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  fn main(${s}) {
    ${i}
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      struct Uniforms { ${t.join(", ")} };
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`)+this.internalVariables.map(t=>t.impl()).join(`
`)}get variablesInfo(){if(this.uniforms.length===0)return;let t=e=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(e)];return this.uniforms.map(e=>[t(e.type),e.length??1])}},El=(t,e)=>new kl(t,e),Kn=(t,e)=>{let r=t.length,n=[];for(let a=0;a<r;a++){let s=r-1-a,i=t[s]||1;(e[e.length-1-a]||1)>1&&i===1&&n.unshift(s)}return n}}),Cl,Us,Tl,Il,br,Al,Ml,Yn=ee(()=>{xe(),Oe(),lt(),Ae(),Cl=t=>{if(!t||t.length!==1)throw new Error("Transpose requires 1 input.")},Us=(t,e)=>e&&e.length!==t?[...new Array(t).keys()].reverse():e,Tl=(t,e)=>Q.sortBasedOnPerm(t,Us(t.length,e)),Il=(t,e,r,n)=>{let a=[];a.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
    var a: ${r.type.indices};`);for(let s=0;s<e;++s)a.push(r.indicesSet("a",t[s],`i[${s}]`));return a.push("return a;}"),a.join(`
`)},br=(t,e)=>{let r=t.dataType,n=t.dims.length,a=Us(n,e),s=Tl(t.dims,a),i=_e("output",r,s.length),o=X("a",r,n),u=l=>`
  ${l.registerUniform("output_size","u32").declareVariables(o,i)}

  ${Il(a,n,o,i)}

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

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

    ${i.setByOffset("global_idx",o.getByIndices("aIndices"))}
  }`;return{name:"Transpose",shaderCache:{hint:`${e}`,inputDependencies:["rank"]},getRunData:l=>{let c=Q.size(s);return{outputs:[{dims:s,dataType:l[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:[{type:12,data:c},...ye(l[0].dims,s)]}},getShaderSource:u}},Al=(t,e)=>{Cl(t.inputs),t.compute(br(t.inputs[0],e.perm))},Ml=t=>Ke({perm:t.perm})}),Ol,zl,Pl,Rl,Bl,Dl,Nl,Fl,Ll,Ul,Qt,Wl,Vl,Gl,Hl,ql,jl,Kl,Yl,Ql,Xl,U_=ee(()=>{xe(),Oe(),Ae(),Vs(),Yn(),Ol={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"},zl={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"},Pl={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},Rl={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},Bl=(t,e)=>{let r=[];for(let n=e-t;n<e;++n)r.push(n);return r},Dl=(t,e)=>{let r=[],n=t.length;for(let s=0;s<n;s++)e.indexOf(s)===-1&&r.push(t[s]);let a=e.map(s=>t[s]);return[r,a]},Nl=(t,e)=>{let r=t.length+e.length,n=[],a=0;for(let s=0;s<r;s++)e.indexOf(s)===-1?n.push(t[a++]):n.push(1);return n},Fl=(t,e)=>{for(let r=0;r<t.length;++r)if(t[t.length-r-1]!==e-1-r)return!1;return!0},Ll=(t,e)=>{let r=[];if(!Fl(t,e)){for(let n=0;n<e;++n)t.indexOf(n)===-1&&r.push(n);t.forEach(n=>r.push(n))}return r},Ul=(t,e,r,n,a,s,i)=>{let o=r[0].dims,u=Q.size(s),l=Q.size(i),c=X("_A",r[0].dataType,o),h=_e("output",a,s),m=32,_=`
          var<workgroup> aBestValues : array<f32, ${m}>;
       `;return{name:t,shaderCache:e,getShaderSource:p=>`
        ${p.registerUniform("reduceSize","u32").declareVariables(c,h)}
        ${_}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${p.mainStart(m)}

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

          var bestValue = f32(${Pl[n]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${m}) {
           let candidate = f32(${c.getByOffset("offset + k")});
           bestValue = ${Ol[n]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

         var reduceSize = min(Length, ${m}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 = ${zl[n]};
            aBestValues[local_idx] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (local_idx == 0u) {
          ${h.setByOffset("outputIndex",`${n==="mean"?`${h.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${h.type.storage}(${Rl[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:s,dataType:a}],dispatchGroup:{x:u},programUniforms:[{type:12,data:l}]})}},Qt=(t,e,r,n)=>{let a=t.inputs.length===1?r:Ws(t.inputs,r),s=a.axes;s.length===0&&!a.noopWithEmptyAxes&&(s=t.inputs[0].dims.map((_,p)=>p));let i=Q.normalizeAxes(s,t.inputs[0].dims.length),o=i,u=t.inputs[0],l=Ll(o,t.inputs[0].dims.length);l.length>0&&(u=t.compute(br(t.inputs[0],l),{inputs:[0],outputs:[-1]})[0],o=Bl(o.length,u.dims.length));let[c,h]=Dl(u.dims,o),m=c;a.keepDims&&(m=Nl(c,i)),t.compute(Ul(e,{hint:a.cacheKey,inputDependencies:["type"]},[u],n,t.inputs[0].dataType,m,h),{inputs:[u]})},Wl=(t,e)=>{Qt(t,"ReduceMeanShared",e,"mean")},Vl=(t,e)=>{Qt(t,"ReduceL1Shared",e,"l1")},Gl=(t,e)=>{Qt(t,"ReduceL2Shared",e,"l2")},Hl=(t,e)=>{Qt(t,"ReduceLogSumExpShared",e,"logSumExp")},ql=(t,e)=>{Qt(t,"ReduceMaxShared",e,"max")},jl=(t,e)=>{Qt(t,"ReduceMinShared",e,"min")},Kl=(t,e)=>{Qt(t,"ReduceProdShared",e,"prod")},Yl=(t,e)=>{Qt(t,"ReduceSumShared",e,"sum")},Ql=(t,e)=>{Qt(t,"ReduceSumSquareShared",e,"sumSquare")},Xl=(t,e)=>{Qt(t,"ReduceLogSumShared",e,"logSum")}}),Xt,Jl,oi,Ws,Jt,Zl,ed,td,rd,nd,ad,id,sd,od,ud,Zt,ld,dd,cd,pd,hd,fd,md,gd,_d,wd,Vs=ee(()=>{xe(),Oe(),lt(),Ae(),U_(),Xt=t=>{if(!t||t.length===0||t.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(t.length===2&&t[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Jl=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],oi=(t,e,r,n,a,s,i=!1,o=!1)=>{let u=[],l=r[0].dims,c=l.length,h=Q.normalizeAxes(a,c),m=!o&&h.length===0;l.forEach((b,$)=>{m||h.indexOf($)>=0?i&&u.push(1):u.push(b)});let _=u.length,p=Q.size(u);return{name:t,shaderCache:e,getShaderSource:b=>{let $=[],S=X("_A",r[0].dataType,c),x=_e("output",s,_),C=n(S,x,h),T=C[2];for(let I=0,P=0;I<c;I++)m||h.indexOf(I)>=0?(i&&P++,T=`for(var j${I}: u32 = 0; j${I} < ${l[I]}; j${I}++) {
                  ${C[2].includes("last_index")?`let last_index = j${I};`:""}
                  ${S.indicesSet("input_indices",I,`j${I}`)}
                  ${T}
                }`):($.push(`${S.indicesSet("input_indices",I,x.indicesGet("output_indices",P))};`),P++);return`

        ${b.registerUniform("output_size","u32").declareVariables(S,x)}

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

          ${$.join(`
`)}
          ${C[0]}       // init ops for reduce max/min
          ${C[1]}
          ${T}
          ${C[3]}
          ${C.length===4?x.setByOffset("global_idx","value"):C.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:u,dataType:s}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...ye(l,u)]})}},Ws=(t,e)=>{let r=[];return t[1].dims[0]>0&&t[1].getBigInt64Array().forEach(n=>r.push(Number(n))),Ke({axes:r,keepDims:e.keepDims,noopWithEmptyAxes:e.noopWithEmptyAxes})},Jt=(t,e,r,n)=>{let a=t.inputs,s=a.length===1?r:Ws(a,r);t.compute(oi(e,{hint:s.cacheKey,inputDependencies:["rank"]},[a[0]],s.noopWithEmptyAxes&&s.axes.length===0?Jl:n,s.axes,a[0].dataType,s.keepDims,s.noopWithEmptyAxes),{inputs:[0]})},Zl=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},ed=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceL1",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},td=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceL2",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},rd=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceLogSumExp",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},nd=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceMax",e,(r,n,a)=>{let s=[];for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&s.push(r.indicesSet("input_indices",i,0));return[`${s.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = max(value, ${r.getByIndices("input_indices")});`,""]})},ad=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceMean",e,(r,n,a)=>{let s=1;for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&(s*=t.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${s});`]})},id=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceMin",e,(r,n,a)=>{let s=[];for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&s.push(`input_indices[${i}] = 0;`);return[`${s.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},sd=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},od=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},ud=(t,e)=>{Xt(t.inputs),Jt(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},Zt=(t,e,r)=>{if(e.length===0)return r;let n=1,a=1;for(let s=0;s<e.length;s++)e.indexOf(s)===-1?n*=t[s]:a*=t[s];return a<32&&n>1024},ld=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?ad(t,e):Wl(t,e)},dd=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?ed(t,e):Vl(t,e)},cd=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?td(t,e):Gl(t,e)},pd=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?rd(t,e):Hl(t,e)},hd=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?nd(t,e):ql(t,e)},fd=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?id(t,e):jl(t,e)},md=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?sd(t,e):Kl(t,e)},gd=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?od(t,e):Yl(t,e)},_d=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?ud(t,e):Ql(t,e)},wd=(t,e)=>{Zt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Zl(t,e):Xl(t,e)}}),Gs,yd,bd,Hs,W_=ee(()=>{xe(),lt(),Vs(),Gs=t=>{if(!t||t.length===0||t.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(t[0].dataType!==1)throw new Error("Invalid input type.")},yd=(t,e)=>{Gs(t.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o<n.rank;o++)(s.indexOf(o)>=0||s.length===0)&&i.push(`input_indices[${o}] = 0;`);return[`${i.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?"<=":"<"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(oi("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},bd=(t,e)=>{Gs(t.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o<n.rank;o++)(s.indexOf(o)>=0||s.length===0)&&i.push(`input_indices[${o}] = 0;`);return[`${i.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?">=":">"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(oi("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Hs=t=>Ke(t)}),vd,$d,xd,ui,Sd,kd,Ed=ee(()=>{xe(),Oe(),lt(),Ae(),vd=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");let r=0,n=t[r],a=n.dataType,s=n.dims.length;t.forEach((i,o)=>{if(o!==r){if(i.dataType!==a)throw new Error("input tensors should be one type");if(i.dims.length!==s)throw new Error("input tensors should have the same shape");i.dims.forEach((u,l)=>{if(l!==e&&u!==n.dims[l])throw new Error("non concat dimensions must match")})}})},$d=(t,e)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${t}u>(${e});
    for (var i: u32 = 0u; i < ${t}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${t}u;
  }`,xd=(t,e)=>{let r=t.length,n=[];for(let a=0;a<r;++a){let s=e.setByOffset("global_idx",t[a].getByIndices("indices"));r===1?n.push(s):a===0?n.push(`if (inputIndex == ${a}u) { ${s} }`):a===r-1?n.push(`else { ${s} }`):n.push(`else if (inputIndex == ${a}) { ${s} }`)}return n.join(`
`)},ui=(t,e,r,n)=>{let a=Q.size(r),s=new Array(t.length),i=new Array(t.length),o=0,u=[],l=[],c=[{type:12,data:a}];for(let b=0;b<t.length;++b)o+=t[b].dims[e],s[b]=o,l.push(t[b].dims.length),i[b]=X(`input${b}`,n,l[b]),u.push("rank"),c.push({type:12,data:s[b]});for(let b=0;b<t.length;++b)c.push(...ye(t[b].dims));c.push(...ye(r));let h=_e("output",n,r.length),m=h.indicesGet("indices",e),_=Array.from(Array(s.length).keys()).map(b=>`uniforms.sizeInConcatAxis${b}`).join(","),p=b=>`

  ${(()=>{b.registerUniform("outputSize","u32");for(let $=0;$<t.length;$++)b.registerUniform(`sizeInConcatAxis${$}`,"u32");return b.declareVariables(...i,h)})()}

  ${$d(s.length,_)}

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

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

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

    ${xd(i,h)}
  }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c}),getShaderSource:p}},Sd=(t,e)=>{let r=t.inputs,n=r[0].dims,a=Q.normalizeAxis(e.axis,n.length);vd(r,a);let s=n.slice();s[a]=r.reduce((o,u)=>o+(u.dims.length>a?u.dims[a]:0),0);let i=r.filter(o=>Q.size(o.dims)>0);t.compute(ui(i,a,s,r[0].dataType),{inputs:i})},kd=t=>Ke({axis:t.axis})}),Cd,Td,Id,Ad,li,Md,Od,zd=ee(()=>{xe(),Ps(),Ae(),Ed(),Cd=(t,e)=>{let r=t[0],n=t[1],a=t[2],s=t[3],i=t[4],o=t[5];if(i&&o)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 u=r.dims[0],l=r.dims[1],c=r.dims[2];if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==c)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(a.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let h=a.dims[0]/3,m=h,_=m;if(e.qkvHiddenSizes.length>0){if(e.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let C of e.qkvHiddenSizes)if(C%e.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");h=e.qkvHiddenSizes[0],m=e.qkvHiddenSizes[1],_=e.qkvHiddenSizes[2]}let p=l;if(h!==m)throw new Error("qkv_hidden_sizes first element should be same as the second");if(a.dims[0]!==h+m+_)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let b=0;if(i){if(m!==_)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==u)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==e.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==m/e.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');e.pastPresentShareBuffer||(b=i.dims[3])}let $=p+b,S=-1,x=0;if(s)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");return{batchSize:u,sequenceLength:l,pastSequenceLength:b,kvSequenceLength:p,totalSequenceLength:$,maxSequenceLength:S,inputHiddenSize:c,hiddenSize:h,vHiddenSize:_,headSize:Math.floor(h/e.numHeads),vHeadSize:Math.floor(_/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:x,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Td=(t,e,r,n)=>{let a=at(n),s=64,i=n/a;i<s?s=1:i/8<64&&(s=Math.ceil(i/8));let o=Math.ceil(n/a/s),u=[{type:e.dataType,data:1/n},{type:12,data:i},{type:12,data:o}],l=ft(e.dataType,a),c=Et(1,a),h=m=>{let _=_e("x",e.dataType,e.dims,a),p=[{name:"d_inv",type:Et(e.dataType)},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${s}>;
  var<workgroup> thread_sum: array<f32, ${s}>;
  ${m.registerUniforms(p).declareVariables(_)}
  ${m.mainStart([s,1,1])}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = workgroup_id.x * uniforms.d_comp + local_offset;

    var thread_max_vector = ${c}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
      thread_max_vector = max(${c}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(a){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${a}`)}})()};
    workgroupBarrier();

    var max_value =  f32(-3.402823e+38f);
    for (var i = 0u; i < ${s}; i++) {
      max_value = max(thread_max[i], max_value);
    }

    var sum_vector = ${c}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
      sum_vector += exp(${c}(x[offset + i]) - max_value);
    }
    thread_sum[local_idx] = ${(()=>{switch(a){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${a}`)}})()};
    workgroupBarrier();

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

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        x[offset + i] = ${_.type.value}(uniforms.d_inv);
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        var f32input = ${c}(x[offset + i]);
        x[offset + i] = ${_.type.value}(exp(f32input - max_value) / sum);
      }
    }
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${l};${a}`},getShaderSource:h,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:u})}},Id=(t,e,r,n,a,s,i)=>{let o=i+a.kvSequenceLength,u=[a.batchSize,a.numHeads,a.sequenceLength,o],l=s.scale===0?1/Math.sqrt(a.headSize):s.scale,c=at(a.headSize),h=a.headSize/c,m=12,_={x:Math.ceil(o/m),y:Math.ceil(a.sequenceLength/m),z:a.batchSize*a.numHeads},p=[{type:12,data:a.sequenceLength},{type:12,data:h},{type:12,data:o},{type:12,data:a.numHeads},{type:1,data:l}],b=n?["type","type","type"]:["type","type"],$=S=>{let x=X("q",e.dataType,e.dims,c),C=X("key",r.dataType,r.dims,c),T=[x,C];n&&T.push(X("relative_position_bias",n.dataType,n.dims));let I=_e("output",e.dataType,u),P=Et(1,c),R=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return`
  const TILE_SIZE = ${m}u;

  var<workgroup> tileQ: array<${x.type.storage}, ${m*m}>;
  var<workgroup> tileK: array<${x.type.storage}, ${m*m}>;
  ${S.registerUniforms(R).declareVariables(...T,I)}
  ${S.mainStart([m,m,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 qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;
    let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;

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

      workgroupBarrier();
    }

    let headOffset = headIdx * uniforms.M * uniforms.N;
    if (global_id.y < uniforms.M && global_id.x < uniforms.N) {
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(c){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${c}`)}})()};
        output[outputIdx] = ${I.type.value} (sum * uniforms.alpha) + ${n?"relative_position_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${c}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:u,dataType:e.dataType,gpuDataType:0}],dispatchGroup:_,programUniforms:p}),getShaderSource:$}},Ad=(t,e,r,n,a)=>{let s=a+n.kvSequenceLength,i=[n.batchSize,n.sequenceLength,n.vHiddenSize],o=12,u={x:Math.ceil(n.vHeadSize/o),y:Math.ceil(n.sequenceLength/o),z:n.batchSize*n.numHeads},l=[{type:12,data:n.sequenceLength},{type:12,data:s},{type:12,data:n.vHeadSize},{type:12,data:n.numHeads},{type:12,data:n.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:l}),getShaderSource:c=>{let h=X("probs",e.dataType,e.dims),m=X("v",r.dataType,r.dims),_=_e("output",e.dataType,i),p=[{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 = ${o}u;
  var<workgroup> tileQ: array<${h.type.value}, ${o*o}>;
  var<workgroup> tileK: array<${h.type.value}, ${o*o}>;
  ${c.registerUniforms(p).declareVariables(h,m,_)}
  ${c.mainStart([o,o,1])}
   let headIdx = workgroup_id.z;
   let m = global_id.y;
   let n = global_id.x;

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

   var value = ${h.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;
   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;
   }
  }`}}},li=(t,e,r,n,a,s,i,o,u,l,c)=>{let h=t.outputCount>1,m=t.outputCount>2,_=h&&m?l.pastSequenceLength:0,p=_+l.kvSequenceLength,b=[l.batchSize,l.numHeads,p,l.headSize],$=i?[i,r]:[r],S=h?t.compute(ui($,2,b,r.dataType),{inputs:$,outputs:[1]})[0]:r,x=[l.batchSize,l.numHeads,p,l.headSize],C=o?[o,n]:[n],T=m?t.compute(ui(C,2,x,n.dataType),{inputs:C,outputs:[2]})[0]:n,I=[e,S];u&&I.push(u);let P=t.compute(Id(t,e,S,u,l,c,_),{inputs:I,outputs:[-1]})[0];t.compute(Td(t,P,l.batchSize*l.numHeads*l.sequenceLength,p),{inputs:[P],outputs:[]});let R=[P,T];t.compute(Ad(t,P,T,l,_),{inputs:R,outputs:[0]})},Md=(t,e)=>{let r=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],n=e.sequenceLength,a=e.inputHiddenSize,s=e.headSize,i=12,o={x:Math.ceil(e.headSize/i),y:Math.ceil(e.sequenceLength/i),z:e.batchSize*e.numHeads},u=[t.inputs[0],t.inputs[1],t.inputs[2]],l=[{type:12,data:n},{type:12,data:a},{type:12,data:s},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],c=h=>{let m=_e("output_q",u[0].dataType,r),_=_e("output_k",u[0].dataType,r),p=_e("output_v",u[0].dataType,r),b=X("input",u[0].dataType,u[0].dims),$=X("weight",u[1].dataType,u[1].dims),S=X("bias",u[2].dataType,u[2].dims),x=b.type.storage,C=[{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 = ${i}u;
  var<workgroup> tileInput: array<${x}, ${i*i}>;
  var<workgroup> tileWeightQ: array<${x}, ${i*i}>;
  var<workgroup> tileWeightK: array<${x}, ${i*i}>;
  var<workgroup> tileWeightV: array<${x}, ${i*i}>;
  ${h.registerUniforms(C).declareVariables(b,$,S,m,_,p)}
  ${h.mainStart([i,i,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_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 = ${x}(0);
    var valueK = ${x}(0);
    var valueV = ${x}(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 t.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:l}),getShaderSource:c},{inputs:u,outputs:[-1,-1,-1]})},Od=(t,e)=>{let r=Cd(t.inputs,e),[n,a,s]=Md(t,r);return li(t,n,a,s,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),Pd,Rd,Bd,Dd,V_=ee(()=>{Yt(),xe(),Oe(),lt(),Ae(),Pd=(t,e)=>{if(!t||t.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,a,s)=>{let i=a.length;if(i!==n.length)throw new Error(`${s}: num dimensions != ${i}`);a.forEach((o,u)=>{if(o!==n[u])throw new Error(`${s}: dim[${u}] do not match`)})};if(t[0].dims.length>1){let n=e.format==="NHWC"?e.spatial?t[0].dims.slice(-1):t[0].dims.slice(-1).concat(t[0].dims.slice(1,t[0].dims.length-1)):t[0].dims.slice(1,e.spatial?2:void 0);r(t[1].dims,n,"Invalid input scale"),r(t[2].dims,n,"Invalid input B"),r(t[3].dims,n,"Invalid input mean"),r(t[4].dims,n,"Invalid input var")}else r(t[1].dims,[1],"Invalid input scale"),r(t[2].dims,[1],"Invalid input B"),r(t[3].dims,[1],"Invalid input mean"),r(t[4].dims,[1],"Invalid input var")},Rd=(t,e)=>{let{epsilon:r,spatial:n,format:a}=e,s=t[0].dims,i=n?at(s[s.length-1]):1,o=a==="NHWC"&&s.length>1?i:1,u=Q.size(s)/i,l=n,c=l?s.length:s,h=X("x",t[0].dataType,t[0].dims,i),m=X("scale",t[1].dataType,t[1].dims,o),_=X("bias",t[2].dataType,t[2].dims,o),p=X("inputMean",t[3].dataType,t[3].dims,o),b=X("inputVar",t[4].dataType,t[4].dims,o),$=_e("y",t[0].dataType,c,i),S=()=>{let C="";if(n)C=`let cOffset = ${s.length===1?"0u":a==="NHWC"?`outputIndices[${s.length-1}] / ${i}`:"outputIndices[1]"};`;else if(a==="NCHW")C=`
            ${$.indicesSet("outputIndices","0","0")}
            let cOffset = ${$.indicesToOffset("outputIndices")};`;else{C=`var cIndices = ${m.type.indices}(0);
                       cIndices[0] = outputIndices[${s.length-1}];`;for(let T=1;T<m.rank;T++)C+=`cIndices[${T}] = outputIndices[${T}];`;C+=`let cOffset = ${m.indicesToOffset("cIndices")};`}return C},x=C=>`
  const epsilon = ${r};
  ${C.registerUniform("outputSize","u32").declareVariables(h,m,_,p,b,$)}
  ${C.mainStart()}
  ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${$.offsetToIndices(`global_idx * ${i}`)};
    ${S()}
    let scale = ${m.getByOffset("cOffset")};
    let bias = ${_.getByOffset("cOffset")};
    let inputMean = ${p.getByOffset("cOffset")};
    let inputVar = ${b.getByOffset("cOffset")};
    let x = ${h.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${$.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${e.epsilon}_${e.format}_${n}_${i}`,inputDependencies:l?["rank","type","type","type","type"]:void 0},getShaderSource:x,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l?[{type:12,data:u},...ye(s)]:[{type:12,data:u}]})}},Bd=t=>Ke(t),Dd=(t,e)=>{let{inputs:r,outputCount:n}=t,a=Bd({...e,outputCount:n});if(Ue.webgpu.validateInputContent&&Pd(r,a),e.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");t.compute(Rd(r,a))}}),Nd,Fd,Ld,G_=ee(()=>{Oe(),Ae(),Nd=t=>{if(t[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(t[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(t[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Fd=t=>{let e=t[0].dims,r=t[0].dims[2],n=Q.size(e)/4,a=t[0].dataType,s=X("input",a,e,4),i=X("bias",a,[r],4),o=X("residual",a,e,4),u=_e("output",a,e,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:l=>`
  const channels = ${r}u / 4;
  ${l.declareVariables(s,i,o,u)}

  ${l.mainStart()}
    ${l.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${s.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${o.getByOffset("global_idx")};
    ${u.setByOffset("global_idx","value")}
  }`}},Ld=t=>{Nd(t.inputs),t.compute(Fd(t.inputs))}}),Ud,De,Wd,Vd,Gd,Hd,qd,jd,Kd,Yd,Qd,Xd,Jd,Zd,ec,tc,di,rc,ci,nc,ac,ic,sc,oc,uc,lc,dc,cc,pc,hc,fc,mc,gc,_c,wc,qs,yc,js,Ks,bc,vc,$c,Ys=ee(()=>{xe(),Oe(),lt(),Ae(),Ud=(t,e,r,n,a,s)=>{let i=Math.ceil(e/4),o="";typeof a=="string"?o=`${a}(a)`:o=a("a");let u=X("inputData",r,[i],4),l=_e("outputData",n,[i],4);return`
      ${t.registerUniform("vec_size","u32").declareVariables(u,l)}

  ${s??""}

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

    let a = ${u.getByOffset("global_idx")};
    ${l.setByOffset("global_idx",o)}
  }`},De=(t,e,r,n,a,s=t.dataType)=>({name:e,shaderCache:{hint:a,inputDependencies:["type"]},getShaderSource:i=>Ud(i,Q.size(t.dims),t.dataType,s,r,n),getRunData:i=>({outputs:[{dims:t.dims,dataType:s}],dispatchGroup:{x:Math.ceil(Q.size(i[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(Q.size(t.dims)/4)}]})}),Wd=t=>{t.compute(De(t.inputs[0],"Abs","abs"))},Vd=t=>{t.compute(De(t.inputs[0],"Acos","acos"))},Gd=t=>{t.compute(De(t.inputs[0],"Acosh","acosh"))},Hd=t=>{t.compute(De(t.inputs[0],"Asin","asin"))},qd=t=>{t.compute(De(t.inputs[0],"Asinh","asinh"))},jd=t=>{t.compute(De(t.inputs[0],"Atan","atan"))},Kd=t=>{t.compute(De(t.inputs[0],"Atanh","atanh"))},Yd=t=>Ke(t),Qd=(t,e)=>{let r;switch(e.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): ${e.to}`)}t.compute(De(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},Xd=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:Ns,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:Fs;return Ke({min:e,max:r})},Jd=(t,e)=>{let r=t.inputs.length===1?e:Xd(t.inputs),n=Et(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Clip",a=>`clamp(${a}, clip_min_, clip_max_)`,`
    const clip_min_: vec4<${n}> = vec4(${n}(${r.min}));
    const clip_max_: vec4<${n}> = vec4(${n}(${r.max}));
`,r.cacheKey),{inputs:[0]})},Zd=t=>{t.compute(De(t.inputs[0],"Ceil","ceil"))},ec=t=>{t.compute(De(t.inputs[0],"Cos","cos"))},tc=t=>{t.compute(De(t.inputs[0],"Cosh","cosh"))},di=t=>Ke(t),rc=(t,e)=>{let r=Et(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Elu",n=>`elu_vf32(${n})`,`
  const elu_alpha_ = ${r}(${e.alpha});

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

  fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,e.cacheKey))},ci=(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: vec4<${t}>) -> vec4<${t}> {
  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));
}`,nc=t=>{let e=Et(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,ci(e)))},ac=t=>{t.compute(De(t.inputs[0],"Exp","exp"))},ic=t=>{t.compute(De(t.inputs[0],"Floor","floor"))},sc=t=>{let e=Et(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,ci(e)))},oc=(t,e)=>{let r=Et(t.inputs[0].dataType);t.compute(De(t.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${e.alpha});`,e.cacheKey))},uc=t=>{t.compute(De(t.inputs[0],"Not",e=>`!${e}`))},lc=t=>{t.compute(De(t.inputs[0],"Neg",e=>`-${e}`))},dc=t=>{t.compute(De(t.inputs[0],"Reciprocal",e=>`1.0/${e}`))},cc=t=>{let e=Et(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Relu",r=>`select(vec4<${e}>(0.0), ${r}, ${r} > vec4<${e}>(0.0))`))},pc=t=>{t.compute(De(t.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},hc=t=>Ke(t),fc=(t,e)=>{let r=Et(t.inputs[0].dataType);t.compute(De(t.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${e.alpha} * ${n} + vec4<${r}>(${e.beta})))`,void 0,e.cacheKey))},mc=t=>{t.compute(De(t.inputs[0],"Sin","sin"))},gc=t=>{t.compute(De(t.inputs[0],"Sinh","sinh"))},_c=t=>{t.compute(De(t.inputs[0],"Sqrt","sqrt"))},wc=t=>{t.compute(De(t.inputs[0],"Tan","tan"))},qs=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,yc=t=>{t.compute(De(t.inputs[0],"Tanh",qs))},js=(t="f32")=>`
const fast_gelu_a: ${t} = 0.5;
const fast_gelu_b: ${t} = 0.7978845608028654;
const fast_gelu_c: ${t} = 0.035677408136300125;

fn tanh_v(v: vec4<${t}>) -> vec4<${t}> {
  return ${qs("v")};
}
`,Ks=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,bc=t=>{let e=Et(t.inputs[0].dataType);t.compute(De(t.inputs[0],"FastGelu",Ks,js(e),void 0,t.inputs[0].dataType))},vc=(t,e)=>{let r=Et(t.inputs[0].dataType);return t.compute(De(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},$c=t=>{t.compute(De(t.inputs[0],"Log","log"))}}),xc,Sc,kc,H_=ee(()=>{Oe(),Ae(),Ys(),xc=t=>{if(t[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(t[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(t[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Sc=t=>{let e=t[0].dims.slice();e[2]=e[2]/2;let r=X("input",t[0].dataType,t[0].dims,4),n=X("bias",t[0].dataType,[t[0].dims[2]],4),a=_e("output",t[0].dataType,e,4),s=Q.size(e)/4,i=ft(t[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:o=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${t[0].dims[2]/4/2}u;

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

  ${ci(i)}

  ${o.mainStart()}
    ${o.guardAgainstOutOfBoundsWorkgroupSizes(s)}
    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);

    ${a.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},kc=t=>{xc(t.inputs),t.compute(Sc(t.inputs))}}),Ec,Cc,er,Tc,Ic,Ac,Mc,Oc,zc,Pc,Rc,Bc,Dc,q_=ee(()=>{xe(),Oe(),Ae(),Ec=(t,e,r,n,a,s,i,o,u,l,c,h)=>{let m,_;typeof o=="string"?m=_=(x,C)=>`${o}((${x}),(${C}))`:typeof o=="function"?m=_=o:(m=o.scalar,_=o.vector);let p=_e("outputData",c,n.length,4),b=X("aData",u,e.length,4),$=X("bData",l,r.length,4),S;if(a)if(s){let x=Q.size(e)===1,C=Q.size(r)===1,T=e.length>0&&e[e.length-1]%4===0,I=r.length>0&&r[r.length-1]%4===0;x||C?S=p.setByOffset("global_idx",_(x?`${b.type.value}(${b.getByOffset("0")}.x)`:b.getByOffset("global_idx"),C?`${$.type.value}(${$.getByOffset("0")}.x)`:$.getByOffset("global_idx"))):S=`
            let outputIndices = ${p.offsetToIndices("global_idx * 4u")};
            let offsetA = ${b.broadcastedIndicesToOffset("outputIndices",p)};
            let offsetB = ${$.broadcastedIndicesToOffset("outputIndices",p)};
            ${p.setByOffset("global_idx",_(i||T?b.getByOffset("offsetA / 4u"):`${b.type.value}(${b.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||I?$.getByOffset("offsetB / 4u"):`${$.type.value}(${$.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else S=p.setByOffset("global_idx",_(b.getByOffset("global_idx"),$.getByOffset("global_idx")));else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let x=(C,T,I="")=>{let P=`aData[indexA${T}][componentA${T}]`,R=`bData[indexB${T}][componentB${T}]`;return`
            let outputIndices${T} = ${p.offsetToIndices(`global_idx * 4u + ${T}u`)};
            let offsetA${T} = ${b.broadcastedIndicesToOffset(`outputIndices${T}`,p)};
            let offsetB${T} = ${$.broadcastedIndicesToOffset(`outputIndices${T}`,p)};
            let indexA${T} = offsetA${T} / 4u;
            let indexB${T} = offsetB${T} / 4u;
            let componentA${T} = offsetA${T} % 4u;
            let componentB${T} = offsetB${T} % 4u;
            ${C}[${T}] = ${I}(${m(P,R)});
          `};c===9?S=`
            var data = vec4<u32>(0);
            ${x("data",0,"u32")}
            ${x("data",1,"u32")}
            ${x("data",2,"u32")}
            ${x("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:S=`
            ${x("outputData[global_idx]",0)}
            ${x("outputData[global_idx]",1)}
            ${x("outputData[global_idx]",2)}
            ${x("outputData[global_idx]",3)}
          `}return`
        ${t.registerUniform("vec_size","u32").declareVariables(b,$,p)}

        ${h??""}

        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${S}
      }`},Cc=(t,e,r,n,a,s,i=r.dataType)=>{let o=!Q.areEqual(r.dims,n.dims),u=r.dims,l=Q.size(r.dims),c=!1,h=!1,m=[o];if(o){let _=mn.calcShape(r.dims,n.dims,!1);if(!_)throw new Error("Can't perform binary op on the given tensors");u=_,l=Q.size(u);let p=Q.size(r.dims)===1,b=Q.size(n.dims)===1,$=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,S=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;m.push(p),m.push(b),m.push($),m.push(S);let x=1;for(let C=1;C<u.length;C++){let T=r.dims[r.dims.length-C]??1,I=n.dims[n.dims.length-C]??1;if(T===I)x*=T;else break}x%4===0?(h=!0,c=!0):(p||b||$||S)&&(c=!0)}else c=!0;return m.push(c),{name:t,shaderCache:{hint:e+m.map(_=>_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>Ec(_,r.dims,n.dims,u,c,o,h,a,r.dataType,n.dataType,i,s),getRunData:()=>({outputs:[{dims:u,dataType:i}],dispatchGroup:{x:Math.ceil(l/64/4)},programUniforms:[{type:12,data:Math.ceil(Q.size(u)/4)},...ye(r.dims,n.dims,u)]})}},er=(t,e,r,n,a,s)=>{t.compute(Cc(e,a??"",t.inputs[0],t.inputs[1],r,n,s))},Tc=t=>{er(t,"Add",(e,r)=>`${e}+${r}`)},Ic=t=>{er(t,"Div",(e,r)=>`${e}/${r}`)},Ac=t=>{er(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4<u32>(${e}==${r})`},void 0,void 0,9)},Mc=t=>{er(t,"Mul",(e,r)=>`${e}*${r}`)},Oc=t=>{let e=X("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;er(t,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},`
    fn pow_custom(a : ${e}, b : ${e}) -> ${e} {
      if (b == ${e}(0.0)) {
        return ${e}(1.0);
      } else if (a < ${e}(0.0) && f32(b) != floor(f32(b))) {
        return ${e}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${e}(1.0), round(f32(abs(b) % ${e}(2.0))) != 1.0) * ${e}(${e==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${e}>, b : vec4<${e}>) -> vec4<${e}> {
      // TODO: implement vectorized pow
      return vec4<${e}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},zc=t=>{er(t,"Sub",(e,r)=>`${e}-${r}`)},Pc=t=>{er(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4<u32>(${e}>${r})`},void 0,void 0,9)},Rc=t=>{er(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4<u32>(${e}<${r})`},void 0,void 0,9)},Bc=t=>{er(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4<u32>(${e}>=${r})`},void 0,void 0,9)},Dc=t=>{er(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4<u32>(${e}<=${r})`},void 0,void 0,9)}}),Hr,qr,jr,Qs,Kr=ee(()=>{xe(),Oe(),Hr=(t,e,r="f32")=>{switch(t.activation){case"Relu":return`value = max(value, ${e}(0.0));`;case"Sigmoid":return`value = (${e}(1.0) / (${e}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${e}(${r}(uniforms.clip_min)), ${e}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${e}(0.0), min(${e}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${e}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${t.activation}`)}},qr=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},jr=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},Qs=t=>{let e=(t==null?void 0:t.activation)||"";if(e==="HardSigmoid"){let[r,n]=(t==null?void 0:t.activation_params)||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=(t==null?void 0:t.activation_params)||[Ns,Fs];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=(t==null?void 0:t.activation_params)||[.01];return{activation:e,alpha:r}}return{activation:e}}}),vt,Xs,Js=ee(()=>{vt=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},Xs=t=>`
      ${t?"value = value + getBiasByOutputCoords(coords);":""}
      `}),Zs,Nc=ee(()=>{Zs=t=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${t}.x), i32(${t}.y), i32(${t}.z), 1));
}
`}),Fc,Lc,pi,eo,Uc,hi,Wc,to,fi=ee(()=>{xe(),Oe(),Ae(),Kr(),Js(),Fc=(t,e)=>t?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${e?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${e?", batchIndices":""});
        `,Lc=(t,e)=>t?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${e===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
        for (var i = 0; i < rowPerThread; i = i + 1) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${e===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
        }`:`
        for (var i = 0; i < rowPerThread; i = i + 1) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${e===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,pi=(t,e,r="f32",n,a=!1,s=32,i=!1,o=32)=>{let u=e[1]*t[1],l=e[0]*t[0],c=a?u:s,h=a?s:u,m=c/e[0],_=s/e[1];if(!((a&&m===4&&t[1]===4||!a&&(m===3||m===4))&&c%e[0]===0&&s%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${m} and workPerThread[1] ${t[1]} must be 4.
      Otherwise, innerElementSize ${m} must be 3 or 4.
  tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${m}<${r}>, ${c/m}>, ${h}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${l/t[0]}>, ${s}>;

const rowPerThread = ${t[1]};
const colPerThread = ${t[0]};
const innerElementSize = ${m};
const tileInner = ${s};

@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[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 = ${i?"0":"i32(globalId.z)"};
  ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
  let globalRowStart = i32(workgroupId.y) * ${u};

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

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

  // Loop over shared dimension.
  let tileRowB = localRow * ${_};
  for (var t = 0; t < num_tiles; 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;
          ${Fc(a,n)}
      }

      // Load one tile of B into local memory.
      for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {
          let inputRow = tileRowB + innerRow;
          let inputCol = tileCol;
          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${n?", 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];
          ${m===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}

          ${Lc(a,m)}
      }

      workgroupBarrier();
  }

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

    // Loop over shared dimension.
    for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var inputRow = localRow; inputRow < ${m}; inputRow = inputRow + ${e[1]}) {
        for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${e[0]}) {
          ${eo(a,n)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${e[1]}) {
            for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {
          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
            kStart + inputRow,
            globalColStart + inputCol${n?", batchIndices":""});
        }
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

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

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

let tileRowA = i32(localId.y) * ${_};
let tileColA = i32(localId.x) * ${p};
let tileRowB = i32(localId.y) * ${b};
// Loop over shared dimension.
for (var t = 0; t < num_tiles; t = t + 1) {
  // Load one tile of A into local memory.
  for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${p}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${eo(a,n)}
    }
  }

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

  // Compute acc values for a single thread.
  var BCached : array<${r}, 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) {
      ${Uc(a)}
      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<${r}, ${h}>, ${m}>;
  var<workgroup> mm_Bsub : array<array<${r}, ${c}>, ${s}>;
  const rowPerThread = ${t[1]};
  const colPerThread = ${t[0]};
  const tileInner = ${s};

@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[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 = ${i?"0":"i32(globalId.z)"};
    ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
    let num_tiles = ${i?`${Math.ceil(o/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
    var kStart = ${i?`i32(globalId.z) * ${o}`:"0"};

    var acc : array<array<${r}, 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;
      }
    }
    ${$}
  }
`},Wc=(t,e,r,n,a,s=!1)=>{let[i,o,u]=a,[l,c,h,m]=n,_=Kn(i,u),p=Kn(o,u),b=ft(n[0].type.tensor),$=()=>{let x=c.rank,C=l.rank,T=`var aIndices: ${c.type.indices};`;for(let I=x-2-1,P=C-1;I>=0;I--,P--)T+=`
aIndices[${I}] = ${C>1?`batchIndices[${P}]`:"batchIndices"};`;return _.forEach(I=>{T+=`
aIndices[${I}] = 0;`}),T+=`
aIndices[${x-2}] = u32(row);
                   aIndices[${x-1}] = u32(colIn);`,T},S=()=>{let x=h.rank,C=l.rank,T=`var bIndices: ${h.type.indices};`;for(let I=x-2-1,P=C-1;I>=0;I--,P--)T+=`
bIndices[${I}] = ${C>1?`batchIndices[${P}]`:"batchIndices"};`;return p.forEach(I=>{T+=`
bIndices[${I}] = 0;`}),T+=`
bIndices[${x-2}] = u32(row);
                   bIndices[${x-1}] = u32(colIn);`,T};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${l.type.indices}) -> ${vt(t,b)} {
      var value = ${vt(t,b)}(0.0);
      let col = colIn * ${t};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${$()}
        value = ${c.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${l.type.indices}) -> ${vt(t,b)} {
      var value = ${vt(t,b)}(0.0);
      let col = colIn * ${t};
      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
      {
        ${S()}
        value = ${h.getByIndices("bIndices")};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${vt(t,b)}) {
      let col = colIn * ${t};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${e?`value = value + ${s?"bias[colIn]":`${vt(t,b)}(bias[row])`};`:""}
        ${r}
        ${m.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},to=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s.slice(0,-2),u=i.slice(0,-2),l=n?n.slice(0,-2):r.slice(0,-2),c=Q.size(l),h=s[s.length-2],m=s[s.length-1],_=i[i.length-1],p=m%4===0&&_%4===0,b=h<=8?[4,1,1]:[4,4,1],$=[8,8,1],S=[Math.ceil(_/$[0]/b[0]),Math.ceil(h/$[1]/b[1]),Math.ceil(c/$[2]/b[2])],x=p?4:1,C=[...o,h,m/x],T=C.length,I=[...u,m,_/x],P=I.length,R=[c,h,_/x],W=[{type:6,data:h},{type:6,data:_},{type:6,data:m}];qr(e,W),W.push(...ye(l,C,I));let q=["rank","rank"],K=t.length>2;K&&(W.push(...ye(t[2].dims)),q.push("rank")),W.push(...ye(R));let ue=ne=>{let re=l.length,oe=Ls("batchDims",t[0].dataType,re,1),D=ft(t[0].dataType),O=X("a",t[0].dataType,T,x),G=X("b",t[1].dataType,P,x),j=_e("result",t[0].dataType,R.length,x),pe=[O,G];if(K){let Se=a?x:1;pe.push(X("bias",t[2].dataType,t[2].dims.length,Se))}let le=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];jr(e,le);let B=ft(j.type.tensor),ae=Hr(e,j.type.value,B),ce=Wc(x,K,ae,[oe,O,G,j],[o,u,l],a);return`
  ${ne.registerUniforms(le).registerInternalVariables(oe).declareVariables(...pe,j)}
  ${ce}
  ${p?pi(b,$,D,oe):hi(b,$,D,oe)}
                   `};return{name:"MatMul",shaderCache:{hint:`${b};${e.activation};${p};${a}`,inputDependencies:q},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:S[0],y:S[1],z:S[2]},programUniforms:W}),getShaderSource:ue}}}),Vc,Gc,j_=ee(()=>{xe(),Gr(),Ae(),Kr(),Js(),Nc(),fi(),Vc=(t,e,r,n,a=!1,s,i=4,o=4,u=4,l="f32")=>{let c=q=>{switch(q){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${l}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${q} is not supported.`)}},h=q=>{switch(q){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 ${q} is not supported.`)}},m=t?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,_=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,p=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",b=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",$=t?"row":"col",S=t?"col":"row",x=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${$} / outWidth;
    let outCol = ${$} % outWidth;

    let WRow = ${S} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${S} / inChannels % i32(uniforms.w_shape[1]);
    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
    let xCh = ${S} % inChannels;
    var resData = ${vt(i,l)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${p} && xCol >= 0 && xCol < ${b}) {
      ${m}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${c(i)}
    }
    return resData;`,C=t?e&&n?`
    let col = colIn * ${i};
    ${x}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${x}
    }
    return ${vt(i,l)}(0.0);`:n&&r?`
    let col = colIn * ${i};
    ${x}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${x}
    }
    return ${vt(i,l)}(0.0);`,T=`${h(o)}`,I=vt(u,l),P=vt(t?i:o,l),R=vt(t?o:i,l),W=Hr(s,I,l);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${P} {
      ${t?C:T}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${R} {
      ${t?T:C}
    }

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${I}) {
      let col = colIn * ${u};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${_}
      ${Xs(a)}
      ${W}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Gc=(t,e,r,n,a,s,i,o)=>{let u=e.format==="NHWC",l=u?t[0].dims[3]:t[0].dims[1],c=r[0],h=u?r[2]:r[3],m=u?r[1]:r[2],_=u?r[3]:r[1],p=u&&(l%4===0||l%3===0)&&_%4===0,b=u?_:h*m,$=u?h*m:_,S=[8,8,1],x=n<=8?[4,1,1]:[4,4,1],C=[Math.ceil(b/S[0]/x[0]),Math.ceil($/S[1]/x[1]),Math.ceil(c/S[2]/x[2])];tt("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${C}`);let T=p?u&&l%4!==0?3:4:1,I=S[1]*x[1],P=S[0]*x[0],R=Math.max(S[0]*T,S[1]),W=n%I===0,q=a%P===0,K=s%R===0,ue=p?[T,4,4]:[1,1,1],ne=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];qr(e,ne),ne.push(...ye(t[0].dims,t[1].dims));let re=["rank","rank"];i&&(ne.push(...ye(t[2].dims)),re.push("rank")),ne.push(...ye(r));let oe=D=>{let O=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];jr(e,O);let G=p?4:1,j=ft(t[0].dataType),pe=`
      fn setOutputAtIndex(flatIndex : i32, value : ${p?`vec4<${j}>`:j}) {
        result[flatIndex] = ${p?`vec4<${j}>`:j}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${p?`vec4<${j}>`:j}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${p?"/ 4":""}, value);
      }`,le=X("x",t[0].dataType,t[0].dims.length,T===3?1:T),B=X("w",t[1].dataType,t[1].dims.length,G),ae=[le,B],ce=_e("result",t[0].dataType,r.length,G);if(i){let Se=X("bias",t[2].dataType,t[2].dims.length,G);ae.push(Se),pe+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${p?`vec4<${j}>`:j} {
          return bias[coords.${u?"w":"y"}${p?"/ 4":""}];
        }`}return`
        ${Zs("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 };
        ${D.registerUniforms(O).declareVariables(...ae,ce)}
        ${pe}
        ${Vc(u,W,q,K,i,e,ue[0],ue[1],ue[2],j)}
        ${p?pi(x,S,j,void 0,!u,R):hi(x,S,j,void 0,!u,R,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${T};${p};${W};${q};${K};${I};${P};${R}`,inputDependencies:re},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:ne}),getShaderSource:oe}}}),ro,Hc,K_=ee(()=>{xe(),Oe(),Ae(),Jc(),Kr(),ro=(t,e,r)=>{let n=t.length>2,a=n?"value += b[output_channel];":"",s=t[0].dims,i=t[1].dims,o=i[0]/e.group,u=e.format==="NHWC",l=mi(s,i,e.dilations,e.pads,e.strides,u),c=Q.size(l),h=[{type:12,data:c},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];qr(e,h),h.push(...ye(s,i));let m=["rank","rank"];n&&(h.push(...ye(t[2].dims)),m.push("rank")),h.push(...ye(l));let _=p=>{let b=_e("output",t[0].dataType,l.length),$=ft(b.type.tensor),S=Hr(e,b.type.value,$),x=X("x",t[0].dataType,s.length),C=X("w",t[1].dataType,i.length),T=[x,C];n&&T.push(X("b",t[2].dataType,t[2].dims.length));let I=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:e.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return jr(e,I),`
  ${p.registerUniforms(I).declareVariables(...T,b)}

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

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

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

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

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

          let xVal = ${u?x.get("batch","xHeight","xWidth","input_channel"):x.get("batch","input_channel","xHeight","xWidth")};
          let wVal = ${C.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${a}
    ${S}
    ${b.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:m},getRunData:()=>({outputs:[{dims:r?r(l):l,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:h}),getShaderSource:_}},Hc=(t,e,r)=>{let n=t.length>2,a=at(r[3]),s=at(r[2]),i=Q.size(r)/a/s,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],u=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],l=[r[0],r[1],r[2],r[3]/a],c=[{type:12,data:i},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];qr(e,c),c.push(...ye(o,u,l));let h=(s-1)*e.strides[1]+u[1],m=_=>{let p=_e("output",t[0].dataType,l.length,a),b=ft(p.type.tensor),$=Hr(e,p.type.value,b),S=X("x",t[0].dataType,o.length,a),x=X("w",t[1].dataType,u.length,a),C=[S,x];n&&C.push(X("b",t[2].dataType,t[2].dims,a));let T=n?"value += b[output_channel];":"",I=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return jr(e,I),`
  ${_.registerUniforms(I).declareVariables(...C,p)}
  ${_.mainStart()}
    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let width0 = uniforms.output_shape[3];
    let output_channel = global_idx % width0;
    var index1 = global_idx / width0;
    let width1 = uniforms.output_shape[2] / ${s}u;
    let col = (index1 % width1) * ${s}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;

    var x_vals: array<${S.type.value}, ${h}>;
    var values: array<${p.type.value}, ${s}>;
    let input_channel = output_channel;
    // Use constant instead of uniform can give better performance for w's height/width.
    for (var w_height: u32 = 0u; w_height < ${u[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${h}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${S.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${S.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${u[1]}; w_width++) {
          let w_val = ${x.get("w_height","w_width","0","output_channel")};
          for (var i = 0u; i < ${s}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${s}u; i++) {
      var value = values[i];
      ${T}
      ${$}
      ${p.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${s};${h};${u[0]};${u[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:c}),getShaderSource:m}}}),no,qc,jc,Kc=ee(()=>{xe(),Oe(),fi(),Ae(),Kr(),no=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s[s.length-2],u=i[i.length-1],l=s[s.length-1],c=at(u),h=at(l),m=at(o),_=Q.size(r)/c/m,p=t.length>2,b=n?n.slice(0,-2):r.slice(0,-2),$=[Q.size(b),o,u],S=[{type:12,data:_},{type:12,data:o},{type:12,data:u},{type:12,data:l}];qr(e,S),S.push(...ye(b,s,i)),p&&S.push(...ye(t[2].dims)),S.push(...ye($));let x=C=>{let T=Ls("batch_dims",t[0].dataType,b.length),I=X("a",t[0].dataType,s.length,h),P=X("b",t[1].dataType,i.length,c),R=_e("output",t[0].dataType,$.length,c),W=ft(R.type.tensor),q=Hr(e,R.type.value,W),K=[I,P],ue="";if(p){let pe=a?c:1;K.push(X("bias",t[2].dataType,t[2].dims.length,pe)),ue=`${a?`value += bias[col / ${pe}];`:`value += ${R.type.value}(bias[row + i]);`}`}let ne=s.slice(0,-2),re=i.slice(0,-2),oe=Kn(ne,b),D=Kn(re,b),O=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];jr(e,O);let G=(pe,le)=>{let B=pe.rank,ae=pe.name;if(B===2)return`var ${ae}_indices = ${pe.type.indices}(0u, 0u);`;let ce=T.rank,Se=`var ${ae}_indices: ${pe.type.indices};`;for(let Te=B-2-1,Qe=ce-1;Te>=0;Te--,Qe--)Se+=`
${ae}_indices[${Te}] = ${ce>1?`batch_indices[${Qe}]`:"batch_indices"};`;return le.forEach(Te=>{Se+=`
${ae}_indices[${Te}] = 0;`}),Se+=`${ae}_indices[${B-2}] = 0u;
                     ${ae}_indices[${B-1}] = 0u;`,Se},j=()=>{let pe=`var a_data: ${I.type.value};`;for(let le=0;le<h;le++)pe+=`
              let b_data${le} = b[(b_offset + (k + ${le}) * uniforms.N + col) / ${c}];`;for(let le=0;le<m;le++){pe+=`a_data = a[(a_offset + (row + ${le}) * uniforms.K + k) / ${h}];`;for(let B=0;B<h;B++)pe+=`
            values[${le}] = fma(${P.type.value}(a_data${h===1?"":`[${B}]`}), b_data${B}, values[${le}]);
`}return pe};return`
  ${C.registerUniforms(O).registerInternalVariables(T).declareVariables(...K,R)}
  ${C.mainStart()}
    ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${c})) * ${c};
    var index1 = global_idx / (uniforms.N / ${c});
    let stride1 = uniforms.M / ${m};
    let row = (index1 % stride1) * ${m};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${T.offsetToIndices("batch")};`}
    ${G(I,oe)}
    let a_offset = ${I.indicesToOffset("a_indices")};
    ${G(P,D)}
    let b_offset = ${P.indicesToOffset("b_indices")};
    var values: array<${R.type.value}, ${m}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${h}) {
      ${j()}
    }
    for (var i = 0u; i < ${m}u; i++) {
      var value = values[i];
      ${ue}
      ${q}
      let cur_indices = ${R.type.indices}(batch, row + i, col);
      let offset = ${R.indicesToOffset("cur_indices")};
      ${R.setByOffset(`offset / ${c}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${c};${h};${m};${a}`,inputDependencies:p?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:S}),getShaderSource:x}},qc=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.")},jc=t=>{qc(t.inputs);let e=mn.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!e)throw new Error("Can't use matmul on the given tensors");let r=e[e.length-1],n=t.inputs[0].dims[t.inputs[0].dims.length-1];r<8&&n<8?t.compute(no(t.inputs,{activation:""},e)):t.compute(to(t.inputs,{activation:""},e))}}),mi,gi,Yc,ao,io,Qc,Xc,so,Jc=ee(()=>{Oe(),j_(),fi(),K_(),Kr(),Kc(),Yn(),mi=(t,e,r,n,a,s)=>{let i=t[0],o=t.slice(s?1:2,s?3:4),u=o.length,l=e[0],c=e.slice(2).map((m,_)=>m+(m-1)*(r[_]-1)),h=o.map((m,_)=>m+n[_]+n[_+u]).map((m,_)=>Math.floor((m-c[_]+a[_])/a[_]));return h.splice(0,0,i),h.splice(s?3:1,0,l),h},gi=[2,3,1,0],Yc=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[1]*e.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let a=t[0].dims.length-2;if(e.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(e.strides.length!==a)throw new Error(`strides should be ${a}D`);if(e.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},ao=(t,e)=>{let r=t.kernelShape.slice();for(let s=2;s<e[1].dims.length;++s)r[s-2]===0&&(r[s-2]=e[1].dims[s]);let n=t.pads.slice();ai.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,r,n,t.format==="NHWC",t.autoPad);let a=Object.assign({},t);return Object.assign(a,{kernelShape:r,pads:n}),a},io=t=>{let e=Qs(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,s=t.group,i=t.kernel_shape,o=t.pads,u=t.strides,l=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:s,kernelShape:i,pads:o,strides:u,wIsConst:l,...e,cacheKey:`${t.format};${e.activation};`}},Qc=(t,e,r)=>{let n=ao(r,e),a=r.format==="NHWC";if(r.group!==1){if(!t.adapterInfo.isArchitecture("ampere")&&a&&e[1].dims[0]===r.group&&e[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let P=mi(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),R=t.kernelCustomData.wT??t.compute(br(e[1],gi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=R);let W=[e[0],R];e.length===3&&W.push(e[2]),t.compute(Hc(W,n,P),{inputs:W})}else t.compute(ro(e,n));return}let s=e.length===3,i=e[0].dims[a?1:2],o=e[0].dims[a?2:3],u=e[0].dims[a?3:1],l=e[1].dims[2],c=e[1].dims[3],h=mi(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),m=h[a?1:2],_=h[a?2:3],p=h[a?3:1],b=a&&l===i&&c===o&&r.pads[0]===0&&r.pads[1]===0;if(b||l===1&&c===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 P=h[0],R,W,q,K=[];if(a){let re=t.kernelCustomData.wT??t.compute(br(e[1],gi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=re),b){let oe=i*o*u;R=e[0].reshape([1,P,oe]),W=re.reshape([1,oe,p]),q=[1,P,p]}else R=e[0].reshape([P,i*o,u]),W=re.reshape([1,u,p]),q=[P,m*_,p];K.push(R),K.push(W)}else R=e[0].reshape([P,u,i*o]),W=e[1].reshape([1,p,u]),q=[P,p,m*_],K.push(W),K.push(R);s&&K.push(e[2]);let ue=q[2],ne=K[0].dims[K[0].dims.length-1];ue<8&&ne<8?t.compute(no(K,n,h,q,a),{inputs:K}):t.compute(to(K,n,h,q,a),{inputs:K});return}let $=!0,S=t.kernelCustomData.wT??t.compute(br(e[1],gi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=S);let x=[e[0],S];s&&x.push(e[2]);let C=a?m*_:p,T=a?p:m*_,I=l*c*u;t.compute(Gc(x,n,h,C,T,I,s,$),{inputs:x})},Xc=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&n.push(t.inputs[2]);let a=[0,e.pads[0],0,e.pads[1]],s=[1].concat(e.strides),i=[1].concat(e.dilations),o=[1].concat(e.kernelShape),u=ao({...e,pads:a,strides:s,dilations:i,kernelShape:o},n);t.compute(ro(n,u,l=>r?[l[0],l[2],l[3]]:[]))},so=(t,e)=>{Yc(t.inputs,e),t.inputs[0].dims.length===3?Xc(t,e):Qc(t,t.inputs,e)}}),Zc,ep,Y_=ee(()=>{xe(),Gr(),Ae(),Kr(),Js(),Nc(),fi(),Zc=(t,e=!1,r,n,a=4)=>{let s=$=>{switch($){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 ${n}(v0, v1, v2, v3);
            `;default:throw new Error(`innerElementSize ${$} is not supported.`)}},i=t?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,o=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,u=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",l=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",c=t?"row":"col",h=t?"col":"row",m=`
      let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${c} / outWidth;
      let outCol = ${c} % outWidth;

      let WRow = ${h} / (uniforms.filter_dims[1] * inChannels);
      let WCol = ${h} / inChannels % uniforms.filter_dims[1];
      let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);
      let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);
      if (xR < 0.0 || xR >= f32(${u}) || fract(xR) > 0.0) {
        return ${n}(0.0);
      }
      if (xC < 0.0 || xC >= f32(${l}) || fract(xC) > 0.0) {
        return ${n}(0.0);
      }
      let iXR = i32(xR);
      let iXC = i32(xC);
      let xCh = ${h} % inChannels;
      ${i}
      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${a}];`,_=t?`
      let col = colIn * ${a};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
        ${m}
      }
      return ${n}(0.0);`:`
      let col = colIn * ${a};
      if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
        ${m}
      }
      return ${n}(0.0);`,p=`
      let col = colIn * ${a};
      let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);
      let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];
      if (${t?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"}  && coordX >= 0 && coordY >= 0) {
        let rowInner = row % inChannels;
        let coord = vec4<i32>(coordX, coordY, col, rowInner);
        ${s(a)}
      }
      return ${n}(0.0);
      `,b=Hr(r,n);return`
  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} {
    ${t?_:p}
  }

  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} {
    ${t?p:_}
  }

  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) {
    let col = colIn * ${a};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
      var value = valueInput;
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${o}
      ${Xs(e)}
      ${b}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${a}] = value;
    }
  }`},ep=(t,e,r,n,a,s,i,o)=>{let u=e.format==="NHWC",l=u?t[0].dims[3]:t[0].dims[1],c=r[0],h=u?r[2]:r[3],m=u?r[1]:r[2],_=u?r[3]:r[1],p=u&&l%4===0&&l%3&&_%4===0,b=u?_:h*m,$=u?h*m:_,S=[8,8,1],x=n<=8?[4,1,1]:[4,4,1],C=[Math.ceil(b/S[0]/x[0]),Math.ceil($/S[1]/x[1]),Math.ceil(c/S[2]/x[2])];tt("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${C}`);let T=p?4:1,I=Math.max(S[0]*T,S[1]),P=p?4:1,R=[e.kernelShape[u?1:2],e.kernelShape[u?2:3]],W=[R[0]+(e.dilations[0]<=1?0:(R[0]-1)*(e.dilations[0]-1)),R[1]+(e.dilations[1]<=1?0:(R[1]-1)*(e.dilations[1]-1))],q=[W[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),W[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],K=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:e.strides},{type:6,data:e.dilations},{type:6,data:R},{type:6,data:q}];qr(e,K),K.push(...ye(t[0].dims,t[1].dims));let ue=["rank","rank"];i&&(K.push(...ye(t[2].dims)),ue.push("rank")),K.push(...ye(r));let ne=re=>{let oe=X("x",t[0].dataType,t[0].dims.length,P),D=X("w",t[1].dataType,t[1].dims.length,1),O=_e("result",t[0].dataType,r.length,P),G=[oe,D],j="";if(i){let B=X("bias",t[2].dataType,t[2].dims.length,P);G.push(B),j+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${B.type.value} {
            return bias[coords.${u?"w":"y"}${p?"/ 4":""}];
          }`}let pe=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:R.length},{name:"pads",type:"i32",length:q.length}];jr(e,pe);let le=ft(t[0].dataType,1);if(le!=="f16"&&le!=="f32")throw new Error(`elemType ${le} is not supported.`);return`
        ${Zs("uniforms.result_strides")}
        ${re.registerUniforms(pe).declareVariables(...G,O)};
        ${j}
        ${Zc(u,i,e,oe.type.value,T)}
        ${p?pi(x,S,le,void 0,!u,I):hi(x,S,le,void 0,!u,I,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${x};${S};${p}`,inputDependencies:ue},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:K}),getShaderSource:ne}}}),tp,oo,Q_=ee(()=>{xe(),Gr(),Oe(),Ae(),tp=(t,e,r,n,a,s=!1,i,o,u=!1)=>{let l=u?1:2,c=u?2:3,h=u?3:1,m=s?2:1,_=`
  fn setOutputAtIndex(flatIndex : u32, value : ${s?`vec4<${i}>`:i}) {
    result[flatIndex] = ${s?`vec4<${i}>`:i}(value);
  }`;n&&(_+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${s?`vec4<${i}>`:i} {
      return bias[coords.${u?"w":"y"}${s?"/ 4":""}];
    }`);let p=s?4:1,b=X("W",e[1].dataType,e[1].dims.length,p),$=X("Dy",e[0].dataType,e[0].dims.length,p),S=[$,b];n&&S.push(X("bias",e[2].dataType,[r[h]].length,p));let x=_e("result",e[0].dataType,r.length,p),C=`{
        let batch: u32 = ${a?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];
        let r = ${a?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];
        let c = ${a?"global_id.y":"workgroup_id.y"} * ${m};
        let d1: u32 = ${a?"global_id.x":"workgroup_id.x"} * 4;

        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(uniforms.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<${i}>, ${m}>;
        for (var i = 0; i < ${m}; i++) {
          dotProd[i] = vec4<${i}>(0.0);
        }
        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {
          var dyR = (${i}(dyCorner.x) + ${i}(wR)) / ${i}(uniforms.strides.x);
          let wRPerm = uniforms.filter_dims[0] - 1 - wR;
          if (dyR < 0.0 || dyR >= ${i}(uniforms.Dy_shape[1]) ||
              fract(dyR) > 0.0 || wRPerm < 0) {
            continue;
          }
          let idyR: u32 = u32(dyR);

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

            let idyC: u32 = u32(dyC);
            let idyC2: u32 = u32(dyC2);
            if (bDyCVal && bDyCVal2) {
              let d2Length = uniforms.Dy_shape[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 = ${$.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${i}>(dot(xValue, wValue0),
                                      dot(xValue, wValue1),
                                      dot(xValue, wValue2),
                                      dot(xValue, wValue3));
                dotProd[0] = dotProd[0] + tmpval;

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

                dotProd[1] = dotProd[1] + vec4<${i}>(dot(xValue, wValue0),
                                                    dot(xValue, wValue1),
                                                    dot(xValue, wValue2),
                                                    dot(xValue, wValue3));
              }
            } else if (bDyCVal) {
              let d2Length = uniforms.Dy_shape[${h}];
              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 = ${$.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${i}>(dot(xValue, wValue0),
                                      dot(xValue, wValue1),
                                      dot(xValue, wValue2),
                                      dot(xValue, wValue3));
                dotProd[0] = dotProd[0] + tmpval;
              }
            } else if (bDyCVal2) {
              let d2Length = uniforms.Dy_shape[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 = ${$.get("batch","idyR","idyC2","d2")};
                let tmpval = vec4<${i}>(dot(xValue, wValue0),
                                      dot(xValue, wValue1),
                                      dot(xValue, wValue2),
                                      dot(xValue, wValue3));
                dotProd[1] = dotProd[1] + tmpval;
              }
            }
          }
        }

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

            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
              if (wC % uniforms.dilations.y != 0) {
                continue;
              }
              let dyC = (${i}(dyCCorner) + ${i}(wC)) / ${i}(uniforms.strides.y);
              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
              if (dyC < 0.0 || dyC >= ${i}(uniforms.Dy_shape[${c}]) ||
                  fract(dyC) > 0.0 || wCPerm < 0) {
                continue;
              }
              let idyC: u32 = u32(dyC);
              var inputChannel = groupId * uniforms.input_channels_per_group;
              for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {
                let xValue = ${u?$.get("batch","idyR","idyC","inputChannel"):$.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 + ${n?"bias[d1]":`${i}(0.0)`};
          ${x.setByOffset("global_idx","value")};
        `;return`
  ${t.registerUniforms(o).declareVariables(...S,x)}
  ${_}

    ${t.mainStart()}
    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
  ${s?C:T}}`},oo=(t,e,r)=>{let n=t.length>2,a=e.outputShape,s=Q.size(a),i=[Math.ceil(s/64),1,1];tt("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${i}`);let o=e.format==="NHWC",u=["rank","rank"],l=[e.strides[0],e.strides[1]],c=[e.kernelShape[o?1:2],e.kernelShape[o?2:3]],h=[e.dilations[0],e.dilations[1]],m=[c[0]+(e.dilations[0]<=1?0:(e.kernelShape[o?1:2]-1)*(e.dilations[0]-1)),c[1]+(e.dilations[1]<=1?0:(e.kernelShape[o?2:3]-1)*(e.dilations[1]-1))],_=[m[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),m[1]-1-Math.floor(e.pads[1]+e.pads[3])/2],p=!1,b=e.group,$=t[1].dims,S=$[0]/b,x=$[1],C=[{type:12,data:s},{type:12,data:l},{type:12,data:c},{type:12,data:h},{type:12,data:m},{type:6,data:_},{type:12,data:S},{type:12,data:x},...ye(t[0].dims,t[1].dims)];n&&(C.push(...ye(t[2].dims)),u.push("rank")),C.push(...ye(a));let T=i[1]===1&&i[2]===1,I=P=>{let R=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:l.length},{name:"filter_dims",type:"u32",length:c.length},{name:"dilations",type:"u32",length:c.length},{name:"effective_filter_dims",type:"u32",length:m.length},{name:"pads",type:"i32",length:_.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],W=ft(t[0].dataType);return`${tp(P,t,a,n,T,p,W,R,o)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${e.cacheKey};`,inputDependencies:u},getRunData:()=>({dispatchGroup:{x:i[0],y:i[1],z:i[2]},outputs:[{dims:r?r(a):a,dataType:t[0].dataType}],programUniforms:C}),getShaderSource:I}}}),rp,np,ap,uo,ip,sp,op,up,lp,dp,X_=ee(()=>{Y_(),Q_(),Kr(),Yn(),rp=(t,e,r,n,a,s)=>(t-1)*e+r+(n-1)*a+1-s,np=(t,e,r,n,a)=>{let s=Math.floor(t/2);e==="SAME_UPPER"?(r[n]=s,r[a]=t-s):e==="SAME_LOWER"&&(r[n]=t-s,r[a]=s)},ap=(t,e,r,n,a,s,i,o,u,l)=>{let c=t.length-2,h=l.length===0;if(u.length===0)for(let p=0;p<c;++p)u.push(0);let m=t[0],_=e[o?3:1]*a;for(let p=0,b=t.length-c-(o?1:0);p<c;++p,++b){let $=t[b],S=h?$*i[p]:l[p],x=rp($,i[p],s[p],e[b],r[p],S);np(x,n,s,p,p+c),h&&l.push(i[p]*($-1)+u[p]+(e[b]-1)*r[p]+1-s[p]-s[p+c])}l.splice(0,0,m),l.splice(o?3:1,0,_)},uo=(t,e)=>{let r=t.kernelShape.slice();if(t.kernelShape.length===0||t.kernelShape.reduce((h,m)=>h*m,1)===0){r.length=0;for(let h=2;h<e[1].dims.length;++h)r.push(e[1].dims[h])}let n=t.format==="NHWC";r.splice(0,0,e[1].dims[0]),r.splice(n?3:1,0,e[1].dims[1]);let a=t.pads.slice(),s=t.outputShape.slice(),i=t.outputPadding.slice(),o=e[0].dims,u=t.dilations.slice();if(u.reduce((h,m)=>h+m,0)===0){let h=e[0].dims.length-2;u=new Array(h).fill(1)}let l=t.strides.slice();if(l.reduce((h,m)=>h+m,0)===0){let h=e[0].dims.length-2;l=new Array(h).fill(1)}ap(o,r,u,t.autoPad,t.group,a,l,n,i,s);let c=Object.assign({},t);return Object.assign(c,{kernelShape:r,pads:a,outputPadding:i,outputShape:s,dilations:u,strides:l}),c},ip=t=>{let e=Qs(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof t.autoPad>"u"?0:t.autoPad],a=t.dilations,s=t.group,i=t.kernelShape,o=t.pads,u=t.strides,l=t.wIsConst(),c=t.outputPadding,h=t.outputShape;return{autoPad:n,format:r,dilations:a,group:s,kernelShape:i,outputPadding:c,outputShape:h,pads:o,strides:u,wIsConst:l,...e,cacheKey:`${t.format};${e.activation};`}},sp=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[0];if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let a=t[1].dims[1]*e.group;if(t.length===3&&(t[2].dims.length!==1||t[2].dims[0]!==a))throw new Error("invalid bias");let s=t[0].dims.length-2;if(e.dilations.reduce((i,o)=>i+o,0)>0&&e.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(e.strides.reduce((i,o)=>i+o,0)>0&&e.strides.length!==s)throw new Error(`strides should be ${s}D`);if(e.pads.reduce((i,o)=>i+o,0)>0&&e.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(e.outputPadding.length!==s&&e.outputPadding.length!==0)throw new Error(`output_padding should be ${s}D`);if(e.kernelShape.reduce((i,o)=>i+o,0)>0&&e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(e.outputShape.length!==0&&e.outputShape.length!==t[0].dims.length-2)throw new Error("invalid output shape")},op=[2,3,1,0],up=(t,e,r)=>{let n=uo(r,e),a=r.format==="NHWC",s=n.outputShape,i=s[a?3:1],o=e[0].dims[a?3:1];if(n.group!==1||i===1&&o===1){t.compute(oo(e,n));return}let u=s[a?1:2],l=s[a?2:3],c=e[1].dims[2],h=e[1].dims[3],m=a?u*l:i,_=a?i:u*l,p=c*h*o,b=!0,$=t.kernelCustomData.wT??t.compute(br(e[1],op),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=$);let S=[e[0],$],x=e.length===3;x&&(!a&&e[2].dims.length===1?S.push(e[2].reshape([e[2].dims[0],1,1])):S.push(e[2])),t.compute(ep(S,n,s,m,_,p,x,b),{inputs:S})},lp=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&n.push(t.inputs[2]);let a=e.kernelShape;(a.length===0||a[0]===0)&&(a=[t.inputs[1].dims[2]]);let s=e.dilations;(s.length===0||s[0]===0)&&(s=[1]);let i=e.strides;(i.length===0||i[0]===0)&&(i=[1]);let o=e.pads;o.length===0&&(o=[0,0]),o=[0,o[0],0,o[1]],i=[1].concat(i),s=[1].concat(s),a=[1].concat(a);let u=uo({...e,pads:o,strides:i,dilations:s,kernelShape:a},n);t.compute(oo(n,u,l=>r?[l[0],l[2],l[3]]:[l[0],l[1],l[3]]))},dp=(t,e)=>{sp(t.inputs,e),t.inputs[0].dims.length===3?lp(t,e):up(t,t.inputs,e)}}),cp,pp,hp,J_=ee(()=>{xe(),Oe(),lt(),Ae(),cp=(t,e,r,n)=>{let a=Q.size(e),s=e.length,i=X("input",t,s),o=_e("output",t,s),u=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),l=Q.normalizeAxis(u,s),c=h=>{let m=` i32(${i.indicesGet("inputIndices","uniforms.axis")}) `,_=ke("uniforms.input_shape","uniforms.axis",s),p=n.reverse?m+(n.exclusive?" + 1":""):"0",b=n.reverse?_:m+(n.exclusive?"":" + 1");return`
                ${h.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(i,o)}
                ${h.mainStart()}
                  ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
                  var inputIndices = ${o.offsetToIndices("global_idx")};
                  var sum = ${o.type.value}(0);
                  let first : i32 = ${p};
                  let last : i32 = ${b};
                  for (var i : i32 = first; i < last; i++) {
                    ${i.indicesSet("inputIndices","uniforms.axis","u32(i)")};
                    sum = sum + ${i.getByIndices("inputIndices")};
                  }
                  ${o.setByOffset("global_idx","sum")};
                }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:e,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},{type:12,data:l},...ye(e,e)]}),getShaderSource:c}},pp=(t,e)=>{let r=t.inputs[0].dims,n=t.inputs[0].dataType,a=t.inputs[1];t.compute(cp(n,r,a,e),{inputs:[0]})},hp=t=>{let e=t.exclusive===1,r=t.reverse===1;return Ke({exclusive:e,reverse:r})}}),fp,mp,gp,_p,wp,Z_=ee(()=>{xe(),Oe(),lt(),Ae(),fp=t=>{if(!t||t.length!==1)throw new Error("DepthToSpace requires 1 input.");if(t[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},mp=(t,e,r,n)=>{let a=[];a.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
    var a: ${r.type.indices};`);for(let s=0;s<e;++s)a.push(r.indicesSet("a",t[s],`i[${s}]`));return a.push("return a;}"),a.join(`
`)},gp=(t,e)=>{let r,n,a,s,i,o,u=e.format==="NHWC",l=e.blocksize,c=e.mode==="DCR";u?([r,n,a,s]=t.dims,i=c?[r,n,a,l,l,s/l**2]:[r,n,a,s/l**2,l,l],o=c?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([r,n,a,s]=[t.dims[0],t.dims[2],t.dims[3],t.dims[1]],i=c?[r,l,l,s/l**2,n,a]:[r,s/l**2,l,l,n,a],o=c?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let h=t.reshape(i),m=h.dims.length,_=t.dataType,p=X("a",_,m),b=_e("output",_,m),$=S=>`
  ${S.registerUniform("output_size","u32").declareVariables(p,b)}

  ${mp(o,m,p,b)}

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

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

    ${b.setByOffset("global_idx",p.getByIndices("aIndices"))}
  }`;return{name:"DepthToSpace",shaderCache:{hint:`${t.dims};${e.blocksize};${e.mode}`,inputDependencies:["rank"]},getRunData:S=>{let x=u?[r,n*l,a*l,s/l**2]:[r,s/l**2,n*l,a*l],C=Q.size(x),T=h.dims,I=Q.sortBasedOnPerm(T,o);return{outputs:[{dims:x,dataType:S[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:[{type:12,data:C},...ye(T,I)]}},getShaderSource:$}},_p=(t,e)=>{fp(t.inputs),t.compute(gp(t.inputs[0],e))},wp=t=>Ke({blocksize:t.blocksize,mode:t.mode,format:t.format})}),_i,Qn,lo,yp,bp,vp,$p,co,xp,Sp,kp,ew=ee(()=>{xe(),Oe(),lt(),Ae(),_i="[a-zA-Z]|\\.\\.\\.",Qn="("+_i+")+",lo="^"+Qn+"$",yp="("+Qn+",)*"+Qn,bp="^"+yp+"$",vp=class{constructor(t=-1){this.symbolToIndices=new Map,this.inputIndex=t}addSymbol(t,e){let r=this.symbolToIndices.get(t);r===void 0?r=[e]:r.push(e),this.symbolToIndices.set(t,r)}},$p=class{constructor(t,e){var a;this.equation=e,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[r,n]=e.includes("->")?e.split("->",2):[e,""];if(!r.match(RegExp(bp)))throw new Error("Invalid LHS term");if(r.split(",").forEach((s,i)=>{let o=t[i].dims.slice();if(!s.match(RegExp(lo)))throw new Error("Invalid LHS term");let u=this.processTerm(s,!0,o,i);this.lhs.push(u)}),n==="")n+=[...this.symbolToInfo.entries()].filter(([s,i])=>i.count===1||s==="...").map(([s])=>s).join("");else if(!n.match(RegExp(Qn)))throw new Error("Invalid RHS");(a=n.match(RegExp(_i,"g")))==null||a.forEach(s=>{if(s==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let i=this.symbolToInfo.get(s);if(i===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(i.dimValue)}}),this.rhs=this.processTerm(n,!1,this.outputDims)}addSymbol(t,e,r){let n=this.symbolToInfo.get(t);if(n!==void 0){if(n.dimValue!==e&&n.count!==1)throw new Error("Dimension mismatch");n.count++,n.inputIndices.push(r)}else n={count:1,dimValue:e,inputIndices:[r]};this.symbolToInfo.set(t,n)}processTerm(t,e,r,n=-1){let a=r.length,s=!1,i=[],o=0;if(!t.match(RegExp(lo))&&!e&&t!=="")throw new Error("Invalid LHS term");let u=t.match(RegExp(_i,"g")),l=new vp(n);return u==null||u.forEach((c,h)=>{if(c==="..."){if(s)throw new Error("Only one ellipsis is allowed per input term");s=!0;let m=a-u.length+1;if(m<0)throw new Error("Ellipsis out of bounds");if(i=r.slice(o,o+m),this.hasEllipsis){if(this.ellipsisDims.length!==i.length||this.ellipsisDims.toString()!==i.toString())throw new Error("Ellipsis dimensions mismatch")}else if(e)this.hasEllipsis=!0,this.ellipsisDims=i;else throw new Error("Ellipsis must be specified in the LHS");for(let _=0;_<i.length;_++){let p=String.fromCharCode(48+_);l.addSymbol(p,h+_),this.addSymbol(p,r[o++],n)}}else l.addSymbol(c,h+(this.hasEllipsis?this.ellipsisDims.length-1:0)),this.addSymbol(c,r[o++],n)}),l}},co=t=>t+"_max",xp=(t,e,r,n)=>{let a=t.map(l=>l.length).map((l,c)=>X(`input${c}`,e,l)),s=Q.size(n),i=_e("output",e,n.length),o=[...r.symbolToInfo.keys()].filter(l=>!r.rhs.symbolToIndices.has(l)),u=l=>{let c=[],h="var prod = 1.0;",m="var sum = 0.0;",_="sum += prod;",p=[],b=[],$=[],S=[],x=r.symbolToInfo.size===r.rhs.symbolToIndices.size;r.symbolToInfo.forEach((T,I)=>{var P;if(r.rhs.symbolToIndices.has(I)){let R=(P=r.rhs.symbolToIndices.get(I))==null?void 0:P[0];R!==void 0&&r.lhs.forEach((W,q)=>{if(T.inputIndices.includes(q)){let K=W.symbolToIndices.get(I);if(K===void 0)throw new Error("Invalid symbol error");K.forEach(ue=>{c.push(`${a[q].indicesSet(`input${q}Indices`,ue,i.indicesGet("outputIndices",R))}`)})}})}else r.lhs.forEach((R,W)=>{if(T.inputIndices.includes(W)){let q=R.symbolToIndices.get(I);if(q===void 0)throw new Error("Invalid symbol error");q.forEach(K=>{p.push(`${a[W].indicesSet(`input${W}Indices`,K,`${I}`)}`)}),S.push(`prod *= ${a[W].getByIndices(`input${W}Indices`)};`)}}),b.push(`for(var ${I}: u32 = 0; ${I} < uniforms.${co(I)}; ${I}++) {`),$.push("}")});let C=x?[...c,`let sum = ${a.map((T,I)=>T.getByIndices(`input${I}Indices`)).join(" * ")};`]:[...c,m,...b,...p,h,...S,_,...$];return`
            ${l.registerUniforms(o.map(T=>({name:`${co(T)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...a,i)}

            ${l.mainStart()}
            ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
            var outputIndices = ${i.offsetToIndices("global_idx")};
            ${a.map((T,I)=>`var input${I}Indices: ${a[I].type.indices};`).join(`
`)}
            ${C.join(`
`)};
            ${i.setByOffset("global_idx","sum")};
          }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:t.map(()=>"rank")},getRunData:()=>{let l=o.filter(h=>r.symbolToInfo.has(h)).map(h=>{var m;return{type:12,data:((m=r.symbolToInfo.get(h))==null?void 0:m.dimValue)||0}});l.push({type:12,data:s});let c=t.map((h,m)=>[...ye(h)]).reduce((h,m)=>h.concat(m),l);return c.push(...ye(n)),{outputs:[{dims:n,dataType:e}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:c}},getShaderSource:u}},Sp=(t,e)=>{let r=new $p(t.inputs,e.equation),n=r.outputDims,a=t.inputs.map((s,i)=>s.dims);t.compute(xp(a,t.inputs[0].dataType,r,n))},kp=t=>{let e=t.equation.replace(/\s+/g,"");return Ke({equation:e})}}),Ep,po,Cp,Tp,Ip,tw=ee(()=>{xe(),Oe(),Ae(),Ep=t=>{if(!t||t.length!==2)throw new Error("Expand requires 2 input.");let e=t[0].dims,r=Array.from(t[1].getBigInt64Array(),Number),n=r.length<e.length?0:r.length-e.length,a=e.length<r.length?0:e.length-r.length;for(;n<r.length&&a<e.length;++n,++a)if(r[n]!==e[a]&&r[n]!==1&&e[a]!==1)throw new Error("Expand requires shape to be broadcastable to input")},po=(t,e)=>{let r=t.length-e.length,n=[];for(let a=0;a<r;++a)n.push(t[a]);for(let a=0;a<e.length;++a)n.push(e[a]===1?t[a+r]:e[a]);return n},Cp=(t,e)=>t.length>e.length?po(t,e):po(e,t),Tp=t=>{let e=t[0].dims,r=Array.from(t[1].getBigInt64Array(),Number),n=Cp(e,r),a=t[0].dataType,s=a===9?4:1,i=Math.ceil(Q.size(n)/s),o=l=>{let c=X("input",a,e.length,s),h=_e("output",a,n.length,s),m;if(a===9){let _=(p,b,$="")=>`
          let outputIndices${b} = ${h.offsetToIndices(`outputOffset + ${b}u`)};
          let offset${b} = ${c.broadcastedIndicesToOffset(`outputIndices${b}`,h)};
          let index${b} = offset${b} / 4u;
          let component${b} = offset${b} % 4u;
          ${p}[${b}] = ${$}(${c.getByOffset(`index${b}`)}[component${b}]);
        `;m=`
        let outputOffset = global_idx * ${s};
        var data = vec4<u32>(0);
        ${_("data",0,"u32")}
        ${_("data",1,"u32")}
        ${_("data",2,"u32")}
        ${_("data",3,"u32")}
        ${h.setByOffset("global_idx","data")}
      }`}else m=`
        let outputIndices = ${h.offsetToIndices("global_idx")};
        let inputOffset = ${c.broadcastedIndicesToOffset("outputIndices",h)};
        ${h.setByOffset("global_idx",c.getByOffset("inputOffset"))}
      }`;return`
    ${l.registerUniform("vec_size","u32").declareVariables(c,h)}
    ${l.mainStart()}
    ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
    ${m}`},u=[{type:12,data:i},...ye(e,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:o,getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:u})}},Ip=t=>{Ep(t.inputs),t.compute(Tp(t.inputs),{inputs:[0]})}}),Ap,Mp,rw=ee(()=>{xe(),Oe(),Ae(),Ys(),Ap=t=>{let e=t[0].dataType,r=Q.size(t[0].dims),n=Q.size(t[1].dims),a=n%4===0,s=i=>{let o=X("x",e,[1],4),u=X("bias",e,[1],4),l=_e("y",e,[1],4),c=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],h=_=>`
      let bias${_}_offset: u32 = (global_idx * 4 + ${_}) % uniforms.bias_size;
      let bias${_} = ${u.getByOffset(`bias${_}_offset / 4`)}[bias${_}_offset % 4];`,m=a?`
      let bias = ${u.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${h(0)}${h(1)}${h(2)}${h(3)}
      let bias = ${o.type.value}(bias0, bias1, bias2, bias3);`;return`${i.registerUniforms(c).declareVariables(o,u,l)}

    ${js(Et(e))}

    ${i.mainStart(gn)}
      ${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}

      let x = ${o.getByOffset("global_idx")};
      ${m}
      let x_in = x + bias;
      ${l.setByOffset("global_idx",Ks("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getShaderSource:s,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/gn/4)}})}},Mp=t=>{t.inputs.length<2||Q.size(t.inputs[1].dims)===0?bc(t):t.compute(Ap(t.inputs))}}),Op,zp,Pp,Rp,nw=ee(()=>{xe(),Oe(),lt(),Ae(),Op=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},zp=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,s=Q.normalizeAxis(e.axis,a),i=r.slice(0);i.splice(s,1,...n);let o=r[s],u=t[0].dataType===9?4:1,l=Math.ceil(Q.size(i)/u),c=[{type:12,data:l},{type:6,data:o},{type:12,data:s},...ye(t[0].dims,t[1].dims,i)],h=m=>{let _=X("data",t[0].dataType,t[0].dims.length,u),p=X("inputIndices",t[1].dataType,t[1].dims.length),b=_e("output",t[0].dataType,i.length,u),$=x=>{let C=n.length,T=`var indicesIndices${x}  = ${p.type.indices}(0);`;for(let I=0;I<C;I++)T+=`${C>1?`indicesIndices${x}[${I}]`:`indicesIndices${x}`} = ${i.length>1?`outputIndices${x}[uniforms.axis + ${I}]`:`outputIndices${x}`};`;T+=`
          var idx${x} = ${p.getByIndices(`indicesIndices${x}`)};
          if (idx${x} < 0) {
            idx${x} = idx${x} + uniforms.axisDimLimit;
          }
          var dataIndices${x} : ${_.type.indices};
        `;for(let I=0,P=0;I<a;I++)I===s?(T+=`${a>1?`dataIndices${x}[${I}]`:`dataIndices${x}`} = u32(idx${x});`,P+=C):(T+=`${a>1?`dataIndices${x}[${I}]`:`dataIndices${x}`} = ${i.length>1?`outputIndices${x}[${P}]`:`outputIndices${x}`};`,P++);return T},S;if(t[0].dataType===9){let x=(C,T,I="")=>`
          let outputIndices${T} = ${b.offsetToIndices(`outputOffset + ${T}u`)};
          ${$(T)};
          let offset${T} = ${_.indicesToOffset(`dataIndices${T}`)};
          let index${T} = offset${T} / 4u;
          let component${T} = offset${T} % 4u;
          ${C}[${T}] = ${I}(${_.getByOffset(`index${T}`)}[component${T}]);
        `;S=`
        let outputOffset = global_idx * ${u};
        var value = vec4<u32>(0);
        ${x("value",0,"u32")}
        ${x("value",1,"u32")}
        ${x("value",2,"u32")}
        ${x("value",3,"u32")}
        ${b.setByOffset("global_idx","value")}
      `}else S=`
      let outputIndices = ${b.offsetToIndices("global_idx")};
      ${$("")};
      let value = ${_.getByIndices("dataIndices")};
      ${b.setByOffset("global_idx","value")};
      `;return`
      ${m.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(_,p,b)}
      ${m.mainStart()}
        ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${S}
      }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:c}),getShaderSource:h}},Pp=t=>Ke({axis:t.axis}),Rp=(t,e)=>{let r=t.inputs;Op(r),t.compute(zp(t.inputs,e))}}),Bp,Dp,Np,Fp,aw=ee(()=>{xe(),Oe(),lt(),Ae(),Bp=t=>{if(!t||t.length!==2)throw new Error("GatherElements requires 2 inputs.");if(t[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(t[0].dims.length!==t[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},Dp=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,s=t[1].dims,i=t[1].dataType,o=Q.normalizeAxis(e.axis,a),u=r[o],l=s.slice(0),c=Q.size(l),h=X("input",n,a),m=X("indicesInput",i,s.length),_=_e("output",n,l.length),p=[{type:12,data:c},{type:6,data:u},{type:12,data:o}];return p.push(...ye(r,s,l)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:l,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:p}),getShaderSource:b=>`
      ${b.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(h,m,_)}
      ${b.mainStart()}
      ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${_.setByOffset("global_idx","value")};
  }`}},Np=t=>Ke({axis:t.axis}),Fp=(t,e)=>{let r=t.inputs;Bp(r),t.compute(Dp(t.inputs,e))}}),Lp,Up,Wp,Vp,iw=ee(()=>{xe(),Oe(),Ae(),Lp=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},Up=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,s,i]=Sl.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[a,s];if(!o)throw new Error("Can't use gemm on the given tensors");let u=Q.size(o),l=[{type:12,data:u},{type:12,data:a},{type:12,data:s},{type:12,data:i},{type:1,data:e.alpha},{type:1,data:e.beta}],c=["type","type"];t.length===3&&(l.push(...ye(t[2].dims)),c.push("rank")),l.push(...ye(o));let h=m=>{let _="";e.transA&&e.transB?_="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?_="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?_="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(_="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let p=e.alpha===1?"":"value *= uniforms.alpha;",b=X("a",t[0].dataType,t[0].dims),$=X("b",t[1].dataType,t[1].dims),S=b.type.value,x=null,C=[b,$];t.length===3&&(x=X("c",t[2].dataType,t[2].dims.length),C.push(x));let T=_e("output",t[0].dataType,o.length);C.push(T);let I=[{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`
  ${m.registerUniforms(I).declareVariables(...C)}

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

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

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

    ${p}
    ${x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",T)}; value += ${S}(uniforms.beta) * ${x.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:h}},Wp=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},Vp=(t,e)=>{Lp(t.inputs),t.compute(Up(t.inputs,e))}}),Gp,Hp,qp,jp,sw=ee(()=>{xe(),Oe(),Ae(),Gp=(t,e)=>{let r=t[0].dims,n=r,a=2,s=Q.sizeToDimension(r,a),i=Q.sizeFromDimension(r,a),o=at(i),u=i/o,l=[r[0],r[1],u],c=["rank","type","type"],h=[{type:12,data:i},{type:12,data:u}];h.push(...ye(l,l));let m=_=>{let p=X("x",t[0].dataType,l.length,o),b=X("scale",t[1].dataType,t[1].dims),$=X("bias",t[2].dataType,t[2].dims),S=_e("output",t[0].dataType,l.length,o),x=[p,b,$,S],C=p.type.value,T=o===1?"f32":`vec${o}<f32>`,I=64,P=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`
  var<workgroup> meanShared : f32;
  var<workgroup> squaredNormShared : f32;
  var<workgroup> workgroupShared : array<${T}, ${I}>;
  const workgroupSize = ${I}u;
  ${_.registerUniforms(P).declareVariables(...x)}
  ${_.mainStart(I)}
    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 = ${T}(0);
    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
      initial = initial + ${T}(${p.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 = ${Mr("workgroupShared[0]",o)} / f32(uniforms.normSize);
    }
    workgroupBarrier();

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

    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${e.epsilon}));
    let channelScale = invStdDev * f32(${b.getByOffset("channel")});
    let channelShift = f32(${$.getByOffset("channel")}) - meanShared * channelScale;
    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
      let value = ${p.get("batch","channel","h")} * ${C}(${T}(channelScale)) + ${C}(${T}(channelShift));
      ${S.set("batch","channel","h","value")};
    }
  }`};return{name:"InstanceNormalization",shaderCache:{hint:`${e.epsilon};${o}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:s},programUniforms:h}),getShaderSource:m}},Hp=(t,e,r,n,a,s,i,o)=>{let u=at(i),l=64,c=u===1?"vec2f":`mat2x${u}f`,h=u===1?"f32":`vec${u}f`,m=(P,R)=>`${c}(${P}, ${R})`,_=a*i/u,p=Math.ceil(s/l),b=["type"],$=[{type:12,data:p},{type:12,data:s},{type:12,data:Math.floor(i/u)},{type:12,data:Math.floor(s*i/u)}],S=P=>{let R=X("input",e.dataType,e.dims,u);return`
  ${P.declareVariables(R)}
  @group(0) @binding(1) var<storage, read_write> output : array<${c}>;
  struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};
  @group(0) @binding(2) var<uniform> uniforms: Uniforms;

  ${P.mainStart(l)}
    let currentImageNumber = global_idx / ${l} / uniforms.C;
    let currentChannelNumber = (global_idx / ${l}) % uniforms.C;
    let wgOffset = local_id.x * 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 = ${yr("f32",u)};
    var squaredSum = ${yr("f32",u)};
    for (var i: u32 = wgOffset; i < wgMax; i++) {
        let value = ${h}(input[offset + i * uniforms.C]);
        sum += value;
        squaredSum += value * value;
    }
    output[global_idx] = ${m("sum","squaredSum")};
  }`},x=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${u}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:[a,i,l,2],dataType:1}],dispatchGroup:{x:a*i/u},programUniforms:$}),getShaderSource:S},{inputs:[e],outputs:[-1]})[0],C=[{type:12,data:_},{type:12,data:s},{type:12,data:Math.floor(i/u)},{type:12,data:Math.floor(l*i/u)}],T=["type","type","type"],I=P=>{let R=X("scale",r.dataType,r.dims,u),W=X("bias",n.dataType,n.dims,u);return`
  @group(0) @binding(0) var<storage, read> input : array<${c}>;
  @group(0) @binding(1) var<storage, read> scale : array<${R.type.storage}>;
  @group(0) @binding(2) var<storage, read> bias : array<${W.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;

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

    output[global_idx] = ${m("channelScale","channelShift")};
  }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${u};${o}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:[a,i,2],dataType:1}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:C}),getShaderSource:I},{inputs:[x,r,n],outputs:[-1]})[0]},qp=(t,e,r)=>{let n=e[0].dims,a=n,s=n[0],i=n[n.length-1],o=Q.sizeFromDimension(n,1)/i,u=at(i),l=Q.size(a)/u,c=[{type:12,data:o},{type:12,data:Math.floor(i/u)}],h=["type","type"],m=Hp(t,e[0],e[1],e[2],s,o,i,r.epsilon),_=p=>{let b=ft(e[0].dataType),$=u===1?"vec2f":`mat2x${u}f`,S=u===1?b:`vec${u}<${b}>`,x=X("input",e[0].dataType,e[0].dims,u),C=_e("output",e[0].dataType,a,u);return`
  @group(0) @binding(0) var<storage, read> input : array<${x.type.storage}>;
  @group(0) @binding(1) var<storage, read> scaleInput : array<${$}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${C.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${p.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], ${S}(scale[0]), ${S}(scale[1]));
  }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${u}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:c}),getShaderSource:_},{inputs:[e[0],m]})},jp=(t,e)=>{e.format==="NHWC"?qp(t,t.inputs,e):t.compute(Gp(t.inputs,e))}}),Kp,Yp,Qp,ow=ee(()=>{xe(),Oe(),Ae(),Kp=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Yp=(t,e,r)=>{let n=e.simplified,a=t[0].dims,s=t[1],i=!n&&t[2],o=a,u=Q.normalizeAxis(e.axis,a.length),l=Q.sizeToDimension(a,u),c=Q.sizeFromDimension(a,u),h=Q.size(s.dims),m=i?Q.size(i.dims):0;if(h!==c||i&&m!==c)throw new Error(`Size of X.shape()[axis:] == ${c}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${h} and bias size of ${m}`);let _=[];for(let I=0;I<a.length;++I)I<u?_.push(a[I]):_.push(1);let p=at(c),b=["type","type"],$=[{type:12,data:l},{type:1,data:c},{type:12,data:Math.floor(c/p)},{type:1,data:e.epsilon}];i&&b.push("type");let S=r>1,x=r>2,C=I=>{let P=ft(t[0].dataType),R=[X("x",t[0].dataType,t[0].dims,p),X("scale",s.dataType,s.dims,p)];i&&R.push(X("bias",i.dataType,i.dims,p)),R.push(_e("output",t[0].dataType,o,p)),S&&R.push(_e("mean_data_output",1,_)),x&&R.push(_e("inv_std_output",1,_));let W=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${I.registerUniforms(W).declareVariables(...R)}
  ${I.mainStart()}
    ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${yr("f32",p)};
    var mean_square_vector = ${yr("f32",p)};

    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
      let value = ${_n(P,p,"x[h + offset]")};
      mean_vector += value;
      mean_square_vector += value * value;
    }
    let mean = ${Mr("mean_vector",p)} / uniforms.norm_size;
    let inv_std_dev = inverseSqrt(${Mr("mean_square_vector",p)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon);

    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
      let f32input = ${_n(P,p,"x[j + offset]")};
      let f32scale = ${_n(P,p,"scale[j]")};
      output[j + offset] = ${R[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale
        ${i?`+ ${_n(P,p,"bias[j]")}`:""}
      );
    }

    ${S?"mean_data_output[global_idx] = mean":""};
    ${x?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},T=[{dims:o,dataType:t[0].dataType}];return S&&T.push({dims:_,dataType:1}),x&&T.push({dims:_,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${p};${r};${n}`,inputDependencies:b},getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:$}),getShaderSource:C}},Qp=(t,e)=>{Kp(t.inputs),t.compute(Yp(t.inputs,e,t.outputCount))}}),Xp,Jp,Zp,eh,uw=ee(()=>{xe(),Oe(),lt(),Ae(),Xp=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((e.k+e.blockSize-1)/e.blockSize),s=e.blockSize/8*e.bits,i=t[1];if(!Q.areEqual(i.dims,[e.n,a,s]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(Q.size(o)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let u=t[3].dims,l=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(Q.size(u)!==l)throw new Error("zeroPoints input size error.")}},Jp=(t,e,r,n)=>{let a=t[0].dims,s=a.length,i=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=a[s-2],u=e.k,l=e.n,c=a.slice(0,s-2),h=Q.size(c),m=e.blockSize/8*e.bits/4,_=t[0].dataType,p=at(o),b=at(e.k),$=at(m),S=jn(_),x=o*i*S,C=Math.floor(n/x),T=i<=r[0]&&C>0,I=!T||C>=4?at(l):C>=2&&at(l)>=2?2:1,P=c.concat([o,l]),R=Q.size(P)/I/p,W=T?[]:[{type:12,data:R},{type:12,data:e.blockSize}],q=[h,o,u/b],K=Q.convertShape(t[1].dims).slice();K.splice(-1,1,m/$),W.push(...ye(q)),W.push(...ye(K)),W.push(...ye(t[2].dims)),t.length===4&&W.push(...ye(Q.convertShape(t[3].dims)));let ue=[h,o,l/I];W.push(...ye(ue));let ne=re=>{let oe=q.length,D=X("a",t[0].dataType,oe,b),O=X("b",12,K.length,$),G=X("scales",t[2].dataType,t[2].dims.length),j=[D,O,G],pe=t.length===4?X("zero_points",12,t[3].dims.length):void 0;pe&&j.push(pe);let le=ue.length,B=_e("output",t[0].dataType,le,I),ae=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],ce=ft(t[0].dataType),Se=(()=>{switch(b){case 1:return`array<${ce}, 8>`;case 2:return`mat4x2<${ce}>`;case 4:return`mat2x4<${ce}>`;default:throw new Error(`${b}-component is not supported.`)}})(),Te=`
        for (var word: u32 = 0; word < ${m}; word += ${$}) {
          ${O.indicesSet("b_indices","2","word")};
          let b_data = ${O.getByIndices("b_indices")};
          for (var i: u32 = 0; i < ${$}; i++) {
            let b_value: u32 = ${$===1?"b_data":"b_data[word + i]"};
            let b_mask: u32 = 0x0F0F0F0Fu;
            let b_value_lower: vec4<u32> = unpack4xU8(b_value & b_mask);
            let b_value_upper: vec4<u32> = unpack4xU8((b_value >> 4) & b_mask);
            let b_quantized_values = ${Se}(${Array.from({length:4},(it,qe)=>`${ce}(b_value_lower[${qe}]), ${ce}(b_value_upper[${qe}])`).join(", ")});
            let b_dequantized_values = ${b===1?`${Se}(${Array.from({length:8},(it,qe)=>`(b_quantized_values[${qe}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Se}(${Array(8).fill("zero_point").join(",")})) * scale;`};
            // Number of B elements per 32-bit word is 32/bits = 32/4 = 8
            for (var m: u32 = 0; m < ${T?o:p}u; m++) {
              ${D.indicesSet("a_indices",oe-2,T?"m":`row * ${p} + m`)};
              ${D.indicesSet("a_indices",oe-1,"word_offset")};
              var input_offset = ${D.indicesToOffset("a_indices")};
              var a_data: ${Se};
              for (var j: u32 = 0; j < ${8/b}; j++) {
                a_data[j] = ${D.getByOffset("input_offset")};
                input_offset++;
              }
              ${T?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${I>1?"[c]":""} += ${Array.from({length:8/b},(it,qe)=>`${b===1?`a_data[${qe}] * b_dequantized_values[${qe}]`:`dot(a_data[${qe}], b_dequantized_values[${qe}])`}`).join(" + ")};
            }
            word_offset += ${8/b};
          }
        }`,Qe=pe?`
          zero_point_offset += 4;
          if (zero_point_offset == 32) {
            zero_point_offset = 0;
            zero_point_index++;
            zero_point_word = ${pe.getByOffset("zero_point_index")};
          }`:"";return T?`
        var<workgroup> workgroup_shared: array<${B.type.value}, ${o*i}>;
        ${re.declareVariables(...j,B)}
        ${re.mainStart([i,1,1])}
          var a_indices: ${D.type.indices};
          var block = local_id.x;
          var col = workgroup_id.y;
          var batch = workgroup_id.z;
          ${D.indicesSet("a_indices","0","batch")};
          // Two zero points are packed into one byte when uniforms.bits is 4.
          for (var c: u32 = 0; c < ${I}; c++) {
            let col_times_components_plus_c = col * ${I} + c;
              ${pe?`
            var zero_point_bytes_per_col: u32 = (${i} + 1) / 2;
            var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u);
            var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u;
            var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u;
            var zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            var zero_point_word: u32 = ${pe.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}
            var b_indices: ${O.type.indices};
            ${O.indicesSet("b_indices","0","col_times_components_plus_c")};
            // The scale and zero points are computed per block.
            var scales_index = col_times_components_plus_c * ${i} + block;
            let scale = ${G.getByOffset("scales_index")};
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${ce}(${pe?"(zero_point_word) & 0xFu":8});
            ${O.indicesSet("b_indices","1","block")};
            var word_offset: u32 = block * ${e.blockSize/b};
            var workgroup_shared_offset: u32 = block * ${o};
            ${Te}
          }
          workgroupBarrier();
          if (local_id.x == 0u) {
            var output_indices: ${B.type.indices};
            ${B.indicesSet("output_indices","0","batch")};
            ${B.indicesSet("output_indices",le-1,"col")};
            ${B.indicesSet("output_indices",le-2,"0")};
            var output_offset = ${B.indicesToOffset("output_indices")};
            for (var m: u32 = 0u; m < ${o}u; m++) {
              var output_value: ${B.type.value} = ${B.type.value}(0);
              var workgroup_shared_offset: u32 = m;
              for (var b: u32 = 0u; b < ${i}u; b++) {
                output_value += workgroup_shared[workgroup_shared_offset];
                workgroup_shared_offset += ${o};
              }
              ${B.setByOffset("output_offset","output_value")};
              output_offset += ${l/I};
            }
          }
        }`:`
        ${re.registerUniforms(ae).declareVariables(...j,B)}
        ${re.mainStart()}
          ${re.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var output_values: array<${B.type.value}, ${p}>;
          var output_indices = ${B.offsetToIndices("global_idx")};
          var col = ${B.indicesGet("output_indices",le-1)};
          var row = ${B.indicesGet("output_indices",le-2)};
          var a_indices: ${D.type.indices} = output_indices;
          // Two zero points are packed into one byte because uniforms.bits <= 4.
          // zero_point_offset is either 0 or 4. It is bit offset within one byte.
          // TODO support zero_point_offset for bits > 4
          ${pe?`
          var zero_point_abs_offset = col * ${I} * ((${i} + 1) / 2);
          var zero_point_index: u32 = zero_point_abs_offset / 4;
          var zero_point_word: u32 = ${pe.getByOffset("zero_point_index")};
          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}
          var scale_index = col * ${i*I};
          var b_indices: ${O.type.indices};
          for (var c: u32 = 0; c < ${I}; c++) {
            ${O.indicesSet("b_indices","0",`col * ${I} + c`)};
            var block_offset: u32 = 0;
            for (var block: u32 = 0; block < ${i}; block++) {
              // The scale and zero points are computed per block.
              let scale = ${G.getByOffset("scale_index")};
              // The default zero point is 8 for unsigned 4-bit quantization.
              let zero_point = ${ce}(${pe?"extractBits(zero_point_word, zero_point_offset, 4)":8});
              ${O.indicesSet("b_indices","1","block")};
              var word_offset: u32 = block_offset;
              ${Te}
              scale_index++;
              ${Qe}
              block_offset += uniforms.block_size / ${b};
            }
            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.
            ${pe?`if (zero_point_offset % 8 > 0) {
                ${Qe}
              }`:""}
            }
            for (var k: u32 = 0u; k < ${p}u; k++) {
              ${B.indicesSet("output_indices",le-2,`${p} * row + k`)};
              ${B.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:T?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${_};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:P,dataType:_}],name:T?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:T?{x:1,y:Math.ceil(l/I),z:h}:{x:Math.ceil(R/64)},programUniforms:W}),getShaderSource:ne}},Zp=(t,e)=>{Xp(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(Jp(t.inputs,e,r,n))},eh=t=>Ke(t)}),$t,th,rh,ho,nh,wi,ah,lw=ee(()=>{xe(),Oe(),lt(),Ps(),zd(),Ae(),Yn(),$t=(t,e)=>t.length>e&&t[e].dims.length>0&&Q.size(t[e].dims)>0?t[e]:void 0,th=(t,e)=>{let r=t[0],n=$t(t,1),a=$t(t,2),s=$t(t,3),i=$t(t,4),o=$t(t,5),u=$t(t,6),l=$t(t,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let c=!1,h=r.dims[0],m=r.dims[1],_=r.dims.length===3?c?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],p=m,b=0,$=0,S=Math.floor(_/e.numHeads);if(u&&l){if(u.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(u.dims[0]!==h||u.dims[1]!==e.numHeads||u.dims[3]!==S)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[0]!==h||l.dims[1]!==e.numHeads||l.dims[3]!==S)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[2]!==l.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(l.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');b=u.dims[2],$=u.dims[2]}else if(u||l)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let x;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');x=2,p=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==S)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(a)throw new Error('Expect "value" be none when "key" has packed kv format.');x=5,p=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==S)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');x=0,p=n.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]!==e.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(a&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let C=0;if(i){C=8;let W=i.dims;throw W.length===1?W[0]===h?C=1:W[0]===3*h+2&&(C=3):W.length===2&&W[0]===h&&W[1]===p&&(C=5),C===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let T=!1,I=_;if(a){if(a.dims.length!==3&&a.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==a.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(a.dims.length===3){if(p!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');I=a.dims[2]}else{if(p!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');I=a.dims[1]*a.dims[3],T=!0}}let P=b+p,R=!1;if(i)throw new Error("Key padding mask is not supported");if(o){if(o.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(o.dims[0]!==h&&o.dims[0]!==1||o.dims[1]!==e.numHeads||o.dims[2]!==m||o.dims[3]!==P)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:h,sequenceLength:m,pastSequenceLength:b,kvSequenceLength:p,totalSequenceLength:P,maxSequenceLength:$,inputHiddenSize:0,hiddenSize:_,vHiddenSize:I,headSize:S,vHeadSize:Math.floor(I/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:C,scale:e.scale,broadcastResPosBias:R,passPastInKv:T,qkvFormat:x}},rh=t=>Ke({...t}),ho=Ke({perm:[0,2,1,3]}),nh=(t,e,r,n,a,s,i)=>{let o=[n,a,s],u=Q.size(o),l=[{type:12,data:u},{type:12,data:i},{type:12,data:s}],c=h=>{let m=_e("qkv_with_bias",e.dataType,o),_=X("qkv",e.dataType,o),p=X("bias",r.dataType,o),b=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${h.registerUniforms(b).declareVariables(_,p,m)}
  ${h.mainStart()}
    ${h.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 t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:c},{inputs:[e,r],outputs:[-1]})[0]},wi=(t,e,r,n,a,s,i,o)=>{let u=s;if(i){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return u=nh(t,s,i,e,n,r*a,o),u=u.reshape([e,n,r,a]),t.compute(br(u,ho.perm),{inputs:[u],outputs:[-1]})[0]}else return s.dims.length===3&&(u=s.reshape([e,n,r,a])),t.compute(br(u,ho.perm),{inputs:[u],outputs:[-1]})[0]},ah=(t,e)=>{let r=th(t.inputs,e),n=t.inputs[0],a=$t(t.inputs,1),s=$t(t.inputs,2),i=$t(t.inputs,3),o=$t(t.inputs,4),u=$t(t.inputs,5),l=$t(t.inputs,6),c=$t(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((a==null?void 0:a.dims.length)===5)throw new Error("Packed KV is not implemented");let h=a&&s&&a.dims.length===4&&s.dims.length===4,m=wi(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(h)return li(t,m,a,s,o,void 0,l,c,u,r,e);if(!a||!s)throw new Error("key and value must be provided");let _=wi(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,i,r.hiddenSize),p=wi(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,s,i,2*r.hiddenSize);li(t,m,_,p,o,void 0,l,c,u,r,e)}}),ih,sh,oh,uh,lh,dh,ch,ph,hh,dw=ee(()=>{xe(),Oe(),Ae(),ih=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1&&t[0].dataType!==10)throw new Error("Input type must be float or float16.");if(t.length>=2){let e=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(e=t[3].dims[0]*2===t[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},sh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
            k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${ke("uniforms.x_shape",a,e)})) {
              break;
            }
            offset += k * i32(${ke("uniforms.x_strides",a,e)});
        `;return`
          value = ${t.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${n}
            value = x[offset];
          }
      `},oh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${ke("uniforms.x_shape",a,e)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${ke("uniforms.x_shape",a,e)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${ke("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},uh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${ke("uniforms.x_shape",a,e)})) {
                  k = i32(${ke("uniforms.x_shape",a,e)}) - 1;
                }
                offset += k * i32(${ke("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},lh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
                if (k < 0)  {
                  k += i32(${ke("uniforms.x_shape",a,e)}]);
                }
                if (k >= i32(${ke("uniforms.x_shape",a,e)})) {
                  k -= i32(${ke("uniforms.x_shape",a,e)});
                }
                offset += k * i32(${ke("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},dh=(t,e,r)=>{switch(r.mode){case 0:return sh(t,e,r.pads.length);case 1:return oh(t,e,r.pads.length);case 2:return uh(t,e,r.pads.length);case 3:return lh(t,e,r.pads.length);default:throw new Error("Invalid mode")}},ch=(t,e)=>{let r=Q.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=Q.size(r),s=[{type:12,data:a},{type:6,data:e.pads}];e.mode===0&&s.push({type:t[0].dataType,data:e.value}),s.push(...ye(t[0].dims,r));let i=["rank"],o=u=>{let l=_e("output",t[0].dataType,r.length),c=X("x",t[0].dataType,n.length),h=c.type.value,m=dh(l,n.length,e),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&_.push({name:"constant_value",type:h}),`
            ${u.registerUniforms(_).declareVariables(c,l)}
            ${u.mainStart()}
            ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${h}(0);
            ${m}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(Q.size(r)/64)},programUniforms:s}),getShaderSource:o}},ph=(t,e)=>{if(t.length>1){let r=t[1].getBigInt64Array(),n=t.length>=3&&t[2].data?t[2].getFloat32Array()[0]:0,a=t[0].dims.length,s=new Int32Array(2*a).fill(0);if(t.length>=4){let o=t[3].getBigInt64Array();for(let u=0;u<o.length;u++)s[Number(o[u])]=Number(r[u]),s[Number(o[u])+a]=Number(r[u+o.length])}else r.forEach((o,u)=>s[Number(u)]=Number(o));let i=[];return s.forEach(o=>i.push(o)),{mode:e.mode,value:n,pads:i}}else return e},hh=(t,e)=>{ih(t.inputs);let r=ph(t.inputs,e);t.compute(ch(t.inputs,r),{inputs:[0]})}}),Xn,fo,mo,go,_o,fh,mh,wo,yo,gh,_h,bo,wh,yh,vo,bh,vh,$h,xh,cw=ee(()=>{Yt(),xe(),Oe(),Ae(),Xn=t=>{if(Ue.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},fo=(t,e,r)=>{let n=e.format==="NHWC",a=t.dims.slice();n&&a.splice(1,0,a.pop());let s=Object.hasOwnProperty.call(e,"dilations"),i=e.kernelShape.slice(),o=e.strides.slice(),u=s?e.dilations.slice():[],l=e.pads.slice();ai.adjustPoolAttributes(r,a,i,o,u,l);let c=ai.computePoolOutputShape(r,a,o,u,i,l,e.autoPad),h=Object.assign({},e);s?Object.assign(h,{kernelShape:i,strides:o,pads:l,dilations:u,cacheKey:e.cacheKey}):Object.assign(h,{kernelShape:i,strides:o,pads:l,cacheKey:e.cacheKey});let m=c.slice();return m.push(m.splice(1,1)[0]),[h,n?m:c]},mo=(t,e)=>{let r=e.format==="NHWC",n=Q.size(t),a=Q.size(e.kernelShape),s=[{type:12,data:n},{type:12,data:a}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let o=e.kernelShape[e.kernelShape.length-1],u=e.strides[e.strides.length-1],l=e.pads[e.pads.length/2-1],c=e.pads[e.pads.length-1],h=!!(l+c);s.push({type:12,data:o},{type:12,data:u},{type:12,data:l},{type:12,data:c}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let m=!1;if(e.kernelShape.length===2){let _=e.kernelShape[e.kernelShape.length-2],p=e.strides[e.strides.length-2],b=e.pads[e.pads.length/2-2],$=e.pads[e.pads.length-2];m=!!(b+$),s.push({type:12,data:_},{type:12,data:p},{type:12,data:b},{type:12,data:$}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,i,!0,h,m]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=Q.computeStrides(e.kernelShape);s.push({type:12,data:o},{type:12,data:e.pads},{type:12,data:e.strides}),i.push({name:"kernelStrides",type:"u32",length:o.length},{name:"pads",type:"u32",length:e.pads.length},{name:"strides",type:"u32",length:e.strides.length});let u=e.pads.reduce((l,c)=>l+c);return[s,i,!!u,!1,!1]}},go=(t,e,r,n,a,s,i,o,u,l,c,h)=>{let m=a.format==="NHWC",_=e.type.value,p=_e("output",e.type.tensor,n);if(a.kernelShape.length<=2){let b="",$="",S="",x=r-(m?2:1);if(c?b=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${x}] < 0 || xIndices[${x}]
                      >= uniforms.x_shape[${x}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${s}
                }`:b=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${s}
                }`,a.kernelShape.length===2){let C=r-(m?3:2);h?$=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${C}] = indices[${C}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${C}] < 0 || xIndices[${C}] >= uniforms.x_shape[${C}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:$=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${C}] = indices[${C}] * uniforms.sh - uniforms.phStart + j;
                `,S=`
              }
            `}return`
            ${t.registerUniforms(u).declareVariables(e,p)}

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

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

              var value = ${_}(${o});
              var pad = 0;
              ${$}
              ${b}
              ${S}
              ${i}

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

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

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

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

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

                isPad = false;
                for (var j = ${r-b}u; j < ${r}u; j++) {
                  xIndices[j] = indices[j] * ${ke("uniforms.strides",`j - ${r-b}u`,b)}
                    + offsets[j - ${r-b}u] - ${ke("uniforms.pads","j - 2u",$)};
                  ${S}
              }
              ${i}

              output[global_idx] = value;
            }`}},_o=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,fh=t=>`${_o(t)};${t.countIncludePad}`,mh=t=>`${_o(t)};${t.storageOrder};${t.dilations}`,wo=t=>({format:t.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],ceilMode:t.ceil_mode,kernelShape:t.kernel_shape,strides:t.strides,pads:t.pads}),yo=(t,e,r,n)=>{let[a,s]=fo(e,n,r),i=X("x",e.dataType,e.dims.length),o=i.type.value,u="value += x_val;",l="";a.countIncludePad?l+=`value /= ${o}(uniforms.kernelSize);`:l+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[c,h,m,_,p]=mo(s,a);c.push(...ye(e.dims,s));let b=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${m};${_};${p}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(Q.size(s)/64)},programUniforms:c}),getShaderSource:$=>go($,i,e.dims.length,s.length,a,u,l,0,h,m,_,p)}},gh=t=>{let e=t.count_include_pad!==0,r=wo(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:fh(n)}},_h=(t,e)=>{Xn(t.inputs),t.compute(yo("AveragePool",t.inputs[0],!1,e))},bo={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},wh=t=>{let e=t.format;return{format:e,...bo,cacheKey:e}},yh=(t,e)=>{Xn(t.inputs),t.compute(yo("GlobalAveragePool",t.inputs[0],!0,e))},vo=(t,e,r,n)=>{let[a,s]=fo(e,n,r),i=`
      value = max(x_val, value);
    `,o="",u=X("x",e.dataType,e.dims.length),l=["rank"],[c,h,m,_,p]=mo(s,a);return c.push(...ye(e.dims,s)),{name:t,shaderCache:{hint:`${n.cacheKey};${m};${_};${p}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(Q.size(s)/64)},programUniforms:c}),getShaderSource:b=>go(b,u,e.dims.length,s.length,a,i,o,e.dataType===10?-65504:-1e5,h,m,_,p)}},bh=(t,e)=>{Xn(t.inputs),t.compute(vo("MaxPool",t.inputs[0],!1,e))},vh=t=>{let e=t.storage_order,r=t.dilations,n=wo(t);if(e!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let a={storageOrder:e,dilations:r,...n,cacheKey:""};return{...a,cacheKey:mh(a)}},$h=t=>{let e=t.format;return{format:e,...bo,cacheKey:e}},xh=(t,e)=>{Xn(t.inputs),t.compute(vo("GlobalMaxPool",t.inputs[0],!0,e))}}),Sh,kh,Eh,pw=ee(()=>{Yt(),xe(),Ae(),Sh=(t,e,r)=>{let n=t===e,a=t<e&&r<0,s=t>e&&r>0;if(n||a||s)throw new Error("Range these inputs' contents are invalid.")},kh=(t,e,r,n)=>{let a=Math.abs(Math.ceil((e-t)/r)),s=[a],i=a,o=[{type:12,data:i},{type:n,data:t},{type:n,data:r},...ye(s)],u=l=>{let c=_e("output",n,s.length),h=c.type.value,m=[{name:"outputSize",type:"u32"},{name:"start",type:h},{name:"delta",type:h}];return`
        ${l.registerUniforms(m).declareVariables(c)}
        ${l.mainStart()}
        ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${h}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:u,getRunData:()=>({outputs:[{dims:s,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:o})}},Eh=t=>{let e=0,r=0,n=0;t.inputs[0].dataType===6?(e=t.inputs[0].getInt32Array()[0],r=t.inputs[1].getInt32Array()[0],n=t.inputs[2].getInt32Array()[0]):t.inputs[0].dataType===1&&(e=t.inputs[0].getFloat32Array()[0],r=t.inputs[1].getFloat32Array()[0],n=t.inputs[2].getFloat32Array()[0]),Ue.webgpu.validateInputContent&&Sh(e,r,n),t.compute(kh(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),Ch,Th,Ih,Ah,Mh,Oh,zh,Ph,Rh,Bh,Dh,$o,Nh,Fh,Lh,Uh,Wh,Vh,Gh,hw=ee(()=>{xe(),Oe(),lt(),Ae(),Ch=(t,e)=>{if(t.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),t.length>0){if(e.mode==="linear"){if(!(t.length===2||t.length===3||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1||t.length===5&&t[0]===1&&t[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(e.mode==="cubic"&&!(t.length===2||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},Th=(t,e,r)=>{e.every(a=>a>=0&&a<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return e.forEach((a,s)=>n[a]=t[s]),n},Ih=(t,e,r,n,a,s)=>{let[i,o,u]=r>10?[1,2,3]:[-1,t.length>1?1:-1,-1],l=t[0].dims.length;if(i>0&&t.length>i&&t[i].dims.length>0)t[i].getFloat32Array().forEach(c=>s.push(c));else if(e.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&t.length>o&&t[o].dims.length>0){if(t[o].getFloat32Array().forEach(c=>n.push(c)),n.length!==0&&n.length!==l&&r>=18&&n.length!==e.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");Ch(n,e),e.axes.length>0&&Th(n,e.axes,l).forEach((c,h)=>n[h]=c)}if(u>0&&t.length>u&&(t[u].getBigInt64Array().forEach(c=>a.push(Number(c))),a.length!==l||r>=18&&a.length===e.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(e.axes.length>0){if(n.length!==e.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(a.length!==e.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof a<"u"&&n.length>0&&a.length>l)throw new Error("Resize requires only of scales or sizes to be specified")},Ah=(t,e)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${e} { `+(()=>{switch(t){case"asymmetric":return`return ${e}(xResized) / ${e}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${e}(xResized) + 0.5) / ${e}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${e}(xResized) + 0.5) / ${e}(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 = ${e}(xResized * (lengthOriginal - 1) / (lengthResized - 1));
                    let fract =
                        ${e}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${e}(lengthResized - 1);
                    return whole + fract;
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${e}(roiStart) * ${e}(lengthOriginal - 1) +
                        (${e}(xResized) * ${e}(roiEnd - roiStart) * ${e}(lengthOriginal - 1)) /
                        ${e}(lengthResized - 1);
                  } else {
                    return 0.5 * ${e}(roiStart + roiEnd) * ${e}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${e}xScale * ${e}(lengthResized);
                  const adjustment = ${e}(lengthResized) / outputWidth;
                  const center = ${e}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;case"half_pixel":return`return ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${t} is not supported`)}})()+"}",Mh=(t,e,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(t){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(e<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${t} is not supported`)}})()+"}",Oh=(t,e,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),a=t.length===0?n:t.slice();return e.length>0?(e.forEach((s,i)=>{n[s]=a[i],n[i+r]=a[e.length+i]}),n):a},zh=(t,e,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(t.forEach(s=>a.push(s)),Math.max(...n)>t.length)throw new Error("axes is out of bound");n.forEach((s,i)=>a[s]=r[i])}else r.forEach(s=>a.push(s));else{if(e.length===0)throw new Error("Resize requires either scales or sizes.");a=t.map((s,i)=>Math.round(s*e[i]))}return a},Ph=(t,e,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>e[s]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>e[s]),Number.MIN_VALUE):Math.max(...e,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();e.fill(1,0,e.length);let a=t.slice();return r.axes.length>0?(r.axes.forEach(s=>e[s]=n),r.axes.forEach(s=>a[s]=Math.round(t[s]*e[s]))):(e.fill(n,0,e.length),a.forEach((s,i)=>a[i]=Math.round(s*e[i]))),a},Rh=(t,e,r,n,a)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> array<${t.type.value}, ${r.length}> {
      var original_indices: array<${t.type.value}, ${r.length}>;
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var output_index = ${t.indicesGet("output_indices","i")};
        var scale = ${ke("uniforms.scales","i",n)};
        var roi_low = ${ke("uniforms.roi","i",a)};
        var roi_hi = ${ke("uniforms.roi",`i + ${e.length}`,a)};
        if (scale == 1.0) {
          original_indices[i] = ${t.type.value}(output_index);
        } else {
          var input_shape_i = ${ke("uniforms.input_shape","i",e.length)};
          var output_shape_i = ${ke("uniforms.output_shape","i",r.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,Bh=(t,e,r,n,a,s,i)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} {
      var input_indices: ${t.type.indices};
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${ke("uniforms.scales","i",a)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${ke("uniforms.roi","i",s)};
          var roi_hi = ${ke("uniforms.roi",`i + ${r.length}`,s)};
          var input_shape_i = ${ke("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${ke("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${i} || (original_idx >= 0 && original_idx < ${e.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${e.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);
          }
        }
        ${t.indicesSet("input_indices","i"," input_index")}
      }
      return input_indices;
    }`,Dh=(t,e)=>`
    fn checkInputIndices(input_indices: ${t.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${e.length}; i++) {
        var input_index = ${t.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${ke("uniforms.input_shape","i",e.length)}) {
          return false;
        }
      }
      return true;
    }`,$o=(t,e,r,n)=>t.rank>n?`
    ${t.indicesSet("input_indices",e,"channel")};
    ${t.indicesSet("input_indices",r,"batch")};
`:"",Nh=(t,e,r,n,a)=>{let[s,i,o,u]=r.length===2?[-1,0,1,-1]:[0,2,3,1],l=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${l} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",i,`max(0, min(row, ${r[i]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)};
      ${$o(t,u,s,2)}
      return ${t.getByIndices("input_indices")};
    }

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

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

  fn cubicInterpolation1D(x: array<${_}, 4>, coefs: array<${_}, 4>) -> ${_} {
    var coefsSum: ${_} = 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: ${e.type.indices}) -> ${_} {
    var input_indices: ${t.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},Lh=(t,e,r,n,a)=>{let[s,i,o,u,l]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],c=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${c} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",i,`max(0, min(depth, ${r[i]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(height, ${r[o]} - 1))`)};
      ${t.indicesSet("input_indices",u,`max(0, min(width, ${r[u]} - 1))`)};
      ${$o(t,l,s,3)}
      return ${t.getByIndices("input_indices")};
    }

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

    depth = max(0, min(depth, ${r[i]} - 1));
      height = max(0, min(height, ${r[o]} - 1));
      width = max(0, min(width, ${r[u]} - 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 = ${r.length>3?`u32(originalIndices[${l}])`:"0"};
      var batch: u32 =  ${r.length>3?`u32(originalIndices[${s}])`:"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);
    }`},Uh=(t,e,r,n,a,s)=>{let i=t.dims,o=Oh(s,e.axes,i.length),u=zh(i,n,a,e.axes),l=n.slice();n.length===0&&(l=i.map((x,C)=>x===0?1:u[C]/x),e.keepAspectRatioPolicy!=="stretch"&&(u=Ph(i,l,e)));let c=_e("output",t.dataType,u.length),h=X("input",t.dataType,i.length),m=Q.size(u),_=i.length===u.length&&i.every((x,C)=>x===u[C]),p=e.coordinateTransformMode==="tf_crop_and_resize",b=e.extrapolationValue,$=h.type.value,S=x=>`
      ${_?"":`
      ${Ah(e.coordinateTransformMode,$)};
      ${(()=>{switch(e.mode){case"nearest":return`
              ${Dh(h,i)};
              ${Mh(e.nearestMode,r,$)};
              ${Bh(h,c,i,u,l.length,o.length,p)};
              `;case"linear":return`
              ${Rh(c,i,u,l.length,o.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${Nh(h,c,i,p,b)}`;if(i.length===3||i.length===5)return`${Lh(h,c,i,p,b)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${Fh(h,c,i,u,l,o,e.cubicCoeffA,p,e.extrapolationValue,e.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${x.registerUniform("output_size","u32").registerUniform("scales","f32",l.length).registerUniform("roi","f32",o.length).declareVariables(h,c)}
      ${x.mainStart()}
        ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${_?"output[global_idx] = input[global_idx];":`
        let output_indices = ${c.offsetToIndices("global_idx")};
        var input_indices: ${h.type.indices};
        ${(()=>{switch(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${h.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${e.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${e.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${e.cacheKey}|${r}|${l.length>0?l:""}|${a.length>0?a:""}|${o.length>0?o:""}|${_}|${i}`,inputDependencies:["rank"]},getShaderSource:S,getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:[{type:12,data:m},{type:1,data:l},{type:1,data:o},...ye(i,u)]})}},Wh=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},Vh=(t,e)=>{let r=[],n=[],a=[],s=Wh(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Ih(t.inputs,e,s,r,n,a),t.compute(Uh(t.inputs[0],e,s,r,n,a),{inputs:[0]})},Gh=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,a=t.cubicCoeffA,s=t.excludeOutside!==0,i=t.extrapolationValue,o=t.keepAspectRatioPolicy,u=t.mode,l=t.nearestMode===""?"simple":t.nearestMode;return Ke({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:s,extrapolationValue:i,keepAspectRatioPolicy:o,mode:u,nearestMode:l})}}),Hh,qh,jh,fw=ee(()=>{xe(),Oe(),lt(),Ae(),Hh=(t,e)=>{let[r,n,a,s]=t,{numHeads:i,rotaryEmbeddingDim:o}=e;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!Q.areEqual(n.dims,[])&&!Q.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(a.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(s.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${s.dims.length}`);if(!Q.areEqual(a.dims,s.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(o>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let u=r.dims[0],l=r.dims[r.dims.length-2],c=a.dims[0],h=Q.sizeFromDimension(r.dims,1)/l,m=o===0?a.dims[1]*2:h/i;if(o>m)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(u!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(l!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(m/2!==a.dims[1]&&o/2!==a.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${a.dims[1]}`);if(l>c)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},qh=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:s}=e,i=t[0].dims[0],o=Q.sizeFromDimension(t[0].dims,1),u=t[0].dims[t[0].dims.length-2],l=o/u,c=t[2].dims[1],h=a===0?c*2:l/n,m=new Array(i,u,l/h,h-c),_=Q.computeStrides(m),p=[{type:1,data:s},{type:12,data:m},{type:12,data:_},...t[0].dims.length===3?new Array({type:12,data:[o,l,h,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,h,u*h,1]}):[],...ye(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],b=$=>{let S=X("input",t[0].dataType,t[0].dims.length),x=X("position_ids",t[1].dataType,t[1].dims.length),C=X("cos_cache",t[2].dataType,t[2].dims.length),T=X("sin_cache",t[3].dataType,t[3].dims.length),I=_e("output",t[0].dataType,t[0].dims.length);return $.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:m.length},{name:"global_strides",type:"u32",length:_.length},{name:"input_output_strides",type:"u32",length:_.length}]),`
        ${$.declareVariables(S,x,C,T,I)}

        ${$.mainStart(gn)}
          let half_rotary_emb_dim = uniforms.${C.name}_shape[1];
          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;
          let size = uniforms.global_shape[0] * uniforms.global_strides[0];
          ${$.guardAgainstOutOfBoundsWorkgroupSizes("size")}

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${x.broadcastedIndicesToOffset("bsnh.xy",_e("",x.type.tensor,2))};
            let position_id =
                u32(${x.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r});
            let j = i + select(half_rotary_emb_dim, 1, ${r});
            let re = ${S.getByOffset("i")} * ${C.get("position_id","bsnh[3]")} -
                ${S.getByOffset("j")} * ${T.get("position_id","bsnh[3]")};
            ${I.setByOffset("i","re")}
            let im = ${S.getByOffset("i")} * ${T.get("position_id","bsnh[3]")} +
                ${S.getByOffset("j")} * ${C.get("position_id","bsnh[3]")};
            ${I.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${I.setByOffset("k",S.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:Ke({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:b,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(Q.size(m)/gn)},programUniforms:p})}},jh=(t,e)=>{Hh(t.inputs,e),t.compute(qh(t.inputs,e))}}),Kh,Yh,Qh,mw=ee(()=>{xe(),Oe(),Ae(),Kh=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let a=e.dims[e.dims.length-1],s=e.dims[e.dims.length-2];if(r.dims[r.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==a)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let i=t[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let i=t[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},Yh=(t,e,r,n)=>{let a=e.simplified,s=t[0].dims,i=Q.size(s),o=s,u=i,l=s.slice(-1)[0],c=n?s.slice(0,-1).concat(1):[],h=!a&&t.length>3,m=t.length>4,_=n&&r>1,p=n&&r>2,b=r>3,$=at(l),S=[{type:12,data:u},{type:12,data:$},{type:12,data:l},{type:1,data:e.epsilon}],x=T=>{let I=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],P=[X("x",t[0].dataType,t[0].dims,$),X("skip",t[1].dataType,t[1].dims,$),X("gamma",t[2].dataType,t[2].dims,$)];h&&P.push(X("beta",t[3].dataType,t[3].dims,$)),m&&P.push(X("bias",t[4].dataType,t[4].dims,$)),P.push(_e("output",t[0].dataType,o,$)),_&&P.push(_e("mean_output",1,c)),p&&P.push(_e("inv_std_output",1,c)),b&&P.push(_e("input_skip_bias_sum",t[0].dataType,o,$));let R=ft(t[0].dataType);return`

      ${T.registerUniforms(I).declareVariables(...P)}

      ${T.mainStart()}
        ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}
        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
        let offset = global_idx * hidden_size_vectorized;
        var sum = ${yr("f32",$)};
        var squareSum = ${yr("f32",$)};
        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {
          let skip_value = skip[offset + i];
          let bias_value = ${m?"bias[i]":R+"(0.0)"};
          let input_value = x[offset + i];
          let value = input_value + skip_value + bias_value;
          ${b?"input_skip_bias_sum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32_value = ${_n(R,$,"value")};
          sum += f32_value;
          squareSum += f32_value * f32_value;
        }
        let mean = ${Mr("sum",$)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Mr("squareSum",$)} / f32(uniforms.hidden_size) ${a?"":"- mean * mean"} + uniforms.epsilon);
        ${_?"mean_output[global_idx] = mean;":""}
        ${p?"inv_std_output[global_idx] = inv_std_dev;":""}
        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {
          output[offset + i] = (output[offset + i] ${a?"":`- ${R}(mean)`}) * ${R}(inv_std_dev) * gamma[i] ${h?"+ beta[i]":""};
        }
      }`},C=[{dims:o,dataType:t[0].dataType}];return r>1&&C.push({dims:c,dataType:1}),r>2&&C.push({dims:c,dataType:1}),r>3&&C.push({dims:s,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${$};${_};${p};${b}`,inputDependencies:t.map((T,I)=>"type")},getShaderSource:x,getRunData:()=>({outputs:C,dispatchGroup:{x:Math.ceil(u/l/64)},programUniforms:S})}},Qh=(t,e)=>{Kh(t.inputs);let r=[0];t.outputCount>1&&r.push(-3),t.outputCount>2&&r.push(-3),t.outputCount>3&&r.push(3),t.compute(Yh(t.inputs,e,t.outputCount,!1),{outputs:r})}}),Xh,Jn,Jh,xo,Zh,ef,tf,rf,gw=ee(()=>{xe(),Oe(),lt(),Ae(),Xh=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");if(e.axes.length!==0){if(e.axes.length!==e.starts.length||e.axes.length!==e.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(e.starts.length!==e.ends.length)throw new Error("starts and ends must have the same length");t.slice(1).forEach((r,n)=>{if(t[n+1].dataType!==6&&t[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},Jn=(t,e)=>{let r=[];if(t.length>e)if(t[e].dataType===7)t[e].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(t[e].dataType===6)t[e].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${e} must be an array of int32 or int64`);return r},Jh=(t,e)=>{if(t.length>1){let r=Jn(t,1),n=Jn(t,2),a=Jn(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),Ke({starts:r,ends:n,axes:a})}else return e},xo=(t,e,r,n,a)=>{let s=t;return t<0&&(s+=r[n[e]]),a[e]<0?Math.max(0,Math.min(s,r[n[e]]-1)):Math.max(0,Math.min(s,r[n[e]]))},Zh=(t,e,r)=>`fn calculateInputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} {
          var input_indices: ${t.type.indices};
          var carry = 0u;
          for (var i = ${r.length}; i >= 0; i--) {
            let input_shape_i = ${ke("uniforms.input_shape","i",r.length)};
            let steps_i = ${ke("uniforms.steps","i",r.length)};
            let signs_i = ${ke("uniforms.signs","i",r.length)};
            let starts_i = ${ke("uniforms.starts","i",r.length)};
            var output_index = ${e.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;
            }
            ${t.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,ef=(t,e)=>{let r=t[0].dims,n=Q.size(r),a=e.axes.length>0?Q.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],s=Jn(t,4);s.forEach(S=>S!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(a.length).fill(1));let i=e.starts.map((S,x)=>xo(S,x,r,a,s)),o=e.ends.map((S,x)=>xo(S,x,r,a,s));if(a.length!==i.length||a.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(a.length!==r.length)for(let S=0;S<r.length;++S)a.includes(S)||(i.splice(S,0,0),o.splice(S,0,r[S]),s.splice(S,0,1));let u=s.map(S=>Math.sign(S));s.forEach((S,x,C)=>{if(S<0){let T=(o[x]-i[x])/S,I=i[x],P=I+T*s[x];i[x]=P,o[x]=I,C[x]=-S}});let l=r.slice(0);a.forEach((S,x)=>{l[S]=Math.ceil((o[S]-i[S])/s[S])});let c={dims:l,dataType:t[0].dataType},h=_e("output",t[0].dataType,l.length),m=X("input",t[0].dataType,t[0].dims.length),_=Q.size(l),p=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:u.length},{name:"steps",type:"u32",length:s.length}],b=[{type:12,data:_},{type:12,data:i},{type:6,data:u},{type:12,data:s},...ye(t[0].dims,l)],$=S=>`
      ${S.registerUniforms(p).declareVariables(m,h)}
        ${Zh(m,h,r)}
        ${S.mainStart()}
          ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${h.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${h.setByOffset("global_idx",m.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${u.length}_${i.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:$,getRunData:()=>({outputs:[c],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:b})}},tf=(t,e)=>{Xh(t.inputs,e);let r=Jh(t.inputs,e);t.compute(ef(t.inputs,r),{inputs:[0]})},rf=t=>{let e=t.starts,r=t.ends,n=t.axes;return Ke({starts:e,ends:r,axes:n})}}),nf,af,sf,of,_w=ee(()=>{xe(),Oe(),lt(),Ae(),nf=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},af=(t,e)=>{let r=t.dims,n=Q.size(r),a=64,s=e.axis;if(s<0&&(s=r.length+s),s<r.length-1)throw new Error("softmax only supports last axis for now.");let i=r[s],o=n/i,u=at(i),l=i/u,c=($,S)=>S===4?`max(max(${$}.x, ${$}.y), max(${$}.z, ${$}.w))`:S===2?`max(${$}.x, ${$}.y)`:S===3?`max(max(${$}.x, ${$}.y), ${$}.z)`:$,h=X("x",t.dataType,t.dims,u),m=_e("result",t.dataType,t.dims,u),_=h.type.value,p=ft(t.dataType)==="f32"?`var threadMax = ${_}(-3.402823e+38f);`:`var threadMax = ${_}(-65504.0h);`,b=$=>`
      var<workgroup> rowMaxShared : ${_};
      var<workgroup> rowSumShared : ${_};
      var<workgroup> threadShared : array<${_}, ${a}>;

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

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

        // find the rows max
        ${p}
        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 = ${_}(${c("threadShared[0]",u)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${_}(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 = ${_}(${Mr("threadShared[0]",u)});
        }
        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:`${u}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:6,data:l}]}),getShaderSource:b}},sf=(t,e)=>{nf(t.inputs),t.compute(af(t.inputs[0],e))},of=t=>Ke({axis:t.axis})}),uf,lf,df,cf,pf,hf,ff,ww=ee(()=>{xe(),Oe(),lt(),Ae(),uf=t=>{if(!t||t.length<1)throw new Error("too few inputs")},lf=(t,e)=>{let r=[],n=e.numOutputs;return t[1].dims[0]>0&&(t[1].getBigInt64Array().forEach(a=>r.push(Number(a))),n=r.length),Ke({numOutputs:n,axis:e.axis,splitSizes:r})},df=t=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${t}u; i += 1u ) {
    if (index < ${ke("uniforms.size_in_split_axis","i",t)}) {
        return i;
    }
    }
    return ${t}u;
}`,cf=t=>{let e=t.length,r=[];for(let n=0;n<e;++n){let a=t[n].setByIndices("indices","input[global_idx]");e===1?r.push(a):n===0?r.push(`if (output_number == ${n}u) { ${a} }`):n===e-1?r.push(`else { ${a} }`):r.push(`else if (output_number == ${n}) { ${a} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${t[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},pf=(t,e)=>{let r=t[0].dims,n=Q.size(r),a=t[0].dataType,s=Q.normalizeAxis(e.axis,r.length),i=new Array(e.numOutputs),o=X("input",a,r.length),u=new Array(e.numOutputs),l=[],c=[],h=0,m=[{type:12,data:n}];for(let p=0;p<e.numOutputs;p++){h+=e.splitSizes[p],u[p]=h;let b=r.slice();b[e.axis]=e.splitSizes[p],c.push(b),i[p]=_e(`output${p}`,a,b.length),l.push({dims:c[p],dataType:t[0].dataType})}m.push({type:12,data:u},...ye(r,...c));let _=p=>`
  ${p.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",u.length).declareVariables(o,...i)}
  ${df(u.length)}
  ${cf(i)}

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

    var indices = ${o.offsetToIndices("global_idx")};
    var index = ${o.indicesGet("indices",s)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${ke("uniforms.size_in_split_axis","output_number - 1u",u.length)};
      ${o.indicesSet("indices",s,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:_,getRunData:()=>({outputs:l,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:m})}},hf=(t,e)=>{uf(t.inputs);let r=t.inputs.length===1?e:lf(t.inputs,e);t.compute(pf(t.inputs,r),{inputs:[0]})},ff=t=>{let e=t.axis,r=t.splitSizes,n=t.numOutputs<0?r.length:t.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Ke({axis:e,numOutputs:n,splitSizes:r})}}),So,mf,gf,_f,wf,yw=ee(()=>{xe(),Oe(),Ae(),So=t=>Array.from(t.getBigInt64Array(),Number),mf=t=>{if(!t||t.length!==2)throw new Error("Tile requires 2 inputs.");if(t[0].dataType!==1&&t[0].dataType!==6&&t[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(t[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(t[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(So(t[1]).length!==t[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},gf=(t,e)=>{let r=[];for(let n=0;n<t.length;++n)r.push(t[n]*e[n]);return r},_f=t=>{let e=t[0].dims,r=So(t[1]),n=gf(e,r),a=Q.size(n),s=t[0].dataType,i=X("input",s,e.length),o=_e("output",s,n.length),u=l=>`
      const inputShape = ${i.indices(...e)};
      ${l.registerUniform("output_size","u32").declareVariables(i,o)}
      ${l.mainStart()}
      ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${o.offsetToIndices("global_idx")};
      var input_indices: ${i.type.indices};
      for (var i = 0; i < ${e.length}; i++) {
        let input_dim_i = ${i.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${o.indicesGet("output_indices","i")}  % input_dim_i;

        ${i.indicesSet("input_indices","i","input_dim_value")}
      }
      ${o.setByOffset("global_idx",i.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...ye(t[0].dims,n)]}),getShaderSource:u}},wf=t=>{mf(t.inputs),t.compute(_f(t.inputs),{inputs:[0]})}}),yf,bf,vf,bw=ee(()=>{xe(),Oe(),Ae(),yf=(t,e,r,n,a)=>{let s=_e("output_data",a,r.length,4),i=X("a_data",e[1].dataType,e[1].dims.length,4),o=X("b_data",e[2].dataType,e[2].dims.length,4),u=X("c_data",e[0].dataType,e[0].dims.length,4),l,c=(h,m,_)=>`select(${m}, ${h}, ${_})`;if(!n)l=s.setByOffset("global_idx",c(i.getByOffset("global_idx"),o.getByOffset("global_idx"),u.getByOffset("global_idx")));else{let h=(m,_,p="")=>{let b=`a_data[index_a${_}][component_a${_}]`,$=`b_data[index_b${_}][component_b${_}]`,S=`bool(c_data[index_c${_}] & (0xffu << (component_c${_} * 8)))`;return`
            let output_indices${_} = ${s.offsetToIndices(`global_idx * 4u + ${_}u`)};
            let offset_a${_} = ${i.broadcastedIndicesToOffset(`output_indices${_}`,s)};
            let offset_b${_} = ${o.broadcastedIndicesToOffset(`output_indices${_}`,s)};
            let offset_c${_} = ${u.broadcastedIndicesToOffset(`output_indices${_}`,s)};
            let index_a${_} = offset_a${_} / 4u;
            let index_b${_} = offset_b${_} / 4u;
            let index_c${_} = offset_c${_} / 4u;
            let component_a${_} = offset_a${_} % 4u;
            let component_b${_} = offset_b${_} % 4u;
            let component_c${_} = offset_c${_} % 4u;
            ${m}[${_}] = ${p}(${c(b,$,S)});
          `};a===9?l=`
            var data = vec4<u32>(0);
            ${h("data",0,"u32")}
            ${h("data",1,"u32")}
            ${h("data",2,"u32")}
            ${h("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:l=`
            ${h("output_data[global_idx]",0)}
            ${h("output_data[global_idx]",1)}
            ${h("output_data[global_idx]",2)}
            ${h("output_data[global_idx]",3)}
          `}return`
        ${t.registerUniform("vec_size","u32").declareVariables(u,i,o,s)}
        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${l}
      }`},bf=t=>{let e=t[1].dims,r=t[2].dims,n=t[0].dims,a=t[1].dataType,s=!(Q.areEqual(e,r)&&Q.areEqual(r,n)),i=e,o=Q.size(e);if(s){let l=mn.calcShape(mn.calcShape(e,r,!1),n,!1);if(!l)throw new Error("Can't perform where op on the given tensors");i=l,o=Q.size(i)}let u=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:l=>yf(l,t,i,s,a),getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:u},...ye(n,e,r,i)]})}},vf=t=>{t.compute(bf(t.inputs))}}),$f,vw=ee(()=>{W_(),zd(),V_(),G_(),H_(),q_(),Ed(),Jc(),X_(),J_(),Z_(),ew(),tw(),rw(),nw(),aw(),iw(),sw(),ow(),Kc(),uw(),lw(),dw(),cw(),pw(),Vs(),hw(),fw(),mw(),gw(),_w(),ww(),yw(),Yn(),Ys(),bw(),$f=new Map([["Abs",[Wd]],["Acos",[Vd]],["Acosh",[Gd]],["Add",[Tc]],["ArgMax",[bd,Hs]],["ArgMin",[yd,Hs]],["Asin",[Hd]],["Asinh",[qd]],["Atan",[jd]],["Atanh",[Kd]],["Attention",[Od]],["AveragePool",[_h,gh]],["BatchNormalization",[Dd]],["BiasAdd",[Ld]],["BiasSplitGelu",[kc]],["Cast",[Qd,Yd]],["Ceil",[Zd]],["Clip",[Jd]],["Concat",[Sd,kd]],["Conv",[so,io]],["ConvTranspose",[dp,ip]],["Cos",[ec]],["Cosh",[tc]],["CumSum",[pp,hp]],["DepthToSpace",[_p,wp]],["Div",[Ic]],["Einsum",[Sp,kp]],["Elu",[rc,di]],["Equal",[Ac]],["Erf",[nc]],["Exp",[ac]],["Expand",[Ip]],["FastGelu",[Mp]],["Floor",[ic]],["FusedConv",[so,io]],["Gather",[Rp,Pp]],["GatherElements",[Fp,Np]],["Gelu",[sc]],["Gemm",[Vp,Wp]],["GlobalAveragePool",[yh,wh]],["GlobalMaxPool",[xh,$h]],["Greater",[Pc]],["GreaterOrEqual",[Bc]],["HardSigmoid",[fc,hc]],["InstanceNormalization",[jp]],["LayerNormalization",[Qp]],["LeakyRelu",[oc,di]],["Less",[Rc]],["LessOrEqual",[Dc]],["Log",[$c]],["MatMul",[jc]],["MatMulNBits",[Zp,eh]],["MaxPool",[bh,vh]],["Mul",[Mc]],["MultiHeadAttention",[ah,rh]],["Neg",[lc]],["Not",[uc]],["Pad",[hh]],["Pow",[Oc]],["Range",[Eh]],["Reciprocal",[dc]],["ReduceMin",[fd]],["ReduceMean",[ld]],["ReduceMax",[hd]],["ReduceSum",[gd]],["ReduceProd",[md]],["ReduceL1",[dd]],["ReduceL2",[cd]],["ReduceLogSum",[wd]],["ReduceLogSumExp",[pd]],["ReduceSumSquare",[_d]],["Relu",[cc]],["Resize",[Vh,Gh]],["RotaryEmbedding",[jh]],["Sigmoid",[pc]],["Sin",[mc]],["Sinh",[gc]],["Slice",[tf,rf]],["SkipLayerNormalization",[Qh]],["Split",[hf,ff]],["Sqrt",[_c]],["Softmax",[sf,of]],["Sub",[zc]],["Tan",[wc]],["Tanh",[yc]],["ThresholdedRelu",[vc,di]],["Tile",[wf]],["Transpose",[Al,Ml]],["Where",[vf]]])}),xf,$w=ee(()=>{Yt(),Gr(),Ae(),xf=class{constructor(t){this.backend=t,this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,e){this.repo.set(t,e)}run(t,e,r,n,a){Kt(t.programInfo.name);let s=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let o=[];for(let l of e)o.push({binding:o.length,resource:{buffer:l.buffer}});for(let l of r)o.push({binding:o.length,resource:{buffer:l.buffer}});a&&o.push({binding:o.length,resource:a});let u=s.createBindGroup({layout:t.computePipeline.getBindGroupLayout(0),entries:o,label:t.programInfo.name});if(this.backend.sessionStatus==="capturing"){let l={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:u,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(l)}i.setPipeline(t.computePipeline),i.setBindGroup(0,u),i.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Ut(t.programInfo.name)}dispose(){}build(t,e){Kt(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=El(e,this.backend.device.limits),s=t.getShaderSource(a),i=`${n.join(`
`)}
${a.additionalImplementations}
${s}`,o=r.createShaderModule({code:i,label:t.name});tt("verbose",()=>`[WebGPU] ${t.name} shader code: ${i}`);let u=r.createComputePipeline({compute:{module:o,entryPoint:"main"},layout:"auto",label:t.name});return Ut(t.name),{programInfo:t,computePipeline:u,uniformVariablesInfo:a.variablesInfo}}normalizeDispatchGroupSize(t){let e=typeof t=="number"?t:t.x,r=typeof t=="number"?1:t.y||1,n=typeof t=="number"?1:t.z||1,a=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(e<=a&&r<=a&&n<=a)return[e,r,n];let s=e*r*n,i=Math.ceil(Math.sqrt(s));if(i>a){if(i=Math.ceil(Math.cbrt(s)),i>a)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),Sf,kf,Ef,Cf,xw=ee(()=>{Yt(),xe(),Gr(),F_(),L_(),vw(),$w(),Sf=(t,e)=>{if(e.length!==t.length)throw new Error(`inputDependencies length ${e.length} is not equal to inputTensors length ${t.length}.`);let r=[];for(let n=0;n<t.length;++n){let a=t[n].dataType;switch(e[n]){case"none":{r.push("");break}case"type":{r.push(`${a}`);break}case"rank":{let s=t[n].dims.length;r.push(`${a};${s}`);break}case"dims":{let s=t[n].dims.join(",");r.push(`${a};${s}`);break}default:throw new Error(`unsupported input dependency: ${e[n]}`)}}return r.join("|")},kf=(t,e,r)=>{var a,s;let n=t.name;return(a=t.shaderCache)!=null&&a.hint&&(n+="["+t.shaderCache.hint+"]"),n+=":"+r+`:${Sf(e,((s=t.shaderCache)==null?void 0:s.inputDependencies)??new Array(e.length).fill("dims"))}`,n},Ef=class{constructor(t){t&&(this.architecture=t.architecture,this.vendor=t.vendor)}isArchitecture(t){return this.architecture===t}isVendor(t){return this.vendor===t}},Cf=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. (should not happen)");let t=this.kernelCustomData.get(this.currentKernelId);return t||(t={},this.kernelCustomData.set(this.currentKernelId,t)),t}async initialize(t,e){this.env=t;let r=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:e.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:e.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:e.limits.maxStorageBufferBindingSize,maxBufferSize:e.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:e.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:e.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:e.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:e.limits.maxComputeWorkgroupSizeZ},requiredFeatures:r};e.features.has("chromium-experimental-timestamp-query-inside-passes")?r.push("chromium-experimental-timestamp-query-inside-passes"):e.features.has("timestamp-query")&&r.push("timestamp-query"),e.features.has("shader-f16")&&r.push("shader-f16"),this.device=await e.requestDevice(n),this.adapterInfo=new Ef(await e.requestAdapterInfo()),this.gpuDataManager=vl(this),this.programManager=new xf(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,ml(t.logLevel,!!t.debug),this.device.onuncapturederror=a=>{a.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${a.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:e,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let t=this.getCommandEncoder(),e={};this.queryType==="at-passes"&&(e.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=t.beginComputePass(e)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Kt(),this.endComputePass();let t;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&t.mapAsync(GPUMapMode.READ).then(()=>{var n;let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let a=0;a<e.length/2;a++){let s=r[a],i=s.kernelId,o=this.kernels.get(i),u=o.kernelType,l=o.kernelName,c=s.programName,h=s.inputTensorViews,m=s.outputTensorViews,_=e[a*2],p=e[a*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=_);let b=Number(_-this.queryTimeBase),$=Number(p-this.queryTimeBase);if(!Number.isSafeInteger(b)||!Number.isSafeInteger($))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:h.map(S=>({dims:S.dims,dataType:Vr(S.dataType)})),outputsMetadata:m.map(S=>({dims:S.dims,dataType:Vr(S.dataType)})),kernelId:i,kernelType:u,kernelName:l,programName:c,startTime:b,endTime:$});else{let S="";h.forEach((C,T)=>{S+=`input[${T}]: [${C.dims}] | ${Vr(C.dataType)}, `});let x="";m.forEach((C,T)=>{x+=`output[${T}]: [${C.dims}] | ${Vr(C.dataType)}, `}),console.log(`[profiling] kernel "${i}|${u}|${l}|${c}" ${S}${x}execution time: ${$-b} ns`)}Gn("GPU",`${c}::${_}::${p}`)}t.unmap(),this.pendingQueries.delete(t)}),Ut()}run(t,e,r,n,a,s){Kt(t.name);let i=[];for(let x=0;x<e.length;++x){let C=e[x].data;if(C===0)continue;let T=this.gpuDataManager.get(C);if(!T)throw new Error(`no GPU data for input: ${C}`);i.push(T)}let{outputs:o,dispatchGroup:u,programUniforms:l}=t.getRunData(e),c=r.length===0?o.map((x,C)=>C):r;if(c.length!==o.length)throw new Error(`Output size ${c.length} must be equal to ${o.length}.`);let h=[],m=[];for(let x=0;x<o.length;++x){if(!Number.isInteger(c[x])||c[x]<-3||c[x]>=s)throw new Error(`Invalid output index: ${c[x]}`);if(c[x]===-3)continue;let C=c[x]===-1,T=c[x]===-2,I=C||T?a(o[x].dataType,o[x].dims):n(c[x],o[x].dataType,o[x].dims);if(h.push(I),I.data===0)continue;let P=this.gpuDataManager.get(I.data);if(!P)throw new Error(`no GPU data for output: ${I.data}`);if(C&&this.temporaryData.push(P),T){let R=this.kernelPersistentData.get(this.currentKernelId);R||(R=[],this.kernelPersistentData.set(this.currentKernelId,R)),R.push(P)}m.push(P)}if(i.length!==e.length||m.length!==h.length){if(m.length===0)return Ut(t.name),h;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let _;if(l){let x=0,C=[];l.forEach(R=>{let W=typeof R.data=="number"?[R.data]:R.data;if(W.length===0)return;let q=R.type===10?2:4,K,ue;R.type===10?(ue=W.length>4?16:W.length>2?8:W.length*q,K=W.length>4?16:q*W.length):(ue=W.length<=2?W.length*q:16,K=16),x=Math.ceil(x/ue)*ue,C.push(x);let ne=R.type===10?8:4;x+=W.length>4?Math.ceil(W.length/ne)*K:W.length*q});let T=16;x=Math.ceil(x/T)*T;let I=new ArrayBuffer(x);l.forEach((R,W)=>{let q=C[W],K=typeof R.data=="number"?[R.data]:R.data;if(R.type===6)new Int32Array(I,q,K.length).set(K);else if(R.type===12)new Uint32Array(I,q,K.length).set(K);else if(R.type===10)new Uint16Array(I,q,K.length).set(K);else if(R.type===1)new Float32Array(I,q,K.length).set(K);else throw new Error(`Unsupported uniform type: ${Vr(R.type)}`)});let P=this.gpuDataManager.create(x,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(P.buffer,0,I,0,x),this.gpuDataManager.release(P.id),_={offset:0,size:x,buffer:P.buffer}}let p=this.programManager.normalizeDispatchGroupSize(u),b=p[1]===1&&p[2]===1,$=kf(t,e,b),S=this.programManager.getArtifact($);if(S||(S=this.programManager.build(t,p),this.programManager.setArtifact($,S),tt("info",()=>`[artifact] key: ${$}, programName: ${t.name}`)),l&&S.uniformVariablesInfo){if(l.length!==S.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${S.uniformVariablesInfo.length}, got ${l.length} in program "${S.programInfo.name}".`);for(let x=0;x<l.length;x++){let C=l[x],T=C.type,I=typeof C.data=="number"?1:C.data.length,[P,R]=S.uniformVariablesInfo[x];if(T!==P||I!==R)throw new Error(`Uniform variable ${x} mismatch: expect type ${P} with size ${R}, got type ${T} with size ${I} in program "${S.programInfo.name}".`)}}if(tt("info",()=>`[ProgramManager] run "${t.name}" (key=${$}) with ${p[0]}x${p[1]}x${p[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let x={kernelId:this.currentKernelId,programName:S.programInfo.name,inputTensorViews:e,outputTensorViews:h};this.pendingKernels.push(x),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(x)}return this.programManager.run(S,i,m,p,_),Ut(t.name),h}upload(t,e){this.gpuDataManager.upload(t,e)}memcpy(t,e){this.gpuDataManager.memcpy(t,e)}async download(t,e){await this.gpuDataManager.download(t,e)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,e,r,n){let a=$f.get(t);if(!a)throw new Error(`kernel not implemented: ${t}`);let s={kernelType:t,kernelName:n,kernelEntry:a[0],attributes:[a[1],r]};this.kernels.set(e,s)}releaseKernel(t){let e=this.kernelPersistentData.get(t);if(e){for(let r of e)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,e,r){let n=this.kernels.get(t);if(!n)throw new Error(`kernel not created: ${t}`);let a=n.kernelType,s=n.kernelName,i=n.kernelEntry,o=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${a}] ${s}" is not allowed to be called recursively`);this.currentKernelId=t,o[0]&&(o[1]=o[0](o[1]),o[0]=void 0),tt("info",()=>`[WebGPU] Start to run kernel "[${a}] ${s}"...`);let u=this.env.debug;this.temporaryData=[];try{return u&&this.device.pushErrorScope("validation"),i(e,o[1]),0}catch(l){return r.push(Promise.resolve(`[WebGPU] Kernel "[${a}] ${s}" failed. ${l}`)),1}finally{u&&r.push(this.device.popErrorScope().then(l=>l?`GPU validation error for kernel "[${a}] ${s}": ${l.message}`:null));for(let l of this.temporaryData)this.gpuDataManager.release(l.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,e,r,n){let a=this.sessionExternalDataMapping.get(t);a||(a=new Map,this.sessionExternalDataMapping.set(t,a));let s=a.get(e),i=this.gpuDataManager.registerExternalBuffer(r,n,s==null?void 0:s[1]);return a.set(e,[i,r]),i}unregisterBuffers(t){let e=this.sessionExternalDataMapping.get(t);e&&(e.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[1])),this.sessionExternalDataMapping.delete(t))}getBuffer(t){let e=this.gpuDataManager.get(t);if(!e)throw new Error(`no GPU data for buffer: ${t}`);return e.buffer}createDownloader(t,e,r){return async()=>{let n=await Ds(this,t,e);return _l(n.buffer,r)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){var t;this.queryType="none",(((t=this.env.webgpu.profiling)==null?void 0:t.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.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"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){tt("info","captureBegin"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus="capturing"}captureEnd(){tt("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){tt("info","replay"),this.sessionStatus="replaying";let t=this.capturedCommandList.get(this.currentSessionId),e=this.capturedPendingKernels.get(this.currentSessionId),r=t.length;this.pendingKernels=[];for(let n=0;n<r;n++){let a=this.getComputePassEncoder(),s=t[n];this.writeTimestamp(this.pendingDispatchNumber*2),a.setPipeline(s.computePipeline),a.setBindGroup(0,s.bindGroup),a.dispatchWorkgroups(...s.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(e[n]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onReleaseSession(t){this.unregisterBuffers(t),this.capturedCommandList.has(t)&&this.capturedCommandList.delete(t),this.capturedPendingKernels.has(t)&&this.capturedPendingKernels.delete(t),this.gpuDataManager.onReleaseSession(t)}onRunStart(t){this.currentSessionId=t,this.setQueryType()}}}),Tf={};fn(Tf,{init:()=>Af});var yi,If,Af,Sw=ee(()=>{xe(),xw(),Gr(),Oe(),yi=class j0{constructor(e,r,n,a){this.module=e,this.dataType=r,this.data=n,this.dims=a}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let e=Q.size(this.dims);return e===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,e)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let e=Q.size(this.dims);return e===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,e)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let e=Q.size(this.dims);return e===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,e)}reshape(e){if(Q.size(e)!==Q.size(this.dims))throw new Error("Invalid new shape");return new j0(this.module,this.dataType,this.data,e)}},If=class{constructor(t,e,r){this.module=t,this.backend=e,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=e.adapterInfo;let n=t.HEAPU32,a=r>>>2;this.opKernelContext=n[a++];let s=n[a++];this.outputCount=n[a++],this.customDataOffset=n[a++],this.customDataSize=n[a++];let i=[];for(let o=0;o<s;o++){let u=n[a++],l=n[a++],c=n[a++],h=[];for(let m=0;m<c;m++)h.push(n[a++]);i.push(new yi(t,u,l,h))}this.inputs=i}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}getMaxComputeWorkgroupSizes(){return[this.backend.device.limits.maxComputeWorkgroupSizeX,this.backend.device.limits.maxComputeWorkgroupSizeY,this.backend.device.limits.maxComputeWorkgroupSizeZ]}getMaxComputeWorkgroupStoragesize(){return this.backend.device.limits.maxComputeWorkgroupStorageSize}compute(t,e){var i;let r=((i=e==null?void 0:e.inputs)==null?void 0:i.map(o=>typeof o=="number"?this.inputs[o]:o))??this.inputs,n=(e==null?void 0:e.outputs)??[],a=(o,u,l)=>new yi(this.module,u,this.output(o,l),l),s=(o,u)=>{let l=jn(o);if(!l)throw new Error(`Unsupported data type: ${o}`);let c=l*Q.size(u),h=c>0?this.backend.gpuDataManager.create(c).id:0;return new yi(this.module,o,h,u)};return this.backend.run(t,r,n,a,s,this.outputCount)}output(t,e){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+e.length)*4),a=n>>2;this.module.HEAPU32[a++]=e.length;for(let s=0;s<e.length;s++)this.module.HEAPU32[a++]=e[s];return this.module._JsepOutput(this.opKernelContext,t,n)}catch(n){throw new Error(`Failed to generate kernel's output[${t}] with dims [${e}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(r)}}},Af=async(t,e,r,n)=>{let a=e.jsepInit;if(!a)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(t==="webgpu"){let s=new Cf;await s.initialize(r,n),a("webgpu",[s,i=>s.alloc(i),i=>s.free(i),(i,o,u,l=!1)=>{if(l)tt("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${i}, dst=${o}, size=${u}`),s.memcpy(i,o);else{tt("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${i}, gpuDataId=${o}, size=${u}`);let c=e.HEAPU8.subarray(i>>>0,(i>>>0)+u);s.upload(o,c)}},async(i,o,u)=>{tt("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${i}, dataOffset=${o}, size=${u}`),await s.download(i,()=>e.HEAPU8.subarray(o>>>0,(o>>>0)+u))},(i,o,u)=>s.createKernel(i,o,u,e.UTF8ToString(e._JsepGetNodeName(o))),i=>s.releaseKernel(i),(i,o,u,l)=>{tt("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${u}, kernel=${i}, contextDataOffset=${o}`);let c=new If(e,s,o);return s.computeKernel(i,c,l)},()=>s.captureBegin(),()=>s.captureEnd(),()=>s.replay()])}else a("webnn")}}),Mf,Of,zf,Or,Pf,ko,Rf,Bf,Eo,Df,Nf,Ff,kw=ee(()=>{D_(),N_(),xe(),qn(),Is(),dl(),Mf=(t,e)=>{nt()._OrtInit(t,e)!==0&&je("Can't initialize onnxruntime.")},Of=async t=>{Mf(t.wasm.numThreads,ei(t.logLevel))},zf=async(t,e)=>{{let r=(Sw(),Lr(Tf)).init;if(e==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let n=t.webgpu.adapter;if(n){if(typeof n.limits!="object"||typeof n.features!="object"||typeof n.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. 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}`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${u};${h};${i}`},getShaderSource:y,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:p})}},rc=(e,t,r,o,i,u,a)=>{let c=a+i.kvSequenceLength,p=[i.batchSize,i.numHeads,i.sequenceLength,c],h=u.scale===0?1/Math.sqrt(i.headSize):u.scale,d=Me(i.headSize),y=i.headSize/d,w=12,_={x:Math.ceil(c/w),y:Math.ceil(i.sequenceLength/w),z:i.batchSize*i.numHeads},v=[{type:12,data:i.sequenceLength},{type:12,data:y},{type:12,data:c},{type:12,data:i.numHeads},{type:1,data:h}],S=o?["type","type","type"]:["type","type"],A=I=>{let x=U("q",t.dataType,t.dims,d),E=U("key",r.dataType,r.dims,d),P=[x,E];o&&P.push(U("relative_position_bias",o.dataType,o.dims));let O=j("output",t.dataType,p),R=et(1,d),L=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return`\n  const TILE_SIZE = ${w}u;\n\n  var<workgroup> tileQ: array<${x.type.storage}, ${w*w}>;\n  var<workgroup> tileK: array<${x.type.storage}, ${w*w}>;\n  ${I.registerUniforms(L).declareVariables(...P,O)}\n  ${I.mainStart([w,w,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 qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n    let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;\n\n    var value = ${R}(0);\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (global_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 += ${R}(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 (global_id.y < uniforms.M && global_id.x < uniforms.N) {\n      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;\n      var sum: f32 = ${(()=>{switch(d){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${d}`)}})()};\n        output[outputIdx] = ${O.type.value} (sum * uniforms.alpha) + ${o?"relative_position_bias[outputIdx]":"0.0"};\n    }\n  }`};return{name:"AttentionProbs",shaderCache:{hint:`${d}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:p,dataType:t.dataType,gpuDataType:0}],dispatchGroup:_,programUniforms:v}),getShaderSource:A}},nc=(e,t,r,o,i)=>{let u=i+o.kvSequenceLength,a=[o.batchSize,o.sequenceLength,o.vHiddenSize],c=12,p={x:Math.ceil(o.vHeadSize/c),y:Math.ceil(o.sequenceLength/c),z:o.batchSize*o.numHeads},h=[{type:12,data:o.sequenceLength},{type:12,data:u},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType,gpuDataType:0}],dispatchGroup:p,programUniforms:h}),getShaderSource:w=>{let _=U("probs",t.dataType,t.dims),v=U("v",r.dataType,r.dims),S=j("output",t.dataType,a),A=[{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 = ${c}u;\n  var<workgroup> tileQ: array<${_.type.value}, ${c*c}>;\n  var<workgroup> tileK: array<${_.type.value}, ${c*c}>;\n  ${w.registerUniforms(A).declareVariables(_,v,S)}\n  ${w.mainStart([c,c,1])}\n   let headIdx = workgroup_id.z;\n   let m = global_id.y;\n   let n = global_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 = ${_.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   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  }`}}},Pn=(e,t,r,o,i,u,a,c,p,h,d)=>{let y=e.outputCount>1,w=e.outputCount>2,_=y&&w?h.pastSequenceLength:0,v=_+h.kvSequenceLength,S=[h.batchSize,h.numHeads,v,h.headSize],A=a?[a,r]:[r],I=y?e.compute(En(A,2,S,r.dataType),{inputs:A,outputs:[1]})[0]:r,x=[h.batchSize,h.numHeads,v,h.headSize],E=c?[c,o]:[o],P=w?e.compute(En(E,2,x,o.dataType),{inputs:E,outputs:[2]})[0]:o,O=[t,I];p&&O.push(p);let R=e.compute(rc(e,t,I,p,h,d,_),{inputs:O,outputs:[-1]})[0];e.compute(tc(e,R,h.batchSize*h.numHeads*h.sequenceLength,v),{inputs:[R],outputs:[]});let L=[R,P];e.compute(nc(e,R,P,h,_),{inputs:L,outputs:[0]})},oc=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,i=t.inputHiddenSize,u=t.headSize,a=12,c={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:o},{type:12,data:i},{type:12,data:u},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],d=y=>{let w=j("output_q",p[0].dataType,r),_=j("output_k",p[0].dataType,r),v=j("output_v",p[0].dataType,r),S=U("input",p[0].dataType,p[0].dims),A=U("weight",p[1].dataType,p[1].dims),I=U("bias",p[2].dataType,p[2].dims),x=S.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 = ${a}u;\n  var<workgroup> tileInput: array<${x}, ${a*a}>;\n  var<workgroup> tileWeightQ: array<${x}, ${a*a}>;\n  var<workgroup> tileWeightK: array<${x}, ${a*a}>;\n  var<workgroup> tileWeightV: array<${x}, ${a*a}>;\n  ${y.registerUniforms(E).declareVariables(S,A,I,w,_,v)}\n  ${y.mainStart([a,a,1])}\n    let batchIndex = workgroup_id.z / uniforms.num_heads;\n    let headNumber = workgroup_id.z % uniforms.num_heads;\n    let m = global_id.y;\n    let n = global_id.x;\n\n    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;\n    let biasOffsetQ = headNumber * uniforms.head_size;\n    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;\n    let biasOffsetV = uniforms.hidden_size + biasOffsetK;\n\n    var valueQ = ${x}(0);\n    var valueK = ${x}(0);\n    var valueV = ${x}(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        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + 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:c,programUniforms:h}),getShaderSource:d},{inputs:p,outputs:[-1,-1,-1]})},Xa=(e,t)=>{let r=ec(e.inputs,t),[o,i,u]=oc(e,r);return Pn(e,o,i,u,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var ic,ac,sc,Qa,Ja=Y(()=>{"use strict";$r();ye();Se();Ze();_e();ic=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(o,i,u)=>{let a=i.length;if(a!==o.length)throw new Error(`${u}: num dimensions != ${a}`);i.forEach((c,p)=>{if(c!==o[p])throw new Error(`${u}: dim[${p}] do not match`)})};if(e[0].dims.length>1){let o=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,o,"Invalid input scale"),r(e[2].dims,o,"Invalid input B"),r(e[3].dims,o,"Invalid input mean"),r(e[4].dims,o,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input 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${E.registerUniform("outputSize","u32").declareVariables(y,w,_,v,S,A)}\n  ${E.mainStart()}\n  ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    var outputIndices = ${A.offsetToIndices(`global_idx * ${a}`)};\n    ${I()}\n    let scale = ${w.getByOffset("cOffset")};\n    let bias = ${_.getByOffset("cOffset")};\n    let inputMean = ${v.getByOffset("cOffset")};\n    let inputVar = ${S.getByOffset("cOffset")};\n    let x = ${y.getByOffset("global_idx")};\n    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;\n    ${A.setByOffset("global_idx","value")}\n  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${o}_${a}`,inputDependencies:h?["rank","type","type","type","type"]:void 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t=e[0].dims,r=e[0].dims[2],o=M.size(t)/4,i=e[0].dataType,u=U("input",i,t,4),a=U("bias",i,[r],4),c=U("residual",i,t,4),p=j("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:d=>`\n  const channels = ${r}u / 4;\n  ${d.declareVariables(u,a,c,p)}\n\n  ${d.mainStart()}\n    ${d.guardAgainstOutOfBoundsWorkgroupSizes(o)}\n    let value = ${u.getByOffset("global_idx")}\n      + ${a.getByOffset("global_idx % channels")} + ${c.getByOffset("global_idx")};\n    ${p.setByOffset("global_idx","value")}\n  }`}},es=e=>{uc(e.inputs),e.compute(dc(e.inputs))}});var lc,ke,rs,ns,os,is,as,ss,us,ds,ls,cc,cs,ps,ms,fs,kn,hs,On,gs,ys,bs,ws,vs,$s,_s,Ss,xs,Cs,As,Is,Ts,Es,Ps,ks,Os,Rs,Bo,Do,Bs,Ds,zs,Rn=Y(()=>{"use strict";ye();Se();Ze();_e();lc=(e,t,r,o,i,u)=>{let a=Math.ceil(t/4),c="";typeof i=="string"?c=`${i}(a)`:c=i("a");let p=U("inputData",r,[a],4),h=j("outputData",o,[a],4);return`\n      ${e.registerUniform("vec_size","u32").declareVariables(p,h)}\n\n  ${u??""}\n\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n\n    let a = ${p.getByOffset("global_idx")};\n    ${h.setByOffset("global_idx",c)}\n  }`},ke=(e,t,r,o,i,u=e.dataType)=>({name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:a=>lc(a,M.size(e.dims),e.dataType,u,r,o),getRunData:a=>({outputs:[{dims:e.dims,dataType:u}],dispatchGroup:{x:Math.ceil(M.size(a[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(e.dims)/4)}]})}),rs=e=>{e.compute(ke(e.inputs[0],"Abs","abs"))},ns=e=>{e.compute(ke(e.inputs[0],"Acos","acos"))},os=e=>{e.compute(ke(e.inputs[0],"Acosh","acosh"))},is=e=>{e.compute(ke(e.inputs[0],"Asin","asin"))},as=e=>{e.compute(ke(e.inputs[0],"Asinh","asinh"))},ss=e=>{e.compute(ke(e.inputs[0],"Atan","atan"))},us=e=>{e.compute(ke(e.inputs[0],"Atanh","atanh"))},ds=e=>ve(e),ls=(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): ${t.to}`)}e.compute(ke(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},cc=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:xn,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:Cn;return ve({min:t,max:r})},cs=(e,t)=>{let r=e.inputs.length===1?t:cc(e.inputs),o=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Clip",i=>`clamp(${i}, 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]})},ps=e=>{e.compute(ke(e.inputs[0],"Ceil","ceil"))},ms=e=>{e.compute(ke(e.inputs[0],"Cos","cos"))},fs=e=>{e.compute(ke(e.inputs[0],"Cosh","cosh"))},kn=e=>ve(e),hs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Elu",o=>`elu_vf32(${o})`,`\n  const elu_alpha_ 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0.7071067811865475))`,On(t)))},vs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(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))},$s=e=>{e.compute(ke(e.inputs[0],"Not",t=>`!${t}`))},_s=e=>{e.compute(ke(e.inputs[0],"Neg",t=>`-${t}`))},Ss=e=>{e.compute(ke(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},xs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Cs=e=>{e.compute(ke(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},As=e=>ve(e),Is=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"HardSigmoid",o=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${o} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Ts=e=>{e.compute(ke(e.inputs[0],"Sin","sin"))},Es=e=>{e.compute(ke(e.inputs[0],"Sinh","sinh"))},Ps=e=>{e.compute(ke(e.inputs[0],"Sqrt","sqrt"))},ks=e=>{e.compute(ke(e.inputs[0],"Tan","tan"))},Os=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Rs=e=>{e.compute(ke(e.inputs[0],"Tanh",Os))},Bo=(e="f32")=>`\nconst fast_gelu_a: ${e} = 0.5;\nconst fast_gelu_b: ${e} = 0.7978845608028654;\nconst fast_gelu_c: ${e} = 0.035677408136300125;\n\nfn tanh_v(v: vec4<${e}>) -> vec4<${e}> {\n  return ${Os("v")};\n}\n`,Do=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Bs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"FastGelu",Do,Bo(t),void 0,e.inputs[0].dataType))},Ds=(e,t)=>{let r=et(e.inputs[0].dataType);return e.compute(ke(e.inputs[0],"ThresholdedRelu",o=>`select(vec4<${r}>(0.0), ${o}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = 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${p.guardAgainstOutOfBoundsWorkgroupSizes(u)}\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    ${i.setByOffset("global_idx","valueLeft * geluRight")}\n  }`}},Us=e=>{pc(e.inputs),e.compute(mc(e.inputs))}});var fc,hc,Ot,Ws,Ns,Gs,Hs,Ls,Fs,qs,js,Ks,Ys,Zs=Y(()=>{"use strict";ye();Se();_e();fc=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w,_;typeof c=="string"?w=_=(x,E)=>`${c}((${x}),(${E}))`:typeof c=="function"?w=_=c:(w=c.scalar,_=c.vector);let v=j("outputData",d,o.length,4),S=U("aData",p,t.length,4),A=U("bData",h,r.length,4),I;if(i)if(u){let x=M.size(t)===1,E=M.size(r)===1,P=t.length>0&&t[t.length-1]%4===0,O=r.length>0&&r[r.length-1]%4===0;x||E?I=v.setByOffset("global_idx",_(x?`${S.type.value}(${S.getByOffset("0")}.x)`:S.getByOffset("global_idx"),E?`${A.type.value}(${A.getByOffset("0")}.x)`:A.getByOffset("global_idx"))):I=`\n            let outputIndices = ${v.offsetToIndices("global_idx * 4u")};\n            let offsetA = ${S.broadcastedIndicesToOffset("outputIndices",v)};\n            let offsetB = ${A.broadcastedIndicesToOffset("outputIndices",v)};\n            ${v.setByOffset("global_idx",_(a||P?S.getByOffset("offsetA / 4u"):`${S.type.value}(${S.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||O?A.getByOffset("offsetB / 4u"):`${A.type.value}(${A.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n          `}else I=v.setByOffset("global_idx",_(S.getByOffset("global_idx"),A.getByOffset("global_idx")));else{if(!u)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let x=(E,P,O="")=>{let R=`aData[indexA${P}][componentA${P}]`,L=`bData[indexB${P}][componentB${P}]`;return`\n            let outputIndices${P} = ${v.offsetToIndices(`global_idx * 4u + ${P}u`)};\n            let offsetA${P} = ${S.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n            let offsetB${P} = ${A.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n            let indexA${P} = offsetA${P} / 4u;\n            let indexB${P} = offsetB${P} / 4u;\n            let componentA${P} = offsetA${P} % 4u;\n            let componentB${P} = offsetB${P} % 4u;\n            ${E}[${P}] = ${O}(${w(R,L)});\n          `};d===9?I=`\n            var data = vec4<u32>(0);\n            ${x("data",0,"u32")}\n            ${x("data",1,"u32")}\n            ${x("data",2,"u32")}\n            ${x("data",3,"u32")}\n            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:I=`\n            ${x("outputData[global_idx]",0)}\n            ${x("outputData[global_idx]",1)}\n            ${x("outputData[global_idx]",2)}\n            ${x("outputData[global_idx]",3)}\n          `}return`\n        ${e.registerUniform("vec_size","u32").declareVariables(S,A,v)}\n\n        ${y??""}\n\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n        ${I}\n      }`},hc=(e,t,r,o,i,u,a=r.dataType)=>{let c=!M.areEqual(r.dims,o.dims),p=r.dims,h=M.size(r.dims),d=!1,y=!1,w=[c];if(c){let _=It.calcShape(r.dims,o.dims,!1);if(!_)throw new Error("Can\'t perform binary op on the given tensors");p=_,h=M.size(p);let v=M.size(r.dims)===1,S=M.size(o.dims)===1,A=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,I=o.dims.length>0&&o.dims[o.dims.length-1]%4===0;w.push(v),w.push(S),w.push(A),w.push(I);let x=1;for(let E=1;E<p.length;E++){let P=r.dims[r.dims.length-E]??1,O=o.dims[o.dims.length-E]??1;if(P===O)x*=P;else break}x%4===0?(y=!0,d=!0):(v||S||A||I)&&(d=!0)}else d=!0;return w.push(d),{name:e,shaderCache:{hint:t+w.map(_=>_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>fc(_,r.dims,o.dims,p,d,c,y,i,r.dataType,o.dataType,a,u),getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(h/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(p)/4)},...Z(r.dims,o.dims,p)]})}},Ot=(e,t,r,o,i,u)=>{e.compute(hc(t,i??"",e.inputs[0],e.inputs[1],r,o,u))},Ws=e=>{Ot(e,"Add",(t,r)=>`${t}+${r}`)},Ns=e=>{Ot(e,"Div",(t,r)=>`${t}/${r}`)},Gs=e=>{Ot(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},Hs=e=>{Ot(e,"Mul",(t,r)=>`${t}*${r}`)},Ls=e=>{let t=U("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Ot(e,"Pow",{scalar:(o,i)=>`pow_custom(${o},${i})`,vector:(o,i)=>`pow_vector_custom(${o},${i})`},`\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      `)},Fs=e=>{Ot(e,"Sub",(t,r)=>`${t}-${r}`)},qs=e=>{Ot(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},js=e=>{Ot(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},Ks=e=>{Ot(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},Ys=e=>{Ot(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}});var St,xt,Ct,Bn,Ft=Y(()=>{"use strict";ye();Se();St=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},xt=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Ct=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Bn=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[r,o]=e?.activation_params||[.2,.5];return{activation:t,alpha:r,beta:o}}else if(t==="Clip"){let[r,o]=e?.activation_params||[xn,Cn];return{activation:t,clipMax:o,clipMin:r}}else if(t==="LeakyRelu"){let[r]=e?.activation_params||[.01];return{activation:t,alpha:r}}return{activation:t}}});var tt,Dn,zn=Y(()=>{"use strict";tt=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw 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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        }`,Hr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32)=>{let p=t[1]*e[1],h=t[0]*e[0],d=i?p:u,y=i?u:p,w=d/t[0],_=u/t[1];if(!((i&&w===4&&e[1]===4||!i&&(w===3||w===4))&&d%t[0]===0&&u%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${w} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${w} must be 3 or 4.\n  tileAWidth ${d} must be divisible by workgroupSize[0]${t[0]}. tileInner ${u} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${w}<${r}>, ${d/w}>, ${y}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${r}>, ${h/e[0]}>, ${u}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${w};\nconst tileInner = ${u};\n\n@compute @workgroup_size(${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 = ${a?"0":"i32(globalId.z)"};\n  ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n  let globalRowStart = i32(workgroupId.y) * ${p};\n\n  let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n  var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};\n\n  var acc: array<vec4<${r}>, rowPerThread>;\n\n  // Loop over shared dimension.\n  let tileRowB = localRow * ${_};\n  for (var t = 0; t < num_tiles; 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          ${yc(i,o)}\n      }\n\n      // Load one tile of B into local memory.\n      for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n          let inputRow = tileRowB + innerRow;\n          let inputCol = tileCol;\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${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          ${w===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n          ${bc(i,w)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},Xs=(e,t)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${t?", batchIndices":""});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${t?", batchIndices":""});\n            `,wc=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Lr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32,p=!1)=>{let h=e[1]*t[1],d=e[0]*t[0],y=i?h:u,w=i?u:h;if(!(w%t[1]===0&&y%t[0]===0&&u%t[1]===0))throw new Error(`tileAHight ${w} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${y} must be divisible by workgroupSize[0]${t[0]}, tileInner ${u} must be divisible by workgroupSize[1]${t[1]}`);let _=w/t[1],v=y/t[0],S=u/t[1],A=p?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${h};\n    let globalColStart = i32(workgroupId.x) * ${d};\n\n    // Loop over shared dimension.\n    for (var t = 0; t < num_tiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var inputRow = localRow; inputRow < ${w}; inputRow = inputRow + ${t[1]}) {\n        for (var inputCol = localCol; inputCol < ${y}; inputCol = inputCol + ${t[0]}) {\n          ${Xs(i,o)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${u}; inputRow = inputRow + ${t[1]}) {\n            for (var inputCol = localCol; inputCol < ${d}; 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 = ${i?`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) * ${h};\n\nlet tileRowA = i32(localId.y) * ${_};\nlet tileColA = i32(localId.x) * ${v};\nlet tileRowB = i32(localId.y) * ${S};\n// Loop over shared dimension.\nfor (var t = 0; t < num_tiles; t = t + 1) {\n  // Load one tile of A into local memory.\n  for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < ${v}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${Xs(i,o)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${S}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n      let inputRow = tileRowB + innerRow;\n      let inputCol = tileCol + innerCol;\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n        kStart + inputRow,\n        globalCol + innerCol${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      ${wc(i)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${r}, ${y}>, ${w}>;\n  var<workgroup> mm_Bsub : array<array<${r}, ${d}>, ${u}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${u};\n\n@compute @workgroup_size(${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 = ${a?"0":"i32(globalId.z)"};\n    ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n    let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n    var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};\n\n    var acc : array<array<${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    ${A}\n  }\n`},vc=(e,t,r,o,i,u=!1)=>{let[a,c,p]=i,[h,d,y,w]=o,_=_r(a,p),v=_r(c,p),S=De(o[0].type.tensor),A=()=>{let E=d.rank,P=h.rank,O=`var aIndices: ${d.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\naIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return _.forEach(R=>{O+=`\naIndices[${R}] = 0;`}),O+=`\naIndices[${E-2}] = u32(row);\n                   aIndices[${E-1}] = u32(colIn);`,O},I=()=>{let E=y.rank,P=h.rank,O=`var bIndices: ${y.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\nbIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return v.forEach(R=>{O+=`\nbIndices[${R}] = 0;`}),O+=`\nbIndices[${E-2}] = u32(row);\n                   bIndices[${E-1}] = u32(colIn);`,O};return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n      var value = ${tt(e,S)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n      {\n        ${A()}\n        value = ${d.getByIndices("aIndices")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n      var value = ${tt(e,S)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n      {\n        ${I()}\n        value = ${y.getByIndices("bIndices")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${tt(e,S)}) {\n      let col = colIn * ${e};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n        var value = valueIn;\n        let coords = vec3<i32>(batch, row, colIn);\n        ${t?`value = value + ${u?"bias[colIn]":`${tt(e,S)}(bias[row])`};`:""}\n        ${r}\n        ${w.setByIndices("vec3<u32>(coords)","value")}\n      }\n    }\n    `},Un=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u.slice(0,-2),p=a.slice(0,-2),h=o?o.slice(0,-2):r.slice(0,-2),d=M.size(h),y=u[u.length-2],w=u[u.length-1],_=a[a.length-1],v=w%4===0&&_%4===0,S=y<=8?[4,1,1]:[4,4,1],A=[8,8,1],I=[Math.ceil(_/A[0]/S[0]),Math.ceil(y/A[1]/S[1]),Math.ceil(d/A[2]/S[2])],x=v?4:1,E=[...c,y,w/x],P=E.length,O=[...p,w,_/x],R=O.length,L=[d,y,_/x],N=[{type:6,data:y},{type:6,data:_},{type:6,data:w}];xt(t,N),N.push(...Z(h,E,O));let K=["rank","rank"],Q=e.length>2;Q&&(N.push(...Z(e[2].dims)),K.push("rank")),N.push(...Z(L));let he=W=>{let se=h.length,Ce=An("batchDims",e[0].dataType,se,1),We=De(e[0].dataType),ee=U("a",e[0].dataType,P,x),ae=U("b",e[1].dataType,R,x),Ae=j("result",e[0].dataType,L.length,x),me=[ee,ae];if(Q){let G=i?x:1;me.push(U("bias",e[2].dataType,e[2].dims.length,G))}let ie=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ct(t,ie);let ue=De(Ae.type.tensor),le=St(t,Ae.type.value,ue),qe=vc(x,Q,le,[Ce,ee,ae,Ae],[c,p,h],i);return`\n  ${W.registerUniforms(ie).registerInternalVariables(Ce).declareVariables(...me,Ae)}\n  ${qe}\n  ${v?Hr(S,A,We,Ce):Lr(S,A,We,Ce)}\n                   `};return{name:"MatMul",shaderCache:{hint:`${S};${t.activation};${v};${i}`,inputDependencies:K},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:I[0],y:I[1],z:I[2]},programUniforms:N}),getShaderSource:he}}});var $c,Qs,Js=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();$c=(e,t,r,o,i=!1,u,a=4,c=4,p=4,h="f32")=>{let d=Q=>{switch(Q){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Q} is not supported.`)}},y=Q=>{switch(Q){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 ${Q} is not supported.`)}},w=e?`\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    `:`\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    `,_=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    `,v=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",S=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",A=e?"row":"col",I=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 = ${A} / outWidth;\n    let outCol = ${A} % outWidth;\n\n    let WRow = ${I} / (i32(uniforms.w_shape[1]) * inChannels);\n    let WCol = ${I} / inChannels % i32(uniforms.w_shape[1]);\n    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n    let xCh = ${I} % inChannels;\n    var resData = ${tt(a,h)}(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 < ${v} && xCol >= 0 && xCol < ${S}) {\n      ${w}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${d(a)}\n    }\n    return resData;`,E=e?t&&o?`\n    let col = colIn * ${a};\n    ${x}`:`\n    let col = colIn * ${a};\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n      ${x}\n    }\n    return ${tt(a,h)}(0.0);`:o&&r?`\n    let col = colIn * ${a};\n    ${x}`:`\n    let col = colIn * ${a};\n    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n      ${x}\n    }\n    return ${tt(a,h)}(0.0);`,P=`${y(c)}`,O=tt(p,h),R=e?tt(a,h):tt(c,h),L=e?tt(c,h):tt(a,h),N=St(u,O,h);return`\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {\n      ${e?E:P}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${L} {\n      ${e?P:E}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${O}) {\n      let col = colIn * ${p};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)\n      {\n      var value = valueIn;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${_}\n      ${Dn(i)}\n      ${N}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},Qs=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&(h%4===0||h%3===0)&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let P=v?p&&h%4!==0?3:4:1,O=I[1]*x[1],R=I[0]*x[0],L=Math.max(I[0]*P,I[1]),N=o%O===0,K=i%R===0,Q=u%L===0,he=v?[P,4,4]:[1,1,1],W=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];xt(t,W),W.push(...Z(e[0].dims,e[1].dims));let se=["rank","rank"];a&&(W.push(...Z(e[2].dims)),se.push("rank")),W.push(...Z(r));let Ce=We=>{let ee=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Ct(t,ee);let ae=v?4:1,Ae=De(e[0].dataType),me=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n        result[flatIndex] = ${v?`vec4<${Ae}>`:Ae}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${v?"/ 4":""}, value);\n      }`,ie=U("x",e[0].dataType,e[0].dims.length,P===3?1:P),ue=U("w",e[1].dataType,e[1].dims.length,ae),le=[ie,ue],qe=j("result",e[0].dataType,r.length,ae);if(a){let G=U("bias",e[2].dataType,e[2].dims.length,ae);le.push(G),me+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${v?`vec4<${Ae}>`:Ae} {\n          return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n        }`}return`\n        ${Mn("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        ${We.registerUniforms(ee).declareVariables(...le,qe)}\n        ${me}\n        ${$c(p,N,K,Q,a,t,he[0],he[1],he[2],Ae)}\n        ${v?Hr(x,I,Ae,void 0,!p,L):Lr(x,I,Ae,void 0,!p,L,!1,void 0,c)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${P};${v};${N};${K};${Q};${O};${R};${L}`,inputDependencies:se},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:W}),getShaderSource:Ce}}});var Mo,eu,tu=Y(()=>{"use strict";ye();Se();_e();Uo();Ft();Mo=(e,t,r)=>{let o=e.length>2,i=o?"value += b[output_channel];":"",u=e[0].dims,a=e[1].dims,c=a[0]/t.group,p=t.format==="NHWC",h=Vn(u,a,t.dilations,t.pads,t.strides,p),d=M.size(h),y=[{type:12,data:d},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:c}];xt(t,y),y.push(...Z(u,a));let w=["rank","rank"];o&&(y.push(...Z(e[2].dims)),w.push("rank")),y.push(...Z(h));let _=v=>{let S=j("output",e[0].dataType,h.length),A=De(S.type.tensor),I=St(t,S.type.value,A),x=U("x",e[0].dataType,u.length),E=U("w",e[1].dataType,a.length),P=[x,E];o&&P.push(U("b",e[2].dataType,e[2].dims.length));let O=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Ct(t,O),`\n  ${v.registerUniforms(O).declareVariables(...P,S)}\n\n  ${v.mainStart()}\n    ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n    let outputIndices = ${S.offsetToIndices("global_idx")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${p?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${p?1:2}], outputIndices[${p?2:3}]) * uniforms.strides - uniforms.pads;\n    let group_id: u32 = output_channel / uniforms.output_channels_per_group;\n\n    var value: ${S.type.value} = ${S.type.value}(0);\n    for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\n      let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\n      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\n        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\n\n        if (xHeight < 0u || xHeight >= uniforms.x_shape[${p?1:2}]) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];\n          if (xWidth < 0u || xWidth >= uniforms.x_shape[${p?2:3}]) {\n            continue;\n          }\n\n          let xVal = ${p?x.get("batch","xHeight","xWidth","input_channel"):x.get("batch","input_channel","xHeight","xWidth")};\n          let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${i}\n    ${I}\n    ${S.setByOffset("global_idx","value")}\n  }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:w},getRunData:()=>({outputs:[{dims:r?r(h):h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:y}),getShaderSource:_}},eu=(e,t,r)=>{let o=e.length>2,i=Me(r[3]),u=Me(r[2]),a=M.size(r)/i/u,c=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/i],p=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/i],h=[r[0],r[1],r[2],r[3]/i],d=[{type:12,data:a},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];xt(t,d),d.push(...Z(c,p,h));let y=(u-1)*t.strides[1]+p[1],w=_=>{let v=j("output",e[0].dataType,h.length,i),S=De(v.type.tensor),A=St(t,v.type.value,S),I=U("x",e[0].dataType,c.length,i),x=U("w",e[1].dataType,p.length,i),E=[I,x];o&&E.push(U("b",e[2].dataType,e[2].dims,i));let P=o?"value += b[output_channel];":"",O=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Ct(t,O),`\n  ${_.registerUniforms(O).declareVariables(...E,v)}\n  ${_.mainStart()}\n    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let width0 = uniforms.output_shape[3];\n    let output_channel = global_idx % width0;\n    var index1 = global_idx / width0;\n    let width1 = uniforms.output_shape[2] / ${u}u;\n    let col = (index1 % width1) * ${u}u;\n    index1 = index1 / width1;\n    let row = index1 % uniforms.output_shape[1];\n    let batch = index1 / uniforms.output_shape[1];\n\n    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;\n\n    var x_vals: array<${I.type.value}, ${y}>;\n    var values: array<${v.type.value}, ${u}>;\n    let input_channel = output_channel;\n    // Use constant instead of uniform can give better performance for w\'s height/width.\n    for (var w_height: u32 = 0u; w_height < ${p[0]}; w_height++) {\n      let x_height = x_corner.x + i32(w_height);\n      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {\n        for (var i = 0; i < ${y}; i++) {\n          let x_width = x_corner.y + i;\n          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {\n            x_vals[i] = ${I.get("batch","u32(x_height)","u32(x_width)","input_channel")};\n          } else {\n            x_vals[i] = ${I.type.value}(0);\n          }\n        }\n        for (var w_width: u32 = 0u; w_width < ${p[1]}; w_width++) {\n          let w_val = ${x.get("w_height","w_width","0","output_channel")};\n          for (var i = 0u; i < ${u}u; i++) {\n            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);\n          }\n        }\n      }\n    }\n\n    for (var i = 0u; i < ${u}u; i++) {\n      var value = values[i];\n      ${P}\n      ${A}\n      ${v.set("batch","row","col + i","output_channel","value")};\n    }\n  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${i};${u};${y};${p[0]};${p[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:w}}});var Vo,_c,ru,Wo=Y(()=>{"use strict";ye();Se();Fr();_e();Ft();Vo=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u[u.length-2],p=a[a.length-1],h=u[u.length-1],d=Me(p),y=Me(h),w=Me(c),_=M.size(r)/d/w,v=e.length>2,S=o?o.slice(0,-2):r.slice(0,-2),I=[M.size(S),c,p],x=[{type:12,data:_},{type:12,data:c},{type:12,data:p},{type:12,data:h}];xt(t,x),x.push(...Z(S,u,a)),v&&x.push(...Z(e[2].dims)),x.push(...Z(I));let E=P=>{let O=An("batch_dims",e[0].dataType,S.length),R=U("a",e[0].dataType,u.length,y),L=U("b",e[1].dataType,a.length,d),N=j("output",e[0].dataType,I.length,d),K=De(N.type.tensor),Q=St(t,N.type.value,K),he=[R,L],W="";if(v){let ie=i?d:1;he.push(U("bias",e[2].dataType,e[2].dims.length,ie)),W=`${i?`value += bias[col / ${ie}];`:`value += ${N.type.value}(bias[row + i]);`}`}let se=u.slice(0,-2),Ce=a.slice(0,-2),We=_r(se,S),ee=_r(Ce,S),ae=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Ct(t,ae);let Ae=(ie,ue)=>{let le=ie.rank,qe=ie.name;if(le===2)return`var ${qe}_indices = ${ie.type.indices}(0u, 0u);`;let G=O.rank,ne=`var ${qe}_indices: ${ie.type.indices};`;for(let xe=le-2-1,Ke=G-1;xe>=0;xe--,Ke--)ne+=`\n${qe}_indices[${xe}] = ${G>1?`batch_indices[${Ke}]`:"batch_indices"};`;return ue.forEach(xe=>{ne+=`\n${qe}_indices[${xe}] = 0;`}),ne+=`${qe}_indices[${le-2}] = 0u;\n                     ${qe}_indices[${le-1}] = 0u;`,ne},me=()=>{let ie=`var a_data: ${R.type.value};`;for(let ue=0;ue<y;ue++)ie+=`\n              let b_data${ue} = b[(b_offset + (k + ${ue}) * uniforms.N + col) / ${d}];`;for(let ue=0;ue<w;ue++){ie+=`a_data = a[(a_offset + (row + ${ue}) * uniforms.K + k) / ${y}];`;for(let le=0;le<y;le++)ie+=`\n            values[${ue}] = fma(${L.type.value}(a_data${y===1?"":`[${le}]`}), b_data${le}, values[${ue}]);\n`}return ie};return`\n  ${P.registerUniforms(ae).registerInternalVariables(O).declareVariables(...he,N)}\n  ${P.mainStart()}\n    ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let col = (global_idx % (uniforms.N / ${d})) * ${d};\n    var index1 = global_idx / (uniforms.N / ${d});\n    let stride1 = uniforms.M / ${w};\n    let row = (index1 % stride1) * ${w};\n    let batch = index1 / stride1;\n\n    ${r.length===2?"":`let batch_indices = ${O.offsetToIndices("batch")};`}\n    ${Ae(R,We)}\n    let a_offset = ${R.indicesToOffset("a_indices")};\n    ${Ae(L,ee)}\n    let b_offset = ${L.indicesToOffset("b_indices")};\n    var values: array<${N.type.value}, ${w}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${y}) {\n      ${me()}\n    }\n    for (var i = 0u; i < ${w}u; i++) {\n      var value = values[i];\n      ${W}\n      ${Q}\n      let cur_indices = ${N.type.indices}(batch, row + i, col);\n      let offset = ${N.indicesToOffset("cur_indices")};\n      ${N.setByOffset(`offset / ${d}`,"value")};\n    }\n  }\n  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${d};${y};${w};${i}`,inputDependencies:v?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:x}),getShaderSource:E}},_c=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.")},ru=e=>{_c(e.inputs);let t=It.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(Vo(e.inputs,{activation:""},t)):e.compute(Un(e.inputs,{activation:""},t))}});var Vn,No,Sc,nu,Go,xc,Cc,Ho,Uo=Y(()=>{"use strict";Se();Js();Fr();tu();Ft();Wo();Sr();Vn=(e,t,r,o,i,u)=>{let a=e[0],c=e.slice(u?1:2,u?3:4),p=c.length,h=t[0],y=t.slice(2).map((v,S)=>v+(v-1)*(r[S]-1)),_=c.map((v,S)=>v+o[S]+o[S+p]).map((v,S)=>Math.floor((v-y[S]+i[S])/i[S]));return _.splice(0,0,a),_.splice(u?3:1,0,h),_},No=[2,3,1,0],Sc=(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 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 ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},nu=(e,t)=>{let r=e.kernelShape.slice();for(let u=2;u<t[1].dims.length;++u)r[u-2]===0&&(r[u-2]=t[1].dims[u]);let o=e.pads.slice();nr.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,o,e.format==="NHWC",e.autoPad);let i=Object.assign({},e);return Object.assign(i,{kernelShape:r,pads:o}),i},Go=e=>{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,u=e.group,a=e.kernel_shape,c=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},xc=(e,t,r)=>{let o=nu(r,t),i=r.format==="NHWC";if(r.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&i&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let L=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),N=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=N);let K=[t[0],N];t.length===3&&K.push(t[2]),e.compute(eu(K,o,L),{inputs:K})}else e.compute(Mo(t,o));return}let u=t.length===3,a=t[0].dims[i?1:2],c=t[0].dims[i?2:3],p=t[0].dims[i?3:1],h=t[1].dims[2],d=t[1].dims[3],y=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),w=y[i?1:2],_=y[i?2:3],v=y[i?3:1],S=i&&h===a&&d===c&&r.pads[0]===0&&r.pads[1]===0;if(S||h===1&&d===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=y[0],L,N,K,Q=[];if(i){let se=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=se),S){let Ce=a*c*p;L=t[0].reshape([1,R,Ce]),N=se.reshape([1,Ce,v]),K=[1,R,v]}else L=t[0].reshape([R,a*c,p]),N=se.reshape([1,p,v]),K=[R,w*_,v];Q.push(L),Q.push(N)}else L=t[0].reshape([R,p,a*c]),N=t[1].reshape([1,v,p]),K=[R,v,w*_],Q.push(N),Q.push(L);u&&Q.push(t[2]);let he=K[2],W=Q[0].dims[Q[0].dims.length-1];he<8&&W<8?e.compute(Vo(Q,o,y,K,i),{inputs:Q}):e.compute(Un(Q,o,y,K,i),{inputs:Q});return}let A=!0,I=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=I);let x=[t[0],I];u&&x.push(t[2]);let E=i?w*_:v,P=i?v:w*_,O=h*d*p;e.compute(Qs(x,o,y,E,P,O,u,A),{inputs:x})},Cc=(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 i=[0,t.pads[0],0,t.pads[1]],u=[1].concat(t.strides),a=[1].concat(t.dilations),c=[1].concat(t.kernelShape),p=nu({...t,pads:i,strides:u,dilations:a,kernelShape:c},o);e.compute(Mo(o,p,h=>r?[h[0],h[2],h[3]]:[]))},Ho=(e,t)=>{Sc(e.inputs,t),e.inputs[0].dims.length===3?Cc(e,t):xc(e,e.inputs,t)}});var Ac,ou,iu=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();Ac=(e,t=!1,r,o,i=4)=>{let u=I=>{switch(I){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 ${o}(v0, v1, v2, v3);\n            `;default:throw new Error(`innerElementSize ${I} is not supported.`)}},a=e?`\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      `,c=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    `,p=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",h=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",d=e?"row":"col",y=e?"col":"row",w=`\n      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      let outRow = ${d} / outWidth;\n      let outCol = ${d} % outWidth;\n\n      let WRow = ${y} / (uniforms.filter_dims[1] * inChannels);\n      let WCol = ${y} / inChannels % uniforms.filter_dims[1];\n      let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n      let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n      if (xR < 0.0 || xR >= f32(${p}) || fract(xR) > 0.0) {\n        return ${o}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${h}) || fract(xC) > 0.0) {\n        return ${o}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${y} % inChannels;\n      ${a}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${i}];`,_=e?`\n      let col = colIn * ${i};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n        ${w}\n      }\n      return ${o}(0.0);`:`\n      let col = colIn * ${i};\n      if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n        ${w}\n      }\n      return ${o}(0.0);`,v=`\n      let col = colIn * ${i};\n      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n      let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);\n      let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];\n      if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"}  && coordX >= 0 && coordY >= 0) {\n        let rowInner = row % inChannels;\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\n        ${u(i)}\n      }\n      return ${o}(0.0);\n      `,S=St(r,o);return`\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${o} {\n    ${e?_:v}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${o} {\n    ${e?v:_}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${o}) {\n    let col = colIn * ${i};\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n      var value = valueInput;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${c}\n      ${Dn(t)}\n      ${S}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${i}] = value;\n    }\n  }`},ou=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&h%4===0&&h%3&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let P=v?4:1,O=Math.max(I[0]*P,I[1]),R=v?4:1,L=[t.kernelShape[p?1:2],t.kernelShape[p?2:3]],N=[L[0]+(t.dilations[0]<=1?0:(L[0]-1)*(t.dilations[0]-1)),L[1]+(t.dilations[1]<=1?0:(L[1]-1)*(t.dilations[1]-1))],K=[N[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),N[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Q=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:L},{type:6,data:K}];xt(t,Q),Q.push(...Z(e[0].dims,e[1].dims));let he=["rank","rank"];a&&(Q.push(...Z(e[2].dims)),he.push("rank")),Q.push(...Z(r));let W=se=>{let Ce=U("x",e[0].dataType,e[0].dims.length,R),We=U("w",e[1].dataType,e[1].dims.length,1),ee=j("result",e[0].dataType,r.length,R),ae=[Ce,We],Ae="";if(a){let ue=U("bias",e[2].dataType,e[2].dims.length,R);ae.push(ue),Ae+=`\n          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${ue.type.value} {\n            return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n          }`}let me=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:L.length},{name:"pads",type:"i32",length:K.length}];Ct(t,me);let ie=De(e[0].dataType,1);if(ie!=="f16"&&ie!=="f32")throw new Error(`elemType ${ie} is not supported.`);return`\n        ${Mn("uniforms.result_strides")}\n        ${se.registerUniforms(me).declareVariables(...ae,ee)};\n        ${Ae}\n        ${Ac(p,a,t,Ce.type.value,P)}\n        ${v?Hr(x,I,ie,void 0,!p,O):Lr(x,I,ie,void 0,!p,O,!1,void 0,c)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${x};${I};${v}`,inputDependencies:he},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Q}),getShaderSource:W}}});var Ic,Lo,au=Y(()=>{"use strict";ye();Lt();Se();_e();Ic=(e,t,r,o,i,u=!1,a,c,p=!1)=>{let h=p?1:2,d=p?2:3,y=p?3:1,w=u?2:1,_=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${u?`vec4<${a}>`:a}) {\n    result[flatIndex] = ${u?`vec4<${a}>`:a}(value);\n  }`;o&&(_+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${u?`vec4<${a}>`:a} {\n      return bias[coords.${p?"w":"y"}${u?"/ 4":""}];\n    }`);let v=u?4:1,S=U("W",t[1].dataType,t[1].dims.length,v),A=U("Dy",t[0].dataType,t[0].dims.length,v),I=[A,S];o&&I.push(U("bias",t[2].dataType,[r[y]].length,v));let x=j("result",t[0].dataType,r.length,v),E=`{\n        let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];\n        let r = ${i?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];\n        let c = ${i?"global_id.y":"workgroup_id.y"} * ${w};\n        let d1: u32 = ${i?"global_id.x":"workgroup_id.x"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(uniforms.pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${a}>, ${w}>;\n        for (var i = 0; i < ${w}; i++) {\n          dotProd[i] = vec4<${a}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n          var dyR = (${a}(dyCorner.x) + ${a}(wR)) / ${a}(uniforms.strides.x);\n          let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n            let dyC = (${a}(dyCorner.y) + ${a}(wC)) / ${a}(uniforms.strides.y);\n            let dyC2 = (${a}(dyCorner.y) + 1.0 + ${a}(wC)) / ${a}(uniforms.strides.y);\n            let wCPerm = uniforms.filter_dims[1] - 1 - wC;\n            if (wCPerm < 0) {\n              continue;\n            }\n            var bDyCVal = true;\n            var bDyCVal2 = true;\n            if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${a}(uniforms.Dy_shape[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 = uniforms.Dy_shape[3];\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${A.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${a}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${A.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${a}>(dot(xValue, wValue0),\n                                                    dot(xValue, wValue1),\n                                                    dot(xValue, wValue2),\n                                                    dot(xValue, wValue3));\n              }\n            } else if (bDyCVal) {\n              let d2Length = uniforms.Dy_shape[${y}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${A.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${a}>(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 = uniforms.Dy_shape[3];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${A.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${a}>(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] + ${o?"bias[c+i]":`vec4<${a}>(0.0)`};\n          ${x.set("batch","r","c + i","d1","value")};\n        }\n      }`,P=`\n          let outputIndices = ${x.offsetToIndices("global_idx")};\n          let batch = ${x.indicesGet("outputIndices",0)};\n          let d1 = ${x.indicesGet("outputIndices",y)};\n          let r = ${x.indicesGet("outputIndices",h)};\n          let c = ${x.indicesGet("outputIndices",d)};\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;\n          let dyRCorner = dyCorner.x;\n          let dyCCorner = dyCorner.y;\n          let groupId = d1 / uniforms.output_channels_per_group;\n          let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;\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 = ${a}(0.0);\n          for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n            if (wR % uniforms.dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${a}(dyRCorner) + ${a}(wR)) / ${a}(uniforms.strides[0]);\n            let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n            if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[${h}]) || fract(dyR) > 0.0 ||\n                wRPerm < 0) {\n              continue;\n            }\n            let idyR: u32 = u32(dyR);\n\n            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {\n              if (wC % uniforms.dilations.y != 0) {\n                continue;\n              }\n              let dyC = (${a}(dyCCorner) + ${a}(wC)) / ${a}(uniforms.strides.y);\n              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n              if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[${d}]) ||\n                  fract(dyC) > 0.0 || wCPerm < 0) {\n                continue;\n              }\n              let idyC: u32 = u32(dyC);\n              var inputChannel = groupId * uniforms.input_channels_per_group;\n              for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {\n                let xValue = ${p?A.get("batch","idyR","idyC","inputChannel"):A.get("batch","inputChannel","idyR","idyC")};\n                let wValue = ${S.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${o?"bias[d1]":`${a}(0.0)`};\n          ${x.setByOffset("global_idx","value")};\n        `;return`\n  ${e.registerUniforms(c).declareVariables(...I,x)}\n  ${_}\n\n    ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};\n  ${u?E:P}}`},Lo=(e,t,r)=>{let o=e.length>2,i=t.outputShape,u=M.size(i),a=[Math.ceil(u/64),1,1];Ve("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${a}`);let c=t.format==="NHWC",p=["rank","rank"],h=[t.strides[0],t.strides[1]],d=[t.kernelShape[c?1:2],t.kernelShape[c?2:3]],y=[t.dilations[0],t.dilations[1]],w=[d[0]+(t.dilations[0]<=1?0:(t.kernelShape[c?1:2]-1)*(t.dilations[0]-1)),d[1]+(t.dilations[1]<=1?0:(t.kernelShape[c?2:3]-1)*(t.dilations[1]-1))],_=[w[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),w[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],v=!1,S=t.group,A=e[1].dims,I=A[0]/S,x=A[1],E=[{type:12,data:u},{type:12,data:h},{type:12,data:d},{type:12,data:y},{type:12,data:w},{type:6,data:_},{type:12,data:I},{type:12,data:x},...Z(e[0].dims,e[1].dims)];o&&(E.push(...Z(e[2].dims)),p.push("rank")),E.push(...Z(i));let P=a[1]===1&&a[2]===1,O=R=>{let L=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:h.length},{name:"filter_dims",type:"u32",length:d.length},{name:"dilations",type:"u32",length:d.length},{name:"effective_filter_dims",type:"u32",length:w.length},{name:"pads",type:"i32",length:_.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],N=De(e[0].dataType);return`${Ic(R,e,i,o,P,v,N,L,c)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:p},getRunData:()=>({dispatchGroup:{x:a[0],y:a[1],z:a[2]},outputs:[{dims:r?r(i):i,dataType:e[0].dataType}],programUniforms:E}),getShaderSource:O}}});var Tc,Ec,Pc,su,uu,kc,Oc,Rc,Bc,du,lu=Y(()=>{"use strict";iu();au();Ft();Sr();Tc=(e,t,r,o,i,u)=>(e-1)*t+r+(o-1)*i+1-u,Ec=(e,t,r,o,i)=>{let u=Math.floor(e/2);t==="SAME_UPPER"?(r[o]=u,r[i]=e-u):t==="SAME_LOWER"&&(r[o]=e-u,r[i]=u)},Pc=(e,t,r,o,i,u,a,c,p,h)=>{let d=e.length-2,y=h.length===0;if(p.length===0)for(let v=0;v<d;++v)p.push(0);let w=e[0],_=t[c?3:1]*i;for(let v=0,S=e.length-d-(c?1:0);v<d;++v,++S){let A=e[S],I=y?A*a[v]:h[v],x=Tc(A,a[v],u[v],t[S],r[v],I);Ec(x,o,u,v,v+d),y&&h.push(a[v]*(A-1)+p[v]+(t[S]-1)*r[v]+1-u[v]-u[v+d])}h.splice(0,0,w),h.splice(c?3:1,0,_)},su=(e,t)=>{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((y,w)=>y*w,1)===0){r.length=0;for(let y=2;y<t[1].dims.length;++y)r.push(t[1].dims[y])}let o=e.format==="NHWC";r.splice(0,0,t[1].dims[0]),r.splice(o?3:1,0,t[1].dims[1]);let i=e.pads.slice(),u=e.outputShape.slice(),a=e.outputPadding.slice(),c=t[0].dims,p=e.dilations.slice();if(p.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;p=new Array(y).fill(1)}let h=e.strides.slice();if(h.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;h=new Array(y).fill(1)}Pc(c,r,p,e.autoPad,e.group,i,h,o,a,u);let d=Object.assign({},e);return Object.assign(d,{kernelShape:r,pads:i,outputPadding:a,outputShape:u,dilations:p,strides:h}),d},uu=e=>{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,u=e.group,a=e.kernelShape,c=e.pads,p=e.strides,h=e.wIsConst(),d=e.outputPadding,y=e.outputShape;return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,outputPadding:d,outputShape:y,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},kc=(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 r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[0];if(r!==o)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 u=e[0].dims.length-2;if(t.dilations.reduce((d,y)=>d+y,0)>0&&t.dilations.length!==u)throw new Error(`dilations should be ${u}D`);if(t.strides.reduce((d,y)=>d+y,0)>0&&t.strides.length!==u)throw new Error(`strides should be ${u}D`);if(t.pads.reduce((d,y)=>d+y,0)>0&&t.pads.length!==u*2)throw new Error(`pads should be ${u*2}D`);if(t.outputPadding.length!==u&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${u}D`);if(t.kernelShape.reduce((d,y)=>d+y,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Oc=[2,3,1,0],Rc=(e,t,r)=>{let o=su(r,t),i=r.format==="NHWC",u=o.outputShape,a=u[i?3:1],c=t[0].dims[i?3:1];if(o.group!==1||a===1&&c===1){e.compute(Lo(t,o));return}let p=u[i?1:2],h=u[i?2:3],d=t[1].dims[2],y=t[1].dims[3],w=i?p*h:a,_=i?a:p*h,v=d*y*c,S=!0,A=e.kernelCustomData.wT??e.compute(yt(t[1],Oc),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=A);let I=[t[0],A],x=t.length===3;x&&(!i&&t[2].dims.length===1?I.push(t[2].reshape([t[2].dims[0],1,1])):I.push(t[2])),e.compute(ou(I,o,u,w,_,v,x,S),{inputs:I})},Bc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let u=t.dilations;(u.length===0||u[0]===0)&&(u=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let c=t.pads;c.length===0&&(c=[0,0]),c=[0,c[0],0,c[1]],a=[1].concat(a),u=[1].concat(u),i=[1].concat(i);let p=su({...t,pads:c,strides:a,dilations:u,kernelShape:i},o);e.compute(Lo(o,p,h=>r?[h[0],h[2],h[3]]:[h[0],h[1],h[3]]))},du=(e,t)=>{kc(e.inputs,t),e.inputs[0].dims.length===3?Bc(e,t):Rc(e,e.inputs,t)}});var Dc,cu,pu,mu=Y(()=>{"use strict";ye();Se();Ze();_e();Dc=(e,t,r,o)=>{let i=M.size(t),u=t.length,a=U("input",e,u),c=j("output",e,u),p=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),h=M.normalizeAxis(p,u),d=y=>{let w=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,_=fe("uniforms.input_shape","uniforms.axis",u),v=o.reverse?w+(o.exclusive?" + 1":""):"0",S=o.reverse?_:w+(o.exclusive?"":" + 1");return`\n                ${y.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(a,c)}\n                ${y.mainStart()}\n                  ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n                  var inputIndices = ${c.offsetToIndices("global_idx")};\n                  var sum = ${c.type.value}(0);\n                 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r=e[0].dims,o=r,i=2,u=M.sizeToDimension(r,i),a=M.sizeFromDimension(r,i),c=Me(a),p=a/c,h=[r[0],r[1],p],d=["rank","type","type"],y=[{type:12,data:a},{type:12,data:p}];y.push(...Z(h,h));let w=_=>{let v=U("x",e[0].dataType,h.length,c),S=U("scale",e[1].dataType,e[1].dims),A=U("bias",e[2].dataType,e[2].dims),I=j("output",e[0].dataType,h.length,c),x=[v,S,A,I],E=v.type.value,P=c===1?"f32":`vec${c}<f32>`,O=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<${P}, ${O}>;\n  const workgroupSize = ${O}u;\n  ${_.registerUniforms(R).declareVariables(...x)}\n  ${_.mainStart(O)}\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 = ${P}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      initial = initial + ${P}(${v.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 = ${_t("workgroupShared[0]",c)} / f32(uniforms.normSize);\n    }\n    workgroupBarrier();\n\n    // reinitialize workgroup memory.\n    initial = ${P}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let deviation =  ${P}(${v.get("batch","channel","h")}) - ${P}(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 = ${_t("workgroupShared[0]",c)};\n    }\n    workgroupBarrier();\n\n    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n    let channelScale = invStdDev * f32(${S.getByOffset("channel")});\n    let channelShift = f32(${A.getByOffset("channel")}) - meanShared * channelScale;\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let value = ${v.get("batch","channel","h")} * ${E}(${P}(channelScale)) + ${E}(${P}(channelShift));\n      ${I.set("batch","channel","h","value")};\n    }\n  }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${c}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:u},programUniforms:y}),getShaderSource:w}},Jc=(e,t,r,o,i,u,a,c)=>{let p=Me(a),h=64,d=p===1?"vec2f":`mat2x${p}f`,y=p===1?"f32":`vec${p}f`,w=(R,L)=>`${d}(${R}, ${L})`,_=i*a/p,v=Math.ceil(u/h),S=["type"],A=[{type:12,data:v},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(u*a/p)}],I=R=>{let L=U("input",t.dataType,t.dims,p);return`\n  ${R.declareVariables(L)}\n  @group(0) @binding(1) var<storage, read_write> output : array<${d}>;\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(h)}\n    let currentImageNumber = global_idx / ${h} / uniforms.C;\n    let currentChannelNumber = (global_idx / ${h}) % uniforms.C;\n    let wgOffset = local_id.x * 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 = ${$t("f32",p)};\n    var squaredSum = ${$t("f32",p)};\n    for (var i: u32 = wgOffset; i < wgMax; i++) {\n        let value = ${y}(input[offset + i * uniforms.C]);\n        sum += value;\n        squaredSum += value * value;\n    }\n    output[global_idx] = ${w("sum","squaredSum")};\n  }`},x=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${p}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:[i,a,h,2],dataType:1}],dispatchGroup:{x:i*a/p},programUniforms:A}),getShaderSource:I},{inputs:[t],outputs:[-1]})[0],E=[{type:12,data:_},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(h*a/p)}],P=["type","type","type"],O=R=>{let L=U("scale",r.dataType,r.dims,p),N=U("bias",o.dataType,o.dims,p);return`\n  @group(0) @binding(0) var<storage, read> input : array<${d}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${L.type.storage}>;\n  @group(0) @binding(2) var<storage, read> bias : array<${N.type.storage}>;\n  @group(0) @binding(3) var<storage, read_write> output : array<${d}>;\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 = ${$t("f32",p)};\n    var squaredSum = ${$t("f32",p)};\n    for (var i: u32 = 0; i < min(${h}, uniforms.H); i++) {\n        let value = input[offset + i + currentChannelNumber * ${h}];\n        sum += value[0];\n        squaredSum += value[1];\n    }\n    sum = sum / f32(uniforms.H);\n    squaredSum = squaredSum / f32(uniforms.H);\n    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${c}));\n    let channelScale = invStdDev * ${y}(scale[currentChannelNumber]);\n    let channelShift = ${y}(bias[currentChannelNumber]) - sum * channelScale;\n\n    output[global_idx] = ${w("channelScale","channelShift")};\n  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${p};${c}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:[i,a,2],dataType:1}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:E}),getShaderSource:O},{inputs:[x,r,o],outputs:[-1]})[0]},ep=(e,t,r)=>{let o=t[0].dims,i=o,u=o[0],a=o[o.length-1],c=M.sizeFromDimension(o,1)/a,p=Me(a),h=M.size(i)/p,d=[{type:12,data:c},{type:12,data:Math.floor(a/p)}],y=["type","type"],w=Jc(e,t[0],t[1],t[2],u,c,a,r.epsilon),_=v=>{let S=De(t[0].dataType),A=p===1?"vec2f":`mat2x${p}f`,I=p===1?S:`vec${p}<${S}>`,x=U("input",t[0].dataType,t[0].dims,p),E=j("output",t[0].dataType,i,p);return`\n  @group(0) @binding(0) var<storage, read> input : array<${x.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${A}>;\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  ${v.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], ${I}(scale[0]), ${I}(scale[1]));\n  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:d}),getShaderSource:_},{inputs:[t[0],w]})},zu=(e,t)=>{t.format==="NHWC"?ep(e,e.inputs,t):e.compute(Qc(e.inputs,t))}});var tp,rp,Uu,Vu=Y(()=>{"use strict";ye();Se();_e();tp=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},rp=(e,t,r)=>{let o=t.simplified,i=e[0].dims,u=e[1],a=!o&&e[2],c=i,p=M.normalizeAxis(t.axis,i.length),h=M.sizeToDimension(i,p),d=M.sizeFromDimension(i,p),y=M.size(u.dims),w=a?M.size(a.dims):0;if(y!==d||a&&w!==d)throw new Error(`Size of X.shape()[axis:] == ${d}.\n       Size of scale and bias (if provided) must match this.\n       Got scale size of ${y} and bias size of ${w}`);let _=[];for(let O=0;O<i.length;++O)O<p?_.push(i[O]):_.push(1);let v=Me(d),S=["type","type"],A=[{type:12,data:h},{type:1,data:d},{type:12,data:Math.floor(d/v)},{type:1,data:t.epsilon}];a&&S.push("type");let I=r>1,x=r>2,E=O=>{let R=De(e[0].dataType),L=[U("x",e[0].dataType,e[0].dims,v),U("scale",u.dataType,u.dims,v)];a&&L.push(U("bias",a.dataType,a.dims,v)),L.push(j("output",e[0].dataType,c,v)),I&&L.push(j("mean_data_output",1,_)),x&&L.push(j("inv_std_output",1,_));let N=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n  ${O.registerUniforms(N).declareVariables(...L)}\n  ${O.mainStart()}\n    ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var mean_vector = ${$t("f32",v)};\n    var mean_square_vector = ${$t("f32",v)};\n\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n      let value = ${ir(R,v,"x[h + offset]")};\n      mean_vector += value;\n      mean_square_vector += value * value;\n    }\n    let mean = ${_t("mean_vector",v)} / uniforms.norm_size;\n    let inv_std_dev = inverseSqrt(${_t("mean_square_vector",v)} / uniforms.norm_size ${o?"":"- mean * mean"} + uniforms.epsilon);\n\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n      let f32input = ${ir(R,v,"x[j + offset]")};\n      let f32scale = ${ir(R,v,"scale[j]")};\n      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i=Math.floor((t.k+t.blockSize-1)/t.blockSize),u=t.blockSize/8*t.bits,a=e[1];if(!M.areEqual(a.dims,[t.n,i,u]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let p=e[2].dims;if(M.size(p)!==t.n*i)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,y=t.bits>4?t.n*i:t.n*Math.floor((i+1)/2);if(M.size(d)!==y)throw new Error("zeroPoints input size error.")}},op=(e,t,r,o)=>{let i=e[0].dims,u=i.length,a=Math.floor((t.k+t.blockSize-1)/t.blockSize),c=i[u-2],p=t.k,h=t.n,d=i.slice(0,u-2),y=M.size(d),_=t.blockSize/8*t.bits/4,v=e[0].dataType,S=Me(c),A=Me(t.k),I=Me(_),x=tr(v),E=c*a*x,P=Math.floor(o/E),O=a<=r[0]&&P>0,R=!O||P>=4?Me(h):P>=2&&Me(h)>=2?2:1,L=d.concat([c,h]),N=M.size(L)/R/S,K=O?[]:[{type:12,data:N},{type:12,data:t.blockSize}],Q=[y,c,p/A],he=M.convertShape(e[1].dims).slice();he.splice(-1,1,_/I),K.push(...Z(Q)),K.push(...Z(he)),K.push(...Z(e[2].dims)),e.length===4&&K.push(...Z(M.convertShape(e[3].dims)));let W=[y,c,h/R];K.push(...Z(W));let se=Ce=>{let We=Q.length,ee=U("a",e[0].dataType,We,A),ae=U("b",12,he.length,I),Ae=U("scales",e[2].dataType,e[2].dims.length),me=[ee,ae,Ae],ie=e.length===4?U("zero_points",12,e[3].dims.length):void 0;ie&&me.push(ie);let ue=W.length,le=j("output",e[0].dataType,ue,R),qe=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],G=De(e[0].dataType),ne=(()=>{switch(A){case 1:return`array<${G}, 8>`;case 2:return`mat4x2<${G}>`;case 4:return`mat2x4<${G}>`;default:throw new Error(`${A}-component is not supported.`)}})(),xe=`\n        for (var word: u32 = 0; word < ${_}; word += ${I}) {\n          ${ae.indicesSet("b_indices","2","word")};\n          let b_data = ${ae.getByIndices("b_indices")};\n          for (var i: u32 = 0; i < ${I}; i++) {\n            let b_value: u32 = ${I===1?"b_data":"b_data[word + i]"};\n            let b_mask: u32 = 0x0F0F0F0Fu;\n            let b_value_lower: vec4<u32> = unpack4xU8(b_value & b_mask);\n            let b_value_upper: vec4<u32> = unpack4xU8((b_value >> 4) & b_mask);\n            let b_quantized_values = ${ne}(${Array.from({length:4},(Be,Ge)=>`${G}(b_value_lower[${Ge}]), ${G}(b_value_upper[${Ge}])`).join(", ")});\n            let b_dequantized_values = ${(()=>A===1?`${ne}(${Array.from({length:8},(Be,Ge)=>`(b_quantized_values[${Ge}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${ne}(${Array(8).fill("zero_point").join(",")})) * scale;`)()};\n            // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\n            for (var m: u32 = 0; m < ${O?c:S}u; m++) {\n              ${ee.indicesSet("a_indices",We-2,O?"m":`row * ${S} + m`)};\n              ${ee.indicesSet("a_indices",We-1,"word_offset")};\n              var input_offset = ${ee.indicesToOffset("a_indices")};\n              var a_data: ${ne};\n              for (var j: u32 = 0; j < ${8/A}; j++) {\n                a_data[j] = ${ee.getByOffset("input_offset")};\n                input_offset++;\n              }\n              ${O?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${R>1?"[c]":""} += ${Array.from({length:8/A},(Be,Ge)=>`${A===1?`a_data[${Ge}] * b_dequantized_values[${Ge}]`:`dot(a_data[${Ge}], b_dequantized_values[${Ge}])`}`).join(" + ")};\n            }\n            word_offset += ${8/A};\n          }\n        }`,Ke=ie?`\n          zero_point_offset += 4;\n          if (zero_point_offset == 32) {\n            zero_point_offset = 0;\n            zero_point_index++;\n            zero_point_word = ${ie.getByOffset("zero_point_index")};\n          }`:"";return O?`\n        var<workgroup> workgroup_shared: array<${le.type.value}, ${c*a}>;\n        ${Ce.declareVariables(...me,le)}\n        ${Ce.mainStart([a,1,1])}\n          var a_indices: ${ee.type.indices};\n          var block = local_id.x;\n          var col = workgroup_id.y;\n          var batch = workgroup_id.z;\n          ${ee.indicesSet("a_indices","0","batch")};\n          // Two zero points are packed into one byte when uniforms.bits is 4.\n          for (var c: u32 = 0; c < ${R}; c++) {\n            let col_times_components_plus_c = col * ${R} + c;\n              ${ie?`\n            var zero_point_bytes_per_col: u32 = (${a} + 1) / 2;\n            var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u);\n            var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u;\n            var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u;\n            var zero_point_nibble_offset: u32 = block & 0x1u;\n            var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);\n            var zero_point_word: u32 = ${ie.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}\n            var b_indices: ${ae.type.indices};\n            ${ae.indicesSet("b_indices","0","col_times_components_plus_c")};\n            // The scale and zero points are computed per block.\n            var scales_index = col_times_components_plus_c * ${a} + block;\n            let scale = ${Ae.getByOffset("scales_index")};\n            // The default zero point is 8 for unsigned 4-bit quantization.\n            let zero_point = ${G}(${ie?"(zero_point_word) & 0xFu":8});\n            ${ae.indicesSet("b_indices","1","block")};\n            var word_offset: u32 = block * ${t.blockSize/A};\n            var workgroup_shared_offset: u32 = block * ${c};\n            ${xe}\n          }\n          workgroupBarrier();\n          if (local_id.x == 0u) {\n            var output_indices: ${le.type.indices};\n            ${le.indicesSet("output_indices","0","batch")};\n            ${le.indicesSet("output_indices",ue-1,"col")};\n            ${le.indicesSet("output_indices",ue-2,"0")};\n            var output_offset = ${le.indicesToOffset("output_indices")};\n            for (var m: u32 = 0u; m < ${c}u; m++) {\n              var output_value: ${le.type.value} = ${le.type.value}(0);\n              var workgroup_shared_offset: u32 = m;\n              for (var b: u32 = 0u; b < ${a}u; b++) {\n                output_value += workgroup_shared[workgroup_shared_offset];\n                workgroup_shared_offset += ${c};\n              }\n              ${le.setByOffset("output_offset","output_value")};\n              output_offset += ${h/R};\n            }\n          }\n        }`:`\n        ${Ce.registerUniforms(qe).declareVariables(...me,le)}\n        ${Ce.mainStart()}\n          ${Ce.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n          var output_values: array<${le.type.value}, ${S}>;\n          var output_indices = ${le.offsetToIndices("global_idx")};\n          var col = ${le.indicesGet("output_indices",ue-1)};\n          var row = ${le.indicesGet("output_indices",ue-2)};\n          var a_indices: ${ee.type.indices} = output_indices;\n          // Two zero points are packed into one byte because uniforms.bits <= 4.\n          // zero_point_offset is either 0 or 4. It is bit offset within one byte.\n          // TODO support zero_point_offset for bits > 4\n          ${ie?`\n          var zero_point_abs_offset = col * ${R} * ((${a} + 1) / 2);\n          var zero_point_index: u32 = zero_point_abs_offset / 4;\n          var zero_point_word: u32 = ${ie.getByOffset("zero_point_index")};\n          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}\n          var scale_index = col * ${a*R};\n          var b_indices: ${ae.type.indices};\n          for (var c: u32 = 0; c < ${R}; c++) {\n            ${ae.indicesSet("b_indices","0",`col * ${R} + c`)};\n            var block_offset: u32 = 0;\n            for (var block: u32 = 0; block < ${a}; block++) {\n              // The scale and zero points are computed per block.\n              let scale = ${Ae.getByOffset("scale_index")};\n              // The default zero point is 8 for unsigned 4-bit quantization.\n              let zero_point = ${G}(${ie?"extractBits(zero_point_word, zero_point_offset, 4)":8});\n              ${ae.indicesSet("b_indices","1","block")};\n              var word_offset: u32 = block_offset;\n              ${xe}\n              scale_index++;\n              ${Ke}\n              block_offset += uniforms.block_size / ${A};\n            }\n            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.\n            ${ie?`if (zero_point_offset % 8 > 0) {\n                ${Ke}\n              }`:""}\n            }\n            for (var k: u32 = 0u; k < ${S}u; k++) {\n              ${le.indicesSet("output_indices",ue-2,`${S} * row + k`)};\n              ${le.setByIndices("output_indices","output_values[k]")}\n            }\n        }`};return{name:O?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${c};${v};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:L,dataType:v}],name:O?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:O?{x:1,y:Math.ceil(h/R),z:y}:{x:Math.ceil(N/64)},programUniforms:K}),getShaderSource:se}},Wu=(e,t)=>{np(e.inputs,t);let r=e.getMaxComputeWorkgroupSizes(),o=e.getMaxComputeWorkgroupStoragesize();e.compute(op(e.inputs,t,r,o))},Nu=e=>ve(e)});var it,ip,Lu,Hu,ap,Ko,Fu,qu=Y(()=>{"use strict";ye();Se();Ze();_n();Ro();_e();Sr();it=(e,t)=>e.length>t&&e[t].dims.length>0&&M.size(e[t].dims)>0?e[t]:void 0,ip=(e,t)=>{let r=e[0],o=it(e,1),i=it(e,2),u=it(e,3),a=it(e,4),c=it(e,5),p=it(e,6),h=it(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 d=!1,y=r.dims[0],w=r.dims[1],_=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],v=w,S=0,A=0,I=Math.floor(_/t.numHeads);if(p&&h){if(p.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims[0]!==y||p.dims[1]!==t.numHeads||p.dims[3]!==I)throw new Error(\'Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(h.dims[0]!==y||h.dims[1]!==t.numHeads||h.dims[3]!==I)throw new Error(\'Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(p.dims[2]!==h.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)\');if(h.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');S=p.dims[2],A=p.dims[2]}else if(p||h)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,v=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==I)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.\');x=5,v=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==I)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');x=0,v=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(u){if(u.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(i&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(a){E=8;let N=a.dims;throw N.length===1?N[0]===y?E=1:N[0]===3*y+2&&(E=3):N.length===2&&N[0]===y&&N[1]===v&&(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 P=!1,O=_;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(r.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(v!==i.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');O=i.dims[2]}else{if(v!==i.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');O=i.dims[1]*i.dims[3],P=!0}}let R=S+v,L=!1;if(a)throw new Error("Key padding mask is not supported");if(c){if(c.dims.length!==4)throw new Error(\'Input "relative_position_bias" is expected to have 4 dimensions\');if(c.dims[0]!==y&&c.dims[0]!==1||c.dims[1]!==t.numHeads||c.dims[2]!==w||c.dims[3]!==R)throw new Error(\'Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)\')}return{batchSize:y,sequenceLength:w,pastSequenceLength:S,kvSequenceLength:v,totalSequenceLength:R,maxSequenceLength:A,inputHiddenSize:0,hiddenSize:_,vHiddenSize:O,headSize:I,vHeadSize:Math.floor(O/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:L,passPastInKv:P,qkvFormat:x}},Lu=e=>ve({...e}),Hu=ve({perm:[0,2,1,3]}),ap=(e,t,r,o,i,u,a)=>{let c=[o,i,u],p=M.size(c),h=[{type:12,data:p},{type:12,data:a},{type:12,data:u}],d=y=>{let w=j("qkv_with_bias",t.dataType,c),_=U("qkv",t.dataType,c),v=U("bias",r.dataType,c),S=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n  ${y.registerUniforms(S).declareVariables(_,v,w)}\n  ${y.mainStart()}\n    ${y.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:c,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:d},{inputs:[t,r],outputs:[-1]})[0]},Ko=(e,t,r,o,i,u,a,c)=>{let p=u;if(a){if(o===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return p=ap(e,u,a,t,o,r*i,c),p=p.reshape([t,o,r,i]),e.compute(yt(p,Hu.perm),{inputs:[p],outputs:[-1]})[0]}else return u.dims.length===3&&(p=u.reshape([t,o,r,i])),e.compute(yt(p,Hu.perm),{inputs:[p],outputs:[-1]})[0]},Fu=(e,t)=>{let r=ip(e.inputs,t),o=e.inputs[0],i=it(e.inputs,1),u=it(e.inputs,2),a=it(e.inputs,3),c=it(e.inputs,4),p=it(e.inputs,5),h=it(e.inputs,6),d=it(e.inputs,7);if(o.dims.length===5)throw new Error("Packed QKV is not implemented");if(i?.dims.length===5)throw new Error("Packed KV is not implemented");let y=i&&u&&i.dims.length===4&&u.dims.length===4,w=Ko(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,o,a,0);if(y)return Pn(e,w,i,u,c,void 0,h,d,p,r,t);if(!i||!u)throw new Error("key and value must be provided");let _=Ko(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,i,a,r.hiddenSize),v=Ko(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,u,a,2*r.hiddenSize);Pn(e,w,_,v,c,void 0,h,d,p,r,t)}});var sp,up,dp,lp,cp,pp,mp,fp,ju,Ku=Y(()=>{"use strict";ye();Se();_e();sp=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");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].")}},up=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n            k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n            if (k < 0) {\n              break;\n            }\n            if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n              break;\n            }\n            offset += k * i32(${fe("uniforms.x_strides",i,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            ${o}\n            value = x[offset];\n          }\n      `},dp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n                if (k < 0) {\n                  k = -k;\n                }\n                {\n                  let _2n_1 = 2 * (i32(${fe("uniforms.x_shape",i,t)}) - 1);\n                  k = k % _2n_1;\n                  if(k >= i32(${fe("uniforms.x_shape",i,t)})) {\n                    k = _2n_1 - k;\n                  }\n                }\n                offset += k * i32(${fe("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},lp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n                if (k < 0) {\n                  k = 0;\n                }\n                if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n                  k = i32(${fe("uniforms.x_shape",i,t)}) - 1;\n                }\n                offset += k * i32(${fe("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},cp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n                if (k < 0)  {\n                  k += i32(${fe("uniforms.x_shape",i,t)}]);\n                }\n                if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n                  k -= i32(${fe("uniforms.x_shape",i,t)});\n                }\n                offset += k * i32(${fe("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},pp=(e,t,r)=>{switch(r.mode){case 0:return up(e,t,r.pads.length);case 1:return dp(e,t,r.pads.length);case 2:return lp(e,t,r.pads.length);case 3:return cp(e,t,r.pads.length);default:throw new Error("Invalid mode")}},mp=(e,t)=>{let r=M.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,i=M.size(r),u=[{type:12,data:i},{type:6,data:t.pads}];t.mode===0&&u.push({type:e[0].dataType,data:t.value}),u.push(...Z(e[0].dims,r));let a=["rank"],c=p=>{let h=j("output",e[0].dataType,r.length),d=U("x",e[0].dataType,o.length),y=d.type.value,w=pp(h,o.length,t),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&_.push({name:"constant_value",type:y}),`\n            ${p.registerUniforms(_).declareVariables(d,h)}\n            ${p.mainStart()}\n            ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n            let indices = ${h.offsetToIndices("global_idx")};\n\n            var value = ${y}(0);\n            ${w}\n            output[global_idx] = value;\n        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(r)/64)},programUniforms:u}),getShaderSource:c}},fp=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,i=e[0].dims.length,u=new Int32Array(2*i).fill(0);if(e.length>=4){let c=e[3].getBigInt64Array();for(let p=0;p<c.length;p++)u[Number(c[p])]=Number(r[p]),u[Number(c[p])+i]=Number(r[p+c.length])}else r.forEach((c,p)=>u[Number(p)]=Number(c));let a=[];return u.forEach(c=>a.push(c)),{mode:t.mode,value:o,pads:a}}else return t},ju=(e,t)=>{sp(e.inputs);let r=fp(e.inputs,t);e.compute(mp(e.inputs,r),{inputs:[0]})}});var Nn,Yu,Zu,Xu,Qu,hp,gp,Ju,ed,td,rd,nd,od,id,ad,sd,ud,dd,ld,cd=Y(()=>{"use strict";$r();ye();Se();_e();Nn=e=>{if(vr.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Yu=(e,t,r)=>{let o=t.format==="NHWC",i=e.dims.slice();o&&i.splice(1,0,i.pop());let u=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),c=t.strides.slice(),p=u?t.dilations.slice():[],h=t.pads.slice();nr.adjustPoolAttributes(r,i,a,c,p,h);let d=nr.computePoolOutputShape(r,i,c,p,a,h,t.autoPad),y=Object.assign({},t);u?Object.assign(y,{kernelShape:a,strides:c,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(y,{kernelShape:a,strides:c,pads:h,cacheKey:t.cacheKey});let w=d.slice();return w.push(w.splice(1,1)[0]),[y,o?w:d]},Zu=(e,t)=>{let r=t.format==="NHWC",o=M.size(e),i=M.size(t.kernelShape),u=[{type:12,data:o},{type:12,data:i}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let c=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],d=t.pads[t.pads.length-1],y=!!(h+d);u.push({type:12,data:c},{type:12,data:p},{type:12,data:h},{type:12,data:d}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let w=!1;if(t.kernelShape.length===2){let _=t.kernelShape[t.kernelShape.length-2],v=t.strides[t.strides.length-2],S=t.pads[t.pads.length/2-2],A=t.pads[t.pads.length-2];w=!!(S+A),u.push({type:12,data:_},{type:12,data:v},{type:12,data:S},{type:12,data:A}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[u,a,!0,y,w]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let c=M.computeStrides(t.kernelShape);u.push({type:12,data:c},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:c.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,d)=>h+d);return[u,a,!!p,!1,!1]}},Xu=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w=i.format==="NHWC",_=t.type.value,v=j("output",t.type.tensor,o);if(i.kernelShape.length<=2){let S="",A="",I="",x=r-(w?2:1);if(d?S=`\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                  ${u}\n                }`:S=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${u}\n                }`,i.kernelShape.length===2){let P=r-(w?3:2);y?A=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${P}] = indices[${P}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${P}] < 0 || xIndices[${P}] >= uniforms.x_shape[${P}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:A=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${P}] = indices[${P}] * uniforms.sh - uniforms.phStart + j;\n                `,I=`\n              }\n            `}return`\n            ${e.registerUniforms(p).declareVariables(t,v)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n              let indices = ${v.offsetToIndices("global_idx")};\n              var xIndices = ${v.offsetToIndices("global_idx")};\n\n              var value = ${_}(${c});\n              var pad = 0;\n              ${A}\n              ${S}\n              ${I}\n              ${a}\n\n              output[global_idx] = value;\n            }`}else{if(w)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let S=i.kernelShape.length,A=i.pads.length,I="";return h?I=`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset("xIndices")}];\n                ${u}\n              }`:I=`\n              }\n              let x_val = x[${t.indicesToOffset("xIndices")}];\n              ${u}\n            `,`\n            ${e.registerUniforms(p).declareVariables(t,v)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n              let indices = ${v.offsetToIndices("global_idx")};\n              var xIndices = ${v.offsetToIndices("global_idx")};\n\n              var offsets: array<u32, ${S}>;\n\n              var value = ${_}(${c});\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 < ${S-1}u; j++) {\n                  offsets[j] = offset / ${fe("uniforms.kernelStrides","j",S)};\n                  offset -= offsets[j] * ${fe("uniforms.kernelStrides","j",S)};\n                }\n                offsets[${S-1}] = offset;\n\n                isPad = false;\n                for (var j = ${r-S}u; j < ${r}u; j++) {\n                  xIndices[j] = indices[j] * ${fe("uniforms.strides",`j - ${r-S}u`,S)}\n                    + offsets[j - ${r-S}u] - ${fe("uniforms.pads","j - 2u",A)};\n                  ${I}\n              }\n              ${a}\n\n              output[global_idx] = value;\n            }`}},Qu=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,hp=e=>`${Qu(e)};${e.countIncludePad}`,gp=e=>`${Qu(e)};${e.storageOrder};${e.dilations}`,Ju=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}),ed=(e,t,r,o)=>{let[i,u]=Yu(t,o,r),a=U("x",t.dataType,t.dims.length),c=a.type.value,p="value += x_val;",h="";i.countIncludePad?h+=`value /= ${c}(uniforms.kernelSize);`:h+=`value /= ${c}(i32(uniforms.kernelSize) - pad);`;let[d,y,w,_,v]=Zu(u,i);d.push(...Z(t.dims,u));let S=["rank"];return{name:e,shaderCache:{hint:`${o.cacheKey};${w};${_};${v}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(M.size(u)/64)},programUniforms:d}),getShaderSource:A=>Xu(A,a,t.dims.length,u.length,i,p,h,0,y,w,_,v)}},td=e=>{let t=e.count_include_pad!==0,r=Ju(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let o={countIncludePad:t,...r,cacheKey:""};return{...o,cacheKey:hp(o)}},rd=(e,t)=>{Nn(e.inputs),e.compute(ed("AveragePool",e.inputs[0],!1,t))},nd={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},od=e=>{let t=e.format;return{format:t,...nd,cacheKey:t}},id=(e,t)=>{Nn(e.inputs),e.compute(ed("GlobalAveragePool",e.inputs[0],!0,t))},ad=(e,t,r,o)=>{let[i,u]=Yu(t,o,r),a=`\n      value = max(x_val, value);\n    `,c="",p=U("x",t.dataType,t.dims.length),h=["rank"],[d,y,w,_,v]=Zu(u,i);return d.push(...Z(t.dims,u)),{name:e,shaderCache:{hint:`${o.cacheKey};${w};${_};${v}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(M.size(u)/64)},programUniforms:d}),getShaderSource:S=>Xu(S,p,t.dims.length,u.length,i,a,c,t.dataType===10?-65504:-1e5,y,w,_,v)}},sd=(e,t)=>{Nn(e.inputs),e.compute(ad("MaxPool",e.inputs[0],!1,t))},ud=e=>{let t=e.storage_order,r=e.dilations,o=Ju(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(o.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:t,dilations:r,...o,cacheKey:""};return{...i,cacheKey:gp(i)}},dd=e=>{let t=e.format;return{format:t,...nd,cacheKey:t}},ld=(e,t)=>{Nn(e.inputs),e.compute(ad("GlobalMaxPool",e.inputs[0],!0,t))}});var bp,wp,pd,md=Y(()=>{"use strict";$r();ye();_e();bp=(e,t,r)=>{let o=e===t,i=e<t&&r<0,u=e>t&&r>0;if(o||i||u)throw new Error("Range these inputs\' contents are invalid.")},wp=(e,t,r,o)=>{let i=Math.abs(Math.ceil((t-e)/r)),u=[i],a=i,c=[{type:12,data:a},{type:o,data:e},{type:o,data:r},...Z(u)],p=h=>{let d=j("output",o,u.length),y=d.type.value,w=[{name:"outputSize",type:"u32"},{name:"start",type:y},{name:"delta",type:y}];return`\n        ${h.registerUniforms(w).declareVariables(d)}\n        ${h.mainStart()}\n        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        output[global_idx] = uniforms.start + ${y}(global_idx) * uniforms.delta;\n      }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:u,dataType:o}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c})}},pd=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]),vr.webgpu.validateInputContent&&bp(t,r,o),e.compute(wp(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var vp,$p,_p,Sp,xp,Cp,Ap,Ip,Tp,Ep,Pp,fd,kp,Op,Rp,Bp,Dp,hd,gd,yd=Y(()=>{"use strict";ye();Se();Ze();_e();vp=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},$p=(e,t,r)=>{t.every(i=>i>=0&&i<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((i,u)=>o[i]=e[u]),o},_p=(e,t,r,o,i,u)=>{let[a,c,p]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(d=>u.push(d));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(c>0&&e.length>c&&e[c].dims.length>0){if(e[c].getFloat32Array().forEach(d=>o.push(d)),o.length!==0&&o.length!==h&&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");vp(o,t),t.axes.length>0&&$p(o,t.axes,h).forEach((d,y)=>o[y]=d)}if(p>0&&e.length>p&&(e[p].getBigInt64Array().forEach(d=>i.push(Number(d))),i.length!==h||r>=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(o.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 o<"u"&&typeof i<"u"&&o.length>0&&i.length>h)throw new Error("Resize requires only of scales or sizes to be specified")},Sp=(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`)}})()+"}",xp=(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`)}})()+"}",Cp=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),i=e.length===0?o:e.slice();return t.length>0?(t.forEach((u,a)=>{o[u]=i[a],o[a+r]=i[t.length+a]}),o):i},Ap=(e,t,r,o)=>{let i=[];if(r.length>0)if(o.length>0){if(e.forEach(u=>i.push(u)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((u,a)=>i[u]=r[a])}else r.forEach(u=>i.push(u));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((u,a)=>Math.round(u*t[a]))}return i},Ip=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(u=>t[u]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(u=>t[u]),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 i=e.slice();return r.axes.length>0?(r.axes.forEach(u=>t[u]=o),r.axes.forEach(u=>i[u]=Math.round(e[u]*t[u]))):(t.fill(o,0,t.length),i.forEach((u,a)=>i[a]=Math.round(u*t[a]))),i},Tp=(e,t,r,o,i)=>`\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 = ${fe("uniforms.scales","i",o)};\n        var roi_low = ${fe("uniforms.roi","i",i)};\n        var roi_hi = ${fe("uniforms.roi",`i + ${t.length}`,i)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${fe("uniforms.input_shape","i",t.length)};\n          var output_shape_i = ${fe("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    }`,Ep=(e,t,r,o,i,u,a)=>`\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 = ${fe("uniforms.scales","i",i)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${fe("uniforms.roi","i",u)};\n          var roi_hi = ${fe("uniforms.roi",`i + ${r.length}`,u)};\n          var input_shape_i = ${fe("uniforms.input_shape","i",r.length)};\n          var output_shape_i = ${fe("uniforms.output_shape","i",o.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${a} || (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    }`,Pp=(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 >= ${fe("uniforms.input_shape","i",t.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,fd=(e,t,r,o)=>e.rank>o?`\n    ${e.indicesSet("input_indices",t,"channel")};\n    ${e.indicesSet("input_indices",r,"batch")};\n`:"",kp=(e,t,r,o,i)=>{let[a,c,p,h]=r.length===2?[-1,0,1,-1]:[0,2,3,1],d=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",c,`max(0, min(row, ${r[c]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(col, ${r[p]} - 1))`)};\n      ${fd(e,h,a,2)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${d} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${d} = originalIndices[${c}];\n      var col:${d} = originalIndices[${p}];\n      ${o?`if (row < 0 || row > (${r[c]} - 1) || col < 0 || col > (${r[p]} - 1)) {\n        return ${i};\n      }`:""};\n      row = max(0, min(row, ${r[c]} - 1));\n      col = max(0, min(col, ${r[p]} - 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 = ${r.length>2?`u32(originalIndices[${h}])`:"0"};\n      var batch: u32 =  ${r.length>2?`u32(originalIndices[${a}])`:"0"};\n      var x11: ${d} = getInputValue(batch, channel, row1, col1);\n      var x12: ${d} = getInputValue(batch, channel, row1, col2);\n      var x21: ${d} = getInputValue(batch, channel, row2, col1);\n      var x22: ${d} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${d} = abs(row - ${d}(row1));\n      var dx2: ${d} = abs(${d}(row2) - row);\n      var dy1: ${d} = abs(col - ${d}(col1));\n      var dy2: ${d} = abs(${d}(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    }`},Op=(e,t,r,o,i,u,a,c,p,h)=>{let d=r.length===2,y=!0,[w,_]=d?[0,1]:y?[2,3]:[1,2],v=e.type.value,S=A=>{let I=A===w?"row":"col";return`\n      fn ${I}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${v} {\n        var output_index = ${t.indicesGet("output_indices",A)};\n        var originalIdx: ${v} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[A]},\n        ${o[A]}, ${r[A]}, ${u[A]}, ${u[A]} + ${r.length});\n        var fractOriginalIdx: ${v} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${c} && (originalIdx < 0 || originalIdx > (${r[A]} - 1))) {\n          return ${p};\n        }\n        var data: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${I}: ${v} = originalIdx + ${v}(i);\n          if (${I} < 0 || ${I} >= ${r[A]}) {\n            ${(()=>h?`coefs[i + 1] = 0.0;\n                        continue;`:c?`return ${p};`:`${I} = max(0, min(${I}, ${r[A]} - 1));`)()};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",A,`u32(${I})`)};\n          data[i + 1] = ${A===w?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${S(w)};\n    ${S(_)};\n  fn getCubicInterpolationCoefs(s: ${v}) -> array<${v}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${v} = 1.0 - absS;\n    var twoMinusAbsS: ${v} = 2.0 - absS;\n    var onePlusAbsS: ${v} = 1.0 + absS;\n    coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a};\n    coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${v}, 4>, coefs: array<${v}, 4>) -> ${v} {\n    var coefsSum: ${v} = 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}) -> ${v} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},Rp=(e,t,r,o,i)=>{let[a,c,p,h,d]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],y=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${y} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",c,`max(0, min(depth, ${r[c]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(height, ${r[p]} - 1))`)};\n      ${e.indicesSet("input_indices",h,`max(0, min(width, ${r[h]} - 1))`)};\n      ${fd(e,d,a,3)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${y} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${y} = originalIndices[${c}];\n      var height:${y} = originalIndices[${p}];\n      var width:${y} = originalIndices[${h}];\n      ${o?`if (depth < 0 || depth > (${r[c]} - 1) || height < 0 || height > (${r[p]} - 1) || width < 0 || (width > ${r[h]} - 1)) {\n      return ${i};\n        }`:""};\n\n    depth = max(0, min(depth, ${r[c]} - 1));\n      height = max(0, min(height, ${r[p]} - 1));\n      width = max(0, min(width, ${r[h]} - 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 = ${r.length>3?`u32(originalIndices[${d}])`:"0"};\n      var batch: u32 =  ${r.length>3?`u32(originalIndices[${a}])`:"0"};\n\n      var x111: ${y} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${y} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${y} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${y} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${y} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${y} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${y} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${y} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${y} = abs(depth - ${y}(depth1));\n      var dx2: ${y} = abs(${y}(depth2) - depth);\n      var dy1: ${y} = abs(height - ${y}(height1));\n      var dy2: ${y} = abs(${y}(height2) - height);\n      var dz1: ${y} = abs(width - ${y}(width1));\n      var dz2: ${y} = abs(${y}(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    }`},Bp=(e,t,r,o,i,u)=>{let a=e.dims,c=Cp(u,t.axes,a.length),p=Ap(a,o,i,t.axes),h=o.slice();o.length===0&&(h=a.map((x,E)=>x===0?1:p[E]/x),t.keepAspectRatioPolicy!=="stretch"&&(p=Ip(a,h,t)));let d=j("output",e.dataType,p.length),y=U("input",e.dataType,a.length),w=M.size(p),_=a.length===p.length&&a.every((x,E)=>x===p[E]),v=t.coordinateTransformMode==="tf_crop_and_resize",S=t.extrapolationValue,A=y.type.value,I=x=>`\n      ${_?"":`\n      ${Sp(t.coordinateTransformMode,A)};\n      ${(()=>{switch(t.mode){case"nearest":return`\n              ${Pp(y,a)};\n              ${xp(t.nearestMode,r,A)};\n              ${Ep(y,d,a,p,h.length,c.length,v)};\n              `;case"linear":return`\n              ${Tp(d,a,p,h.length,c.length)};\n              ${(()=>{if(a.length===2||a.length===4)return`${kp(y,d,a,v,S)}`;if(a.length===3||a.length===5)return`${Rp(y,d,a,v,S)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n            `;case"cubic":return`\n            ${(()=>{if(a.length===2||a.length===4)return`${Op(y,d,a,p,h,c,t.cubicCoeffA,v,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",h.length).registerUniform("roi","f32",c.length).declareVariables(y,d)}\n      ${x.mainStart()}\n        ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n        ${_?"output[global_idx] = input[global_idx];":`\n        let output_indices = ${d.offsetToIndices("global_idx")};\n        var input_indices: ${y.type.indices};\n        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n                if (checkInputIndices(input_indices)) {\n                  output[global_idx] = ${y.getByIndices("input_indices")};\n                } else {\n                  output[global_idx] = ${t.extrapolationValue};\n                }`;case"linear":return`output[global_idx] = ${a.length===2||a.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}|${r}|${h.length>0?h:""}|${i.length>0?i:""}|${c.length>0?c:""}|${_}|${a}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:[{type:12,data:w},{type:1,data:h},{type:1,data:c},...Z(a,p)]})}},Dp=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},hd=(e,t)=>{let r=[],o=[],i=[],u=Dp(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");_p(e.inputs,t,u,r,o,i),e.compute(Bp(e.inputs[0],t,u,r,o,i),{inputs:[0]})},gd=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,i=e.cubicCoeffA,u=e.excludeOutside!==0,a=e.extrapolationValue,c=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return ve({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:i,excludeOutside:u,extrapolationValue:a,keepAspectRatioPolicy:c,mode:p,nearestMode:h})}});var zp,Mp,bd,wd=Y(()=>{"use strict";ye();Se();Ze();_e();zp=(e,t)=>{let[r,o,i,u]=e,{numHeads:a,rotaryEmbeddingDim:c}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input \'x\' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!M.areEqual(o.dims,[])&&!M.areEqual(o.dims,[1])&&o.dims.length!==2)throw new Error(`Input \'position_ids\' is expected to have 0, 1, or 2 dimensions, got ${o.dims.length}`);if(i.dims.length!==2)throw new Error(`Input \'cos_cache\' is expected to have 2 dimensions, got ${i.dims.length}`);if(u.dims.length!==2)throw new Error(`Input \'sin_cache\' is expected to have 2 dimensions, got ${u.dims.length}`);if(!M.areEqual(i.dims,u.dims))throw new Error("Inputs \'cos_cache\' and \'sin_cache\' are expected to have the same shape");if(c>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=r.dims[0],h=r.dims[r.dims.length-2],d=i.dims[0],y=M.sizeFromDimension(r.dims,1)/h,w=c===0?i.dims[1]*2:y/a;if(c>w)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(o.dims.length===2){if(p!==o.dims[0])throw new Error(`Input \'position_ids\' dimension 0 should be of size batch_size, got ${o.dims[0]}`);if(h!==o.dims[1])throw new Error(`Input \'position_ids\' dimension 1 should be of size sequence_length, got ${o.dims[1]}`)}if(w/2!==i.dims[1]&&c/2!==i.dims[1])throw new Error(`Input \'cos_cache\' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${i.dims[1]}`);if(h>d)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Mp=(e,t)=>{let{interleaved:r,numHeads:o,rotaryEmbeddingDim:i,scale:u}=t,a=e[0].dims[0],c=M.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=c/p,d=e[2].dims[1],y=i===0?d*2:h/o,w=new Array(a,p,h/y,y-d),_=M.computeStrides(w),v=[{type:1,data:u},{type:12,data:w},{type:12,data:_},...e[0].dims.length===3?new Array({type:12,data:[c,h,y,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[c,y,p*y,1]}):[],...Z(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],S=A=>{let I=U("input",e[0].dataType,e[0].dims.length),x=U("position_ids",e[1].dataType,e[1].dims.length),E=U("cos_cache",e[2].dataType,e[2].dims.length),P=U("sin_cache",e[3].dataType,e[3].dims.length),O=j("output",e[0].dataType,e[0].dims.length);return A.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:w.length},{name:"global_strides",type:"u32",length:_.length},{name:"input_output_strides",type:"u32",length:_.length}]),`\n        ${A.declareVariables(I,x,E,P,O)}\n\n        ${A.mainStart(or)}\n          let half_rotary_emb_dim = uniforms.${E.name}_shape[1];\n          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;\n          let size = uniforms.global_shape[0] * uniforms.global_strides[0];\n          ${A.guardAgainstOutOfBoundsWorkgroupSizes("size")}\n\n          if (bsnh[3] < half_rotary_emb_dim) {\n            let position_ids_idx =\n                ${x.broadcastedIndicesToOffset("bsnh.xy",j("",x.type.tensor,2))};\n            let position_id =\n                u32(${x.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);\n            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r});\n            let j = i + select(half_rotary_emb_dim, 1, ${r});\n            let re = ${I.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} -\n                ${I.getByOffset("j")} * ${P.get("position_id","bsnh[3]")};\n            ${O.setByOffset("i","re")}\n            let im = ${I.getByOffset("i")} * ${P.get("position_id","bsnh[3]")} +\n                ${I.getByOffset("j")} * ${E.get("position_id","bsnh[3]")};\n            ${O.setByOffset("j","im")}\n          } else {\n            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;\n            ${O.setByOffset("k",I.getByOffset("k"))}\n          }\n        }`};return{name:"RotaryEmbedding",shaderCache:{hint:ve({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:S,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(w)/or)},programUniforms:v})}},bd=(e,t)=>{zp(e.inputs,t),e.compute(Mp(e.inputs,t))}});var Up,Vp,vd,$d=Y(()=>{"use strict";ye();Se();_e();Up=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 i=t.dims[t.dims.length-1],u=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==u)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]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},Vp=(e,t,r,o)=>{let i=t.simplified,u=e[0].dims,a=M.size(u),c=u,p=a,h=u.slice(-1)[0],d=o?u.slice(0,-1).concat(1):[],y=!i&&e.length>3,w=e.length>4,_=o&&r>1,v=o&&r>2,S=r>3,A=Me(h),I=[{type:12,data:p},{type:12,data:A},{type:12,data:h},{type:1,data:t.epsilon}],x=P=>{let O=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],R=[U("x",e[0].dataType,e[0].dims,A),U("skip",e[1].dataType,e[1].dims,A),U("gamma",e[2].dataType,e[2].dims,A)];y&&R.push(U("beta",e[3].dataType,e[3].dims,A)),w&&R.push(U("bias",e[4].dataType,e[4].dims,A)),R.push(j("output",e[0].dataType,c,A)),_&&R.push(j("mean_output",1,d)),v&&R.push(j("inv_std_output",1,d)),S&&R.push(j("input_skip_bias_sum",e[0].dataType,c,A));let L=De(e[0].dataType);return`\n\n      ${P.registerUniforms(O).declareVariables(...R)}\n\n      ${P.mainStart()}\n        ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}\n        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;\n        let offset = global_idx * hidden_size_vectorized;\n        var sum = ${$t("f32",A)};\n        var squareSum = ${$t("f32",A)};\n        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n          let skip_value = skip[offset + i];\n          let bias_value = ${w?"bias[i]":L+"(0.0)"};\n          let input_value = x[offset + i];\n          let value = input_value + skip_value + bias_value;\n          ${S?"input_skip_bias_sum[offset + i] = value;":""}\n          output[offset + i] = value;\n          let f32_value = ${ir(L,A,"value")};\n          sum += f32_value;\n          squareSum += f32_value * f32_value;\n        }\n        let mean = ${_t("sum",A)} / f32(uniforms.hidden_size);\n        let inv_std_dev = inverseSqrt(${_t("squareSum",A)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon);\n        ${_?"mean_output[global_idx] = mean;":""}\n        ${v?"inv_std_output[global_idx] = inv_std_dev;":""}\n        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n          output[offset + i] = (output[offset + i] ${i?"":`- ${L}(mean)`}) * ${L}(inv_std_dev) * gamma[i] ${y?"+ beta[i]":""};\n        }\n      }`},E=[{dims:c,dataType:e[0].dataType}];return r>1&&E.push({dims:d,dataType:1}),r>2&&E.push({dims:d,dataType:1}),r>3&&E.push({dims:u,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${A};${_};${v};${S}`,inputDependencies:e.map((P,O)=>"type")},getShaderSource:x,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(p/h/64)},programUniforms:I})}},vd=(e,t)=>{Up(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(Vp(e.inputs,t,e.outputCount,!1),{outputs:o})}});var Wp,Gn,Np,_d,Gp,Hp,Sd,xd,Cd=Y(()=>{"use strict";ye();Se();Ze();_e();Wp=(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`)})},Gn=(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},Np=(e,t)=>{if(e.length>1){let r=Gn(e,1),o=Gn(e,2),i=Gn(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),ve({starts:r,ends:o,axes:i})}else return t},_d=(e,t,r,o,i)=>{let u=e;return e<0&&(u+=r[o[t]]),i[t]<0?Math.max(0,Math.min(u,r[o[t]]-1)):Math.max(0,Math.min(u,r[o[t]]))},Gp=(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 = ${fe("uniforms.input_shape","i",r.length)};\n            let steps_i = ${fe("uniforms.steps","i",r.length)};\n            let signs_i = ${fe("uniforms.signs","i",r.length)};\n            let starts_i = ${fe("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      }`,Hp=(e,t)=>{let r=e[0].dims,o=M.size(r),i=t.axes.length>0?M.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],u=Gn(e,4);u.forEach(I=>I!==0||(()=>{throw new Error("step cannot be 0")})),u.length===0&&(u=Array(i.length).fill(1));let a=t.starts.map((I,x)=>_d(I,x,r,i,u)),c=t.ends.map((I,x)=>_d(I,x,r,i,u));if(i.length!==a.length||i.length!==c.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==r.length)for(let I=0;I<r.length;++I)i.includes(I)||(a.splice(I,0,0),c.splice(I,0,r[I]),u.splice(I,0,1));let p=u.map(I=>Math.sign(I));u.forEach((I,x,E)=>{if(I<0){let P=(c[x]-a[x])/I,O=a[x],R=O+P*u[x];a[x]=R,c[x]=O,E[x]=-I}});let h=r.slice(0);i.forEach((I,x)=>{h[I]=Math.ceil((c[I]-a[I])/u[I])});let 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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'}),zr,Ct,wn,Zn,ea,Co,bi,Yr,Qr,Lf,ta,Uf,Wf,Vf,Gf,Hf,qf,jf,Kf=ee(()=>{var t;Yt(),kw(),qn(),zr=()=>!!Ue.wasm.proxy&&typeof document<"u",wn=!1,Zn=!1,ea=!1,bi=new Map,Yr=(e,r)=>{let n=bi.get(e);n?n.push(r):bi.set(e,[r])},Qr=()=>{if(wn||!Zn||ea||!Ct)throw new Error("worker not 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${t[3]}`)}},Qf=class{async fetchModelAndCopyToWasmMemory(t){return Vf(await ti(t))}async loadModel(t,e){Kt();let r;typeof t=="string"?typeof process<"u"&&process.versions&&process.versions.node?r=await ti(t):r=await this.fetchModelAndCopyToWasmMemory(t):r=t,[this.sessionId,this.inputNames,this.outputNames]=await Gf(r,e),Ut()}async dispose(){return Hf(this.sessionId)}async run(t,e,r){Kt();let n=[],a=[];Object.entries(t).forEach(h=>{let m=h[0],_=h[1],p=this.inputNames.indexOf(m);if(p===-1)throw new Error(`invalid input '${m}'`);n.push(_),a.push(p)});let s=[],i=[];Object.entries(e).forEach(h=>{let m=h[0],_=h[1],p=this.outputNames.indexOf(m);if(p===-1)throw new Error(`invalid output '${m}'`);s.push(_),i.push(p)});let o=n.map((h,m)=>To(h,()=>`input "${this.inputNames[a[m]]}"`)),u=s.map((h,m)=>h?To(h,()=>`output "${this.outputNames[i[m]]}"`):null),l=await qf(this.sessionId,a,o,i,u,r),c={};for(let h=0;h<l.length;h++)c[this.outputNames[i[h]]]=s[h]??Yf(l[h]);return Ut(),c}startProfiling(){}endProfiling(){jf(this.sessionId)}}}),Xf,Jf,Tw=ee(()=>{Yt(),Kf(),Cw(),Xf=()=>{if((typeof Ue.wasm.initTimeout!="number"||Ue.wasm.initTimeout<0)&&(Ue.wasm.initTimeout=0),typeof Ue.wasm.simd!="boolean"&&(Ue.wasm.simd=!0),typeof Ue.wasm.proxy!="boolean"&&(Ue.wasm.proxy=!1),typeof Ue.wasm.trace!="boolean"&&(Ue.wasm.trace=!1),typeof Ue.wasm.numThreads!="number"||!Number.isInteger(Ue.wasm.numThreads)||Ue.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(Ue.wasm.numThreads=1);let t=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;Ue.wasm.numThreads=Math.min(4,Math.ceil((t||1)/2))}},Jf=class{async init(t){Xf(),await Uf(),await Wf(t)}async createInferenceSessionHandler(t,e){let r=new Qf;return await r.loadModel(t,e),Promise.resolve(r)}}}),Zf={};fn(Zf,{wasmBackend:()=>em});var em,Iw=ee(()=>{Tw(),em=new Jf});Yt(),Yt(),Yt();var Aw="1.18.0",Mw=Vu;{let t=(Iw(),Lr(Zf)).wasmBackend;Ur("webgpu",t,5),Ur("webnn",t,5),Ur("cpu",t,10),Ur("wasm",t,10)}Object.defineProperty(Ue.versions,"web",{value:Aw,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.
 * =============================================================================
 */var Ow=Object.freeze({__proto__:null,get InferenceSession(){return xs},get TRACE(){return Gn},get TRACE_FUNC_BEGIN(){return Kt},get TRACE_FUNC_END(){return Ut},get Tensor(){return bt},get TrainingSession(){return Ss},default:Mw,get env(){return Ue},get registerBackend(){return Ur}});const zw=(t,e)=>{const r=typeof document<"u"?document.createElement("canvas"):new OffscreenCanvas(1,1);r.width=t.dims[3],r.height=t.dims[2];const n=r.getContext("2d");if(n!=null){let a,s;(e==null?void 0:e.tensorLayout)!==void 0&&e.tensorLayout==="NHWC"?(a=t.dims[2],s=t.dims[3]):(a=t.dims[3],s=t.dims[2]);const i=(e==null?void 0:e.format)!==void 0?e.format:"RGB",o=e==null?void 0:e.norm;let u,l;o===void 0||o.mean===void 0?u=[255,255,255,255]:typeof o.mean=="number"?u=[o.mean,o.mean,o.mean,o.mean]:(u=[o.mean[0],o.mean[1],o.mean[2],0],o.mean[3]!==void 0&&(u[3]=o.mean[3])),o===void 0||o.bias===void 0?l=[0,0,0,0]:typeof o.bias=="number"?l=[o.bias,o.bias,o.bias,o.bias]:(l=[o.bias[0],o.bias[1],o.bias[2],0],o.bias[3]!==void 0&&(l[3]=o.bias[3]));const c=s*a;let h=0,m=c,_=c*2,p=-1;i==="RGBA"?(h=0,m=c,_=c*2,p=c*3):i==="RGB"?(h=0,m=c,_=c*2):i==="RBG"&&(h=0,_=c,m=c*2);for(let b=0;b<s;b++)for(let $=0;$<a;$++){const S=(t.data[h++]-l[0])*u[0],x=(t.data[m++]-l[1])*u[1],C=(t.data[_++]-l[2])*u[2],T=p===-1?255:(t.data[p++]-l[3])*u[3];n.fillStyle="rgba("+S+","+x+","+C+","+T+")",n.fillRect($,b,1,1)}if("toDataURL"in r)return r.toDataURL();throw new Error("toDataURL is not supported")}else throw new Error("Can not access image data")},Pw=(t,e)=>{const r=typeof document<"u"?document.createElement("canvas").getContext("2d"):new OffscreenCanvas(1,1).getContext("2d");let n;if(r!=null){let a,s,i;(e==null?void 0:e.tensorLayout)!==void 0&&e.tensorLayout==="NHWC"?(a=t.dims[2],s=t.dims[1],i=t.dims[3]):(a=t.dims[3],s=t.dims[2],i=t.dims[1]);const o=e!==void 0&&e.format!==void 0?e.format:"RGB",u=e==null?void 0:e.norm;let l,c;u===void 0||u.mean===void 0?l=[255,255,255,255]:typeof u.mean=="number"?l=[u.mean,u.mean,u.mean,u.mean]:(l=[u.mean[0],u.mean[1],u.mean[2],255],u.mean[3]!==void 0&&(l[3]=u.mean[3])),u===void 0||u.bias===void 0?c=[0,0,0,0]:typeof u.bias=="number"?c=[u.bias,u.bias,u.bias,u.bias]:(c=[u.bias[0],u.bias[1],u.bias[2],0],u.bias[3]!==void 0&&(c[3]=u.bias[3]));const h=s*a;if(e!==void 0&&(e.format!==void 0&&i===4&&e.format!=="RGBA"||i===3&&e.format!=="RGB"&&e.format!=="BGR"))throw new Error("Tensor format doesn't match input tensor dims");const m=4;let _=0,p=1,b=2,$=3,S=0,x=h,C=h*2,T=-1;o==="RGBA"?(S=0,x=h,C=h*2,T=h*3):o==="RGB"?(S=0,x=h,C=h*2):o==="RBG"&&(S=0,C=h,x=h*2),n=r.createImageData(a,s);for(let I=0;I<s*a;_+=m,p+=m,b+=m,$+=m,I++)n.data[_]=(t.data[S++]-c[0])*l[0],n.data[p]=(t.data[x++]-c[1])*l[1],n.data[b]=(t.data[C++]-c[2])*l[2],n.data[$]=T===-1?255:(t.data[T++]-c[3])*l[3]}else throw new Error("Can not access image data");return n},Io=(t,e)=>{if(t===void 0)throw new Error("Image buffer must be defined");if(e.height===void 0||e.width===void 0)throw new Error("Image height and width must be defined");if(e.tensorLayout==="NHWC")throw new Error("NHWC Tensor layout is not supported yet");const{height:r,width:n}=e,a=e.norm??{mean:255,bias:0};let s,i;typeof a.mean=="number"?s=[a.mean,a.mean,a.mean,a.mean]:s=[a.mean[0],a.mean[1],a.mean[2],a.mean[3]??255],typeof a.bias=="number"?i=[a.bias,a.bias,a.bias,a.bias]:i=[a.bias[0],a.bias[1],a.bias[2],a.bias[3]??0];const o=e.format!==void 0?e.format:"RGBA",u=e.tensorFormat!==void 0&&e.tensorFormat!==void 0?e.tensorFormat:"RGB",l=r*n,c=u==="RGBA"?new Float32Array(l*4):new Float32Array(l*3);let h=4,m=0,_=1,p=2,b=3,$=0,S=l,x=l*2,C=-1;o==="RGB"&&(h=3,m=0,_=1,p=2,b=-1),u==="RGBA"?C=l*3:u==="RBG"?($=0,x=l,S=l*2):u==="BGR"&&(x=0,S=l,$=l*2);for(let I=0;I<l;I++,m+=h,p+=h,_+=h,b+=h)c[$++]=(t[m]+i[0])/s[0],c[S++]=(t[_]+i[1])/s[1],c[x++]=(t[p]+i[2])/s[2],C!==-1&&b!==-1&&(c[C++]=(t[b]+i[3])/s[3]);return u==="RGBA"?new lr("float32",c,[1,4,r,n]):new lr("float32",c,[1,3,r,n])},Rw=async(t,e)=>{const r=typeof HTMLImageElement<"u"&&t instanceof HTMLImageElement,n=typeof ImageData<"u"&&t instanceof ImageData,a=typeof ImageBitmap<"u"&&t instanceof ImageBitmap,s=typeof t=="string";let i,o=e??{};const u=()=>{if(typeof document<"u")return document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},l=c=>c instanceof HTMLCanvasElement||c instanceof OffscreenCanvas?c.getContext("2d"):null;if(r){const c=u();c.width=t.width,c.height=t.height;const h=l(c);if(h!=null){let m=t.height,_=t.width;if(e!==void 0&&e.resizedHeight!==void 0&&e.resizedWidth!==void 0&&(m=e.resizedHeight,_=e.resizedWidth),e!==void 0){if(o=e,e.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");o.tensorFormat="RGBA",o.height=m,o.width=_}else o.tensorFormat="RGBA",o.height=m,o.width=_;h.drawImage(t,0,0),i=h.getImageData(0,0,_,m).data}else throw new Error("Can not access image data")}else if(n){let c,h;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(c=e.resizedHeight,h=e.resizedWidth):(c=t.height,h=t.width),e!==void 0&&(o=e),o.format="RGBA",o.height=c,o.width=h,e!==void 0){const m=u();m.width=h,m.height=c;const _=l(m);if(_!=null)_.putImageData(t,0,0),i=_.getImageData(0,0,h,c).data;else throw new Error("Can not access image data")}else i=t.data}else if(a){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const c=u();c.width=t.width,c.height=t.height;const h=l(c);if(h!=null){const m=t.height,_=t.width;return h.drawImage(t,0,0,_,m),i=h.getImageData(0,0,_,m).data,o.height=m,o.width=_,Io(i,o)}else throw new Error("Can not access image data")}else{if(s)return new Promise((c,h)=>{const m=u(),_=l(m);if(!t||!_)return h();const p=new Image;p.crossOrigin="Anonymous",p.src=t,p.onload=()=>{m.width=p.width,m.height=p.height,_.drawImage(p,0,0,m.width,m.height);const b=_.getImageData(0,0,m.width,m.height);o.height=m.height,o.width=m.width,c(Io(b.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(i!==void 0)return Io(i,o);throw new Error("Input data provided is not supported - aborted tensor creation")},Bw=(t,e)=>{const{width:r,height:n,download:a,dispose:s}=e,i=[1,n,r,4];return new lr({location:"texture",type:"float32",texture:t,dims:i,download:a,dispose:s})},Dw=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:s}=e;return new lr({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:s})},Nw=(t,e,r)=>new lr({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),yn=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),vi=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let tm=!1;const Fw=()=>{if(!tm){tm=!0;const t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=typeof Float16Array<"u"&&Float16Array.from;t&&(yn.set("int64",BigInt64Array),vi.set(BigInt64Array,"int64")),e&&(yn.set("uint64",BigUint64Array),vi.set(BigUint64Array,"uint64")),r?(yn.set("float16",Float16Array),vi.set(Float16Array,"float16")):yn.set("float16",Uint16Array)}},Lw=t=>{let e=1;for(let r=0;r<t.length;r++){const n=t[r];if(typeof n!="number"||!Number.isSafeInteger(n))throw new TypeError(`dims[${r}] must be an integer, got: ${n}`);if(n<0)throw new RangeError(`dims[${r}] must be a non-negative integer, got: ${n}`);e*=n}return e},Uw=(t,e)=>{switch(t.location){case"cpu":return new lr(t.type,t.data,e);case"cpu-pinned":return new lr({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new lr({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new lr({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let lr=class{constructor(e,r,n){Fw();let a,s;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,a=e.type,s=e.dims,e.location){case"cpu-pinned":{const o=yn.get(a);if(!o)throw new TypeError(`unsupported type "${a}" to create tensor from pinned buffer`);if(!(e.data instanceof o))throw new TypeError(`buffer should be of type ${o.name}`);this.cpuData=e.data;break}case"texture":{if(a!=="float32")throw new TypeError(`unsupported type "${a}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(a!=="float32"&&a!=="float16"&&a!=="int32"&&a!=="int64"&&a!=="uint32"&&a!=="uint8"&&a!=="bool")throw new TypeError(`unsupported type "${a}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let o,u;if(typeof e=="string")if(a=e,u=n,e==="string"){if(!Array.isArray(r))throw new TypeError("A string tensor's data must be a string array.");o=r}else{const l=yn.get(e);if(l===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(r)){if(e==="float16"&&l===Uint16Array)throw new TypeError("Creating a float16 tensor from number array is not supported. Please use Uint16Array as data.");e==="uint64"||e==="int64"?o=l.from(r,BigInt):o=l.from(r)}else if(r instanceof l)o=r;else throw new TypeError(`A ${a} tensor's data must be type of ${l}`)}else if(u=r,Array.isArray(e)){if(e.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");const l=typeof e[0];if(l==="string")a="string",o=e;else if(l==="boolean")a="bool",o=Uint8Array.from(e);else throw new TypeError(`Invalid element type of data array: ${l}.`)}else{const l=vi.get(e.constructor);if(l===void 0)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);a=l,o=e}if(u===void 0)u=[o.length];else if(!Array.isArray(u))throw new TypeError("A tensor's dims must be a number array");s=u,this.cpuData=o,this.dataLocation="cpu"}const i=Lw(s);if(this.cpuData&&i!==this.cpuData.length)throw new Error(`Tensor's size(${i}) does not match data length(${this.cpuData.length}).`);this.type=a,this.dims=s,this.size=i}static async fromImage(e,r){return Rw(e,r)}static fromTexture(e,r){return Bw(e,r)}static fromGpuBuffer(e,r){return Dw(e,r)}static fromPinnedBuffer(e,r,n){return Nw(e,r,n)}toDataURL(e){return zw(this,e)}toImageData(e){return Pw(this,e)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":{if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;const r=await this.downloader();return this.downloader=void 0,this.dataLocation="cpu",this.cpuData=r,e&&this.disposer&&(this.disposer(),this.disposer=void 0),r}finally{this.isDownloading=!1}}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return Uw(this,e)}};const Ww=lr,ra=[];let Ao,Xr;Fr.IS_NODE_ENV?(Xr=Ve??mr,ra.push("cpu"),Ao=["cpu"]):(Xr=Ow,Fr.IS_WEBGPU_AVAILABLE&&ra.push("webgpu"),ra.push("wasm"),Ao=["wasm"]);const Vw=Xr.InferenceSession;function Gw(t){let e=Ao;if(t){if(!ra.includes(t))throw new Error(`Unsupported device: "${t}". Should be one of: ${ra.join(", ")}.`);e=[t]}return e}async function rm(t,e){return await Vw.create(t,e)}function nm(t){return t instanceof Xr.Tensor}const tr=Xr==null?void 0:Xr.env;tr!=null&&tr.wasm&&(tr.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.18.0/dist/",tr.wasm.proxy=!Fr.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(tr.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(tr.wasm.simd=!1));function Hw(){var t;return(t=tr==null?void 0:tr.wasm)==null?void 0:t.proxy}kt.backends.onnx=tr;const bn=async(t,e,r)=>{const n=await rm(new Uint8Array(t),e);return async a=>{const s=Object.fromEntries(Object.entries(a).map(([o,u])=>[o,u.ort_tensor])),i=await n.run(s);return Array.isArray(r)?r.map(o=>new fe(i[o])):new fe(i[r])}};class na{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=bn([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=bn([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=bn([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=bn([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=bn([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=bn([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}}F(na,"session_options",{});const am=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array});class fe{constructor(...e){F(this,"ort_tensor");return nm(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new Ww(e[0],e[1],e[2]),new Proxy(this,{get:(r,n)=>{if(typeof n=="string"){let a=Number(n);if(Number.isInteger(a))return r._getitem(a)}return r[n]},set:(r,n,a)=>r[n]=a})}get dims(){return this.ort_tensor.dims}set dims(e){this.ort_tensor.dims=e}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...r]=this.dims;if(r.length>0){const n=r.reduce((a,s)=>a*s);for(let a=0;a<e;++a)yield this._subarray(a,n,r)}else yield*this.data}_getitem(e){const[r,...n]=this.dims;if(e=dr(e,r),n.length>0){const a=n.reduce((s,i)=>s*i);return this._subarray(e,a,n)}else return new fe(this.type,[this.data[e]],n)}indexOf(e){const r=this.data;for(let n=0;n<r.length;++n)if(r[n]==e)return n;return-1}_subarray(e,r,n){const a=e*r,s=(e+1)*r,i="subarray"in this.data?this.data.subarray(a,s):this.data.slice(a,s);return new fe(this.type,i,n)}item(){const e=this.data;if(e.length!==1)throw new Error(`a Tensor with ${e.length} elements cannot be converted to Scalar`);return e[0]}tolist(){return qw(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=1/(1+Math.exp(-e[r]));return this}mul(e){return this.clone().mul_(e)}mul_(e){const r=this.data;for(let n=0;n<r.length;++n)r[n]*=e;return this}div(e){return this.clone().div_(e)}div_(e){const r=this.data;for(let n=0;n<r.length;++n)r[n]/=e;return this}add(e){return this.clone().add_(e)}add_(e){const r=this.data;for(let n=0;n<r.length;++n)r[n]+=e;return this}clone(){return new fe(this.type,this.data.slice(),this.dims.slice())}slice(...e){let r=[],n=[];for(let l=0;l<this.dims.length;++l){let c=e[l];if(c==null)n.push([0,this.dims[l]]),r.push(this.dims[l]);else if(typeof c=="number")c=dr(c,this.dims[l],l),n.push([c,c+1]);else if(Array.isArray(c)&&c.length===2){let[h,m]=c;if(h=h===null?0:dr(h,this.dims[l],l,!1),m=m===null?this.dims[l]:dr(m,this.dims[l],l,!1),h>m)throw new Error(`Invalid slice: ${c}`);let _=[Math.max(h,0),Math.min(m,this.dims[l])];n.push(_),r.push(_[1]-_[0])}else throw new Error(`Invalid slice: ${c}`)}let a=n.map(([l,c])=>c-l),s=a.reduce((l,c)=>l*c);const i=this.data;let o=new i.constructor(s);const u=this.stride();for(let l=0;l<s;++l){let c=0;for(let h=a.length-1,m=l;h>=0;--h){const _=a[h];c+=(m%_+n[h][0])*u[h],m=Math.floor(m/_)}o[l]=i[c]}return new fe(this.type,o,r)}permute(...e){return jw(this,e)}transpose(...e){return this.permute(...e)}sum(e=null,r=!1){return this.norm(1,e,r)}norm(e="fro",r=null,n=!1){if(e==="fro")e=2;else if(typeof e=="string")throw Error(`Unsupported norm: ${e}`);const a=this.data;if(r===null){let o=a.reduce((u,l)=>u+l**e,0)**(1/e);return new fe(this.type,[o],[])}r=dr(r,this.dims.length);const s=this.dims.slice();s[r]=1;const i=new a.constructor(a.length/this.dims[r]);for(let o=0;o<a.length;++o){let u=0;for(let l=this.dims.length-1,c=o,h=1;l>=0;--l){const m=this.dims[l];if(l!==r){const _=c%m;u+=_*h,h*=s[l]}c=Math.floor(c/m)}i[u]+=a[o]**e}if(e!==1)for(let o=0;o<i.length;++o)i[o]=i[o]**(1/e);return n||s.splice(r,1),new fe(this.type,i,s)}normalize_(e=2,r=1){r=dr(r,this.dims.length);const n=this.norm(e,r,!0),a=this.data;for(let s=0;s<a.length;++s){let i=0;for(let o=this.dims.length-1,u=s,l=1;o>=0;--o){const c=this.dims[o];if(o!==r){const h=u%c;i+=h*l,l*=this.dims[o]}u=Math.floor(u/c)}a[s]/=n.data[i]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return Xw(this.dims)}squeeze(e=null){return new fe(this.type,this.data,um(this.dims,e))}squeeze_(e=null){return this.dims=um(this.dims,e),this}unsqueeze(e=null){return new fe(this.type,this.data,lm(this.dims,e))}unsqueeze_(e=null){return this.dims=lm(this.dims,e),this}flatten_(e=0,r=-1){r=(r+this.dims.length)%this.dims.length;let n=this.dims.slice(0,e),a=this.dims.slice(e,r+1),s=this.dims.slice(r+1);return this.dims=[...n,a.reduce((i,o)=>i*o,1),...s],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let n=0;n<e.length;++n)if(e[n]===-1){if(r!==-1)throw new Error("Only one dimension can be inferred");r=n}if(r!==-1){const n=e.reduce((a,s,i)=>i!==r?a*s:a,1);e[r]=this.data.length/n}return new fe(this.type,this.data,e)}neg_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=-e[r];return this}neg(){return this.clone().neg_()}clamp_(e,r){const n=this.data;for(let a=0;a<n.length;++a)n[a]=Math.min(Math.max(n[a],e),r);return this}clamp(e,r){return this.clone().clamp_(e,r)}round_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=Math.round(e[r]);return this}round(){return this.clone().round_()}mean(e=null,r=!1){return Mo(this,e,r)}to(e){if(this.type===e)return this;if(!am.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);return new fe(e,am[e].from(this.data),this.dims)}}function qw(t,e){const r=t.length,n=e.reduce((s,i)=>s*i);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let a=t;for(let s=e.length-1;s>=0;s--)a=a.reduce((i,o)=>{let u=i[i.length-1];return u.length<e[s]?u.push(o):i.push([o]),i},[[]]);return a[0]}function jw(t,e){const[r,n]=s_(t.data,t.dims,e);return new fe(t.type,r,n)}function im(t,[e,r],n="bilinear",a=!1){const s=t.dims.at(-3)??1,i=t.dims.at(-2),o=t.dims.at(-1);let u=i_(t.data,[s,i,o],[e,r],n,a);return new fe(t.type,u,[s,e,r])}async function sm(t,{size:e=null,mode:r="bilinear"}={}){if(t.dims.length!==4)throw new Error("`interpolate_4d` currently only supports 4D input.");if(!e)throw new Error("`interpolate_4d` requires a `size` argument.");let n;if(e.length===2)n=[...t.dims.slice(0,2),...e];else if(e.length===3)n=[t.dims[0],...e];else if(e.length===4)n=e;else throw new Error("`size` must be of length 2, 3, or 4.");let a;if(r==="bilinear")a=await na.bilinear_interpolate_4d;else if(r==="bicubic")a=await na.bicubic_interpolate_4d;else throw new Error(`Unsupported mode: ${r}`);const s=new fe("int64",new BigInt64Array(n.map(BigInt)),[n.length]);return await a({x:t,s})}async function Kw(t,e){return await(await na.matmul)({a:t,b:e})}async function om(t,e){const r=await na.top_k;return e===null?e=t.dims.at(-1):e=Math.min(e,t.dims.at(-1)),await r({x:t,k:new fe("int64",[BigInt(e)],[1])})}function um(t,e){return t=t.slice(),e===null?t=t.filter(r=>r!==1):typeof e=="number"?t[e]===1&&t.splice(e,1):Array.isArray(e)&&(t=t.filter((r,n)=>r!==1||!e.includes(n))),t}function lm(t,e){return e=dr(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function dr(t,e,r=null,n=!0){if(n&&(t<-e||t>=e))throw new Error(`IndexError: index ${t} is out of bounds for dimension${r===null?"":" "+r} with size ${e}`);return t<0&&(t=(t%e+e)%e),t}function Wt(t,e=0){e=dr(e,t[0].dims.length);const r=t[0].dims.slice();r[e]=t.reduce((i,o)=>i+o.dims[e],0);const n=r.reduce((i,o)=>i*o,1),a=new t[0].data.constructor(n),s=t[0].type;if(e===0){let i=0;for(let o of t)a.set(o.data,i),i+=o.data.length}else{let i=0;for(let o=0;o<t.length;++o){let u=t[o];for(let l=0;l<u.data.length;++l){let c=0;for(let h=u.dims.length-1,m=l,_=1;h>=0;--h){const p=u.dims[h];let b=m%p;h===e&&(b+=i),c+=b*_,_*=r[h],m=Math.floor(m/p)}a[c]=u.data[l]}i+=u.dims[e]}}return new fe(s,a,r)}function aa(t,e=0){return Wt(t.map(r=>r.unsqueeze(e)),e)}function Yw(t,e=null,r=1,n=!1){if(e===null){const l=t.data.reduce((_,p)=>_+p,0)/t.data.length,c=Math.sqrt(t.data.reduce((_,p)=>_+(p-l)**2,0)/(t.data.length-r)),h=new fe(t.type,[l],[]);return[new fe(t.type,[c],[]),h]}e=dr(e,t.dims.length);const a=Mo(t,e,n),s=t.dims.slice();s[e]=1;const i=new t.data.constructor(t.data.length/t.dims[e]);for(let u=0;u<t.data.length;++u){let l=0;for(let c=t.dims.length-1,h=u,m=1;c>=0;--c){const _=t.dims[c];if(c!==e){const p=h%_;l+=p*m,m*=s[c]}h=Math.floor(h/_)}i[l]+=(t.data[u]-a.data[l])**2}for(let u=0;u<i.length;++u)i[u]=Math.sqrt(i[u]/(t.dims[e]-r));return n||s.splice(e,1),[new fe(t.type,i,s),a]}function Mo(t,e=null,r=!1){if(e===null){let s=t.data.reduce((i,o)=>i+o,0);return new fe(t.type,[s/t.data.length],[])}e=dr(e,t.dims.length);const n=t.dims.slice();n[e]=1;const a=new t.data.constructor(t.data.length/t.dims[e]);for(let s=0;s<t.data.length;++s){let i=0;for(let o=t.dims.length-1,u=s,l=1;o>=0;--o){const c=t.dims[o];if(o!==e){const h=u%c;i+=h*l,l*=n[o]}u=Math.floor(u/c)}a[i]+=t.data[s]}if(t.dims[e]!==1)for(let s=0;s<a.length;++s)a[s]=a[s]/t.dims[e];return r||n.splice(e,1),new fe(t.type,a,n)}function Qw(t){const[e,r]=t.dims,n=[e+1,r+1],a=new fe("float32",new Float32Array(n[0]*n[1]).fill(1/0),n),s=new fe("float32",new Float32Array(n[0]*n[1]).fill(-1),n);a[0].data[0]=0;for(let c=1;c<r+1;++c)for(let h=1;h<e+1;++h){const m=a[h-1][c-1].item(),_=a[h-1][c].item(),p=a[h][c-1].item();let b,$;m<_&&m<p?(b=m,$=0):_<m&&_<p?(b=_,$=1):(b=p,$=2),a[h].data[c]=t[h-1][c-1].item()+b,s[h].data[c]=$}let i=e,o=r;s.data.fill(2,0,n[1]);for(let c=0;c<n[0];++c)s[c].data[0]=1;let u=[],l=[];for(;i>0||o>0;)switch(u.push(i-1),l.push(o-1),s[i][o].item()){case 0:--i,--o;break;case 1:--i;break;case 2:--o;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${i}, ${o}]. 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r=this.evaluate(t.object,e);let n;if(t.computed){if(t.property.type==="SliceExpression")return this.evaluateSliceExpression(r,t.property,e);n=this.evaluate(t.property,e)}else n=new Re(t.property.value);let a;if(r instanceof rr){if(!(n instanceof Re))throw new Error(`Cannot access property with non-string: got ${n.type}`);a=r.value.get(n.value)??r.builtins.get(n.value)}else if(r instanceof Ze||r instanceof Re)if(n instanceof Ye)a=r.value.at(n.value),r instanceof Re&&(a=new Re(r.value.at(n.value)));else if(n instanceof Re)a=r.builtins.get(n.value);else throw new Error(`Cannot access property with non-string/non-number: got ${n.type}`);else{if(!(n instanceof Re))throw new Error(`Cannot access property with non-string: got ${n.type}`);a=r.builtins.get(n.value)}return a instanceof vr?a:new nr}evaluateSet(t,e){const r=this.evaluate(t.value,e);if(t.assignee.type==="Identifier"){const n=t.assignee.value;e.setVariable(n,r)}else if(t.assignee.type==="MemberExpression"){const n=t.assignee,a=this.evaluate(n.object,e);if(!(a instanceof rr))throw new Error("Cannot assign to member of non-object");if(n.property.type!=="Identifier")throw new Error("Cannot assign to member with non-identifier property");a.value.set(n.property.value,r)}else throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(t.assignee)}`);return new ua}evaluateIf(t,e){const r=this.evaluate(t.test,e);return this.evaluateBlock(r.__bool__().value?t.body:t.alternate,e)}evaluateFor(t,e){const r=new Po(e),n=this.evaluate(t.iterable,r);if(!(n instanceof Ze))throw new Error(`Expected iterable type in for loop: got ${n.type}`);let a="";for(let s=0;s<n.value.length;++s){const i=new Map([["index",new Ye(s+1)],["index0",new Ye(s)],["revindex",new Ye(n.value.length-s)],["revindex0",new Ye(n.value.length-s-1)],["first",new rt(s===0)],["last",new rt(s===n.value.length-1)],["length",new Ye(n.value.length)],["previtem",s>0?n.value[s-1]:new nr],["nextitem",s<n.value.length-1?n.value[s+1]:new nr]]);r.setVariable("loop",new rr(i));const o=n.value[s];if(t.loopvar.type==="Identifier")r.setVariable(t.loopvar.value,o);else if(t.loopvar.type==="TupleLiteral"){const l=t.loopvar;if(o.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${o.type}`);const c=o;if(l.value.length!==c.value.length)throw new Error(`Too ${l.value.length>c.value.length?"few":"many"} items to unpack`);for(let h=0;h<l.value.length;++h){if(l.value[h].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${l.value[h].type}`);r.setVariable(l.value[h].value,c.value[h])}}const u=this.evaluateBlock(t.body,r);a+=u.value}return new Re(a)}evaluate(t,e){if(t===void 0)return new nr;switch(t.type){case"Program":return this.evalProgram(t,e);case"Set":return this.evaluateSet(t,e);case"If":return this.evaluateIf(t,e);case"For":return this.evaluateFor(t,e);case"NumericLiteral":return new Ye(Number(t.value));case"StringLiteral":return new Re(t.value);case"BooleanLiteral":return new rt(t.value);case"ArrayLiteral":return new Ze(t.value.map(r=>this.evaluate(r,e)));case"TupleLiteral":return new Sy(t.value.map(r=>this.evaluate(r,e)));case"ObjectLiteral":{const r=new Map;for(const[n,a]of t.value){const s=this.evaluate(n,e);if(!(s instanceof Re))throw new Error(`Object keys must be strings: got ${s.type}`);r.set(s.value,this.evaluate(a,e))}return new rr(r)}case"Identifier":return this.evaluateIdentifier(t,e);case"CallExpression":return this.evaluateCallExpression(t,e);case"MemberExpression":return this.evaluateMemberExpression(t,e);case"UnaryExpression":return this.evaluateUnaryExpression(t,e);case"BinaryExpression":return this.evaluateBinaryExpression(t,e);case"FilterExpression":return this.evaluateFilterExpression(t,e);case"TestExpression":return this.evaluateTestExpression(t,e);default:throw new SyntaxError(`Unknown node type: ${t.type}`)}}};function xi(t){switch(typeof t){case"number":return new Ye(t);case"string":return new Re(t);case"boolean":return new rt(t);case"object":return t===null?new ua:Array.isArray(t)?new Ze(t.map(xi)):new rr(new Map(Object.entries(t).map(([e,r])=>[e,xi(r)])));case"function":return new $r((e,r)=>{const n=t(...e.map(a=>a.value))??null;return xi(n)});default:throw new Error(`Cannot convert to runtime value: ${t}`)}}var Ey=class{constructor(t){F(this,"parsed");const e=uy(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=$y(e)}render(t){const e=new Po;e.set("false",!1),e.set("true",!0),e.set("raise_exception",a=>{throw new Error(a)}),e.set("range",xy);for(const[a,s]of Object.entries(t))e.set(a,s);return new ky(e).run(this.parsed).value}};const ym=[["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"]],Si=new Map(ym),Cy=new Map([...ym.map(([t,e])=>[e,t]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function bm(t){t=t.toLowerCase();let e=Cy.get(t);if(e===void 0)if(Si.has(t))e=t;else{const n=t.length===2?Si.keys():Si.values();throw new Error(`Language "${t}" is not supported. 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Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class Py{constructor(e){this.content=e.content,this.id=e.id,this.single_word=e.single_word??!1,this.lstrip=e.lstrip??!1,this.rstrip=e.rstrip??!1,this.special=e.special??!1,this.normalized=e.normalized??null}}class da extends yt{constructor(e){super(),this.config=e,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(e,...r){switch(e.type){case"WordPiece":return new Ry(e);case"Unigram":return new By(e,...r);case"BPE":return new Ny(e);default:if(e.vocab)return new Fy(e,...r);throw new Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){let r=this.encode(e);return this.fuse_unk&&(r=My(r,this.unk_token_id,this.tokens_to_ids)),r}encode(e){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(e){return e.map(r=>this.tokens_to_ids.get(r)??this.unk_token_id)}convert_ids_to_tokens(e){return e.map(r=>this.vocab[r]??this.unk_token)}}class Ry extends da{constructor(e){super(e),this.tokens_to_ids=Bo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.max_input_chars_per_word=e.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r}encode(e){const r=[];for(const n of e){const a=[...n];if(a.length>this.max_input_chars_per_word){r.push(this.unk_token);continue}let s=!1,i=0;const o=[];for(;i<a.length;){let u=a.length,l=null;for(;i<u;){let c=a.slice(i,u).join("");if(i>0&&(c=this.config.continuing_subword_prefix+c),this.tokens_to_ids.has(c)){l=c;break}--u}if(l===null){s=!0;break}o.push(l),i=u}s?r.push(this.unk_token):r.push(...o)}return r}}class By extends da{constructor(e,r){super(e);const n=e.vocab.length;this.vocab=new Array(n),this.scores=new Array(n);for(let a=0;a<n;++a){const s=e.vocab[a];this.vocab[a]=s[0],this.scores[a]=s[1]}this.unk_token_id=e.unk_id,this.unk_token=this.vocab[e.unk_id],this.tokens_to_ids=new Map(this.vocab.map((a,s)=>[a,s])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=r.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=Su(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new ny,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(e){const r=e.sentence,n=r.length;let a=0;for(;a<n;){let i=!1;for(let o of this.trie.commonPrefixSearch(r.slice(a))){const u=this.tokens_to_ids.get(o),l=this.scores[u],c=o.length;e.insert(a,c,l,u),!i&&c===1&&(i=!0)}i||e.insert(a,1,this.unkScore,this.unk_token_id),a+=1}}tokenize(e){const r=new ay(e,this.bosTokenId,this.eosTokenId);return this.populateNodes(r),r.tokens()}encode(e){const r=[];for(const n of e){const a=this.tokenize(n);r.push(...a)}return r}}const Sm=(()=>{const t=[...Array.from({length:94},(a,s)=>s+33),...Array.from({length:12},(a,s)=>s+161),...Array.from({length:82},(a,s)=>s+174)],e=t.slice();let r=0;for(let a=0;a<256;++a)t.includes(a)||(t.push(a),e.push(256+r),r+=1);const n=e.map(a=>String.fromCharCode(a));return Object.fromEntries(t.map((a,s)=>[a,n[s]]))})(),Dy=Z0(Sm);class Ny extends da{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=Bo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r;this.bpe_ranks=new Map(e.merges.map((r,n)=>[r,n])),this.merges=e.merges.map(r=>r.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(e){if(e.length===0)return[];const r=this.cache.get(e);if(r!==void 0)return r;const n=Array.from(e);this.end_of_word_suffix&&(n[n.length-1]+=this.end_of_word_suffix);let a=[];if(n.length>1){const s=new ry((u,l)=>u.score<l.score);let i={token:n[0],bias:0,prev:null,next:null},o=i;for(let u=1;u<n.length;++u){const l={bias:u/n.length,token:n[u],prev:o,next:null};o.next=l,this._add_node(s,o),o=l}for(;!s.isEmpty();){const u=s.pop();if(u.deleted||!u.next||u.next.deleted)continue;if(u.deleted=!0,u.next.deleted=!0,u.prev){const c={...u.prev};u.prev.deleted=!0,u.prev=c,c.prev?c.prev.next=c:i=c}const l={token:u.token+u.next.token,bias:u.bias,prev:u.prev,next:u.next.next};l.prev?(l.prev.next=l,this._add_node(s,l.prev)):i=l,l.next&&(l.next.prev=l,this._add_node(s,l))}for(let u=i;u!==null;u=u.next)a.push(u.token)}else a=n;if(this.continuing_subword_suffix)for(let s=0;s<a.length-1;++s)a[s]+=this.continuing_subword_suffix;return this.cache.set(e,a),a}_add_node(e,r){const n=this.bpe_ranks.get(r.token+this.BPE_SPLIT_TOKEN+r.next.token);n!==void 0&&(r.score=n+r.bias,e.push(r))}encode(e){const r=[];for(const n of e){if(this.ignore_merges&&this.tokens_to_ids.has(n)){r.push(n);continue}const a=this.bpe(n);for(const s of a)this.tokens_to_ids.has(s)?r.push(s):this.byte_fallback?r.push(...Array.from(this.text_encoder.encode(s)).map(i=>`<0x${i.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class Fy extends da{constructor(e,r){super(e),this.tokens_to_ids=Bo(r.target_lang?e.vocab[r.target_lang]:e.vocab),this.bos_token=r.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=r.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=r.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=r.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[n,a]of this.tokens_to_ids)this.vocab[a]=n}encode(e){return e}}class Pt extends yt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new Yy(e);case"Precompiled":return new fb(e);case"Sequence":return new Ky(e);case"Replace":return new Ly(e);case"NFC":return new Uy(e);case"NFKC":return new Wy(e);case"NFKD":return new Vy(e);case"Strip":return new Gy(e);case"StripAccents":return new Hy(e);case"Lowercase":return new qy(e);case"Prepend":return new jy(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class Ly extends Pt{normalize(e){const r=ki(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class Uy extends Pt{normalize(e){return e=e.normalize("NFC"),e}}class Wy extends Pt{normalize(e){return e=e.normalize("NFKC"),e}}class Vy extends Pt{normalize(e){return e=e.normalize("NFKD"),e}}class Gy extends Pt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class Hy extends Pt{normalize(e){return e=xm(e),e}}class qy extends Pt{normalize(e){return e=e.toLowerCase(),e}}class jy extends Pt{normalize(e){return e=this.config.prepend+e,e}}class Ky extends Pt{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>Pt.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class Yy extends Pt{_tokenize_chinese_chars(e){const r=[];for(let n=0;n<e.length;++n){const a=e[n],s=a.charCodeAt(0);Ay(s)?(r.push(" "),r.push(a),r.push(" ")):r.push(a)}return r.join("")}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(e){switch(e){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(e)}}_clean_text(e){const r=[];for(const n of e){const a=n.charCodeAt(0);a===0||a===65533||this._is_control(n)||(/^\s$/.test(n)?r.push(" "):r.push(n))}return r.join("")}normalize(e){return this.config.clean_text&&(e=this._clean_text(e)),this.config.handle_chinese_chars&&(e=this._tokenize_chinese_chars(e)),this.config.lowercase?(e=e.toLowerCase(),this.config.strip_accents!==!1&&(e=this.stripAccents(e))):this.config.strip_accents&&(e=this.stripAccents(e)),e}}class Vt extends yt{static fromConfig(e){if(e===null)return null;switch(e.type){case"BertPreTokenizer":return new Qy(e);case"Sequence":return new mb(e);case"Whitespace":return new gb(e);case"WhitespaceSplit":return new _b(e);case"Metaspace":return new Tm(e);case"ByteLevel":return new Xy(e);case"Split":return new Jy(e);case"Punctuation":return new Zy(e);case"Digits":return new eb(e);case"Replace":return new wb(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,r){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,r){return(Array.isArray(e)?e.map(n=>this.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class Qy extends Vt{constructor(e){super(),this.pattern=new RegExp(`[^\\s${la}]+|[${la}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class Xy extends Vt{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=Sm,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),s=>this.byte_encoder[s]).join(""))}}class Jy extends Vt{constructor(e){super(),this.config=e,this.pattern=ki(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:Ty(e,this.pattern)}}class Zy extends Vt{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${la}]+|[${la}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class eb extends Vt{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Sn extends yt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new tb(e);case"ByteLevel":return new Cm(e);case"RobertaProcessing":return new Em(e);case"BertProcessing":return new km(e);case"Sequence":return new rb(e);default:throw new Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...r){throw Error("post_process should be implemented in subclass.")}_call(e,...r){return this.post_process(e,...r)}}class km extends Sn{constructor(e){super(e),this.cls=e.cls[0],this.sep=e.sep[0]}post_process(e,r=null,{add_special_tokens:n=!0}={}){n&&(e=ht([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const s=n&&this instanceof Em?[this.sep]:[],i=n?[this.sep]:[];e=ht(e,s,r,i),a=ht(a,new Array(r.length+s.length+i.length).fill(1))}return{tokens:e,token_type_ids:a}}}class Em extends km{}class tb extends Sn{constructor(e){super(e),this.single=e.single,this.pair=e.pair}post_process(e,r=null,{add_special_tokens:n=!0}={}){const a=r===null?this.single:this.pair;let s=[],i=[];for(const o of a)"SpecialToken"in o?n&&(s.push(o.SpecialToken.id),i.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(s=ht(s,e),i=ht(i,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(s=ht(s,r),i=ht(i,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:s,token_type_ids:i}}}class Cm extends Sn{post_process(e,r=null){return r&&(e=ht(e,r)),{tokens:e}}}class rb extends Sn{constructor(e){super(e),this.processors=e.processors.map(r=>Sn.fromConfig(r))}post_process(e,r=null,n={}){let a;for(const s of this.processors)if(s instanceof Cm)e=s.post_process(e).tokens,r&&(r=s.post_process(r).tokens);else{const i=s.post_process(e,r,n);e=i.tokens,a=i.token_type_ids}return{tokens:e,token_type_ids:a}}}class Rt extends yt{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=e.trim_offsets}static fromConfig(e){if(e===null)return null;switch(e.type){case"WordPiece":return new ob(e);case"Metaspace":return new hb(e);case"ByteLevel":return new ub(e);case"Replace":return new nb(e);case"ByteFallback":return new ab(e);case"Fuse":return new ib(e);case"Strip":return new sb(e);case"Sequence":return new db(e);case"CTC":return new lb(e);case"BPEDecoder":return new cb(e);default:throw new Error(`Unknown Decoder type: ${e.type}`)}}_call(e){return this.decode(e)}decode(e){return this.decode_chain(e).join("")}decode_chain(e){throw Error("`decode_chain` should be implemented in subclass.")}}class nb extends Rt{decode_chain(e){const r=ki(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class ab extends Rt{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const r=[];let n=[];for(const a of e){let s=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const i=parseInt(a.slice(3,5),16);isNaN(i)||(s=i)}if(s!==null)n.push(s);else{if(n.length>0){const i=this.text_decoder.decode(Uint8Array.from(n));r.push(i),n=[]}r.push(a)}}if(n.length>0){const a=this.text_decoder.decode(Uint8Array.from(n));r.push(a),n=[]}return r}}class ib extends Rt{decode_chain(e){return[e.join("")]}}class sb extends Rt{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map(r=>{let n=0;for(let s=0;s<this.start&&r[s]===this.content;++s){n=s+1;continue}let a=r.length;for(let s=0;s<this.stop;++s){const i=r.length-s-1;if(r[i]===this.content){a=i;continue}else break}return r.slice(n,a)})}}class ob extends Rt{constructor(e){super(e),this.cleanup=e.cleanup}decode_chain(e){return e.map((r,n)=>(n!==0&&(r.startsWith(this.config.prefix)?r=r.replace(this.config.prefix,""):r=" "+r),this.cleanup&&(r=Do(r)),r))}}class ub extends Rt{constructor(e){super(e),this.byte_decoder=Dy,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(e){const r=e.join(""),n=new Uint8Array([...r].map(s=>this.byte_decoder[s]));return this.text_decoder.decode(n)}decode_chain(e){const r=[];let n=[];for(const a of e)this.added_tokens.find(s=>s.content===a)!==void 0?(n.length>0&&(r.push(this.convert_tokens_to_string(n)),n=[]),r.push(a)):n.push(a);return n.length>0&&r.push(this.convert_tokens_to_string(n)),r}}class lb extends Rt{constructor(e){super(e),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(e){if(e.length===0)return"";const r=[e[0]];for(let s=1;s<e.length;++s)e[s]!==r.at(-1)&&r.push(e[s]);let a=r.filter(s=>s!==this.pad_token).join("");return this.cleanup&&(a=Do(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class db extends Rt{constructor(e){super(e),this.decoders=e.decoders.map(r=>Rt.fromConfig(r))}decode_chain(e){return this.decoders.reduce((r,n)=>n.decode_chain(r),e)}}class cb extends Rt{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map((r,n)=>r.replaceAll(this.suffix,n===e.length-1?"":" "))}}class pb extends Rt{decode_chain(e){let r="";for(let n=1;n<e.length;n+=2)r+=e[n];return[r]}}class Tm extends Vt{constructor(e){super(),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement,this.strRep=e.str_rep||this.replacement,this.prepend_scheme=e.prepend_scheme??"always"}pre_tokenize_text(e,{section_index:r=void 0}={}){let n=e.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!n.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&r===0)&&(n=this.strRep+n),[n]}}class hb extends Rt{constructor(e){super(e),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement}decode_chain(e){const r=[];for(let n=0;n<e.length;++n){let a=e[n].replaceAll(this.replacement," ");this.addPrefixSpace&&n==0&&a.startsWith(" ")&&(a=a.substring(1)),r.push(a)}return r}}class fb extends Pt{constructor(e){super(e),this.charsmap=e.precompiled_charsmap}normalize(e){return e=e.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),e=e.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),e.includes("～")?e=e.split("～").map(n=>n.normalize("NFKC")).join("～"):e=e.normalize("NFKC"),e}}class mb extends Vt{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>Vt.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class gb extends Vt{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class _b extends Vt{constructor(e){super()}pre_tokenize_text(e,r){return Oy(e)}}class wb extends Vt{constructor(e){super(),this.config=e,this.pattern=ki(this.config.pattern),this.content=this.config.content}pre_tokenize_text(e,r){return this.pattern===null?[e]:[e.replaceAll(this.pattern,this.config.content)]}}const yb=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function bb(t,e,r,n){for(const a of Object.keys(t)){const s=e-t[a].length,i=r(a),o=new Array(s).fill(i);t[a]=n==="right"?ht(t[a],o):ht(o,t[a])}}function vb(t,e){for(const r of Object.keys(t))t[r].length=e}class Ce extends yt{constructor(r,n){super();F(this,"return_token_type_ids",!1);F(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 %}`);F(this,"padding_side","right");this._tokenizer_config=n,this.normalizer=Pt.fromConfig(r.normalizer),this.pre_tokenizer=Vt.fromConfig(r.pre_tokenizer),this.model=da.fromConfig(r.model,n),this.post_processor=Sn.fromConfig(r.post_processor),this.decoder=Rt.fromConfig(r.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const a of r.added_tokens){const s=new Py(a);this.added_tokens.push(s),this.model.tokens_to_ids.set(s.content,s.id),this.model.vocab[s.id]=s.content,s.special&&(this.special_tokens.push(s.content),this.all_special_ids.push(s.id))}if(this.additional_special_tokens=n.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(a=>`${a.lstrip?"\\s*":""}(${vu(a.content)})${a.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=n.model_max_length,this.remove_space=n.remove_space,this.clean_up_tokenization_spaces=n.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=n.do_lowercase_and_remove_accent??!1,n.padding_side&&(this.padding_side=n.padding_side),this.legacy=!1,this.chat_template=n.chat_template??null,Array.isArray(this.chat_template)){const a=Object.create(null);for(const{name:s,template:i}of this.chat_template){if(typeof s!="string"||typeof i!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');a[s]=i}this.chat_template=a}this._compiled_template_cache=new Map}getToken(...r){for(const n of r){const a=this._tokenizer_config[n];if(a)if(typeof a=="object"){if(a.__type==="AddedToken")return a.content;throw Error(`Unknown token: ${a}`)}else return a}return null}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:s=null,local_files_only:i=!1,revision:o="main",legacy:u=null}={}){const l=await vm(r,{progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,legacy:u});return new this(...l)}_call(r,{text_pair:n=null,add_special_tokens:a=!0,padding:s=!1,truncation:i=null,max_length:o=null,return_tensor:u=!0,return_token_type_ids:l=null}={}){const c=Array.isArray(r);let h;if(c){if(r.length===0)throw Error("text array must be non-empty");if(n!==null){if(Array.isArray(n)){if(r.length!==n.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");h=r.map((_,p)=>this._encode_plus(_,{text_pair:n[p],add_special_tokens:a,return_token_type_ids:l}))}else h=r.map(_=>this._encode_plus(_,{add_special_tokens:a,return_token_type_ids:l}))}else{if(r==null)throw Error("text may not be null or undefined");if(Array.isArray(n))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");h=[this._encode_plus(r,{text_pair:n,add_special_tokens:a,return_token_type_ids:l})]}if(o===null?s==="max_length"?o=this.model_max_length:o=wr(h.map(_=>_.input_ids.length))[0]:i||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??1/0),s||i)for(let _=0;_<h.length;++_)h[_].input_ids.length!==o&&(h[_].input_ids.length>o?i&&vb(h[_],o):s&&bb(h[_],o,p=>p==="input_ids"?this.pad_token_id:0,this.padding_side));const m={};if(u){if(!(s&&i)&&h.some(p=>{var b;for(const $ of Object.keys(p))if(p[$].length!==((b=h[0][$])==null?void 0:b.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 _=[h.length,h[0].input_ids.length];for(const p of Object.keys(h[0]))m[p]=new fe("int64",BigInt64Array.from(h.flatMap(b=>b[p]).map(BigInt)),_)}else{for(const _ of Object.keys(h[0]))m[_]=h.map(p=>p[_]);if(!c)for(const _ of Object.keys(m))m[_]=m[_][0]}return m}_encode_text(r){return r===null?null:(this.added_tokens_regex?r.split(this.added_tokens_regex).filter(s=>s):[r]).map((s,i)=>{if(this.added_tokens.find(u=>u.content===s)!==void 0)return s;{if(this.remove_space===!0&&(s=s.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(s=Iy(s)),this.normalizer!==null&&(s=this.normalizer(s)),s.length===0)return[];const u=this.pre_tokenizer!==null?this.pre_tokenizer(s,{section_index:i}):[s];return this.model(u)}}).flat()}_encode_plus(r,{text_pair:n=null,add_special_tokens:a=!0,return_token_type_ids:s=null}={}){const{tokens:i,token_type_ids:o}=this._tokenize_helper(r,{pair:n,add_special_tokens:a}),u=this.model.convert_tokens_to_ids(i),l={input_ids:u,attention_mask:new Array(u.length).fill(1)};return(s??this.return_token_type_ids)&&o&&(l.token_type_ids=o),l}_tokenize_helper(r,{pair:n=null,add_special_tokens:a=!1}={}){const s=this._encode_text(r),i=this._encode_text(n);return this.post_processor?this.post_processor(s,i,{add_special_tokens:a}):{tokens:ht(s??[],i??[])}}tokenize(r,{pair:n=null,add_special_tokens:a=!1}={}){return this._tokenize_helper(r,{pair:n,add_special_tokens:a}).tokens}encode(r,{text_pair:n=null,add_special_tokens:a=!0,return_token_type_ids:s=null}={}){return this._encode_plus(r,{text_pair:n,add_special_tokens:a,return_token_type_ids:s}).input_ids}batch_decode(r,n={}){return r instanceof fe&&(r=r.tolist()),r.map(a=>this.decode(a,n))}decode(r,n={}){if(r instanceof fe&&(r=$m(r)),!Array.isArray(r)||r.length===0||!e_(r[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(r,n)}decode_single(r,{skip_special_tokens:n=!1,clean_up_tokenization_spaces:a=null}){let s=this.model.convert_ids_to_tokens(r);n&&(s=s.filter(o=>!this.special_tokens.includes(o)));let i=this.decoder?this.decoder(s):s.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(i=i.replaceAll(this.decoder.end_of_word_suffix," "),n&&(i=i.trim())),(a??this.clean_up_tokenization_spaces)&&(i=Do(i)),i}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(r,{chat_template:n=null,add_generation_prompt:a=!1,tokenize:s=!0,padding:i=!1,truncation:o=!1,max_length:u=null,return_tensor:l=!0,return_dict:c=!1,tokenizer_kwargs:h={},...m}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const $=this.chat_template??this.default_chat_template;if(n!==null&&Object.hasOwn($,n))n=$[n];else if(n===null&&"default"in $)n=$.default;else if(n===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 n??(n=this.chat_template??this.default_chat_template);if(typeof n!="string")throw Error(`chat_template must be a string, but got ${typeof n}`);let _=this._compiled_template_cache.get(n);_===void 0&&(_=new Ey(n),this._compiled_template_cache.set(n,_));const p=Object.create(null);for(const $ of yb){const S=this.getToken($);S&&(p[$]=S)}const b=_.render({messages:r,add_generation_prompt:a,...p,...m});if(s){const $=this._call(b,{add_special_tokens:!1,padding:i,truncation:o,max_length:u,return_tensor:l,...h});return c?$:$.input_ids}return b}}class $b extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class xb extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class Sb extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class kb extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class Eb extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class Cb extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class Tb extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class Ib extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class Ab extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class Mb extends Ce{}class Ob extends Ce{}class zb extends Ce{constructor(r,n){super(r,n);F(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Pb extends Ce{constructor(){super(...arguments);F(this,"return_token_type_ids",!0)}}class Rb extends Ce{}class Im extends Ce{constructor(){super(...arguments);F(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}}class Bb extends Ce{}class Am extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return No(this,e,r,n)}}class Db extends Am{}class Nb extends Ce{}class Fb extends Im{constructor(e,r){var s,i;const n=".,!?…。，、।۔،",a=(i=(s=e.pre_tokenizer)==null?void 0:s.pretokenizers[0])==null?void 0:i.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const Ei="▁";class Mm extends Ce{constructor(r,n){super(r,n);F(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 %}`);F(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.`);F(this,"padding_side","left");this.use_default_system_prompt=n.use_default_system_prompt??!1,this.legacy=n.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new Tm({replacement:Ei,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(r){if(r===null)return null;if(this.legacy||r.length===0)return super._encode_text(r);let n=super._encode_text(Ei+r.replaceAll(Ei," "));return n.length>1&&n[0]===Ei&&this.special_tokens.includes(n[1])&&(n=n.slice(1)),n}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 Lb extends Mm{}class Ub extends Ce{}class Wb extends Ce{}class Vb extends Ce{}class Gb extends Ce{}class Hb extends Ce{}class qb extends Ce{}class jb extends Ce{constructor(){super(...arguments);F(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 Kb extends Ce{}function No(t,e,r,n){if(!("language_codes"in t)||!Array.isArray(t.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in t)||!(t.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in t)||typeof t.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const a=n.src_lang,s=n.tgt_lang;if(!t.language_codes.includes(s))throw new Error(`Target language code "${s}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);if(a!==void 0){if(!t.language_codes.includes(a))throw new Error(`Source language code "${a}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);for(const i of t.post_processor.config.single)if("SpecialToken"in i&&t.languageRegex.test(i.SpecialToken.id)){i.SpecialToken.id=t.lang_to_token(a);break}}return n.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(s)])[0],t._call(e,r)}class Yb extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return No(this,e,r,n)}}class Qb extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)).map(n=>n.slice(2,-2)),this.lang_to_token=n=>`__${n}__`}_build_translation_inputs(e,r,n){return No(this,e,r,n)}}class Xb extends Ce{constructor(){super(...arguments);F(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(r,{return_timestamps:n=!1,return_language:a=!1,time_precision:s=null,force_full_sequences:i=!0}={}){if(s===null)throw Error("Must specify time_precision");let o=null;const u=n==="word";function l(){return{language:o,timestamp:[null,null],text:""}}const c=[];let h=l(),m=0;const _=this.timestamp_begin;let p=[],b=[],$=!1,S=null;const x=new Set(this.all_special_ids);for(const I of r){const P=I.tokens,R=u?I.token_timestamps:null;let W=null,q=_;if("stride"in I){const[ne,re,oe]=I.stride;if(m-=re,S=ne-oe,re&&(q=re/s+_),oe)for(let D=P.length-1;D>=0;--D){const O=Number(P[D]);if(O>=_){if(W!==null&&(O-_)*s<S)break;W=O}}}let K=[],ue=[];for(let ne=0;ne<P.length;++ne){const re=Number(P[ne]);if(x.has(re)){const oe=this.decode([re]),D=Si.get(oe.slice(2,-2));if(D!==void 0){if(o!==null&&D!==o&&!n){p.push(K);const O=this.findLongestCommonSequence(p)[0],G=this.decode(O);h.text=G,c.push(h),p=[],K=[],h=l()}o=h.language=D}}else if(re>=_){const oe=(re-_)*s+m,D=fs(oe,2);if(W!==null&&re>=W)$=!0;else if($||p.length>0&&re<q)$=!1;else if(h.timestamp[0]===null)h.timestamp[0]=D;else if(D!==h.timestamp[0]){h.timestamp[1]=D,p.push(K),u&&b.push(ue);const[O,G]=this.findLongestCommonSequence(p,b),j=this.decode(O);h.text=j,u&&(h.words=this.collateWordTimestamps(O,G,o)),c.push(h),p=[],K=[],b=[],ue=[],h=l()}}else if(K.push(re),u){let oe=fs(R[ne]+m,2),D;ne+1<R.length?D=fs(R[ne+1]+m,2):D=null,ue.push([oe,D])}}if("stride"in I){const[ne,re,oe]=I.stride;m+=ne-oe}K.length>0?(p.push(K),u&&b.push(ue)):p.every(ne=>ne.length===0)&&(h=l(),p=[],K=[],b=[],ue=[])}if(p.length>0){if(i&&n)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[I,P]=this.findLongestCommonSequence(p,b),R=this.decode(I);h.text=R,u&&(h.words=this.collateWordTimestamps(I,P,o)),c.push(h)}let C=Object.create(null);const T=c.map(I=>I.text).join("");if(n||a){for(let I=0;I<c.length;++I){const P=c[I];n||delete P.timestamp,a||delete P.language}if(u){const I=[];for(const P of c)for(const R of P.words)I.push(R);C={chunks:I}}else C={chunks:c}}return[T,C]}findLongestCommonSequence(r,n=null){let a=r[0],s=a.length,i=[];const o=Array.isArray(n)&&n.length>0;let u=o?[]:null,l=o?n[0]:null;for(let c=1;c<r.length;++c){const h=r[c];let m=0,_=[s,s,0,0];const p=h.length;for(let I=1;I<s+p;++I){const P=I/1e4,R=Math.max(0,s-I),W=Math.min(s,s+p-I),q=a.slice(R,W),K=Math.max(0,I-s),ue=Math.min(p,I),ne=h.slice(K,ue);if(q.length!==ne.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const re=q.filter((D,O)=>D===ne[O]).length,oe=re/I+P;re>1&&oe>m&&(m=oe,_=[R,W,K,ue])}const[b,$,S,x]=_,C=Math.floor(($+b)/2),T=Math.floor((x+S)/2);i.push(...a.slice(0,C)),a=h.slice(T),s=a.length,o&&(u.push(...l.slice(0,C)),l=n[c].slice(T))}return i.push(...a),o?(u.push(...l),[i,u]):[i,[]]}collateWordTimestamps(r,n,a){const[s,i,o]=this.combineTokensIntoWords(r,a),u=[];for(let l=0;l<s.length;++l){const c=o[l];u.push({text:s[l],timestamp:[n[c.at(0)][0],n[c.at(-1)][1]]})}return u}combineTokensIntoWords(r,n,a=`"'“¡¿([{-`,s=`"'.。,，!！?？:：”)]}、`){n=n??"english";let i,o,u;return["chinese","japanese","thai","lao","myanmar"].includes(n)?[i,o,u]=this.splitTokensOnUnicode(r):[i,o,u]=this.splitTokensOnSpaces(r),this.mergePunctuations(i,o,u,a,s)}decode(r,n){let a;return n!=null&&n.decode_with_timestamps?(r instanceof fe&&(r=$m(r)),a=this.decodeWithTimestamps(r,n)):a=super.decode(r,n),a}decodeWithTimestamps(r,n){const a=(n==null?void 0:n.time_precision)??.02,s=Array.from(this.all_special_ids).at(-1)+1;let i=[[]];for(let o of r)if(o=Number(o),o>=s){const u=((o-s)*a).toFixed(2);i.push(`<|${u}|>`),i.push([])}else i[i.length-1].push(o);return i=i.map(o=>typeof o=="string"?o:super.decode(o,n)),i.join("")}splitTokensOnUnicode(r){const n=this.decode(r,{decode_with_timestamps:!0}),a="�",s=[],i=[],o=[];let u=[],l=[],c=0;for(let h=0;h<r.length;++h){const m=r[h];u.push(m),l.push(h);const _=this.decode(u,{decode_with_timestamps:!0});(!_.includes(a)||n[c+_.indexOf(a)]===a)&&(s.push(_),i.push(u),o.push(l),u=[],l=[],c+=_.length)}return[s,i,o]}splitTokensOnSpaces(r){const[n,a,s]=this.splitTokensOnUnicode(r),i=[],o=[],u=[],l=new RegExp(`^[${la}]$`,"gu");for(let c=0;c<n.length;++c){const h=n[c],m=a[c],_=s[c],p=m[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),b=h.startsWith(" "),$=h.trim(),S=l.test($);if(p||b||S||i.length===0)i.push(h),o.push(m),u.push(_);else{const x=i.length-1;i[x]+=h,o[x].push(...m),u[x].push(..._)}}return[i,o,u]}mergePunctuations(r,n,a,s,i){const o=structuredClone(r),u=structuredClone(n),l=structuredClone(a);let c=o.length-2,h=o.length-1;for(;c>=0;)o[c].startsWith(" ")&&s.includes(o[c].trim())?(o[h]=o[c]+o[h],u[h]=ht(u[c],u[h]),l[h]=ht(l[c],l[h]),o[c]="",u[c]=[],l[c]=[]):h=c,--c;for(c=0,h=1;h<o.length;)!o[c].endsWith(" ")&&i.includes(o[h])?(o[c]+=o[h],u[c]=ht(u[c],u[h]),l[c]=ht(l[c],l[h]),o[h]="",u[h]=[],l[h]=[]):c=h,++h;return[o.filter(m=>m),u.filter(m=>m.length>0),l.filter(m=>m.length>0)]}get_decoder_prompt_ids({language:r=null,task:n=null,no_timestamps:a=!0}={}){const s=[];if(r){const i=bm(r),o=this.model.tokens_to_ids.get(`<|${i}|>`);if(o===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at ${Ro}.`);s.push(o)}else s.push(null);if(n){if(n=n.toLowerCase(),n!=="transcribe"&&n!=="translate")throw new Error(`Task "${n}" is not supported. Must be one of: ["transcribe", "translate"]`);const i=this.model.tokens_to_ids.get(`<|${n}|>`);if(i===void 0)throw new Error(`Unable to find task "${n}" in model vocabulary. Please report this issue at ${Ro}.`);s.push(i)}else s.push(null);if(a){const i=this.model.tokens_to_ids.get("<|notimestamps|>");if(i===void 0)throw new Error(`Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at ${Ro}.`);s.push(i)}return s.map((i,o)=>[o+1,i]).filter(i=>i[1]!==null)}}class Jb extends Ce{}class Zb extends Ce{}class ev extends Ce{}class tv extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(n=>this.languageRegex.test(n)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[r,...n]=e.trim().split(this.languageRegex);if(n.length===0)return super._encode_text(r);if(n.length===2){const[a,s]=n;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),ht([a],super._encode_text(s))}}}class rv extends Ce{}class Om extends Ce{constructor(){super(...arguments);F(this,"_default_chat_template","{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}")}}class nv extends Om{}class av extends Ce{}class iv extends Ce{}class sv extends Ce{constructor(e,r){super(e,r),this.decoder=new pb({})}}class ov extends Ce{}class zm{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",legacy:o=null}={}){var m;const[u,l]=await vm(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,legacy:o}),c=((m=l.tokenizer_class)==null?void 0:m.replace(/Fast$/,""))??"PreTrainedTokenizer";let h=this.TOKENIZER_CLASS_MAPPING[c];return h||(console.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),h=Ce),new h(u,l)}}F(zm,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:Rb,DistilBertTokenizer:Mb,CamembertTokenizer:Ob,DebertaTokenizer:Eb,DebertaV2Tokenizer:Cb,BertTokenizer:$b,HerbertTokenizer:Tb,ConvBertTokenizer:Ib,RoFormerTokenizer:Ab,XLMTokenizer:zb,ElectraTokenizer:Pb,MobileBertTokenizer:Sb,SqueezeBertTokenizer:kb,AlbertTokenizer:xb,GPT2Tokenizer:Im,BartTokenizer:Bb,MBartTokenizer:Am,MBart50Tokenizer:Db,RobertaTokenizer:Nb,WhisperTokenizer:Xb,CodeGenTokenizer:Jb,CLIPTokenizer:Zb,SiglipTokenizer:ev,MarianTokenizer:tv,BloomTokenizer:Fb,NllbTokenizer:Yb,M2M100Tokenizer:Qb,LlamaTokenizer:Mm,CodeLlamaTokenizer:Lb,XLMRobertaTokenizer:Ub,MPNetTokenizer:Wb,FalconTokenizer:Vb,GPTNeoXTokenizer:Gb,EsmTokenizer:Hb,Wav2Vec2CTCTokenizer:rv,BlenderbotTokenizer:Om,BlenderbotSmallTokenizer:nv,SpeechT5Tokenizer:av,NougatTokenizer:iv,VitsTokenizer:sv,Qwen2Tokenizer:qb,GemmaTokenizer:jb,Grok1Tokenizer:Kb,CohereTokenizer:ov,PreTrainedTokenizer:Ce});async function uv(t,e){return await Ir(t,"config.json",!0,e)}function ca(t){const e={};let r={};switch(t.model_type){case"llava":case"paligemma":case"florence2":r=ca(t.text_config);break;case"moondream1":r=ca(t.phi_config);break;case"musicgen":r=ca(t.decoder);break;case"gpt2":case"gptj":case"codegen":case"gpt_bigcode":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"phi":case"phi3":case"falcon":e.num_heads="num_attention_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size";break;case"llama":case"cohere":case"mistral":case"starcoder2":case"qwen2":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size",e.num_attention_heads="num_attention_heads";break;case"gemma":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="head_dim";break;case"openelm":e.num_heads="num_kv_heads",e.num_layers="num_transformer_layers",e.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":e.num_heads="num_heads",e.num_layers="num_layers",e.hidden_size="hidden_size";break;case"bloom":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="hidden_size";break;case"mpt":e.num_heads="n_heads",e.num_layers="n_layers",e.hidden_size="d_model";break;case"t5":case"mt5":case"longt5":e.num_decoder_layers="num_decoder_layers",e.num_decoder_heads="num_heads",e.decoder_dim_kv="d_kv",e.num_encoder_layers="num_layers",e.num_encoder_heads="num_heads",e.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="d_model",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="d_model";break;case"speecht5":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="hidden_size",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="hidden_size";break;case"trocr":e.num_encoder_layers=e.num_decoder_layers="decoder_layers",e.num_encoder_heads=e.num_decoder_heads="decoder_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="d_model";break;case"musicgen_decoder":e.num_encoder_layers=e.num_decoder_layers="num_hidden_layers",e.num_encoder_heads=e.num_decoder_heads="num_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const a=ca(t.decoder),s="num_decoder_layers"in a,i=gr(t,["model_type","is_encoder_decoder"]);return s?(i.num_decoder_layers=a.num_decoder_layers,i.num_decoder_heads=a.num_decoder_heads,i.decoder_hidden_size=a.decoder_hidden_size,i.num_encoder_layers=a.num_encoder_layers,i.num_encoder_heads=a.num_encoder_heads,i.encoder_hidden_size=a.encoder_hidden_size):(i.num_layers=a.num_layers,i.num_heads=a.num_heads,i.hidden_size=a.hidden_size),i}const n={...r,...gr(t,["model_type","multi_query","is_encoder_decoder"])};for(const a in e)n[a]=t[e[a]];return n}function Pm(t,{prefix:e="past_key_values"}={}){const r={},n=t.normalized_config,a=1;if(n.is_encoder_decoder&&"num_encoder_heads"in n&&"num_decoder_heads"in n){const s=n.encoder_dim_kv??n.encoder_hidden_size/n.num_encoder_heads,i=n.decoder_dim_kv??n.decoder_hidden_size/n.num_decoder_heads,o=[a,n.num_encoder_heads,0,s],u=[a,n.num_decoder_heads,0,i];for(let l=0;l<n.num_decoder_layers;++l)r[`${e}.${l}.encoder.key`]=o,r[`${e}.${l}.encoder.value`]=o,r[`${e}.${l}.decoder.key`]=u,r[`${e}.${l}.decoder.value`]=u}else{const s=n.num_heads,i=n.num_layers,o=n.dim_kv??n.hidden_size/(n.num_attention_heads??s);if(n.model_type==="falcon"){const u=[a*s,0,o];for(let l=0;l<i;++l)r[`${e}.${l}.key`]=u,r[`${e}.${l}.value`]=u}else if(n.multi_query){const u=[a*s,0,2*o];for(let l=0;l<i;++l)r[`${e}.${l}.key_value`]=u}else if(n.model_type==="bloom"){const u=[a*s,o,0],l=[a*s,0,o];for(let c=0;c<i;++c)r[`${e}.${c}.key`]=u,r[`${e}.${c}.value`]=l}else if(n.model_type==="openelm")for(let u=0;u<i;++u){const l=[a,s[u],0,o];r[`${e}.${u}.key`]=l,r[`${e}.${u}.value`]=l}else{const u=[a,s,0,o];for(let l=0;l<i;++l)r[`${e}.${l}.key`]=u,r[`${e}.${l}.value`]=u}}return r}class Ci{constructor(e){F(this,"max_position_embeddings");this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e),this.normalized_config=ca(this)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main"}={}){n&&!(n instanceof Ci)&&(n=new Ci(n));const o=n??await uv(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i});return new this(o)}}class Rm{static async from_pretrained(...e){return Ci.from_pretrained(...e)}}const Ti=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),lv=function(){let t;return async function(){if(t===void 0)if(Fr.IS_NODE_ENV)t=!0;else if(!Fr.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}}(),Tt=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),dv=Object.freeze({[Ti.cpu]:Tt.q8,[Ti.gpu]:Tt.fp32,[Ti.wasm]:Tt.q8,[Ti.webgpu]:Tt.fp32}),Bm=Object.freeze({[Tt.fp32]:"",[Tt.fp16]:"_fp16",[Tt.int8]:"_int8",[Tt.uint8]:"_uint8",[Tt.q8]:"_quantized",[Tt.q4]:"_q4",[Tt.q4f16]:"_q4f16",[Tt.bnb4]:"_bnb4"});class pr extends yt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class Fo extends yt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class Lo extends yt{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,r){let n=r;for(const a of this.processors)n=a(e,n);return n}[Symbol.iterator](){return this.processors.values()}}class cv extends pr{constructor(e){super(),this.bos_token_id=e}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===1){const a=r[n];a.data.fill(-1/0),a.data[this.bos_token_id]=0}return r}}class pv extends pr{constructor(e,r){super(),this.max_length=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===this.max_length-1){const a=r[n];a.data.fill(-1/0);for(const s of this.eos_token_id)a.data[s]=0}return r}}class hv extends pr{constructor(e,r){super(),this.begin_suppress_tokens=e,this.begin_index=r}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===this.begin_index){const a=r[n];for(const s of this.begin_suppress_tokens)a.data[s]=-1/0}return r}}class fv extends pr{constructor(e,r){super(),this.eos_token_id=Array.isArray(e.eos_token_id)?e.eos_token_id[0]:e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=r.length,r.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,r){for(let n=0;n<e.length;++n){const s=r[n].data;if(s[this.no_timestamps_token_id]=-1/0,e[n].length===this.begin_index-1){s.fill(-1/0),s[this.timestamp_begin]=0;continue}const i=e[n].slice(this.begin_index),o=i.length>=1&&i[i.length-1]>=this.timestamp_begin,u=i.length<2||i[i.length-2]>=this.timestamp_begin;if(o&&(u?s.subarray(this.timestamp_begin).fill(-1/0):s.subarray(0,this.eos_token_id).fill(-1/0)),e[n].length===this.begin_index&&this.max_initial_timestamp_index!==null){const m=this.timestamp_begin+this.max_initial_timestamp_index;s.subarray(m+1).fill(-1/0)}const l=o_(s),c=Math.log(l.subarray(this.timestamp_begin).map(Math.exp).reduce((m,_)=>m+_)),h=wr(l.subarray(0,this.timestamp_begin))[0];c>h&&s.subarray(0,this.timestamp_begin).fill(-1/0)}return r}}class mv extends pr{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const r=e.length,n=[];for(let s=0;s<r+1-this.no_repeat_ngram_size;++s){const i=[];for(let o=0;o<this.no_repeat_ngram_size;++o)i.push(e[s+o]);n.push(i)}const a=new Map;for(const s of n){const i=s.slice(0,s.length-1),o=JSON.stringify(i.map(Number)),u=a.get(o)??[];u.push(s[s.length-1]),a.set(o,u)}return a}getGeneratedNgrams(e,r){const n=r.slice(r.length+1-this.no_repeat_ngram_size,r.length);return e.get(JSON.stringify(n.map(Number)))??[]}calcBannedNgramTokens(e){const r=[];if(e.length+1<this.no_repeat_ngram_size)return r;{const n=this.getNgrams(e);return this.getGeneratedNgrams(n,e)}}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n],s=this.calcBannedNgramTokens(e[n]);for(const i of s)a.data[i]=-1/0}return r}}class gv extends pr{constructor(e){super(),this.penalty=e}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n];for(const s of e[n])a.data[s]<0?a.data[s]*=this.penalty:a.data[s]/=this.penalty}return r}}class _v extends pr{constructor(e,r){super(),this.min_length=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length<this.min_length){const a=r[n];for(const s of this.eos_token_id)a.data[s]=-1/0}return r}}class wv extends pr{constructor(e,r,n){super(),this.prompt_length_to_skip=e,this.min_new_tokens=r,this.eos_token_id=Array.isArray(n)?n:[n]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length-this.prompt_length_to_skip<this.min_new_tokens){const s=r[n];for(const i of this.eos_token_id)s[i]=-1/0}return r}}class yv extends pr{constructor(e,r){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n];for(const s of this.bad_words_ids){let i=!0;for(let o=1;o<=s.length-1&&s.length<e[o].length;++o)if(s.at(-o-1)!==e[o].at(-o)){i=!1;break}i&&(a[s.at(-1)]=-1/0)}}return r}}class bv extends pr{constructor(e){if(super(),e<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,r){if(r.dims[0]!==2*e.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 ${r.dims[0]} for the logits and ${e.length} for the input ids.`);const n=e.length,a=r.slice([0,n],null),s=r.slice([n,r.dims[0]],null);for(let i=0;i<s.data.length;++i)s.data[i]+=(a.data[i]-s.data[i])*this.guidance_scale;return s}}class vv extends Fo{constructor(e){super(),this.temperature=e}_call(e,r){const n=r.data;for(let a=0;a<n.length;++a)n[a]/=this.temperature;return r}}class $v extends Fo{constructor(e,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),e<0||e>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${e}`);if(!Number.isInteger(n)||n<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${n}`);this.top_p=e,this.filter_value=r,this.min_tokens_to_keep=n}}class xv extends Fo{constructor(e,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),!Number.isInteger(e)||e<0)throw new Error(`\`top_k\` must be a positive integer, but is ${e}`);this.top_k=Math.max(e,n),this.filter_value=r}}class Dm{constructor(e){F(this,"max_length",20);F(this,"max_new_tokens",null);F(this,"min_length",0);F(this,"min_new_tokens",null);F(this,"early_stopping",!1);F(this,"max_time",null);F(this,"do_sample",!1);F(this,"num_beams",1);F(this,"num_beam_groups",1);F(this,"penalty_alpha",null);F(this,"use_cache",!0);F(this,"temperature",1);F(this,"top_k",50);F(this,"top_p",1);F(this,"typical_p",1);F(this,"epsilon_cutoff",0);F(this,"eta_cutoff",0);F(this,"diversity_penalty",0);F(this,"repetition_penalty",1);F(this,"encoder_repetition_penalty",1);F(this,"length_penalty",1);F(this,"no_repeat_ngram_size",0);F(this,"bad_words_ids",null);F(this,"force_words_ids",null);F(this,"renormalize_logits",!1);F(this,"constraints",null);F(this,"forced_bos_token_id",null);F(this,"forced_eos_token_id",null);F(this,"remove_invalid_values",!1);F(this,"exponential_decay_length_penalty",null);F(this,"suppress_tokens",null);F(this,"begin_suppress_tokens",null);F(this,"forced_decoder_ids",null);F(this,"guidance_scale",null);F(this,"num_return_sequences",1);F(this,"output_attentions",!1);F(this,"output_hidden_states",!1);F(this,"output_scores",!1);F(this,"return_dict_in_generate",!1);F(this,"pad_token_id",null);F(this,"bos_token_id",null);F(this,"eos_token_id",null);F(this,"encoder_no_repeat_ngram_size",0);F(this,"decoder_start_token_id",null);F(this,"generation_kwargs",{});Object.assign(this,gr(e,Object.getOwnPropertyNames(this)))}}class Uo extends yt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class Wo extends yt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof Wo?e=e.criteria:e instanceof Uo&&(e=[e]),this.criteria.push(...e)}_call(e,r){const n=new Array(e.length).fill(!1);for(const a of this.criteria){const s=a(e,r);for(let i=0;i<n.length;++i)n[i]||(n[i]=s[i])}return n}[Symbol.iterator](){return this.criteria.values()}}class Sv extends Uo{constructor(e,r=null){super(),this.max_length=e,this.max_position_embeddings=r}_call(e){return e.map(r=>r.length>=this.max_length)}}class kv extends Uo{constructor(e){super(),Array.isArray(e)||(e=[e]),this.eos_token_id=e}_call(e,r){return e.map(n=>{const a=n.at(-1);return this.eos_token_id.some(s=>a==s)})}}class Ii extends yt{constructor(e){super(),this.generation_config=e}async _call(e){return this.sample(e)}async sample(e){throw Error("sample should be implemented in subclasses.")}getLogits(e,r){let n=e.dims.at(-1),a=e.data;if(r===-1)a=a.slice(-n);else{let s=r*n;a=a.slice(s,s+n)}return a}randomSelect(e){let r=0;for(let a=0;a<e.length;++a)r+=e[a];let n=Math.random()*r;for(let a=0;a<e.length;++a)if(n-=e[a],n<=0)return a;return 0}static getSampler(e){if(e.do_sample)return new Cv(e);if(e.num_beams>1)return new Tv(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new Ev(e)}}class Ev extends Ii{async sample(e){const r=wr(e.data)[1];return[[BigInt(r),0]]}}class Cv extends Ii{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await om(e,r),s=Un(n.data);return Array.from({length:this.generation_config.num_beams},()=>{const i=this.randomSelect(s);return[a.data[i],Math.log(s[i])]})}}class Tv extends Ii{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await om(e,r),s=Un(n.data);return Array.from({length:this.generation_config.num_beams},(i,o)=>[a.data[o],Math.log(s[o])])}}class Iv extends Dm{constructor(){super(...arguments);F(this,"return_timestamps",null);F(this,"return_token_timestamps",null);F(this,"num_frames",null);F(this,"alignment_heads",null);F(this,"task",null);F(this,"language",null);F(this,"no_timestamps_token_id",null);F(this,"prompt_ids",null);F(this,"is_multilingual",null);F(this,"lang_to_id",null);F(this,"task_to_id",null);F(this,"max_initial_timestamp_index",1)}}const $e={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},Ai=new Map,Nm=new Map,pa=new Map;async function Av(t,e,r){let n=r.device;n&&typeof n!="string"&&(n.hasOwnProperty(e)?n=n[e]:(console.warn(`Device not specified for ${e}. Using the default device.`),n=null));const a=Gw(n);let s=r.dtype;if(typeof s!="string"&&(s&&s.hasOwnProperty(e)?s=s[e]:(s=dv[a[0]],console.warn(`Dtype not specified for ${e}. Using the default dtype: ${s}.`))),Bm.hasOwnProperty(s)){if(s===Tt.fp16&&!await lv())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${s}. Should be one of: ${Object.keys(Tt).join(", ")}`);const i=Bm[s],o=`${r.subfolder??""}/${e}${i}.onnx`,u={...r.session_options};u.executionProviders??(u.executionProviders=a);const l=Ya(t,o,!0,r);let c=[];if(r.use_external_data_format){if(Fr.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const m=`${e}${i}.onnx_data`,_=`${r.subfolder??""}/${m}`;c.push(new Promise(async(p,b)=>{const $=await Ya(t,_,!0,r);p({path:m,data:$})}))}else u.externalData!==void 0&&(c=u.externalData.map(async m=>{if(typeof m.data=="string"){const _=await Ya(t,m.data,!0,r);return{...m,data:_}}return m}));if(c.length>0&&(u.externalData=await Promise.all(c)),n==="webgpu"){const m=Pm(r.config,{prefix:"present"});if(Object.keys(m).length>0){const _={};for(const p in m)_[p]="gpu-buffer";u.preferredOutputLocation=_}}return{buffer:await l,session_options:u}}async function Zr(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async i=>Av(t,e[i],r))),s={};for(let i=0;i<n.length;++i){const{buffer:o,session_options:u}=a[i],l=await rm(o,u);s[n[i]]=l}return s}function Mv(t,e){const r=Object.create(null),n=[];for(const i of t.inputNames){const o=e[i];if(!(o instanceof fe)){n.push(i);continue}r[i]=Hw()?o.clone():o}if(n.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${n.join(", ")}.`);const a=Object.keys(e).length,s=t.inputNames.length;if(a>s){let i=Object.keys(e).filter(o=>!t.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${s}). The following inputs will be ignored: "${i.join(", ")}".`)}return r}async function Pr(t,e){const r=Mv(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([s,i])=>[s,i.ort_tensor]));let a=await t.run(n);return a=Fm(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Fm(t){for(let e in t)nm(t[e])?t[e]=new fe(t[e]):typeof t[e]=="object"&&Fm(t[e]);return t}function Lm(t){if(t instanceof fe)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[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 fe("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new fe("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function Um(t){return new fe("bool",[t],[1])}async function Wm(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const u=gr(e,t.sessions.model.inputNames);r=(await kn(t,u)).last_hidden_state}const{input_ids:a,decoder_input_ids:s,...i}=e;return i.input_ids=s,i.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(i.encoder_attention_mask=e.attention_mask),await Mi(t,i,!0)}async function kn(t,e){const r=t.sessions.model,n=gr(e,r.inputNames);if(r.inputNames.includes("inputs_embeds")&&!n.inputs_embeds){if(!e.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");n.inputs_embeds=await t.encode_text({input_ids:e.input_ids})}return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new fe("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await Pr(r,n)}async function Mi(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...s}=e;n.inputNames.includes("use_cache_branch")&&(s.use_cache_branch=Um(!!a)),n.inputNames.includes("position_ids")&&s.attention_mask&&!s.position_ids&&(s.position_ids=zv(s,a)),t.addPastKeyValues(s,a);const i=gr(s,n.inputNames);return await Pr(n,i)}async function Ov(t,{input_ids:e=null,attention_mask:r=null,pixel_values:n=null,position_ids:a=null,inputs_embeds:s=null,past_key_values:i=null,generation_config:o=null,logits_processor:u=null,...l}){if(!s){if(s=await t.encode_text({input_ids:e}),n&&e.dims[1]!==1){const h=await t.encode_image({pixel_values:n});({inputs_embeds:s,attention_mask:r}=t._merge_input_ids_with_image_features({image_features:h,inputs_embeds:s,input_ids:e,attention_mask:r}))}else if(i&&n&&e.dims[1]===1){const h=e.dims[1],m=Object.values(i)[0].dims.at(-2);r=Wt([vn([e.dims[0],m]),r.slice(null,[r.dims[1]-h,r.dims[1]])],1)}}return await Mi(t,{inputs_embeds:s,past_key_values:i,attention_mask:r,position_ids:a,generation_config:o,logits_processor:u},!0)}function zv(t,e=null){const{input_ids:r,inputs_embeds:n,attention_mask:a}=t,[s,i]=a.dims,o=new BigInt64Array(a.data.length);for(let l=0;l<s;++l){const c=l*i;let h=BigInt(0);for(let m=0;m<i;++m){const _=c+m;a.data[_]===0n?o[_]=BigInt(1):(o[_]=h,h+=a.data[_])}}let u=new fe("int64",o,a.dims);if(e){const l=-(r??n).dims.at(1);u=u.slice(null,[l,null])}return u}function Vm(t,e,r,n){if(r.past_key_values){const a=Object.values(r.past_key_values)[0].dims.at(-2),{input_ids:s,attention_mask:i}=r;if(!(i&&i.dims[1]>s.dims[1])){if(a<s.dims[1])r.input_ids=s.slice(null,[a,null]);else if(t.config.image_token_index!=null&&s.data.some(o=>o==t.config.image_token_index)){const o=t.config.num_image_tokens;if(!o)throw new Error("`num_image_tokens` is missing in the model configuration.");const u=s.dims[1]-(a-o);r.input_ids=s.slice(null,[-u,null]),r.attention_mask=vn([1,a+u])}}}return r}function Gm(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(i=>[i.at(-1)])),a.decoder_input_ids=Lm(e),a}function Pv(t,...e){return t.config.is_encoder_decoder?Gm(t,...e):Vm(t,...e)}class J extends yt{constructor(r,n){super();F(this,"main_input_name","input_ids");F(this,"forward_params",["input_ids","attention_mask"]);this.config=r,this.sessions=n;const a=pa.get(this.constructor),s=Ai.get(a);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,s){case $e.DecoderOnly:this.can_generate=!0,this._forward=Mi,this._prepare_inputs_for_generation=Vm;break;case $e.Seq2Seq:case $e.Vision2Seq:case $e.Musicgen:this.can_generate=!0,this._forward=Wm,this._prepare_inputs_for_generation=Gm;break;case $e.EncoderDecoder:this._forward=Wm;break;case $e.ImageTextToText:this.can_generate=!0,this._forward=Ov,this._prepare_inputs_for_generation=Pv;break;default:this._forward=kn;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var n;const r=[];for(const a of Object.values(this.sessions))(n=a==null?void 0:a.handler)!=null&&n.dispose&&r.push(a.handler.dispose());return await Promise.all(r)}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:s=null,local_files_only:i=!1,revision:o="main",model_file_name:u=null,subfolder:l="onnx",device:c=null,dtype:h=null,use_external_data_format:m=null,session_options:_={}}={}){let p={progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,model_file_name:u,subfolder:l,device:c,dtype:h,use_external_data_format:m,session_options:_};const b=pa.get(this),$=Ai.get(b);a=p.config=await Rm.from_pretrained(r,p);let S;if($===$e.DecoderOnly)S=await Promise.all([Zr(r,{model:p.model_file_name??"model"},p),Ir(r,"generation_config.json",!1,p)]);else if($===$e.Seq2Seq||$===$e.Vision2Seq)S=await Promise.all([Zr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p),Ir(r,"generation_config.json",!1,p)]);else if($===$e.MaskGeneration)S=await Promise.all([Zr(r,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},p)]);else if($===$e.EncoderDecoder)S=await Promise.all([Zr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p)]);else if($===$e.ImageTextToText){const x={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};a.is_encoder_decoder&&(x.model="encoder_model"),S=await Promise.all([Zr(r,x,p),Ir(r,"generation_config.json",!1,p)])}else $===$e.Musicgen?S=await Promise.all([Zr(r,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},p),Ir(r,"generation_config.json",!1,p)]):($!==$e.EncoderOnly&&console.warn(`Model type for '${b??(a==null?void 0:a.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),S=await Promise.all([Zr(r,{model:p.model_file_name??"model"},p)]));return new this(a,...S)}async _call(r){return await this.forward(r)}async forward(r){return await this._forward(this,r)}_get_logits_warper(r){const n=new Lo;return r.temperature!==null&&r.temperature!==1&&n.push(new vv(r.temperature)),r.top_k!==null&&r.top_k!==0&&n.push(new xv(r.top_k)),r.top_p!==null&&r.top_p<1&&n.push(new $v(r.top_p)),n}_get_logits_processor(r,n,a=null){const s=new Lo;if(r.repetition_penalty!==null&&r.repetition_penalty!==1&&s.push(new gv(r.repetition_penalty)),r.no_repeat_ngram_size!==null&&r.no_repeat_ngram_size>0&&s.push(new mv(r.no_repeat_ngram_size)),r.bad_words_ids!==null&&s.push(new yv(r.bad_words_ids,r.eos_token_id)),r.min_length!==null&&r.eos_token_id!==null&&r.min_length>0&&s.push(new _v(r.min_length,r.eos_token_id)),r.min_new_tokens!==null&&r.eos_token_id!==null&&r.min_new_tokens>0&&s.push(new wv(n,r.min_new_tokens,r.eos_token_id)),r.forced_bos_token_id!==null&&s.push(new cv(r.forced_bos_token_id)),r.forced_eos_token_id!==null&&s.push(new pv(r.max_length,r.forced_eos_token_id)),r.begin_suppress_tokens!==null){const i=n>1||r.forced_bos_token_id===null?n:n+1;s.push(new hv(r.begin_suppress_tokens,i))}return r.guidance_scale!==null&&r.guidance_scale>1&&s.push(new bv(r.guidance_scale)),a!==null&&s.extend(a),s}_prepare_generation_config(r,n,a=Dm){const s={...this.config};for(const o of["decoder","generator","text_config"])o in s&&Object.assign(s,s[o]);const i=new a(s);return"generation_config"in this&&Object.assign(i,this.generation_config),r&&Object.assign(i,r),n&&Object.assign(i,gr(n,Object.getOwnPropertyNames(i))),i}_get_stopping_criteria(r,n=null){const a=new Wo;return r.max_length!==null&&a.push(new Sv(r.max_length,this.config.max_position_embeddings??null)),r.eos_token_id!==null&&a.push(new kv(r.eos_token_id)),n&&a.extend(n),a}_validate_model_class(){if(!this.can_generate){const r=[e0,t0,Zg,Jg],n=pa.get(this.constructor),a=new Set,s=this.config.model_type;for(const o of r){const u=o.get(s);u&&a.add(u[0])}let i=`The current model class (${n}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw a.size>0&&(i+=` Please use the following class instead: ${[...a].join(", ")}`),Error(i)}}prepare_inputs_for_generation(...r){return this._prepare_inputs_for_generation(this,...r)}_update_model_kwargs_for_generation({generated_input_ids:r,outputs:n,model_inputs:a,is_encoder_decoder:s}){return a.past_key_values=this.getPastKeyValues(n,a.past_key_values),a.input_ids=new fe("int64",r.flat(),[r.length,1]),s||(a.attention_mask=Wt([a.attention_mask,vn([a.attention_mask.dims[0],1])],1)),a.position_ids=null,a}_prepare_model_inputs({inputs:r,bos_token_id:n,model_kwargs:a}){const s=gr(a,this.forward_params),i=this.main_input_name;if(i in s){if(r)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else s[i]=r;return{inputs_tensor:s[i],model_inputs:s,model_input_name:i}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:r,model_inputs:n,model_input_name:a,generation_config:s}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!n.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:o,pixel_values:u,attention_mask:l,...c}=n,h=await this._prepare_inputs_embeds(n);n={...c,...gr(h,["inputs_embeds","attention_mask"])}}let{last_hidden_state:i}=await kn(this,n);return s.guidance_scale!==null&&s.guidance_scale>1&&(i=Wt([i,Jw(i,0)],0),"attention_mask"in n&&(n.attention_mask=Wt([n.attention_mask,ty(n.attention_mask)],0))),n.encoder_outputs=i,n}_prepare_decoder_input_ids_for_generation({batch_size:r,model_input_name:n,model_kwargs:a,decoder_start_token_id:s,bos_token_id:i,generation_config:o}){let{decoder_input_ids:u,...l}=a;if(!u)if(s??(s=i),this.config.model_type==="musicgen")u=Array.from({length:r*this.config.decoder.num_codebooks},()=>[s]);else if(Array.isArray(s)){if(s.length!==r)throw new Error(`\`decoder_start_token_id\` expcted to have length ${r} but got ${s.length}`);u=s}else u=Array.from({length:r},()=>[s]);return u=Lm(u),a.decoder_attention_mask=Zw(u),{input_ids:u,model_inputs:l}}async generate({inputs:r=null,generation_config:n=null,logits_processor:a=null,stopping_criteria:s=null,streamer:i=null,...o}){this._validate_model_class(),n=this._prepare_generation_config(n,o);let{inputs_tensor:u,model_inputs:l,model_input_name:c}=this._prepare_model_inputs({inputs:r,model_kwargs:o});const h=this.config.is_encoder_decoder;h&&("encoder_outputs"in l||(l=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:u,model_inputs:l,model_input_name:c,generation_config:n})));let m;h?{input_ids:m,model_inputs:l}=this._prepare_decoder_input_ids_for_generation({batch_size:l[c].dims.at(0),model_input_name:c,model_kwargs:l,decoder_start_token_id:n.decoder_start_token_id,bos_token_id:n.bos_token_id,generation_config:n}):m=l[c];let _=m.dims.at(-1);n.max_new_tokens!==null&&(n.max_length=_+n.max_new_tokens);const p=this._get_logits_processor(n,_,a),b=this._get_stopping_criteria(n,s),$=l[c].dims.at(0),S=Ii.getSampler(n),x=new Array($).fill(0),C=m.tolist();i&&i.put(C);let T=null;for(;;){l=this.prepare_inputs_for_generation(C,l,n);const P=await this.forward(l),R=P.logits.slice(null,-1,null),W=p(C,R),q=[];for(let ue=0;ue<W.dims.at(0);++ue){const ne=W[ue],re=await S(ne);for(const[oe,D]of re){const O=BigInt(oe);x[ue]+=D,C[ue].push(O),q.push([O]);break}}if(i&&i.put(q),b(C).every(ue=>ue)){n.return_dict_in_generate&&(T=this.getPastKeyValues(P,l.past_key_values,!1));break}l=this._update_model_kwargs_for_generation({generated_input_ids:q,outputs:P,model_inputs:l,is_encoder_decoder:h})}i&&i.end();const I=new fe("int64",C.flat(),[C.length,C[0].length]);return n.return_dict_in_generate?{sequences:I,past_key_values:T}:I}addAttentionsToBeam(r,n){if(this.config.is_encoder_decoder){if(!n.cross_attentions||n.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`.");r.cross_attentions||(r.cross_attentions=[]),r.cross_attentions.push(n.cross_attentions)}if(!n.decoder_attentions||n.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`.");r.decoder_attentions||(r.decoder_attentions=[]),r.decoder_attentions.push(n.decoder_attentions)}groupBeams(r){const n=Object.create(null);for(const a of r)n[a.id]===void 0?n[a.id]=[a]:n[a.id].push(a);return Object.values(n)}getPastKeyValues(r,n,a=!0){const s=Object.create(null);for(const i in r)if(i.startsWith("present")){let o=i.replace("present","past_key_values");if(n&&i.includes("encoder"))s[o]=n[o];else{if(a&&n){const u=n[o];u.location==="gpu-buffer"&&u.dispose()}s[o]=r[i]}}return s}getAttentions(r){const n=Object.create(null);for(const a of["cross_attentions","decoder_attentions"]){const s=[];for(const i in r)if(i.startsWith(a)){const o=i.split(".").pop();s[o]=r[i]}n[a]=s}return n}addPastKeyValues(r,n){if(n)Object.assign(r,n);else{const a=this.custom_config.kv_cache_dtype??"float32",s=a==="float16"?new Uint16Array:[],i=Pm(this.config);for(const o in i)r[o]=new fe(a,s,i[o])}}async encode_image({pixel_values:r}){const n=(await Pr(this.sessions.vision_encoder,{pixel_values:r})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${n.dims[1]}).`),this.config.num_image_tokens=n.dims[1]),n}async encode_text({input_ids:r}){return(await Pr(this.sessions.embed_tokens,{input_ids:r})).inputs_embeds}}class Gt{}class ha extends J{}class Rv extends ha{}class Bv extends ha{async _call(e){return new _t(await super._call(e))}}class Dv extends ha{async _call(e){return new Me(await super._call(e))}}class Nv extends ha{async _call(e){return new gt(await super._call(e))}}class Fv extends ha{async _call(e){return new xt(await super._call(e))}}class Lv extends J{}class Uv extends Lv{}class fa extends J{}class Wv extends fa{}class Vv extends fa{async _call(e){return new _t(await super._call(e))}}class Gv extends fa{async _call(e){return new Me(await super._call(e))}}class Hv extends fa{async _call(e){return new gt(await super._call(e))}}class qv extends fa{async _call(e){return new xt(await super._call(e))}}class ma extends J{}class jv extends ma{}class Kv extends ma{async _call(e){return new _t(await super._call(e))}}class Yv extends ma{async _call(e){return new Me(await super._call(e))}}class Qv extends ma{async _call(e){return new gt(await super._call(e))}}class Xv extends ma{async _call(e){return new xt(await super._call(e))}}class ga extends J{}class Jv extends ga{}class Zv extends ga{async _call(e){return new _t(await super._call(e))}}class e1 extends ga{async _call(e){return new Me(await super._call(e))}}class t1 extends ga{async _call(e){return new gt(await super._call(e))}}class r1 extends ga{async _call(e){return new xt(await super._call(e))}}class _a extends J{}class n1 extends _a{}class a1 extends _a{async _call(e){return new _t(await super._call(e))}}class i1 extends _a{async _call(e){return new Me(await super._call(e))}}class s1 extends _a{async _call(e){return new gt(await super._call(e))}}class o1 extends _a{async _call(e){return new xt(await super._call(e))}}class wa extends J{}class u1 extends wa{}class l1 extends wa{async _call(e){return new _t(await super._call(e))}}class d1 extends wa{async _call(e){return new Me(await super._call(e))}}class c1 extends wa{async _call(e){return new gt(await super._call(e))}}class p1 extends wa{async _call(e){return new xt(await super._call(e))}}class ya extends J{}class h1 extends ya{}class f1 extends ya{async _call(e){return new _t(await super._call(e))}}class m1 extends ya{async _call(e){return new Me(await super._call(e))}}class g1 extends ya{async _call(e){return new gt(await super._call(e))}}class _1 extends ya{async _call(e){return new xt(await super._call(e))}}class ba extends J{}class w1 extends ba{}class y1 extends ba{async _call(e){return new Me(await super._call(e))}}class b1 extends ba{async _call(e){return new gt(await super._call(e))}}class v1 extends ba{async _call(e){return new xt(await super._call(e))}}class $1 extends ba{async _call(e){return new _t(await super._call(e))}}class Oi extends J{}class x1 extends Oi{}class S1 extends Oi{async _call(e){return new _t(await super._call(e))}}class k1 extends Oi{async _call(e){return new Me(await super._call(e))}}class E1 extends Oi{async _call(e){return new gt(await super._call(e))}}class zi extends J{}class C1 extends zi{}class T1 extends zi{async _call(e){return new _t(await super._call(e))}}class I1 extends zi{async _call(e){return new Me(await super._call(e))}}class A1 extends zi{async _call(e){return new xt(await super._call(e))}}class va extends J{}class M1 extends va{}class O1 extends va{async _call(e){return new _t(await super._call(e))}}class z1 extends va{async _call(e){return new Me(await super._call(e))}}class P1 extends va{async _call(e){return new gt(await super._call(e))}}class R1 extends va{async _call(e){return new xt(await super._call(e))}}class Pi extends J{}class B1 extends Pi{}class D1 extends Pi{async _call(e){return new _t(await super._call(e))}}class N1 extends Pi{async _call(e){return new Me(await super._call(e))}}class F1 extends Pi{async _call(e){return new xt(await super._call(e))}}class Ri extends J{}class L1 extends Ri{}class U1 extends Ri{async _call(e){return new Me(await super._call(e))}}class W1 extends Ri{async _call(e){return new xt(await super._call(e))}}class V1 extends Ri{async _call(e){return new _t(await super._call(e))}}class Hm extends J{constructor(r,n,a){super(r,n);F(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class G1 extends Hm{}class H1 extends Hm{}class qm extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class q1 extends qm{}class j1 extends qm{}class jm extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class K1 extends jm{}class Y1 extends jm{}class Vo extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class Q1 extends Vo{}class X1 extends Vo{}class J1 extends Vo{async _call(e){return new Me(await super._call(e))}}class Bi extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class Z1 extends Bi{}class e2 extends Bi{}class t2 extends Bi{async _call(e){return new Me(await super._call(e))}}class r2 extends Bi{}class Km extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class n2 extends Km{}class a2 extends Km{}class Ym extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class i2 extends Ym{}class s2 extends Ym{}class $a extends J{}class o2 extends $a{}class u2 extends $a{async _call(e){return new _t(await super._call(e))}}class l2 extends $a{async _call(e){return new Me(await super._call(e))}}class d2 extends $a{async _call(e){return new gt(await super._call(e))}}class c2 extends $a{async _call(e){return new xt(await super._call(e))}}class xa extends J{}class p2 extends xa{}class h2 extends xa{async _call(e){return new _t(await super._call(e))}}class f2 extends xa{async _call(e){return new Me(await super._call(e))}}class m2 extends xa{async _call(e){return new gt(await super._call(e))}}class g2 extends xa{async _call(e){return new xt(await super._call(e))}}class Sa extends J{}class _2 extends Sa{}class w2 extends Sa{async _call(e){return new _t(await super._call(e))}}class y2 extends Sa{async _call(e){return new Me(await super._call(e))}}class b2 extends Sa{async _call(e){return new gt(await super._call(e))}}class v2 extends Sa{async _call(e){return new xt(await super._call(e))}}class Qm extends J{}class $2 extends Qm{}class x2 extends Qm{}class Xm extends J{constructor(r,n,a){super(r,n);F(this,"requires_attention_mask",!1);F(this,"main_input_name","input_features");F(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class S2 extends Xm{}class k2 extends Xm{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,Iv)}_retrieve_init_tokens(e){const r=[e.decoder_start_token_id];let n=e.language;const a=e.task;if(e.is_multilingual){n||(console.warn("No language specified - defaulting to English (en)."),n="en");const i=`<|${bm(n)}|>`;r.push(e.lang_to_id[i]),r.push(e.task_to_id[a??"transcribe"])}else if(n||a)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!e.return_timestamps&&e.no_timestamps_token_id&&r.at(-1)!==e.no_timestamps_token_id?r.push(e.no_timestamps_token_id):e.return_timestamps&&r.at(-1)===e.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),r.pop()),r.filter(s=>s!=null)}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,...s}){r=this._prepare_generation_config(r,s);const i=this._retrieve_init_tokens(r);return r.return_timestamps&&(n??(n=new Lo),n.push(new fv(r,i))),await super.generate({inputs:e,generation_config:r,logits_processor:n,decoder_input_ids:i,...s})}_extract_token_timestamps(e,r,n=null,a=.02){if(!e.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 s=this.config.median_filter_width;s===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),s=7);const i=e.cross_attentions.map(l=>{let c=Array.from({length:this.config.decoder_layers},($,S)=>Wt(l.map(x=>x[S]),2)),h=aa(r.map(([$,S])=>n?c[$].slice(null,S,null,[0,n]):c[$].slice(null,S)));h=h.transpose(1,0,2,3);let[m,_]=Yw(h,-2,0,!0),p=h.clone();for(let $=0;$<p.dims[0];++$){let S=p[$];for(let x=0;x<S.dims[0];++x){let C=S[x];const T=m[$][x][0],I=_[$][x][0];for(let P=0;P<C.dims[0];++P){let R=C[P];for(let W=0;W<R.data.length;++W)R.data[W]=(R.data[W]-I.data[W])/T.data[W];R.data.set(d_(R.data,s))}}}return Mo(p,1)}),o=[e.sequences.length,e.sequences[0].length],u=new fe("float32",new Float32Array(o[0]*o[1]),o);for(let l=0;l<o[0];++l){const c=i[l].neg().squeeze_(0);let[h,m]=Qw(c),_=Array.from({length:h.length-1},($,S)=>h[S+1]-h[S]),p=ht([1],_).map($=>!!$),b=[];for(let $=0;$<p.length;++$)p[$]&&b.push(m[$]*a);u[l].data.set(b,1)}return u}}class E2 extends J{constructor(r,n,a){super(r,n);F(this,"main_input_name","pixel_values");F(this,"forward_params",["pixel_values","input_ids","encoder_hidden_states","past_key_values"]);this.generation_config=a}}class C2 extends J{constructor(r,n,a){super(r,n);F(this,"forward_params",["input_ids","pixel_values","attention_mask","position_ids","past_key_values"]);this.generation_config=a}}class Jm extends C2{_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){const s=this.config.image_token_index,o=n.tolist().map(m=>m.findIndex(_=>_==s)),u=o.every(m=>m===-1),l=o.every(m=>m!==-1);if(!u&&!l)throw new Error("Every input should contain either 0 or 1 image token.");if(u)return{inputs_embeds:e,attention_mask:a};const c=[],h=[];for(let m=0;m<o.length;++m){const _=o[m],p=e[m],b=r[m],$=a[m];c.push(Wt([p.slice([0,_]),b,p.slice([_+1,p.dims[0]])],0)),h.push(Wt([$.slice([0,_]),vn([b.dims[0]]),$.slice([_+1,$.dims[0]])],0))}return{inputs_embeds:aa(c,0),attention_mask:aa(h,0)}}}class T2 extends Jm{}class I2 extends J{constructor(r,n,a){super(r,n);F(this,"forward_params",["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"]);F(this,"main_input_name","inputs_embeds");this.generation_config=a}}class Zm extends I2{_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){return{inputs_embeds:Wt([r,e],1),attention_mask:Wt([vn(r.dims.slice(0,2)),a],1)}}async _prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:n,attention_mask:a}){if(!e&&!r)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let s,i;return e&&(s=await this.encode_text({input_ids:e})),r&&(i=await this.encode_image({pixel_values:r})),s&&i?{inputs_embeds:n,attention_mask:a}=this._merge_input_ids_with_image_features({inputs_embeds:s,image_features:i,input_ids:e,attention_mask:a}):n=s||i,{inputs_embeds:n,attention_mask:a}}async forward({input_ids:e,pixel_values:r,attention_mask:n,decoder_input_ids:a,decoder_attention_mask:s,encoder_outputs:i,past_key_values:o,inputs_embeds:u,decoder_inputs_embeds:l}){if(u||({inputs_embeds:u,attention_mask:n}=await this._prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:u,attention_mask:n})),!i){let{last_hidden_state:m}=await kn(this,{inputs_embeds:u,attention_mask:n});i=m}if(!l){if(!a)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");l=await this.encode_text({input_ids:a})}return await Mi(this,{inputs_embeds:l,attention_mask:s,encoder_attention_mask:n,encoder_hidden_states:i,past_key_values:o},!0)}}class Di extends J{}class A2 extends Di{}class M2 extends Di{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class O2 extends Di{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(e,r)}}class eg extends J{}class z2 extends eg{}class P2 extends eg{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class R2 extends Di{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(e,r)}}class B2 extends J{}class D2 extends B2{}class tg extends J{}class N2 extends tg{}class F2 extends tg{}class rg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class L2 extends rg{}class U2 extends rg{}class ng extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class W2 extends ng{}class V2 extends ng{}class ag extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class G2 extends ag{}class H2 extends ag{}class ig extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class q2 extends ig{}class j2 extends ig{}class sg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class K2 extends sg{}class Y2 extends sg{}class og extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class Q2 extends og{}class X2 extends og{}class ug extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class J2 extends ug{}class Z2 extends ug{}class lg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class e$ extends lg{}class t$ extends lg{}class dg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class r$ extends dg{}class n$ extends dg{}class cg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class a$ extends cg{}class i$ extends cg{}class pg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class s$ extends pg{}class o$ extends pg{}class hg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class u$ extends hg{}class l$ extends hg{}class fg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class d$ extends fg{}class c$ extends fg{}class mg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class p$ extends mg{}class h$ extends mg{}class gg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class f$ extends gg{}class m$ extends gg{}class _g extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class g$ extends _g{}class _$ extends _g{}class wg extends J{}class w$ extends wg{}class y$ extends wg{async _call(e){return new Me(await super._call(e))}}class yg extends J{}class b$ extends yg{}class v$ extends yg{async _call(e){return new Me(await super._call(e))}}class $$ extends J{}class x$ extends $${async _call(e){return new WS(await super._call(e))}}class bg extends J{}class S$ extends bg{}class k$ extends bg{async _call(e){return new Me(await super._call(e))}}class vg extends J{}class E$ extends vg{}class C$ extends vg{async _call(e){return new Me(await super._call(e))}}class $g extends J{}class T$ extends $g{}class I$ extends $g{}class xg extends J{}class A$ extends xg{}class M$ extends xg{}class Sg extends J{}class O$ extends Sg{}class z$ extends Sg{async _call(e){return new Me(await super._call(e))}}class Go extends J{}class P$ extends Go{}class R$ extends Go{async _call(e){return new kg(await super._call(e))}}class B$ extends Go{async _call(e){return new D$(await super._call(e))}}class kg extends Gt{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class D$ extends Gt{constructor({logits:e,pred_boxes:r,pred_masks:n}){super(),this.logits=e,this.pred_boxes=r,this.pred_masks=n}}class Eg extends J{}class N$ extends Eg{}class F$ extends Eg{async _call(e){return new L$(await super._call(e))}}class L$ extends kg{}class Cg extends J{}class U$ extends Cg{}class W$ extends Cg{async _call(e){return new Me(await super._call(e))}}class Tg extends J{}class V$ extends Tg{}class G$ extends Tg{async _call(e){return new Me(await super._call(e))}}class Ig extends J{}class H$ extends Ig{}class q$ extends Ig{async _call(e){return new Me(await super._call(e))}}class Ag extends J{}class j$ extends Ag{}class K$ extends Ag{}class Mg extends J{}class Y$ extends Mg{}class Q$ extends Mg{}class X$ extends J{}class J$ extends X${}class Og extends J{}class Z$ extends Og{}class ex extends Og{}class tx extends J{}class rx extends tx{}class zg extends J{}class nx extends zg{}class ax extends zg{async _call(e){return new Me(await super._call(e))}}class Pg extends J{}class ix extends Pg{}class sx extends Pg{async _call(e){return new Me(await super._call(e))}}class Rg extends J{}class ox extends Rg{}class ux extends Rg{async _call(e){return new Me(await super._call(e))}}class Bg extends J{}class lx extends Bg{}class dx extends Bg{async _call(e){return new cx(await super._call(e))}}class cx extends Gt{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class px extends J{}class hx extends px{async get_image_embeddings({pixel_values:e}){return await kn(this,{pixel_values:e})}async forward(e){if((!e.image_embeddings||!e.image_positional_embeddings)&&(e={...e,...await this.get_image_embeddings(e)}),!e.input_labels&&e.input_points){const n=e.input_points.dims.slice(0,-1),a=n.reduce((s,i)=>s*i,1);e.input_labels=new fe("int64",new BigInt64Array(a).fill(1n),n)}const r={image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings};return e.input_points&&(r.input_points=e.input_points),e.input_labels&&(r.input_labels=e.input_labels),e.input_boxes&&(r.input_boxes=e.input_boxes),await Pr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new fx(await super._call(e))}}class fx extends Gt{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class Dg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class mx extends Dg{}class gx extends Dg{}class Ng extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class _x extends Ng{}class wx extends Ng{}class en extends J{}class yx extends en{}class bx extends en{async _call(e){return new En(await super._call(e))}}class vx extends en{async _call(e){return new Me(await super._call(e))}}class $x extends en{async _call(e){return new gt(await super._call(e))}}class Ho extends J{}class xx extends Ho{}class Sx extends Ho{async _call(e){return new En(await super._call(e))}}class kx extends Ho{async _call(e){return new Me(await super._call(e))}}class Ni extends J{}class Ex extends Ni{}class Cx extends Ni{async _call(e){return new En(await super._call(e))}}class Tx extends Ni{async _call(e){return new Me(await super._call(e))}}class Ix extends Ni{async _call(e){return new gt(await super._call(e))}}class qo extends J{}class Ax extends qo{}class Mx extends qo{async _call(e){return new En(await super._call(e))}}class Ox extends qo{async _call(e){return new Me(await super._call(e))}}class zx extends en{}class Px extends en{async _call(e){return new En(await super._call(e))}}class Rx extends en{async _call(e){return new Me(await super._call(e))}}class ka extends J{}class Bx extends ka{}class Dx extends ka{async _call(e){return new En(await super._call(e))}}class Nx extends ka{async _call(e){return new Me(await super._call(e))}}class Fx extends ka{async _call(e){return new US(await super._call(e))}}class Lx extends ka{async _call(e){return new gt(await super._call(e))}}class Fg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class Ux extends Fg{}class Wx extends Fg{async generate_speech(e,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:s=20,vocoder:i=null}={}){const o={input_ids:e},{encoder_outputs:u,encoder_attention_mask:l}=await kn(this,o),c=u.dims[1]/this.config.reduction_factor,h=Math.floor(c*s),m=Math.floor(c*a),_=this.config.num_mel_bins;let p=[],b=null,$=null,S=0;for(;;){++S;const T=Um(!!$);let I;$?I=$.output_sequence_out:I=new fe("float32",new Float32Array(_),[1,1,_]);let P={use_cache_branch:T,output_sequence:I,encoder_attention_mask:l,speaker_embeddings:r,encoder_hidden_states:u};this.addPastKeyValues(P,b),$=await Pr(this.sessions.decoder_model_merged,P),b=this.getPastKeyValues($,b);const{prob:R,spectrum:W}=$;if(p.push(W),S>=m&&(Array.from(R.data).filter(q=>q>=n).length>0||S>=h))break}const x=Wt(p),{waveform:C}=await Pr(i.sessions.model,{spectrogram:x});return{spectrogram:x,waveform:C}}}class Vx extends J{constructor(){super(...arguments);F(this,"main_input_name","spectrogram")}}class Gx extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class Hx extends Gx{}class Lg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class qx extends Lg{}class jx extends Lg{}class Ug extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class Kx extends Ug{}class Yx extends Ug{}class Wg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class Qx extends Wg{}class Xx extends Wg{}class jo extends J{}class Jx extends jo{}class Zx extends jo{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class eS extends jo{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="audio_model"),super.from_pretrained(e,r)}}class tS extends J{}class Vg extends tS{async _call(e){return new VS(await super._call(e))}}class Gg extends J{}class rS extends Gg{}class nS extends Gg{}class Hg extends J{constructor(e,r,n){super(e,r),this.generation_config=n}}class aS extends Hg{}class iS extends Hg{}class qg extends J{}class sS extends qg{}class oS extends qg{async _call(e){return new Me(await super._call(e))}}class jg extends J{constructor(r,n,a){super(r,n);F(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}_apply_and_filter_by_delay_pattern_mask(r){const[n,a]=r.dims,s=this.config.decoder.num_codebooks,i=a-s;let o=0;for(let c=0;c<r.size;++c){if(r.data[c]===this.config.decoder.pad_token_id)continue;const h=c%a,m=Math.floor(c/a)%s,_=h-m;_>0&&_<=i&&(r.data[o++]=r.data[c])}const u=Math.floor(n/s),l=o/(u*s);return new fe(r.type,r.data.slice(0,o),[u,s,l])}prepare_inputs_for_generation(r,n,a){let s=structuredClone(r);for(let o=0;o<s.length;++o)for(let u=0;u<s[o].length;++u)o%this.config.decoder.num_codebooks>=u&&(s[o][u]=BigInt(this.config.decoder.pad_token_id));return a.guidance_scale!==null&&a.guidance_scale>1&&(s=s.concat(s)),super.prepare_inputs_for_generation(s,n,a)}async generate(r){const n=await super.generate(r),a=this._apply_and_filter_by_delay_pattern_mask(n).unsqueeze_(0),{audio_values:s}=await Pr(this.sessions.encodec_decode,{audio_codes:a});return s}}class Kg extends J{}class uS extends Kg{}class lS extends Kg{async _call(e){return new Me(await super._call(e))}}class Yg extends J{}class dS extends Yg{}class cS extends Yg{async _call(e){return new Me(await super._call(e))}}class Qg extends J{}class pS extends Qg{}class hS extends Qg{async _call(e){return new Me(await super._call(e))}}class Xg extends J{}class fS extends Xg{}class mS extends Xg{async _call(e){return new Me(await super._call(e))}}class Ko{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",model_file_name:o=null,subfolder:u="onnx",device:l=null,dtype:c=null,use_external_data_format:h=null,session_options:m={}}={}){let _={progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,model_file_name:o,subfolder:u,device:l,dtype:c,use_external_data_format:h,session_options:m};if(_.config=await Rm.from_pretrained(e,_),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let p of this.MODEL_CLASS_MAPPINGS){const b=p.get(_.config.model_type);if(b)return await b[1].from_pretrained(e,_)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${_.config.model_type}", attempting to construct from base class.`),await J.from_pretrained(e,_);throw Error(`Unsupported model type: ${_.config.model_type}`)}}F(Ko,"MODEL_CLASS_MAPPINGS",null),F(Ko,"BASE_IF_FAIL",!1);const gS=new Map([["bert",["BertModel",Rv]],["nomic_bert",["NomicBertModel",Uv]],["roformer",["RoFormerModel",Wv]],["electra",["ElectraModel",Jv]],["esm",["EsmModel",x1]],["convbert",["ConvBertModel",jv]],["camembert",["CamembertModel",n1]],["deberta",["DebertaModel",u1]],["deberta-v2",["DebertaV2Model",h1]],["mpnet",["MPNetModel",M1]],["albert",["AlbertModel",L1]],["distilbert",["DistilBertModel",w1]],["roberta",["RobertaModel",o2]],["xlm",["XLMModel",p2]],["xlm-roberta",["XLMRobertaModel",_2]],["clap",["ClapModel",Jx]],["clip",["CLIPModel",A2]],["clipseg",["CLIPSegModel",N2]],["chinese_clip",["ChineseCLIPModel",D2]],["siglip",["SiglipModel",z2]],["mobilebert",["MobileBertModel",C1]],["squeezebert",["SqueezeBertModel",B1]],["wav2vec2",["Wav2Vec2Model",yx]],["wav2vec2-bert",["Wav2Vec2BertModel",Ax]],["unispeech",["UniSpeechModel",xx]],["unispeech-sat",["UniSpeechSatModel",Ex]],["hubert",["HubertModel",zx]],["wavlm",["WavLMModel",Bx]],["audio-spectrogram-transformer",["ASTModel",$2]],["vits",["VitsModel",Vg]],["detr",["DetrModel",P$]],["table-transformer",["TableTransformerModel",N$]],["vit",["ViTModel",w$]],["fastvit",["FastViTModel",b$]],["mobilevit",["MobileViTModel",S$]],["mobilevitv2",["MobileViTV2Model",E$]],["owlvit",["OwlViTModel",T$]],["owlv2",["Owlv2Model",A$]],["beit",["BeitModel",O$]],["deit",["DeiTModel",U$]],["convnext",["ConvNextModel",nx]],["convnextv2",["ConvNextV2Model",ix]],["dinov2",["Dinov2Model",ox]],["resnet",["ResNetModel",V$]],["swin",["SwinModel",H$]],["swin2sr",["Swin2SRModel",j$]],["donut-swin",["DonutSwinModel",rx]],["yolos",["YolosModel",lx]],["dpt",["DPTModel",Y$]],["glpn",["GLPNModel",Z$]],["hifigan",["SpeechT5HifiGan",Vx]],["efficientnet",["EfficientNetModel",sS]],["mobilenet_v1",["MobileNetV1Model",uS]],["mobilenet_v2",["MobileNetV2Model",dS]],["mobilenet_v3",["MobileNetV3Model",pS]],["mobilenet_v4",["MobileNetV4Model",fS]]]),_S=new Map([["t5",["T5Model",G1]],["longt5",["LongT5Model",q1]],["mt5",["MT5Model",K1]],["bart",["BartModel",Q1]],["mbart",["MBartModel",Z1]],["marian",["MarianModel",mx]],["whisper",["WhisperModel",S2]],["m2m_100",["M2M100Model",_x]],["blenderbot",["BlenderbotModel",n2]],["blenderbot-small",["BlenderbotSmallModel",i2]]]),wS=new Map([["bloom",["BloomModel",p$]],["gpt2",["GPT2Model",L2]],["gptj",["GPTJModel",q2]],["gpt_bigcode",["GPTBigCodeModel",K2]],["gpt_neo",["GPTNeoModel",W2]],["gpt_neox",["GPTNeoXModel",G2]],["codegen",["CodeGenModel",Q2]],["llama",["LlamaModel",J2]],["cohere",["CohereModel",e$]],["gemma",["GemmaModel",r$]],["openelm",["OpenELMModel",a$]],["qwen2",["Qwen2Model",s$]],["phi",["PhiModel",u$]],["phi3",["Phi3Model",d$]],["mpt",["MptModel",f$]],["opt",["OPTModel",g$]],["mistral",["MistralModel",qx]],["starcoder2",["Starcoder2Model",Kx]],["falcon",["FalconModel",Qx]],["stablelm",["StableLmModel",aS]]]),Jg=new Map([["speecht5",["SpeechT5ForSpeechToText",Ux]],["whisper",["WhisperForConditionalGeneration",k2]]]),yS=new Map([["speecht5",["SpeechT5ForTextToSpeech",Wx]]]),bS=new Map([["vits",["VitsModel",Vg]],["musicgen",["MusicgenForConditionalGeneration",jg]]]),vS=new Map([["bert",["BertForSequenceClassification",Dv]],["roformer",["RoFormerForSequenceClassification",Gv]],["electra",["ElectraForSequenceClassification",e1]],["esm",["EsmForSequenceClassification",k1]],["convbert",["ConvBertForSequenceClassification",Yv]],["camembert",["CamembertForSequenceClassification",i1]],["deberta",["DebertaForSequenceClassification",d1]],["deberta-v2",["DebertaV2ForSequenceClassification",m1]],["mpnet",["MPNetForSequenceClassification",z1]],["albert",["AlbertForSequenceClassification",U1]],["distilbert",["DistilBertForSequenceClassification",y1]],["roberta",["RobertaForSequenceClassification",l2]],["xlm",["XLMForSequenceClassification",f2]],["xlm-roberta",["XLMRobertaForSequenceClassification",y2]],["bart",["BartForSequenceClassification",J1]],["mbart",["MBartForSequenceClassification",t2]],["mobilebert",["MobileBertForSequenceClassification",I1]],["squeezebert",["SqueezeBertForSequenceClassification",N1]]]),$S=new Map([["bert",["BertForTokenClassification",Nv]],["roformer",["RoFormerForTokenClassification",Hv]],["electra",["ElectraForTokenClassification",t1]],["esm",["EsmForTokenClassification",E1]],["convbert",["ConvBertForTokenClassification",Qv]],["camembert",["CamembertForTokenClassification",s1]],["deberta",["DebertaForTokenClassification",c1]],["deberta-v2",["DebertaV2ForTokenClassification",g1]],["mpnet",["MPNetForTokenClassification",P1]],["distilbert",["DistilBertForTokenClassification",b1]],["roberta",["RobertaForTokenClassification",d2]],["xlm",["XLMForTokenClassification",m2]],["xlm-roberta",["XLMRobertaForTokenClassification",b2]]]),Zg=new Map([["t5",["T5ForConditionalGeneration",H1]],["longt5",["LongT5ForConditionalGeneration",j1]],["mt5",["MT5ForConditionalGeneration",Y1]],["bart",["BartForConditionalGeneration",X1]],["mbart",["MBartForConditionalGeneration",e2]],["marian",["MarianMTModel",gx]],["m2m_100",["M2M100ForConditionalGeneration",wx]],["blenderbot",["BlenderbotForConditionalGeneration",a2]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",s2]]]),e0=new Map([["bloom",["BloomForCausalLM",h$]],["gpt2",["GPT2LMHeadModel",U2]],["gptj",["GPTJForCausalLM",j2]],["gpt_bigcode",["GPTBigCodeForCausalLM",Y2]],["gpt_neo",["GPTNeoForCausalLM",V2]],["gpt_neox",["GPTNeoXForCausalLM",H2]],["codegen",["CodeGenForCausalLM",X2]],["llama",["LlamaForCausalLM",Z2]],["cohere",["CohereForCausalLM",t$]],["gemma",["GemmaForCausalLM",n$]],["openelm",["OpenELMForCausalLM",i$]],["qwen2",["Qwen2ForCausalLM",o$]],["phi",["PhiForCausalLM",l$]],["phi3",["Phi3ForCausalLM",c$]],["mpt",["MptForCausalLM",m$]],["opt",["OPTForCausalLM",_$]],["mbart",["MBartForCausalLM",r2]],["mistral",["MistralForCausalLM",jx]],["starcoder2",["Starcoder2ForCausalLM",Yx]],["falcon",["FalconForCausalLM",Xx]],["trocr",["TrOCRForCausalLM",Hx]],["stablelm",["StableLmForCausalLM",iS]]]),xS=new Map([["bert",["BertForMaskedLM",Bv]],["roformer",["RoFormerForMaskedLM",Vv]],["electra",["ElectraForMaskedLM",Zv]],["esm",["EsmForMaskedLM",S1]],["convbert",["ConvBertForMaskedLM",Kv]],["camembert",["CamembertForMaskedLM",a1]],["deberta",["DebertaForMaskedLM",l1]],["deberta-v2",["DebertaV2ForMaskedLM",f1]],["mpnet",["MPNetForMaskedLM",O1]],["albert",["AlbertForMaskedLM",V1]],["distilbert",["DistilBertForMaskedLM",$1]],["roberta",["RobertaForMaskedLM",u2]],["xlm",["XLMWithLMHeadModel",h2]],["xlm-roberta",["XLMRobertaForMaskedLM",w2]],["mobilebert",["MobileBertForMaskedLM",T1]],["squeezebert",["SqueezeBertForMaskedLM",D1]]]),SS=new Map([["bert",["BertForQuestionAnswering",Fv]],["roformer",["RoFormerForQuestionAnswering",qv]],["electra",["ElectraForQuestionAnswering",r1]],["convbert",["ConvBertForQuestionAnswering",Xv]],["camembert",["CamembertForQuestionAnswering",o1]],["deberta",["DebertaForQuestionAnswering",p1]],["deberta-v2",["DebertaV2ForQuestionAnswering",_1]],["mpnet",["MPNetForQuestionAnswering",R1]],["albert",["AlbertForQuestionAnswering",W1]],["distilbert",["DistilBertForQuestionAnswering",v1]],["roberta",["RobertaForQuestionAnswering",c2]],["xlm",["XLMForQuestionAnswering",g2]],["xlm-roberta",["XLMRobertaForQuestionAnswering",v2]],["mobilebert",["MobileBertForQuestionAnswering",A1]],["squeezebert",["SqueezeBertForQuestionAnswering",F1]]]),t0=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",E2]]]),kS=new Map([["llava",["LlavaForConditionalGeneration",Jm]],["moondream1",["Moondream1ForConditionalGeneration",T2]],["florence2",["Florence2ForConditionalGeneration",Zm]]]),ES=new Map([["vit",["ViTForImageClassification",y$]],["fastvit",["FastViTForImageClassification",v$]],["mobilevit",["MobileViTForImageClassification",k$]],["mobilevitv2",["MobileViTV2ForImageClassification",C$]],["beit",["BeitForImageClassification",z$]],["deit",["DeiTForImageClassification",W$]],["convnext",["ConvNextForImageClassification",ax]],["convnextv2",["ConvNextV2ForImageClassification",sx]],["dinov2",["Dinov2ForImageClassification",ux]],["resnet",["ResNetForImageClassification",G$]],["swin",["SwinForImageClassification",q$]],["segformer",["SegformerForImageClassification",rS]],["efficientnet",["EfficientNetForImageClassification",oS]],["mobilenet_v1",["MobileNetV1ForImageClassification",lS]],["mobilenet_v2",["MobileNetV2ForImageClassification",cS]],["mobilenet_v3",["MobileNetV3ForImageClassification",hS]],["mobilenet_v4",["MobileNetV4ForImageClassification",mS]]]),CS=new Map([["detr",["DetrForObjectDetection",R$]],["table-transformer",["TableTransformerForObjectDetection",F$]],["yolos",["YolosForObjectDetection",dx]]]),TS=new Map([["owlvit",["OwlViTForObjectDetection",I$]],["owlv2",["Owlv2ForObjectDetection",M$]]]),IS=new Map([["detr",["DetrForSegmentation",B$]],["clipseg",["CLIPSegForImageSegmentation",F2]]]),AS=new Map([["segformer",["SegformerForSemanticSegmentation",nS]]]),MS=new Map([["sam",["SamModel",hx]]]),OS=new Map([["wav2vec2",["Wav2Vec2ForCTC",bx]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Mx]],["unispeech",["UniSpeechForCTC",Sx]],["unispeech-sat",["UniSpeechSatForCTC",Cx]],["wavlm",["WavLMForCTC",Dx]],["hubert",["HubertForCTC",Px]]]),zS=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",vx]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Ox]],["unispeech",["UniSpeechForSequenceClassification",kx]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Tx]],["wavlm",["WavLMForSequenceClassification",Nx]],["hubert",["HubertForSequenceClassification",Rx]],["audio-spectrogram-transformer",["ASTForAudioClassification",x2]]]),PS=new Map([["wavlm",["WavLMForXVector",Fx]]]),RS=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Ix]],["wavlm",["WavLMForAudioFrameClassification",Lx]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",$x]]]),BS=new Map([["vitmatte",["VitMatteForImageMatting",x$]]]),DS=new Map([["swin2sr",["Swin2SRForImageSuperResolution",K$]]]),NS=new Map([["dpt",["DPTForDepthEstimation",Q$]],["depth_anything",["DepthAnythingForDepthEstimation",J$]],["glpn",["GLPNForDepthEstimation",ex]]]),FS=new Map([["clip",["CLIPVisionModelWithProjection",O2]],["siglip",["SiglipVisionModel",R2]]]),r0=[[gS,$e.EncoderOnly],[_S,$e.EncoderDecoder],[wS,$e.DecoderOnly],[vS,$e.EncoderOnly],[$S,$e.EncoderOnly],[Zg,$e.Seq2Seq],[Jg,$e.Seq2Seq],[e0,$e.DecoderOnly],[xS,$e.EncoderOnly],[SS,$e.EncoderOnly],[t0,$e.Vision2Seq],[kS,$e.ImageTextToText],[ES,$e.EncoderOnly],[IS,$e.EncoderOnly],[AS,$e.EncoderOnly],[BS,$e.EncoderOnly],[DS,$e.EncoderOnly],[NS,$e.EncoderOnly],[CS,$e.EncoderOnly],[TS,$e.EncoderOnly],[MS,$e.MaskGeneration],[OS,$e.EncoderOnly],[zS,$e.EncoderOnly],[yS,$e.Seq2Seq],[bS,$e.EncoderOnly],[PS,$e.EncoderOnly],[RS,$e.EncoderOnly],[FS,$e.EncoderOnly]];for(const[t,e]of r0)for(const[r,n]of t.values())Ai.set(r,e),pa.set(n,r),Nm.set(r,n);const LS=[["MusicgenForConditionalGeneration",jg,$e.Musicgen],["CLIPTextModelWithProjection",M2,$e.EncoderOnly],["SiglipTextModel",P2,$e.EncoderOnly],["ClapTextModelWithProjection",Zx,$e.EncoderOnly],["ClapAudioModelWithProjection",eS,$e.EncoderOnly]];for(const[t,e,r]of LS)Ai.set(t,r),pa.set(e,t),Nm.set(t,e);class n0 extends Ko{}F(n0,"MODEL_CLASS_MAPPINGS",r0.map(e=>e[0])),F(n0,"BASE_IF_FAIL",!0);class Me extends Gt{constructor({logits:e}){super(),this.logits=e}}class US extends Gt{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class gt extends Gt{constructor({logits:e}){super(),this.logits=e}}class _t extends Gt{constructor({logits:e}){super(),this.logits=e}}class xt extends Gt{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class En extends Gt{constructor({logits:e}){super(),this.logits=e}}class WS extends Gt{constructor({alphas:e}){super(),this.alphas=e}}class VS extends Gt{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const Ht=typeof self<"u",GS=Ht&&self.constructor.name==="DedicatedWorkerGlobalScope";let tn,a0,Rr;if(Ht)tn=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},Rr=self.createImageBitmap,a0=self.ImageData;else if(Ve)Rr=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),s=new ar(new Uint8ClampedArray(n),a.width,a.height,a.channels);return r!==void 0&&r!==a.channels&&s.convert(r),s};else throw new Error("Unable to load image processing library.");const HS={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},qS=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class ar{constructor(e,r,n,a){this.data=e,this.width=r,this.height=n,this.channels=a}get size(){return[this.width,this.height]}static async read(e){if(e instanceof ar)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!Ht)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new ar(n,e.width,e.height,4)}static async fromURL(e){const r=await hs(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(Ht){const r=await Rr(e),n=tn(r.width,r.height).getContext("2d");return n.drawImage(r,0,0),new this(n.getImageData(0,0,r.width,r.height).data,r.width,r.height,4)}else{const r=Ve(await e.arrayBuffer());return await Rr(r)}}static fromTensor(e,r="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(r==="CHW")e=e.transpose(1,2,0);else if(r!=="HWC")throw new Error(`Unsupported channel format: ${r}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new ar(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let r=0,n=0;r<this.data.length;r+=this.channels){const a=this.data[r],s=this.data[r+1],i=this.data[r+2];e[n++]=Math.round(.2989*a+.587*s+.114*i)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let r=0,n=0;r<this.data.length;++r)e[n++]=this.data[r],e[n++]=this.data[r],e[n++]=this.data[r];break;case 4:for(let r=0,n=0;r<this.data.length;r+=4)e[n++]=this.data[r],e[n++]=this.data[r+1],e[n++]=this.data[r+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let r=0,n=0;r<this.data.length;++r)e[n++]=this.data[r],e[n++]=this.data[r],e[n++]=this.data[r],e[n++]=255;break;case 3:for(let r=0,n=0;r<this.data.length;r+=3)e[n++]=this.data[r],e[n++]=this.data[r+1],e[n++]=this.data[r+2],e[n++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}async resize(e,r,{resample:n=2}={}){let a=HS[n]??n;if(Ht){const s=this.channels,i=this.toCanvas(),o=tn(e,r).getContext("2d");return o.drawImage(i,0,0,e,r),new ar(o.getImageData(0,0,e,r).data,e,r,4).convert(s)}else{let s=this.toSharp();switch(a){case"box":case"hamming":(a==="box"||a==="hamming")&&(console.warn(`Resampling method ${a} is not yet supported. Using bilinear instead.`),a="bilinear");case"nearest":case"bilinear":case"bicubic":s=s.affine([e/this.width,0,0,r/this.height],{interpolator:a});break;case"lanczos":s=s.resize({width:e,height:r,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${a} is not supported.`)}return await Rr(s)}}async pad([e,r,n,a]){if(e=Math.max(e,0),r=Math.max(r,0),n=Math.max(n,0),a=Math.max(a,0),e===0&&r===0&&n===0&&a===0)return this;if(Ht){const s=this.channels,i=this.toCanvas(),o=this.width+e+r,u=this.height+n+a,l=tn(o,u).getContext("2d");return l.drawImage(i,0,0,this.width,this.height,e,n,o,u),new ar(l.getImageData(0,0,o,u).data,o,u,4).convert(s)}else{const s=this.toSharp().extend({left:e,right:r,top:n,bottom:a});return await Rr(s)}}async crop([e,r,n,a]){if(e=Math.max(e,0),r=Math.max(r,0),n=Math.min(n,this.width-1),a=Math.min(a,this.height-1),e===0&&r===0&&n===this.width-1&&a===this.height-1)return this;const s=n-e+1,i=a-r+1;if(Ht){const o=this.channels,u=this.toCanvas(),l=tn(s,i).getContext("2d");return l.drawImage(u,e,r,s,i,0,0,s,i),new ar(l.getImageData(0,0,s,i).data,s,i,4).convert(o)}else{const o=this.toSharp().extract({left:e,top:r,width:s,height:i});return await Rr(o)}}async center_crop(e,r){if(this.width===e&&this.height===r)return this;const n=(this.width-e)/2,a=(this.height-r)/2;if(Ht){const s=this.channels,i=this.toCanvas(),o=tn(e,r).getContext("2d");let u=0,l=0,c=0,h=0;return n>=0?u=n:c=-n,a>=0?l=a:h=-a,o.drawImage(i,u,l,e,r,c,h,e,r),new ar(o.getImageData(0,0,e,r).data,e,r,4).convert(s)}else{let s=this.toSharp();if(n>=0&&a>=0)s=s.extract({left:Math.floor(n),top:Math.floor(a),width:e,height:r});else if(n<=0&&a<=0){const i=Math.floor(-a),o=Math.floor(-n);s=s.extend({top:i,left:o,right:e-this.width-o,bottom:r-this.height-i})}else{let i=[0,0],o=0;a<0?(i[0]=Math.floor(-a),i[1]=r-this.height-i[0]):o=Math.floor(a);let u=[0,0],l=0;n<0?(u[0]=Math.floor(-n),u[1]=e-this.width-u[0]):l=Math.floor(n),s=s.extend({top:i[0],bottom:i[1],left:u[0],right:u[1]}).extract({left:l,top:o,width:e,height:r})}return await Rr(s)}}async toBlob(e="image/png",r=1){if(!Ht)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:r})}toTensor(e="CHW"){let r=new fe("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(e!=="HWC")if(e==="CHW")r=r.permute(2,0,1);else throw new Error(`Unsupported channel format: ${e}`);return r}toCanvas(){if(!Ht)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=tn(e.width,e.height),n=new a0(e.data,e.width,e.height);return r.getContext("2d").putImageData(n,0,0),r}_update(e,r,n,a=null){return this.data=e,this.width=r,this.height=n,a!==null&&(this.channels=a),this}clone(){return new ar(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(Ht){if(GS)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=qS.get(r)??"image/png",a=await this.toBlob(n),s=URL.createObjectURL(a),i=document.createElement("a");i.href=s,i.download=e,i.click(),i.remove()}else{if(kt.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Ht)throw new Error("toSharp() is only supported in server-side environments.");return Ve(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}function i0(t){if(t<1)return new Float64Array;if(t===1)return new Float64Array([1]);const e=t-1,r=Math.PI/e,n=new Float64Array(t);for(let a=0;a<t;++a){const s=2*a-e;n[a]=.5+.5*Math.cos(r*s)}return n}const jS={htk:t=>2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,r=15,n=27/Math.log(6.4))=>t>=e?r+Math.log(t/e)*n:3*t/200};function Yo(t,e="htk"){const r=jS[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}const KS={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,r=15,n=Math.log(6.4)/27)=>t>=r?e*Math.exp(n*(t-r)):200*t/3};function YS(t,e="htk"){const r=KS[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}function QS(t,e){const r=Float64Array.from({length:e.length-1},(i,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let i=0;i<t.length;++i){const o=n[i];for(let u=0;u<e.length;++u)o[u]=e[u]-t[i]}const a=e.length-2,s=Array.from({length:a},()=>new Array(t.length));for(let i=0;i<t.length;++i){const o=n[i];for(let u=0;u<a;++u){const l=-o[u]/r[u],c=o[u+2]/r[u+1];s[u][i]=Math.max(0,Math.min(l,c))}}return s}function s0(t,e,r){const n=(e-t)/(r-1);return Float64Array.from({length:r},(a,s)=>t+n*s)}function Ea(t,e,r,n,a,s=null,i="htk",o=!1){if(s!==null&&s!=="slaney")throw new Error('norm must be one of null or "slaney"');const u=Yo(r,i),l=Yo(n,i),c=s0(u,l,e+2);let h=YS(c,i),m;if(o){const p=a/(t*2);m=Yo(Float64Array.from({length:t},(b,$)=>$*p),i),h=c}else m=s0(0,Math.floor(a/2),t);const _=QS(m,h);if(s!==null&&s==="slaney")for(let p=0;p<e;++p){const b=_[p],$=2/(h[p+2]-h[p]);for(let S=0;S<t;++S)b[S]*=$}return _}function XS(t,e,r){const n=new t.constructor(t.length+e+r),a=t.length-1;for(let s=0;s<t.length;++s)n[e+s]=t[s];for(let s=1;s<=e;++s)n[e-s]=t[ja(s,a)];for(let s=1;s<=r;++s)n[a+e+s]=t[ja(a-s,a)];return n}function o0(t,e,r,n,a){if(r<=0)throw new Error("reference must be greater than zero");if(n<=0)throw new Error("min_value must be greater than zero");r=Math.max(n,r);const s=Math.log10(r);for(let i=0;i<t.length;++i)t[i]=e*Math.log10(Math.max(n,t[i])-s);if(a!==null){if(a<=0)throw new Error("db_range must be greater than zero");const i=wr(t)[0]-a;for(let o=0;o<t.length;++o)t[o]=Math.max(t[o],i)}return t}function JS(t,e=1,r=1e-5,n=null){return o0(t,20,e,r,n)}function ZS(t,e=1,r=1e-10,n=null){return o0(t,10,e,r,n)}async function Fi(t,e,r,n,{fft_length:a=null,power:s=1,center:i=!0,pad_mode:o="reflect",onesided:u=!0,preemphasis:l=null,mel_filters:c=null,mel_floor:h=1e-10,log_mel:m=null,reference:_=1,min_value:p=1e-10,db_range:b=null,remove_dc_offset:$=null,max_num_frames:S=null,do_pad:x=!0,transpose:C=!1}={}){const T=e.length;if(a===null&&(a=r),r>a)throw Error(`frame_length (${r}) may not be larger than fft_length (${a})`);if(T!==r)throw new Error(`Length of the window (${T}) must equal frame_length (${r})`);if(n<=0)throw new Error("hop_length must be greater than zero");if(s===null&&c!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(i){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const O=Math.floor((a-1)/2)+1;t=XS(t,O,O)}const I=Math.floor(1+Math.floor((t.length-r)/n)),P=u?Math.floor(a/2)+1:a;let R=I,W=I;S!==null&&(S>I?x&&(W=S):W=R=S);const q=new l_(a),K=new Float64Array(a),ue=new Float64Array(q.outputBufferSize),ne=new Float32Array(P*W);for(let O=0;O<R;++O){const G=O*n;for(let j=0;j<r;++j)K[j]=t[G+j];if($){let j=0;for(let le=0;le<r;++le)j+=K[le];const pe=j/r;for(let le=0;le<r;++le)K[le]-=pe}if(l!==null){for(let j=r-1;j>=1;--j)K[j]-=l*K[j-1];K[0]*=1-l}for(let j=0;j<e.length;++j)K[j]*=e[j];q.realTransform(ue,K);for(let j=0;j<P;++j){const pe=j<<1;ne[j*W+O]=ue[pe]**2+ue[pe+1]**2}}if(s!==null&&s!==2){const O=2/s;for(let G=0;G<ne.length;++G)ne[G]**=O}const re=c.length;let oe=await Kw(new fe("float32",c.flat(),[re,P]),new fe("float32",ne,[P,W]));C&&(oe=oe.transpose(1,0));const D=oe.data;for(let O=0;O<D.length;++O)D[O]=Math.max(h,D[O]);if(s!==null&&m!==null){const O=Math.min(D.length,R*re);switch(m){case"log":for(let G=0;G<O;++G)D[G]=Math.log(D[G]);break;case"log10":for(let G=0;G<O;++G)D[G]=Math.log10(D[G]);break;case"dB":if(s===1)JS(D,_,p,b);else if(s===2)ZS(D,_,p,b);else throw new Error(`Cannot use log_mel option '${m}' with power ${s}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${m}'`)}}return oe}function Li(t,e,{periodic:r=!0,frame_length:n=null,center:a=!0}={}){const s=r?t+1:t;let i;switch(e){case"boxcar":i=new Float64Array(s).fill(1);break;case"hann":case"hann_window":i=i0(s);break;case"povey":i=i0(s).map(o=>Math.pow(o,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(i=i.subarray(0,t)),n===null)return i;if(t>n)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${n})`);return i}function e3([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function Qo(t,e=.5,r=null,n=!1){const a=t.logits,s=t.pred_boxes,[i,o,u]=a.dims;if(r!==null&&r.length!==i)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let l=[];for(let c=0;c<i;++c){let h=r!==null?r[c]:null,m={boxes:[],classes:[],scores:[]},_=a[c],p=s[c];for(let b=0;b<o;++b){let $=_[b],S=[],x;if(n){x=$.sigmoid().data;for(let C=0;C<x.length;++C)x[C]>e&&S.push(C)}else{let C=wr($.data)[1];if(C===u-1||(x=Un($.data),x[C]<e))continue;S.push(C)}for(const C of S){let T=p[b].data;T=e3(T),h!==null&&(T=T.map((I,P)=>I*h[(P+1)%2])),m.boxes.push(T),m.classes.push(C),m.scores.push(x[C])}}l.push(m)}return l}function Ca(t,e){var r;if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${((r=t==null?void 0:t.constructor)==null?void 0:r.name)??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function u0(t,e,r=0,n=null){const a=t/e;let s=c_(a)*e;return n!==null&&s>n&&(s=Math.floor(a)*e),s<r&&(s=Math.ceil(a)*e),s}function Xo([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}class rn extends yt{constructor(e){super(),this.config=e}}class He extends rn{constructor(e){super(e),this.image_mean=this.config.image_mean??this.config.mean,this.image_std=this.config.image_std??this.config.std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.size_divisibility=this.config.size_divisibility??this.config.size_divisor,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=this.config.do_flip_channel_order??!1}async thumbnail(e,r,n=2){const a=e.height,s=e.width,i=r.height,o=r.width;let u=Math.min(a,i),l=Math.min(s,o);return u===a&&l===s?e:(a>s?l=Math.floor(s*u/a):s>a&&(u=Math.floor(a*l/s)),await e.resize(l,u,{resample:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),a=Su(n.data)[0],i=wr(n.data)[0]-a;if(i===0)return e;const o=r/255;let u=n.width,l=n.height,c=0,h=0;const m=n.data;for(let _=0;_<n.height;++_){const p=_*n.width;for(let b=0;b<n.width;++b)(m[p+b]-a)/i<o&&(u=Math.min(u,b),l=Math.min(l,_),c=Math.max(c,b),h=Math.max(h,_))}return e=await e.crop([u,l,c,h]),e}pad_image(e,r,n,{mode:a="constant",center:s=!1,constant_values:i=0}={}){const[o,u,l]=r;let c,h;if(typeof n=="number"?(c=n,h=n):(c=n.width,h=n.height),c!==u||h!==o){const m=new Float32Array(c*h*l);if(Array.isArray(i))for(let b=0;b<m.length;++b)m[b]=i[b%l];else i!==0&&m.fill(i);const[_,p]=s?[Math.floor((c-u)/2),Math.floor((h-o)/2)]:[0,0];for(let b=0;b<o;++b){const $=(b+p)*c,S=b*u;for(let x=0;x<u;++x){const C=($+x+_)*l,T=(S+x)*l;for(let I=0;I<l;++I)m[C+I]=e[T+I]}}if(a==="symmetric"){if(s)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const b=o-1,$=u-1;for(let S=0;S<h;++S){const x=S*c,C=ja(S,b)*u;for(let T=0;T<c;++T){if(S<o&&T<u)continue;const I=(x+T)*l,P=(C+ja(T,$))*l;for(let R=0;R<l;++R)m[I+R]=e[P+R]}}}e=m,r=[h,c,l]}return[e,r]}rescale(e){for(let r=0;r<e.length;++r)e[r]=this.rescale_factor*e[r]}get_resize_output_image_size(e,r){const[n,a]=e.size;let s,i;if(this.do_thumbnail){const{height:o,width:u}=r;s=Math.min(o,u)}else Number.isInteger(r)?(s=r,i=this.config.max_size??s):r!==void 0&&(s=r.shortest_edge,i=r.longest_edge);if(s!==void 0||i!==void 0){const o=s===void 0?1:Math.max(s/n,s/a),u=n*o,l=a*o,c=i===void 0?1:Math.min(i/u,i/l);let h=Math.floor(Number((u*c).toFixed(2))),m=Math.floor(Number((l*c).toFixed(2)));return this.size_divisibility!==void 0&&([h,m]=Xo([h,m],this.size_divisibility)),[h,m]}else if(r!==void 0&&r.width!==void 0&&r.height!==void 0){let o=r.width,u=r.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let l=u/a,c=o/n;Math.abs(1-c)<Math.abs(1-l)?l=c:c=l,u=u0(l*a,this.config.ensure_multiple_of),o=u0(c*n,this.config.ensure_multiple_of)}return[o,u]}else{if(this.size_divisibility!==void 0)return Xo([n,a],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(r)}`)}}async resize(e){const[r,n]=this.get_resize_output_image_size(e,this.size);return await e.resize(r,n,{resample:this.resample})}async preprocess(e,{do_normalize:r=null,do_pad:n=null,do_convert_rgb:a=null,do_convert_grayscale:s=null,do_flip_channel_order:i=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[o,u]=e.size;if(a??this.do_convert_rgb?e=e.rgb():s&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let _,p;Number.isInteger(this.crop_size)?(_=this.crop_size,p=this.crop_size):(_=this.crop_size.width,p=this.crop_size.height),e=await e.center_crop(_,p)}const l=[e.height,e.width];let c=Float32Array.from(e.data),h=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(c),r??this.do_normalize){let _=this.image_mean;Array.isArray(this.image_mean)||(_=new Array(e.channels).fill(_));let p=this.image_std;if(Array.isArray(this.image_std)||(p=new Array(e.channels).fill(_)),_.length!==e.channels||p.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${_.length}) and \`image_std\` (${p.length}) must match the number of channels in the image (${e.channels}).`);for(let b=0;b<c.length;b+=e.channels)for(let $=0;$<e.channels;++$)c[b+$]=(c[b+$]-_[$])/p[$]}if(n??this.do_pad){if(this.pad_size)[c,h]=this.pad_image(c,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){const[_,p]=Xo([h[1],h[0]],this.size_divisibility);[c,h]=this.pad_image(c,h,{width:_,height:p})}}if(i??this.do_flip_channel_order){if(h[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let _=0;_<c.length;_+=3){const p=c[_];c[_]=c[_+2],c[_+2]=p}}const m=new fe("float32",c,h).permute(2,0,1);return{original_size:[u,o],reshaped_input_size:l,pixel_values:m}}async _call(e,...r){Array.isArray(e)||(e=[e]);const n=await Promise.all(e.map(s=>this.preprocess(s)));return{pixel_values:aa(n.map(s=>s.pixel_values),0),original_sizes:n.map(s=>s.original_size),reshaped_input_sizes:n.map(s=>s.reshaped_input_size)}}}class t3 extends He{post_process_semantic_segmentation(e,r=null){const n=e.logits,a=n.dims[0];if(r!==null&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const s=[];for(let i=0;i<a;++i){const o=r!==null?r[i]:null;let u=n[i];o!==null&&(u=im(u,o,"bilinear",!1));const[l,c]=o??u.dims.slice(-2),h=new fe("int32",new Int32Array(l*c),[l,c]),m=u[0].data,_=h.data;for(let S=1;S<u.dims[0];++S){const x=u[S].data;for(let C=0;C<x.length;++C)x[C]>m[C]&&(m[C]=x[C],_[C]=S)}const p=new Array(u.dims[0]),b=h.data;for(let S=0;S<b.length;++S){const x=b[S];p[x]=x}const $=p.filter(S=>S!==void 0);s.push({segmentation:h,labels:$})}return s}}class l0 extends He{}class r3 extends l0{}class n3 extends He{}class a3 extends He{}class d0 extends He{}class i3 extends d0{}class s3 extends He{}class o3 extends He{}class c0 extends He{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){var n;const r=(n=this.size)==null?void 0:n.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){const a=Math.floor(r/this.crop_pct),[s,i]=this.get_resize_output_image_size(e,{shortest_edge:a});e=await e.resize(s,i,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}}class u3 extends c0{}class l3 extends He{}class d3 extends He{}class c3 extends He{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(r=>r*r))}}class p3 extends He{}class h3 extends He{}class f3 extends He{}class m3 extends He{}class p0 extends He{}class g3 extends p0{}class h0 extends He{post_process_object_detection(...e){return Qo(...e)}}class _3 extends h0{}class w3 extends He{}class y3 extends He{}class f0 extends He{pad_image(e,r,n,a={}){const[s,i,o]=r;let u=this.image_mean;Array.isArray(this.image_mean)||(u=new Array(o).fill(u));let l=this.image_std;Array.isArray(l)||(l=new Array(o).fill(u));const c=u.map((h,m)=>-h/l[m]);return super.pad_image(e,r,n,{center:!0,constant_values:c,...a})}}class b3 extends f0{}class v3 extends He{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new fe("int64",new BigInt64Array(n.reduce((s,i)=>s*i)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return Qo(...e)}remove_low_and_no_objects(e,r,n,a){let s=[],i=[],o=[];for(let u=0;u<e.dims[0];++u){let l=e[u],c=r[u],h=wr(l.data)[1];if(h===a)continue;let _=Un(l.data)[h];_>n&&(s.push(c),i.push(_),o.push(h))}return[s,i,o]}check_segment_validity(e,r,n,a=.5,s=.8){let i=[],o=0,u=0;const l=r[n].data;for(let h=0;h<e.length;++h)e[h]===n&&(i.push(h),++o),l[h]>=a&&++u;let c=o>0&&u>0;return c&&(c=o/u>s),[c,i]}compute_segments(e,r,n,a,s,i=null,o=null){let[u,l]=o??e[0].dims,c=new fe("int32",new Int32Array(u*l),[u,l]),h=[];if(o!==null)for(let $=0;$<e.length;++$)e[$]=im(e[$],o,"bilinear",!1);let m=new Int32Array(e[0].data.length),_=new Float32Array(e[0].data.length);for(let $=0;$<e.length;++$){let S=r[$];const x=e[$].data;for(let C=0;C<x.length;++C)x[C]*=S,x[C]>_[C]&&(m[C]=$,_[C]=x[C])}let p=0;const b=c.data;for(let $=0;$<n.length;++$){let S=n[$],[x,C]=this.check_segment_validity(m,e,$,a,s);if(x){++p;for(let T of C)b[T]=p;h.push({id:p,label_id:S,score:r[$]})}}return[c,h]}post_process_panoptic_segmentation(e,r=.5,n=.5,a=.8,s=null,i=null){s===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),s=new Set);const o=e.logits,l=e.pred_masks.sigmoid();let[c,h,m]=o.dims;if(m-=1,i!==null&&i.length!==c)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let _=[];for(let p=0;p<c;++p){let b=i!==null?i[p]:null,$=o[p],S=l[p],[x,C,T]=this.remove_low_and_no_objects($,S,r,m);if(T.length===0){let[R,W]=b??S.dims.slice(-2),q=new fe("int32",new Int32Array(R*W).fill(-1),[R,W]);_.push({segmentation:q,segments_info:[]});continue}let[I,P]=this.compute_segments(x,C,T,n,a,s,b);_.push({segmentation:I,segments_info:P})}return _}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class $3 extends He{post_process_object_detection(...e){return Qo(...e)}}class x3 extends He{reshape_input_points(e,r,n,a=!1){e=structuredClone(e);let s=$u(e);if(s.length===3)a||(s=[1,...s]),e=[e];else if(s.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let i=0;i<e.length;++i){let o=r[i],u=n[i],l=[u[0]/o[0],u[1]/o[1]];for(let c=0;c<e[i].length;++c)for(let h=0;h<e[i][c].length;++h)for(let m=0;m<e[i][c][h].length;++m)e[i][c][h][m]*=l[m%2]}return new fe("float32",Float32Array.from(e.flat(1/0)),s)}add_input_labels(e,r){let n=$u(e);if(n.length===2)n=[1,...n],e=[e];else if(n.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(n.some((a,s)=>a!==r.dims[s]))throw Error(`The first ${n.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new fe("int64",e.flat(1/0).map(BigInt),n)}async _call(e,{input_points:r=null,input_labels:n=null,input_boxes:a=null}={}){const s=await super._call(e);if(r&&(s.input_points=this.reshape_input_points(r,s.original_sizes,s.reshaped_input_sizes)),n){if(!s.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");s.input_labels=this.add_input_labels(n,s.input_points)}return a&&(s.input_boxes=this.reshape_input_points(a,s.original_sizes,s.reshaped_input_sizes,!0)),s}async post_process_masks(e,r,n,{mask_threshold:a=0,binarize:s=!0,pad_size:i=null}={}){const o=[];i=i??this.pad_size;const u=[i.height,i.width];for(let l=0;l<r.length;++l){const c=r[l],h=n[l];let m=await sm(e[l],{mode:"bilinear",size:u});if(m=m.slice(null,null,[0,h[0]],[0,h[1]]),m=await sm(m,{mode:"bilinear",size:c}),s){const _=m.data,p=new Uint8Array(_.length);for(let b=0;b<_.length;++b)_[b]>a&&(p[b]=1);m=new fe("bool",p,m.dims)}o.push(m)}return o}generate_crop_boxes(e,r,{crop_n_layers:n=0,overlap_ratio:a=512/1500,points_per_crop:s=32,crop_n_points_downscale_factor:i=1}={}){}}class S3 extends He{pad_image(e,r,n,a={}){const[s,i,o]=r;return super.pad_image(e,r,{width:i+(n-i%n)%n,height:s+(n-s%n)%n},{mode:"symmetric",center:!1,constant_values:-1,...a})}}class k3 extends He{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);const n=await Promise.all(e.map(i=>this.preprocess(i))),a=await Promise.all(r.map(i=>this.preprocess(i,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:aa(n.map((i,o)=>Wt([i.pixel_values,a[o].pixel_values],0)),0),original_sizes:n.map(i=>i.original_size),reshaped_input_sizes:n.map(i=>i.reshaped_input_size)}}}class E3 extends rn{constructor(e){var r;super(e),(r=this.config).mel_filters??(r.mel_filters=Ea(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney")),this.window=Li(this.config.n_fft,"hann")}async _extract_fbank_features(e){const r=await Fi(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),n=r.data,a=wr(n)[0];for(let s=0;s<n.length;++s)n[s]=(Math.max(n[s],a-8)+4)/4;return r}async _call(e){Ca(e,"WhisperFeatureExtractor");let r;return e.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),r=e.slice(0,this.config.n_samples)):(r=new Float32Array(this.config.n_samples),r.set(e)),{input_features:(await this._extract_fbank_features(r)).unsqueeze_(0)}}}class C3 extends rn{_zero_mean_unit_var_norm(e){const n=e.reduce((s,i)=>s+i,0)/e.length,a=e.reduce((s,i)=>s+(i-n)**2,0)/e.length;return e.map(s=>(s-n)/Math.sqrt(a+1e-7))}async _call(e){Ca(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=e;this.config.do_normalize&&(r=this._zero_mean_unit_var_norm(r));const n=[1,r.length];return{input_values:new fe("float32",r,n),attention_mask:new fe("int64",new BigInt64Array(r.length).fill(1n),n)}}}class T3 extends rn{constructor(e){super(e);const r=this.config.sampling_rate,n=Ea(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;a<n.length;++a)n[a].push(0);this.mel_filters=n,this.window=Li(400,"povey",{periodic:!1})}async _extract_fbank_features(e,r){return e=e.map(n=>n*32768),Fi(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e,{padding:r=!0,pad_to_multiple_of:n=2,do_normalize_per_mel_bins:a=!0,return_attention_mask:s=!0}={}){Ca(e,"SeamlessM4TFeatureExtractor");let i=await this._extract_fbank_features(e,this.config.max_length);if(a){const[p,b]=i.dims,$=i.data;for(let S=0;S<b;++S){let x=0;for(let P=0;P<p;++P)x+=$[P*b+S];const C=x/p;let T=0;for(let P=0;P<p;++P)T+=($[P*b+S]-C)**2;T/=p-1;const I=Math.sqrt(T+1e-7);for(let P=0;P<p;++P){const R=P*b+S;$[R]=($[R]-C)/I}}}let o;if(r){const[p,b]=i.dims,$=i.data,S=p%n;if(S>0){const x=new Float32Array(b*(p+S));x.set($),x.fill(this.config.padding_value,$.length);const C=p+S;i=new fe(i.type,x,[C,b]),s&&(o=new fe("int64",new BigInt64Array(C),[1,C]),o.data.fill(1n,0,p))}}const[u,l]=i.dims,c=this.config.stride;if(u%c!==0)throw new Error(`The number of frames (${u}) must be a multiple of the stride (${c}).`);const m=i.view(1,Math.floor(u/c),l*c),_={input_features:m};if(s){const p=m.dims[1],b=new BigInt64Array(p);if(o){const $=o.data;for(let S=1,x=0;S<u;S+=c,++x)b[x]=$[S]}else b.fill(1n);_.attention_mask=new fe("int64",b,[1,p])}return _}}class I3 extends rn{constructor(e){super(e);const r=this.config.sampling_rate,n=Ea(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;a<n.length;++a)n[a].push(0);this.mel_filters=n,this.window=Li(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(e,r){return Fi(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e){Ca(e,"ASTFeatureExtractor");const r=await this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){const n=this.std*2,a=r.data;for(let s=0;s<a.length;++s)a[s]=(a[s]-this.mean)/n}return{input_values:r.unsqueeze_(0)}}}class A3 extends rn{constructor(e){super(e),this.mel_filters=Ea(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=Ea(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=Li(this.config.fft_window_size,"hann")}async _get_input_mel(e,r,n,a){let s;const i=e.length-r;if(i>0)if(n==="rand_trunc"){const o=Math.floor(Math.random()*(i+1));e=e.subarray(o,o+r),s=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(i<0){let o=new Float64Array(r);if(o.set(e),a==="repeat")for(let u=e.length;u<r;u+=e.length)o.set(e.subarray(0,Math.min(e.length,r-u)),u);else if(a==="repeatpad")for(let u=e.length;u<-i;u+=e.length)o.set(e,u);e=o}if(n==="fusion")throw new Error(`Truncation strategy "${n}" not implemented`);s=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}return s.unsqueeze_(0)}async _extract_fbank_features(e,r,n=null){return Fi(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:r,log_mel:"dB",max_num_frames:n,do_pad:!1,transpose:!0})}async _call(e,{max_length:r=null}={}){return Ca(e,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(e,r??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}class M3 extends rn{}class nn extends yt{constructor(e){super(),this.feature_extractor=e}async _call(e,...r){return await this.feature_extractor(e,...r)}}class O3 extends nn{async _call(...e){return await this.feature_extractor(...e)}post_process_masks(...e){return this.feature_extractor.post_process_masks(...e)}reshape_input_points(...e){return this.feature_extractor.reshape_input_points(...e)}}class z3 extends nn{async _call(e){return await this.feature_extractor(e)}}class P3 extends nn{async _call(e){return await this.feature_extractor(e)}}class R3 extends nn{async _call(e){return await this.feature_extractor(e)}}class B3 extends nn{}class m0 extends nn{constructor(e){super(e);const{tasks_answer_post_processing_type:r,task_prompts_without_inputs:n,task_prompts_with_input:a}=e.config;this.tasks_answer_post_processing_type=new Map(Object.entries(r??{})),this.task_prompts_without_inputs=new Map(Object.entries(n??{})),this.task_prompts_with_input=new Map(Object.entries(a??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(e){typeof e=="string"&&(e=[e]);const r=[];for(const n of e)if(this.task_prompts_without_inputs.has(n))r.push(this.task_prompts_without_inputs.get(n));else{for(const[a,s]of this.task_prompts_with_input)if(n.includes(a)){r.push(s.replaceAll("{input}",n).replaceAll(a,""));break}r.length!==e.length&&r.push(n)}return r}post_process_generation(e,r,n){const a=this.tasks_answer_post_processing_type.get(r)??"pure_text";e=e.replaceAll("<s>","").replaceAll("</s>","");let s;switch(a){case"pure_text":s=e;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const i=a==="ocr"?"quad_boxes":"bboxes",o=e.matchAll(this.regexes[i]),u=[],l=[];for(const[c,h,...m]of o)u.push(h?h.trim():u.at(-1)??""),l.push(m.map((_,p)=>(Number(_)+.5)/this.size_per_bin*n[p%2]));s={labels:u,[i]:l};break;default:throw new Error(`Task "${r}" (of type "${a}") not yet implemented.`)}return{[r]:s}}}class Jo{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main"}={}){let o=n??await Ir(e,"preprocessor_config.json",!0,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i}),u=o.feature_extractor_type??o.image_processor_type,l=this.FEATURE_EXTRACTOR_CLASS_MAPPING[u];if(!l)if(o.size!==void 0)console.warn(`Feature extractor type "${u}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),l=He;else throw new Error(`Unknown Feature Extractor type: ${u}`);let c=this.PROCESSOR_CLASS_MAPPING[o.processor_class]??nn,h=new l(o);return new c(h)}}F(Jo,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:He,WhisperFeatureExtractor:E3,ViTFeatureExtractor:l3,MobileViTFeatureExtractor:p0,MobileViTImageProcessor:g3,MobileNetV1FeatureExtractor:p3,MobileNetV2FeatureExtractor:h3,MobileNetV3FeatureExtractor:f3,MobileNetV4FeatureExtractor:m3,OwlViTFeatureExtractor:h0,Owlv2ImageProcessor:_3,CLIPFeatureExtractor:d0,CLIPImageProcessor:i3,Florence2Processor:m0,ChineseCLIPFeatureExtractor:s3,SiglipImageProcessor:o3,ConvNextFeatureExtractor:c0,ConvNextImageProcessor:u3,SegformerFeatureExtractor:t3,BitImageProcessor:n3,DPTImageProcessor:r3,DPTFeatureExtractor:l0,GLPNFeatureExtractor:a3,BeitFeatureExtractor:y3,DeiTFeatureExtractor:w3,DetrFeatureExtractor:v3,YolosFeatureExtractor:$3,DonutFeatureExtractor:f0,NougatImageProcessor:b3,EfficientNetImageProcessor:c3,ViTImageProcessor:d3,VitMatteImageProcessor:k3,SamImageProcessor:x3,Swin2SRImageProcessor:S3,Wav2Vec2FeatureExtractor:C3,SeamlessM4TFeatureExtractor:T3,SpeechT5FeatureExtractor:M3,ASTFeatureExtractor:I3,ClapFeatureExtractor:A3}),F(Jo,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:z3,Wav2Vec2ProcessorWithLM:P3,SamProcessor:O3,SpeechT5Processor:R3,OwlViTProcessor:B3,Florence2Processor:m0}),Fr.IS_PROCESS_AVAILABLE;async function D3(){try{return(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{return!1}}class Zo{static async getInstance(e=null){return this.processor??(this.processor=Jo.from_pretrained(this.model_id)),this.tokenizer??(this.tokenizer=zm.from_pretrained(this.model_id)),this.supports_fp16??(this.supports_fp16=await D3()),this.model??(this.model=Zm.from_pretrained(this.model_id,{dtype:{embed_tokens:this.supports_fp16?"fp16":"fp32",vision_encoder:this.supports_fp16?"fp16":"fp32",encoder_model:"q4",decoder_model_merged:"q4"},device:"webgpu",progress_callback:e})),Promise.all([this.model,this.tokenizer,this.processor])}}F(Zo,"model_id","onnx-community/Florence-2-base-ft");async function N3(){self.postMessage({status:"loading",data:"Loading model..."});const[t,e,r]=await Zo.getInstance(s=>{self.postMessage(s)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const n=e("a"),a=dm([1,3,768,768],0);await t.generate({...n,pixel_values:a,max_new_tokens:1}),self.postMessage({status:"ready"})}const F3=["<CAPTION_TO_PHRASE_GROUNDING>"];let Ui,eu;async function L3({text:t,url:e,task:r}){const[n,a,s]=await Zo.getInstance(),i=performance.now();if(!Ui){const p=await ar.fromURL(e);eu=p.size,Ui=await s(p)}let o=r;F3.includes(r)&&t&&(o+=t);const u=s.construct_prompts(o),l=a(u),c=await n.generate({...l,...Ui,max_new_tokens:128,num_beams:1,do_sample:!1}),h=a.batch_decode(c,{skip_special_tokens:!1})[0],m=s.post_process_generation(h,r,eu),_=performance.now();self.postMessage({status:"complete",result:m,time:_-i})}self.addEventListener("message",async t=>{const{type:e,data:r}=t.data;switch(e){case"load":N3();break;case"run":L3(r);break;case"reset":Ui=eu=null;break}})})();