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`)}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])}},kl=(t,e)=>new Sl(t,e),Vn=(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}}),El,Fs,Cl,Tl,gr,Il,Al,Gn=Z(()=>{xe(),Me(),lt(),Ae(),El=t=>{if(!t||t.length!==1)throw new Error("Transpose requires 1 input.")},Fs=(t,e)=>e&&e.length!==t?[...new Array(t).keys()].reverse():e,Cl=(t,e)=>X.sortBasedOnPerm(t,Fs(t.length,e)),Tl=(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(`
`)},gr=(t,e)=>{let r=t.dataType,n=t.dims.length,a=Fs(n,e),s=Cl(t.dims,a),i=_e("output",r,s.length),o=Q("a",r,n),u=l=>`
  ${l.registerUniform("output_size","u32").declareVariables(o,i)}

  ${Tl(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=X.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}},Il=(t,e)=>{El(t.inputs),t.compute(gr(t.inputs[0],e.perm))},Al=t=>He({perm:t.perm})}),Ml,Ol,zl,Rl,Pl,Bl,Dl,Nl,Fl,Ll,qt,Ul,Wl,Vl,Gl,Hl,jl,ql,Kl,Yl,Xl,T_=Z(()=>{xe(),Me(),Ae(),Us(),Gn(),Ml={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"},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",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},zl={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)"},Pl=(t,e)=>{let r=[];for(let n=e-t;n<e;++n)r.push(n);return r},Bl=(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]},Dl=(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},Nl=(t,e)=>{for(let r=0;r<t.length;++r)if(t[t.length-r-1]!==e-1-r)return!1;return!0},Fl=(t,e)=>{let r=[];if(!Nl(t,e)){for(let n=0;n<e;++n)t.indexOf(n)===-1&&r.push(n);t.forEach(n=>r.push(n))}return r},Ll=(t,e,r,n,a,s,i)=>{let o=r[0].dims,u=X.size(s),l=X.size(i),c=Q("_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(${zl[n]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${m}) {
           let candidate = f32(${c.getByOffset("offset + k")});
           bestValue = ${Ml[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 = ${Ol[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:Ls(t.inputs,r),s=a.axes;s.length===0&&!a.noopWithEmptyAxes&&(s=t.inputs[0].dims.map((_,p)=>p));let i=X.normalizeAxes(s,t.inputs[0].dims.length),o=i,u=t.inputs[0],l=Fl(o,t.inputs[0].dims.length);l.length>0&&(u=t.compute(gr(t.inputs[0],l),{inputs:[0],outputs:[-1]})[0],o=Pl(o.length,u.dims.length));let[c,h]=Bl(u.dims,o),m=c;a.keepDims&&(m=Dl(c,i)),t.compute(Ll(e,{hint:a.cacheKey,inputDependencies:["type"]},[u],n,t.inputs[0].dataType,m,h),{inputs:[u]})},Ul=(t,e)=>{qt(t,"ReduceMeanShared",e,"mean")},Wl=(t,e)=>{qt(t,"ReduceL1Shared",e,"l1")},Vl=(t,e)=>{qt(t,"ReduceL2Shared",e,"l2")},Gl=(t,e)=>{qt(t,"ReduceLogSumExpShared",e,"logSumExp")},Hl=(t,e)=>{qt(t,"ReduceMaxShared",e,"max")},jl=(t,e)=>{qt(t,"ReduceMinShared",e,"min")},ql=(t,e)=>{qt(t,"ReduceProdShared",e,"prod")},Kl=(t,e)=>{qt(t,"ReduceSumShared",e,"sum")},Yl=(t,e)=>{qt(t,"ReduceSumSquareShared",e,"sumSquare")},Xl=(t,e)=>{qt(t,"ReduceLogSumShared",e,"logSum")}}),Kt,Ql,ai,Ls,Yt,Jl,Zl,ed,td,rd,nd,ad,id,sd,od,Xt,ud,ld,dd,cd,pd,hd,fd,md,gd,_d,Us=Z(()=>{xe(),Me(),lt(),Ae(),T_(),Kt=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.")},Ql=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],ai=(t,e,r,n,a,s,i=!1,o=!1)=>{let u=[],l=r[0].dims,c=l.length,h=X.normalizeAxes(a,c),m=!o&&h.length===0;l.forEach((y,$)=>{m||h.indexOf($)>=0?i&&u.push(1):u.push(y)});let _=u.length,p=X.size(u);return{name:t,shaderCache:e,getShaderSource:y=>{let $=[],S=Q("_A",r[0].dataType,c),x=_e("output",s,_),C=n(S,x,h),T=C[2];for(let I=0,B=0;I<c;I++)m||h.indexOf(I)>=0?(i&&B++,T=`for(var j${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",B))};`),B++);return`

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

        ${y.mainStart()}
          ${y.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)]})}},Ls=(t,e)=>{let r=[];return t[1].dims[0]>0&&t[1].getBigInt64Array().forEach(n=>r.push(Number(n))),He({axes:r,keepDims:e.keepDims,noopWithEmptyAxes:e.noopWithEmptyAxes})},Yt=(t,e,r,n)=>{let a=t.inputs,s=a.length===1?r:Ls(a,r);t.compute(ai(e,{hint:s.cacheKey,inputDependencies:["rank"]},[a[0]],s.noopWithEmptyAxes&&s.axes.length===0?Ql:n,s.axes,a[0].dataType,s.keepDims,s.noopWithEmptyAxes),{inputs:[0]})},Jl=(t,e)=>{Kt(t.inputs),Yt(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},Zl=(t,e)=>{Kt(t.inputs),Yt(t,"ReduceL1",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},ed=(t,e)=>{Kt(t.inputs),Yt(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);"])},td=(t,e)=>{Kt(t.inputs),Yt(t,"ReduceLogSumExp",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},rd=(t,e)=>{Kt(t.inputs),Yt(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, ${r.getByIndices("input_indices")});`,""]})},nd=(t,e)=>{Kt(t.inputs),Yt(t,"ReduceMean",e,(r,n,a)=>{let s=1;for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&(s*=t.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${s});`]})},ad=(t,e)=>{Kt(t.inputs),Yt(t,"ReduceMin",e,(r,n,a)=>{let s=[];for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&s.push(`input_indices[${i}] = 0;`);return[`${s.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},id=(t,e)=>{Kt(t.inputs),Yt(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},sd=(t,e)=>{Kt(t.inputs),Yt(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},od=(t,e)=>{Kt(t.inputs),Yt(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},Xt=(t,e,r)=>{if(e.length===0)return r;let n=1,a=1;for(let s=0;s<e.length;s++)e.indexOf(s)===-1?n*=t[s]:a*=t[s];return a<32&&n>1024},ud=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?nd(t,e):Ul(t,e)},ld=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Zl(t,e):Wl(t,e)},dd=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?ed(t,e):Vl(t,e)},cd=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?td(t,e):Gl(t,e)},pd=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?rd(t,e):Hl(t,e)},hd=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?ad(t,e):jl(t,e)},fd=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?id(t,e):ql(t,e)},md=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?sd(t,e):Kl(t,e)},gd=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?od(t,e):Yl(t,e)},_d=(t,e)=>{Xt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Jl(t,e):Xl(t,e)}}),Ws,wd,yd,Vs,I_=Z(()=>{xe(),lt(),Us(),Ws=t=>{if(!t||t.length===0||t.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(t[0].dataType!==1)throw new Error("Invalid input type.")},wd=(t,e)=>{Ws(t.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o<n.rank;o++)(s.indexOf(o)>=0||s.length===0)&&i.push(`input_indices[${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 = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(ai("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},yd=(t,e)=>{Ws(t.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o<n.rank;o++)(s.indexOf(o)>=0||s.length===0)&&i.push(`input_indices[${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 = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(ai("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Vs=t=>He(t)}),bd,vd,$d,ii,xd,Sd,kd=Z(()=>{xe(),Me(),lt(),Ae(),bd=(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")})}})},vd=(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 ${t}u;
  }`,$d=(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}) { ${s} }`)}return n.join(`
`)},ii=(t,e,r,n)=>{let a=X.size(r),s=new Array(t.length),i=new Array(t.length),o=0,u=[],l=[],c=[{type:12,data:a}];for(let y=0;y<t.length;++y)o+=t[y].dims[e],s[y]=o,l.push(t[y].dims.length),i[y]=Q(`input${y}`,n,l[y]),u.push("rank"),c.push({type:12,data:s[y]});for(let y=0;y<t.length;++y)c.push(...ye(t[y].dims));c.push(...ye(r));let h=_e("output",n,r.length),m=h.indicesGet("indices",e),_=Array.from(Array(s.length).keys()).map(y=>`uniforms.sizeInConcatAxis${y}`).join(","),p=y=>`

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

  ${vd(s.length,_)}

  ${y.mainStart()}
    ${y.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];
    }

    ${$d(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}},xd=(t,e)=>{let r=t.inputs,n=r[0].dims,a=X.normalizeAxis(e.axis,n.length);bd(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=>X.size(o.dims)>0);t.compute(ii(i,a,s,r[0].dataType),{inputs:i})},Sd=t=>He({axis:t.axis})}),Ed,Cd,Td,Id,si,Ad,Md,Od=Z(()=>{xe(),Os(),Ae(),kd(),Ed=(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 y=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||(y=i.dims[3])}let $=p+y,S=-1,x=0;if(s)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");return{batchSize:u,sequenceLength:l,pastSequenceLength:y,kvSequenceLength:p,totalSequenceLength:$,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}},Cd=(t,e,r,n)=>{let a=nt(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=dt(e.dataType,a),c=kt(1,a),h=m=>{let _=_e("x",e.dataType,e.dims,a),p=[{name:"d_inv",type:kt(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})}},Td=(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=nt(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}],y=n?["type","type","type"]:["type","type"],$=S=>{let x=Q("q",e.dataType,e.dims,c),C=Q("key",r.dataType,r.dims,c),T=[x,C];n&&T.push(Q("relative_position_bias",n.dataType,n.dims));let I=_e("output",e.dataType,u),B=kt(1,c),P=[{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(P).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 = ${B}(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 += ${B}(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:y},getRunData:()=>({outputs:[{dims:u,dataType:e.dataType,gpuDataType:0}],dispatchGroup:_,programUniforms:p}),getShaderSource:$}},Id=(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=Q("probs",e.dataType,e.dims),m=Q("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;
   }
  }`}}},si=(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,y=[l.batchSize,l.numHeads,p,l.headSize],$=i?[i,r]:[r],S=h?t.compute(ii($,2,y,r.dataType),{inputs:$,outputs:[1]})[0]:r,x=[l.batchSize,l.numHeads,p,l.headSize],C=o?[o,n]:[n],T=m?t.compute(ii(C,2,x,n.dataType),{inputs:C,outputs:[2]})[0]:n,I=[e,S];u&&I.push(u);let B=t.compute(Td(t,e,S,u,l,c,_),{inputs:I,outputs:[-1]})[0];t.compute(Cd(t,B,l.batchSize*l.numHeads*l.sequenceLength,p),{inputs:[B],outputs:[]});let P=[B,T];t.compute(Id(t,B,T,l,_),{inputs:P,outputs:[0]})},Ad=(t,e)=>{let r=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],n=e.sequenceLength,a=e.inputHiddenSize,s=e.headSize,i=12,o={x:Math.ceil(e.headSize/i),y:Math.ceil(e.sequenceLength/i),z:e.batchSize*e.numHeads},u=[t.inputs[0],t.inputs[1],t.inputs[2]],l=[{type:12,data:n},{type:12,data:a},{type:12,data:s},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],c=h=>{let m=_e("output_q",u[0].dataType,r),_=_e("output_k",u[0].dataType,r),p=_e("output_v",u[0].dataType,r),y=Q("input",u[0].dataType,u[0].dims),$=Q("weight",u[1].dataType,u[1].dims),S=Q("bias",u[2].dataType,u[2].dims),x=y.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}>;
  ${h.registerUniforms(C).declareVariables(y,$,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]})},Md=(t,e)=>{let r=Ed(t.inputs,e),[n,a,s]=Ad(t,r);return si(t,n,a,s,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),zd,Rd,Pd,Bd,A_=Z(()=>{jt(),xe(),Me(),lt(),Ae(),zd=(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[${u}] do not match`)})};if(t[0].dims.length>1){let n=e.format==="NHWC"?e.spatial?t[0].dims.slice(-1):t[0].dims.slice(-1).concat(t[0].dims.slice(1,t[0].dims.length-1)):t[0].dims.slice(1,e.spatial?2:void 0);r(t[1].dims,n,"Invalid input scale"),r(t[2].dims,n,"Invalid input B"),r(t[3].dims,n,"Invalid input mean"),r(t[4].dims,n,"Invalid input var")}else r(t[1].dims,[1],"Invalid input scale"),r(t[2].dims,[1],"Invalid input B"),r(t[3].dims,[1],"Invalid input mean"),r(t[4].dims,[1],"Invalid input var")},Rd=(t,e)=>{let{epsilon:r,spatial:n,format:a}=e,s=t[0].dims,i=n?nt(s[s.length-1]):1,o=a==="NHWC"&&s.length>1?i:1,u=X.size(s)/i,l=n,c=l?s.length:s,h=Q("x",t[0].dataType,t[0].dims,i),m=Q("scale",t[1].dataType,t[1].dims,o),_=Q("bias",t[2].dataType,t[2].dims,o),p=Q("inputMean",t[3].dataType,t[3].dims,o),y=Q("inputVar",t[4].dataType,t[4].dims,o),$=_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};
  ${C.registerUniform("outputSize","u32").declareVariables(h,m,_,p,y,$)}
  ${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 = ${y.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}]})}},Pd=t=>He(t),Bd=(t,e)=>{let{inputs:r,outputCount:n}=t,a=Pd({...e,outputCount:n});if(Ue.webgpu.validateInputContent&&zd(r,a),e.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");t.compute(Rd(r,a))}}),Dd,Nd,Fd,M_=Z(()=>{Me(),Ae(),Dd=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")},Nd=t=>{let e=t[0].dims,r=t[0].dims[2],n=X.size(e)/4,a=t[0].dataType,s=Q("input",a,e,4),i=Q("bias",a,[r],4),o=Q("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")};
    ${u.setByOffset("global_idx","value")}
  }`}},Fd=t=>{Dd(t.inputs),t.compute(Nd(t.inputs))}}),Ld,De,Ud,Wd,Vd,Gd,Hd,jd,qd,Kd,Yd,Xd,Qd,Jd,Zd,ec,oi,tc,ui,rc,nc,ac,ic,sc,oc,uc,lc,dc,cc,pc,hc,fc,mc,gc,_c,Gs,wc,Hs,js,yc,bc,vc,qs=Z(()=>{xe(),Me(),lt(),Ae(),Ld=(t,e,r,n,a,s)=>{let i=Math.ceil(e/4),o="";typeof a=="string"?o=`${a}(a)`:o=a("a");let u=Q("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=>Ld(i,X.size(t.dims),t.dataType,s,r,n),getRunData:i=>({outputs:[{dims:t.dims,dataType:s}],dispatchGroup:{x:Math.ceil(X.size(i[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(X.size(t.dims)/4)}]})}),Ud=t=>{t.compute(De(t.inputs[0],"Abs","abs"))},Wd=t=>{t.compute(De(t.inputs[0],"Acos","acos"))},Vd=t=>{t.compute(De(t.inputs[0],"Acosh","acosh"))},Gd=t=>{t.compute(De(t.inputs[0],"Asin","asin"))},Hd=t=>{t.compute(De(t.inputs[0],"Asinh","asinh"))},jd=t=>{t.compute(De(t.inputs[0],"Atan","atan"))},qd=t=>{t.compute(De(t.inputs[0],"Atanh","atanh"))},Kd=t=>He(t),Yd=(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): ${e.to}`)}t.compute(De(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},Xd=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:Bs,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:Ds;return He({min:e,max:r})},Qd=(t,e)=>{let r=t.inputs.length===1?e:Xd(t.inputs),n=kt(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Clip",a=>`clamp(${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]})},Jd=t=>{t.compute(De(t.inputs[0],"Ceil","ceil"))},Zd=t=>{t.compute(De(t.inputs[0],"Cos","cos"))},ec=t=>{t.compute(De(t.inputs[0],"Cosh","cosh"))},oi=t=>He(t),tc=(t,e)=>{let r=kt(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))},ui=(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));
}`,rc=t=>{let e=kt(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,ui(e)))},nc=t=>{t.compute(De(t.inputs[0],"Exp","exp"))},ac=t=>{t.compute(De(t.inputs[0],"Floor","floor"))},ic=t=>{let e=kt(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,ui(e)))},sc=(t,e)=>{let r=kt(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))},oc=t=>{t.compute(De(t.inputs[0],"Not",e=>`!${e}`))},uc=t=>{t.compute(De(t.inputs[0],"Neg",e=>`-${e}`))},lc=t=>{t.compute(De(t.inputs[0],"Reciprocal",e=>`1.0/${e}`))},dc=t=>{let e=kt(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Relu",r=>`select(vec4<${e}>(0.0), ${r}, ${r} > vec4<${e}>(0.0))`))},cc=t=>{t.compute(De(t.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},pc=t=>He(t),hc=(t,e)=>{let r=kt(t.inputs[0].dataType);t.compute(De(t.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${e.alpha} * ${n} + vec4<${r}>(${e.beta})))`,void 0,e.cacheKey))},fc=t=>{t.compute(De(t.inputs[0],"Sin","sin"))},mc=t=>{t.compute(De(t.inputs[0],"Sinh","sinh"))},gc=t=>{t.compute(De(t.inputs[0],"Sqrt","sqrt"))},_c=t=>{t.compute(De(t.inputs[0],"Tan","tan"))},Gs=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,wc=t=>{t.compute(De(t.inputs[0],"Tanh",Gs))},Hs=(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 ${Gs("v")};
}
`,js=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,yc=t=>{let e=kt(t.inputs[0].dataType);t.compute(De(t.inputs[0],"FastGelu",js,Hs(e),void 0,t.inputs[0].dataType))},bc=(t,e)=>{let r=kt(t.inputs[0].dataType);return t.compute(De(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},vc=t=>{t.compute(De(t.inputs[0],"Log","log"))}}),$c,xc,Sc,O_=Z(()=>{Me(),Ae(),qs(),$c=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")},xc=t=>{let e=t[0].dims.slice();e[2]=e[2]/2;let r=Q("input",t[0].dataType,t[0].dims,4),n=Q("bias",t[0].dataType,[t[0].dims[2]],4),a=_e("output",t[0].dataType,e,4),s=X.size(e)/4,i=dt(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)}

  ${ui(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);

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

        ${h??""}

        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${S}
      }`},Ec=(t,e,r,n,a,s,i=r.dataType)=>{let o=!X.areEqual(r.dims,n.dims),u=r.dims,l=X.size(r.dims),c=!1,h=!1,m=[o];if(o){let _=pn.calcShape(r.dims,n.dims,!1);if(!_)throw new Error("Can't perform binary op on the given tensors");u=_,l=X.size(u);let p=X.size(r.dims)===1,y=X.size(n.dims)===1,$=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(y),m.push($),m.push(S);let x=1;for(let C=1;C<u.length;C++){let T=r.dims[r.dims.length-C]??1,I=n.dims[n.dims.length-C]??1;if(T===I)x*=T;else break}x%4===0?(h=!0,c=!0):(p||y||$||S)&&(c=!0)}else c=!0;return m.push(c),{name:t,shaderCache:{hint:e+m.map(_=>_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>kc(_,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(X.size(u)/4)},...ye(r.dims,n.dims,u)]})}},Qt=(t,e,r,n,a,s)=>{t.compute(Ec(e,a??"",t.inputs[0],t.inputs[1],r,n,s))},Cc=t=>{Qt(t,"Add",(e,r)=>`${e}+${r}`)},Tc=t=>{Qt(t,"Div",(e,r)=>`${e}/${r}`)},Ic=t=>{Qt(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4<u32>(${e}==${r})`},void 0,void 0,9)},Ac=t=>{Qt(t,"Mul",(e,r)=>`${e}*${r}`)},Mc=t=>{let e=Q("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;Qt(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));
    }
      `)},Oc=t=>{Qt(t,"Sub",(e,r)=>`${e}-${r}`)},zc=t=>{Qt(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4<u32>(${e}>${r})`},void 0,void 0,9)},Rc=t=>{Qt(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4<u32>(${e}<${r})`},void 0,void 0,9)},Pc=t=>{Qt(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4<u32>(${e}>=${r})`},void 0,void 0,9)},Bc=t=>{Qt(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4<u32>(${e}<=${r})`},void 0,void 0,9)}}),Gr,Hr,jr,Ks,qr=Z(()=>{xe(),Me(),Gr=(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 ${t.activation}`)}},Hr=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},jr=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},Ks=t=>{let e=(t==null?void 0:t.activation)||"";if(e==="HardSigmoid"){let[r,n]=(t==null?void 0:t.activation_params)||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=(t==null?void 0:t.activation_params)||[Bs,Ds];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=(t==null?void 0:t.activation_params)||[.01];return{activation:e,alpha:r}}return{activation:e}}}),vt,Ys,Xs=Z(()=>{vt=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},Ys=t=>`
      ${t?"value = value + getBiasByOutputCoords(coords);":""}
      `}),Qs,Dc=Z(()=>{Qs=t=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${t}.x), i32(${t}.y), i32(${t}.z), 1));
}
`}),Nc,Fc,li,Js,Lc,di,Uc,Zs,ci=Z(()=>{xe(),Me(),Ae(),qr(),Xs(),Nc=(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":""});
        `,Fc=(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];"}
        }`,li=(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;
          ${Nc(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];"}

          ${Fc(a,m)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},Js=(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":""});
            `,Lc=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",di=(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],y=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]}) {
          ${Js(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) * ${y};
// 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;
      ${Js(a,n)}
    }
  }

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${y}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${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) {
      ${Lc(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;
      }
    }
    ${$}
  }
`},Uc=(t,e,r,n,a,s=!1)=>{let[i,o,u]=a,[l,c,h,m]=n,_=Vn(i,u),p=Vn(o,u),y=dt(n[0].type.tensor),$=()=>{let x=c.rank,C=l.rank,T=`var aIndices: ${c.type.indices};`;for(let I=x-2-1,B=C-1;I>=0;I--,B--)T+=`
aIndices[${I}] = ${C>1?`batchIndices[${B}]`:"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: ${h.type.indices};`;for(let I=x-2-1,B=C-1;I>=0;I--,B--)T+=`
bIndices[${I}] = ${C>1?`batchIndices[${B}]`:"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,y)} {
      var value = ${vt(t,y)}(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,y)} {
      var value = ${vt(t,y)}(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,y)}) {
      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,y)}(bias[row])`};`:""}
        ${r}
        ${m.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},Zs=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s.slice(0,-2),u=i.slice(0,-2),l=n?n.slice(0,-2):r.slice(0,-2),c=X.size(l),h=s[s.length-2],m=s[s.length-1],_=i[i.length-1],p=m%4===0&&_%4===0,y=h<=8?[4,1,1]:[4,4,1],$=[8,8,1],S=[Math.ceil(_/$[0]/y[0]),Math.ceil(h/$[1]/y[1]),Math.ceil(c/$[2]/y[2])],x=p?4:1,C=[...o,h,m/x],T=C.length,I=[...u,m,_/x],B=I.length,P=[c,h,_/x],F=[{type:6,data:h},{type:6,data:_},{type:6,data:m}];Hr(e,F),F.push(...ye(l,C,I));let j=["rank","rank"],q=t.length>2;q&&(F.push(...ye(t[2].dims)),j.push("rank")),F.push(...ye(P));let le=ae=>{let re=l.length,ie=Ns("batchDims",t[0].dataType,re,1),D=dt(t[0].dataType),M=Q("a",t[0].dataType,T,x),V=Q("b",t[1].dataType,B,x),K=_e("result",t[0].dataType,P.length,x),ee=[M,V];if(q){let Se=a?x:1;ee.push(Q("bias",t[2].dataType,t[2].dims.length,Se))}let de=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];jr(e,de);let R=dt(K.type.tensor),se=Gr(e,K.type.value,R),pe=Uc(x,q,se,[ie,M,V,K],[o,u,l],a);return`
  ${ae.registerUniforms(de).registerInternalVariables(ie).declareVariables(...ee,K)}
  ${pe}
  ${p?li(y,$,D,ie):di(y,$,D,ie)}
                   `};return{name:"MatMul",shaderCache:{hint:`${y};${e.activation};${p};${a}`,inputDependencies:j},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:S[0],y:S[1],z:S[2]},programUniforms:F}),getShaderSource:le}}}),Wc,Vc,R_=Z(()=>{xe(),Wr(),Ae(),qr(),Xs(),Dc(),ci(),Wc=(t,e,r,n,a=!1,s,i=4,o=4,u=4,l="f32")=>{let c=j=>{switch(j){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${l}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${j} is not supported.`)}},h=j=>{switch(j){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 ${j} is not supported.`)}},m=t?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,_=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,p=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",y=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",$=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 < ${y}) {
      ${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),B=vt(t?i:o,l),P=vt(t?o:i,l),F=Gr(s,I,l);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${B} {
      ${t?C:T}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${P} {
      ${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])"};
      ${_}
      ${Ys(a)}
      ${F}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Vc=(t,e,r,n,a,s,i,o)=>{let u=e.format==="NHWC",l=u?t[0].dims[3]:t[0].dims[1],c=r[0],h=u?r[2]:r[3],m=u?r[1]:r[2],_=u?r[3]:r[1],p=u&&(l%4===0||l%3===0)&&_%4===0,y=u?_:h*m,$=u?h*m:_,S=[8,8,1],x=n<=8?[4,1,1]:[4,4,1],C=[Math.ceil(y/S[0]/x[0]),Math.ceil($/S[1]/x[1]),Math.ceil(c/S[2]/x[2])];et("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${C}`);let T=p?u&&l%4!==0?3:4:1,I=S[1]*x[1],B=S[0]*x[0],P=Math.max(S[0]*T,S[1]),F=n%I===0,j=a%B===0,q=s%P===0,le=p?[T,4,4]:[1,1,1],ae=[{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}];Hr(e,ae),ae.push(...ye(t[0].dims,t[1].dims));let re=["rank","rank"];i&&(ae.push(...ye(t[2].dims)),re.push("rank")),ae.push(...ye(r));let ie=D=>{let M=[{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,M);let V=p?4:1,K=dt(t[0].dataType),ee=`
      fn setOutputAtIndex(flatIndex : i32, value : ${p?`vec4<${K}>`:K}) {
        result[flatIndex] = ${p?`vec4<${K}>`:K}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${p?`vec4<${K}>`:K}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${p?"/ 4":""}, value);
      }`,de=Q("x",t[0].dataType,t[0].dims.length,T===3?1:T),R=Q("w",t[1].dataType,t[1].dims.length,V),se=[de,R],pe=_e("result",t[0].dataType,r.length,V);if(i){let Se=Q("bias",t[2].dataType,t[2].dims.length,V);se.push(Se),ee+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${p?`vec4<${K}>`:K} {
          return bias[coords.${u?"w":"y"}${p?"/ 4":""}];
        }`}return`
        ${Qs("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(M).declareVariables(...se,pe)}
        ${ee}
        ${Wc(u,F,j,q,i,e,le[0],le[1],le[2],K)}
        ${p?li(x,S,K,void 0,!u,P):di(x,S,K,void 0,!u,P,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${T};${p};${F};${j};${q};${I};${B};${P}`,inputDependencies:re},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:ae}),getShaderSource:ie}}}),eo,Gc,P_=Z(()=>{xe(),Me(),Ae(),Qc(),qr(),eo=(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=pi(s,i,e.dilations,e.pads,e.strides,u),c=X.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}];Hr(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 y=_e("output",t[0].dataType,l.length),$=dt(y.type.tensor),S=Gr(e,y.type.value,$),x=Q("x",t[0].dataType,s.length),C=Q("w",t[1].dataType,i.length),T=[x,C];n&&T.push(Q("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,y)}

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

    let outputIndices = ${y.offsetToIndices("global_idx")};
    let batch: u32 = outputIndices[0];
    let output_channel: u32 = outputIndices[${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: ${y.type.value} = ${y.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}
    ${y.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:m},getRunData:()=>({outputs:[{dims:r?r(l):l,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:h}),getShaderSource:_}},Gc=(t,e,r)=>{let n=t.length>2,a=nt(r[3]),s=nt(r[2]),i=X.size(r)/a/s,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],u=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],l=[r[0],r[1],r[2],r[3]/a],c=[{type:12,data:i},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];Hr(e,c),c.push(...ye(o,u,l));let h=(s-1)*e.strides[1]+u[1],m=_=>{let p=_e("output",t[0].dataType,l.length,a),y=dt(p.type.tensor),$=Gr(e,p.type.value,y),S=Q("x",t[0].dataType,o.length,a),x=Q("w",t[1].dataType,u.length,a),C=[S,x];n&&C.push(Q("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]};${u[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:c}),getShaderSource:m}}}),to,Hc,jc,qc=Z(()=>{xe(),Me(),ci(),Ae(),qr(),to=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s[s.length-2],u=i[i.length-1],l=s[s.length-1],c=nt(u),h=nt(l),m=nt(o),_=X.size(r)/c/m,p=t.length>2,y=n?n.slice(0,-2):r.slice(0,-2),$=[X.size(y),o,u],S=[{type:12,data:_},{type:12,data:o},{type:12,data:u},{type:12,data:l}];Hr(e,S),S.push(...ye(y,s,i)),p&&S.push(...ye(t[2].dims)),S.push(...ye($));let x=C=>{let T=Ns("batch_dims",t[0].dataType,y.length),I=Q("a",t[0].dataType,s.length,h),B=Q("b",t[1].dataType,i.length,c),P=_e("output",t[0].dataType,$.length,c),F=dt(P.type.tensor),j=Gr(e,P.type.value,F),q=[I,B],le="";if(p){let ee=a?c:1;q.push(Q("bias",t[2].dataType,t[2].dims.length,ee)),le=`${a?`value += bias[col / ${ee}];`:`value += ${P.type.value}(bias[row + i]);`}`}let ae=s.slice(0,-2),re=i.slice(0,-2),ie=Vn(ae,y),D=Vn(re,y),M=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];jr(e,M);let V=(ee,de)=>{let R=ee.rank,se=ee.name;if(R===2)return`var ${se}_indices = ${ee.type.indices}(0u, 0u);`;let pe=T.rank,Se=`var ${se}_indices: ${ee.type.indices};`;for(let Te=R-2-1,qe=pe-1;Te>=0;Te--,qe--)Se+=`
${se}_indices[${Te}] = ${pe>1?`batch_indices[${qe}]`:"batch_indices"};`;return de.forEach(Te=>{Se+=`
${se}_indices[${Te}] = 0;`}),Se+=`${se}_indices[${R-2}] = 0u;
                     ${se}_indices[${R-1}] = 0u;`,Se},K=()=>{let ee=`var a_data: ${I.type.value};`;for(let de=0;de<h;de++)ee+=`
              let b_data${de} = b[(b_offset + (k + ${de}) * uniforms.N + col) / ${c}];`;for(let de=0;de<m;de++){ee+=`a_data = a[(a_offset + (row + ${de}) * uniforms.K + k) / ${h}];`;for(let R=0;R<h;R++)ee+=`
            values[${de}] = fma(${B.type.value}(a_data${h===1?"":`[${R}]`}), b_data${R}, values[${de}]);
`}return ee};return`
  ${C.registerUniforms(M).registerInternalVariables(T).declareVariables(...q,P)}
  ${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")};`}
    ${V(I,ie)}
    let a_offset = ${I.indicesToOffset("a_indices")};
    ${V(B,D)}
    let b_offset = ${B.indicesToOffset("b_indices")};
    var values: array<${P.type.value}, ${m}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${h}) {
      ${K()}
    }
    for (var i = 0u; i < ${m}u; i++) {
      var value = values[i];
      ${le}
      ${j}
      let cur_indices = ${P.type.indices}(batch, row + i, col);
      let offset = ${P.indicesToOffset("cur_indices")};
      ${P.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}},Hc=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=>{Hc(t.inputs);let e=pn.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(to(t.inputs,{activation:""},e)):t.compute(Zs(t.inputs,{activation:""},e))}}),pi,hi,Kc,ro,no,Yc,Xc,ao,Qc=Z(()=>{Me(),R_(),ci(),P_(),qr(),qc(),Gn(),pi=(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},hi=[2,3,1,0],Kc=(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 ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},ro=(t,e)=>{let r=t.kernelShape.slice();for(let s=2;s<e[1].dims.length;++s)r[s-2]===0&&(r[s-2]=e[1].dims[s]);let n=t.pads.slice();ti.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,r,n,t.format==="NHWC",t.autoPad);let a=Object.assign({},t);return Object.assign(a,{kernelShape:r,pads:n}),a},no=t=>{let e=Ks(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,s=t.group,i=t.kernel_shape,o=t.pads,u=t.strides,l=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:s,kernelShape:i,pads:o,strides:u,wIsConst:l,...e,cacheKey:`${t.format};${e.activation};`}},Yc=(t,e,r)=>{let n=ro(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 B=pi(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),P=t.kernelCustomData.wT??t.compute(gr(e[1],hi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=P);let F=[e[0],P];e.length===3&&F.push(e[2]),t.compute(Gc(F,n,B),{inputs:F})}else t.compute(eo(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=pi(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],y=a&&l===i&&c===o&&r.pads[0]===0&&r.pads[1]===0;if(y||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 B=h[0],P,F,j,q=[];if(a){let re=t.kernelCustomData.wT??t.compute(gr(e[1],hi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=re),y){let ie=i*o*u;P=e[0].reshape([1,B,ie]),F=re.reshape([1,ie,p]),j=[1,B,p]}else P=e[0].reshape([B,i*o,u]),F=re.reshape([1,u,p]),j=[B,m*_,p];q.push(P),q.push(F)}else P=e[0].reshape([B,u,i*o]),F=e[1].reshape([1,p,u]),j=[B,p,m*_],q.push(F),q.push(P);s&&q.push(e[2]);let le=j[2],ae=q[0].dims[q[0].dims.length-1];le<8&&ae<8?t.compute(to(q,n,h,j,a),{inputs:q}):t.compute(Zs(q,n,h,j,a),{inputs:q});return}let $=!0,S=t.kernelCustomData.wT??t.compute(gr(e[1],hi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=S);let x=[e[0],S];s&&x.push(e[2]);let C=a?m*_:p,T=a?p:m*_,I=l*c*u;t.compute(Vc(x,n,h,C,T,I,s,$),{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=ro({...e,pads:a,strides:s,dilations:i,kernelShape:o},n);t.compute(eo(n,u,l=>r?[l[0],l[2],l[3]]:[]))},ao=(t,e)=>{Kc(t.inputs,e),t.inputs[0].dims.length===3?Xc(t,e):Yc(t,t.inputs,e)}}),Jc,Zc,B_=Z(()=>{xe(),Wr(),Ae(),qr(),Xs(),Dc(),ci(),Jc=(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);
      `,y=Gr(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}
      ${Ys(e)}
      ${y}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${a}] = value;
    }
  }`},Zc=(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,y=u?_:h*m,$=u?h*m:_,S=[8,8,1],x=n<=8?[4,1,1]:[4,4,1],C=[Math.ceil(y/S[0]/x[0]),Math.ceil($/S[1]/x[1]),Math.ceil(c/S[2]/x[2])];et("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${C}`);let T=p?4:1,I=Math.max(S[0]*T,S[1]),B=p?4:1,P=[e.kernelShape[u?1:2],e.kernelShape[u?2:3]],F=[P[0]+(e.dilations[0]<=1?0:(P[0]-1)*(e.dilations[0]-1)),P[1]+(e.dilations[1]<=1?0:(P[1]-1)*(e.dilations[1]-1))],j=[F[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),F[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],q=[{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:P},{type:6,data:j}];Hr(e,q),q.push(...ye(t[0].dims,t[1].dims));let le=["rank","rank"];i&&(q.push(...ye(t[2].dims)),le.push("rank")),q.push(...ye(r));let ae=re=>{let ie=Q("x",t[0].dataType,t[0].dims.length,B),D=Q("w",t[1].dataType,t[1].dims.length,1),M=_e("result",t[0].dataType,r.length,B),V=[ie,D],K="";if(i){let R=Q("bias",t[2].dataType,t[2].dims.length,B);V.push(R),K+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${R.type.value} {
            return bias[coords.${u?"w":"y"}${p?"/ 4":""}];
          }`}let ee=[{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:P.length},{name:"pads",type:"i32",length:j.length}];jr(e,ee);let de=dt(t[0].dataType,1);if(de!=="f16"&&de!=="f32")throw new Error(`elemType ${de} is not supported.`);return`
        ${Qs("uniforms.result_strides")}
        ${re.registerUniforms(ee).declareVariables(...V,M)};
        ${K}
        ${Jc(u,i,e,ie.type.value,T)}
        ${p?li(x,S,de,void 0,!u,I):di(x,S,de,void 0,!u,I,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${x};${S};${p}`,inputDependencies:le},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:q}),getShaderSource:ae}}}),ep,io,D_=Z(()=>{xe(),Wr(),Me(),Ae(),ep=(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,y=Q("W",e[1].dataType,e[1].dims.length,p),$=Q("Dy",e[0].dataType,e[0].dims.length,p),S=[$,y];n&&S.push(Q("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 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${y.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 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${y.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 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${y.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 = ${y.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}}`},io=(t,e,r)=>{let n=t.length>2,a=e.outputShape,s=X.size(a),i=[Math.ceil(s/64),1,1];et("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${i}`);let o=e.format==="NHWC",u=["rank","rank"],l=[e.strides[0],e.strides[1]],c=[e.kernelShape[o?1:2],e.kernelShape[o?2:3]],h=[e.dilations[0],e.dilations[1]],m=[c[0]+(e.dilations[0]<=1?0:(e.kernelShape[o?1:2]-1)*(e.dilations[0]-1)),c[1]+(e.dilations[1]<=1?0:(e.kernelShape[o?2:3]-1)*(e.dilations[1]-1))],_=[m[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),m[1]-1-Math.floor(e.pads[1]+e.pads[3])/2],p=!1,y=e.group,$=t[1].dims,S=$[0]/y,x=$[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=B=>{let P=[{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"}],F=dt(t[0].dataType);return`${ep(B,t,a,n,T,p,F,P,o)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${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}}}),tp,rp,np,so,ap,ip,sp,op,up,lp,N_=Z(()=>{B_(),D_(),qr(),Gn(),tp=(t,e,r,n,a,s)=>(t-1)*e+r+(n-1)*a+1-s,rp=(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)},np=(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,y=t.length-c-(o?1:0);p<c;++p,++y){let $=t[y],S=h?$*i[p]:l[p],x=tp($,i[p],s[p],e[y],r[p],S);rp(x,n,s,p,p+c),h&&l.push(i[p]*($-1)+u[p]+(e[y]-1)*r[p]+1-s[p]-s[p+c])}l.splice(0,0,m),l.splice(o?3:1,0,_)},so=(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)}np(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},ap=t=>{let e=Ks(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};`}},ip=(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 ${s}D`);if(e.kernelShape.reduce((i,o)=>i+o,0)>0&&e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(e.outputShape.length!==0&&e.outputShape.length!==t[0].dims.length-2)throw new Error("invalid output shape")},sp=[2,3,1,0],op=(t,e,r)=>{let n=so(r,e),a=r.format==="NHWC",s=n.outputShape,i=s[a?3:1],o=e[0].dims[a?3:1];if(n.group!==1||i===1&&o===1){t.compute(io(e,n));return}let u=s[a?1:2],l=s[a?2:3],c=e[1].dims[2],h=e[1].dims[3],m=a?u*l:i,_=a?i:u*l,p=c*h*o,y=!0,$=t.kernelCustomData.wT??t.compute(gr(e[1],sp),{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(Zc(S,n,s,m,_,p,x,y),{inputs:S})},up=(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=so({...e,pads:o,strides:i,dilations:s,kernelShape:a},n);t.compute(io(n,u,l=>r?[l[0],l[2],l[3]]:[l[0],l[1],l[3]]))},lp=(t,e)=>{ip(t.inputs,e),t.inputs[0].dims.length===3?up(t,e):op(t,t.inputs,e)}}),dp,cp,pp,F_=Z(()=>{xe(),Me(),lt(),Ae(),dp=(t,e,r,n)=>{let a=X.size(e),s=e.length,i=Q("input",t,s),o=_e("output",t,s),u=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),l=X.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",y=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 = ${y};
                  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}},cp=(t,e)=>{let r=t.inputs[0].dims,n=t.inputs[0].dataType,a=t.inputs[1];t.compute(dp(n,r,a,e),{inputs:[0]})},pp=t=>{let e=t.exclusive===1,r=t.reverse===1;return He({exclusive:e,reverse:r})}}),hp,fp,mp,gp,_p,L_=Z(()=>{xe(),Me(),lt(),Ae(),hp=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.")},fp=(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(`
`)},mp=(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=Q("a",_,m),y=_e("output",_,m),$=S=>`
  ${S.registerUniform("output_size","u32").declareVariables(p,y)}

  ${fp(o,m,p,y)}

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

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

    ${y.setByOffset("global_idx",p.getByIndices("aIndices"))}
  }`;return{name:"DepthToSpace",shaderCache:{hint:`${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=X.size(x),T=h.dims,I=X.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:$}},gp=(t,e)=>{hp(t.inputs),t.compute(mp(t.inputs[0],e))},_p=t=>He({blocksize:t.blocksize,mode:t.mode,format:t.format})}),fi,Hn,oo,wp,yp,bp,vp,uo,$p,xp,Sp,U_=Z(()=>{xe(),Me(),lt(),Ae(),fi="[a-zA-Z]|\\.\\.\\.",Hn="("+fi+")+",oo="^"+Hn+"$",wp="("+Hn+",)*"+Hn,yp="^"+wp+"$",bp=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)}},vp=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(yp)))throw new Error("Invalid LHS term");if(r.split(",").forEach((s,i)=>{let o=t[i].dims.slice();if(!s.match(RegExp(oo)))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(Hn)))throw new Error("Invalid RHS");(a=n.match(RegExp(fi,"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(oo))&&!e&&t!=="")throw new Error("Invalid LHS term");let u=t.match(RegExp(fi,"g")),l=new bp(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}},uo=t=>t+"_max",$p=(t,e,r,n)=>{let a=t.map(l=>l.length).map((l,c)=>Q(`input${c}`,e,l)),s=X.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=[],y=[],$=[],S=[],x=r.symbolToInfo.size===r.rhs.symbolToIndices.size;r.symbolToInfo.forEach((T,I)=>{var B;if(r.rhs.symbolToIndices.has(I)){let P=(B=r.rhs.symbolToIndices.get(I))==null?void 0:B[0];P!==void 0&&r.lhs.forEach((F,j)=>{if(T.inputIndices.includes(j)){let q=F.symbolToIndices.get(I);if(q===void 0)throw new Error("Invalid symbol error");q.forEach(le=>{c.push(`${a[j].indicesSet(`input${j}Indices`,le,i.indicesGet("outputIndices",P))}`)})}})}else r.lhs.forEach((P,F)=>{if(T.inputIndices.includes(F)){let j=P.symbolToIndices.get(I);if(j===void 0)throw new Error("Invalid symbol error");j.forEach(q=>{p.push(`${a[F].indicesSet(`input${F}Indices`,q,`${I}`)}`)}),S.push(`prod *= ${a[F].getByIndices(`input${F}Indices`)};`)}}),y.push(`for(var ${I}: u32 = 0; ${I} < uniforms.${uo(I)}; ${I}++) {`),$.push("}")});let C=x?[...c,`let sum = ${a.map((T,I)=>T.getByIndices(`input${I}Indices`)).join(" * ")};`]:[...c,m,...y,...p,h,...S,_,...$];return`
            ${l.registerUniforms(o.map(T=>({name:`${uo(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(`
`)};
            ${i.setByOffset("global_idx","sum")};
          }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:t.map(()=>"rank")},getRunData:()=>{let l=o.filter(h=>r.symbolToInfo.has(h)).map(h=>{var m;return{type:12,data:((m=r.symbolToInfo.get(h))==null?void 0:m.dimValue)||0}});l.push({type:12,data:s});let c=t.map((h,m)=>[...ye(h)]).reduce((h,m)=>h.concat(m),l);return c.push(...ye(n)),{outputs:[{dims:n,dataType:e}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:c}},getShaderSource:u}},xp=(t,e)=>{let r=new vp(t.inputs,e.equation),n=r.outputDims,a=t.inputs.map((s,i)=>s.dims);t.compute($p(a,t.inputs[0].dataType,r,n))},Sp=t=>{let e=t.equation.replace(/\s+/g,"");return He({equation:e})}}),kp,lo,Ep,Cp,Tp,W_=Z(()=>{xe(),Me(),Ae(),kp=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")},lo=(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},Ep=(t,e)=>t.length>e.length?lo(t,e):lo(e,t),Cp=t=>{let e=t[0].dims,r=Array.from(t[1].getBigInt64Array(),Number),n=Ep(e,r),a=t[0].dataType,s=a===9?4:1,i=Math.ceil(X.size(n)/s),o=l=>{let c=Q("input",a,e.length,s),h=_e("output",a,n.length,s),m;if(a===9){let _=(p,y,$="")=>`
          let outputIndices${y} = ${h.offsetToIndices(`outputOffset + ${y}u`)};
          let offset${y} = ${c.broadcastedIndicesToOffset(`outputIndices${y}`,h)};
          let index${y} = offset${y} / 4u;
          let component${y} = offset${y} % 4u;
          ${p}[${y}] = ${$}(${c.getByOffset(`index${y}`)}[component${y}]);
        `;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})}},Tp=t=>{kp(t.inputs),t.compute(Cp(t.inputs),{inputs:[0]})}}),Ip,Ap,V_=Z(()=>{xe(),Me(),Ae(),qs(),Ip=t=>{let e=t[0].dataType,r=X.size(t[0].dims),n=X.size(t[1].dims),a=n%4===0,s=i=>{let o=Q("x",e,[1],4),u=Q("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)}

    ${Hs(kt(e))}

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

      let x = ${o.getByOffset("global_idx")};
      ${m}
      let x_in = x + bias;
      ${l.setByOffset("global_idx",js("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${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/hn/4)}})}},Ap=t=>{t.inputs.length<2||X.size(t.inputs[1].dims)===0?yc(t):t.compute(Ip(t.inputs))}}),Mp,Op,zp,Rp,G_=Z(()=>{xe(),Me(),lt(),Ae(),Mp=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},Op=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,s=X.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(X.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 _=Q("data",t[0].dataType,t[0].dims.length,u),p=Q("inputIndices",t[1].dataType,t[1].dims.length),y=_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} : ${_.type.indices};
        `;for(let I=0,B=0;I<a;I++)I===s?(T+=`${a>1?`dataIndices${x}[${I}]`:`dataIndices${x}`} = u32(idx${x});`,B+=C):(T+=`${a>1?`dataIndices${x}[${I}]`:`dataIndices${x}`} = ${i.length>1?`outputIndices${x}[${B}]`:`outputIndices${x}`};`,B++);return T},S;if(t[0].dataType===9){let x=(C,T,I="")=>`
          let outputIndices${T} = ${y.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")}
        ${y.setByOffset("global_idx","value")}
      `}else S=`
      let outputIndices = ${y.offsetToIndices("global_idx")};
      ${$("")};
      let value = ${_.getByIndices("dataIndices")};
      ${y.setByOffset("global_idx","value")};
      `;return`
      ${m.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(_,p,y)}
      ${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}},zp=t=>He({axis:t.axis}),Rp=(t,e)=>{let r=t.inputs;Mp(r),t.compute(Op(t.inputs,e))}}),Pp,Bp,Dp,Np,H_=Z(()=>{xe(),Me(),lt(),Ae(),Pp=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.`)},Bp=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,s=t[1].dims,i=t[1].dataType,o=X.normalizeAxis(e.axis,a),u=r[o],l=s.slice(0),c=X.size(l),h=Q("input",n,a),m=Q("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:y=>`
      ${y.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(h,m,_)}
      ${y.mainStart()}
      ${y.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")};

      ${_.setByOffset("global_idx","value")};
  }`}},Dp=t=>He({axis:t.axis}),Np=(t,e)=>{let r=t.inputs;Pp(r),t.compute(Bp(t.inputs,e))}}),Fp,Lp,Up,Wp,j_=Z(()=>{xe(),Me(),Ae(),Fp=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},Lp=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,s,i]=xl.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[a,s];if(!o)throw new Error("Can't use gemm on the given tensors");let u=X.size(o),l=[{type:12,data:u},{type:12,data:a},{type:12,data:s},{type:12,data:i},{type:1,data:e.alpha},{type:1,data:e.beta}],c=["type","type"];t.length===3&&(l.push(...ye(t[2].dims)),c.push("rank")),l.push(...ye(o));let h=m=>{let _="";e.transA&&e.transB?_="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?_="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?_="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(_="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let p=e.alpha===1?"":"value *= uniforms.alpha;",y=Q("a",t[0].dataType,t[0].dims),$=Q("b",t[1].dataType,t[1].dims),S=y.type.value,x=null,C=[y,$];t.length===3&&(x=Q("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}},Up=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};${t.alpha===1}`}},Wp=(t,e)=>{Fp(t.inputs),t.compute(Lp(t.inputs,e))}}),Vp,Gp,Hp,jp,q_=Z(()=>{xe(),Me(),Ae(),Vp=(t,e)=>{let r=t[0].dims,n=r,a=2,s=X.sizeToDimension(r,a),i=X.sizeFromDimension(r,a),o=nt(i),u=i/o,l=[r[0],r[1],u],c=["rank","type","type"],h=[{type:12,data:i},{type:12,data:u}];h.push(...ye(l,l));let m=_=>{let p=Q("x",t[0].dataType,l.length,o),y=Q("scale",t[1].dataType,t[1].dims),$=Q("bias",t[2].dataType,t[2].dims),S=_e("output",t[0].dataType,l.length,o),x=[p,y,$,S],C=p.type.value,T=o===1?"f32":`vec${o}<f32>`,I=64,B=[{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(B).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 = ${Cr("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 = ${Cr("workgroupShared[0]",o)};
    }
    workgroupBarrier();

    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${e.epsilon}));
    let channelScale = invStdDev * f32(${y.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};${o}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:s},programUniforms:h}),getShaderSource:m}},Gp=(t,e,r,n,a,s,i,o)=>{let u=nt(i),l=64,c=u===1?"vec2f":`mat2x${u}f`,h=u===1?"f32":`vec${u}f`,m=(B,P)=>`${c}(${B}, ${P})`,_=a*i/u,p=Math.ceil(s/l),y=["type"],$=[{type:12,data:p},{type:12,data:s},{type:12,data:Math.floor(i/u)},{type:12,data:Math.floor(s*i/u)}],S=B=>{let P=Q("input",e.dataType,e.dims,u);return`
  ${B.declareVariables(P)}
  @group(0) @binding(1) var<storage, read_write> output : array<${c}>;
  struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};
  @group(0) @binding(2) var<uniform> uniforms: Uniforms;

  ${B.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 = ${Vr("f32",u)};
    var squaredSum = ${Vr("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:y},getRunData:()=>({outputs:[{dims:[a,i,l,2],dataType:1}],dispatchGroup:{x:a*i/u},programUniforms:$}),getShaderSource:S},{inputs:[e],outputs:[-1]})[0],C=[{type:12,data:_},{type:12,data:s},{type:12,data:Math.floor(i/u)},{type:12,data:Math.floor(l*i/u)}],T=["type","type","type"],I=B=>{let P=Q("scale",r.dataType,r.dims,u),F=Q("bias",n.dataType,n.dims,u);return`
  @group(0) @binding(0) var<storage, read> input : array<${c}>;
  @group(0) @binding(1) var<storage, read> scale : array<${P.type.storage}>;
  @group(0) @binding(2) var<storage, read> bias : array<${F.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;

  ${B.mainStart()}
    ${B.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 = ${Vr("f32",u)};
    var squaredSum = ${Vr("f32",u)};
    for (var i: u32 = 0; i < min(${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};${o}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:[a,i,2],dataType:1}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:C}),getShaderSource:I},{inputs:[x,r,n],outputs:[-1]})[0]},Hp=(t,e,r)=>{let n=e[0].dims,a=n,s=n[0],i=n[n.length-1],o=X.sizeFromDimension(n,1)/i,u=nt(i),l=X.size(a)/u,c=[{type:12,data:o},{type:12,data:Math.floor(i/u)}],h=["type","type"],m=Gp(t,e[0],e[1],e[2],s,o,i,r.epsilon),_=p=>{let y=dt(e[0].dataType),$=u===1?"vec2f":`mat2x${u}f`,S=u===1?y:`vec${u}<${y}>`,x=Q("input",e[0].dataType,e[0].dims,u),C=_e("output",e[0].dataType,a,u);return`
  @group(0) @binding(0) var<storage, read> input : array<${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"?Hp(t,t.inputs,e):t.compute(Vp(t.inputs,e))}}),qp,Kp,Yp,K_=Z(()=>{xe(),Me(),Ae(),qp=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Kp=(t,e,r)=>{let n=e.simplified,a=t[0].dims,s=t[1],i=!n&&t[2],o=a,u=X.normalizeAxis(e.axis,a.length),l=X.sizeToDimension(a,u),c=X.sizeFromDimension(a,u),h=X.size(s.dims),m=i?X.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 ${m}`);let _=[];for(let I=0;I<a.length;++I)I<u?_.push(a[I]):_.push(1);let p=nt(c),y=["type","type"],$=[{type:12,data:l},{type:1,data:c},{type:12,data:Math.floor(c/p)},{type:1,data:e.epsilon}];i&&y.push("type");let S=r>1,x=r>2,C=I=>{let B=dt(t[0].dataType),P=[Q("x",t[0].dataType,t[0].dims,p),Q("scale",s.dataType,s.dims,p)];i&&P.push(Q("bias",i.dataType,i.dims,p)),P.push(_e("output",t[0].dataType,o,p)),S&&P.push(_e("mean_data_output",1,_)),x&&P.push(_e("inv_std_output",1,_));let F=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${I.registerUniforms(F).declareVariables(...P)}
  ${I.mainStart()}
    ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${Vr("f32",p)};
    var mean_square_vector = ${Vr("f32",p)};

    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
      let value = ${fn(B,p,"x[h + offset]")};
      mean_vector += value;
      mean_square_vector += value * value;
    }
    let mean = ${Cr("mean_vector",p)} / uniforms.norm_size;
    let inv_std_dev = inverseSqrt(${Cr("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 = ${fn(B,p,"x[j + offset]")};
      let f32scale = ${fn(B,p,"scale[j]")};
      output[j + offset] = ${P[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale
        ${i?`+ ${fn(B,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:y},getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:$}),getShaderSource:C}},Yp=(t,e)=>{qp(t.inputs),t.compute(Kp(t.inputs,e,t.outputCount))}}),Xp,Qp,Jp,Zp,Y_=Z(()=>{xe(),Me(),lt(),Ae(),Xp=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((e.k+e.blockSize-1)/e.blockSize),s=e.blockSize/8*e.bits,i=t[1];if(!X.areEqual(i.dims,[e.n,a,s]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(X.size(o)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let u=t[3].dims,l=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(X.size(u)!==l)throw new Error("zeroPoints input size error.")}},Qp=(t,e,r,n)=>{let a=t[0].dims,s=a.length,i=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=a[s-2],u=e.k,l=e.n,c=a.slice(0,s-2),h=X.size(c),m=e.blockSize/8*e.bits/4,_=t[0].dataType,p=nt(o),y=nt(e.k),$=nt(m),S=Wn(_),x=o*i*S,C=Math.floor(n/x),T=i<=r[0]&&C>0,I=!T||C>=4?nt(l):C>=2&&nt(l)>=2?2:1,B=c.concat([o,l]),P=X.size(B)/I/p,F=T?[]:[{type:12,data:P},{type:12,data:e.blockSize}],j=[h,o,u/y],q=X.convertShape(t[1].dims).slice();q.splice(-1,1,m/$),F.push(...ye(j)),F.push(...ye(q)),F.push(...ye(t[2].dims)),t.length===4&&F.push(...ye(X.convertShape(t[3].dims)));let le=[h,o,l/I];F.push(...ye(le));let ae=re=>{let ie=j.length,D=Q("a",t[0].dataType,ie,y),M=Q("b",12,q.length,$),V=Q("scales",t[2].dataType,t[2].dims.length),K=[D,M,V],ee=t.length===4?Q("zero_points",12,t[3].dims.length):void 0;ee&&K.push(ee);let de=le.length,R=_e("output",t[0].dataType,de,I),se=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],pe=dt(t[0].dataType),Se=(()=>{switch(y){case 1:return`array<${pe}, 8>`;case 2:return`mat4x2<${pe}>`;case 4:return`mat2x4<${pe}>`;default:throw new Error(`${y}-component is not supported.`)}})(),Te=`
        for (var word: u32 = 0; word < ${m}; word += ${$}) {
          ${M.indicesSet("b_indices","2","word")};
          let b_data = ${M.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},(at,Ke)=>`${pe}(b_value_lower[${Ke}]), ${pe}(b_value_upper[${Ke}])`).join(", ")});
            let b_dequantized_values = ${y===1?`${Se}(${Array.from({length:8},(at,Ke)=>`(b_quantized_values[${Ke}] - 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",ie-2,T?"m":`row * ${p} + m`)};
              ${D.indicesSet("a_indices",ie-1,"word_offset")};
              var input_offset = ${D.indicesToOffset("a_indices")};
              var a_data: ${Se};
              for (var j: u32 = 0; j < ${8/y}; j++) {
                a_data[j] = ${D.getByOffset("input_offset")};
                input_offset++;
              }
              ${T?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${I>1?"[c]":""} += ${Array.from({length:8/y},(at,Ke)=>`${y===1?`a_data[${Ke}] * b_dequantized_values[${Ke}]`:`dot(a_data[${Ke}], b_dequantized_values[${Ke}])`}`).join(" + ")};
            }
            word_offset += ${8/y};
          }
        }`,qe=ee?`
          zero_point_offset += 4;
          if (zero_point_offset == 32) {
            zero_point_offset = 0;
            zero_point_index++;
            zero_point_word = ${ee.getByOffset("zero_point_index")};
          }`:"";return T?`
        var<workgroup> workgroup_shared: array<${R.type.value}, ${o*i}>;
        ${re.declareVariables(...K,R)}
        ${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;
              ${ee?`
            var zero_point_bytes_per_col: u32 = (${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 = ${ee.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}
            var b_indices: ${M.type.indices};
            ${M.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 = ${V.getByOffset("scales_index")};
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${pe}(${ee?"(zero_point_word) & 0xFu":8});
            ${M.indicesSet("b_indices","1","block")};
            var word_offset: u32 = block * ${e.blockSize/y};
            var workgroup_shared_offset: u32 = block * ${o};
            ${Te}
          }
          workgroupBarrier();
          var output_indices: ${R.type.indices};
          var elements_per_thread: u32 = ${Math.ceil(o/i)};
          ${R.indicesSet("output_indices","0","batch")};
          ${R.indicesSet("output_indices",de-1,"col")};
          ${R.indicesSet("output_indices",de-2,"local_id.x * elements_per_thread")};
          var output_offset = ${R.indicesToOffset("output_indices")};
          for (var m: u32 = 0u; m < elements_per_thread; m++) {
            var row = m + local_id.x * elements_per_thread;
            if (row < ${o}) {
              var output_value: ${R.type.value} = ${R.type.value}(0);
              var workgroup_shared_offset: u32 = row;
              for (var b: u32 = 0u; b < ${i}u; b++) {
                output_value += workgroup_shared[workgroup_shared_offset];
                workgroup_shared_offset += ${o};
              }
              ${R.setByOffset("output_offset","output_value")};
              output_offset += ${l/I};
            }
          }
        }`:`
        ${re.registerUniforms(se).declareVariables(...K,R)}
        ${re.mainStart()}
          ${re.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var output_values: array<${R.type.value}, ${p}>;
          var output_indices = ${R.offsetToIndices("global_idx")};
          var col = ${R.indicesGet("output_indices",de-1)};
          var row = ${R.indicesGet("output_indices",de-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
          ${ee?`
          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 = ${ee.getByOffset("zero_point_index")};
          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}
          var scale_index = col * ${i*I};
          var b_indices: ${M.type.indices};
          for (var c: u32 = 0; c < ${I}; c++) {
            ${M.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 = ${V.getByOffset("scale_index")};
              // The default zero point is 8 for unsigned 4-bit quantization.
              let zero_point = ${pe}(${ee?"extractBits(zero_point_word, zero_point_offset, 4)":8});
              ${M.indicesSet("b_indices","1","block")};
              var word_offset: u32 = block_offset;
              ${Te}
              scale_index++;
              ${qe}
              block_offset += uniforms.block_size / ${y};
            }
            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.
            ${ee?`if (zero_point_offset % 8 > 0) {
                ${qe}
              }`:""}
            }
            for (var k: u32 = 0u; k < ${p}u; k++) {
              ${R.indicesSet("output_indices",de-2,`${p} * row + k`)};
              ${R.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:T?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${_};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:B,dataType:_}],name:T?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:T?{x:1,y:Math.ceil(l/I),z:h}:{x:Math.ceil(P/64)},programUniforms:F}),getShaderSource:ae}},Jp=(t,e)=>{Xp(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(Qp(t.inputs,e,r,n))},Zp=t=>He(t)}),$t,eh,th,co,rh,mi,nh,X_=Z(()=>{xe(),Me(),lt(),Os(),Od(),Ae(),Gn(),$t=(t,e)=>t.length>e&&t[e].dims.length>0&&X.size(t[e].dims)>0?t[e]:void 0,eh=(t,e)=>{let r=t[0],n=$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,y=0,$=0,S=Math.floor(_/e.numHeads);if(u&&l){if(u.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(u.dims[0]!==h||u.dims[1]!==e.numHeads||u.dims[3]!==S)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[0]!==h||l.dims[1]!==e.numHeads||l.dims[3]!==S)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[2]!==l.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(l.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');y=u.dims[2],$=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 F=i.dims;throw F.length===1?F[0]===h?C=1:F[0]===3*h+2&&(C=3):F.length===2&&F[0]===h&&F[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 B=y+p,P=!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]!==B)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:h,sequenceLength:m,pastSequenceLength:y,kvSequenceLength:p,totalSequenceLength:B,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:P,passPastInKv:T,qkvFormat:x}},th=t=>He({...t}),co=He({perm:[0,2,1,3]}),rh=(t,e,r,n,a,s,i)=>{let o=[n,a,s],u=X.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),_=Q("qkv",e.dataType,o),p=Q("bias",r.dataType,o),y=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${h.registerUniforms(y).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]},mi=(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=rh(t,s,i,e,n,r*a,o),u=u.reshape([e,n,r,a]),t.compute(gr(u,co.perm),{inputs:[u],outputs:[-1]})[0]}else return s.dims.length===3&&(u=s.reshape([e,n,r,a])),t.compute(gr(u,co.perm),{inputs:[u],outputs:[-1]})[0]},nh=(t,e)=>{let r=eh(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=$t(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((a==null?void 0:a.dims.length)===5)throw new Error("Packed KV is not implemented");let h=a&&s&&a.dims.length===4&&s.dims.length===4,m=mi(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(h)return si(t,m,a,s,o,void 0,l,c,u,r,e);if(!a||!s)throw new Error("key and value must be provided");let _=mi(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,i,r.hiddenSize),p=mi(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,s,i,2*r.hiddenSize);si(t,m,_,p,o,void 0,l,c,u,r,e)}}),ah,ih,sh,oh,uh,lh,dh,ch,ph,Q_=Z(()=>{xe(),Me(),Ae(),ah=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1&&t[0].dataType!==10)throw new Error("Input type must be float or float16.");if(t.length>=2){let e=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(e=t[3].dims[0]*2===t[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},ih=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
            k = i32(${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;
            ${n}
            value = x[offset];
          }
      `},sh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${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;
              ${n}
              value = x[offset];
          `},oh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${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;
              ${n}
              value = x[offset];
          `},uh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
                if (k < 0)  {
                  k += i32(${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];
          `},lh=(t,e,r)=>{switch(r.mode){case 0:return ih(t,e,r.pads.length);case 1:return sh(t,e,r.pads.length);case 2:return oh(t,e,r.pads.length);case 3:return uh(t,e,r.pads.length);default:throw new Error("Invalid mode")}},dh=(t,e)=>{let r=X.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=X.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=Q("x",t[0].dataType,n.length),h=c.type.value,m=lh(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(X.size(r)/64)},programUniforms:s}),getShaderSource:o}},ch=(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},ph=(t,e)=>{ah(t.inputs);let r=ch(t.inputs,e);t.compute(dh(t.inputs,r),{inputs:[0]})}}),jn,po,ho,fo,mo,hh,fh,go,_o,mh,gh,wo,_h,wh,yo,yh,bh,vh,$h,J_=Z(()=>{jt(),xe(),Me(),Ae(),jn=t=>{if(Ue.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},po=(t,e,r)=>{let n=e.format==="NHWC",a=t.dims.slice();n&&a.splice(1,0,a.pop());let s=Object.hasOwnProperty.call(e,"dilations"),i=e.kernelShape.slice(),o=e.strides.slice(),u=s?e.dilations.slice():[],l=e.pads.slice();ti.adjustPoolAttributes(r,a,i,o,u,l);let c=ti.computePoolOutputShape(r,a,o,u,i,l,e.autoPad),h=Object.assign({},e);s?Object.assign(h,{kernelShape:i,strides:o,pads:l,dilations:u,cacheKey:e.cacheKey}):Object.assign(h,{kernelShape:i,strides:o,pads:l,cacheKey:e.cacheKey});let m=c.slice();return m.push(m.splice(1,1)[0]),[h,n?m:c]},ho=(t,e)=>{let r=e.format==="NHWC",n=X.size(t),a=X.size(e.kernelShape),s=[{type:12,data:n},{type:12,data:a}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let o=e.kernelShape[e.kernelShape.length-1],u=e.strides[e.strides.length-1],l=e.pads[e.pads.length/2-1],c=e.pads[e.pads.length-1],h=!!(l+c);s.push({type:12,data:o},{type:12,data:u},{type:12,data:l},{type:12,data:c}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let m=!1;if(e.kernelShape.length===2){let _=e.kernelShape[e.kernelShape.length-2],p=e.strides[e.strides.length-2],y=e.pads[e.pads.length/2-2],$=e.pads[e.pads.length-2];m=!!(y+$),s.push({type:12,data:_},{type:12,data:p},{type:12,data:y},{type:12,data:$}),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=X.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]}},fo=(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 y="",$="",S="",x=r-(m?2:1);if(c?y=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${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")}];
                  ${s}
                }`:y=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${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;
              ${$}
              ${y}
              ${S}
              ${i}

              output[global_idx] = value;
            }`}else{if(m)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let y=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, ${y}>;

              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 < ${y-1}u; j++) {
                  offsets[j] = offset / ${ke("uniforms.kernelStrides","j",y)};
                  offset -= offsets[j] * ${ke("uniforms.kernelStrides","j",y)};
                }
                offsets[${y-1}] = offset;

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

              output[global_idx] = value;
            }`}},mo=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,hh=t=>`${mo(t)};${t.countIncludePad}`,fh=t=>`${mo(t)};${t.storageOrder};${t.dilations}`,go=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}),_o=(t,e,r,n)=>{let[a,s]=po(e,n,r),i=Q("x",e.dataType,e.dims.length),o=i.type.value,u="value += x_val;",l="";a.countIncludePad?l+=`value /= ${o}(uniforms.kernelSize);`:l+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[c,h,m,_,p]=ho(s,a);c.push(...ye(e.dims,s));let y=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${m};${_};${p}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(X.size(s)/64)},programUniforms:c}),getShaderSource:$=>fo($,i,e.dims.length,s.length,a,u,l,0,h,m,_,p)}},mh=t=>{let e=t.count_include_pad!==0,r=go(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:hh(n)}},gh=(t,e)=>{jn(t.inputs),t.compute(_o("AveragePool",t.inputs[0],!1,e))},wo={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},_h=t=>{let e=t.format;return{format:e,...wo,cacheKey:e}},wh=(t,e)=>{jn(t.inputs),t.compute(_o("GlobalAveragePool",t.inputs[0],!0,e))},yo=(t,e,r,n)=>{let[a,s]=po(e,n,r),i=`
      value = max(x_val, value);
    `,o="",u=Q("x",e.dataType,e.dims.length),l=["rank"],[c,h,m,_,p]=ho(s,a);return c.push(...ye(e.dims,s)),{name:t,shaderCache:{hint:`${n.cacheKey};${m};${_};${p}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(X.size(s)/64)},programUniforms:c}),getShaderSource:y=>fo(y,u,e.dims.length,s.length,a,i,o,e.dataType===10?-65504:-1e5,h,m,_,p)}},yh=(t,e)=>{jn(t.inputs),t.compute(yo("MaxPool",t.inputs[0],!1,e))},bh=t=>{let e=t.storage_order,r=t.dilations,n=go(t);if(e!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let a={storageOrder:e,dilations:r,...n,cacheKey:""};return{...a,cacheKey:fh(a)}},vh=t=>{let e=t.format;return{format:e,...wo,cacheKey:e}},$h=(t,e)=>{jn(t.inputs),t.compute(yo("GlobalMaxPool",t.inputs[0],!0,e))}}),xh,Sh,kh,Z_=Z(()=>{jt(),xe(),Ae(),xh=(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.")},Sh=(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})}},kh=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&&xh(e,r,n),t.compute(Sh(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),Eh,Ch,Th,Ih,Ah,Mh,Oh,zh,Rh,Ph,Bh,bo,Dh,Nh,Fh,Lh,Uh,Wh,Vh,ew=Z(()=>{xe(),Me(),lt(),Ae(),Eh=(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")}},Ch=(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},Th=(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");Eh(n,e),e.axes.length>0&&Ch(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")},Ih=(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`)}})()+"}",Ah=(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`)}})()+"}",Mh=(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},Oh=(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},zh=(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;
    }`,Ph=(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;
    }`,Bh=(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;
    }`,bo=(t,e,r,n)=>t.rank>n?`
    ${t.indicesSet("input_indices",e,"channel")};
    ${t.indicesSet("input_indices",r,"batch")};
`:"",Dh=(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))`)};
      ${bo(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);
    }`},Nh=(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=y=>{let $=y===h?"row":"col";return`
      fn ${$}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${_} {
        var output_index = ${e.indicesGet("output_indices",y)};
        var originalIdx: ${_} = getOriginalCoordinateFromResizedCoordinate(output_index, ${a[y]},
        ${n[y]}, ${r[y]}, ${s[y]}, ${s[y]} + ${r.length});
        var fractOriginalIdx: ${_} = originalIdx - floor(originalIdx);
        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);

        if (${o} && (originalIdx < 0 || originalIdx > (${r[y]} - 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[y]}) {
            ${l?`coefs[i + 1] = 0.0;
                        continue;`:o?`return ${u};`:`${$} = max(0, min(${$}, ${r[y]} - 1));`};
          }
        var input_indices_copy: ${t.type.indices} = input_indices;
          ${t.indicesSet("input_indices_copy",y,`u32(${$})`)};
          data[i + 1] = ${y===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);
  }
    `},Fh=(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))`)};
      ${bo(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);
    }`},Lh=(t,e,r,n,a,s)=>{let i=t.dims,o=Mh(s,e.axes,i.length),u=Oh(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=zh(i,l,e)));let c=_e("output",t.dataType,u.length),h=Q("input",t.dataType,i.length),m=X.size(u),_=i.length===u.length&&i.every((x,C)=>x===u[C]),p=e.coordinateTransformMode==="tf_crop_and_resize",y=e.extrapolationValue,$=h.type.value,S=x=>`
      ${_?"":`
      ${Ih(e.coordinateTransformMode,$)};
      ${(()=>{switch(e.mode){case"nearest":return`
              ${Bh(h,i)};
              ${Ah(e.nearestMode,r,$)};
              ${Ph(h,c,i,u,l.length,o.length,p)};
              `;case"linear":return`
              ${Rh(c,i,u,l.length,o.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${Dh(h,c,i,p,y)}`;if(i.length===3||i.length===5)return`${Fh(h,c,i,p,y)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${Nh(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)]})}},Uh=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},Wh=(t,e)=>{let r=[],n=[],a=[],s=Uh(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Th(t.inputs,e,s,r,n,a),t.compute(Lh(t.inputs[0],e,s,r,n,a),{inputs:[0]})},Vh=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 He({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:s,extrapolationValue:i,keepAspectRatioPolicy:o,mode:u,nearestMode:l})}}),Gh,Hh,jh,tw=Z(()=>{xe(),Me(),lt(),Ae(),Gh=(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(!X.areEqual(n.dims,[])&&!X.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(!X.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=X.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 ${a.dims[1]}`);if(l>c)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Hh=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:s}=e,i=t[0].dims[0],o=X.sizeFromDimension(t[0].dims,1),u=t[0].dims[t[0].dims.length-2],l=o/u,c=t[2].dims[1],h=a===0?c*2:l/n,m=new Array(i,u,l/h,h-c),_=X.computeStrides(m),p=[{type:1,data:s},{type:12,data:m},{type:12,data:_},...t[0].dims.length===3?new Array({type:12,data:[o,l,h,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,h,u*h,1]}):[],...ye(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],y=$=>{let S=Q("input",t[0].dataType,t[0].dims.length),x=Q("position_ids",t[1].dataType,t[1].dims.length),C=Q("cos_cache",t[2].dataType,t[2].dims.length),T=Q("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(hn)}
          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;
            ${I.setByOffset("k",S.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:He({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:y,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(X.size(m)/hn)},programUniforms:p})}},jh=(t,e)=>{Gh(t.inputs,e),t.compute(Hh(t.inputs,e))}}),qh,Kh,Yh,rw=Z(()=>{xe(),Me(),Ae(),qh=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let a=e.dims[e.dims.length-1],s=e.dims[e.dims.length-2];if(r.dims[r.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==a)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let i=t[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let i=t[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},Kh=(t,e,r,n)=>{let a=e.simplified,s=t[0].dims,i=X.size(s),o=s,u=i,l=s.slice(-1)[0],c=n?s.slice(0,-1).concat(1):[],h=!a&&t.length>3,m=t.length>4,_=n&&r>1,p=n&&r>2,y=r>3,$=64,S=nt(l),x=[{type:12,data:u},{type:12,data:S},{type:12,data:l},{type:1,data:e.epsilon}],C=I=>{let B=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],P=[Q("x",t[0].dataType,t[0].dims,S),Q("skip",t[1].dataType,t[1].dims,S),Q("gamma",t[2].dataType,t[2].dims,S)];h&&P.push(Q("beta",t[3].dataType,t[3].dims,S)),m&&P.push(Q("bias",t[4].dataType,t[4].dims,S)),P.push(_e("output",t[0].dataType,o,S)),_&&P.push(_e("mean_output",1,c)),p&&P.push(_e("inv_std_output",1,c)),y&&P.push(_e("input_skip_bias_sum",t[0].dataType,o,S));let F=dt(t[0].dataType),j=dt(1,S);return`

      ${I.registerUniforms(B).declareVariables(...P)}
      var<workgroup> sum_shared : array<${j}, ${$}>;
      var<workgroup> sum_squared_shared : array<${j}, ${$}>;

      ${I.mainStart([$,1,1])}
        let ix = local_id.x;
        let iy = global_id.x / ${$};

        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
        var stride = hidden_size_vectorized / ${$};
        let offset = ix * stride + iy * hidden_size_vectorized;
        let offset1d = stride * ix;
        if (ix == ${$-1}) {
          stride = hidden_size_vectorized - stride * ix;
        }
        for (var i: u32 = 0; i < stride; i++) {
          let skip_value = skip[offset + i];
          let bias_value = ${m?"bias[offset1d + i]":F+"(0.0)"};
          let input_value = x[offset + i];
          let value = input_value + skip_value + bias_value;
          ${y?"input_skip_bias_sum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32_value = ${fn(F,S,"value")};
          sum_shared[ix] += f32_value;
          sum_squared_shared[ix] += f32_value * f32_value;
        }
        workgroupBarrier();

        var reduce_size : u32 = ${$};
        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {
          reduce_size = curr_size + (reduce_size & 1);
          if (ix < curr_size) {
            sum_shared[ix] += sum_shared[ix + reduce_size];
            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
          }
          workgroupBarrier();
        }

        let sum = sum_shared[0];
        let square_sum = sum_squared_shared[0];
        let mean = ${Cr("sum",S)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Cr("square_sum",S)} / 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 < stride; i++) {
          output[offset + i] = (output[offset + i] ${a?"":`- ${F}(mean)`}) *
            ${F}(inv_std_dev) * gamma[offset1d + i]
            ${h?"+ beta[offset1d + i]":""};
        }
      }`},T=[{dims:o,dataType:t[0].dataType}];return r>1&&T.push({dims:c,dataType:1}),r>2&&T.push({dims:c,dataType:1}),r>3&&T.push({dims:s,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${S};${_};${p};${y}`,inputDependencies:t.map((I,B)=>"type")},getShaderSource:C,getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(u/l)},programUniforms:x})}},Yh=(t,e)=>{qh(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(Kh(t.inputs,e,t.outputCount,!1),{outputs:r})}}),Xh,qn,Qh,vo,Jh,Zh,ef,tf,nw=Z(()=>{xe(),Me(),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`)})},qn=(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},Qh=(t,e)=>{if(t.length>1){let r=qn(t,1),n=qn(t,2),a=qn(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),He({starts:r,ends:n,axes:a})}else return e},vo=(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]]))},Jh=(t,e,r)=>`fn calculateInputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} {
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          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")};
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            carry = input_index / input_shape_i;
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            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${t.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,Zh=(t,e)=>{let r=t[0].dims,n=X.size(r),a=e.axes.length>0?X.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],s=qn(t,4);s.forEach(S=>S!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(a.length).fill(1));let i=e.starts.map((S,x)=>vo(S,x,r,a,s)),o=e.ends.map((S,x)=>vo(S,x,r,a,s));if(a.length!==i.length||a.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(a.length!==r.length)for(let S=0;S<r.length;++S)a.includes(S)||(i.splice(S,0,0),o.splice(S,0,r[S]),s.splice(S,0,1));let u=s.map(S=>Math.sign(S));s.forEach((S,x,C)=>{if(S<0){let T=(o[x]-i[x])/S,I=i[x],B=I+T*s[x];i[x]=B,o[x]=I,C[x]=-S}});let l=r.slice(0);a.forEach((S,x)=>{l[S]=Math.ceil((o[S]-i[S])/s[S])});let c={dims:l,dataType:t[0].dataType},h=_e("output",t[0].dataType,l.length),m=Q("input",t[0].dataType,t[0].dims.length),_=X.size(l),p=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:u.length},{name:"steps",type:"u32",length:s.length}],y=[{type:12,data:_},{type:12,data:i},{type:6,data:u},{type:12,data:s},...ye(t[0].dims,l)],$=S=>`
      ${S.registerUniforms(p).declareVariables(m,h)}
        ${Jh(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:$,getRunData:()=>({outputs:[c],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:y})}},ef=(t,e)=>{Xh(t.inputs,e);let r=Qh(t.inputs,e);t.compute(Zh(t.inputs,r),{inputs:[0]})},tf=t=>{let e=t.starts,r=t.ends,n=t.axes;return He({starts:e,ends:r,axes:n})}}),rf,nf,af,sf,aw=Z(()=>{xe(),Me(),lt(),Ae(),rf=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},nf=(t,e)=>{let r=t.dims,n=X.size(r),a=64,s=e.axis;if(s<0&&(s=r.length+s),s<r.length-1)throw new Error("softmax only supports last axis for now.");let i=r[s],o=n/i,u=nt(i),l=i/u,c=($,S)=>S===4?`max(max(${$}.x, ${$}.y), max(${$}.z, ${$}.w))`:S===2?`max(${$}.x, ${$}.y)`:S===3?`max(max(${$}.x, ${$}.y), ${$}.z)`:$,h=Q("x",t.dataType,t.dims,u),m=_e("result",t.dataType,t.dims,u),_=h.type.value,p=dt(t.dataType)==="f32"?`var threadMax = ${_}(-3.402823e+38f);`:`var threadMax = ${_}(-65504.0h);`,y=$=>`
      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 = ${_}(${Cr("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:y}},af=(t,e)=>{rf(t.inputs),t.compute(nf(t.inputs[0],e))},sf=t=>He({axis:t.axis})}),of,uf,lf,df,cf,pf,hf,iw=Z(()=>{xe(),Me(),lt(),Ae(),of=t=>{if(!t||t.length<1)throw new Error("too few inputs")},uf=(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),He({numOutputs:n,axis:e.axis,splitSizes:r})},lf=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;
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      fn writeBufferData(output_number: u32, indices: ${t[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},cf=(t,e)=>{let r=t[0].dims,n=X.size(r),a=t[0].dataType,s=X.normalizeAxis(e.axis,r.length),i=new Array(e.numOutputs),o=Q("input",a,r.length),u=new Array(e.numOutputs),l=[],c=[],h=0,m=[{type:12,data:n}];for(let p=0;p<e.numOutputs;p++){h+=e.splitSizes[p],u[p]=h;let y=r.slice();y[e.axis]=e.splitSizes[p],c.push(y),i[p]=_e(`output${p}`,a,y.length),l.push({dims:c[p],dataType:t[0].dataType})}m.push({type:12,data:u},...ye(r,...c));let _=p=>`
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  ${lf(u.length)}
  ${df(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)};
      ${o.indicesSet("indices",s,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:_,getRunData:()=>({outputs:l,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:m})}},pf=(t,e)=>{of(t.inputs);let r=t.inputs.length===1?e:uf(t.inputs,e);t.compute(cf(t.inputs,r),{inputs:[0]})},hf=t=>{let e=t.axis,r=t.splitSizes,n=t.numOutputs<0?r.length:t.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return He({axis:e,numOutputs:n,splitSizes:r})}}),$o,ff,mf,gf,_f,sw=Z(()=>{xe(),Me(),Ae(),$o=t=>Array.from(t.getBigInt64Array(),Number),ff=t=>{if(!t||t.length!==2)throw new Error("Tile requires 2 inputs.");if(t[0].dataType!==1&&t[0].dataType!==6&&t[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(t[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(t[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if($o(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")},mf=(t,e)=>{let r=[];for(let n=0;n<t.length;++n)r.push(t[n]*e[n]);return r},gf=t=>{let e=t[0].dims,r=$o(t[1]),n=mf(e,r),a=X.size(n),s=t[0].dataType,i=Q("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}},_f=t=>{ff(t.inputs),t.compute(gf(t.inputs),{inputs:[0]})}}),wf,yf,bf,ow=Z(()=>{xe(),Me(),Ae(),wf=(t,e,r,n,a)=>{let s=_e("output_data",a,r.length,4),i=Q("a_data",e[1].dataType,e[1].dims.length,4),o=Q("b_data",e[2].dataType,e[2].dims.length,4),u=Q("c_data",e[0].dataType,e[0].dims.length,4),l,c=(h,m,_)=>`select(${m}, ${h}, ${_})`;if(!n)l=s.setByOffset("global_idx",c(i.getByOffset("global_idx"),o.getByOffset("global_idx"),u.getByOffset("global_idx")));else{let h=(m,_,p="")=>{let y=`a_data[index_a${_}][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(y,$,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()}
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        ${l}
      }`},yf=t=>{let e=t[1].dims,r=t[2].dims,n=t[0].dims,a=t[1].dataType,s=!(X.areEqual(e,r)&&X.areEqual(r,n)),i=e,o=X.size(e);if(s){let l=pn.calcShape(pn.calcShape(e,r,!1),n,!1);if(!l)throw new Error("Can't perform where op on the given tensors");i=l,o=X.size(i)}let u=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:l=>wf(l,t,i,s,a),getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:u},...ye(n,e,r,i)]})}},bf=t=>{t.compute(yf(t.inputs))}}),vf,uw=Z(()=>{I_(),Od(),A_(),M_(),O_(),z_(),kd(),Qc(),N_(),F_(),L_(),U_(),W_(),V_(),G_(),H_(),j_(),q_(),K_(),qc(),Y_(),X_(),Q_(),J_(),Z_(),Us(),ew(),tw(),rw(),nw(),aw(),iw(),sw(),Gn(),qs(),ow(),vf=new Map([["Abs",[Ud]],["Acos",[Wd]],["Acosh",[Vd]],["Add",[Cc]],["ArgMax",[yd,Vs]],["ArgMin",[wd,Vs]],["Asin",[Gd]],["Asinh",[Hd]],["Atan",[jd]],["Atanh",[qd]],["Attention",[Md]],["AveragePool",[gh,mh]],["BatchNormalization",[Bd]],["BiasAdd",[Fd]],["BiasSplitGelu",[Sc]],["Cast",[Yd,Kd]],["Ceil",[Jd]],["Clip",[Qd]],["Concat",[xd,Sd]],["Conv",[ao,no]],["ConvTranspose",[lp,ap]],["Cos",[Zd]],["Cosh",[ec]],["CumSum",[cp,pp]],["DepthToSpace",[gp,_p]],["Div",[Tc]],["Einsum",[xp,Sp]],["Elu",[tc,oi]],["Equal",[Ic]],["Erf",[rc]],["Exp",[nc]],["Expand",[Tp]],["FastGelu",[Ap]],["Floor",[ac]],["FusedConv",[ao,no]],["Gather",[Rp,zp]],["GatherElements",[Np,Dp]],["Gelu",[ic]],["Gemm",[Wp,Up]],["GlobalAveragePool",[wh,_h]],["GlobalMaxPool",[$h,vh]],["Greater",[zc]],["GreaterOrEqual",[Pc]],["HardSigmoid",[hc,pc]],["InstanceNormalization",[jp]],["LayerNormalization",[Yp]],["LeakyRelu",[sc,oi]],["Less",[Rc]],["LessOrEqual",[Bc]],["Log",[vc]],["MatMul",[jc]],["MatMulNBits",[Jp,Zp]],["MaxPool",[yh,bh]],["Mul",[Ac]],["MultiHeadAttention",[nh,th]],["Neg",[uc]],["Not",[oc]],["Pad",[ph]],["Pow",[Mc]],["Range",[kh]],["Reciprocal",[lc]],["ReduceMin",[hd]],["ReduceMean",[ud]],["ReduceMax",[pd]],["ReduceSum",[md]],["ReduceProd",[fd]],["ReduceL1",[ld]],["ReduceL2",[dd]],["ReduceLogSum",[_d]],["ReduceLogSumExp",[cd]],["ReduceSumSquare",[gd]],["Relu",[dc]],["Resize",[Wh,Vh]],["RotaryEmbedding",[jh]],["Sigmoid",[cc]],["Sin",[fc]],["Sinh",[mc]],["Slice",[ef,tf]],["SkipLayerNormalization",[Yh]],["Split",[pf,hf]],["Sqrt",[gc]],["Softmax",[af,sf]],["Sub",[Oc]],["Tan",[_c]],["Tanh",[wc]],["ThresholdedRelu",[bc,oi]],["Tile",[_f]],["Transpose",[Il,Al]],["Where",[bf]]])}),$f,lw=Z(()=>{jt(),Wr(),Ae(),$f=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){Ht(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(),Ft(t.programInfo.name)}dispose(){}build(t,e){Ht(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=kl(e,this.backend.device.limits),s=t.getShaderSource(a),i=`${n.join(`
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${a.additionalImplementations}
${s}`,o=r.createShaderModule({code:i,label:t.name});et("verbose",()=>`[WebGPU] ${t.name} shader code: ${i}`);let u=r.createComputePipeline({compute:{module:o,entryPoint:"main"},layout:"auto",label:t.name});return Ft(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]}}}),xf,Sf,kf,Ef,dw=Z(()=>{jt(),xe(),Wr(),E_(),C_(),uw(),lw(),xf=(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: ${e[n]}`)}}return r.join("|")},Sf=(t,e,r)=>{var a,s;let n=t.name;return(a=t.shaderCache)!=null&&a.hint&&(n+="["+t.shaderCache.hint+"]"),n+=":"+r+`:${xf(e,((s=t.shaderCache)==null?void 0:s.inputDependencies)??new Array(e.length).fill("dims"))}`,n},kf=class{constructor(t){t&&(this.architecture=t.architecture,this.vendor=t.vendor)}isArchitecture(t){return this.architecture===t}isVendor(t){return this.vendor===t}},Ef=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 kf(await e.requestAdapterInfo()),this.gpuDataManager=bl(this),this.programManager=new $f(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,fl(t.logLevel,!!t.debug),this.device.onuncapturederror=a=>{a.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${a.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:e,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let t=this.getCommandEncoder(),e={};this.queryType==="at-passes"&&(e.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=t.beginComputePass(e)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Ht(),this.endComputePass();let t;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&t.mapAsync(GPUMapMode.READ).then(()=>{var n;let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let a=0;a<e.length/2;a++){let s=r[a],i=s.kernelId,o=this.kernels.get(i),u=o.kernelType,l=o.kernelName,c=s.programName,h=s.inputTensorViews,m=s.outputTensorViews,_=e[a*2],p=e[a*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=_);let y=Number(_-this.queryTimeBase),$=Number(p-this.queryTimeBase);if(!Number.isSafeInteger(y)||!Number.isSafeInteger($))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:h.map(S=>({dims:S.dims,dataType:Ur(S.dataType)})),outputsMetadata:m.map(S=>({dims:S.dims,dataType:Ur(S.dataType)})),kernelId:i,kernelType:u,kernelName:l,programName:c,startTime:y,endTime:$});else{let S="";h.forEach((C,T)=>{S+=`input[${T}]: [${C.dims}] | ${Ur(C.dataType)}, `});let x="";m.forEach((C,T)=>{x+=`output[${T}]: [${C.dims}] | ${Ur(C.dataType)}, `}),console.log(`[profiling] kernel "${i}|${u}|${l}|${c}" ${S}${x}execution time: ${$-y} ns`)}Fn("GPU",`${c}::${_}::${p}`)}t.unmap(),this.pendingQueries.delete(t)}),Ft()}run(t,e,r,n,a,s){Ht(t.name);let i=[];for(let x=0;x<e.length;++x){let C=e[x].data;if(C===0)continue;let T=this.gpuDataManager.get(C);if(!T)throw new Error(`no GPU data for input: 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this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){et("info","captureBegin"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus="capturing"}captureEnd(){et("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){et("info","replay"),this.sessionStatus="replaying";let t=this.capturedCommandList.get(this.currentSessionId),e=this.capturedPendingKernels.get(this.currentSessionId),r=t.length;this.pendingKernels=[];for(let n=0;n<r;n++){let 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}`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${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"],I=A=>{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),H=[{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  ${A.registerUniforms(H).declareVariables(...P,O)}\n  ${A.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:I}},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),I=[{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(I).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],I=a?[a,r]:[r],A=y?e.compute(En(I,2,S,r.dataType),{inputs:I,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,A];p&&O.push(p);let R=e.compute(rc(e,t,A,p,h,d,_),{inputs:O,outputs:[-1]})[0];e.compute(tc(e,R,h.batchSize*h.numHeads*h.sequenceLength,v),{inputs:[R],outputs:[]});let H=[R,P];e.compute(nc(e,R,P,h,_),{inputs:H,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),I=U("weight",p[1].dataType,p[1].dims),A=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,I,A,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,I)}\n  ${E.mainStart()}\n  ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    var outputIndices = ${I.offsetToIndices(`global_idx * ${a}`)};\n    ${A()}\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    ${I.setByOffset("global_idx","value")}\n  }`;return{name:"BatchNormalization",shaderCache:{hint:`${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_ = ${r}(${t.alpha});\n\n  fn elu_f32(a: ${r}) -> ${r} {\n  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);\n  }\n\n  fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {\n  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));\n  }`,t.cacheKey))},On=(e="f32")=>`\nconst r0: ${e} = 0.3275911;\nconst r1: ${e} = 0.254829592;\nconst r2: ${e} = -0.284496736;\nconst r3: ${e} = 1.421413741;\nconst r4: ${e} = -1.453152027;\nconst r5: ${e} = 1.061405429;\n\nfn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {\n  let absv = abs(v);\n  let x = 1.0 / (1.0 + r0 * absv);\n  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));\n}`,gs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,On(t)))},ys=e=>{e.compute(ke(e.inputs[0],"Exp","exp"))},bs=e=>{e.compute(ke(e.inputs[0],"Floor","floor"))},ws=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 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}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},zs=e=>{e.compute(ke(e.inputs[0],"Log","log"))}});var pc,mc,Us,Vs=Y(()=>{"use strict";Se();_e();Rn();pc=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},mc=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=U("input",e[0].dataType,e[0].dims,4),o=U("bias",e[0].dataType,[e[0].dims[2]],4),i=j("output",e[0].dataType,t,4),u=M.size(t)/4,a=Be(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)}}),getShaderSource:p=>`\n  const M_SQRT2 = sqrt(2.0);\n  const halfChannels = ${e[0].dims[2]/4/2}u;\n\n  ${p.declareVariables(r,o,i)}\n\n  ${On(a)}\n\n  ${p.mainStart()}\n    ${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    ${i.setByOffset("global_idx","valueLeft * geluRight")}\n  }`}},Us=e=>{pc(e.inputs),e.compute(mc(e.inputs))}});var fc,hc,kt,Ws,Ns,Gs,Hs,Ls,Fs,qs,js,Ks,Ys,Zs=Y(()=>{"use strict";ye();Se();_e();fc=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w,_;typeof c=="string"?w=_=(x,E)=>`${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),I=U("bData",h,r.length,4),A;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?A=v.setByOffset("global_idx",_(x?`${S.type.value}(${S.getByOffset("0")}.x)`:S.getByOffset("global_idx"),E?`${I.type.value}(${I.getByOffset("0")}.x)`:I.getByOffset("global_idx"))):A=`\n            let outputIndices = ${v.offsetToIndices("global_idx * 4u")};\n            let offsetA = ${S.broadcastedIndicesToOffset("outputIndices",v)};\n            let offsetB = ${I.broadcastedIndicesToOffset("outputIndices",v)};\n            ${v.setByOffset("global_idx",_(a||P?S.getByOffset("offsetA / 4u"):`${S.type.value}(${S.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||O?I.getByOffset("offsetB / 4u"):`${I.type.value}(${I.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n          `}else A=v.setByOffset("global_idx",_(S.getByOffset("global_idx"),I.getByOffset("global_idx")));else{if(!u)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let x=(E,P,O="")=>{let R=`aData[indexA${P}][componentA${P}]`,H=`bData[indexB${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} = ${I.broadcastedIndicesToOffset(`outputIndices${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,H)});\n          `};d===9?A=`\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));`:A=`\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,I,v)}\n\n        ${y??""}\n\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n        ${A}\n      }`},hc=(e,t,r,o,i,u,a=r.dataType)=>{let c=!M.areEqual(r.dims,o.dims),p=r.dims,h=M.size(r.dims),d=!1,y=!1,w=[c];if(c){let _=At.calcShape(r.dims,o.dims,!1);if(!_)throw new Error("Can\'t perform binary op on the given tensors");p=_,h=M.size(p);let v=M.size(r.dims)===1,S=M.size(o.dims)===1,I=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,A=o.dims.length>0&&o.dims[o.dims.length-1]%4===0;w.push(v),w.push(S),w.push(I),w.push(A);let x=1;for(let E=1;E<p.length;E++){let P=r.dims[r.dims.length-E]??1,O=o.dims[o.dims.length-E]??1;if(P===O)x*=P;else break}x%4===0?(y=!0,d=!0):(v||S||I||A)&&(d=!0)}else d=!0;return w.push(d),{name:e,shaderCache:{hint:t+w.map(_=>_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>fc(_,r.dims,o.dims,p,d,c,y,i,r.dataType,o.dataType,a,u),getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(h/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(p)/4)},...Z(r.dims,o.dims,p)]})}},kt=(e,t,r,o,i,u)=>{e.compute(hc(t,i??"",e.inputs[0],e.inputs[1],r,o,u))},Ws=e=>{kt(e,"Add",(t,r)=>`${t}+${r}`)},Ns=e=>{kt(e,"Div",(t,r)=>`${t}/${r}`)},Gs=e=>{kt(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},Hs=e=>{kt(e,"Mul",(t,r)=>`${t}*${r}`)},Ls=e=>{let t=U("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;kt(e,"Pow",{scalar:(o,i)=>`pow_custom(${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=>{kt(e,"Sub",(t,r)=>`${t}-${r}`)},qs=e=>{kt(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},js=e=>{kt(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},Ks=e=>{kt(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},Ys=e=>{kt(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}});var _t,St,xt,Bn,Ft=Y(()=>{"use strict";ye();Se();_t=(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 ${e.activation}`)}},St=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},xt=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Bn=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[r,o]=e?.activation_params||[.2,.5];return{activation:t,alpha:r,beta:o}}else if(t==="Clip"){let[r,o]=e?.activation_params||[xn,Cn];return{activation:t,clipMax:o,clipMin:r}}else if(t==="LeakyRelu"){let[r]=e?.activation_params||[.01];return{activation:t,alpha:r}}return{activation:t}}});var tt,Dn,zn=Y(()=>{"use strict";tt=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Dn=e=>`\n      ${e?"value = value + getBiasByOutputCoords(coords);":""}\n      `});var Mn,zo=Y(()=>{"use strict";Mn=e=>`\nfn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var yc,bc,Hr,Xs,wc,Lr,vc,Un,Fr=Y(()=>{"use strict";ye();Se();_e();Ft();zn();yc=(e,t)=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `,bc=(e,t)=>e?`\n        let ACached0 = mm_Asub[k * innerElementSize][localRow];\n        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n        }`:`\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n        }`,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],I=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    ${I}\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=Be(o[0].type.tensor),I=()=>{let E=d.rank,P=h.rank,O=`var aIndices: ${d.type.indices};`;for(let R=E-2-1,H=P-1;R>=0;R--,H--)O+=`\naIndices[${R}] = ${P>1?`batchIndices[${H}]`:"batchIndices"};`;return _.forEach(R=>{O+=`\naIndices[${R}] = 0;`}),O+=`\naIndices[${E-2}] = u32(row);\n                   aIndices[${E-1}] = u32(colIn);`,O},A=()=>{let E=y.rank,P=h.rank,O=`var bIndices: ${y.type.indices};`;for(let R=E-2-1,H=P-1;R>=0;R--,H--)O+=`\nbIndices[${R}] = ${P>1?`batchIndices[${H}]`:"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        ${I()}\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        ${A()}\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],I=[8,8,1],A=[Math.ceil(_/I[0]/S[0]),Math.ceil(y/I[1]/S[1]),Math.ceil(d/I[2]/S[2])],x=v?4:1,E=[...c,y,w/x],P=E.length,O=[...p,w,_/x],R=O.length,H=[d,y,_/x],N=[{type:6,data:y},{type:6,data:_},{type:6,data:w}];St(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(H));let he=W=>{let se=h.length,Ce=An("batchDims",e[0].dataType,se,1),We=Be(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,H.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"}];xt(t,ie);let ue=Be(Ae.type.tensor),le=_t(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  ${v?Hr(S,I,We,Ce):Lr(S,I,We,Ce)}\n                   `};return{name:"MatMul",shaderCache:{hint:`${S};${t.activation};${v};${i}`,inputDependencies:K},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:N}),getShaderSource:he}}});var $c,Qs,Js=Y(()=>{"use strict";ye();Ht();_e();Ft();zn();zo();Fr();$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])",I=e?"row":"col",A=e?"col":"row",x=`\n    let inChannels = i32(uniforms.w_shape[2]);\n    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n    let outRow = ${I} / outWidth;\n    let outCol = ${I} % outWidth;\n\n    let WRow = ${A} / (i32(uniforms.w_shape[1]) * inChannels);\n    let WCol = ${A} / 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 = ${A} % 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),H=e?tt(c,h):tt(a,h),N=_t(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) -> ${H} {\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,I=p?y*w:_,A=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/A[0]/x[0]),Math.ceil(I/A[1]/x[1]),Math.ceil(d/A[2]/x[2])];Ve("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let P=v?p&&h%4!==0?3:4:1,O=A[1]*x[1],R=A[0]*x[0],H=Math.max(A[0]*P,A[1]),N=o%O===0,K=i%R===0,Q=u%H===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}];St(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}];xt(t,ee);let ae=v?4:1,Ae=Be(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        ${v?Hr(x,A,Ae,void 0,!p,H):Lr(x,A,Ae,void 0,!p,H,!1,void 0,c)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${P};${v};${N};${K};${Q};${O};${R};${H}`,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}];St(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),I=Be(S.type.tensor),A=_t(t,S.type.value,I),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 xt(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    ${A}\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]]}];St(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=Be(v.type.tensor),I=_t(t,v.type.value,S),A=U("x",e[0].dataType,c.length,i),x=U("w",e[1].dataType,p.length,i),E=[A,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 xt(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) * ${u}u;\n    index1 = index1 / width1;\n    let row = index1 % uniforms.output_shape[1];\n    let batch = index1 / uniforms.output_shape[1];\n\n    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;\n\n    var x_vals: array<${A.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] = ${A.get("batch","u32(x_height)","u32(x_width)","input_channel")};\n          } else {\n            x_vals[i] = ${A.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      ${I}\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]};${p[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:w}}});var Vo,_c,ru,Wo=Y(()=>{"use strict";ye();Se();Fr();_e();Ft();Vo=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u[u.length-2],p=a[a.length-1],h=u[u.length-1],d=Me(p),y=Me(h),w=Me(c),_=M.size(r)/d/w,v=e.length>2,S=o?o.slice(0,-2):r.slice(0,-2),A=[M.size(S),c,p],x=[{type:12,data:_},{type:12,data:c},{type:12,data:p},{type:12,data:h}];St(t,x),x.push(...Z(S,u,a)),v&&x.push(...Z(e[2].dims)),x.push(...Z(A));let E=P=>{let O=An("batch_dims",e[0].dataType,S.length),R=U("a",e[0].dataType,u.length,y),H=U("b",e[1].dataType,a.length,d),N=j("output",e[0].dataType,A.length,d),K=Be(N.type.tensor),Q=_t(t,N.type.value,K),he=[R,H],W="";if(v){let ie=i?d:1;he.push(U("bias",e[2].dataType,e[2].dims.length,ie)),W=`${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"}];xt(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(${H.type.value}(a_data${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(H,ee)}\n    let b_offset = ${H.indicesToOffset("b_indices")};\n    var values: array<${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=At.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 H=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,H),{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],H,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;H=t[0].reshape([1,R,Ce]),N=se.reshape([1,Ce,v]),K=[1,R,v]}else H=t[0].reshape([R,a*c,p]),N=se.reshape([1,p,v]),K=[R,w*_,v];Q.push(H),Q.push(N)}else H=t[0].reshape([R,p,a*c]),N=t[1].reshape([1,v,p]),K=[R,v,w*_],Q.push(N),Q.push(H);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 I=!0,A=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=A);let x=[t[0],A];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,I),{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();Ht();_e();Ft();zn();zo();Fr();Ac=(e,t=!1,r,o,i=4)=>{let u=A=>{switch(A){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 ${A} 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=_t(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,I=p?y*w:_,A=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/A[0]/x[0]),Math.ceil(I/A[1]/x[1]),Math.ceil(d/A[2]/x[2])];Ve("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let P=v?4:1,O=Math.max(A[0]*P,A[1]),R=v?4:1,H=[t.kernelShape[p?1:2],t.kernelShape[p?2:3]],N=[H[0]+(t.dilations[0]<=1?0:(H[0]-1)*(t.dilations[0]-1)),H[1]+(t.dilations[1]<=1?0:(H[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:H},{type:6,data:K}];St(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:H.length},{name:"pads",type:"i32",length:K.length}];xt(t,me);let ie=Be(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        ${v?Hr(x,A,ie,void 0,!p,O):Lr(x,A,ie,void 0,!p,O,!1,void 0,c)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${x};${A};${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();Ht();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),I=U("Dy",t[0].dataType,t[0].dims.length,v),A=[I,S];o&&A.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 = ${I.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${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 =  ${I.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${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 = ${I.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${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 = ${I.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${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?I.get("batch","idyR","idyC","inputChannel"):I.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(...A,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 = ${a}`);let c=t.format==="NHWC",p=["rank","rank"],h=[t.strides[0],t.strides[1]],d=[t.kernelShape[c?1:2],t.kernelShape[c?2:3]],y=[t.dilations[0],t.dilations[1]],w=[d[0]+(t.dilations[0]<=1?0:(t.kernelShape[c?1:2]-1)*(t.dilations[0]-1)),d[1]+(t.dilations[1]<=1?0:(t.kernelShape[c?2:3]-1)*(t.dilations[1]-1))],_=[w[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),w[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],v=!1,S=t.group,I=e[1].dims,A=I[0]/S,x=I[1],E=[{type:12,data:u},{type:12,data:h},{type:12,data:d},{type:12,data:y},{type:12,data:w},{type:6,data:_},{type:12,data:A},{type:12,data:x},...Z(e[0].dims,e[1].dims)];o&&(E.push(...Z(e[2].dims)),p.push("rank")),E.push(...Z(i));let P=a[1]===1&&a[2]===1,O=R=>{let H=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:h.length},{name:"filter_dims",type:"u32",length:d.length},{name:"dilations",type:"u32",length:d.length},{name:"effective_filter_dims",type:"u32",length:w.length},{name:"pads",type:"i32",length:_.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],N=Be(e[0].dataType);return`${Ic(R,e,i,o,P,v,N,H,c)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:p},getRunData:()=>({dispatchGroup:{x:a[0],y:a[1],z:a[2]},outputs:[{dims:r?r(i):i,dataType:e[0].dataType}],programUniforms:E}),getShaderSource:O}}});var Tc,Ec,Pc,su,uu,kc,Oc,Rc,Bc,du,lu=Y(()=>{"use strict";iu();au();Ft();Sr();Tc=(e,t,r,o,i,u)=>(e-1)*t+r+(o-1)*i+1-u,Ec=(e,t,r,o,i)=>{let u=Math.floor(e/2);t==="SAME_UPPER"?(r[o]=u,r[i]=e-u):t==="SAME_LOWER"&&(r[o]=e-u,r[i]=u)},Pc=(e,t,r,o,i,u,a,c,p,h)=>{let d=e.length-2,y=h.length===0;if(p.length===0)for(let v=0;v<d;++v)p.push(0);let w=e[0],_=t[c?3:1]*i;for(let v=0,S=e.length-d-(c?1:0);v<d;++v,++S){let I=e[S],A=y?I*a[v]:h[v],x=Tc(I,a[v],u[v],t[S],r[v],A);Ec(x,o,u,v,v+d),y&&h.push(a[v]*(I-1)+p[v]+(t[S]-1)*r[v]+1-u[v]-u[v+d])}h.splice(0,0,w),h.splice(c?3:1,0,_)},su=(e,t)=>{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((y,w)=>y*w,1)===0){r.length=0;for(let y=2;y<t[1].dims.length;++y)r.push(t[1].dims[y])}let o=e.format==="NHWC";r.splice(0,0,t[1].dims[0]),r.splice(o?3:1,0,t[1].dims[1]);let i=e.pads.slice(),u=e.outputShape.slice(),a=e.outputPadding.slice(),c=t[0].dims,p=e.dilations.slice();if(p.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;p=new Array(y).fill(1)}let h=e.strides.slice();if(h.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;h=new Array(y).fill(1)}Pc(c,r,p,e.autoPad,e.group,i,h,o,a,u);let d=Object.assign({},e);return Object.assign(d,{kernelShape:r,pads:i,outputPadding:a,outputShape:u,dilations:p,strides:h}),d},uu=e=>{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,u=e.group,a=e.kernelShape,c=e.pads,p=e.strides,h=e.wIsConst(),d=e.outputPadding,y=e.outputShape;return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,outputPadding:d,outputShape:y,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${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 ${u}D`);if(t.kernelShape.reduce((d,y)=>d+y,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Oc=[2,3,1,0],Rc=(e,t,r)=>{let o=su(r,t),i=r.format==="NHWC",u=o.outputShape,a=u[i?3:1],c=t[0].dims[i?3:1];if(o.group!==1||a===1&&c===1){e.compute(Lo(t,o));return}let p=u[i?1:2],h=u[i?2:3],d=t[1].dims[2],y=t[1].dims[3],w=i?p*h:a,_=i?a:p*h,v=d*y*c,S=!0,I=e.kernelCustomData.wT??e.compute(yt(t[1],Oc),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=I);let A=[t[0],I],x=t.length===3;x&&(!i&&t[2].dims.length===1?A.push(t[2].reshape([t[2].dims[0],1,1])):A.push(t[2])),e.compute(ou(A,o,u,w,_,v,x,S),{inputs:A})},Bc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let u=t.dilations;(u.length===0||u[0]===0)&&(u=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let c=t.pads;c.length===0&&(c=[0,0]),c=[0,c[0],0,c[1]],a=[1].concat(a),u=[1].concat(u),i=[1].concat(i);let 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r=e[0].dims,o=r,i=2,u=M.sizeToDimension(r,i),a=M.sizeFromDimension(r,i),c=Me(a),p=a/c,h=[r[0],r[1],p],d=["rank","type","type"],y=[{type:12,data:a},{type:12,data:p}];y.push(...Z(h,h));let w=_=>{let v=U("x",e[0].dataType,h.length,c),S=U("scale",e[1].dataType,e[1].dims),I=U("bias",e[2].dataType,e[2].dims),A=j("output",e[0].dataType,h.length,c),x=[v,S,I,A],E=v.type.value,P=c===1?"f32":`vec${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(${I.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      ${A.set("batch","channel","h","value")};\n    }\n  }`};return{name:"InstanceNormalization",shaderCache:{hint:`${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${p}f`,w=(R,H)=>`${d}(${R}, ${H})`,_=i*a/p,v=Math.ceil(u/h),S=["type"],I=[{type:12,data:v},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(u*a/p)}],A=R=>{let H=U("input",t.dataType,t.dims,p);return`\n  ${R.declareVariables(H)}\n  @group(0) @binding(1) var<storage, read_write> output : array<${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 = ${Lt("f32",p)};\n    var squaredSum = ${Lt("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:I}),getShaderSource:A},{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 H=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<${d}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${H.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 = ${Lt("f32",p)};\n    var squaredSum = ${Lt("f32",p)};\n    for (var i: u32 = 0; i < min(${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};${c}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:[i,a,2],dataType:1}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:E}),getShaderSource:O},{inputs:[x,r,o],outputs:[-1]})[0]},ep=(e,t,r)=>{let o=t[0].dims,i=o,u=o[0],a=o[o.length-1],c=M.sizeFromDimension(o,1)/a,p=Me(a),h=M.size(i)/p,d=[{type:12,data:c},{type:12,data:Math.floor(a/p)}],y=["type","type"],w=Jc(e,t[0],t[1],t[2],u,c,a,r.epsilon),_=v=>{let S=Be(t[0].dataType),I=p===1?"vec2f":`mat2x${p}f`,A=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<${x.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${I}>;\n  @group(0) @binding(2) var<storage, read_write> output : array<${E.type.storage}>;\n  struct Uniforms {H: u32, C : u32};\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\n\n  ${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], ${A}(scale[0]), ${A}(scale[1]));\n  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${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"],I=[{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 A=r>1,x=r>2,E=O=>{let R=Be(e[0].dataType),H=[U("x",e[0].dataType,e[0].dims,v),U("scale",u.dataType,u.dims,v)];a&&H.push(U("bias",a.dataType,a.dims,v)),H.push(j("output",e[0].dataType,c,v)),A&&H.push(j("mean_data_output",1,_)),x&&H.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(...H)}\n  ${O.mainStart()}\n    ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var mean_vector = ${Lt("f32",v)};\n    var mean_square_vector = ${Lt("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      output[j + offset] = ${H[0].type.value}((f32input ${o?"":"- mean"}) * inv_std_dev * f32scale\n        ${a?`+ ${ir(R,v,"bias[j]")}`:""}\n      );\n    }\n\n    ${A?"mean_data_output[global_idx] = mean":""};\n    ${x?"inv_std_output[global_idx] = inv_std_dev":""};\n  }`},P=[{dims:c,dataType:e[0].dataType}];return A&&P.push({dims:_,dataType:1}),x&&P.push({dims:_,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${v};${r};${o}`,inputDependencies:S},getRunData:()=>({outputs:P,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:I}),getShaderSource:E}},Uu=(e,t)=>{tp(e.inputs),e.compute(rp(e.inputs,t,e.outputCount))}});var np,op,Wu,Nu,Gu=Y(()=>{"use strict";ye();Se();Ze();_e();np=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],o=r.dims.length;if(r.dims[o-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let 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),I=Me(t.k),A=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,H=d.concat([c,h]),N=M.size(H)/R/S,K=O?[]:[{type:12,data:N},{type:12,data:t.blockSize}],Q=[y,c,p/I],he=M.convertShape(e[1].dims).slice();he.splice(-1,1,_/A),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,I),ae=U("b",12,he.length,A),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=Be(e[0].dataType),ne=(()=>{switch(I){case 1:return`array<${G}, 8>`;case 2:return`mat4x2<${G}>`;case 4:return`mat2x4<${G}>`;default:throw new Error(`${I}-component is not supported.`)}})(),xe=`\n        for (var word: u32 = 0; word < ${_}; word += ${A}) {\n          ${ae.indicesSet("b_indices","2","word")};\n          let b_data = ${ae.getByIndices("b_indices")};\n          for (var i: u32 = 0; i < ${A}; i++) {\n            let b_value: u32 = ${A===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},(De,Ge)=>`${G}(b_value_lower[${Ge}]), ${G}(b_value_upper[${Ge}])`).join(", ")});\n            let b_dequantized_values = ${(()=>I===1?`${ne}(${Array.from({length:8},(De,Ge)=>`(b_quantized_values[${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/I}; 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]":""} += ${Array.from({length:8/I},(De,Ge)=>`${I===1?`a_data[${Ge}] * b_dequantized_values[${Ge}]`:`dot(a_data[${Ge}], b_dequantized_values[${Ge}])`}`).join(" + ")};\n            }\n            word_offset += ${8/I};\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/I};\n            var workgroup_shared_offset: u32 = block * ${c};\n            ${xe}\n          }\n          workgroupBarrier();\n          var output_indices: ${le.type.indices};\n          var elements_per_thread: u32 = ${Math.ceil(c/a)};\n          ${le.indicesSet("output_indices","0","batch")};\n          ${le.indicesSet("output_indices",ue-1,"col")};\n          ${le.indicesSet("output_indices",ue-2,"local_id.x * elements_per_thread")};\n          var output_offset = ${le.indicesToOffset("output_indices")};\n          for (var m: u32 = 0u; m < elements_per_thread; m++) {\n            var row = m + local_id.x * elements_per_thread;\n            if (row < ${c}) {\n              var output_value: ${le.type.value} = ${le.type.value}(0);\n              var workgroup_shared_offset: u32 = row;\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 / ${I};\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:H,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,I=0,A=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]!==A)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]!==A)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],I=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]!==A)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]!==A)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,H=!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:I,inputHiddenSize:0,hiddenSize:_,vHiddenSize:O,headSize:A,vHeadSize:Math.floor(O/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:H,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],I=t.pads[t.pads.length-2];w=!!(S+I),u.push({type:12,data:_},{type:12,data:v},{type:12,data:S},{type:12,data:I}),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="",I="",A="",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                  ${u}\n                }`,i.kernelShape.length===2){let P=r-(w?3:2);y?I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${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              `:I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${P}] = indices[${P}] * uniforms.sh - uniforms.phStart + j;\n                `,A=`\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              ${I}\n              ${S}\n              ${A}\n              ${a}\n\n              output[global_idx] = value;\n            }`}else{if(w)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let S=i.kernelShape.length,I=i.pads.length,A="";return h?A=`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset("xIndices")}];\n                ${u}\n              }`:A=`\n              }\n              let x_val = x[${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",I)};\n                  ${A}\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};${w};${_};${v}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(M.size(u)/64)},programUniforms:d}),getShaderSource:I=>Xu(I,a,t.dims.length,u.length,i,p,h,0,y,w,_,v)}},td=e=>{let t=e.count_include_pad!==0,r=Ju(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let o={countIncludePad:t,...r,cacheKey:""};return{...o,cacheKey:hp(o)}},rd=(e,t)=>{Nn(e.inputs),e.compute(ed("AveragePool",e.inputs[0],!1,t))},nd={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},od=e=>{let t=e.format;return{format:t,...nd,cacheKey:t}},id=(e,t)=>{Nn(e.inputs),e.compute(ed("GlobalAveragePool",e.inputs[0],!0,t))},ad=(e,t,r,o)=>{let[i,u]=Yu(t,o,r),a=`\n      value = max(x_val, value);\n    `,c="",p=U("x",t.dataType,t.dims.length),h=["rank"],[d,y,w,_,v]=Zu(u,i);return d.push(...Z(t.dims,u)),{name:e,shaderCache:{hint:`${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=I=>{let A=I===w?"row":"col";return`\n      fn ${A}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${v} {\n        var output_index = ${t.indicesGet("output_indices",I)};\n        var originalIdx: ${v} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[I]},\n        ${o[I]}, ${r[I]}, ${u[I]}, ${u[I]} + ${r.length});\n        var fractOriginalIdx: ${v} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${c} && (originalIdx < 0 || originalIdx > (${r[I]} - 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 ${A}: ${v} = originalIdx + ${v}(i);\n          if (${A} < 0 || ${A} >= ${r[I]}) {\n            ${(()=>h?`coefs[i + 1] = 0.0;\n                        continue;`:c?`return ${p};`:`${A} = max(0, min(${A}, ${r[I]} - 1));`)()};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",I,`u32(${A})`)};\n          data[i + 1] = ${I===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,I=y.type.value,A=x=>`\n      ${_?"":`\n      ${Sp(t.coordinateTransformMode,I)};\n      ${(()=>{switch(t.mode){case"nearest":return`\n              ${Pp(y,a)};\n              ${xp(t.nearestMode,r,I)};\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:A,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=I=>{let A=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 I.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        ${I.declareVariables(A,x,E,P,O)}\n\n        ${I.mainStart(or)}\n          let half_rotary_emb_dim = uniforms.${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          ${I.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 = ${A.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} -\n                ${A.getByOffset("j")} * ${P.get("position_id","bsnh[3]")};\n            ${O.setByOffset("i","re")}\n            let im = ${A.getByOffset("i")} * ${P.get("position_id","bsnh[3]")} +\n                ${A.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            ${O.setByOffset("k",A.getByOffset("k"))}\n          }\n        }`};return{name:"RotaryEmbedding",shaderCache:{hint:ve({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:S,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(w)/or)},programUniforms:v})}},bd=(e,t)=>{zp(e.inputs,t),e.compute(Mp(e.inputs,t))}});var Up,Vp,vd,$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,I=64,A=Me(h),x=[{type:12,data:p},{type:12,data:A},{type:12,data:h},{type:1,data:t.epsilon}],E=O=>{let R=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],H=[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&&H.push(U("beta",e[3].dataType,e[3].dims,A)),w&&H.push(U("bias",e[4].dataType,e[4].dims,A)),H.push(j("output",e[0].dataType,c,A)),_&&H.push(j("mean_output",1,d)),v&&H.push(j("inv_std_output",1,d)),S&&H.push(j("input_skip_bias_sum",e[0].dataType,c,A));let N=Be(e[0].dataType),K=Be(1,A);return`\n\n      ${O.registerUniforms(R).declareVariables(...H)}\n      var<workgroup> sum_shared : array<${K}, ${I}>;\n      var<workgroup> sum_squared_shared : array<${K}, ${I}>;\n\n      ${O.mainStart([I,1,1])}\n        let ix = local_id.x;\n        let iy = global_id.x / ${I};\n\n        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;\n        var stride = hidden_size_vectorized / ${I};\n        let offset = ix * stride + iy * hidden_size_vectorized;\n        let offset1d = stride * ix;\n        if (ix == ${I-1}) {\n          stride = hidden_size_vectorized - stride * ix;\n        }\n        for (var i: u32 = 0; i < stride; i++) {\n          let skip_value = skip[offset + i];\n          let bias_value = ${w?"bias[offset1d + i]":N+"(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(N,A,"value")};\n          sum_shared[ix] += f32_value;\n          sum_squared_shared[ix] += f32_value * f32_value;\n        }\n        workgroupBarrier();\n\n        var reduce_size : u32 = ${I};\n        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {\n          reduce_size = curr_size + (reduce_size & 1);\n          if (ix < curr_size) {\n            sum_shared[ix] += sum_shared[ix + reduce_size];\n            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];\n          }\n          workgroupBarrier();\n        }\n\n        let sum = sum_shared[0];\n        let square_sum = sum_squared_shared[0];\n        let mean = ${$t("sum",A)} / f32(uniforms.hidden_size);\n        let inv_std_dev = inverseSqrt(${$t("square_sum",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\n        for (var i: u32 = 0; i < stride; i++) {\n          output[offset + i] = (output[offset + i] ${i?"":`- ${N}(mean)`}) *\n            ${N}(inv_std_dev) * gamma[offset1d + i]\n            ${y?"+ beta[offset1d + i]":""};\n        }\n      }`},P=[{dims:c,dataType:e[0].dataType}];return r>1&&P.push({dims:d,dataType:1}),r>2&&P.push({dims:d,dataType:1}),r>3&&P.push({dims:u,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${A};${_};${v};${S}`,inputDependencies:e.map((O,R)=>"type")},getShaderSource:E,getRunData:()=>({outputs:P,dispatchGroup:{x:Math.ceil(p/h)},programUniforms:x})}},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 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H=this.gpuDataManager.create(E,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(H.buffer,0,R,0,E),this.gpuDataManager.release(H.id),v={offset:0,size:E,buffer:H.buffer}}let S=this.programManager.normalizeDispatchGroupSize(h),I=S[1]===1&&S[2]===1,A=nm(t,r,I),x=this.programManager.getArtifact(A);if(x||(x=this.programManager.build(t,S),this.programManager.setArtifact(A,x),Ve("info",()=>`[artifact] key: ${A}, programName: ${t.name}`)),d&&x.uniformVariablesInfo){if(d.length!==x.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${x.uniformVariablesInfo.length}, got ${d.length} in program "${x.programInfo.name}".`);for(let E=0;E<d.length;E++){let P=d[E],O=P.type,R=typeof P.data=="number"?1:P.data.length,[H,N]=x.uniformVariablesInfo[E];if(O!==H||R!==N)throw new Error(`Uniform variable ${E} mismatch: expect type ${H} with size ${N}, got type ${O} with size ${R} in program 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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'}),Ir,Et,mn,Kn,Yn,ko,_i,Kr,Yr,Ff,Xn,Lf,Uf,Wf,Vf,Gf,Hf,jf,qf=Z(()=>{var t;jt(),pw(),Un(),Ir=()=>!!Ue.wasm.proxy&&typeof document<"u",mn=!1,Kn=!1,Yn=!1,_i=new Map,Kr=(e,r)=>{let n=_i.get(e);n?n.push(r):_i.set(e,[r])},Yr=()=>{if(mn||!Kn||Yn||!Et)throw new Error("worker not 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* @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 yw=Object.freeze({__proto__:null,get InferenceSession(){return vs},get TRACE(){return Fn},get TRACE_FUNC_BEGIN(){return Ht},get TRACE_FUNC_END(){return Ft},get Tensor(){return bt},get TrainingSession(){return $s},default:ww,get env(){return Ue},get registerBackend(){return Fr}});const bw=(t,e)=>{const r=typeof document<"u"?document.createElement("canvas"):new OffscreenCanvas(1,1);r.width=t.dims[3],r.height=t.dims[2];const n=r.getContext("2d");if(n!=null){let a,s;(e==null?void 0:e.tensorLayout)!==void 0&&e.tensorLayout==="NHWC"?(a=t.dims[2],s=t.dims[3]):(a=t.dims[3],s=t.dims[2]);const i=(e==null?void 0:e.format)!==void 0?e.format:"RGB",o=e==null?void 0:e.norm;let u,l;o===void 0||o.mean===void 0?u=[255,255,255,255]:typeof o.mean=="number"?u=[o.mean,o.mean,o.mean,o.mean]:(u=[o.mean[0],o.mean[1],o.mean[2],0],o.mean[3]!==void 0&&(u[3]=o.mean[3])),o===void 0||o.bias===void 0?l=[0,0,0,0]:typeof 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Error("Can not access image data")}else if(n){let c,h;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(c=e.resizedHeight,h=e.resizedWidth):(c=t.height,h=t.width),e!==void 0&&(o=e),o.format="RGBA",o.height=c,o.width=h,e!==void 0){const m=u();m.width=h,m.height=c;const _=l(m);if(_!=null)_.putImageData(t,0,0),i=_.getImageData(0,0,h,c).data;else throw new Error("Can not access image data")}else i=t.data}else if(a){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const c=u();c.width=t.width,c.height=t.height;const h=l(c);if(h!=null){const m=t.height,_=t.width;return h.drawImage(t,0,0,_,m),i=h.getImageData(0,0,_,m).data,o.height=m,o.width=_,Co(i,o)}else throw new Error("Can not access image data")}else{if(s)return new Promise((c,h)=>{const m=u(),_=l(m);if(!t||!_)return h();const p=new Image;p.crossOrigin="Anonymous",p.src=t,p.onload=()=>{m.width=p.width,m.height=p.height,_.drawImage(p,0,0,m.width,m.height);const 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gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${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=wi.get(e);if(l===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(r)){if(e==="float16")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=To.get(e.constructor);if(l===void 0)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);a=l,o=e}if(u===void 0)u=[o.length];else if(!Array.isArray(u))throw new TypeError("A tensor's dims must be a number array");s=u,this.cpuData=o,this.dataLocation="cpu"}const i=Cw(s);if(this.cpuData&&i!==this.cpuData.length)throw new Error(`Tensor's size(${i}) does not match data length(${this.cpuData.length}).`);this.type=a,this.dims=s,this.size=i}static async fromImage(e,r){return $w(e,r)}static fromTexture(e,r){return xw(e,r)}static fromGpuBuffer(e,r){return Sw(e,r)}static fromPinnedBuffer(e,r,n){return kw(e,r,n)}toDataURL(e){return bw(this,e)}toImageData(e){return vw(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: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return Tw(this,e)}};const Iw=ur,Qn=[];let Io,Xr;Br.IS_NODE_ENV?(Xr=We??fr,Qn.push("cpu"),Io=["cpu"]):(Xr=yw,Br.IS_WEBGPU_AVAILABLE&&Qn.push("webgpu"),Qn.push("wasm"),Io=["wasm"]);const Aw=Xr.InferenceSession;function Mw(t){let e=Io;if(t){if(!Qn.includes(t))throw new Error(`Unsupported device: "${t}". Should be one of: ${Qn.join(", ")}.`);e=[t]}return e}async function tm(t,e){return await Aw.create(t,e)}function rm(t){return t instanceof Xr.Tensor}const Jt=Xr==null?void 0:Xr.env;Jt!=null&&Jt.wasm&&(Jt.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.19.0-dev.20240509-69cfcba38a/dist/",Jt.wasm.proxy=!Br.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(Jt.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(Jt.wasm.simd=!1));function Ow(){var t;return(t=Jt==null?void 0:Jt.wasm)==null?void 0:t.proxy}wt.backends.onnx=Jt;const Ao=async(t,e,r)=>{const n=await tm(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 new fe(i[r])}};class Mo{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=Ao(new Uint8Array([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=Ao(new Uint8Array([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=Ao(new Uint8Array([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,"y")),this._matmul}}G(Mo,"session_options",{});const nm=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){G(this,"ort_tensor");return rm(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new Iw(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=lr(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 zw(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}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=lr(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:lr(h,this.dims[l],l,!1),m=m===null?this.dims[l]:lr(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 Rw(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=lr(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=lr(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 Dw(this.dims)}squeeze(e=null){return new fe(this.type,this.data,sm(this.dims,e))}squeeze_(e=null){return this.dims=sm(this.dims,e),this}unsqueeze(e=null){return new fe(this.type,this.data,om(this.dims,e))}unsqueeze_(e=null){return this.dims=om(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 Oo(this,e,r)}to(e){if(this.type===e)return this;if(!nm.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);return new fe(e,nm[e].from(this.data),this.dims)}}function zw(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 Rw(t,e){const[r,n]=K0(t.data,t.dims,e);return new fe(t.type,r,n)}function am(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=q0(t.data,[s,i,o],[e,r],n,a);return new fe(t.type,u,[s,e,r])}async function im(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 Mo.bilinear_interpolate_4d;else if(r==="bicubic")a=await Mo.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})}function sm(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 om(t,e){return e=lr(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function lr(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 dr(t,e=0){e=lr(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 y=m%p;h===e&&(y+=i),c+=y*_,_*=r[h],m=Math.floor(m/p)}a[c]=u.data[l]}i+=u.dims[e]}}return new fe(s,a,r)}function Jn(t,e=0){return dr(t.map(r=>r.unsqueeze(e)),e)}function Pw(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=lr(e,t.dims.length);const a=Oo(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 Oo(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=lr(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 Bw(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 y,$;m<_&&m<p?(y=m,$=0):_<m&&_<p?(y=_,$=1):(y=p,$=2),a[h].data[c]=t[h-1][c-1].item()+y,s[h].data[c]=$}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}, ${o}]. 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Je)switch(t.operator.value){case"+":return new Je(r.value.concat(n.value))}else if(n instanceof Je){const a=n.value.find(s=>s.value===r.value)!==void 0;switch(t.operator.value){case"in":return new tt(a);case"not in":return new tt(!a)}}if(r instanceof Re||n instanceof Re)switch(t.operator.value){case"+":return new Re(r.value.toString()+n.value.toString())}if(r instanceof Re&&n instanceof Re)switch(t.operator.value){case"in":return new tt(n.value.includes(r.value));case"not in":return new tt(!n.value.includes(r.value))}if(r instanceof Re&&n instanceof Zt)switch(t.operator.value){case"in":return new tt(n.value.has(r.value));case"not in":return new tt(!n.value.has(r.value))}throw new SyntaxError(`Unknown operator "${t.operator.value}" between ${r.type} and ${n.type}`)}evaluateFilterExpression(t,e){const r=this.evaluate(t.operand,e);if(t.filter.type==="Identifier"){const n=t.filter;if(r instanceof Je)switch(n.value){case"list":return r;case"first":return r.value[0];case"last":return r.value[r.value.length-1];case"length":return new je(r.value.length);case"reverse":return new Je(r.value.reverse());case"sort":return new Je(r.value.sort((a,s)=>{if(a.type!==s.type)throw new Error(`Cannot compare different types: ${a.type} and ${s.type}`);switch(a.type){case"NumericValue":return a.value-s.value;case"StringValue":return a.value.localeCompare(s.value);default:throw new Error(`Cannot compare type: ${a.type}`)}}));default:throw new Error(`Unknown ArrayValue filter: ${n.value}`)}else if(r instanceof Re)switch(n.value){case"length":return new je(r.value.length);case"upper":return new Re(r.value.toUpperCase());case"lower":return new Re(r.value.toLowerCase());case"title":return new Re(gm(r.value));case"capitalize":return new Re(r.value.charAt(0).toUpperCase()+r.value.slice(1));case"trim":return new Re(r.value.trim());default:throw new Error(`Unknown StringValue filter: ${n.value}`)}else if(r instanceof je)switch(n.value){case"abs":return new je(Math.abs(r.value));default:throw new Error(`Unknown NumericValue filter: ${n.value}`)}else if(r instanceof Zt)switch(n.value){case"items":return new Je(Array.from(r.value.entries()).map(([a,s])=>new Je([new Re(a),s])));case"length":return new je(r.value.size);default:throw new Error(`Unknown ObjectValue filter: ${n.value}`)}throw new Error(`Cannot apply filter "${n.value}" to type: ${r.type}`)}else if(t.filter.type==="CallExpression"){const n=t.filter;if(n.callee.type!=="Identifier")throw new Error(`Unknown filter: ${n.callee.type}`);const a=n.callee.value;if(r instanceof Je){switch(a){case"selectattr":{if(r.value.some(c=>!(c instanceof Zt)))throw new Error("`selectattr` can only be applied to array of objects");if(n.args.some(c=>c.type!=="StringLiteral"))throw new Error("arguments of `selectattr` must be strings");const[s,i,o]=n.args.map(c=>this.evaluate(c,e));let u;if(i){const c=e.tests.get(i.value);if(!c)throw new Error(`Unknown test: ${i.value}`);u=c}else u=(...c)=>c[0].__bool__().value;const l=r.value.filter(c=>{const h=c.value.get(s.value);return h?u(h,o):!1});return new Je(l)}}throw new Error(`Unknown ArrayValue filter: ${a}`)}else throw new Error(`Cannot apply filter "${a}" to type: ${r.type}`)}throw new Error(`Unknown filter: ${t.filter.type}`)}evaluateTestExpression(t,e){const r=this.evaluate(t.operand,e),n=e.tests.get(t.test.value);if(!n)throw new Error(`Unknown test: ${t.test.value}`);const a=n(r);return new tt(t.negate?!a:a)}evaluateUnaryExpression(t,e){const r=this.evaluate(t.argument,e);switch(t.operator.value){case"not":return new tt(!r.value);default:throw new SyntaxError(`Unknown operator: ${t.operator.value}`)}}evalProgram(t,e){return this.evaluateBlock(t.body,e)}evaluateBlock(t,e){let r="";for(const n of t){const a=this.evaluate(n,e);a.type!=="NullValue"&&a.type!=="UndefinedValue"&&(r+=a.value)}return new Re(r)}evaluateIdentifier(t,e){return e.lookupVariable(t.value)}evaluateCallExpression(t,e){const r=[],n=new Map;for(const s of t.args)if(s.type==="KeywordArgumentExpression"){const i=s;n.set(i.key.value,this.evaluate(i.value,e))}else r.push(this.evaluate(s,e));n.size>0&&r.push(new Zt(n));const a=this.evaluate(t.callee,e);if(a.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${a.type}`);return a.value(r,e)}evaluateSliceExpression(t,e,r){if(!(t instanceof Je||t instanceof Re))throw new Error("Slice object must be an array or string");const n=this.evaluate(e.start,r),a=this.evaluate(e.stop,r),s=this.evaluate(e.step,r);if(!(n instanceof je||n instanceof er))throw new Error("Slice start must be numeric or undefined");if(!(a instanceof je||a instanceof er))throw new Error("Slice stop must be numeric or undefined");if(!(s instanceof je||s instanceof er))throw new Error("Slice step must be numeric or undefined");return t instanceof Je?new Je(mm(t.value,n.value,a.value,s.value)):new Re(mm(Array.from(t.value),n.value,a.value,s.value).join(""))}evaluateMemberExpression(t,e){const 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 Zt){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 Je||r instanceof Re)if(n instanceof je)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 _r?a:new er}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 Zt))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 na}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 Je))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 je(s+1)],["index0",new je(s)],["revindex",new je(n.value.length-s)],["revindex0",new je(n.value.length-s-1)],["first",new tt(s===0)],["last",new tt(s===n.value.length-1)],["length",new je(n.value.length)],["previtem",s>0?n.value[s-1]:new er],["nextitem",s<n.value.length-1?n.value[s+1]:new er]]);r.setVariable("loop",new Zt(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 er;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 je(Number(t.value));case"StringLiteral":return new Re(t.value);case"BooleanLiteral":return new tt(t.value);case"ArrayLiteral":return new Je(t.value.map(r=>this.evaluate(r,e)));case"TupleLiteral":return new dy(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 Zt(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 bi(t){switch(typeof t){case"number":return new je(t);case"string":return new Re(t);case"boolean":return new tt(t);case"object":return t===null?new na:Array.isArray(t)?new Je(t.map(bi)):new Zt(new Map(Object.entries(t).map(([e,r])=>[e,bi(r)])));case"function":return new wr((e,r)=>{const n=t(...e.map(a=>a.value))??null;return bi(n)});default:throw new Error(`Cannot convert to runtime value: ${t}`)}}var py=class{constructor(t){G(this,"parsed");const e=Kw(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=uy(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",ly);for(const[a,s]of Object.entries(t))e.set(a,s);return new cy(e).run(this.parsed).value}};async function _m(t,e){const r=await Promise.all([Sr(t,"tokenizer.json",!0,e),Sr(t,"tokenizer_config.json",!0,e)]);return e.legacy!==null&&(r[1].legacy=e.legacy),r}function hy(t,e){const r=[];let n=0;for(const a of t.matchAll(e)){const s=a[0];n<a.index&&r.push(t.slice(n,a.index)),s.length>0&&r.push(s),n=a.index+s.length}return n<t.length&&r.push(t.slice(n)),r}function vi(t,e=!0){if(t.Regex!==void 0){let r=t.Regex.replace(/\\([#&~])/g,"$1");for(const[n,a]of _y)r=r.replaceAll(n,a);return new RegExp(r,"gu")}else if(t.String!==void 0){const r=yu(t.String);return new RegExp(e?r:`(${r})`,"gu")}else return console.warn("Unknown pattern type:",t),null}function Bo(t){return new Map(Object.entries(t))}function wm(t){const e=t.dims;switch(e.length){case 1:return t.tolist();case 2:if(e[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return t.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${e.length}.`)}}function Do(t){return t.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function ym(t){return t.replace(/[\u0300-\u036f]/g,"")}function fy(t){return ym(t.toLowerCase())}function bm(t){return t>=19968&&t<=40959||t>=13312&&t<=19903||t>=131072&&t<=173791||t>=173824&&t<=177983||t>=177984&&t<=178207||t>=178208&&t<=183983||t>=63744&&t<=64255||t>=194560&&t<=195103}function my(t,e,r){const n=[];let a=0;for(;a<t.length;){if(n.push(t[a]),(r.get(t[a])??e)!==e){++a;continue}for(;a<t.length&&(r.get(t[a])??e)===e;)++a}return n}function gy(t){return t.match(/\S+/g)||[]}const aa="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",_y=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class wy{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 ia 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 yy(e);case"Unigram":return new by(e,...r);case"BPE":return new $y(e);default:if(e.vocab)return new xy(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 yy extends ia{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 ia{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=xu(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new Vw,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 Gw(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 vm=(()=>{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]]))})(),vy=U0(vm);class $y extends ia{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=Bo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r;this.bpe_ranks=new Map(e.merges.map((r,n)=>[r,n])),this.merges=e.merges.map(r=>r.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(e){if(e.length===0)return[];const r=this.cache.get(e);if(r!==void 0)return r;const n=Array.from(e);this.end_of_word_suffix&&(n[n.length-1]+=this.end_of_word_suffix);let a=[];if(n.length>1){const s=new Ww((u,l)=>u.score<l.score);let i={token:n[0],bias:0,prev:null,next:null},o=i;for(let u=1;u<n.length;++u){const l={bias:u/n.length,token:n[u],prev:o,next:null};o.next=l,this._add_node(s,o),o=l}for(;!s.isEmpty();){const u=s.pop();if(u.deleted||!u.next||u.next.deleted)continue;if(u.deleted=!0,u.next.deleted=!0,u.prev){const c={...u.prev};u.prev.deleted=!0,u.prev=c,c.prev?c.prev.next=c:i=c}const l={token:u.token+u.next.token,bias:u.bias,prev:u.prev,next:u.next.next};l.prev?(l.prev.next=l,this._add_node(s,l.prev)):i=l,l.next&&(l.next.prev=l,this._add_node(s,l))}for(let u=i;u!==null;u=u.next)a.push(u.token)}else a=n;if(this.continuing_subword_suffix)for(let s=0;s<a.length-1;++s)a[s]+=this.continuing_subword_suffix;return this.cache.set(e,a),a}_add_node(e,r){const n=this.bpe_ranks.get(r.token+this.BPE_SPLIT_TOKEN+r.next.token);n!==void 0&&(r.score=n+r.bias,e.push(r))}encode(e){const r=[];for(const n of e){const a=this.bpe(n);for(const s of a)this.tokens_to_ids.has(s)?r.push(s):this.byte_fallback?r.push(...Array.from(this.text_encoder.encode(s)).map(i=>`<0x${i.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class xy extends ia{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 Ot extends yt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new zy(e);case"Precompiled":return new Jy(e);case"Sequence":return new Oy(e);case"Replace":return new Sy(e);case"NFC":return new ky(e);case"NFKC":return new Ey(e);case"NFKD":return new Cy(e);case"Strip":return new Ty(e);case"StripAccents":return new Iy(e);case"Lowercase":return new Ay(e);case"Prepend":return new My(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class Sy extends Ot{normalize(e){const r=vi(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class ky extends Ot{normalize(e){return e=e.normalize("NFC"),e}}class Ey extends Ot{normalize(e){return e=e.normalize("NFKC"),e}}class Cy extends Ot{normalize(e){return e=e.normalize("NFKD"),e}}class Ty extends Ot{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class Iy extends Ot{normalize(e){return e=ym(e),e}}class Ay extends Ot{normalize(e){return e=e.toLowerCase(),e}}class My extends Ot{normalize(e){return e=this.config.prepend+e,e}}class Oy extends Ot{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>Ot.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class zy extends Ot{_tokenize_chinese_chars(e){const r=[];for(let n=0;n<e.length;++n){const a=e[n],s=a.charCodeAt(0);bm(s)?(r.push(" "),r.push(a),r.push(" ")):r.push(a)}return r.join("")}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(e){switch(e){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","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 Lt extends yt{static fromConfig(e){if(e===null)return null;switch(e.type){case"BertPreTokenizer":return new Ry(e);case"Sequence":return new Zy(e);case"Whitespace":return new eb(e);case"WhitespaceSplit":return new tb(e);case"Metaspace":return new Sm(e);case"ByteLevel":return new Py(e);case"Split":return new By(e);case"Punctuation":return new Dy(e);case"Digits":return new Ny(e);case"Replace":return new rb(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,r){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,r){return(Array.isArray(e)?e.map(n=>this.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class Ry extends Lt{constructor(e){super(),this.pattern=new RegExp(`[^\\s${aa}]+|[${aa}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class Py extends Lt{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=vm,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),s=>this.byte_encoder[s]).join(""))}}class By extends Lt{constructor(e){super(),this.config=e,this.pattern=vi(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:hy(e,this.pattern)}}class Dy extends Lt{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${aa}]+|[${aa}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Ny extends Lt{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class $i extends yt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new Fy(e);case"ByteLevel":return new Ly(e);case"RobertaProcessing":return new xm(e);case"BertProcessing":return new $m(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 $m extends $i{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=ut([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const s=n&&this instanceof xm?[this.sep]:[],i=n?[this.sep]:[];e=ut(e,s,r,i),a=ut(a,new Array(r.length+s.length+i.length).fill(1))}return{tokens:e,token_type_ids:a}}}class xm extends $m{}class Fy extends $i{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=ut(s,e),i=ut(i,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(s=ut(s,r),i=ut(i,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:s,token_type_ids:i}}}class Ly extends $i{post_process(e,r=null){return r&&(e=ut(e,r)),{tokens:e}}}class zt 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 Hy(e);case"Metaspace":return new Qy(e);case"ByteLevel":return new jy(e);case"Replace":return new Uy(e);case"ByteFallback":return new Wy(e);case"Fuse":return new Vy(e);case"Strip":return new Gy(e);case"Sequence":return new Ky(e);case"CTC":return new qy(e);case"BPEDecoder":return new Yy(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 Uy extends zt{decode_chain(e){const r=vi(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class Wy extends zt{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 Vy extends zt{decode_chain(e){return[e.join("")]}}class Gy extends zt{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 Hy extends zt{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 jy extends zt{constructor(e){super(e),this.byte_decoder=vy,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 qy extends zt{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 Ky extends zt{constructor(e){super(e),this.decoders=e.decoders.map(r=>zt.fromConfig(r))}decode_chain(e){return this.decoders.reduce((r,n)=>n.decode_chain(r),e)}}class Yy extends zt{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 Xy extends zt{decode_chain(e){let r="";for(let n=1;n<e.length;n+=2)r+=e[n];return[r]}}class Sm extends Lt{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 Qy extends zt{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 Jy extends Ot{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 Zy extends Lt{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>Lt.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class eb extends Lt{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class tb extends Lt{constructor(e){super()}pre_tokenize_text(e,r){return gy(e)}}class rb extends Lt{constructor(e){super(),this.config=e,this.pattern=vi(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 nb=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function ab(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"?ut(t[a],o):ut(o,t[a])}}function ib(t,e){for(const r of Object.keys(t))t[r].length=e}class Ce extends yt{constructor(r,n){super();G(this,"return_token_type_ids",!1);G(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 %}`);G(this,"padding_side","right");this._tokenizer_config=n,this.normalizer=Ot.fromConfig(r.normalizer),this.pre_tokenizer=Lt.fromConfig(r.pre_tokenizer),this.model=ia.fromConfig(r.model,n),this.post_processor=$i.fromConfig(r.post_processor),this.decoder=zt.fromConfig(r.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const a of r.added_tokens){const s=new wy(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=>`${a.lstrip?"\\s*":""}(${yu(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 _m(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}={}){const l=Array.isArray(r);let c;if(l){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");c=r.map((m,_)=>this._encode_plus(m,{text_pair:n[_],add_special_tokens:a}))}else c=r.map(m=>this._encode_plus(m,{add_special_tokens:a}))}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).");c=[this._encode_plus(r,{text_pair:n,add_special_tokens:a})]}if(o===null?s==="max_length"?o=this.model_max_length:o=kr(c.map(m=>m.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),s||i)for(let m=0;m<c.length;++m)c[m].input_ids.length!==o&&(c[m].input_ids.length>o?i&&ib(c[m],o):s&&ab(c[m],o,_=>_==="input_ids"?this.pad_token_id:0,this.padding_side));const h={};if(u){if(!(s&&i)&&c.some(_=>{var p;for(const y of Object.keys(_))if(_[y].length!==((p=c[0][y])==null?void 0:p.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 m=[c.length,c[0].input_ids.length];for(const _ of Object.keys(c[0]))h[_]=new fe("int64",BigInt64Array.from(c.flatMap(p=>p[_]).map(BigInt)),m)}else{for(const m of Object.keys(c[0]))h[m]=c.map(_=>_[m]);if(!l)for(const m of Object.keys(h))h[m]=h[m][0]}return h}_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=fy(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}={}){const{tokens:s,token_type_ids:i}=this._tokenize_helper(r,{pair:n,add_special_tokens:a}),o=this.model.convert_tokens_to_ids(s),u={input_ids:o,attention_mask:new Array(o.length).fill(1)};return this.return_token_type_ids&&i&&(u.token_type_ids=i),u}_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:ut(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 this._encode_plus(r,{text_pair:n,add_special_tokens:a}).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=wm(r)),!Array.isArray(r)||r.length===0||!W0(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 py(n),this._compiled_template_cache.set(n,_));const p=Object.create(null);for(const $ of nb){const S=this.getToken($);S&&(p[$]=S)}const y=_.render({messages:r,add_generation_prompt:a,...p,...m});if(s){const $=this._call(y,{add_special_tokens:!1,padding:i,truncation:o,max_length:u,return_tensor:l,...h});return c?$:$.input_ids}return y}}class sb extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class ob extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class ub extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class lb extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class db extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class cb extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class pb extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class hb extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class fb extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class mb extends Ce{}class gb extends Ce{}class _b extends Ce{constructor(r,n){super(r,n);G(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class wb extends Ce{constructor(){super(...arguments);G(this,"return_token_type_ids",!0)}}class yb extends Ce{}class km extends Ce{constructor(){super(...arguments);G(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}}class bb extends Ce{}class Em extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,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 vb extends Em{}class $b extends Ce{}class xb extends km{constructor(e,r){var s,i;const n=".,!?…。，、।۔،",a=(i=(s=e.pre_tokenizer)==null?void 0:s.pretokenizers[0])==null?void 0:i.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const xi="▁";class Cm extends Ce{constructor(r,n){super(r,n);G(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 %}`);G(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.`);G(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 Sm({replacement:xi,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(xi+r.replaceAll(xi," "));return n.length>1&&n[0]===xi&&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 Sb extends Cm{}class kb extends Ce{}class Eb extends Ce{}class Cb extends Ce{}class Tb extends Ce{}class Ib extends Ce{}class Ab extends Ce{}class Mb extends Ce{constructor(){super(...arguments);G(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 Ob 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 zb extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return No(this,e,r,n)}}class Rb extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,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)}}const Tm=[["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(Tm),Pb=new Map([...Tm.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"]]);class Bb extends Ce{constructor(){super(...arguments);G(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}_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.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let p=[],y=[],$=!1,S=null;const x=new Set(this.all_special_ids);for(const I of r){const B=I.tokens,P=u?I.token_timestamps:null;let F=null,j=_;if("stride"in I){const[ae,re,ie]=I.stride;if(m-=re,S=ae-ie,re&&(j=re/s+_),ie)for(let D=B.length-1;D>=0;--D){const M=B[D];if(M>=_){if(F!==null&&(M-_)*s<S)break;F=M}}}let q=[],le=[];for(let ae=0;ae<B.length;++ae){const re=B[ae];if(x.has(re)){const ie=this.decode([re]),D=Si.get(ie.slice(2,-2));if(D!==void 0){if(o!==null&&D!==o&&!n){p.push(q);const M=this.findLongestCommonSequence(p)[0],V=this.decode(M);h.text=V,c.push(h),p=[],q=[],h=l()}o=h.language=D}}else if(re>=_){const ie=(re-_)*s+m,D=qa(ie,2);if(F!==null&&re>=F)$=!0;else if($||p.length>0&&re<j)$=!1;else if(h.timestamp[0]===null)h.timestamp[0]=D;else if(D!==h.timestamp[0]){h.timestamp[1]=D,p.push(q),u&&y.push(le);const[M,V]=this.findLongestCommonSequence(p,y),K=this.decode(M);h.text=K,u&&(h.words=this.collateWordTimestamps(M,V,o)),c.push(h),p=[],q=[],y=[],le=[],h=l()}}else if(q.push(re),u){let ie=qa(P[ae]+m,2),D;ae+1<P.length?D=qa(P[ae+1]+m,2):D=null,le.push([ie,D])}}if("stride"in I){const[ae,re,ie]=I.stride;m+=ae-ie}q.length>0?(p.push(q),u&&y.push(le)):p.every(ae=>ae.length===0)&&(h=l(),p=[],q=[],y=[],le=[])}if(p.length>0){if(i&&n)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[I,B]=this.findLongestCommonSequence(p,y),P=this.decode(I);h.text=P,u&&(h.words=this.collateWordTimestamps(I,B,o)),c.push(h)}let C=Object.create(null);const T=c.map(I=>I.text).join("");if(n||a){for(let I=0;I<c.length;++I){const B=c[I];n||delete B.timestamp,a||delete B.language}if(u){const I=[];for(const B of c)for(const P of B.words)I.push(P);C={chunks:I}}else C={chunks:c}}return[T,C]}findLongestCommonSequence(r,n=null){let a=r[0],s=a.length,i=[];const o=Array.isArray(n)&&n.length>0;let u=o?[]:null,l=o?n[0]:null;for(let c=1;c<r.length;++c){const h=r[c];let m=0,_=[s,s,0,0];const p=h.length;for(let I=1;I<s+p;++I){const B=I/1e4,P=Math.max(0,s-I),F=Math.min(s,s+p-I),j=a.slice(P,F),q=Math.max(0,I-s),le=Math.min(p,I),ae=h.slice(q,le);if(j.length!==ae.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const re=j.filter((D,M)=>D===ae[M]).length,ie=re/I+B;re>1&&ie>m&&(m=ie,_=[P,F,q,le])}const[y,$,S,x]=_,C=Math.floor(($+y)/2),T=Math.floor((x+S)/2);i.push(...a.slice(0,C)),a=h.slice(T),s=a.length,o&&(u.push(...l.slice(0,C)),l=n[c].slice(T))}return i.push(...a),o?(u.push(...l),[i,u]):[i,[]]}collateWordTimestamps(r,n,a){const[s,i,o]=this.combineTokensIntoWords(r,a),u=[];for(let l=0;l<s.length;++l){const c=o[l];u.push({text:s[l],timestamp:[n[c.at(0)][0],n[c.at(-1)][1]]})}return u}combineTokensIntoWords(r,n,a=`"'“¡¿([{-`,s=`"'.。,，!！?？:：”)]}、`){n=n??"english";let i,o,u;return["chinese","japanese","thai","lao","myanmar"].includes(n)?[i,o,u]=this.splitTokensOnUnicode(r):[i,o,u]=this.splitTokensOnSpaces(r),this.mergePunctuations(i,o,u,a,s)}decode(r,n){let a;return n&&n.decode_with_timestamps?(r instanceof fe&&(r=wm(r)),a=this.decodeWithTimestamps(r,n)):a=super.decode(r,n),a}decodeWithTimestamps(r,n){const a=(n==null?void 0:n.time_precision)??.02,s=Array.from(this.all_special_ids).at(-1)+1;let i=[[]];for(const o of r)if(o>=s){const u=qa((Number(o)-s)*a,2);i.push(`<|${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(`^[${aa}]$`,"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|>"),y=h.startsWith(" "),$=h.trim(),S=l.test($);if(p||y||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]=ut(u[c],u[h]),l[h]=ut(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]=ut(u[c],u[h]),l[c]=ut(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){r=r.toLowerCase();let i=Pb.get(r);if(i===void 0)if(Si.has(r))i=r;else{const l=r.length===2?Si.keys():Si.values();throw new Error(`Language "${r}" is not supported. Must be one of: ${JSON.stringify(l)}`)}const o=this.model.tokens_to_ids.get(`<|${i}|>`);if(o===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);s.push(o)}else s.push(null);if(n){if(n=n.toLowerCase(),n!=="transcribe"&&n!=="translate")throw new Error(`Task "${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 https://github.com/xenova/transformers.js/issues/new/choose.`);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 https://github.com/xenova/transformers.js/issues/new/choose.');s.push(i)}return s.map((i,o)=>[o+1,i]).filter(i=>i[1]!==null)}}class Db extends Ce{}class Nb extends Ce{}class Fb extends Ce{}class Lb 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: ${JSON.stringify(this.supported_language_codes)}`),ut([a],super._encode_text(s))}}}class Ub extends Ce{}class Im extends Ce{constructor(){super(...arguments);G(this,"_default_chat_template","{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}")}}class Wb extends Im{}class Vb extends Ce{}class Gb extends Ce{}class Hb extends Ce{constructor(e,r){super(e,r),this.decoder=new Xy({})}}class jb extends Ce{}class Am{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",legacy:o=null}={}){var m;const[u,l]=await _m(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,legacy:o}),c=((m=l.tokenizer_class)==null?void 0:m.replace(/Fast$/,""))??"PreTrainedTokenizer";let h=this.TOKENIZER_CLASS_MAPPING[c];return h||(console.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),h=Ce),new h(u,l)}}G(Am,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:yb,DistilBertTokenizer:mb,CamembertTokenizer:gb,DebertaTokenizer:db,DebertaV2Tokenizer:cb,BertTokenizer:sb,HerbertTokenizer:pb,ConvBertTokenizer:hb,RoFormerTokenizer:fb,XLMTokenizer:_b,ElectraTokenizer:wb,MobileBertTokenizer:ub,SqueezeBertTokenizer:lb,AlbertTokenizer:ob,GPT2Tokenizer:km,BartTokenizer:bb,MBartTokenizer:Em,MBart50Tokenizer:vb,RobertaTokenizer:$b,WhisperTokenizer:Bb,CodeGenTokenizer:Db,CLIPTokenizer:Nb,SiglipTokenizer:Fb,MarianTokenizer:Lb,BloomTokenizer:xb,NllbTokenizer:zb,M2M100Tokenizer:Rb,LlamaTokenizer:Cm,CodeLlamaTokenizer:Sb,XLMRobertaTokenizer:kb,MPNetTokenizer:Eb,FalconTokenizer:Cb,GPTNeoXTokenizer:Tb,EsmTokenizer:Ib,Wav2Vec2CTCTokenizer:Ub,BlenderbotTokenizer:Im,BlenderbotSmallTokenizer:Wb,SpeechT5Tokenizer:Vb,NougatTokenizer:Gb,VitsTokenizer:Hb,Qwen2Tokenizer:Ab,GemmaTokenizer:Mb,Grok1Tokenizer:Ob,CohereTokenizer:jb,PreTrainedTokenizer:Ce});async function qb(t,e){return await Sr(t,"config.json",!0,e)}function ki(t){const e={};let r={};switch(t.model_type){case"llava":case"paligemma":r=ki(t.text_config);break;case"moondream1":r=ki(t.phi_config);break;case"musicgen":r=ki(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"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":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":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}const n={...r,...Dr(t,["model_type","multi_query","is_encoder_decoder"])};for(const a in e)n[a]=t[e[a]];return n}function Mm(t,{prefix:e="past_key_values",encoder_add_pkv:r=!0}={}){const n={},a=t.normalized_config,s=1;if(a.is_encoder_decoder&&r){const i=a.encoder_dim_kv??a.encoder_hidden_size/a.num_encoder_heads,o=a.decoder_dim_kv??a.decoder_hidden_size/a.num_decoder_heads,u=[s,a.num_encoder_heads,0,i],l=[s,a.num_decoder_heads,0,o];for(let c=0;c<a.num_decoder_layers;++c)n[`${e}.${c}.encoder.key`]=u,n[`${e}.${c}.encoder.value`]=u,n[`${e}.${c}.decoder.key`]=l,n[`${e}.${c}.decoder.value`]=l}else{const i=a.num_heads,o=a.num_layers,u=a.dim_kv??a.hidden_size/(a.num_attention_heads??i);if(a.model_type==="falcon"){const l=[s*i,0,u];for(let c=0;c<o;++c)n[`${e}.${c}.key`]=l,n[`${e}.${c}.value`]=l}else if(a.multi_query){const l=[s*i,0,2*u];for(let c=0;c<o;++c)n[`${e}.${c}.key_value`]=l}else if(a.model_type==="bloom"){const l=[s*i,u,0],c=[s*i,0,u];for(let h=0;h<o;++h)n[`${e}.${h}.key`]=l,n[`${e}.${h}.value`]=c}else if(a.model_type==="openelm")for(let l=0;l<o;++l){const c=[s,i[l],0,u];n[`${e}.${l}.key`]=c,n[`${e}.${l}.value`]=c}else{const l=[s,i,0,u];for(let c=0;c<o;++c)n[`${e}.${c}.key`]=l,n[`${e}.${c}.value`]=l}}return n}class Ei{constructor(e){G(this,"max_position_embeddings");this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e),this.normalized_config=ki(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 Ei)&&(n=new Ei(n));const o=n??await qb(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i});return new this(o)}}class Om{static async from_pretrained(...e){return Ei.from_pretrained(...e)}}const Ci=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),Kb=function(){let t;return async function(){if(t===void 0)if(Br.IS_NODE_ENV)t=!0;else if(!Br.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}}(),Ct=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),Yb=Object.freeze({[Ci.cpu]:Ct.q8,[Ci.gpu]:Ct.fp32,[Ci.wasm]:Ct.q8,[Ci.webgpu]:Ct.fp32}),zm=Object.freeze({[Ct.fp32]:"",[Ct.fp16]:"_fp16",[Ct.int8]:"_int8",[Ct.uint8]:"_uint8",[Ct.q8]:"_quantized",[Ct.q4]:"_q4",[Ct.q4f16]:"_q4f16",[Ct.bnb4]:"_bnb4"});class yr 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 Rm 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 Xb extends yr{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 Qb extends yr{constructor(e,r){super(),this.max_length=e,this.forced_eos_token_id=r}_call(e,r){}}class Jb extends yr{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 Zb extends yr{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),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))??[]}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 ev extends yr{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 tv extends yr{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 rv extends yr{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 nv extends yr{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 av extends yr{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 iv 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 sv 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 ov 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 uv{constructor(e){G(this,"max_length",20);G(this,"max_new_tokens",null);G(this,"min_length",0);G(this,"min_new_tokens",null);G(this,"early_stopping",!1);G(this,"max_time",null);G(this,"do_sample",!1);G(this,"num_beams",1);G(this,"num_beam_groups",1);G(this,"penalty_alpha",null);G(this,"use_cache",!0);G(this,"temperature",1);G(this,"top_k",50);G(this,"top_p",1);G(this,"typical_p",1);G(this,"epsilon_cutoff",0);G(this,"eta_cutoff",0);G(this,"diversity_penalty",0);G(this,"repetition_penalty",1);G(this,"encoder_repetition_penalty",1);G(this,"length_penalty",1);G(this,"no_repeat_ngram_size",0);G(this,"bad_words_ids",null);G(this,"force_words_ids",null);G(this,"renormalize_logits",!1);G(this,"constraints",null);G(this,"forced_bos_token_id",null);G(this,"forced_eos_token_id",null);G(this,"remove_invalid_values",!1);G(this,"exponential_decay_length_penalty",null);G(this,"suppress_tokens",null);G(this,"begin_suppress_tokens",null);G(this,"forced_decoder_ids",null);G(this,"guidance_scale",null);G(this,"num_return_sequences",1);G(this,"output_attentions",!1);G(this,"output_hidden_states",!1);G(this,"output_scores",!1);G(this,"return_dict_in_generate",!1);G(this,"pad_token_id",null);G(this,"bos_token_id",null);G(this,"eos_token_id",null);G(this,"encoder_no_repeat_ngram_size",0);G(this,"decoder_start_token_id",null);G(this,"generation_kwargs",{});Object.assign(this,Dr(e,Object.getOwnPropertyNames(this)))}}class Ti extends yt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class Lo extends yt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof Lo?e=e.criteria:e instanceof Ti&&(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 lv extends Ti{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 dv extends Ti{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}_call(e,r=-1){return this.sample(e,r)}sample(e,r){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=e.reduce((a,s)=>a+s,0),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 pv(e);if(e.num_beams>1)return new hv(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new cv(e)}}class cv extends Ii{sample(e,r=-1){let n=this.getLogits(e,r);return[[kr(n)[1],0]]}}class pv extends Ii{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),s=$u(a,n),i=ja(s.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},()=>{const o=this.randomSelect(i);return[s[o][0],Math.log(i[o])]})}}class hv extends Ii{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),s=$u(a,n),i=ja(s.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},(o,u)=>[s[u][0],Math.log(i[u])])}}const $e={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},Ai=new Map,Pm=new Map,sa=new Map;async function fv(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=Mw(n);let s=r.dtype;if(typeof s!="string"&&(s&&s.hasOwnProperty(e)?s=s[e]:(s=Yb[a[0]],console.warn(`Dtype not specified for ${e}. Using the default dtype: ${s}.`))),zm.hasOwnProperty(s)){if(s===Ct.fp16&&!await Kb())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${s}. Should be one of: ${Object.keys(Ct).join(", ")}`);const i=zm[s],o=`${r.subfolder??""}/${e}${i}.onnx`,u={...r.session_options};u.executionProviders??(u.executionProviders=a);const l=Ga(t,o,!0,r);let c=[];if(r.use_external_data_format){if(Br.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,y)=>{const $=await Ga(t,_,!0,r);p({path:m,data:$})}))}else u.externalData!==void 0&&(c=u.externalData.map(async m=>{if(typeof m.data=="string"){const _=await Ga(t,m.data,!0,r);return{...m,data:_}}return m}));if(c.length>0&&(u.externalData=await Promise.all(c)),n==="webgpu"){const m=Mm(r.config,{prefix:"present"}),_={};for(const p in m)_[p]="gpu-buffer";u.preferredOutputLocation=_}return{buffer:await l,session_options:u}}async function Jr(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async i=>fv(t,e[i],r))),s={};for(let i=0;i<n.length;++i){const{buffer:o,session_options:u}=a[i],l=await tm(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]=Ow()?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 Ar(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=Bm(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Bm(t){for(let e in t)rm(t[e])?t[e]=new fe(t[e]):typeof t[e]=="object"&&Bm(t[e]);return t}function gv(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 Dm(t){return new fe("bool",[t],[1])}async function Nm(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const u=Dr(e,t.sessions.model.inputNames);r=(await oa(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 Uo(t,i,!0)}async function oa(t,e){const r=t.sessions.model,n=Object.create(null);for(const a of r.inputNames)n[a]=e[a];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 Ar(r,n)}async function Uo(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=Dm(!!a)),n.inputNames.includes("position_ids")&&s.attention_mask&&!s.position_ids&&(s.position_ids=wv(s,a)),t.addPastKeyValues(s,a);const i=Dr(s,n.inputNames);return await Ar(n,i)}async function _v(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=dr([Zn([e.dims[0],m]),r.slice(null,[r.dims[1]-h,r.dims[1]])],1)}}return await Uo(t,{inputs_embeds:s,past_key_values:i,attention_mask:r,position_ids:a,generation_config:o,logits_processor:u},!0)}function wv(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 Fm(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=Zn([1,a+u])}}}return r}function yv(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(i=>[i.at(-1)])),a.decoder_input_ids=gv(e),a}class ne extends yt{constructor(r,n){super();G(this,"main_input_name","input_ids");G(this,"forward_params",["input_ids","attention_mask"]);this.config=r,this.sessions=n;const a=sa.get(this.constructor),s=Ai.get(a);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,s===$e.DecoderOnly?(this.can_generate=!0,this._forward=Uo,this._prepare_inputs_for_generation=Fm):s===$e.Seq2Seq||s===$e.Vision2Seq||s===$e.Musicgen?(this.can_generate=!0,this._forward=Nm,this._prepare_inputs_for_generation=yv):s===$e.EncoderDecoder?this._forward=Nm:s===$e.ImageTextToText?(this.can_generate=!0,this._forward=_v,this._prepare_inputs_for_generation=Fm):this._forward=oa,this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var n;const r=[];for(const a of Object.values(this.sessions))(n=a==null?void 0:a.handler)!=null&&n.dispose&&r.push(a.handler.dispose());return await Promise.all(r)}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:s=null,local_files_only:i=!1,revision:o="main",model_file_name:u=null,subfolder:l="onnx",device:c=null,dtype:h=null,use_external_data_format:m=null,session_options:_={}}={}){let p={progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,model_file_name:u,subfolder:l,device:c,dtype:h,use_external_data_format:m,session_options:_};const y=sa.get(this),$=Ai.get(y);p.config=await Om.from_pretrained(r,p);let S;return $===$e.DecoderOnly?S=await Promise.all([Jr(r,{model:p.model_file_name??"model"},p),Sr(r,"generation_config.json",!1,p)]):$===$e.Seq2Seq||$===$e.Vision2Seq?S=await Promise.all([Jr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p),Sr(r,"generation_config.json",!1,p)]):$===$e.MaskGeneration?S=await Promise.all([Jr(r,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},p)]):$===$e.EncoderDecoder?S=await Promise.all([Jr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p)]):$===$e.ImageTextToText?S=await Promise.all([Jr(r,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},p),Sr(r,"generation_config.json",!1,p)]):$===$e.Musicgen?S=await Promise.all([Jr(r,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},p),Sr(r,"generation_config.json",!1,p)]):($!==$e.EncoderOnly&&console.warn(`Model type for '${y??(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([Jr(r,{model:p.model_file_name??"model"},p)])),new this(p.config,...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 Rm;return r.temperature!==null&&r.temperature!==1&&n.push(new iv(r.temperature)),r.top_k!==null&&r.top_k!==0&&n.push(new ov(r.top_k)),r.top_p!==null&&r.top_p<1&&n.push(new sv(r.top_p)),n}_get_logits_processor(r,n,a=null){const s=new Rm;if(r.repetition_penalty!==null&&r.repetition_penalty!==1&&s.push(new ev(r.repetition_penalty)),r.no_repeat_ngram_size!==null&&r.no_repeat_ngram_size>0&&s.push(new Zb(r.no_repeat_ngram_size)),r.bad_words_ids!==null&&s.push(new nv(r.bad_words_ids,r.eos_token_id)),r.min_length!==null&&r.eos_token_id!==null&&r.min_length>0&&s.push(new tv(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 rv(n,r.min_new_tokens,r.eos_token_id)),r.forced_bos_token_id!==null&&s.push(new Xb(r.forced_bos_token_id)),r.forced_eos_token_id!==null&&s.push(new Qb(r.max_length,r.forced_eos_token_id)),r.begin_suppress_tokens!==null){let i=n>1||r.forced_bos_token_id===null?n:n+1;r.forced_decoder_ids!==null&&(i+=r.forced_decoder_ids[r.forced_decoder_ids.length-1][0]),s.push(new Jb(r.begin_suppress_tokens,i))}return r.guidance_scale!==null&&r.guidance_scale>1&&s.push(new av(r.guidance_scale)),a!==null&&s.extend(a),s}_prepare_generation_config(r,n){const a=new uv(this.config);return"generation_config"in this&&Object.assign(a,this.generation_config),r&&Object.assign(a,r),n&&Object.assign(a,Dr(n,Object.getOwnPropertyNames(a))),a}_get_stopping_criteria(r,n=null){const a=new Lo;return r.max_length!==null&&a.push(new lv(r.max_length,this.config.max_position_embeddings??null)),r.eos_token_id!==null&&a.push(new dv(r.eos_token_id)),n&&a.extend(n),a}_validate_model_class(){if(!this.can_generate){const r=[Wg,Vg,Ug,Lg],n=sa.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=dr([a.attention_mask,Zn([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=Dr(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}){const i=Dr(n,this.sessions.model.inputNames);let{last_hidden_state:o}=await oa(this,i);return s.guidance_scale!==null&&s.guidance_scale>1&&(o=dr([o,Nw(o,0)],0),"attention_mask"in n&&(n.attention_mask=dr([n.attention_mask,Uw(n.attention_mask)],0))),n.encoder_outputs=o,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}){s=s??i;let u;if(this.config.model_type==="musicgen")u=new Array(r*this.config.decoder.num_codebooks).fill(s);else if(Array.isArray(s)){if(s.length!==r)throw new Error(`\`decoder_start_token_id\` expcted to have length ${r} but got ${s.length}`);u=s}else u=new Array(r).fill(s);const c=new fe("int64",u,[u.length,1]);return a.decoder_attention_mask=Fw(c),{input_ids:c,model_inputs:a}}async generate({inputs:r=null,generation_config:n=null,logits_processor:a=null,stopping_criteria:s=null,streamer:i=null,...o}){this._validate_model_class(),n=this._prepare_generation_config(n,o);let{inputs_tensor:u,model_inputs:l,model_input_name:c}=this._prepare_model_inputs({inputs:r,model_kwargs:o});const h=this.config.is_encoder_decoder;h&&("encoder_outputs"in l||(l=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:u,model_inputs:l,model_input_name:c,generation_config:n})));let m;h?{input_ids:m,model_inputs:l}=this._prepare_decoder_input_ids_for_generation({batch_size:l[c].dims.at(0),model_input_name:c,model_kwargs:l,decoder_start_token_id:n.decoder_start_token_id,bos_token_id:n.bos_token_id,generation_config:n}):m=l[c];let _=m.dims.at(-1);n.max_new_tokens!==null&&(n.max_length=_+n.max_new_tokens);const p=this._get_logits_processor(n,_,a),y=this._get_stopping_criteria(n,s),$=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 B=await this.forward(l),P=B.logits.slice(null,-1,null),F=p(C,P),j=[];for(let le=0;le<F.dims.at(0);++le){const ae=F[le],re=S(ae);for(const[ie,D]of re){const M=BigInt(ie);x[le]+=D,C[le].push(M),j.push([M])}}if(i&&i.put(j),y(C).every(le=>le)){n.return_dict_in_generate&&(T=this.getPastKeyValues(B,l.past_key_values,!1));break}l=this._update_model_kwargs_for_generation({generated_input_ids:j,outputs:B,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=Mm(this.config,{encoder_add_pkv:this.add_encoder_pkv??!0});for(const o in i)r[o]=new fe(a,s,i[o])}}}class Ut{}class ua extends ne{}class bv extends ua{}class vv extends ua{async _call(e){return new mt(await super._call(e))}}class $v extends ua{async _call(e){return new Pe(await super._call(e))}}class xv extends ua{async _call(e){return new ft(await super._call(e))}}class Sv extends ua{async _call(e){return new xt(await super._call(e))}}class kv extends ne{}class Ev extends kv{}class la extends ne{}class Cv extends la{}class Tv extends la{async _call(e){return new mt(await super._call(e))}}class Iv extends la{async _call(e){return new Pe(await super._call(e))}}class Av extends la{async _call(e){return new ft(await super._call(e))}}class Mv extends la{async _call(e){return new xt(await super._call(e))}}class da extends ne{}class Ov extends da{}class zv extends da{async _call(e){return new mt(await super._call(e))}}class Rv extends da{async _call(e){return new Pe(await super._call(e))}}class Pv extends da{async _call(e){return new ft(await super._call(e))}}class Bv extends da{async _call(e){return new xt(await super._call(e))}}class ca extends ne{}class Dv extends ca{}class Nv extends ca{async _call(e){return new mt(await super._call(e))}}class Fv extends ca{async _call(e){return new Pe(await super._call(e))}}class Lv extends ca{async _call(e){return new ft(await super._call(e))}}class Uv extends ca{async _call(e){return new xt(await super._call(e))}}class pa extends ne{}class Wv extends pa{}class Vv extends pa{async _call(e){return new mt(await super._call(e))}}class Gv extends pa{async _call(e){return new Pe(await super._call(e))}}class Hv extends pa{async _call(e){return new ft(await super._call(e))}}class jv extends pa{async _call(e){return new xt(await super._call(e))}}class ha extends ne{}class qv extends ha{}class Kv extends ha{async _call(e){return new mt(await super._call(e))}}class Yv extends ha{async _call(e){return new Pe(await super._call(e))}}class Xv extends ha{async _call(e){return new ft(await super._call(e))}}class Qv extends ha{async _call(e){return new xt(await super._call(e))}}class fa extends ne{}class Jv extends fa{}class Zv extends fa{async _call(e){return new mt(await super._call(e))}}class e2 extends fa{async _call(e){return new Pe(await super._call(e))}}class t2 extends fa{async _call(e){return new ft(await super._call(e))}}class r2 extends fa{async _call(e){return new xt(await super._call(e))}}class ma extends ne{}class n2 extends ma{}class a2 extends ma{async _call(e){return new Pe(await super._call(e))}}class i2 extends ma{async _call(e){return new ft(await super._call(e))}}class s2 extends ma{async _call(e){return new xt(await super._call(e))}}class o2 extends ma{async _call(e){return new mt(await super._call(e))}}class Mi extends ne{}class u2 extends Mi{}class l2 extends Mi{async _call(e){return new mt(await super._call(e))}}class d2 extends Mi{async _call(e){return new Pe(await super._call(e))}}class c2 extends Mi{async _call(e){return new ft(await super._call(e))}}class Oi extends ne{}class p2 extends Oi{}class h2 extends Oi{async _call(e){return new mt(await super._call(e))}}class f2 extends Oi{async _call(e){return new Pe(await super._call(e))}}class m2 extends Oi{async _call(e){return new xt(await super._call(e))}}class ga extends ne{}class g2 extends ga{}class _2 extends ga{async _call(e){return new mt(await super._call(e))}}class w2 extends ga{async _call(e){return new Pe(await super._call(e))}}class y2 extends ga{async _call(e){return new ft(await super._call(e))}}class b2 extends ga{async _call(e){return new xt(await super._call(e))}}class zi extends ne{}class v2 extends zi{}class $2 extends zi{async _call(e){return new mt(await super._call(e))}}class x2 extends zi{async _call(e){return new Pe(await super._call(e))}}class S2 extends zi{async _call(e){return new xt(await super._call(e))}}class Ri extends ne{}class k2 extends Ri{}class E2 extends Ri{async _call(e){return new Pe(await super._call(e))}}class C2 extends Ri{async _call(e){return new xt(await super._call(e))}}class T2 extends Ri{async _call(e){return new mt(await super._call(e))}}class Lm extends ne{constructor(r,n,a){super(r,n);G(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class I2 extends Lm{}class A2 extends Lm{}class Um extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class M2 extends Um{}class O2 extends Um{}class Wm extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class z2 extends Wm{}class R2 extends Wm{}class Wo extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class P2 extends Wo{}class B2 extends Wo{}class D2 extends Wo{async _call(e){return new Pe(await super._call(e))}}class Pi extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class N2 extends Pi{}class F2 extends Pi{}class L2 extends Pi{async _call(e){return new Pe(await super._call(e))}}class U2 extends Pi{}class Vm extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class W2 extends Vm{}class V2 extends Vm{}class Gm extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class G2 extends Gm{}class H2 extends Gm{}class _a extends ne{}class j2 extends _a{}class q2 extends _a{async _call(e){return new mt(await super._call(e))}}class K2 extends _a{async _call(e){return new Pe(await super._call(e))}}class Y2 extends _a{async _call(e){return new ft(await super._call(e))}}class X2 extends _a{async _call(e){return new xt(await super._call(e))}}class wa extends ne{}class Q2 extends wa{}class J2 extends wa{async _call(e){return new mt(await super._call(e))}}class Z2 extends wa{async _call(e){return new Pe(await super._call(e))}}class e1 extends wa{async _call(e){return new ft(await super._call(e))}}class t1 extends wa{async _call(e){return new xt(await super._call(e))}}class ya extends ne{}class r1 extends ya{}class n1 extends ya{async _call(e){return new mt(await super._call(e))}}class a1 extends ya{async _call(e){return new Pe(await super._call(e))}}class i1 extends ya{async _call(e){return new ft(await super._call(e))}}class s1 extends ya{async _call(e){return new xt(await super._call(e))}}class Hm extends ne{}class o1 extends Hm{}class u1 extends Hm{}class jm extends ne{constructor(r,n,a){super(r,n);G(this,"requires_attention_mask",!1);G(this,"main_input_name","input_features");G(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class l1 extends jm{}class d1 extends jm{_retrieve_init_tokens(e){throw e.decoder_start_token_id,new Error("Not implemented yet")}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,language:s=null,task:i=null,...o}){throw new Error("WhisperForConditionalGeneration.generate is not yet in Transformers.js v3.")}_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)=>dr(l.map(x=>x[S]),2)),h=Jn(r.map(([$,S])=>n?c[$].slice(null,S,null,[0,n]):c[$].slice(null,S)));h=h.transpose(1,0,2,3);let[m,_]=Pw(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 B=0;B<C.dims[0];++B){let P=C[B];for(let F=0;F<P.data.length;++F)P.data[F]=(P.data[F]-I.data[F])/T.data[F];P.data.set(Q0(P.data,s))}}}return Oo(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]=Bw(c),_=Array.from({length:h.length-1},($,S)=>h[S+1]-h[S]),p=ut([1],_).map($=>!!$),y=[];for(let $=0;$<p.length;++$)p[$]&&y.push(m[$]*a);u[l].data.set(y,1)}return u}}class c1 extends ne{constructor(r,n,a){super(r,n);G(this,"main_input_name","pixel_values");throw this.generation_config=a,new Error("Not implemented yet")}}class p1 extends ne{constructor(r,n,a){super(r,n);G(this,"forward_params",["input_ids","pixel_values","attention_mask","position_ids","past_key_values"]);this.generation_config=a}}class qm extends p1{async encode_image({pixel_values:e}){const r=(await Ar(this.sessions.vision_encoder,{pixel_values:e})).image_features;return this.config.num_image_tokens||(console.warn("The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder."),this.config.num_image_tokens=r.dims[1]),r}async encode_text({input_ids:e}){return(await Ar(this.sessions.embed_tokens,{input_ids:e})).inputs_embeds}_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){const s=this.config.image_token_index,o=n.tolist().map(m=>m.findIndex(_=>_==s)),u=o.every(m=>m===-1),l=o.every(m=>m!==-1);if(!u&&!l)throw new Error("Every input should contain either 0 or 1 image token.");if(u)return{inputs_embeds:e,attention_mask:a};const c=[],h=[];for(let m=0;m<o.length;++m){const _=o[m],p=e[m],y=r[m],$=a[m];c.push(dr([p.slice([0,_]),y,p.slice([_+1,p.dims[0]])],0)),h.push(dr([$.slice([0,_]),Zn([y.dims[0]]),$.slice([_+1,$.dims[0]])],0))}return{inputs_embeds:Jn(c,0),attention_mask:Jn(h,0)}}}class Km extends qm{}class Bi extends ne{}class h1 extends Bi{}class f1 extends Bi{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class m1 extends Bi{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(e,r)}}class Ym extends ne{}class g1 extends Ym{}class _1 extends Ym{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class w1 extends Bi{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(e,r)}}class y1 extends ne{}class b1 extends y1{}class Xm extends ne{}class v1 extends Xm{}class $1 extends Xm{}class Qm extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class x1 extends Qm{}class S1 extends Qm{}class Jm extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class k1 extends Jm{}class E1 extends Jm{}class Zm extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class C1 extends Zm{}class T1 extends Zm{}class eg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class I1 extends eg{}class A1 extends eg{}class tg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class M1 extends tg{}class O1 extends tg{}class rg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class z1 extends rg{}class R1 extends rg{}class ng extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class P1 extends ng{}class B1 extends ng{}class ag extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class D1 extends ag{}class N1 extends ag{}class ig extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class F1 extends ig{}class L1 extends ig{}class sg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class U1 extends sg{}class W1 extends sg{}class og extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class V1 extends og{}class G1 extends og{}class ug extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class H1 extends ug{}class j1 extends ug{}class lg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class q1 extends lg{}class K1 extends lg{}class dg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class Y1 extends dg{}class X1 extends dg{}class cg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class Q1 extends cg{}class J1 extends cg{}class pg extends ne{}class Z1 extends pg{}class e$ extends pg{async _call(e){return new Pe(await super._call(e))}}class t$ extends ne{}class r$ extends t${async _call(e){return new cS(await super._call(e))}}class hg extends ne{}class n$ extends hg{}class a$ extends hg{async _call(e){return new Pe(await super._call(e))}}class fg extends ne{}class i$ extends fg{}class s$ extends fg{}class mg extends ne{}class o$ extends mg{}class u$ extends mg{}class gg extends ne{}class l$ extends gg{}class d$ extends gg{async _call(e){return new Pe(await super._call(e))}}class Vo extends ne{}class c$ extends Vo{}class p$ extends Vo{async _call(e){return new _g(await super._call(e))}}class h$ extends Vo{async _call(e){return new f$(await super._call(e))}}class _g extends Ut{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class f$ extends Ut{constructor({logits:e,pred_boxes:r,pred_masks:n}){super(),this.logits=e,this.pred_boxes=r,this.pred_masks=n}}class wg extends ne{}class m$ extends wg{}class g$ extends wg{async _call(e){return new _$(await super._call(e))}}class _$ extends _g{}class yg extends ne{}class w$ extends yg{}class y$ extends yg{async _call(e){return new Pe(await super._call(e))}}class bg extends ne{}class b$ extends bg{}class v$ extends bg{async _call(e){return new Pe(await super._call(e))}}class vg extends ne{}class $$ extends vg{}class x$ extends vg{async _call(e){return new Pe(await super._call(e))}}class $g extends ne{}class S$ extends $g{}class k$ extends $g{}class xg extends ne{}class E$ extends xg{}class C$ extends xg{}class T$ extends ne{}class I$ extends T${}class Sg extends ne{}class A$ extends Sg{}class M$ extends Sg{}class O$ extends ne{}class z$ extends O${}class kg extends ne{}class R$ extends kg{}class P$ extends kg{async _call(e){return new Pe(await super._call(e))}}class Eg extends ne{}class B$ extends Eg{}class D$ extends Eg{async _call(e){return new Pe(await super._call(e))}}class Cg extends ne{}class N$ extends Cg{}class F$ extends Cg{async _call(e){return new Pe(await super._call(e))}}class Tg extends ne{}class L$ extends Tg{}class U$ extends Tg{async _call(e){return new W$(await super._call(e))}}class W$ extends Ut{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class V$ extends ne{}class G$ extends V${async get_image_embeddings({pixel_values:e}){return await oa(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 Ar(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new H$(await super._call(e))}}class H$ extends Ut{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class Ig extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class j$ extends Ig{}class q$ extends Ig{}class Ag extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class K$ extends Ag{}class Y$ extends Ag{}class Zr extends ne{}class X$ extends Zr{}class Q$ extends Zr{async _call(e){return new wn(await super._call(e))}}class J$ extends Zr{async _call(e){return new Pe(await super._call(e))}}class Z$ extends Zr{async _call(e){return new ft(await super._call(e))}}class Go extends ne{}class ex extends Go{}class tx extends Go{async _call(e){return new wn(await super._call(e))}}class rx extends Go{async _call(e){return new Pe(await super._call(e))}}class Di extends ne{}class nx extends Di{}class ax extends Di{async _call(e){return new wn(await super._call(e))}}class ix extends Di{async _call(e){return new Pe(await super._call(e))}}class sx extends Di{async _call(e){return new ft(await super._call(e))}}class Ho extends ne{}class ox extends Ho{}class ux extends Ho{async _call(e){return new wn(await super._call(e))}}class lx extends Ho{async _call(e){return new Pe(await super._call(e))}}class dx extends Zr{}class cx extends Zr{async _call(e){return new wn(await super._call(e))}}class px extends Zr{async _call(e){return new Pe(await super._call(e))}}class ba extends ne{}class hx extends ba{}class fx extends ba{async _call(e){return new wn(await super._call(e))}}class mx extends ba{async _call(e){return new Pe(await super._call(e))}}class gx extends ba{async _call(e){return new dS(await super._call(e))}}class _x extends ba{async _call(e){return new ft(await super._call(e))}}class Mg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class wx extends Mg{}class yx extends Mg{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 oa(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=[],y=null,$=null,S=0;for(;;){++S;const T=Dm(!!$);let I;$?I=$.output_sequence_out:I=new fe("float32",new Float32Array(_),[1,1,_]);let B={use_cache_branch:T,output_sequence:I,encoder_attention_mask:l,speaker_embeddings:r,encoder_hidden_states:u};this.addPastKeyValues(B,y),$=await Ar(this.sessions.decoder_model_merged,B),y=this.getPastKeyValues($,y);const{prob:P,spectrum:F}=$;if(p.push(F),S>=m&&(Array.from(P.data).filter(j=>j>=n).length>0||S>=h))break}const x=dr(p),{waveform:C}=await Ar(i.sessions.model,{spectrogram:x});return{spectrogram:x,waveform:C}}}class bx extends ne{constructor(){super(...arguments);G(this,"main_input_name","spectrogram")}}class vx extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class $x extends vx{}class Og extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class xx extends Og{}class Sx extends Og{}class zg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class kx extends zg{}class Ex extends zg{}class Rg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class Cx extends Rg{}class Tx extends Rg{}class jo extends ne{}class Ix extends jo{}class Ax 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 Mx 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 Ox extends ne{}class Pg extends Ox{async _call(e){return new pS(await super._call(e))}}class Bg extends ne{}class zx extends Bg{}class Rx extends Bg{}class Dg extends ne{constructor(e,r,n){super(e,r),this.generation_config=n}}class Px extends Dg{}class Bx extends Dg{}class Ng extends ne{}class Dx extends Ng{}class Nx extends Ng{async _call(e){return new Pe(await super._call(e))}}class Fg extends ne{constructor(r,n,a){super(r,n);G(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 Ar(this.sessions.encodec_decode,{audio_codes:a});return s}}class qo{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 Om.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 y=p.get(_.config.model_type);if(y)return await y[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 ne.from_pretrained(e,_);throw Error(`Unsupported model type: ${_.config.model_type}`)}}G(qo,"MODEL_CLASS_MAPPINGS",null),G(qo,"BASE_IF_FAIL",!1);const Fx=new Map([["bert",["BertModel",bv]],["nomic_bert",["NomicBertModel",Ev]],["roformer",["RoFormerModel",Cv]],["electra",["ElectraModel",Dv]],["esm",["EsmModel",u2]],["convbert",["ConvBertModel",Ov]],["camembert",["CamembertModel",Wv]],["deberta",["DebertaModel",qv]],["deberta-v2",["DebertaV2Model",Jv]],["mpnet",["MPNetModel",g2]],["albert",["AlbertModel",k2]],["distilbert",["DistilBertModel",n2]],["roberta",["RobertaModel",j2]],["xlm",["XLMModel",Q2]],["xlm-roberta",["XLMRobertaModel",r1]],["clap",["ClapModel",Ix]],["clip",["CLIPModel",h1]],["clipseg",["CLIPSegModel",v1]],["chinese_clip",["ChineseCLIPModel",b1]],["siglip",["SiglipModel",g1]],["mobilebert",["MobileBertModel",p2]],["squeezebert",["SqueezeBertModel",v2]],["wav2vec2",["Wav2Vec2Model",X$]],["wav2vec2-bert",["Wav2Vec2BertModel",ox]],["unispeech",["UniSpeechModel",ex]],["unispeech-sat",["UniSpeechSatModel",nx]],["hubert",["HubertModel",dx]],["wavlm",["WavLMModel",hx]],["audio-spectrogram-transformer",["ASTModel",o1]],["vits",["VitsModel",Pg]],["detr",["DetrModel",c$]],["table-transformer",["TableTransformerModel",m$]],["vit",["ViTModel",Z1]],["mobilevit",["MobileViTModel",n$]],["owlvit",["OwlViTModel",i$]],["owlv2",["Owlv2Model",o$]],["beit",["BeitModel",l$]],["deit",["DeiTModel",w$]],["convnext",["ConvNextModel",R$]],["convnextv2",["ConvNextV2Model",B$]],["dinov2",["Dinov2Model",N$]],["resnet",["ResNetModel",b$]],["swin",["SwinModel",$$]],["swin2sr",["Swin2SRModel",S$]],["donut-swin",["DonutSwinModel",z$]],["yolos",["YolosModel",L$]],["dpt",["DPTModel",E$]],["glpn",["GLPNModel",A$]],["hifigan",["SpeechT5HifiGan",bx]],["efficientnet",["EfficientNetModel",Dx]]]),Lx=new Map([["t5",["T5Model",I2]],["longt5",["LongT5Model",M2]],["mt5",["MT5Model",z2]],["bart",["BartModel",P2]],["mbart",["MBartModel",N2]],["marian",["MarianModel",j$]],["whisper",["WhisperModel",l1]],["m2m_100",["M2M100Model",K$]],["blenderbot",["BlenderbotModel",W2]],["blenderbot-small",["BlenderbotSmallModel",G2]]]),Ux=new Map([["bloom",["BloomModel",q1]],["gpt2",["GPT2Model",x1]],["gptj",["GPTJModel",I1]],["gpt_bigcode",["GPTBigCodeModel",M1]],["gpt_neo",["GPTNeoModel",k1]],["gpt_neox",["GPTNeoXModel",C1]],["codegen",["CodeGenModel",z1]],["llama",["LlamaModel",P1]],["gemma",["GemmaModel",D1]],["openelm",["OpenELMModel",F1]],["qwen2",["Qwen2Model",U1]],["phi",["PhiModel",V1]],["phi3",["Phi3Model",H1]],["mpt",["MptModel",Y1]],["opt",["OPTModel",Q1]],["mistral",["MistralModel",xx]],["starcoder2",["Starcoder2Model",kx]],["falcon",["FalconModel",Cx]],["stablelm",["StableLmModel",Px]]]),Lg=new Map([["speecht5",["SpeechT5ForSpeechToText",wx]],["whisper",["WhisperForConditionalGeneration",d1]]]),Wx=new Map([["speecht5",["SpeechT5ForTextToSpeech",yx]]]),Vx=new Map([["vits",["VitsModel",Pg]],["musicgen",["MusicgenForConditionalGeneration",Fg]]]),Gx=new Map([["bert",["BertForSequenceClassification",$v]],["roformer",["RoFormerForSequenceClassification",Iv]],["electra",["ElectraForSequenceClassification",Fv]],["esm",["EsmForSequenceClassification",d2]],["convbert",["ConvBertForSequenceClassification",Rv]],["camembert",["CamembertForSequenceClassification",Gv]],["deberta",["DebertaForSequenceClassification",Yv]],["deberta-v2",["DebertaV2ForSequenceClassification",e2]],["mpnet",["MPNetForSequenceClassification",w2]],["albert",["AlbertForSequenceClassification",E2]],["distilbert",["DistilBertForSequenceClassification",a2]],["roberta",["RobertaForSequenceClassification",K2]],["xlm",["XLMForSequenceClassification",Z2]],["xlm-roberta",["XLMRobertaForSequenceClassification",a1]],["bart",["BartForSequenceClassification",D2]],["mbart",["MBartForSequenceClassification",L2]],["mobilebert",["MobileBertForSequenceClassification",f2]],["squeezebert",["SqueezeBertForSequenceClassification",x2]]]),Hx=new Map([["bert",["BertForTokenClassification",xv]],["roformer",["RoFormerForTokenClassification",Av]],["electra",["ElectraForTokenClassification",Lv]],["esm",["EsmForTokenClassification",c2]],["convbert",["ConvBertForTokenClassification",Pv]],["camembert",["CamembertForTokenClassification",Hv]],["deberta",["DebertaForTokenClassification",Xv]],["deberta-v2",["DebertaV2ForTokenClassification",t2]],["mpnet",["MPNetForTokenClassification",y2]],["distilbert",["DistilBertForTokenClassification",i2]],["roberta",["RobertaForTokenClassification",Y2]],["xlm",["XLMForTokenClassification",e1]],["xlm-roberta",["XLMRobertaForTokenClassification",i1]]]),Ug=new Map([["t5",["T5ForConditionalGeneration",A2]],["longt5",["LongT5ForConditionalGeneration",O2]],["mt5",["MT5ForConditionalGeneration",R2]],["bart",["BartForConditionalGeneration",B2]],["mbart",["MBartForConditionalGeneration",F2]],["marian",["MarianMTModel",q$]],["m2m_100",["M2M100ForConditionalGeneration",Y$]],["blenderbot",["BlenderbotForConditionalGeneration",V2]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",H2]]]),Wg=new Map([["bloom",["BloomForCausalLM",K1]],["gpt2",["GPT2LMHeadModel",S1]],["gptj",["GPTJForCausalLM",A1]],["gpt_bigcode",["GPTBigCodeForCausalLM",O1]],["gpt_neo",["GPTNeoForCausalLM",E1]],["gpt_neox",["GPTNeoXForCausalLM",T1]],["codegen",["CodeGenForCausalLM",R1]],["llama",["LlamaForCausalLM",B1]],["gemma",["GemmaForCausalLM",N1]],["openelm",["OpenELMForCausalLM",L1]],["qwen2",["Qwen2ForCausalLM",W1]],["phi",["PhiForCausalLM",G1]],["phi3",["Phi3ForCausalLM",j1]],["mpt",["MptForCausalLM",X1]],["opt",["OPTForCausalLM",J1]],["mbart",["MBartForCausalLM",U2]],["mistral",["MistralForCausalLM",Sx]],["starcoder2",["Starcoder2ForCausalLM",Ex]],["falcon",["FalconForCausalLM",Tx]],["trocr",["TrOCRForCausalLM",$x]],["stablelm",["StableLmForCausalLM",Bx]]]),jx=new Map([["bert",["BertForMaskedLM",vv]],["roformer",["RoFormerForMaskedLM",Tv]],["electra",["ElectraForMaskedLM",Nv]],["esm",["EsmForMaskedLM",l2]],["convbert",["ConvBertForMaskedLM",zv]],["camembert",["CamembertForMaskedLM",Vv]],["deberta",["DebertaForMaskedLM",Kv]],["deberta-v2",["DebertaV2ForMaskedLM",Zv]],["mpnet",["MPNetForMaskedLM",_2]],["albert",["AlbertForMaskedLM",T2]],["distilbert",["DistilBertForMaskedLM",o2]],["roberta",["RobertaForMaskedLM",q2]],["xlm",["XLMWithLMHeadModel",J2]],["xlm-roberta",["XLMRobertaForMaskedLM",n1]],["mobilebert",["MobileBertForMaskedLM",h2]],["squeezebert",["SqueezeBertForMaskedLM",$2]]]),qx=new Map([["bert",["BertForQuestionAnswering",Sv]],["roformer",["RoFormerForQuestionAnswering",Mv]],["electra",["ElectraForQuestionAnswering",Uv]],["convbert",["ConvBertForQuestionAnswering",Bv]],["camembert",["CamembertForQuestionAnswering",jv]],["deberta",["DebertaForQuestionAnswering",Qv]],["deberta-v2",["DebertaV2ForQuestionAnswering",r2]],["mpnet",["MPNetForQuestionAnswering",b2]],["albert",["AlbertForQuestionAnswering",C2]],["distilbert",["DistilBertForQuestionAnswering",s2]],["roberta",["RobertaForQuestionAnswering",X2]],["xlm",["XLMForQuestionAnswering",t1]],["xlm-roberta",["XLMRobertaForQuestionAnswering",s1]],["mobilebert",["MobileBertForQuestionAnswering",m2]],["squeezebert",["SqueezeBertForQuestionAnswering",S2]]]),Vg=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",c1]]]),Kx=new Map([["llava",["LlavaForConditionalGeneration",qm]],["moondream1",["Moondream1ForConditionalGeneration",Km]]]),Yx=new Map([["vit",["ViTForImageClassification",e$]],["mobilevit",["MobileViTForImageClassification",a$]],["beit",["BeitForImageClassification",d$]],["deit",["DeiTForImageClassification",y$]],["convnext",["ConvNextForImageClassification",P$]],["convnextv2",["ConvNextV2ForImageClassification",D$]],["dinov2",["Dinov2ForImageClassification",F$]],["resnet",["ResNetForImageClassification",v$]],["swin",["SwinForImageClassification",x$]],["segformer",["SegformerForImageClassification",zx]],["efficientnet",["EfficientNetForImageClassification",Nx]]]),Xx=new Map([["detr",["DetrForObjectDetection",p$]],["table-transformer",["TableTransformerForObjectDetection",g$]],["yolos",["YolosForObjectDetection",U$]]]),Qx=new Map([["owlvit",["OwlViTForObjectDetection",s$]],["owlv2",["Owlv2ForObjectDetection",u$]]]),Jx=new Map([["detr",["DetrForSegmentation",h$]],["clipseg",["CLIPSegForImageSegmentation",$1]]]),Zx=new Map([["segformer",["SegformerForSemanticSegmentation",Rx]]]),eS=new Map([["sam",["SamModel",G$]]]),tS=new Map([["wav2vec2",["Wav2Vec2ForCTC",Q$]],["wav2vec2-bert",["Wav2Vec2BertForCTC",ux]],["unispeech",["UniSpeechForCTC",tx]],["unispeech-sat",["UniSpeechSatForCTC",ax]],["wavlm",["WavLMForCTC",fx]],["hubert",["HubertForCTC",cx]]]),rS=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",J$]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",lx]],["unispeech",["UniSpeechForSequenceClassification",rx]],["unispeech-sat",["UniSpeechSatForSequenceClassification",ix]],["wavlm",["WavLMForSequenceClassification",mx]],["hubert",["HubertForSequenceClassification",px]],["audio-spectrogram-transformer",["ASTForAudioClassification",u1]]]),nS=new Map([["wavlm",["WavLMForXVector",gx]]]),aS=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",sx]],["wavlm",["WavLMForAudioFrameClassification",_x]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Z$]]]),iS=new Map([["vitmatte",["VitMatteForImageMatting",r$]]]),sS=new Map([["swin2sr",["Swin2SRForImageSuperResolution",k$]]]),oS=new Map([["dpt",["DPTForDepthEstimation",C$]],["depth_anything",["DepthAnythingForDepthEstimation",I$]],["glpn",["GLPNForDepthEstimation",M$]]]),uS=new Map([["clip",["CLIPVisionModelWithProjection",m1]],["siglip",["SiglipVisionModel",w1]]]),Gg=[[Fx,$e.EncoderOnly],[Lx,$e.EncoderDecoder],[Ux,$e.DecoderOnly],[Gx,$e.EncoderOnly],[Hx,$e.EncoderOnly],[Ug,$e.Seq2Seq],[Lg,$e.Seq2Seq],[Wg,$e.DecoderOnly],[jx,$e.EncoderOnly],[qx,$e.EncoderOnly],[Vg,$e.Vision2Seq],[Kx,$e.ImageTextToText],[Yx,$e.EncoderOnly],[Jx,$e.EncoderOnly],[Zx,$e.EncoderOnly],[iS,$e.EncoderOnly],[sS,$e.EncoderOnly],[oS,$e.EncoderOnly],[Xx,$e.EncoderOnly],[Qx,$e.EncoderOnly],[eS,$e.MaskGeneration],[tS,$e.EncoderOnly],[rS,$e.EncoderOnly],[Wx,$e.Seq2Seq],[Vx,$e.EncoderOnly],[nS,$e.EncoderOnly],[aS,$e.EncoderOnly],[uS,$e.EncoderOnly]];for(const[t,e]of Gg)for(const[r,n]of t.values())Ai.set(r,e),sa.set(n,r),Pm.set(r,n);const lS=[["MusicgenForConditionalGeneration",Fg,$e.Musicgen],["CLIPTextModelWithProjection",f1,$e.EncoderOnly],["SiglipTextModel",_1,$e.EncoderOnly],["ClapTextModelWithProjection",Ax,$e.EncoderOnly],["ClapAudioModelWithProjection",Mx,$e.EncoderOnly]];for(const[t,e,r]of lS)Ai.set(t,r),sa.set(e,t),Pm.set(t,e);class Hg extends qo{}G(Hg,"MODEL_CLASS_MAPPINGS",Gg.map(e=>e[0])),G(Hg,"BASE_IF_FAIL",!0);class Pe extends Ut{constructor({logits:e}){super(),this.logits=e}}class dS extends Ut{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class ft extends Ut{constructor({logits:e}){super(),this.logits=e}}class mt extends Ut{constructor({logits:e}){super(),this.logits=e}}class xt extends Ut{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class wn extends Ut{constructor({logits:e}){super(),this.logits=e}}class cS extends Ut{constructor({alphas:e}){super(),this.alphas=e}}class pS extends Ut{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const Wt=typeof self<"u",hS=Wt&&self.constructor.name==="DedicatedWorkerGlobalScope";let en,jg,Mr;if(Wt)en=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},Mr=self.createImageBitmap,jg=self.ImageData;else if(We)Mr=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.raw().toBuffer({resolveWithObject:!0}),s=new tr(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 fS={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},mS=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class tr{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 tr)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!Wt)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new tr(n,e.width,e.height,4)}static async fromURL(e){const r=await ps(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(Wt){const r=await Mr(e),n=en(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=We(await e.arrayBuffer());return await Mr(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 tr(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=fS[n]??n;if(Wt){const s=this.channels,i=this.toCanvas(),o=en(e,r).getContext("2d");return o.drawImage(i,0,0,e,r),new tr(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 Mr(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(Wt){const s=this.channels,i=this.toCanvas(),o=this.width+e+r,u=this.height+n+a,l=en(o,u).getContext("2d");return l.drawImage(i,0,0,this.width,this.height,e,n,o,u),new tr(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 Mr(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(Wt){const o=this.channels,u=this.toCanvas(),l=en(s,i).getContext("2d");return l.drawImage(u,e,r,s,i,0,0,s,i),new tr(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 Mr(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(Wt){const s=this.channels,i=this.toCanvas(),o=en(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 tr(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 Mr(s)}}async toBlob(e="image/png",r=1){if(!Wt)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(!Wt)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=en(e.width,e.height),n=new jg(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 tr(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(Wt){if(hS)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=mS.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(wt.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Wt)throw new Error("toSharp() is only supported in server-side environments.");return We(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}function qg(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 gS={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 Ko(t,e="htk"){const r=gS[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 _S={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 wS(t,e="htk"){const r=_S[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 yS(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 Kg(t,e,r){const n=(e-t)/(r-1);return Float64Array.from({length:r},(a,s)=>t+n*s)}function va(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=Ko(r,i),l=Ko(n,i),c=Kg(u,l,e+2);let h=wS(c,i),m;if(o){const p=a/(t*2);m=Ko(Float64Array.from({length:t},(y,$)=>$*p),i),h=c}else m=Kg(0,Math.floor(a/2),t);const _=yS(m,h);if(s!==null&&s==="slaney")for(let p=0;p<e;++p){const y=_[p],$=2/(h[p+2]-h[p]);for(let S=0;S<t;++S)y[S]*=$}return _}function bS(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[Wa(s,a)];for(let s=1;s<=r;++s)n[a+e+s]=t[Wa(a-s,a)];return n}function Yg(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=kr(t)[0]-a;for(let o=0;o<t.length;++o)t[o]=Math.max(t[o],i)}return t}function vS(t,e=1,r=1e-5,n=null){return Yg(t,20,e,r,n)}function $S(t,e=1,r=1e-10,n=null){return Yg(t,10,e,r,n)}function Ni(t,e,r,n,{fft_length:a=null,power:s=1,center:i=!0,pad_mode:o="reflect",onesided:u=!0,preemphasis:l=null,mel_filters:c=null,mel_floor:h=1e-10,log_mel:m=null,reference:_=1,min_value:p=1e-10,db_range:y=null,remove_dc_offset:$=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(i){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const M=Math.floor((a-1)/2)+1;t=bS(t,M,M)}const I=Math.floor(1+Math.floor((t.length-r)/n)),B=u?Math.floor(a/2)+1:a;let P=I,F=I;S!==null&&(S>I?x&&(F=S):F=P=S);const j=new X0(a),q=new Float64Array(a),le=new Float64Array(j.outputBufferSize),ae=new Array(P);for(let M=0;M<P;++M){const V=M*n;for(let ee=0;ee<r;++ee)q[ee]=t[V+ee];if($){let ee=0;for(let R=0;R<r;++R)ee+=q[R];const de=ee/r;for(let R=0;R<r;++R)q[R]-=de}if(l!==null){for(let ee=r-1;ee>=1;--ee)q[ee]-=l*q[ee-1];q[0]*=1-l}for(let ee=0;ee<e.length;++ee)q[ee]*=e[ee];j.realTransform(le,q);const K=new Array(B);for(let ee=0;ee<K.length;++ee){const de=ee<<1;K[ee]=le[de]**2+le[de+1]**2}ae[M]=K}if(s!==null&&s!==2){const M=2/s;for(let V=0;V<ae.length;++V){const K=ae[V];for(let ee=0;ee<K.length;++ee)K[ee]**=M}}const re=c.length,ie=new Float32Array(re*F),D=C?[F,re]:[re,F];for(let M=0;M<re;++M){const V=c[M];for(let K=0;K<P;++K){const ee=ae[K];let de=0;for(let R=0;R<B;++R)de+=V[R]*ee[R];ie[C?K*re+M:M*P+K]=Math.max(h,de)}}if(s!==null&&m!==null){const M=Math.min(ie.length,P*re);switch(m){case"log":for(let V=0;V<M;++V)ie[V]=Math.log(ie[V]);break;case"log10":for(let V=0;V<M;++V)ie[V]=Math.log10(ie[V]);break;case"dB":if(s===1)vS(ie,_,p,y);else if(s===2)$S(ie,_,p,y);else throw new Error(`Cannot use log_mel option '${m}' with power ${s}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${m}'`)}}return{data:ie,dims:D}}function Fi(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=qg(s);break;case"povey":i=qg(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 xS([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function Yo(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 y=0;y<o;++y){let $=_[y],S=[],x;if(n){x=$.sigmoid().data;for(let C=0;C<x.length;++C)x[C]>e&&S.push(C)}else{let C=kr($.data)[1];if(C===u-1||(x=ja($.data),x[C]<e))continue;S.push(C)}for(const C of S){let T=p[y].data;T=xS(T),h!==null&&(T=T.map((I,B)=>I*h[(B+1)%2])),m.boxes.push(T),m.classes.push(C),m.scores.push(x[C])}}l.push(m)}return l}function $a(t,e){var r;if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${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 Xg(t,e,r=0,n=null){const a=t/e;let s=J0(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 tn extends yt{constructor(e){super(),this.config=e}}class Xe extends tn{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)}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=xu(n.data)[0],i=kr(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 y=0;y<n.width;++y)(m[p+y]-a)/i<o&&(u=Math.min(u,y),l=Math.min(l,_),c=Math.max(c,y),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 y=0;y<m.length;++y)m[y]=i[y%l];else i!==0&&m.fill(i);const[_,p]=s?[Math.floor((c-u)/2),Math.floor((h-o)/2)]:[0,0];for(let y=0;y<o;++y){const $=(y+p)*c,S=y*u;for(let x=0;x<u;++x){const C=($+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 y=o-1,$=u-1;for(let S=0;S<h;++S){const x=S*c,C=Wa(S,y)*u;for(let T=0;T<c;++T){if(S<o&&T<u)continue;const I=(x+T)*l,B=(C+Wa(T,$))*l;for(let P=0;P<l;++P)m[I+P]=e[B+P]}}}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=Xg(l*a,this.config.ensure_multiple_of),o=Xg(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: ${JSON.stringify(r)}`)}}async resize(e){const[r,n]=this.get_resize_output_image_size(e,this.size);return await e.resize(r,n,{resample:this.resample})}async preprocess(e,{do_normalize:r=null,do_pad:n=null,do_convert_rgb:a=null,do_convert_grayscale:s=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[i,o]=e.size;if(a??this.do_convert_rgb?e=e.rgb():s&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let m,_;Number.isInteger(this.crop_size)?(m=this.crop_size,_=this.crop_size):(m=this.crop_size.width,_=this.crop_size.height),e=await e.center_crop(m,_)}const u=[e.height,e.width];let l=Float32Array.from(e.data),c=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(l),r??this.do_normalize){let m=this.image_mean;Array.isArray(this.image_mean)||(m=new Array(e.channels).fill(m));let _=this.image_std;if(Array.isArray(this.image_std)||(_=new Array(e.channels).fill(m)),m.length!==e.channels||_.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${m.length}) and \`image_std\` (${_.length}) must match the number of channels in the image (${e.channels}).`);for(let p=0;p<l.length;p+=e.channels)for(let y=0;y<e.channels;++y)l[p+y]=(l[p+y]-m[y])/_[y]}if(n??this.do_pad){if(this.pad_size)[l,c]=this.pad_image(l,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){const[m,_]=Xo([c[1],c[0]],this.size_divisibility);[l,c]=this.pad_image(l,c,{width:m,height:_})}}const h=new fe("float32",l,c).permute(2,0,1);return{original_size:[o,i],reshaped_input_size:u,pixel_values:h}}async _call(e,...r){Array.isArray(e)||(e=[e]);const n=await Promise.all(e.map(s=>this.preprocess(s)));return{pixel_values:Jn(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 SS extends Xe{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=am(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]),y=h.data;for(let S=0;S<y.length;++S){const x=y[S];p[x]=x}const $=p.filter(S=>S!==void 0);s.push({segmentation:h,labels:$})}return s}}class Qg extends Xe{}class kS extends Qg{}class ES extends Xe{}class CS extends Xe{}class Jg extends Xe{}class TS extends Jg{}class IS extends Xe{}class AS extends Xe{}class Zg extends Xe{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 MS extends Zg{}class OS extends Xe{}class zS extends Xe{}class RS extends Xe{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 PS extends Xe{}class e0 extends Xe{post_process_object_detection(...e){return Yo(...e)}}class BS extends e0{}class DS extends Xe{}class NS extends Xe{}class t0 extends Xe{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 FS extends t0{}class LS extends Xe{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 Yo(...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=kr(l.data)[1];if(h===a)continue;let _=ja(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;++$)e[$]=am(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 y=c.data;for(let $=0;$<n.length;++$){let S=n[$],[x,C]=this.check_segment_validity(m,e,$,a,s);if(x){++p;for(let T of C)y[T]=p;h.push({id:p,label_id:S,score:r[$]})}}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 y=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[P,F]=y??S.dims.slice(-2),j=new fe("int32",new Int32Array(P*F).fill(-1),[P,F]);_.push({segmentation:j,segments_info:[]});continue}let[I,B]=this.compute_segments(x,C,T,n,a,s,y);_.push({segmentation:I,segments_info:B})}return _}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class US extends Xe{post_process_object_detection(...e){return Yo(...e)}}class WS extends Xe{reshape_input_points(e,r,n,a=!1){e=structuredClone(e);let s=bu(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=bu(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 im(e[l],{mode:"bilinear",size:u});if(m=m.slice(null,null,[0,h[0]],[0,h[1]]),m=await im(m,{mode:"bilinear",size:c}),s){const _=m.data,p=new Uint8Array(_.length);for(let y=0;y<_.length;++y)_[y]>a&&(p[y]=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 VS extends Xe{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 GS extends Xe{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:Jn(n.map((i,o)=>dr([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 HS extends tn{constructor(e){var r;super(e),(r=this.config).mel_filters??(r.mel_filters=va(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney")),this.window=Fi(this.config.n_fft,"hann")}_extract_fbank_features(e){const{data:r,dims:n}=Ni(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}),a=kr(r)[0];for(let s=0;s<r.length;++s)r[s]=(Math.max(r[s],a-8)+4)/4;return{data:r,dims:n}}async _call(e){$a(e,"WhisperFeatureExtractor");let r;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));const{data:n,dims:a}=this._extract_fbank_features(r);return{input_features:new fe("float32",n,[1,...a])}}}class jS extends tn{_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){$a(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=e;this.config.do_normalize&&(r=this._zero_mean_unit_var_norm(r));const n=[1,r.length];return{input_values:new fe("float32",r,n),attention_mask:new fe("int64",new BigInt64Array(r.length).fill(1n),n)}}}class qS extends tn{constructor(e){super(e);const r=this.config.sampling_rate,n=va(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;a<n.length;++a)n[a].push(0);this.mel_filters=n,this.window=Fi(400,"povey",{periodic:!1})}_extract_fbank_features(e,r){return e=e.map(n=>n*32768),Ni(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e,{padding:r=!0,pad_to_multiple_of:n=2,do_normalize_per_mel_bins:a=!0,return_attention_mask:s=!0}={}){$a(e,"SeamlessM4TFeatureExtractor");let i=this._extract_fbank_features(e,this.config.max_length);const o=i.data;if(a){const[y,$]=i.dims;for(let S=0;S<$;++S){let x=0;for(let B=0;B<y;++B)x+=o[B*$+S];const C=x/y;let T=0;for(let B=0;B<y;++B)T+=(o[B*$+S]-C)**2;T/=y-1;const I=Math.sqrt(T+1e-7);for(let B=0;B<y;++B){const P=B*$+S;o[P]=(o[P]-C)/I}}}let u;if(r){const[y,$]=i.dims,S=y%n;if(S>0){const x=new Float32Array($*(y+S));x.set(o),x.fill(this.config.padding_value,o.length);const C=y+S;i={data:x,dims:[C,$]},s&&(u=new fe("int64",new BigInt64Array(C),[1,C]),u.data.fill(1n,0,y))}}const[l,c]=i.dims,h=this.config.stride;if(l%h!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${h}).`);const _=new fe("float32",o,i.dims).view(1,Math.floor(l/h),c*h),p={input_features:_};if(s){const y=_.dims[1],$=new BigInt64Array(y);if(u){const S=u.data;for(let x=1,C=0;x<l;x+=h,++C)$[C]=S[x]}else $.fill(1n);p.attention_mask=new fe("int64",$,[1,y])}return p}}class KS extends tn{constructor(e){super(e);const r=this.config.sampling_rate,n=va(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=Fi(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(e,r){return Ni(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){$a(e,"ASTFeatureExtractor");const r=this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){const n=this.std*2,a=r.data;for(let s=0;s<a.length;++s)a[s]=(a[s]-this.mean)/n}return{input_values:new fe("float32",r.data,[1,...r.dims])}}}class YS extends tn{constructor(e){super(e),this.mel_filters=va(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=va(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=Fi(this.config.fft_window_size,"hann")}_get_input_mel(e,r,n,a){let s,i=!1;const o=e.length-r;if(o>0)if(n==="rand_trunc"){i=!0;const u=Math.floor(Math.random()*(o+1));e=e.subarray(u,u+r),s=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),s.dims=[1,...s.dims]}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(o<0){let u=new Float64Array(r);if(u.set(e),a==="repeat")for(let l=e.length;l<r;l+=e.length)u.set(e.subarray(0,Math.min(e.length,r-l)),l);else if(a==="repeatpad")for(let l=e.length;l<-o;l+=e.length)u.set(e,l);e=u}if(n==="fusion")throw new Error(`Truncation strategy "${n}" not implemented`);s=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),s.dims=[1,...s.dims]}return{...s,longer:i}}_extract_fbank_features(e,r,n=null){return Ni(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:r,log_mel:"dB",max_num_frames:n,do_pad:!1,transpose:!0})}async _call(e,{max_length:r=null}={}){$a(e,"ClapFeatureExtractor");const n=this._get_input_mel(e,r??this.config.nb_max_samples,this.config.truncation,this.config.padding);return{input_features:new fe("float32",n.data,[1,...n.dims])}}}class XS extends tn{}class yn extends yt{constructor(e){super(),this.feature_extractor=e}async _call(e,...r){return await this.feature_extractor(e,...r)}}class QS extends yn{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 JS extends yn{async _call(e){return await this.feature_extractor(e)}}class ZS extends yn{async _call(e){return await this.feature_extractor(e)}}class e3 extends yn{async _call(e){return await this.feature_extractor(e)}}class t3 extends yn{}class Qo{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 Sr(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=Xe;else throw new Error(`Unknown Feature Extractor type: ${u}`);let c=this.PROCESSOR_CLASS_MAPPING[o.processor_class]??yn,h=new l(o);return new c(h)}}G(Qo,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:Xe,WhisperFeatureExtractor:HS,ViTFeatureExtractor:OS,MobileViTFeatureExtractor:PS,OwlViTFeatureExtractor:e0,Owlv2ImageProcessor:BS,CLIPFeatureExtractor:Jg,CLIPImageProcessor:TS,ChineseCLIPFeatureExtractor:IS,SiglipImageProcessor:AS,ConvNextFeatureExtractor:Zg,ConvNextImageProcessor:MS,SegformerFeatureExtractor:SS,BitImageProcessor:ES,DPTImageProcessor:kS,DPTFeatureExtractor:Qg,GLPNFeatureExtractor:CS,BeitFeatureExtractor:NS,DeiTFeatureExtractor:DS,DetrFeatureExtractor:LS,YolosFeatureExtractor:US,DonutFeatureExtractor:t0,NougatImageProcessor:FS,EfficientNetImageProcessor:RS,ViTImageProcessor:zS,VitMatteImageProcessor:GS,SamImageProcessor:WS,Swin2SRImageProcessor:VS,Wav2Vec2FeatureExtractor:jS,SeamlessM4TFeatureExtractor:qS,SpeechT5FeatureExtractor:XS,ASTFeatureExtractor:KS,ClapFeatureExtractor:YS}),G(Qo,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:JS,Wav2Vec2ProcessorWithLM:ZS,SamProcessor:QS,SpeechT5Processor:e3,OwlViTProcessor:t3});class r3{put(e){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const r0=Br.IS_PROCESS_AVAILABLE?t=>process.stdout.write(t):t=>console.log(t);class n3 extends r3{constructor(e,{skip_prompt:r=!1,...n}={}){super(),this.tokenizer=e,this.skip_prompt=r,this.decode_kwargs=n,this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(e){if(e.length>1)throw Error("TextStreamer only supports batch size of 1");const r=e[0];if(this.skip_prompt&&this.next_tokens_are_prompt){this.next_tokens_are_prompt=!1;return}this.token_cache=ut(this.token_cache,r);const n=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let a;n.endsWith(`
`)?(a=n.slice(this.print_len),this.token_cache=[],this.print_len=0):n.length>0&&bm(n.charCodeAt(n.length-1))?(a=n.slice(this.print_len),this.print_len+=a.length):(a=n.slice(this.print_len,n.lastIndexOf(" ")+1),this.print_len+=a.length),this.on_finalized_text(a,!1)}end(){let e;this.token_cache.length>0?(e=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):e="",this.next_tokens_are_prompt=!0,this.on_finalized_text(e,!0)}on_finalized_text(e,r){r0(e),r&&r0(`
`)}}const n0="webgpu",a3=256;wt.backends.onnx.wasm.proxy=n0!=="webgpu";async function i3(){try{return(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{return!1}}class bn{static async getInstance(e=null){const r=await i3()?"fp16":"fp32";return this.tokenizer??(this.tokenizer=Am.from_pretrained(this.model_id,{progress_callback:e})),this.processor=Qo.from_pretrained(this.model_id),this.model??(this.model=Km.from_pretrained(this.model_id,{dtype:{embed_tokens:r?"fp16":"fp32",vision_encoder:r?"fp16":"fp32",decoder_model_merged:"q4"},device:n0,progress_callback:e})),Promise.all([this.tokenizer,this.processor,this.model])}}G(bn,"model_id","Xenova/nanoLLaVA"),G(bn,"model",null),G(bn,"tokenizer",null),G(bn,"streamer",null);class s3 extends n3{constructor(e,r){super(e,{skip_prompt:!0,skip_special_tokens:!0}),this.cb=r}on_finalized_text(e){this.cb(e)}}class o3 extends Ti{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(e,r){return new Array(e.length).fill(this.interrupted)}}const Li=new o3;let Jo=null;async function u3(t){const e=t.filter(p=>p.image).map(p=>p.image);if(e.length>1){self.postMessage({status:"error",error:"Currently, at most one image is supported."});return}t=t.map(({role:p,content:y,image:$})=>({role:p,content:`${$?`<image>
`:""}${y}`})),console.log("messages",t);const[r,n,a]=await bn.getInstance(),s=r.apply_chat_template(t,{tokenize:!1,add_generation_prompt:!0});let i=r(s);if(e.length>0){const p=await tr.fromURL(e[0]),y=await n(p);i={...i,...y}}let o,u=0;const l=p=>{o??(o=performance.now());let y;u++>0&&(y=u/(performance.now()-o)*1e3),self.postMessage({status:"update",output:p,tps:y,numTokens:u})},c=new s3(r,l);self.postMessage({status:"start"});const{past_key_values:h,sequences:m}=await a.generate({...i,past_key_values:Jo,do_sample:!1,max_new_tokens:a3,streamer:c,stopping_criteria:Li,return_dict_in_generate:!0});Jo=h;const _=r.batch_decode(m,{skip_special_tokens:!1});self.postMessage({status:"complete",output:_})}async function l3(){self.postMessage({status:"loading",data:"Loading model..."});const[t,e,r]=await bn.getInstance(s=>{self.postMessage(s)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const n=t("a"),a={pixel_values:um([1,3,378,378],0)};await r.generate({...n,...a,max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async t=>{const{type:e,data:r}=t.data;switch(e){case"load":l3();break;case"generate":Li.reset(),u3(r);break;case"interrupt":Li.interrupt();break;case"reset":Jo=null,Li.reset();break}})})();