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

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          var<workgroup> aBestValues : array<f32, ${g}>;
       `;return{name:e,shaderCache:t,getShaderSource:p=>`
        ${p.registerUniform("reduceSize","u32").declareVariables(c,f)}
        ${y}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${p.mainStart(g)}

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

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

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

         if (local_idx == 0u) {
          ${f.setByOffset("outputIndex",`${n==="mean"?`${f.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${f.type.storage}(${hl[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:s,dataType:a}],dispatchGroup:{x:l},programUniforms:[{type:12,data:d}]})}},Gt=(e,t,r,n)=>{let a=e.inputs.length===1?r:Es(e.inputs,r),s=a.axes;s.length===0&&!a.noopWithEmptyAxes&&(s=e.inputs[0].dims.map((y,p)=>p));let i=K.normalizeAxes(s,e.inputs[0].dims.length),o=i,l=e.inputs[0],d=yl(o,e.inputs[0].dims.length);d.length>0&&(l=e.compute(pr(e.inputs[0],d),{inputs:[0],outputs:[-1]})[0],o=fl(o.length,l.dims.length));let[c,f]=ml(l.dims,o),g=c;a.keepDims&&(g=gl(c,i)),e.compute(wl(t,{hint:a.cacheKey,inputDependencies:["type"]},[l],n,e.inputs[0].dataType,g,f),{inputs:[l]})},bl=(e,t)=>{Gt(e,"ReduceMeanShared",t,"mean")},vl=(e,t)=>{Gt(e,"ReduceL1Shared",t,"l1")},$l=(e,t)=>{Gt(e,"ReduceL2Shared",t,"l2")},xl=(e,t)=>{Gt(e,"ReduceLogSumExpShared",t,"logSumExp")},Sl=(e,t)=>{Gt(e,"ReduceMaxShared",t,"max")},kl=(e,t)=>{Gt(e,"ReduceMinShared",t,"min")},El=(e,t)=>{Gt(e,"ReduceProdShared",t,"prod")},Cl=(e,t)=>{Gt(e,"ReduceSumShared",t,"sum")},Tl=(e,t)=>{Gt(e,"ReduceSumSquareShared",t,"sumSquare")},Il=(e,t)=>{Gt(e,"ReduceLogSumShared",t,"logSum")}}),Ht,Al,Ga,Es,qt,Ol,Ml,zl,Rl,Pl,Bl,Dl,Nl,Fl,Ll,jt,Ul,Wl,Vl,Gl,Hl,ql,jl,Kl,Yl,Ql,Cs=X(()=>{$e(),Ie(),st(),Te(),U0(),Ht=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Al=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],Ga=(e,t,r,n,a,s,i=!1,o=!1)=>{let l=[],d=r[0].dims,c=d.length,f=K.normalizeAxes(a,c),g=!o&&f.length===0;d.forEach((v,S)=>{g||f.indexOf(S)>=0?i&&l.push(1):l.push(v)});let y=l.length,p=K.size(l);return{name:e,shaderCache:t,getShaderSource:v=>{let S=[],E=Y("_A",r[0].dataType,c),k=me("output",s,y),O=n(E,k,f),I=O[2];for(let T=0,F=0;T<c;T++)g||f.indexOf(T)>=0?(i&&F++,I=`for(var j${T}: u32 = 0; j${T} < ${d[T]}; j${T}++) {
                  ${O[2].includes("last_index")?`let last_index = j${T};`:""}
                  ${E.indicesSet("input_indices",T,`j${T}`)}
                  ${I}
                }`):(S.push(`${E.indicesSet("input_indices",T,k.indicesGet("output_indices",F))};`),F++);return`

        ${v.registerUniform("output_size","u32").declareVariables(E,k)}

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

          ${S.join(`
`)}
          ${O[0]}       // init ops for reduce max/min
          ${O[1]}
          ${I}
          ${O[3]}
          ${O.length===4?k.setByOffset("global_idx","value"):O.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:l,dataType:s}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},..._e(d,l)]})}},Es=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),He({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},qt=(e,t,r,n)=>{let a=e.inputs,s=a.length===1?r:Es(a,r);e.compute(Ga(t,{hint:s.cacheKey,inputDependencies:["rank"]},[a[0]],s.noopWithEmptyAxes&&s.axes.length===0?Al:n,s.axes,a[0].dataType,s.keepDims,s.noopWithEmptyAxes),{inputs:[0]})},Ol=(e,t)=>{Ht(e.inputs),qt(e,"ReduceLogSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},Ml=(e,t)=>{Ht(e.inputs),qt(e,"ReduceL1",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},zl=(e,t)=>{Ht(e.inputs),qt(e,"ReduceL2",t,(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);"])},Rl=(e,t)=>{Ht(e.inputs),qt(e,"ReduceLogSumExp",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},Pl=(e,t)=>{Ht(e.inputs),qt(e,"ReduceMax",t,(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, ${r.getByIndices("input_indices")});`,""]})},Bl=(e,t)=>{Ht(e.inputs),qt(e,"ReduceMean",t,(r,n,a)=>{let s=1;for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&(s*=e.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${s});`]})},Dl=(e,t)=>{Ht(e.inputs),qt(e,"ReduceMin",t,(r,n,a)=>{let s=[];for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&s.push(`input_indices[${i}] = 0;`);return[`${s.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},Nl=(e,t)=>{Ht(e.inputs),qt(e,"ReduceProd",t,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},Fl=(e,t)=>{Ht(e.inputs),qt(e,"ReduceSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},Ll=(e,t)=>{Ht(e.inputs),qt(e,"ReduceSumSquare",t,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},jt=(e,t,r)=>{if(t.length===0)return r;let n=1,a=1;for(let s=0;s<t.length;s++)t.indexOf(s)===-1?n*=e[s]:a*=e[s];return a<32&&n>1024},Ul=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Bl(e,t):bl(e,t)},Wl=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ml(e,t):vl(e,t)},Vl=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?zl(e,t):$l(e,t)},Gl=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Rl(e,t):xl(e,t)},Hl=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Pl(e,t):Sl(e,t)},ql=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Dl(e,t):kl(e,t)},jl=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Nl(e,t):El(e,t)},Kl=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Fl(e,t):Cl(e,t)},Yl=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ll(e,t):Tl(e,t)},Ql=(e,t)=>{jt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ol(e,t):Il(e,t)}}),Ts,Xl,Jl,Is,W0=X(()=>{$e(),st(),Cs(),Ts=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Xl=(e,t)=>{Ts(e.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o<n.rank;o++)(s.indexOf(o)>=0||s.length===0)&&i.push(`input_indices[${o}] = 0;`);return[`${i.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};e.compute(Ga("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Jl=(e,t)=>{Ts(e.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o<n.rank;o++)(s.indexOf(o)>=0||s.length===0)&&i.push(`input_indices[${o}] = 0;`);return[`${i.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};e.compute(Ga("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Is=e=>He(e)}),Zl,ed,td,Ha,rd,nd,ad=X(()=>{$e(),Ie(),st(),Te(),Zl=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],a=n.dataType,s=n.dims.length;e.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((l,d)=>{if(d!==t&&l!==n.dims[d])throw new Error("non concat dimensions must match")})}})},ed=(e,t)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${e}u>(${t});
    for (var i: u32 = 0u; i < ${e}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${e}u;
  }`,td=(e,t)=>{let r=e.length,n=[];for(let a=0;a<r;++a){let s=t.setByOffset("global_idx",e[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}) { ${s} }`)}return n.join(`
`)},Ha=(e,t,r,n)=>{let a=K.size(r),s=new Array(e.length),i=new Array(e.length),o=0,l=[],d=[],c=[{type:12,data:a}];for(let v=0;v<e.length;++v)o+=e[v].dims[t],s[v]=o,d.push(e[v].dims.length),i[v]=Y(`input${v}`,n,d[v]),l.push("rank"),c.push({type:12,data:s[v]});for(let v=0;v<e.length;++v)c.push(..._e(e[v].dims));c.push(..._e(r));let f=me("output",n,r.length),g=f.indicesGet("indices",t),y=Array.from(Array(s.length).keys()).map(v=>`uniforms.sizeInConcatAxis${v}`).join(","),p=v=>`

  ${(()=>{v.registerUniform("outputSize","u32");for(let S=0;S<e.length;S++)v.registerUniform(`sizeInConcatAxis${S}`,"u32");return v.declareVariables(...i,f)})()}

  ${ed(s.length,y)}

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

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

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

    ${td(i,f)}
  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c}),getShaderSource:p}},rd=(e,t)=>{let r=e.inputs,n=r[0].dims,a=K.normalizeAxis(t.axis,n.length);Zl(r,a);let s=n.slice();s[a]=r.reduce((o,l)=>o+(l.dims.length>a?l.dims[a]:0),0);let i=r.filter(o=>K.size(o.dims)>0);e.compute(Ha(i,a,s,r[0].dataType),{inputs:i})},nd=e=>He({axis:e.axis})}),id,sd,od,ud,qa,ld,dd,cd=X(()=>{$e(),ys(),Te(),ad(),id=(e,t)=>{let r=e[0],n=e[1],a=e[2],s=e[3],i=e[4],o=e[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 l=r.dims[0],d=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 f=a.dims[0]/3,g=f,y=g;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let O of t.qkvHiddenSizes)if(O%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");f=t.qkvHiddenSizes[0],g=t.qkvHiddenSizes[1],y=t.qkvHiddenSizes[2]}let p=d;if(f!==g)throw new Error("qkv_hidden_sizes first element should be same as the second");if(a.dims[0]!==f+g+y)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let v=0;if(i){if(g!==y)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]!==l)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==g/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(v=i.dims[3])}let S=p+v,E=-1,k=0;if(s)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");return{batchSize:l,sequenceLength:d,pastSequenceLength:v,kvSequenceLength:p,totalSequenceLength:S,maxSequenceLength:E,inputHiddenSize:c,hiddenSize:f,vHiddenSize:y,headSize:Math.floor(f/t.numHeads),vHeadSize:Math.floor(y/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:k,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},sd=(e,t,r,n)=>{let a=et(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),l=[{type:t.dataType,data:1/n},{type:12,data:i},{type:12,data:o}],d=ot(t.dataType,a),c=wt(1,a),f=g=>{let y=me("x",t.dataType,t.dims,a),p=[{name:"d_inv",type:wt(t.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}>;
  ${g.registerUniforms(p).declareVariables(y)}
  ${g.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] = ${y.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] = ${y.type.value}(exp(f32input - max_value) / sum);
      }
    }
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${d};${a}`},getShaderSource:f,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:l})}},od=(e,t,r,n,a,s,i)=>{let o=i+a.kvSequenceLength,l=[a.batchSize,a.numHeads,a.sequenceLength,o],d=s.scale===0?1/Math.sqrt(a.headSize):s.scale,c=et(a.headSize),f=a.headSize/c,g=12,y={x:Math.ceil(o/g),y:Math.ceil(a.sequenceLength/g),z:a.batchSize*a.numHeads},p=[{type:12,data:a.sequenceLength},{type:12,data:f},{type:12,data:o},{type:12,data:a.numHeads},{type:1,data:d}],v=n?["type","type","type"]:["type","type"],S=E=>{let k=Y("q",t.dataType,t.dims,c),O=Y("key",r.dataType,r.dims,c),I=[k,O];n&&I.push(Y("relative_position_bias",n.dataType,n.dims));let T=me("output",t.dataType,l),F=wt(1,c),B=[{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 = ${g}u;

  var<workgroup> tileQ: array<${k.type.storage}, ${g*g}>;
  var<workgroup> tileK: array<${k.type.storage}, ${g*g}>;
  ${E.registerUniforms(B).declareVariables(...I,T)}
  ${E.mainStart([g,g,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 = ${F}(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 += ${F}(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] = ${T.type.value} (sum * uniforms.alpha) + ${n?"relative_position_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${c}`,inputDependencies:v},getRunData:()=>({outputs:[{dims:l,dataType:t.dataType,gpuDataType:0}],dispatchGroup:y,programUniforms:p}),getShaderSource:S}},ud=(e,t,r,n,a)=>{let s=a+n.kvSequenceLength,i=[n.batchSize,n.sequenceLength,n.vHiddenSize],o=12,l={x:Math.ceil(n.vHeadSize/o),y:Math.ceil(n.sequenceLength/o),z:n.batchSize*n.numHeads},d=[{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:t.dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:d}),getShaderSource:c=>{let f=Y("probs",t.dataType,t.dims),g=Y("v",r.dataType,r.dims),y=me("output",t.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 = ${o}u;
  var<workgroup> tileQ: array<${f.type.value}, ${o*o}>;
  var<workgroup> tileK: array<${f.type.value}, ${o*o}>;
  ${c.registerUniforms(p).declareVariables(f,g,y)}
  ${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 = ${f.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;
   }
  }`}}},qa=(e,t,r,n,a,s,i,o,l,d,c)=>{let f=e.outputCount>1,g=e.outputCount>2,y=f&&g?d.pastSequenceLength:0,p=y+d.kvSequenceLength,v=[d.batchSize,d.numHeads,p,d.headSize],S=i?[i,r]:[r],E=f?e.compute(Ha(S,2,v,r.dataType),{inputs:S,outputs:[1]})[0]:r,k=[d.batchSize,d.numHeads,p,d.headSize],O=o?[o,n]:[n],I=g?e.compute(Ha(O,2,k,n.dataType),{inputs:O,outputs:[2]})[0]:n,T=[t,E];l&&T.push(l);let F=e.compute(od(e,t,E,l,d,c,y),{inputs:T,outputs:[-1]})[0];e.compute(sd(e,F,d.batchSize*d.numHeads*d.sequenceLength,p),{inputs:[F],outputs:[]});let B=[F,I];e.compute(ud(e,F,I,d,y),{inputs:B,outputs:[0]})},ld=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,a=t.inputHiddenSize,s=t.headSize,i=12,o={x:Math.ceil(t.headSize/i),y:Math.ceil(t.sequenceLength/i),z:t.batchSize*t.numHeads},l=[e.inputs[0],e.inputs[1],e.inputs[2]],d=[{type:12,data:n},{type:12,data:a},{type:12,data:s},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],c=f=>{let g=me("output_q",l[0].dataType,r),y=me("output_k",l[0].dataType,r),p=me("output_v",l[0].dataType,r),v=Y("input",l[0].dataType,l[0].dims),S=Y("weight",l[1].dataType,l[1].dims),E=Y("bias",l[2].dataType,l[2].dims),k=v.type.storage,O=[{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 = ${i}u;
  var<workgroup> tileInput: array<${k}, ${i*i}>;
  var<workgroup> tileWeightQ: array<${k}, ${i*i}>;
  var<workgroup> tileWeightK: array<${k}, ${i*i}>;
  var<workgroup> tileWeightV: array<${k}, ${i*i}>;
  ${f.registerUniforms(O).declareVariables(v,S,E,g,y,p)}
  ${f.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 = ${k}(0);
    var valueK = ${k}(0);
    var valueV = ${k}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims: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:o,programUniforms:d}),getShaderSource:c},{inputs:l,outputs:[-1,-1,-1]})},dd=(e,t)=>{let r=id(e.inputs,t),[n,a,s]=ld(e,r);return qa(e,n,a,s,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}}),pd,hd,fd,md,V0=X(()=>{Vt(),$e(),Ie(),st(),Te(),pd=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,a,s)=>{let i=a.length;if(i!==n.length)throw new Error(`${s}: num dimensions != ${i}`);a.forEach((o,l)=>{if(o!==n[l])throw new Error(`${s}: dim[${l}] do not match`)})};if(e[0].dims.length>1){let n=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,n,"Invalid input scale"),r(e[2].dims,n,"Invalid input B"),r(e[3].dims,n,"Invalid input mean"),r(e[4].dims,n,"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 var")},hd=(e,t)=>{let{epsilon:r,spatial:n,format:a}=t,s=e[0].dims,i=n?et(s[s.length-1]):1,o=a==="NHWC"&&s.length>1?i:1,l=K.size(s)/i,d=n,c=d?s.length:s,f=Y("x",e[0].dataType,e[0].dims,i),g=Y("scale",e[1].dataType,e[1].dims,o),y=Y("bias",e[2].dataType,e[2].dims,o),p=Y("inputMean",e[3].dataType,e[3].dims,o),v=Y("inputVar",e[4].dataType,e[4].dims,o),S=me("y",e[0].dataType,c,i),E=()=>{let O="";if(n)O=`let cOffset = ${s.length===1?"0u":a==="NHWC"?`outputIndices[${s.length-1}] / ${i}`:"outputIndices[1]"};`;else if(a==="NCHW")O=`
            ${S.indicesSet("outputIndices","0","0")}
            let cOffset = ${S.indicesToOffset("outputIndices")};`;else{O=`var cIndices = ${g.type.indices}(0);
                       cIndices[0] = outputIndices[${s.length-1}];`;for(let I=1;I<g.rank;I++)O+=`cIndices[${I}] = outputIndices[${I}];`;O+=`let cOffset = ${g.indicesToOffset("cIndices")};`}return O},k=O=>`
  const epsilon = ${r};
  ${O.registerUniform("outputSize","u32").declareVariables(f,g,y,p,v,S)}
  ${O.mainStart()}
  ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${S.offsetToIndices(`global_idx * ${i}`)};
    ${E()}
    let scale = ${g.getByOffset("cOffset")};
    let bias = ${y.getByOffset("cOffset")};
    let inputMean = ${p.getByOffset("cOffset")};
    let inputVar = ${v.getByOffset("cOffset")};
    let x = ${f.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${S.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${i}`,inputDependencies:d?["rank","type","type","type","type"]:void 0},getShaderSource:k,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d?[{type:12,data:l},..._e(s)]:[{type:12,data:l}]})}},fd=e=>He(e),md=(e,t)=>{let{inputs:r,outputCount:n}=e,a=fd({...t,outputCount:n});if(Ue.webgpu.validateInputContent&&pd(r,a),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(hd(r,a))}}),gd,_d,yd,G0=X(()=>{Ie(),Te(),gd=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},_d=e=>{let t=e[0].dims,r=e[0].dims[2],n=K.size(t)/4,a=e[0].dataType,s=Y("input",a,t,4),i=Y("bias",a,[r],4),o=Y("residual",a,t,4),l=me("output",a,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:d=>`
  const channels = ${r}u / 4;
  ${d.declareVariables(s,i,o,l)}

  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${s.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${o.getByOffset("global_idx")};
    ${l.setByOffset("global_idx","value")}
  }`}},yd=e=>{gd(e.inputs),e.compute(_d(e.inputs))}}),wd,De,bd,vd,$d,xd,Sd,kd,Ed,Cd,Td,Id,Ad,Od,Md,zd,ja,Rd,Ka,Pd,Bd,Dd,Nd,Fd,Ld,Ud,Wd,Vd,Gd,Hd,qd,jd,Kd,Yd,Qd,As,Xd,Os,Ms,Jd,Zd,ec,zs=X(()=>{$e(),Ie(),st(),Te(),wd=(e,t,r,n,a,s)=>{let i=Math.ceil(t/4),o="";typeof a=="string"?o=`${a}(a)`:o=a("a");let l=Y("inputData",r,[i],4),d=me("outputData",n,[i],4);return`
      ${e.registerUniform("vec_size","u32").declareVariables(l,d)}

  ${s??""}

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

    let a = ${l.getByOffset("global_idx")};
    ${d.setByOffset("global_idx",o)}
  }`},De=(e,t,r,n,a,s=e.dataType)=>({name:t,shaderCache:{hint:a,inputDependencies:["type"]},getShaderSource:i=>wd(i,K.size(e.dims),e.dataType,s,r,n),getRunData:i=>({outputs:[{dims:e.dims,dataType:s}],dispatchGroup:{x:Math.ceil(K.size(i[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(K.size(e.dims)/4)}]})}),bd=e=>{e.compute(De(e.inputs[0],"Abs","abs"))},vd=e=>{e.compute(De(e.inputs[0],"Acos","acos"))},$d=e=>{e.compute(De(e.inputs[0],"Acosh","acosh"))},xd=e=>{e.compute(De(e.inputs[0],"Asin","asin"))},Sd=e=>{e.compute(De(e.inputs[0],"Asinh","asinh"))},kd=e=>{e.compute(De(e.inputs[0],"Atan","atan"))},Ed=e=>{e.compute(De(e.inputs[0],"Atanh","atanh"))},Cd=e=>He(e),Td=(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): ${t.to}`)}e.compute(De(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},Id=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:$s,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:xs;return He({min:t,max:r})},Ad=(e,t)=>{let r=e.inputs.length===1?t:Id(e.inputs),n=wt(e.inputs[0].dataType);e.compute(De(e.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}(${r.max}));
`,r.cacheKey),{inputs:[0]})},Od=e=>{e.compute(De(e.inputs[0],"Ceil","ceil"))},Md=e=>{e.compute(De(e.inputs[0],"Cos","cos"))},zd=e=>{e.compute(De(e.inputs[0],"Cosh","cosh"))},ja=e=>He(e),Rd=(e,t)=>{let r=wt(e.inputs[0].dataType);e.compute(De(e.inputs[0],"Elu",n=>`elu_vf32(${n})`,`
  const elu_alpha_ = ${r}(${t.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));
  }`,t.cacheKey))},Ka=(e="f32")=>`
const r0: ${e} = 0.3275911;
const r1: ${e} = 0.254829592;
const r2: ${e} = -0.284496736;
const r3: ${e} = 1.421413741;
const r4: ${e} = -1.453152027;
const r5: ${e} = 1.061405429;

fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {
  let absv = abs(v);
  let x = 1.0 / (1.0 + r0 * absv);
  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
}`,Pd=e=>{let t=wt(e.inputs[0].dataType);e.compute(De(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,Ka(t)))},Bd=e=>{e.compute(De(e.inputs[0],"Exp","exp"))},Dd=e=>{e.compute(De(e.inputs[0],"Floor","floor"))},Nd=e=>{let t=wt(e.inputs[0].dataType);e.compute(De(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,Ka(t)))},Fd=(e,t)=>{let r=wt(e.inputs[0].dataType);e.compute(De(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},Ld=e=>{e.compute(De(e.inputs[0],"Not",t=>`!${t}`))},Ud=e=>{e.compute(De(e.inputs[0],"Neg",t=>`-${t}`))},Wd=e=>{e.compute(De(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Vd=e=>{let t=wt(e.inputs[0].dataType);e.compute(De(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Gd=e=>{e.compute(De(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},Hd=e=>He(e),qd=(e,t)=>{let r=wt(e.inputs[0].dataType);e.compute(De(e.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${n} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},jd=e=>{e.compute(De(e.inputs[0],"Sin","sin"))},Kd=e=>{e.compute(De(e.inputs[0],"Sinh","sinh"))},Yd=e=>{e.compute(De(e.inputs[0],"Sqrt","sqrt"))},Qd=e=>{e.compute(De(e.inputs[0],"Tan","tan"))},As=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Xd=e=>{e.compute(De(e.inputs[0],"Tanh",As))},Os=(e="f32")=>`
const fast_gelu_a: ${e} = 0.5;
const fast_gelu_b: ${e} = 0.7978845608028654;
const fast_gelu_c: ${e} = 0.035677408136300125;

fn tanh_v(v: vec4<${e}>) -> vec4<${e}> {
  return ${As("v")};
}
`,Ms=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Jd=e=>{let t=wt(e.inputs[0].dataType);e.compute(De(e.inputs[0],"FastGelu",Ms,Os(t),void 0,e.inputs[0].dataType))},Zd=(e,t)=>{let r=wt(e.inputs[0].dataType);return e.compute(De(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},ec=e=>{e.compute(De(e.inputs[0],"Log","log"))}}),tc,rc,nc,H0=X(()=>{Ie(),Te(),zs(),tc=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")},rc=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=Y("input",e[0].dataType,e[0].dims,4),n=Y("bias",e[0].dataType,[e[0].dims[2]],4),a=me("output",e[0].dataType,t,4),s=K.size(t)/4,i=ot(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:o=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

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

  ${Ka(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);

    ${a.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},nc=e=>{tc(e.inputs),e.compute(rc(e.inputs))}}),ac,ic,Kt,sc,oc,uc,lc,dc,cc,pc,hc,fc,mc,q0=X(()=>{$e(),Ie(),Te(),ac=(e,t,r,n,a,s,i,o,l,d,c,f)=>{let g,y;typeof o=="string"?g=y=(k,O)=>`${o}((${k}),(${O}))`:typeof o=="function"?g=y=o:(g=o.scalar,y=o.vector);let p=me("outputData",c,n.length,4),v=Y("aData",l,t.length,4),S=Y("bData",d,r.length,4),E;if(a)if(s){let k=K.size(t)===1,O=K.size(r)===1,I=t.length>0&&t[t.length-1]%4===0,T=r.length>0&&r[r.length-1]%4===0;k||O?E=p.setByOffset("global_idx",y(k?`${v.type.value}(${v.getByOffset("0")}.x)`:v.getByOffset("global_idx"),O?`${S.type.value}(${S.getByOffset("0")}.x)`:S.getByOffset("global_idx"))):E=`
            let outputIndices = ${p.offsetToIndices("global_idx * 4u")};
            let offsetA = ${v.broadcastedIndicesToOffset("outputIndices",p)};
            let offsetB = ${S.broadcastedIndicesToOffset("outputIndices",p)};
            ${p.setByOffset("global_idx",y(i||I?v.getByOffset("offsetA / 4u"):`${v.type.value}(${v.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||T?S.getByOffset("offsetB / 4u"):`${S.type.value}(${S.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else E=p.setByOffset("global_idx",y(v.getByOffset("global_idx"),S.getByOffset("global_idx")));else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let k=(O,I,T="")=>{let F=`aData[indexA${I}][componentA${I}]`,B=`bData[indexB${I}][componentB${I}]`;return`
            let outputIndices${I} = ${p.offsetToIndices(`global_idx * 4u + ${I}u`)};
            let offsetA${I} = ${v.broadcastedIndicesToOffset(`outputIndices${I}`,p)};
            let offsetB${I} = ${S.broadcastedIndicesToOffset(`outputIndices${I}`,p)};
            let indexA${I} = offsetA${I} / 4u;
            let indexB${I} = offsetB${I} / 4u;
            let componentA${I} = offsetA${I} % 4u;
            let componentB${I} = offsetB${I} % 4u;
            ${O}[${I}] = ${T}(${g(F,B)});
          `};c===9?E=`
            var data = vec4<u32>(0);
            ${k("data",0,"u32")}
            ${k("data",1,"u32")}
            ${k("data",2,"u32")}
            ${k("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:E=`
            ${k("outputData[global_idx]",0)}
            ${k("outputData[global_idx]",1)}
            ${k("outputData[global_idx]",2)}
            ${k("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(v,S,p)}

        ${f??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${E}
      }`},ic=(e,t,r,n,a,s,i=r.dataType)=>{let o=!K.areEqual(r.dims,n.dims),l=r.dims,d=K.size(r.dims),c=!1,f=!1,g=[o];if(o){let y=an.calcShape(r.dims,n.dims,!1);if(!y)throw new Error("Can't perform binary op on the given tensors");l=y,d=K.size(l);let p=K.size(r.dims)===1,v=K.size(n.dims)===1,S=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,E=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;g.push(p),g.push(v),g.push(S),g.push(E);let k=1;for(let O=1;O<l.length;O++){let I=r.dims[r.dims.length-O]??1,T=n.dims[n.dims.length-O]??1;if(I===T)k*=I;else break}k%4===0?(f=!0,c=!0):(p||v||S||E)&&(c=!0)}else c=!0;return g.push(c),{name:e,shaderCache:{hint:t+g.map(y=>y.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:y=>ac(y,r.dims,n.dims,l,c,o,f,a,r.dataType,n.dataType,i,s),getRunData:()=>({outputs:[{dims:l,dataType:i}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:Math.ceil(K.size(l)/4)},..._e(r.dims,n.dims,l)]})}},Kt=(e,t,r,n,a,s)=>{e.compute(ic(t,a??"",e.inputs[0],e.inputs[1],r,n,s))},sc=e=>{Kt(e,"Add",(t,r)=>`${t}+${r}`)},oc=e=>{Kt(e,"Div",(t,r)=>`${t}/${r}`)},uc=e=>{Kt(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},lc=e=>{Kt(e,"Mul",(t,r)=>`${t}*${r}`)},dc=e=>{let t=Y("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Kt(e,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},`
    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {
      if (b == ${t}(0.0)) {
        return ${t}(1.0);
      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {
        return ${t}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {
      // TODO: implement vectorized pow
      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},cc=e=>{Kt(e,"Sub",(t,r)=>`${t}-${r}`)},pc=e=>{Kt(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},hc=e=>{Kt(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},fc=e=>{Kt(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},mc=e=>{Kt(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}}),Fr,Lr,Ur,Rs,Wr=X(()=>{$e(),Ie(),Fr=(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 ${e.activation}`)}},Lr=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Ur=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Rs=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[r,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=(e==null?void 0:e.activation_params)||[$s,xs];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:r}}return{activation:t}}}),mt,Ps,Bs=X(()=>{mt=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Ps=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),Ds,gc=X(()=>{Ds=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),_c,yc,Ya,Ns,wc,Qa,bc,Fs,Xa=X(()=>{$e(),Ie(),Te(),Wr(),Bs(),_c=(e,t)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${t?", batchIndices":""});
        `,yc=(e,t)=>e?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
        for (var i = 0; i < rowPerThread; i = i + 1) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
        }`:`
        for (var i = 0; i < rowPerThread; i = i + 1) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,Ya=(e,t,r="f32",n,a=!1,s=32,i=!1,o=32)=>{let l=t[1]*e[1],d=t[0]*e[0],c=a?l:s,f=a?s:l,g=c/t[0],y=s/t[1];if(!((a&&g===4&&e[1]===4||!a&&(g===3||g===4))&&c%t[0]===0&&s%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${g} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${g} must be 3 or 4.
  tileAWidth ${c} must be divisible by workgroupSize[0]${t[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${g}<${r}>, ${c/g}>, ${f}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${d/e[0]}>, ${s}>;

const rowPerThread = ${e[1]};
const colPerThread = ${e[0]};
const innerElementSize = ${g};
const tileInner = ${s};

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

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

  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 * ${y};
  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;
          ${_c(a,n)}
      }

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

          ${yc(a,g)}
      }

      workgroupBarrier();
  }

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

    // Loop over shared dimension.
    for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var inputRow = localRow; inputRow < ${g}; inputRow = inputRow + ${t[1]}) {
        for (var inputCol = localCol; inputCol < ${f}; inputCol = inputCol + ${t[0]}) {
          ${Ns(a,n)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${t[1]}) {
            for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${t[0]}) {
          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
            kStart + inputRow,
            globalColStart + inputCol${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 * ${t[0]}];
        }
        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let ACached = ${a?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}
          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
            acc[innerRow][innerCol] = acc[innerRow][innerCol] +
                ACached * BCached[innerCol];
          }
        }
      }
      workgroupBarrier();
    }
    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      let gRow = globalRowStart + localRow + innerRow * ${t[1]};
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        let gCol = globalColStart + localCol + innerCol * ${t[0]};
        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
      }
    }
    `:`
let tileRow = i32(localId.y) * rowPerThread;
let tileCol = i32(localId.x) * colPerThread;

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

let tileRowA = i32(localId.y) * ${y};
let tileColA = i32(localId.x) * ${p};
let tileRowB = i32(localId.y) * ${v};
// 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 < ${y}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${p}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${Ns(a,n)}
    }
  }

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${v}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${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) {
      ${wc(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}, ${f}>, ${g}>;
  var<workgroup> mm_Bsub : array<array<${r}, ${c}>, ${s}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${s};

@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
        @builtin(global_invocation_id) globalId : vec3<u32>,
        @builtin(workgroup_id) workgroupId : vec3<u32>) {
    let batch = ${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;
      }
    }
    ${S}
  }
`},bc=(e,t,r,n,a,s=!1)=>{let[i,o,l]=a,[d,c,f,g]=n,y=Pn(i,l),p=Pn(o,l),v=ot(n[0].type.tensor),S=()=>{let k=c.rank,O=d.rank,I=`var aIndices: ${c.type.indices};`;for(let T=k-2-1,F=O-1;T>=0;T--,F--)I+=`
aIndices[${T}] = ${O>1?`batchIndices[${F}]`:"batchIndices"};`;return y.forEach(T=>{I+=`
aIndices[${T}] = 0;`}),I+=`
aIndices[${k-2}] = u32(row);
                   aIndices[${k-1}] = u32(colIn);`,I},E=()=>{let k=f.rank,O=d.rank,I=`var bIndices: ${f.type.indices};`;for(let T=k-2-1,F=O-1;T>=0;T--,F--)I+=`
bIndices[${T}] = ${O>1?`batchIndices[${F}]`:"batchIndices"};`;return p.forEach(T=>{I+=`
bIndices[${T}] = 0;`}),I+=`
bIndices[${k-2}] = u32(row);
                   bIndices[${k-1}] = u32(colIn);`,I};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${mt(e,v)} {
      var value = ${mt(e,v)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${S()}
        value = ${c.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${mt(e,v)} {
      var value = ${mt(e,v)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
      {
        ${E()}
        value = ${f.getByIndices("bIndices")};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${mt(e,v)}) {
      let col = colIn * ${e};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${t?`value = value + ${s?"bias[colIn]":`${mt(e,v)}(bias[row])`};`:""}
        ${r}
        ${g.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},Fs=(e,t,r,n,a=!1)=>{let s=e[0].dims,i=e[1].dims,o=s.slice(0,-2),l=i.slice(0,-2),d=n?n.slice(0,-2):r.slice(0,-2),c=K.size(d),f=s[s.length-2],g=s[s.length-1],y=i[i.length-1],p=g%4===0&&y%4===0,v=f<=8?[4,1,1]:[4,4,1],S=[8,8,1],E=[Math.ceil(y/S[0]/v[0]),Math.ceil(f/S[1]/v[1]),Math.ceil(c/S[2]/v[2])],k=p?4:1,O=[...o,f,g/k],I=O.length,T=[...l,g,y/k],F=T.length,B=[c,f,y/k],G=[{type:6,data:f},{type:6,data:y},{type:6,data:g}];Lr(t,G),G.push(..._e(d,O,T));let H=["rank","rank"],Z=e.length>2;Z&&(G.push(..._e(e[2].dims)),H.push("rank")),G.push(..._e(B));let pe=oe=>{let ne=d.length,de=Ss("batchDims",e[0].dataType,ne,1),D=ot(e[0].dataType),z=Y("a",e[0].dataType,I,k),q=Y("b",e[1].dataType,F,k),te=me("result",e[0].dataType,B.length,k),ye=[z,q];if(Z){let Ae=a?k:1;ye.push(Y("bias",e[2].dataType,e[2].dims.length,Ae))}let he=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ur(t,he);let R=ot(te.type.tensor),ie=Fr(t,te.type.value,R),le=bc(k,Z,ie,[de,z,q,te],[o,l,d],a);return`
  ${oe.registerUniforms(he).registerInternalVariables(de).declareVariables(...ye,te)}
  ${le}
  ${p?Ya(v,S,D,de):Qa(v,S,D,de)}
                   `};return{name:"MatMul",shaderCache:{hint:`${v};${t.activation};${p};${a}`,inputDependencies:H},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:G}),getShaderSource:pe}}}),vc,$c,j0=X(()=>{$e(),Dr(),Te(),Wr(),Bs(),gc(),Xa(),vc=(e,t,r,n,a=!1,s,i=4,o=4,l=4,d="f32")=>{let c=H=>{switch(H){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${d}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${H} is not supported.`)}},f=H=>{switch(H){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 ${H} is not supported.`)}},g=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,y=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,p=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",v=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",S=e?"row":"col",E=e?"col":"row",k=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${S} / outWidth;
    let outCol = ${S} % outWidth;

    let WRow = ${E} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${E} / 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 = ${E} % inChannels;
    var resData = ${mt(i,d)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${p} && xCol >= 0 && xCol < ${v}) {
      ${g}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${c(i)}
    }
    return resData;`,O=e?t&&n?`
    let col = colIn * ${i};
    ${k}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${k}
    }
    return ${mt(i,d)}(0.0);`:n&&r?`
    let col = colIn * ${i};
    ${k}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${k}
    }
    return ${mt(i,d)}(0.0);`,I=`${f(o)}`,T=mt(l,d),F=mt(e?i:o,d),B=mt(e?o:i,d),G=Fr(s,T,d);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${F} {
      ${e?O:I}
    }

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${T}) {
      let col = colIn * ${l};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${y}
      ${Ps(a)}
      ${G}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},$c=(e,t,r,n,a,s,i,o)=>{let l=t.format==="NHWC",d=l?e[0].dims[3]:e[0].dims[1],c=r[0],f=l?r[2]:r[3],g=l?r[1]:r[2],y=l?r[3]:r[1],p=l&&(d%4===0||d%3===0)&&y%4===0,v=l?y:f*g,S=l?f*g:y,E=[8,8,1],k=n<=8?[4,1,1]:[4,4,1],O=[Math.ceil(v/E[0]/k[0]),Math.ceil(S/E[1]/k[1]),Math.ceil(c/E[2]/k[2])];Xe("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${O}`);let I=p?l&&d%4!==0?3:4:1,T=E[1]*k[1],F=E[0]*k[0],B=Math.max(E[0]*I,E[1]),G=n%T===0,H=a%F===0,Z=s%B===0,pe=p?[I,4,4]:[1,1,1],oe=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Lr(t,oe),oe.push(..._e(e[0].dims,e[1].dims));let ne=["rank","rank"];i&&(oe.push(..._e(e[2].dims)),ne.push("rank")),oe.push(..._e(r));let de=D=>{let z=[{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}];Ur(t,z);let q=p?4:1,te=ot(e[0].dataType),ye=`
      fn setOutputAtIndex(flatIndex : i32, value : ${p?`vec4<${te}>`:te}) {
        result[flatIndex] = ${p?`vec4<${te}>`:te}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${p?`vec4<${te}>`:te}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${p?"/ 4":""}, value);
      }`,he=Y("x",e[0].dataType,e[0].dims.length,I===3?1:I),R=Y("w",e[1].dataType,e[1].dims.length,q),ie=[he,R],le=me("result",e[0].dataType,r.length,q);if(i){let Ae=Y("bias",e[2].dataType,e[2].dims.length,q);ie.push(Ae),ye+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${p?`vec4<${te}>`:te} {
          return bias[coords.${l?"w":"y"}${p?"/ 4":""}];
        }`}return`
        ${Ds("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(z).declareVariables(...ie,le)}
        ${ye}
        ${vc(l,G,H,Z,i,t,pe[0],pe[1],pe[2],te)}
        ${p?Ya(k,E,te,void 0,!l,B):Qa(k,E,te,void 0,!l,B,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${I};${p};${G};${H};${Z};${T};${F};${B}`,inputDependencies:ne},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:O[0],y:O[1],z:O[2]},programUniforms:oe}),getShaderSource:de}}}),Ls,xc,K0=X(()=>{$e(),Ie(),Te(),Ac(),Wr(),Ls=(e,t,r)=>{let n=e.length>2,a=n?"value += b[output_channel];":"",s=e[0].dims,i=e[1].dims,o=i[0]/t.group,l=t.format==="NHWC",d=Ja(s,i,t.dilations,t.pads,t.strides,l),c=K.size(d),f=[{type:12,data:c},{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:o}];Lr(t,f),f.push(..._e(s,i));let g=["rank","rank"];n&&(f.push(..._e(e[2].dims)),g.push("rank")),f.push(..._e(d));let y=p=>{let v=me("output",e[0].dataType,d.length),S=ot(v.type.tensor),E=Fr(t,v.type.value,S),k=Y("x",e[0].dataType,s.length),O=Y("w",e[1].dataType,i.length),I=[k,O];n&&I.push(Y("b",e[2].dataType,e[2].dims.length));let T=[{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 Ur(t,T),`
  ${p.registerUniforms(T).declareVariables(...I,v)}

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

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

    var value: ${v.type.value} = ${v.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[${l?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[${l?2:3}]) {
            continue;
          }

          let xVal = ${l?k.get("batch","xHeight","xWidth","input_channel"):k.get("batch","input_channel","xHeight","xWidth")};
          let wVal = ${O.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${a}
    ${E}
    ${v.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:g},getRunData:()=>({outputs:[{dims:r?r(d):d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:f}),getShaderSource:y}},xc=(e,t,r)=>{let n=e.length>2,a=et(r[3]),s=et(r[2]),i=K.size(r)/a/s,o=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],l=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],d=[r[0],r[1],r[2],r[3]/a],c=[{type:12,data:i},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Lr(t,c),c.push(..._e(o,l,d));let f=(s-1)*t.strides[1]+l[1],g=y=>{let p=me("output",e[0].dataType,d.length,a),v=ot(p.type.tensor),S=Fr(t,p.type.value,v),E=Y("x",e[0].dataType,o.length,a),k=Y("w",e[1].dataType,l.length,a),O=[E,k];n&&O.push(Y("b",e[2].dataType,e[2].dims,a));let I=n?"value += b[output_channel];":"",T=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Ur(t,T),`
  ${y.registerUniforms(T).declareVariables(...O,p)}
  ${y.mainStart()}
    ${y.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<${E.type.value}, ${f}>;
    var values: array<${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 < ${l[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 < ${f}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${E.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${E.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${l[1]}; w_width++) {
          let w_val = ${k.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];
      ${I}
      ${S}
      ${p.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${a};${s};${f};${l[0]};${l[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:c}),getShaderSource:g}}}),Us,Sc,kc,Ec=X(()=>{$e(),Ie(),Xa(),Te(),Wr(),Us=(e,t,r,n,a=!1)=>{let s=e[0].dims,i=e[1].dims,o=s[s.length-2],l=i[i.length-1],d=s[s.length-1],c=et(l),f=et(d),g=et(o),y=K.size(r)/c/g,p=e.length>2,v=n?n.slice(0,-2):r.slice(0,-2),S=[K.size(v),o,l],E=[{type:12,data:y},{type:12,data:o},{type:12,data:l},{type:12,data:d}];Lr(t,E),E.push(..._e(v,s,i)),p&&E.push(..._e(e[2].dims)),E.push(..._e(S));let k=O=>{let I=Ss("batch_dims",e[0].dataType,v.length),T=Y("a",e[0].dataType,s.length,f),F=Y("b",e[1].dataType,i.length,c),B=me("output",e[0].dataType,S.length,c),G=ot(B.type.tensor),H=Fr(t,B.type.value,G),Z=[T,F],pe="";if(p){let ye=a?c:1;Z.push(Y("bias",e[2].dataType,e[2].dims.length,ye)),pe=`${a?`value += bias[col / ${ye}];`:`value += ${B.type.value}(bias[row + i]);`}`}let oe=s.slice(0,-2),ne=i.slice(0,-2),de=Pn(oe,v),D=Pn(ne,v),z=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Ur(t,z);let q=(ye,he)=>{let R=ye.rank,ie=ye.name;if(R===2)return`var ${ie}_indices = ${ye.type.indices}(0u, 0u);`;let le=I.rank,Ae=`var ${ie}_indices: ${ye.type.indices};`;for(let Me=R-2-1,rt=le-1;Me>=0;Me--,rt--)Ae+=`
${ie}_indices[${Me}] = ${le>1?`batch_indices[${rt}]`:"batch_indices"};`;return he.forEach(Me=>{Ae+=`
${ie}_indices[${Me}] = 0;`}),Ae+=`${ie}_indices[${R-2}] = 0u;
                     ${ie}_indices[${R-1}] = 0u;`,Ae},te=()=>{let ye=`var a_data: ${T.type.value};`;for(let he=0;he<f;he++)ye+=`
              let b_data${he} = b[(b_offset + (k + ${he}) * uniforms.N + col) / ${c}];`;for(let he=0;he<g;he++){ye+=`a_data = a[(a_offset + (row + ${he}) * uniforms.K + k) / ${f}];`;for(let R=0;R<f;R++)ye+=`
            values[${he}] = fma(${F.type.value}(a_data${f===1?"":`[${R}]`}), b_data${R}, values[${he}]);
`}return ye};return`
  ${O.registerUniforms(z).registerInternalVariables(I).declareVariables(...Z,B)}
  ${O.mainStart()}
    ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${c})) * ${c};
    var index1 = global_idx / (uniforms.N / ${c});
    let stride1 = uniforms.M / ${g};
    let row = (index1 % stride1) * ${g};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${I.offsetToIndices("batch")};`}
    ${q(T,de)}
    let a_offset = ${T.indicesToOffset("a_indices")};
    ${q(F,D)}
    let b_offset = ${F.indicesToOffset("b_indices")};
    var values: array<${B.type.value}, ${g}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${f}) {
      ${te()}
    }
    for (var i = 0u; i < ${g}u; i++) {
      var value = values[i];
      ${pe}
      ${H}
      let cur_indices = ${B.type.indices}(batch, row + i, col);
      let offset = ${B.indicesToOffset("cur_indices")};
      ${B.setByOffset(`offset / ${c}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${c};${f};${g};${a}`,inputDependencies:p?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:E}),getShaderSource:k}},Sc=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.")},kc=e=>{Sc(e.inputs);let t=an.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],n=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&n<8?e.compute(Us(e.inputs,{activation:""},t)):e.compute(Fs(e.inputs,{activation:""},t))}}),Ja,Za,Cc,Ws,Vs,Tc,Ic,Gs,Ac=X(()=>{Ie(),j0(),Xa(),K0(),Wr(),Ec(),Bn(),Ja=(e,t,r,n,a,s)=>{let i=e[0],o=e.slice(s?1:2,s?3:4),l=o.length,d=t[0],c=t.slice(2).map((g,y)=>g+(g-1)*(r[y]-1)),f=o.map((g,y)=>g+n[y]+n[y+l]).map((g,y)=>Math.floor((g-c[y]+a[y])/a[y]));return f.splice(0,0,i),f.splice(s?3:1,0,d),f},Za=[2,3,1,0],Cc=(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],n=e[1].dims[1]*t.group;if(r!==n)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 a=e[0].dims.length-2;if(t.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(t.strides.length!==a)throw new Error(`strides should be ${a}D`);if(t.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},Ws=(e,t)=>{let r=e.kernelShape.slice();for(let s=2;s<t[1].dims.length;++s)r[s-2]===0&&(r[s-2]=t[1].dims[s]);let n=e.pads.slice();Ua.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,n,e.format==="NHWC",e.autoPad);let a=Object.assign({},e);return Object.assign(a,{kernelShape:r,pads:n}),a},Vs=e=>{let t=Rs(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],a=e.dilations,s=e.group,i=e.kernel_shape,o=e.pads,l=e.strides,d=e.w_is_const();return{autoPad:n,format:r,dilations:a,group:s,kernelShape:i,pads:o,strides:l,wIsConst:d,...t,cacheKey:`${e.format};${t.activation};`}},Tc=(e,t,r)=>{let n=Ws(r,t),a=r.format==="NHWC";if(r.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&a&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let F=Ja(t[0].dims,t[1].dims,r.dilations,n.pads,r.strides,a),B=e.kernelCustomData.wT??e.compute(pr(t[1],Za),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=B);let G=[t[0],B];t.length===3&&G.push(t[2]),e.compute(xc(G,n,F),{inputs:G})}else e.compute(Ls(t,n));return}let s=t.length===3,i=t[0].dims[a?1:2],o=t[0].dims[a?2:3],l=t[0].dims[a?3:1],d=t[1].dims[2],c=t[1].dims[3],f=Ja(t[0].dims,t[1].dims,r.dilations,n.pads,r.strides,a),g=f[a?1:2],y=f[a?2:3],p=f[a?3:1],v=a&&d===i&&c===o&&r.pads[0]===0&&r.pads[1]===0;if(v||d===1&&c===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let F=f[0],B,G,H,Z=[];if(a){let ne=e.kernelCustomData.wT??e.compute(pr(t[1],Za),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=ne),v){let de=i*o*l;B=t[0].reshape([1,F,de]),G=ne.reshape([1,de,p]),H=[1,F,p]}else B=t[0].reshape([F,i*o,l]),G=ne.reshape([1,l,p]),H=[F,g*y,p];Z.push(B),Z.push(G)}else B=t[0].reshape([F,l,i*o]),G=t[1].reshape([1,p,l]),H=[F,p,g*y],Z.push(G),Z.push(B);s&&Z.push(t[2]);let pe=H[2],oe=Z[0].dims[Z[0].dims.length-1];pe<8&&oe<8?e.compute(Us(Z,n,f,H,a),{inputs:Z}):e.compute(Fs(Z,n,f,H,a),{inputs:Z});return}let S=!0,E=e.kernelCustomData.wT??e.compute(pr(t[1],Za),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=E);let k=[t[0],E];s&&k.push(t[2]);let O=a?g*y:p,I=a?p:g*y,T=d*c*l;e.compute($c(k,n,f,O,I,T,s,S),{inputs:k})},Ic=(e,t)=>{let r=t.format==="NHWC",n=[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&&n.push(e.inputs[2]);let a=[0,t.pads[0],0,t.pads[1]],s=[1].concat(t.strides),i=[1].concat(t.dilations),o=[1].concat(t.kernelShape),l=Ws({...t,pads:a,strides:s,dilations:i,kernelShape:o},n);e.compute(Ls(n,l,d=>r?[d[0],d[2],d[3]]:[]))},Gs=(e,t)=>{Cc(e.inputs,t),e.inputs[0].dims.length===3?Ic(e,t):Tc(e,e.inputs,t)}}),Oc,Mc,Y0=X(()=>{$e(),Dr(),Te(),Wr(),Bs(),gc(),Xa(),Oc=(e,t=!1,r,n,a=4)=>{let s=S=>{switch(S){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 ${S} is not supported.`)}},i=e?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,o=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,l=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",d=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",c=e?"row":"col",f=e?"col":"row",g=`
      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${c} / outWidth;
      let outCol = ${c} % outWidth;

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

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

  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 = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${o}
      ${Ps(t)}
      ${v}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${a}] = value;
    }
  }`},Mc=(e,t,r,n,a,s,i,o)=>{let l=t.format==="NHWC",d=l?e[0].dims[3]:e[0].dims[1],c=r[0],f=l?r[2]:r[3],g=l?r[1]:r[2],y=l?r[3]:r[1],p=l&&d%4===0&&d%3&&y%4===0,v=l?y:f*g,S=l?f*g:y,E=[8,8,1],k=n<=8?[4,1,1]:[4,4,1],O=[Math.ceil(v/E[0]/k[0]),Math.ceil(S/E[1]/k[1]),Math.ceil(c/E[2]/k[2])];Xe("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${O}`);let I=p?4:1,T=Math.max(E[0]*I,E[1]),F=p?4:1,B=[t.kernelShape[l?1:2],t.kernelShape[l?2:3]],G=[B[0]+(t.dilations[0]<=1?0:(B[0]-1)*(t.dilations[0]-1)),B[1]+(t.dilations[1]<=1?0:(B[1]-1)*(t.dilations[1]-1))],H=[G[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),G[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Z=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:B},{type:6,data:H}];Lr(t,Z),Z.push(..._e(e[0].dims,e[1].dims));let pe=["rank","rank"];i&&(Z.push(..._e(e[2].dims)),pe.push("rank")),Z.push(..._e(r));let oe=ne=>{let de=Y("x",e[0].dataType,e[0].dims.length,F),D=Y("w",e[1].dataType,e[1].dims.length,1),z=me("result",e[0].dataType,r.length,F),q=[de,D],te="";if(i){let R=Y("bias",e[2].dataType,e[2].dims.length,F);q.push(R),te+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${R.type.value} {
            return bias[coords.${l?"w":"y"}${p?"/ 4":""}];
          }`}let ye=[{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:B.length},{name:"pads",type:"i32",length:H.length}];Ur(t,ye);let he=ot(e[0].dataType,1);if(he!=="f16"&&he!=="f32")throw new Error(`elemType ${he} is not supported.`);return`
        ${Ds("uniforms.result_strides")}
        ${ne.registerUniforms(ye).declareVariables(...q,z)};
        ${te}
        ${Oc(l,i,t,de.type.value,I)}
        ${p?Ya(k,E,he,void 0,!l,T):Qa(k,E,he,void 0,!l,T,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${k};${E};${p}`,inputDependencies:pe},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:O[0],y:O[1],z:O[2]},programUniforms:Z}),getShaderSource:oe}}}),zc,Hs,Q0=X(()=>{$e(),Dr(),Ie(),Te(),zc=(e,t,r,n,a,s=!1,i,o,l=!1)=>{let d=l?1:2,c=l?2:3,f=l?3:1,g=s?2:1,y=`
  fn setOutputAtIndex(flatIndex : u32, value : ${s?`vec4<${i}>`:i}) {
    result[flatIndex] = ${s?`vec4<${i}>`:i}(value);
  }`;n&&(y+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${s?`vec4<${i}>`:i} {
      return bias[coords.${l?"w":"y"}${s?"/ 4":""}];
    }`);let p=s?4:1,v=Y("W",t[1].dataType,t[1].dims.length,p),S=Y("Dy",t[0].dataType,t[0].dims.length,p),E=[S,v];n&&E.push(Y("bias",t[2].dataType,[r[f]].length,p));let k=me("result",t[0].dataType,r.length,p),O=`{
        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"} * ${g};
        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}>, ${g}>;
        for (var i = 0; i < ${g}; i++) {
          dotProd[i] = vec4<${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 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

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

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

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

                var xValue = ${S.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${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 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${v.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

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

        for (var i: u32 = 0; i < ${g}; i = i + 1) {
          let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${i}>(0.0)`};
          ${k.set("batch","r","c + i","d1","value")};
        }
      }`,I=`
          let outputIndices = ${k.offsetToIndices("global_idx")};
          let batch = ${k.indicesGet("outputIndices",0)};
          let d1 = ${k.indicesGet("outputIndices",f)};
          let r = ${k.indicesGet("outputIndices",d)};
          let c = ${k.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 >= ${i}(uniforms.Dy_shape[${d}]) || 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 = ${l?S.get("batch","idyR","idyC","inputChannel"):S.get("batch","inputChannel","idyR","idyC")};
                let wValue = ${v.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};
                dotProd = dotProd + xValue * wValue;
                inputChannel = inputChannel + 1;
              }
            }
          }
          let value = dotProd + ${n?"bias[d1]":`${i}(0.0)`};
          ${k.setByOffset("global_idx","value")};
        `;return`
  ${e.registerUniforms(o).declareVariables(...E,k)}
  ${y}

    ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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      let x_in = x + bias;
      ${d.setByOffset("global_idx",Ms("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${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/sn/4)}})}},lp=e=>{e.inputs.length<2||K.size(e.inputs[1].dims)===0?Jd(e):e.compute(up(e.inputs))}}),dp,cp,pp,hp,n_=X(()=>{$e(),Ie(),st(),Te(),dp=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},cp=(e,t)=>{let r=e[0].dims,n=e[1].dims,a=r.length,s=K.normalizeAxis(t.axis,a),i=r.slice(0);i.splice(s,1,...n);let o=r[s],l=e[0].dataType===9?4:1,d=Math.ceil(K.size(i)/l),c=[{type:12,data:d},{type:6,data:o},{type:12,data:s},..._e(e[0].dims,e[1].dims,i)],f=g=>{let y=Y("data",e[0].dataType,e[0].dims.length,l),p=Y("inputIndices",e[1].dataType,e[1].dims.length),v=me("output",e[0].dataType,i.length,l),S=k=>{let O=n.length,I=`var indicesIndices${k}  = ${p.type.indices}(0);`;for(let T=0;T<O;T++)I+=`${O>1?`indicesIndices${k}[${T}]`:`indicesIndices${k}`} = ${i.length>1?`outputIndices${k}[uniforms.axis + ${T}]`:`outputIndices${k}`};`;I+=`
          var idx${k} = ${p.getByIndices(`indicesIndices${k}`)};
          if (idx${k} < 0) {
            idx${k} = idx${k} + uniforms.axisDimLimit;
          }
          var dataIndices${k} : ${y.type.indices};
        `;for(let T=0,F=0;T<a;T++)T===s?(I+=`${a>1?`dataIndices${k}[${T}]`:`dataIndices${k}`} = u32(idx${k});`,F+=O):(I+=`${a>1?`dataIndices${k}[${T}]`:`dataIndices${k}`} = ${i.length>1?`outputIndices${k}[${F}]`:`outputIndices${k}`};`,F++);return I},E;if(e[0].dataType===9){let k=(O,I,T="")=>`
          let outputIndices${I} = ${v.offsetToIndices(`outputOffset + ${I}u`)};
          ${S(I)};
          let offset${I} = ${y.indicesToOffset(`dataIndices${I}`)};
          let index${I} = offset${I} / 4u;
          let component${I} = offset${I} % 4u;
          ${O}[${I}] = ${T}(${y.getByOffset(`index${I}`)}[component${I}]);
        `;E=`
        let outputOffset = global_idx * ${l};
        var value = vec4<u32>(0);
        ${k("value",0,"u32")}
        ${k("value",1,"u32")}
        ${k("value",2,"u32")}
        ${k("value",3,"u32")}
        ${v.setByOffset("global_idx","value")}
      `}else E=`
      let outputIndices = ${v.offsetToIndices("global_idx")};
      ${S("")};
      let value = ${y.getByIndices("dataIndices")};
      ${v.setByOffset("global_idx","value")};
      `;return`
      ${g.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(y,p,v)}
      ${g.mainStart()}
        ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${E}
      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:f}},pp=e=>He({axis:e.axis}),hp=(e,t)=>{let r=e.inputs;dp(r),e.compute(cp(e.inputs,t))}}),fp,mp,gp,_p,a_=X(()=>{$e(),Ie(),st(),Te(),fp=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},mp=(e,t)=>{let r=e[0].dims,n=e[0].dataType,a=r.length,s=e[1].dims,i=e[1].dataType,o=K.normalizeAxis(t.axis,a),l=r[o],d=s.slice(0),c=K.size(d),f=Y("input",n,a),g=Y("indicesInput",i,s.length),y=me("output",n,d.length),p=[{type:12,data:c},{type:6,data:l},{type:12,data:o}];return p.push(..._e(r,s,d)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:p}),getShaderSource:v=>`
      ${v.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(f,g,y)}
      ${v.mainStart()}
      ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${y.setByOffset("global_idx","value")};
  }`}},gp=e=>He({axis:e.axis}),_p=(e,t)=>{let r=e.inputs;fp(r),e.compute(mp(e.inputs,t))}}),yp,wp,bp,vp,i_=X(()=>{$e(),Ie(),Te(),yp=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},wp=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[a,s,i]=rl.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),o=[a,s];if(!o)throw new Error("Can't use gemm on the given tensors");let l=K.size(o),d=[{type:12,data:l},{type:12,data:a},{type:12,data:s},{type:12,data:i},{type:1,data:t.alpha},{type:1,data:t.beta}],c=["type","type"];e.length===3&&(d.push(..._e(e[2].dims)),c.push("rank")),d.push(..._e(o));let f=g=>{let y="";t.transA&&t.transB?y="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?y="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?y="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(y="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let p=t.alpha===1?"":"value *= uniforms.alpha;",v=Y("a",e[0].dataType,e[0].dims),S=Y("b",e[1].dataType,e[1].dims),E=v.type.value,k=null,O=[v,S];e.length===3&&(k=Y("c",e[2].dataType,e[2].dims.length),O.push(k));let I=me("output",e[0].dataType,o.length);O.push(I);let T=[{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`
  ${g.registerUniforms(T).declareVariables(...O)}

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

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

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

    ${p}
    ${k!=null?`let cOffset = ${k.broadcastedIndicesToOffset("vec2(m, n)",I)}; value += ${E}(uniforms.beta) * ${k.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:f}},bp=e=>{let t=e.transA,r=e.transB,n=e.alpha,a=e.beta;return{transA:t,transB:r,alpha:n,beta:a,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},vp=(e,t)=>{yp(e.inputs),e.compute(wp(e.inputs,t))}}),$p,xp,Sp,kp,s_=X(()=>{$e(),Ie(),Te(),$p=(e,t)=>{let r=e[0].dims,n=r,a=2,s=K.sizeToDimension(r,a),i=K.sizeFromDimension(r,a),o=et(i),l=i/o,d=[r[0],r[1],l],c=["rank","type","type"],f=[{type:12,data:i},{type:12,data:l}];f.push(..._e(d,d));let g=y=>{let p=Y("x",e[0].dataType,d.length,o),v=Y("scale",e[1].dataType,e[1].dims),S=Y("bias",e[2].dataType,e[2].dims),E=me("output",e[0].dataType,d.length,o),k=[p,v,S,E],O=p.type.value,I=o===1?"f32":`vec${o}<f32>`,T=64,F=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`
  var<workgroup> meanShared : f32;
  var<workgroup> squaredNormShared : f32;
  var<workgroup> workgroupShared : array<${I}, ${T}>;
  const workgroupSize = ${T}u;
  ${y.registerUniforms(F).declareVariables(...k)}
  ${y.mainStart(T)}
    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 = ${I}(0);
    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
      initial = initial + ${I}(${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 = ${$r("workgroupShared[0]",o)} / f32(uniforms.normSize);
    }
    workgroupBarrier();

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

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

  ${F.mainStart(d)}
    let currentImageNumber = global_idx / ${d} / uniforms.C;
    let currentChannelNumber = (global_idx / ${d}) % 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 = ${Nr("f32",l)};
    var squaredSum = ${Nr("f32",l)};
    for (var i: u32 = wgOffset; i < wgMax; i++) {
        let value = ${f}(input[offset + i * uniforms.C]);
        sum += value;
        squaredSum += value * value;
    }
    output[global_idx] = ${g("sum","squaredSum")};
  }`},k=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:v},getRunData:()=>({outputs:[{dims:[a,i,d,2],dataType:1}],dispatchGroup:{x:a*i/l},programUniforms:S}),getShaderSource:E},{inputs:[t],outputs:[-1]})[0],O=[{type:12,data:y},{type:12,data:s},{type:12,data:Math.floor(i/l)},{type:12,data:Math.floor(d*i/l)}],I=["type","type","type"],T=F=>{let B=Y("scale",r.dataType,r.dims,l),G=Y("bias",n.dataType,n.dims,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${c}>;
  @group(0) @binding(1) var<storage, read> scale : array<${B.type.storage}>;
  @group(0) @binding(2) var<storage, read> bias : array<${G.type.storage}>;
  @group(0) @binding(3) var<storage, read_write> output : array<${c}>;
  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};
  @group(0) @binding(4) var<uniform> uniforms: Uniforms;

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

    output[global_idx] = ${g("channelScale","channelShift")};
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${o}`,inputDependencies:I},getRunData:()=>({outputs:[{dims:[a,i,2],dataType:1}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:O}),getShaderSource:T},{inputs:[k,r,n],outputs:[-1]})[0]},Sp=(e,t,r)=>{let n=t[0].dims,a=n,s=n[0],i=n[n.length-1],o=K.sizeFromDimension(n,1)/i,l=et(i),d=K.size(a)/l,c=[{type:12,data:o},{type:12,data:Math.floor(i/l)}],f=["type","type"],g=xp(e,t[0],t[1],t[2],s,o,i,r.epsilon),y=p=>{let v=ot(t[0].dataType),S=l===1?"vec2f":`mat2x${l}f`,E=l===1?v:`vec${l}<${v}>`,k=Y("input",t[0].dataType,t[0].dims,l),O=me("output",t[0].dataType,a,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${k.type.storage}>;
  @group(0) @binding(1) var<storage, read> scaleInput : array<${S}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${O.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], ${E}(scale[0]), ${E}(scale[1]));
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:a,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:y},{inputs:[t[0],g]})},kp=(e,t)=>{t.format==="NHWC"?Sp(e,e.inputs,t):e.compute($p(e.inputs,t))}}),Ep,Cp,Tp,o_=X(()=>{$e(),Ie(),Te(),Ep=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Cp=(e,t,r)=>{let n=t.simplified,a=e[0].dims,s=e[1],i=!n&&e[2],o=a,l=K.normalizeAxis(t.axis,a.length),d=K.sizeToDimension(a,l),c=K.sizeFromDimension(a,l),f=K.size(s.dims),g=i?K.size(i.dims):0;if(f!==c||i&&g!==c)throw new Error(`Size of X.shape()[axis:] == ${c}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${f} and bias size of ${g}`);let y=[];for(let T=0;T<a.length;++T)T<l?y.push(a[T]):y.push(1);let p=et(c),v=["type","type"],S=[{type:12,data:d},{type:1,data:c},{type:12,data:Math.floor(c/p)},{type:1,data:t.epsilon}];i&&v.push("type");let E=r>1,k=r>2,O=T=>{let F=ot(e[0].dataType),B=[Y("x",e[0].dataType,e[0].dims,p),Y("scale",s.dataType,s.dims,p)];i&&B.push(Y("bias",i.dataType,i.dims,p)),B.push(me("output",e[0].dataType,o,p)),E&&B.push(me("mean_data_output",1,y)),k&&B.push(me("inv_std_output",1,y));let G=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${T.registerUniforms(G).declareVariables(...B)}
  ${T.mainStart()}
    ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${Nr("f32",p)};
    var mean_square_vector = ${Nr("f32",p)};

    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
      let value = ${on(F,p,"x[h + offset]")};
      mean_vector += value;
      mean_square_vector += value * value;
    }
    let mean = ${$r("mean_vector",p)} / uniforms.norm_size;
    let inv_std_dev = inverseSqrt(${$r("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 = ${on(F,p,"x[j + offset]")};
      let f32scale = ${on(F,p,"scale[j]")};
      output[j + offset] = ${B[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale
        ${i?`+ ${on(F,p,"bias[j]")}`:""}
      );
    }

    ${E?"mean_data_output[global_idx] = mean":""};
    ${k?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},I=[{dims:o,dataType:e[0].dataType}];return E&&I.push({dims:y,dataType:1}),k&&I.push({dims:y,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${p};${r};${n}`,inputDependencies:v},getRunData:()=>({outputs:I,dispatchGroup:{x:Math.ceil(d/64)},programUniforms:S}),getShaderSource:O}},Tp=(e,t)=>{Ep(e.inputs),e.compute(Cp(e.inputs,t,e.outputCount))}}),Ip,Ap,Op,Mp,u_=X(()=>{$e(),Ie(),st(),Te(),Ip=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],n=r.dims.length;if(r.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((t.k+t.blockSize-1)/t.blockSize),s=t.blockSize/8*t.bits,i=e[1];if(!K.areEqual(i.dims,[t.n,a,s]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=e[2].dims;if(K.size(o)!==t.n*a)throw new Error("scales input size error.");if(e.length===4){let l=e[3].dims,d=t.bits>4?t.n*a:t.n*Math.floor((a+1)/2);if(K.size(l)!==d)throw new Error("zeroPoints input size error.")}},Ap=(e,t,r,n)=>{let a=e[0].dims,s=a.length,i=Math.floor((t.k+t.blockSize-1)/t.blockSize),o=a[s-2],l=t.k,d=t.n,c=a.slice(0,s-2),f=K.size(c),g=t.blockSize/8*t.bits/4,y=e[0].dataType,p=et(o),v=et(t.k),S=et(g),E=Rn(y),k=o*i*E,O=Math.floor(n/k),I=i<=r[0]&&O>0,T=!I||O>=4?et(d):O>=2&&et(d)>=2?2:1,F=c.concat([o,d]),B=K.size(F)/T/p,G=I?[]:[{type:12,data:B},{type:12,data:t.blockSize}],H=[f,o,l/v],Z=K.convertShape(e[1].dims).slice();Z.splice(-1,1,g/S),G.push(..._e(H)),G.push(..._e(Z)),G.push(..._e(e[2].dims)),e.length===4&&G.push(..._e(K.convertShape(e[3].dims)));let pe=[f,o,d/T];G.push(..._e(pe));let oe=ne=>{let de=H.length,D=Y("a",e[0].dataType,de,v),z=Y("b",12,Z.length,S),q=Y("scales",e[2].dataType,e[2].dims.length),te=[D,z,q],ye=e.length===4?Y("zero_points",12,e[3].dims.length):void 0;ye&&te.push(ye);let he=pe.length,R=me("output",e[0].dataType,he,T),ie=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],le=ot(e[0].dataType),Ae=(()=>{switch(v){case 1:return`array<${le}, 8>`;case 2:return`mat4x2<${le}>`;case 4:return`mat2x4<${le}>`;default:throw new Error(`${v}-component is not supported.`)}})(),Me=`
        for (var word: u32 = 0; word < ${g}; word += ${S}) {
          ${z.indicesSet("b_indices","2","word")};
          let b_data = ${z.getByIndices("b_indices")};
          for (var i: u32 = 0; i < ${S}; i++) {
            let b_value: u32 = ${S===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 = ${Ae}(${Array.from({length:4},($t,nt)=>`${le}(b_value_lower[${nt}]), ${le}(b_value_upper[${nt}])`).join(", ")});
            let b_dequantized_values = ${v===1?`${Ae}(${Array.from({length:8},($t,nt)=>`(b_quantized_values[${nt}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Ae}(${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 < ${I?o:p}u; m++) {
              ${D.indicesSet("a_indices",de-2,I?"m":`row * ${p} + m`)};
              ${D.indicesSet("a_indices",de-1,"word_offset")};
              var input_offset = ${D.indicesToOffset("a_indices")};
              var a_data: ${Ae};
              for (var j: u32 = 0; j < ${8/v}; j++) {
                a_data[j] = ${D.getByOffset("input_offset")};
                input_offset++;
              }
              ${I?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${T>1?"[c]":""} += ${Array.from({length:8/v},($t,nt)=>`${v===1?`a_data[${nt}] * b_dequantized_values[${nt}]`:`dot(a_data[${nt}], b_dequantized_values[${nt}])`}`).join(" + ")};
            }
            word_offset += ${8/v};
          }
        }`,rt=ye?`
          zero_point_offset += 4;
          if (zero_point_offset == 32) {
            zero_point_offset = 0;
            zero_point_index++;
            zero_point_word = ${ye.getByOffset("zero_point_index")};
          }`:"";return I?`
        var<workgroup> workgroup_shared: array<${R.type.value}, ${o*i}>;
        ${ne.declareVariables(...te,R)}
        ${ne.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 < ${T}; c++) {
            let col_times_components_plus_c = col * ${T} + c;
              ${ye?`
            var zero_point_bytes_per_col: u32 = (${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 = ${ye.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}
            var b_indices: ${z.type.indices};
            ${z.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 = ${q.getByOffset("scales_index")};
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${le}(${ye?"(zero_point_word) & 0xFu":8});
            ${z.indicesSet("b_indices","1","block")};
            var word_offset: u32 = block * ${t.blockSize/v};
            var workgroup_shared_offset: u32 = block * ${o};
            ${Me}
          }
          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",he-1,"col")};
          ${R.indicesSet("output_indices",he-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 += ${d/T};
            }
          }
        }`:`
        ${ne.registerUniforms(ie).declareVariables(...te,R)}
        ${ne.mainStart()}
          ${ne.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var output_values: array<${R.type.value}, ${p}>;
          var output_indices = ${R.offsetToIndices("global_idx")};
          var col = ${R.indicesGet("output_indices",he-1)};
          var row = ${R.indicesGet("output_indices",he-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
          ${ye?`
          var zero_point_abs_offset = col * ${T} * ((${i} + 1) / 2);
          var zero_point_index: u32 = zero_point_abs_offset / 4;
          var zero_point_word: u32 = ${ye.getByOffset("zero_point_index")};
          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}
          var scale_index = col * ${i*T};
          var b_indices: ${z.type.indices};
          for (var c: u32 = 0; c < ${T}; c++) {
            ${z.indicesSet("b_indices","0",`col * ${T} + 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 = ${q.getByOffset("scale_index")};
              // The default zero point is 8 for unsigned 4-bit quantization.
              let zero_point = ${le}(${ye?"extractBits(zero_point_word, zero_point_offset, 4)":8});
              ${z.indicesSet("b_indices","1","block")};
              var word_offset: u32 = block_offset;
              ${Me}
              scale_index++;
              ${rt}
              block_offset += uniforms.block_size / ${v};
            }
            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.
            ${ye?`if (zero_point_offset % 8 > 0) {
                ${rt}
              }`:""}
            }
            for (var k: u32 = 0u; k < ${p}u; k++) {
              ${R.indicesSet("output_indices",he-2,`${p} * row + k`)};
              ${R.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:I?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${o};${y};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:F,dataType:y}],name:I?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:I?{x:1,y:Math.ceil(d/T),z:f}:{x:Math.ceil(B/64)},programUniforms:G}),getShaderSource:oe}},Op=(e,t)=>{Ip(e.inputs,t);let r=e.getMaxComputeWorkgroupSizes(),n=e.getMaxComputeWorkgroupStoragesize();e.compute(Ap(e.inputs,t,r,n))},Mp=e=>He(e)}),gt,zp,Rp,Qs,Pp,ti,Bp,l_=X(()=>{$e(),Ie(),st(),ys(),cd(),Te(),Bn(),gt=(e,t)=>e.length>t&&e[t].dims.length>0&&K.size(e[t].dims)>0?e[t]:void 0,zp=(e,t)=>{let r=e[0],n=gt(e,1),a=gt(e,2),s=gt(e,3),i=gt(e,4),o=gt(e,5),l=gt(e,6),d=gt(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 c=!1,f=r.dims[0],g=r.dims[1],y=r.dims.length===3?c?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],p=g,v=0,S=0,E=Math.floor(y/t.numHeads);if(l&&d){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==f||l.dims[1]!==t.numHeads||l.dims[3]!==E)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[0]!==f||d.dims[1]!==t.numHeads||d.dims[3]!==E)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==d.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(d.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');v=l.dims[2],S=l.dims[2]}else if(l||d)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let k;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)');k=2,p=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==E)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.');k=5,p=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==E)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');k=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]!==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');k=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 O=0;if(i){O=8;let G=i.dims;throw G.length===1?G[0]===f?O=1:G[0]===3*f+2&&(O=3):G.length===2&&G[0]===f&&G[1]===p&&(O=5),O===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let I=!1,T=y;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)');T=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)');T=a.dims[1]*a.dims[3],I=!0}}let F=v+p,B=!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]!==f&&o.dims[0]!==1||o.dims[1]!==t.numHeads||o.dims[2]!==g||o.dims[3]!==F)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:f,sequenceLength:g,pastSequenceLength:v,kvSequenceLength:p,totalSequenceLength:F,maxSequenceLength:S,inputHiddenSize:0,hiddenSize:y,vHiddenSize:T,headSize:E,vHeadSize:Math.floor(T/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:O,scale:t.scale,broadcastResPosBias:B,passPastInKv:I,qkvFormat:k}},Rp=e=>He({...e}),Qs=He({perm:[0,2,1,3]}),Pp=(e,t,r,n,a,s,i)=>{let o=[n,a,s],l=K.size(o),d=[{type:12,data:l},{type:12,data:i},{type:12,data:s}],c=f=>{let g=me("qkv_with_bias",t.dataType,o),y=Y("qkv",t.dataType,o),p=Y("bias",r.dataType,o),v=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${f.registerUniforms(v).declareVariables(y,p,g)}
  ${f.mainStart()}
    ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:c},{inputs:[t,r],outputs:[-1]})[0]},ti=(e,t,r,n,a,s,i,o)=>{let l=s;if(i){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=Pp(e,s,i,t,n,r*a,o),l=l.reshape([t,n,r,a]),e.compute(pr(l,Qs.perm),{inputs:[l],outputs:[-1]})[0]}else return s.dims.length===3&&(l=s.reshape([t,n,r,a])),e.compute(pr(l,Qs.perm),{inputs:[l],outputs:[-1]})[0]},Bp=(e,t)=>{let r=zp(e.inputs,t),n=e.inputs[0],a=gt(e.inputs,1),s=gt(e.inputs,2),i=gt(e.inputs,3),o=gt(e.inputs,4),l=gt(e.inputs,5),d=gt(e.inputs,6),c=gt(e.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 f=a&&s&&a.dims.length===4&&s.dims.length===4,g=ti(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(f)return qa(e,g,a,s,o,void 0,d,c,l,r,t);if(!a||!s)throw new Error("key and value must be provided");let y=ti(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,i,r.hiddenSize),p=ti(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,s,i,2*r.hiddenSize);qa(e,g,y,p,o,void 0,d,c,l,r,t)}}),Dp,Np,Fp,Lp,Up,Wp,Vp,Gp,Hp,d_=X(()=>{$e(),Ie(),Te(),Dp=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Np=(e,t,r)=>{let n="";for(let a=t-1;a>=0;--a)n+=`
            k = i32(${e.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${xe("uniforms.x_shape",a,t)})) {
              break;
            }
            offset += k * i32(${xe("uniforms.x_strides",a,t)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${n}
            value = x[offset];
          }
      `},Fp=(e,t,r)=>{let n="";for(let a=t-1;a>=0;--a)n+=`
                k = i32(${e.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${xe("uniforms.x_shape",a,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${xe("uniforms.x_shape",a,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${xe("uniforms.x_strides",a,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Lp=(e,t,r)=>{let n="";for(let a=t-1;a>=0;--a)n+=`
                k = i32(${e.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${xe("uniforms.x_shape",a,t)})) {
                  k = i32(${xe("uniforms.x_shape",a,t)}) - 1;
                }
                offset += k * i32(${xe("uniforms.x_strides",a,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Up=(e,t,r)=>{let n="";for(let a=t-1;a>=0;--a)n+=`
                k = i32(${e.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,r)};
                if (k < 0)  {
                  k += i32(${xe("uniforms.x_shape",a,t)}]);
                }
                if (k >= i32(${xe("uniforms.x_shape",a,t)})) {
                  k -= i32(${xe("uniforms.x_shape",a,t)});
                }
                offset += k * i32(${xe("uniforms.x_strides",a,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Wp=(e,t,r)=>{switch(r.mode){case 0:return Np(e,t,r.pads.length);case 1:return Fp(e,t,r.pads.length);case 2:return Lp(e,t,r.pads.length);case 3:return Up(e,t,r.pads.length);default:throw new Error("Invalid mode")}},Vp=(e,t)=>{let r=K.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,a=K.size(r),s=[{type:12,data:a},{type:6,data:t.pads}];t.mode===0&&s.push({type:e[0].dataType,data:t.value}),s.push(..._e(e[0].dims,r));let i=["rank"],o=l=>{let d=me("output",e[0].dataType,r.length),c=Y("x",e[0].dataType,n.length),f=c.type.value,g=Wp(d,n.length,t),y=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&y.push({name:"constant_value",type:f}),`
            ${l.registerUniforms(y).declareVariables(c,d)}
            ${l.mainStart()}
            ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${f}(0);
            ${g}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(K.size(r)/64)},programUniforms:s}),getShaderSource:o}},Gp=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,a=e[0].dims.length,s=new Int32Array(2*a).fill(0);if(e.length>=4){let o=e[3].getBigInt64Array();for(let l=0;l<o.length;l++)s[Number(o[l])]=Number(r[l]),s[Number(o[l])+a]=Number(r[l+o.length])}else r.forEach((o,l)=>s[Number(l)]=Number(o));let i=[];return s.forEach(o=>i.push(o)),{mode:t.mode,value:n,pads:i}}else return t},Hp=(e,t)=>{Dp(e.inputs);let r=Gp(e.inputs,t);e.compute(Vp(e.inputs,r),{inputs:[0]})}}),Nn,Xs,Js,Zs,eo,qp,jp,to,ro,Kp,Yp,no,Qp,Xp,ao,Jp,Zp,eh,th,c_=X(()=>{Vt(),$e(),Ie(),Te(),Nn=e=>{if(Ue.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Xs=(e,t,r)=>{let n=t.format==="NHWC",a=e.dims.slice();n&&a.splice(1,0,a.pop());let s=Object.hasOwnProperty.call(t,"dilations"),i=t.kernelShape.slice(),o=t.strides.slice(),l=s?t.dilations.slice():[],d=t.pads.slice();Ua.adjustPoolAttributes(r,a,i,o,l,d);let c=Ua.computePoolOutputShape(r,a,o,l,i,d,t.autoPad),f=Object.assign({},t);s?Object.assign(f,{kernelShape:i,strides:o,pads:d,dilations:l,cacheKey:t.cacheKey}):Object.assign(f,{kernelShape:i,strides:o,pads:d,cacheKey:t.cacheKey});let g=c.slice();return g.push(g.splice(1,1)[0]),[f,n?g:c]},Js=(e,t)=>{let r=t.format==="NHWC",n=K.size(e),a=K.size(t.kernelShape),s=[{type:12,data:n},{type:12,data:a}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let o=t.kernelShape[t.kernelShape.length-1],l=t.strides[t.strides.length-1],d=t.pads[t.pads.length/2-1],c=t.pads[t.pads.length-1],f=!!(d+c);s.push({type:12,data:o},{type:12,data:l},{type:12,data:d},{type:12,data:c}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let g=!1;if(t.kernelShape.length===2){let y=t.kernelShape[t.kernelShape.length-2],p=t.strides[t.strides.length-2],v=t.pads[t.pads.length/2-2],S=t.pads[t.pads.length-2];g=!!(v+S),s.push({type:12,data:y},{type:12,data:p},{type:12,data:v},{type:12,data:S}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,i,!0,f,g]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=K.computeStrides(t.kernelShape);s.push({type:12,data:o},{type:12,data:t.pads},{type:12,data:t.strides}),i.push({name:"kernelStrides",type:"u32",length:o.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let l=t.pads.reduce((d,c)=>d+c);return[s,i,!!l,!1,!1]}},Zs=(e,t,r,n,a,s,i,o,l,d,c,f)=>{let g=a.format==="NHWC",y=t.type.value,p=me("output",t.type.tensor,n);if(a.kernelShape.length<=2){let v="",S="",E="",k=r-(g?2:1);if(c?v=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${k}] = indices[${k}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${k}] < 0 || xIndices[${k}]
                      >= uniforms.x_shape[${k}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${s}
                }`:v=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${k}] = indices[${k}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${s}
                }`,a.kernelShape.length===2){let O=r-(g?3:2);f?S=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${O}] = indices[${O}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${O}] < 0 || xIndices[${O}] >= uniforms.x_shape[${O}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:S=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${O}] = indices[${O}] * uniforms.sh - uniforms.phStart + j;
                `,E=`
              }
            `}return`
            ${e.registerUniforms(l).declareVariables(t,p)}

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

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

              var value = ${y}(${o});
              var pad = 0;
              ${S}
              ${v}
              ${E}
              ${i}

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

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

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

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

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

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

              output[global_idx] = value;
            }`}},eo=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,qp=e=>`${eo(e)};${e.countIncludePad}`,jp=e=>`${eo(e)};${e.storageOrder};${e.dilations}`,to=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}),ro=(e,t,r,n)=>{let[a,s]=Xs(t,n,r),i=Y("x",t.dataType,t.dims.length),o=i.type.value,l="value += x_val;",d="";a.countIncludePad?d+=`value /= ${o}(uniforms.kernelSize);`:d+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[c,f,g,y,p]=Js(s,a);c.push(..._e(t.dims,s));let v=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${g};${y};${p}`,inputDependencies:v},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(K.size(s)/64)},programUniforms:c}),getShaderSource:S=>Zs(S,i,t.dims.length,s.length,a,l,d,0,f,g,y,p)}},Kp=e=>{let t=e.count_include_pad!==0,r=to(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...r,cacheKey:""};return{...n,cacheKey:qp(n)}},Yp=(e,t)=>{Nn(e.inputs),e.compute(ro("AveragePool",e.inputs[0],!1,t))},no={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Qp=e=>{let t=e.format;return{format:t,...no,cacheKey:t}},Xp=(e,t)=>{Nn(e.inputs),e.compute(ro("GlobalAveragePool",e.inputs[0],!0,t))},ao=(e,t,r,n)=>{let[a,s]=Xs(t,n,r),i=`
      value = max(x_val, value);
    `,o="",l=Y("x",t.dataType,t.dims.length),d=["rank"],[c,f,g,y,p]=Js(s,a);return c.push(..._e(t.dims,s)),{name:e,shaderCache:{hint:`${n.cacheKey};${g};${y};${p}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(K.size(s)/64)},programUniforms:c}),getShaderSource:v=>Zs(v,l,t.dims.length,s.length,a,i,o,t.dataType===10?-65504:-1e5,f,g,y,p)}},Jp=(e,t)=>{Nn(e.inputs),e.compute(ao("MaxPool",e.inputs[0],!1,t))},Zp=e=>{let t=e.storage_order,r=e.dilations,n=to(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let a={storageOrder:t,dilations:r,...n,cacheKey:""};return{...a,cacheKey:jp(a)}},eh=e=>{let t=e.format;return{format:t,...no,cacheKey:t}},th=(e,t)=>{Nn(e.inputs),e.compute(ao("GlobalMaxPool",e.inputs[0],!0,t))}}),rh,nh,ah,p_=X(()=>{Vt(),$e(),Te(),rh=(e,t,r)=>{let n=e===t,a=e<t&&r<0,s=e>t&&r>0;if(n||a||s)throw new Error("Range these inputs' contents are invalid.")},nh=(e,t,r,n)=>{let a=Math.abs(Math.ceil((t-e)/r)),s=[a],i=a,o=[{type:12,data:i},{type:n,data:e},{type:n,data:r},..._e(s)],l=d=>{let c=me("output",n,s.length),f=c.type.value,g=[{name:"outputSize",type:"u32"},{name:"start",type:f},{name:"delta",type:f}];return`
        ${d.registerUniforms(g).declareVariables(c)}
        ${d.mainStart()}
        ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${f}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:s,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:o})}},ah=e=>{let t=0,r=0,n=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),Ue.webgpu.validateInputContent&&rh(t,r,n),e.compute(nh(t,r,n,e.inputs[0].dataType),{inputs:[]})}}),ih,sh,oh,uh,lh,dh,ch,ph,hh,fh,mh,io,gh,_h,yh,wh,bh,vh,$h,h_=X(()=>{$e(),Ie(),st(),Te(),ih=(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
            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")}},sh=(e,t,r)=>{t.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 t.forEach((a,s)=>n[a]=e[s]),n},oh=(e,t,r,n,a,s)=>{let[i,o,l]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],d=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(c=>s.push(c));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&e.length>o&&e[o].dims.length>0){if(e[o].getFloat32Array().forEach(c=>n.push(c)),n.length!==0&&n.length!==d&&r>=18&&n.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");ih(n,t),t.axes.length>0&&sh(n,t.axes,d).forEach((c,f)=>n[f]=c)}if(l>0&&e.length>l&&(e[l].getBigInt64Array().forEach(c=>a.push(Number(c))),a.length!==d||r>=18&&a.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(n.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(a.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 n<"u"&&typeof a<"u"&&n.length>0&&a.length>d)throw new Error("Resize requires only of scales or sizes to be specified")},uh=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating
                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
                    // offset-by-one error later in floor().
                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));
                    let fract =
                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);
                    return whole + fract;
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +
                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /
                        ${t}(lengthResized - 1);
                  } else {
                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);
                  const adjustment = ${t}(lengthResized) / outputWidth;
                  const center = ${t}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",lh=(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`)}})()+"}",dh=(e,t,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),a=e.length===0?n:e.slice();return t.length>0?(t.forEach((s,i)=>{n[s]=a[i],n[i+r]=a[t.length+i]}),n):a},ch=(e,t,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(e.forEach(s=>a.push(s)),Math.max(...n)>e.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(t.length===0)throw new Error("Resize requires either scales or sizes.");a=e.map((s,i)=>Math.round(s*t[i]))}return a},ph=(e,t,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>t[s]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>t[s]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let a=e.slice();return r.axes.length>0?(r.axes.forEach(s=>t[s]=n),r.axes.forEach(s=>a[s]=Math.round(e[s]*t[s]))):(t.fill(n,0,t.length),a.forEach((s,i)=>a[i]=Math.round(s*t[i]))),a},hh=(e,t,r,n,a)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {
      var original_indices: array<${e.type.value}, ${r.length}>;
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var scale = ${xe("uniforms.scales","i",n)};
        var roi_low = ${xe("uniforms.roi","i",a)};
        var roi_hi = ${xe("uniforms.roi",`i + ${t.length}`,a)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${xe("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${xe("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;
    }`,fh=(e,t,r,n,a,s,i)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
      var input_indices: ${e.type.indices};
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${t.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${xe("uniforms.scales","i",a)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${xe("uniforms.roi","i",s)};
          var roi_hi = ${xe("uniforms.roi",`i + ${r.length}`,s)};
          var input_shape_i = ${xe("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${xe("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${i} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${e.indicesSet("input_indices","i"," input_index")}
      }
      return input_indices;
    }`,mh=(e,t)=>`
    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var input_index = ${e.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${xe("uniforms.input_shape","i",t.length)}) {
          return false;
        }
      }
      return true;
    }`,io=(e,t,r,n)=>e.rank>n?`
    ${e.indicesSet("input_indices",t,"channel")};
    ${e.indicesSet("input_indices",r,"batch")};
`:"",gh=(e,t,r,n,a)=>{let[s,i,o,l]=r.length===2?[-1,0,1,-1]:[0,2,3,1],d=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",i,`max(0, min(row, ${r[i]} - 1))`)};
      ${e.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)};
      ${io(e,l,s,2)}
      return ${e.getByIndices("input_indices")};
    }

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

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

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

    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${c} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var depth:${c} = originalIndices[${i}];
      var height:${c} = originalIndices[${o}];
      var width:${c} = originalIndices[${l}];
      ${n?`if (depth < 0 || depth > (${r[i]} - 1) || height < 0 || height > (${r[o]} - 1) || width < 0 || (width > ${r[l]} - 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[l]} - 1));
      var depth1: u32 = u32(depth);
      var height1: u32 = u32(height);
      var width1: u32 = u32(width);
      var depth2: u32 = u32(depth + 1);
      var height2: u32 = u32(height + 1);
      var width2: u32 = u32(width + 1);
      var channel: u32 = ${r.length>3?`u32(originalIndices[${d}])`:"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);
    }`},wh=(e,t,r,n,a,s)=>{let i=e.dims,o=dh(s,t.axes,i.length),l=ch(i,n,a,t.axes),d=n.slice();n.length===0&&(d=i.map((k,O)=>k===0?1:l[O]/k),t.keepAspectRatioPolicy!=="stretch"&&(l=ph(i,d,t)));let c=me("output",e.dataType,l.length),f=Y("input",e.dataType,i.length),g=K.size(l),y=i.length===l.length&&i.every((k,O)=>k===l[O]),p=t.coordinateTransformMode==="tf_crop_and_resize",v=t.extrapolationValue,S=f.type.value,E=k=>`
      ${y?"":`
      ${uh(t.coordinateTransformMode,S)};
      ${(()=>{switch(t.mode){case"nearest":return`
              ${mh(f,i)};
              ${lh(t.nearestMode,r,S)};
              ${fh(f,c,i,l,d.length,o.length,p)};
              `;case"linear":return`
              ${hh(c,i,l,d.length,o.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${gh(f,c,i,p,v)}`;if(i.length===3||i.length===5)return`${yh(f,c,i,p,v)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${_h(f,c,i,l,d,o,t.cubicCoeffA,p,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${k.registerUniform("output_size","u32").registerUniform("scales","f32",d.length).registerUniform("roi","f32",o.length).declareVariables(f,c)}
      ${k.mainStart()}
        ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${y?"output[global_idx] = input[global_idx];":`
        let output_indices = ${c.offsetToIndices("global_idx")};
        var input_indices: ${f.type.indices};
        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${f.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${t.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: ${t.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${d.length>0?d:""}|${a.length>0?a:""}|${o.length>0?o:""}|${y}|${i}`,inputDependencies:["rank"]},getShaderSource:E,getRunData:()=>({outputs:[{dims:l,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:12,data:g},{type:1,data:d},{type:1,data:o},..._e(i,l)]})}},bh=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},vh=(e,t)=>{let r=[],n=[],a=[],s=bh(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");oh(e.inputs,t,s,r,n,a),e.compute(wh(e.inputs[0],t,s,r,n,a),{inputs:[0]})},$h=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,a=e.cubicCoeffA,s=e.excludeOutside!==0,i=e.extrapolationValue,o=e.keepAspectRatioPolicy,l=e.mode,d=e.nearestMode===""?"simple":e.nearestMode;return He({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:s,extrapolationValue:i,keepAspectRatioPolicy:o,mode:l,nearestMode:d})}}),xh,Sh,kh,f_=X(()=>{$e(),Ie(),st(),Te(),xh=(e,t)=>{let[r,n,a,s]=e,{numHeads:i,rotaryEmbeddingDim:o}=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(!K.areEqual(n.dims,[])&&!K.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(!K.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 l=r.dims[0],d=r.dims[r.dims.length-2],c=a.dims[0],f=K.sizeFromDimension(r.dims,1)/d,g=o===0?a.dims[1]*2:f/i;if(o>g)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(l!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(d!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(g/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(d>c)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Sh=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:s}=t,i=e[0].dims[0],o=K.sizeFromDimension(e[0].dims,1),l=e[0].dims[e[0].dims.length-2],d=o/l,c=e[2].dims[1],f=a===0?c*2:d/n,g=new Array(i,l,d/f,f-c),y=K.computeStrides(g),p=[{type:1,data:s},{type:12,data:g},{type:12,data:y},...e[0].dims.length===3?new Array({type:12,data:[o,d,f,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[o,f,l*f,1]}):[],..._e(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],v=S=>{let E=Y("input",e[0].dataType,e[0].dims.length),k=Y("position_ids",e[1].dataType,e[1].dims.length),O=Y("cos_cache",e[2].dataType,e[2].dims.length),I=Y("sin_cache",e[3].dataType,e[3].dims.length),T=me("output",e[0].dataType,e[0].dims.length);return S.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:g.length},{name:"global_strides",type:"u32",length:y.length},{name:"input_output_strides",type:"u32",length:y.length}]),`
        ${S.declareVariables(E,k,O,I,T)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${k.broadcastedIndicesToOffset("bsnh.xy",me("",k.type.tensor,2))};
            let position_id =
                u32(${k.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 = ${E.getByOffset("i")} * ${O.get("position_id","bsnh[3]")} -
                ${E.getByOffset("j")} * ${I.get("position_id","bsnh[3]")};
            ${T.setByOffset("i","re")}
            let im = ${E.getByOffset("i")} * ${I.get("position_id","bsnh[3]")} +
                ${E.getByOffset("j")} * ${O.get("position_id","bsnh[3]")};
            ${T.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${T.setByOffset("k",E.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:He({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:v,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(K.size(g)/sn)},programUniforms:p})}},kh=(e,t)=>{xh(e.inputs,t),e.compute(Sh(e.inputs,t))}}),Eh,Ch,Th,m_=X(()=>{$e(),Ie(),Te(),Eh=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],n=e[2];if(t.dataType!==r.dataType||t.dataType!==n.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 a=t.dims[t.dims.length-1],s=t.dims[t.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(e.length>3){let i=e[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(e.length>4){let i=e[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")}},Ch=(e,t,r,n)=>{let a=t.simplified,s=e[0].dims,i=K.size(s),o=s,l=i,d=s.slice(-1)[0],c=n?s.slice(0,-1).concat(1):[],f=!a&&e.length>3,g=e.length>4,y=n&&r>1,p=n&&r>2,v=r>3,S=64,E=et(d),k=[{type:12,data:l},{type:12,data:E},{type:12,data:d},{type:1,data:t.epsilon}],O=T=>{let F=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],B=[Y("x",e[0].dataType,e[0].dims,E),Y("skip",e[1].dataType,e[1].dims,E),Y("gamma",e[2].dataType,e[2].dims,E)];f&&B.push(Y("beta",e[3].dataType,e[3].dims,E)),g&&B.push(Y("bias",e[4].dataType,e[4].dims,E)),B.push(me("output",e[0].dataType,o,E)),y&&B.push(me("mean_output",1,c)),p&&B.push(me("inv_std_output",1,c)),v&&B.push(me("input_skip_bias_sum",e[0].dataType,o,E));let G=ot(e[0].dataType),H=ot(1,E);return`

      ${T.registerUniforms(F).declareVariables(...B)}
      var<workgroup> sum_shared : array<${H}, ${S}>;
      var<workgroup> sum_squared_shared : array<${H}, ${S}>;

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

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

        var reduce_size : u32 = ${S};
        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 = ${$r("sum",E)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${$r("square_sum",E)} / f32(uniforms.hidden_size) ${a?"":"- mean * mean"} + uniforms.epsilon);
        ${y?"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?"":`- ${G}(mean)`}) *
            ${G}(inv_std_dev) * gamma[offset1d + i]
            ${f?"+ beta[offset1d + i]":""};
        }
      }`},I=[{dims:o,dataType:e[0].dataType}];return r>1&&I.push({dims:c,dataType:1}),r>2&&I.push({dims:c,dataType:1}),r>3&&I.push({dims:s,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${E};${y};${p};${v}`,inputDependencies:e.map((T,F)=>"type")},getShaderSource:O,getRunData:()=>({outputs:I,dispatchGroup:{x:Math.ceil(l/d)},programUniforms:k})}},Th=(e,t)=>{Eh(e.inputs);let r=[0];e.outputCount>1&&r.push(-3),e.outputCount>2&&r.push(-3),e.outputCount>3&&r.push(3),e.compute(Ch(e.inputs,t,e.outputCount,!1),{outputs:r})}}),Ih,Fn,Ah,so,Oh,Mh,zh,Rh,g_=X(()=>{$e(),Ie(),st(),Te(),Ih=(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,n)=>{if(e[n+1].dataType!==6&&e[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},Fn=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Ah=(e,t)=>{if(e.length>1){let r=Fn(e,1),n=Fn(e,2),a=Fn(e,3);return a.length===0&&(a=[...Array(e[0].dims.length).keys()]),He({starts:r,ends:n,axes:a})}else return t},so=(e,t,r,n,a)=>{let s=e;return e<0&&(s+=r[n[t]]),a[t]<0?Math.max(0,Math.min(s,r[n[t]]-1)):Math.max(0,Math.min(s,r[n[t]]))},Oh=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${r.length}; i >= 0; i--) {
            let input_shape_i = ${xe("uniforms.input_shape","i",r.length)};
            let steps_i = ${xe("uniforms.steps","i",r.length)};
            let signs_i = ${xe("uniforms.signs","i",r.length)};
            let starts_i = ${xe("uniforms.starts","i",r.length)};
            var output_index = ${t.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${e.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,Mh=(e,t)=>{let r=e[0].dims,n=K.size(r),a=t.axes.length>0?K.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],s=Fn(e,4);s.forEach(E=>E!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(a.length).fill(1));let i=t.starts.map((E,k)=>so(E,k,r,a,s)),o=t.ends.map((E,k)=>so(E,k,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 E=0;E<r.length;++E)a.includes(E)||(i.splice(E,0,0),o.splice(E,0,r[E]),s.splice(E,0,1));let l=s.map(E=>Math.sign(E));s.forEach((E,k,O)=>{if(E<0){let I=(o[k]-i[k])/E,T=i[k],F=T+I*s[k];i[k]=F,o[k]=T,O[k]=-E}});let d=r.slice(0);a.forEach((E,k)=>{d[E]=Math.ceil((o[E]-i[E])/s[E])});let c={dims:d,dataType:e[0].dataType},f=me("output",e[0].dataType,d.length),g=Y("input",e[0].dataType,e[0].dims.length),y=K.size(d),p=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:s.length}],v=[{type:12,data:y},{type:12,data:i},{type:6,data:l},{type:12,data:s},..._e(e[0].dims,d)],S=E=>`
      ${E.registerUniforms(p).declareVariables(g,f)}
        ${Oh(g,f,r)}
        ${E.mainStart()}
          ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${f.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${f.setByOffset("global_idx",g.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${i.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:S,getRunData:()=>({outputs:[c],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:v})}},zh=(e,t)=>{Ih(e.inputs,t);let r=Ah(e.inputs,t);e.compute(Mh(e.inputs,r),{inputs:[0]})},Rh=e=>{let t=e.starts,r=e.ends,n=e.axes;return He({starts:t,ends:r,axes:n})}}),Ph,Bh,Dh,Nh,__=X(()=>{$e(),Ie(),st(),Te(),Ph=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Bh=(e,t)=>{let r=e.dims,n=K.size(r),a=64,s=t.axis;if(s<0&&(s=r.length+s),s<r.length-1)throw new Error("softmax only supports last axis for now.");let i=r[s],o=n/i,l=et(i),d=i/l,c=(S,E)=>E===4?`max(max(${S}.x, ${S}.y), max(${S}.z, ${S}.w))`:E===2?`max(${S}.x, ${S}.y)`:E===3?`max(max(${S}.x, ${S}.y), ${S}.z)`:S,f=Y("x",e.dataType,e.dims,l),g=me("result",e.dataType,e.dims,l),y=f.type.value,p=ot(e.dataType)==="f32"?`var threadMax = ${y}(-3.402823e+38f);`:`var threadMax = ${y}(-65504.0h);`,v=S=>`
      var<workgroup> rowMaxShared : ${y};
      var<workgroup> rowSumShared : ${y};
      var<workgroup> threadShared : array<${y}, ${a}>;

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

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${y}) {
        let index = row * row_stride + col;
        result[index] = value;
      }
      ${S.registerUniform("packedCols","i32").declareVariables(f,g)}
      ${S.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 = ${y}(${c("threadShared[0]",l)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${y}(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 = ${y}(${$r("threadShared[0]",l)});
        }
        workgroupBarrier();

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          setValue(row, col, row_stride, value);
        }
      }`;return{name:"Softmax",shaderCache:{hint:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:o},programUniforms:[{type:6,data:d}]}),getShaderSource:v}},Dh=(e,t)=>{Ph(e.inputs),e.compute(Bh(e.inputs[0],t))},Nh=e=>He({axis:e.axis})}),Fh,Lh,Uh,Wh,Vh,Gh,Hh,y_=X(()=>{$e(),Ie(),st(),Te(),Fh=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Lh=(e,t)=>{let r=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(a=>r.push(Number(a))),n=r.length),He({numOutputs:n,axis:t.axis,splitSizes:r})},Uh=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${xe("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,Wh=e=>{let t=e.length,r=[];for(let n=0;n<t;++n){let a=e[n].setByIndices("indices","input[global_idx]");t===1?r.push(a):n===0?r.push(`if (output_number == ${n}u) { ${a} }`):n===t-1?r.push(`else { ${a} }`):r.push(`else if (output_number == ${n}) { ${a} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},Vh=(e,t)=>{let r=e[0].dims,n=K.size(r),a=e[0].dataType,s=K.normalizeAxis(t.axis,r.length),i=new Array(t.numOutputs),o=Y("input",a,r.length),l=new Array(t.numOutputs),d=[],c=[],f=0,g=[{type:12,data:n}];for(let p=0;p<t.numOutputs;p++){f+=t.splitSizes[p],l[p]=f;let v=r.slice();v[t.axis]=t.splitSizes[p],c.push(v),i[p]=me(`output${p}`,a,v.length),d.push({dims:c[p],dataType:e[0].dataType})}g.push({type:12,data:l},..._e(r,...c));let y=p=>`
  ${p.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...i)}
  ${Uh(l.length)}
  ${Wh(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 -= ${xe("uniforms.size_in_split_axis","output_number - 1u",l.length)};
      ${o.indicesSet("indices",s,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:y,getRunData:()=>({outputs:d,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:g})}},Gh=(e,t)=>{Fh(e.inputs);let r=e.inputs.length===1?t:Lh(e.inputs,t);e.compute(Vh(e.inputs,r),{inputs:[0]})},Hh=e=>{let t=e.axis,r=e.splitSizes,n=e.numOutputs<0?r.length:e.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return He({axis:t,numOutputs:n,splitSizes:r})}}),oo,qh,jh,Kh,Yh,w_=X(()=>{$e(),Ie(),Te(),oo=e=>Array.from(e.getBigInt64Array(),Number),qh=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(oo(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},jh=(e,t)=>{let r=[];for(let n=0;n<e.length;++n)r.push(e[n]*t[n]);return r},Kh=e=>{let t=e[0].dims,r=oo(e[1]),n=jh(t,r),a=K.size(n),s=e[0].dataType,i=Y("input",s,t.length),o=me("output",s,n.length),l=d=>`
      const inputShape = ${i.indices(...t)};
      ${d.registerUniform("output_size","u32").declareVariables(i,o)}
      ${d.mainStart()}
      ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${o.offsetToIndices("global_idx")};
      var input_indices: ${i.type.indices};
      for (var i = 0; i < ${t.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:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},..._e(e[0].dims,n)]}),getShaderSource:l}},Yh=e=>{qh(e.inputs),e.compute(Kh(e.inputs),{inputs:[0]})}}),Qh,Xh,Jh,b_=X(()=>{$e(),Ie(),Te(),Qh=(e,t,r,n,a)=>{let s=me("output_data",a,r.length,4),i=Y("a_data",t[1].dataType,t[1].dims.length,4),o=Y("b_data",t[2].dataType,t[2].dims.length,4),l=Y("c_data",t[0].dataType,t[0].dims.length,4),d,c=(f,g,y)=>`select(${g}, ${f}, ${y})`;if(!n)d=s.setByOffset("global_idx",c(i.getByOffset("global_idx"),o.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let f=(g,y,p="")=>{let v=`a_data[index_a${y}][component_a${y}]`,S=`b_data[index_b${y}][component_b${y}]`,E=`bool(c_data[index_c${y}] & (0xffu << (component_c${y} * 8)))`;return`
            let output_indices${y} = ${s.offsetToIndices(`global_idx * 4u + ${y}u`)};
            let offset_a${y} = ${i.broadcastedIndicesToOffset(`output_indices${y}`,s)};
            let offset_b${y} = ${o.broadcastedIndicesToOffset(`output_indices${y}`,s)};
            let offset_c${y} = ${l.broadcastedIndicesToOffset(`output_indices${y}`,s)};
            let index_a${y} = offset_a${y} / 4u;
            let index_b${y} = offset_b${y} / 4u;
            let index_c${y} = offset_c${y} / 4u;
            let component_a${y} = offset_a${y} % 4u;
            let component_b${y} = offset_b${y} % 4u;
            let component_c${y} = offset_c${y} % 4u;
            ${g}[${y}] = ${p}(${c(v,S,E)});
          `};a===9?d=`
            var data = vec4<u32>(0);
            ${f("data",0,"u32")}
            ${f("data",1,"u32")}
            ${f("data",2,"u32")}
            ${f("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:d=`
            ${f("output_data[global_idx]",0)}
            ${f("output_data[global_idx]",1)}
            ${f("output_data[global_idx]",2)}
            ${f("output_data[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(l,i,o,s)}
        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${d}
      }`},Xh=e=>{let t=e[1].dims,r=e[2].dims,n=e[0].dims,a=e[1].dataType,s=!(K.areEqual(t,r)&&K.areEqual(r,n)),i=t,o=K.size(t);if(s){let d=an.calcShape(an.calcShape(t,r,!1),n,!1);if(!d)throw new Error("Can't perform where op on the given tensors");i=d,o=K.size(i)}let l=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:d=>Qh(d,e,i,s,a),getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:l},..._e(n,t,r,i)]})}},Jh=e=>{e.compute(Xh(e.inputs))}}),Zh,v_=X(()=>{W0(),cd(),V0(),G0(),H0(),q0(),ad(),Ac(),X0(),J0(),Z0(),e_(),t_(),r_(),n_(),a_(),i_(),s_(),o_(),Ec(),u_(),l_(),d_(),c_(),p_(),Cs(),h_(),f_(),m_(),g_(),__(),y_(),w_(),Bn(),zs(),b_(),Zh=new Map([["Abs",[bd]],["Acos",[vd]],["Acosh",[$d]],["Add",[sc]],["ArgMax",[Jl,Is]],["ArgMin",[Xl,Is]],["Asin",[xd]],["Asinh",[Sd]],["Atan",[kd]],["Atanh",[Ed]],["Attention",[dd]],["AveragePool",[Yp,Kp]],["BatchNormalization",[md]],["BiasAdd",[yd]],["BiasSplitGelu",[nc]],["Cast",[Td,Cd]],["Ceil",[Od]],["Clip",[Ad]],["Concat",[rd,nd]],["Conv",[Gs,Vs]],["ConvTranspose",[Wc,Dc]],["Cos",[Md]],["Cosh",[zd]],["CumSum",[Gc,Hc]],["DepthToSpace",[Yc,Qc]],["Div",[oc]],["Einsum",[rp,np]],["Elu",[Rd,ja]],["Equal",[uc]],["Erf",[Pd]],["Exp",[Bd]],["Expand",[op]],["FastGelu",[lp]],["Floor",[Dd]],["FusedConv",[Gs,Vs]],["Gather",[hp,pp]],["GatherElements",[_p,gp]],["Gelu",[Nd]],["Gemm",[vp,bp]],["GlobalAveragePool",[Xp,Qp]],["GlobalMaxPool",[th,eh]],["Greater",[pc]],["GreaterOrEqual",[fc]],["HardSigmoid",[qd,Hd]],["InstanceNormalization",[kp]],["LayerNormalization",[Tp]],["LeakyRelu",[Fd,ja]],["Less",[hc]],["LessOrEqual",[mc]],["Log",[ec]],["MatMul",[kc]],["MatMulNBits",[Op,Mp]],["MaxPool",[Jp,Zp]],["Mul",[lc]],["MultiHeadAttention",[Bp,Rp]],["Neg",[Ud]],["Not",[Ld]],["Pad",[Hp]],["Pow",[dc]],["Range",[ah]],["Reciprocal",[Wd]],["ReduceMin",[ql]],["ReduceMean",[Ul]],["ReduceMax",[Hl]],["ReduceSum",[Kl]],["ReduceProd",[jl]],["ReduceL1",[Wl]],["ReduceL2",[Vl]],["ReduceLogSum",[Ql]],["ReduceLogSumExp",[Gl]],["ReduceSumSquare",[Yl]],["Relu",[Vd]],["Resize",[vh,$h]],["RotaryEmbedding",[kh]],["Sigmoid",[Gd]],["Sin",[jd]],["Sinh",[Kd]],["Slice",[zh,Rh]],["SkipLayerNormalization",[Th]],["Split",[Gh,Hh]],["Sqrt",[Yd]],["Softmax",[Dh,Nh]],["Sub",[cc]],["Tan",[Qd]],["Tanh",[Xd]],["ThresholdedRelu",[Zd,ja]],["Tile",[Yh]],["Transpose",[ul,ll]],["Where",[Jh]]])}),ef,$_=X(()=>{Vt(),Dr(),Te(),ef=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,t){this.repo.set(e,t)}run(e,t,r,n,a){Wt(e.programInfo.name);let s=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let o=[];for(let d of t)o.push({binding:o.length,resource:{buffer:d.buffer}});for(let d of r)o.push({binding:o.length,resource:{buffer:d.buffer}});a&&o.push({binding:o.length,resource:a});let l=s.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:o,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let d={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:l,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(d)}i.setPipeline(e.computePipeline),i.setBindGroup(0,l),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(),Dt(e.programInfo.name)}dispose(){}build(e,t){Wt(e.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=al(t,this.backend.device.limits),s=e.getShaderSource(a),i=`${n.join(`
`)}
${a.additionalImplementations}
${s}`,o=r.createShaderModule({code:i,label:e.name});Xe("verbose",()=>`[WebGPU] ${e.name} shader code: ${i}`);let l=r.createComputePipeline({compute:{module:o,entryPoint:"main"},layout:"auto",label:e.name});return Dt(e.name),{programInfo:e,computePipeline:l,uniformVariablesInfo:a.variablesInfo}}normalizeDispatchGroupSize(e){let t=typeof e=="number"?e:e.x,r=typeof e=="number"?1:e.y||1,n=typeof e=="number"?1:e.z||1,a=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(t<=a&&r<=a&&n<=a)return[t,r,n];let s=t*r*n,i=Math.ceil(Math.sqrt(s));if(i>a){if(i=Math.ceil(Math.cbrt(s)),i>a)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),tf,rf,nf,af,x_=X(()=>{Vt(),$e(),Dr(),F0(),L0(),v_(),$_(),tf=(e,t)=>{if(t.length!==e.length)throw new Error(`inputDependencies length ${t.length} is not equal to inputTensors length ${e.length}.`);let r=[];for(let n=0;n<e.length;++n){let a=e[n].dataType;switch(t[n]){case"none":{r.push("");break}case"type":{r.push(`${a}`);break}case"rank":{let s=e[n].dims.length;r.push(`${a};${s}`);break}case"dims":{let s=e[n].dims.join(",");r.push(`${a};${s}`);break}default:throw new Error(`unsupported input dependency: ${t[n]}`)}}return r.join("|")},rf=(e,t,r)=>{var a,s;let n=e.name;return(a=e.shaderCache)!=null&&a.hint&&(n+="["+e.shaderCache.hint+"]"),n+=":"+r+`:${tf(t,((s=e.shaderCache)==null?void 0:s.inputDependencies)??new Array(t.length).fill("dims"))}`,n},nf=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},af=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 e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,t){this.env=e;let r=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:t.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:t.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:t.limits.maxStorageBufferBindingSize,maxBufferSize:t.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:t.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:t.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:t.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:t.limits.maxComputeWorkgroupSizeZ},requiredFeatures:r};t.features.has("chromium-experimental-timestamp-query-inside-passes")?r.push("chromium-experimental-timestamp-query-inside-passes"):t.features.has("timestamp-query")&&r.push("timestamp-query"),t.features.has("shader-f16")&&r.push("shader-f16"),this.device=await t.requestDevice(n),this.adapterInfo=new nf(await t.requestAdapterInfo()),this.gpuDataManager=Zu(this),this.programManager=new ef(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,ju(e.logLevel,!!e.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:t,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 e=this.getCommandEncoder(),t={};this.queryType==="at-passes"&&(t.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(t)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Wt(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var n;let t=new BigUint64Array(e.getMappedRange()),r=this.pendingQueries.get(e);for(let a=0;a<t.length/2;a++){let s=r[a],i=s.kernelId,o=this.kernels.get(i),l=o.kernelType,d=o.kernelName,c=s.programName,f=s.inputTensorViews,g=s.outputTensorViews,y=t[a*2],p=t[a*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=y);let v=Number(y-this.queryTimeBase),S=Number(p-this.queryTimeBase);if(!Number.isSafeInteger(v)||!Number.isSafeInteger(S))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:f.map(E=>({dims:E.dims,dataType:Br(E.dataType)})),outputsMetadata:g.map(E=>({dims:E.dims,dataType:Br(E.dataType)})),kernelId:i,kernelType:l,kernelName:d,programName:c,startTime:v,endTime:S});else{let E="";f.forEach((O,I)=>{E+=`input[${I}]: [${O.dims}] | ${Br(O.dataType)}, `});let k="";g.forEach((O,I)=>{k+=`output[${I}]: [${O.dims}] | ${Br(O.dataType)}, `}),console.log(`[profiling] kernel "${i}|${l}|${d}|${c}" ${E}${k}execution time: ${S-v} ns`)}On("GPU",`${c}::${y}::${p}`)}e.unmap(),this.pendingQueries.delete(e)}),Dt()}run(e,t,r,n,a,s){Wt(e.name);let i=[];for(let k=0;k<t.length;++k){let O=t[k].data;if(O===0)continue;let I=this.gpuDataManager.get(O);if(!I)throw new Error(`no GPU data for input: 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e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),r=e.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'}),Sr,bt,un,Ln,Un,co,ni,Vr,Gr,yf,Wn,wf,bf,vf,$f,xf,Sf,kf,Ef=X(()=>{var e;Vt(),k_(),zn(),Sr=()=>!!Ue.wasm.proxy&&typeof document<"u",un=!1,Ln=!1,Un=!1,ni=new Map,Vr=(t,r)=>{let n=ni.get(t);n?n.push(r):ni.set(t,[r])},Gr=()=>{if(un||!Ln||Un||!bt)throw new Error("worker not 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Dt(),c}startProfiling(){}endProfiling(){kf(this.sessionId)}}}),If,Af,T_=X(()=>{Vt(),Ef(),C_(),If=()=>{if((typeof Ue.wasm.initTimeout!="number"||Ue.wasm.initTimeout<0)&&(Ue.wasm.initTimeout=0),typeof Ue.wasm.simd!="boolean"&&(Ue.wasm.simd=!0),typeof Ue.wasm.proxy!="boolean"&&(Ue.wasm.proxy=!1),typeof Ue.wasm.trace!="boolean"&&(Ue.wasm.trace=!1),typeof Ue.wasm.numThreads!="number"||!Number.isInteger(Ue.wasm.numThreads)||Ue.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(Ue.wasm.numThreads=1);let e=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;Ue.wasm.numThreads=Math.min(4,Math.ceil((e||1)/2))}},Af=class{async init(e){If(),await wf(),await bf(e)}async createInferenceSessionHandler(e,t){let r=new Tf;return await r.loadModel(e,t),Promise.resolve(r)}}}),Of={};nn(Of,{wasmBackend:()=>Mf});var Mf,I_=X(()=>{T_(),Mf=new Af});Vt(),Vt(),Vt();var A_="1.19.0-dev.20240509-69cfcba38a",O_=vu;{let e=(I_(),zr(Of)).wasmBackend;Rr("webgpu",e,5),Rr("webnn",e,5),Rr("cpu",e,10),Rr("wasm",e,10)}Object.defineProperty(Ue.versions,"web",{value:A_,enumerable:!0});/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*//**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */var M_=Object.freeze({__proto__:null,get InferenceSession(){return os},get TRACE(){return On},get TRACE_FUNC_BEGIN(){return Wt},get TRACE_FUNC_END(){return Dt},get Tensor(){return ft},get TrainingSession(){return us},default:O_,get env(){return Ue},get registerBackend(){return Rr}});const z_=(e,t)=>{const r=typeof document<"u"?document.createElement("canvas"):new OffscreenCanvas(1,1);r.width=e.dims[3],r.height=e.dims[2];const n=r.getContext("2d");if(n!=null){let a,s;(t==null?void 0:t.tensorLayout)!==void 0&&t.tensorLayout==="NHWC"?(a=e.dims[2],s=e.dims[3]):(a=e.dims[3],s=e.dims[2]);const i=(t==null?void 0:t.format)!==void 0?t.format:"RGB",o=t==null?void 0:t.norm;let l,d;o===void 0||o.mean===void 0?l=[255,255,255,255]:typeof o.mean=="number"?l=[o.mean,o.mean,o.mean,o.mean]:(l=[o.mean[0],o.mean[1],o.mean[2],0],o.mean[3]!==void 0&&(l[3]=o.mean[3])),o===void 0||o.bias===void 0?d=[0,0,0,0]:typeof 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Error("Can not access image data")}else if(n){let c,f;if(t!==void 0&&t.resizedWidth!==void 0&&t.resizedHeight!==void 0?(c=t.resizedHeight,f=t.resizedWidth):(c=e.height,f=e.width),t!==void 0&&(o=t),o.format="RGBA",o.height=c,o.width=f,t!==void 0){const g=l();g.width=f,g.height=c;const y=d(g);if(y!=null)y.putImageData(e,0,0),i=y.getImageData(0,0,f,c).data;else throw new Error("Can not access image data")}else i=e.data}else if(a){if(t===void 0)throw new Error("Please provide image config with format for Imagebitmap");const c=l();c.width=e.width,c.height=e.height;const f=d(c);if(f!=null){const g=e.height,y=e.width;return f.drawImage(e,0,0,y,g),i=f.getImageData(0,0,y,g).data,o.height=g,o.width=y,ho(i,o)}else throw new Error("Can not access image data")}else{if(s)return new Promise((c,f)=>{const g=l(),y=d(g);if(!e||!y)return f();const p=new Image;p.crossOrigin="Anonymous",p.src=e,p.onload=()=>{g.width=p.width,g.height=p.height,y.drawImage(p,0,0,g.width,g.height);const v=y.getImageData(0,0,g.width,g.height);o.height=g.height,o.width=g.width,c(ho(v.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(i!==void 0)return ho(i,o);throw new Error("Input data provided is not supported - aborted tensor creation")},B_=(e,t)=>{const{width:r,height:n,download:a,dispose:s}=t,i=[1,n,r,4];return new ir({location:"texture",type:"float32",texture:e,dims:i,download:a,dispose:s})},D_=(e,t)=>{const{dataType:r,dims:n,download:a,dispose:s}=t;return new ir({location:"gpu-buffer",type:r??"float32",gpuBuffer:e,dims:n,download:a,dispose:s})},N_=(e,t,r)=>new ir({location:"cpu-pinned",type:e,data:t,dims:r??[t.length]}),ai=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["float16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),fo=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let zf=!1;const F_=()=>{if(!zf){zf=!0;const e=typeof BigInt64Array<"u"&&typeof BigInt64Array.from=="function",t=typeof BigUint64Array<"u"&&typeof BigUint64Array.from=="function";e&&(ai.set("int64",BigInt64Array),fo.set(BigInt64Array,"int64")),t&&(ai.set("uint64",BigUint64Array),fo.set(BigUint64Array,"uint64"))}},L_=e=>{let t=1;for(let r=0;r<e.length;r++){const n=e[r];if(typeof n!="number"||!Number.isSafeInteger(n))throw new TypeError(`dims[${r}] must be an integer, got: ${n}`);if(n<0)throw new RangeError(`dims[${r}] must be a non-negative integer, got: ${n}`);t*=n}return t},U_=(e,t)=>{switch(e.location){case"cpu":return new ir(e.type,e.data,t);case"cpu-pinned":return new ir({location:"cpu-pinned",data:e.data,type:e.type,dims:t});case"texture":return new ir({location:"texture",texture:e.texture,type:e.type,dims:t});case"gpu-buffer":return new ir({location:"gpu-buffer",gpuBuffer:e.gpuBuffer,type:e.type,dims:t});default:throw new Error(`tensorReshape: tensor location ${e.location} is not supported`)}};let ir=class{constructor(t,r,n){F_();let a,s;if(typeof t=="object"&&"location"in t)switch(this.dataLocation=t.location,a=t.type,s=t.dims,t.location){case"cpu-pinned":{const o=ai.get(a);if(!o)throw new TypeError(`unsupported type "${a}" to create tensor from pinned buffer`);if(!(t.data instanceof o))throw new TypeError(`buffer should be of type ${o.name}`);this.cpuData=t.data;break}case"texture":{if(a!=="float32")throw new TypeError(`unsupported type "${a}" to create tensor from texture`);this.gpuTextureData=t.texture,this.downloader=t.download,this.disposer=t.dispose;break}case"gpu-buffer":{if(a!=="float32"&&a!=="float16"&&a!=="int32"&&a!=="int64"&&a!=="uint32"&&a!=="bool")throw new TypeError(`unsupported type "${a}" to create tensor from gpu buffer`);this.gpuBufferData=t.gpuBuffer,this.downloader=t.download,this.disposer=t.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let o,l;if(typeof t=="string")if(a=t,l=n,t==="string"){if(!Array.isArray(r))throw new TypeError("A string tensor's data must be a string array.");o=r}else{const d=ai.get(t);if(d===void 0)throw new TypeError(`Unsupported tensor type: ${t}.`);if(Array.isArray(r)){if(t==="float16")throw new TypeError("Creating a float16 tensor from number array is not supported. Please use Uint16Array as data.");t==="uint64"||t==="int64"?o=d.from(r,BigInt):o=d.from(r)}else if(r instanceof d)o=r;else throw new TypeError(`A ${a} tensor's data must be type of ${d}`)}else if(l=r,Array.isArray(t)){if(t.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");const d=typeof t[0];if(d==="string")a="string",o=t;else if(d==="boolean")a="bool",o=Uint8Array.from(t);else throw new TypeError(`Invalid element type of data array: ${d}.`)}else{const d=fo.get(t.constructor);if(d===void 0)throw new TypeError(`Unsupported type for tensor data: ${t.constructor}.`);a=d,o=t}if(l===void 0)l=[o.length];else if(!Array.isArray(l))throw new TypeError("A tensor's dims must be a number array");s=l,this.cpuData=o,this.dataLocation="cpu"}const i=L_(s);if(this.cpuData&&i!==this.cpuData.length)throw new Error(`Tensor's size(${i}) does not match data length(${this.cpuData.length}).`);this.type=a,this.dims=s,this.size=i}static async fromImage(t,r){return P_(t,r)}static fromTexture(t,r){return B_(t,r)}static fromGpuBuffer(t,r){return D_(t,r)}static fromPinnedBuffer(t,r,n){return N_(t,r,n)}toDataURL(t){return z_(this,t)}toImageData(t){return R_(this,t)}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(t){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,t&&this.disposer&&(this.disposer(),this.disposer=void 0),r}finally{this.isDownloading=!1}}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(t){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return U_(this,t)}};const W_=ir,Vn=[];let mo,Hr;Ar.IS_NODE_ENV?(Hr=Ve??dr,Vn.push("cpu"),mo=["cpu"]):(Hr=M_,Ar.IS_WEBGPU_AVAILABLE&&Vn.push("webgpu"),Vn.push("wasm"),mo=["wasm"]);const V_=Hr.InferenceSession;function G_(e){let t=mo;if(e){if(!Vn.includes(e))throw new Error(`Unsupported device: "${e}". Should be one of: ${Vn.join(", ")}.`);t=[e]}return t}async function H_(e,t){return await V_.create(e,t)}function Rf(e){return e instanceof Hr.Tensor}const Yt=Hr==null?void 0:Hr.env;Yt!=null&&Yt.wasm&&(Yt.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.19.0-dev.20240509-69cfcba38a/dist/",Yt.wasm.proxy=!Ar.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(Yt.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(Yt.wasm.simd=!1));function q_(){var e;return(e=Yt==null?void 0:Yt.wasm)==null?void 0:e.proxy}Ct.backends.onnx=Yt;const Pf=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 Ce{constructor(...t){W(this,"ort_tensor");return Rf(t[0])?this.ort_tensor=t[0]:this.ort_tensor=new W_(t[0],t[1],t[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(t){this.ort_tensor.dims=t}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[t,...r]=this.dims;if(r.length>0){const n=r.reduce((a,s)=>a*s);for(let a=0;a<t;++a)yield this._subarray(a,n,r)}else yield*this.data}_getitem(t){const[r,...n]=this.dims;if(t=sr(t,r),n.length>0){const a=n.reduce((s,i)=>s*i);return this._subarray(t,a,n)}else return new Ce(this.type,[this.data[t]],n)}indexOf(t){const r=this.data;for(let n=0;n<r.length;++n)if(r[n]==t)return n;return-1}_subarray(t,r,n){const a=t*r,s=(t+1)*r,i="subarray"in this.data?this.data.subarray(a,s):this.data.slice(a,s);return new Ce(this.type,i,n)}item(){const t=this.data;if(t.length!==1)throw new Error(`a Tensor with ${t.length} elements cannot be converted to Scalar`);return t[0]}tolist(){return j_(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){const t=this.data;for(let r=0;r<t.length;++r)t[r]=1/(1+Math.exp(-t[r]));return this}mul(t){return this.clone().mul_(t)}mul_(t){const r=this.data;for(let n=0;n<r.length;++n)r[n]*=t;return this}add(t){return this.clone().add_(t)}add_(t){const r=this.data;for(let n=0;n<r.length;++n)r[n]+=t;return this}clone(){return new Ce(this.type,this.data.slice(),this.dims.slice())}slice(...t){let r=[],n=[];for(let d=0;d<this.dims.length;++d){let c=t[d];if(c==null)n.push([0,this.dims[d]]),r.push(this.dims[d]);else if(typeof c=="number")c=sr(c,this.dims[d],d),n.push([c,c+1]);else if(Array.isArray(c)&&c.length===2){let[f,g]=c;if(f=f===null?0:sr(f,this.dims[d],d,!1),g=g===null?this.dims[d]:sr(g,this.dims[d],d,!1),f>g)throw new Error(`Invalid slice: ${c}`);let y=[Math.max(f,0),Math.min(g,this.dims[d])];n.push(y),r.push(y[1]-y[0])}else throw new Error(`Invalid slice: ${c}`)}let a=n.map(([d,c])=>c-d),s=a.reduce((d,c)=>d*c);const i=this.data;let o=new i.constructor(s);const l=this.stride();for(let d=0;d<s;++d){let c=0;for(let f=a.length-1,g=d;f>=0;--f){const y=a[f];c+=(g%y+n[f][0])*l[f],g=Math.floor(g/y)}o[d]=i[c]}return new Ce(this.type,o,r)}permute(...t){return K_(this,t)}transpose(...t){return this.permute(...t)}sum(t=null,r=!1){return this.norm(1,t,r)}norm(t="fro",r=null,n=!1){if(t==="fro")t=2;else if(typeof t=="string")throw Error(`Unsupported norm: ${t}`);const a=this.data;if(r===null){let o=a.reduce((l,d)=>l+d**t,0)**(1/t);return new Ce(this.type,[o],[])}r=sr(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 l=0;for(let d=this.dims.length-1,c=o,f=1;d>=0;--d){const g=this.dims[d];if(d!==r){const y=c%g;l+=y*f,f*=s[d]}c=Math.floor(c/g)}i[l]+=a[o]**t}if(t!==1)for(let o=0;o<i.length;++o)i[o]=i[o]**(1/t);return n||s.splice(r,1),new Ce(this.type,i,s)}normalize_(t=2,r=1){r=sr(r,this.dims.length);const n=this.norm(t,r,!0),a=this.data;for(let s=0;s<a.length;++s){let i=0;for(let o=this.dims.length-1,l=s,d=1;o>=0;--o){const c=this.dims[o];if(o!==r){const f=l%c;i+=f*d,d*=this.dims[o]}l=Math.floor(l/c)}a[s]/=n.data[i]}return this}normalize(t=2,r=1){return this.clone().normalize_(t,r)}stride(){return X_(this.dims)}squeeze(t=null){return new Ce(this.type,this.data,Bf(this.dims,t))}squeeze_(t=null){return this.dims=Bf(this.dims,t),this}unsqueeze(t=null){return new Ce(this.type,this.data,Df(this.dims,t))}unsqueeze_(t=null){return this.dims=Df(this.dims,t),this}flatten_(t=0,r=-1){r=(r+this.dims.length)%this.dims.length;let n=this.dims.slice(0,t),a=this.dims.slice(t,r+1),s=this.dims.slice(r+1);return this.dims=[...n,a.reduce((i,o)=>i*o,1),...s],this}flatten(t=0,r=-1){return this.clone().flatten_(t,r)}view(...t){let r=-1;for(let n=0;n<t.length;++n)if(t[n]===-1){if(r!==-1)throw new Error("Only one dimension can be inferred");r=n}if(r!==-1){const n=t.reduce((a,s,i)=>i!==r?a*s:a,1);t[r]=this.data.length/n}return new Ce(this.type,this.data,t)}neg_(){const t=this.data;for(let r=0;r<t.length;++r)t[r]=-t[r];return this}neg(){return this.clone().neg_()}clamp_(t,r){const n=this.data;for(let a=0;a<n.length;++a)n[a]=Math.min(Math.max(n[a],t),r);return this}clamp(t,r){return this.clone().clamp_(t,r)}round_(){const t=this.data;for(let r=0;r<t.length;++r)t[r]=Math.round(t[r]);return this}round(){return this.clone().round_()}mean(t=null,r=!1){return _o(this,t,r)}to(t){if(this.type===t)return this;if(!Pf.hasOwnProperty(t))throw new Error(`Unsupported type: ${t}`);return new Ce(t,Pf[t].from(this.data),this.dims)}}function j_(e,t){const r=e.length,n=t.reduce((s,i)=>s*i);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${t})`);let a=e;for(let s=t.length-1;s>=0;s--)a=a.reduce((i,o)=>{let l=i[i.length-1];return l.length<t[s]?l.push(o):i.push([o]),i},[[]]);return a[0]}function K_(e,t){const[r,n]=l0(e.data,e.dims,t);return new Ce(e.type,r,n)}function Bf(e,t){return e=e.slice(),t===null?e=e.filter(r=>r!==1):typeof t=="number"?e[t]===1&&e.splice(t,1):Array.isArray(t)&&(e=e.filter((r,n)=>r!==1||!t.includes(n))),e}function Df(e,t){return t=sr(t,e.length+1),e=e.slice(),e.splice(t,0,1),e}function sr(e,t,r=null,n=!0){if(n&&(e<-t||e>=t))throw new Error(`IndexError: index ${e} is out of bounds for dimension${r===null?"":" "+r} with size ${t}`);return e<0&&(e=(e%t+t)%t),e}function hr(e,t=0){t=sr(t,e[0].dims.length);const r=e[0].dims.slice();r[t]=e.reduce((i,o)=>i+o.dims[t],0);const n=r.reduce((i,o)=>i*o,1),a=new e[0].data.constructor(n),s=e[0].type;if(t===0){let i=0;for(let o of e)a.set(o.data,i),i+=o.data.length}else{let i=0;for(let o=0;o<e.length;++o){let l=e[o];for(let d=0;d<l.data.length;++d){let c=0;for(let 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dynamic time warping. Unexpected trace[${i}, ${o}]. 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this._heap[0]=t,this._siftDown(),r}_parent(t){return(t+1>>>1)-1}_left(t){return(t<<1)+1}_right(t){return t+1<<1}_greater(t,r){return this._comparator(this._heap[t],this._heap[r])}_swap(t,r){const n=this._heap[t];this._heap[t]=this._heap[r],this._heap[r]=n}_siftUp(){let t=this.size-1;for(;t>0&&this._greater(t,this._parent(t));)this._swap(t,this._parent(t)),t=this._parent(t)}_siftDown(){let t=0;for(;this._left(t)<this.size&&this._greater(this._left(t),t)||this._right(t)<this.size&&this._greater(this._right(t),t);){const r=this._right(t)<this.size&&this._greater(this._right(t),this._left(t))?this._right(t):this._left(t);this._swap(t,r),t=r}}}class ay{constructor(){this.root=si.default()}extend(t){for(let r of t)this.push(r)}push(t){let r=this.root;for(let n of t){let a=r.children.get(n);a===void 0&&(a=si.default(),r.children.set(n,a)),r=a}r.isLeaf=!0}*commonPrefixSearch(t){let r=this.root,n="";for(let a=0;a<t.length&&r!==void 0;++a){const s=t[a];n+=s,r=r.children.get(s),r!==void 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n=e.assignee,a=this.evaluate(n.object,t);if(!(a instanceof Qt))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(e.assignee)}`);return new jn}evaluateIf(e,t){const r=this.evaluate(e.test,t);return this.evaluateBlock(r.__bool__().value?e.body:e.alternate,t)}evaluateFor(e,t){const r=new bo(t),n=this.evaluate(e.iterable,r);if(!(n instanceof Ye))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 qe(s+1)],["index0",new qe(s)],["revindex",new qe(n.value.length-s)],["revindex0",new qe(n.value.length-s-1)],["first",new Je(s===0)],["last",new Je(s===n.value.length-1)],["length",new qe(n.value.length)],["previtem",s>0?n.value[s-1]:new Xt],["nextitem",s<n.value.length-1?n.value[s+1]:new Xt]]);r.setVariable("loop",new Qt(i));const o=n.value[s];if(e.loopvar.type==="Identifier")r.setVariable(e.loopvar.value,o);else if(e.loopvar.type==="TupleLiteral"){const d=e.loopvar;if(o.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${o.type}`);const c=o;if(d.value.length!==c.value.length)throw new Error(`Too ${d.value.length>c.value.length?"few":"many"} items to unpack`);for(let f=0;f<d.value.length;++f){if(d.value[f].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${d.value[f].type}`);r.setVariable(d.value[f].value,c.value[f])}}const l=this.evaluateBlock(e.body,r);a+=l.value}return new Re(a)}evaluate(e,t){if(e===void 0)return new Xt;switch(e.type){case"Program":return this.evalProgram(e,t);case"Set":return this.evaluateSet(e,t);case"If":return this.evaluateIf(e,t);case"For":return this.evaluateFor(e,t);case"NumericLiteral":return new qe(Number(e.value));case"StringLiteral":return new Re(e.value);case"BooleanLiteral":return new Je(e.value);case"ArrayLiteral":return new Ye(e.value.map(r=>this.evaluate(r,t)));case"TupleLiteral":return new ky(e.value.map(r=>this.evaluate(r,t)));case"ObjectLiteral":{const r=new Map;for(const[n,a]of e.value){const s=this.evaluate(n,t);if(!(s instanceof Re))throw new Error(`Object keys must be strings: got ${s.type}`);r.set(s.value,this.evaluate(a,t))}return new Qt(r)}case"Identifier":return this.evaluateIdentifier(e,t);case"CallExpression":return this.evaluateCallExpression(e,t);case"MemberExpression":return this.evaluateMemberExpression(e,t);case"UnaryExpression":return this.evaluateUnaryExpression(e,t);case"BinaryExpression":return this.evaluateBinaryExpression(e,t);case"FilterExpression":return this.evaluateFilterExpression(e,t);case"TestExpression":return this.evaluateTestExpression(e,t);default:throw new SyntaxError(`Unknown node type: ${e.type}`)}}};function oi(e){switch(typeof e){case"number":return new qe(e);case"string":return new Re(e);case"boolean":return new Je(e);case"object":return e===null?new jn:Array.isArray(e)?new Ye(e.map(oi)):new Qt(new Map(Object.entries(e).map(([t,r])=>[t,oi(r)])));case"function":return new mr((t,r)=>{const n=e(...t.map(a=>a.value))??null;return oi(n)});default:throw new Error(`Cannot convert to runtime value: ${e}`)}}var Cy=class{constructor(e){W(this,"parsed");const t=ly(e,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=xy(t)}render(e){const t=new bo;t.set("false",!1),t.set("true",!0),t.set("raise_exception",a=>{throw new Error(a)}),t.set("range",Sy);for(const[a,s]of Object.entries(e))t.set(a,s);return new Ey(t).run(this.parsed).value}};async function qf(e,t){const r=await Promise.all([Mr(e,"tokenizer.json",!0,t),Mr(e,"tokenizer_config.json",!0,t)]);return t.legacy!==null&&(r[1].legacy=t.legacy),r}function Ty(e,t){const r=[];let n=0;for(const a of e.matchAll(t)){const s=a[0];n<a.index&&r.push(e.slice(n,a.index)),s.length>0&&r.push(s),n=a.index+s.length}return n<e.length&&r.push(e.slice(n)),r}function ui(e,t=!0){if(e.Regex!==void 0){let r=e.Regex.replace(/\\([#&~])/g,"$1");for(const[n,a]of My)r=r.replaceAll(n,a);return new RegExp(r,"gu")}else if(e.String!==void 0){const r=eu(e.String);return new RegExp(t?r:`(${r})`,"gu")}else return console.warn("Unknown pattern type:",e),null}function vo(e){return new Map(Object.entries(e))}function jf(e){const t=e.dims;switch(t.length){case 1:return e.tolist();case 2:if(t[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return e.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${t.length}.`)}}function $o(e){return e.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function Kf(e){return e.replace(/[\u0300-\u036f]/g,"")}function Iy(e){return Kf(e.toLowerCase())}function Yf(e){return e>=19968&&e<=40959||e>=13312&&e<=19903||e>=131072&&e<=173791||e>=173824&&e<=177983||e>=177984&&e<=178207||e>=178208&&e<=183983||e>=63744&&e<=64255||e>=194560&&e<=195103}function Ay(e,t,r){const n=[];let a=0;for(;a<e.length;){if(n.push(e[a]),(r.get(e[a])??t)!==t){++a;continue}for(;a<e.length&&(r.get(e[a])??t)===t;)++a}return n}function Oy(e){return e.match(/\S+/g)||[]}const Kn="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",My=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class zy{constructor(t){this.content=t.content,this.id=t.id,this.single_word=t.single_word??!1,this.lstrip=t.lstrip??!1,this.rstrip=t.rstrip??!1,this.special=t.special??!1,this.normalized=t.normalized??null}}class Yn extends Tt{constructor(t){super(),this.config=t,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(t,...r){switch(t.type){case"WordPiece":return new Ry(t);case"Unigram":return new Py(t,...r);case"BPE":return new Dy(t);default:if(t.vocab)return new Ny(t,...r);throw new Error(`Unknown TokenizerModel type: ${t.type}`)}}_call(t){let r=this.encode(t);return this.fuse_unk&&(r=Ay(r,this.unk_token_id,this.tokens_to_ids)),r}encode(t){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(t){return t.map(r=>this.tokens_to_ids.get(r)??this.unk_token_id)}convert_ids_to_tokens(t){return t.map(r=>this.vocab[r]??this.unk_token)}}class Ry extends Yn{constructor(t){super(t),this.tokens_to_ids=vo(t.vocab),this.unk_token_id=this.tokens_to_ids.get(t.unk_token),this.unk_token=t.unk_token,this.max_input_chars_per_word=t.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r}encode(t){const r=[];for(const n of t){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 l=a.length,d=null;for(;i<l;){let c=a.slice(i,l).join("");if(i>0&&(c=this.config.continuing_subword_prefix+c),this.tokens_to_ids.has(c)){d=c;break}--l}if(d===null){s=!0;break}o.push(d),i=l}s?r.push(this.unk_token):r.push(...o)}return r}}class Py extends Yn{constructor(t,r){super(t);const n=t.vocab.length;this.vocab=new Array(n),this.scores=new Array(n);for(let a=0;a<n;++a){const s=t.vocab[a];this.vocab[a]=s[0],this.scores[a]=s[1]}this.unk_token_id=t.unk_id,this.unk_token=this.vocab[t.unk_id],this.tokens_to_ids=new Map(this.vocab.map((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=d0(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new ay,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(t){const r=t.sentence,n=r.length;let a=0;for(;a<n;){let i=!1;for(let o of this.trie.commonPrefixSearch(r.slice(a))){const l=this.tokens_to_ids.get(o),d=this.scores[l],c=o.length;t.insert(a,c,d,l),!i&&c===1&&(i=!0)}i||t.insert(a,1,this.unkScore,this.unk_token_id),a+=1}}tokenize(t){const r=new iy(t,this.bosTokenId,this.eosTokenId);return this.populateNodes(r),r.tokens()}encode(t){const r=[];for(const n of t){const a=this.tokenize(n);r.push(...a)}return r}}const Qf=(()=>{const e=[...Array.from({length:94},(a,s)=>s+33),...Array.from({length:12},(a,s)=>s+161),...Array.from({length:82},(a,s)=>s+174)],t=e.slice();let r=0;for(let a=0;a<256;++a)e.includes(a)||(e.push(a),t.push(256+r),r+=1);const n=t.map(a=>String.fromCharCode(a));return Object.fromEntries(e.map((a,s)=>[a,n[s]]))})(),By=n0(Qf);class Dy extends Yn{constructor(t){super(t),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=vo(t.vocab),this.unk_token_id=this.tokens_to_ids.get(t.unk_token),this.unk_token=t.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r;this.bpe_ranks=new Map(t.merges.map((r,n)=>[r,n])),this.merges=t.merges.map(r=>r.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=t.end_of_word_suffix,this.continuing_subword_suffix=t.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(t){if(t.length===0)return[];const r=this.cache.get(t);if(r!==void 0)return r;const n=Array.from(t);this.end_of_word_suffix&&(n[n.length-1]+=this.end_of_word_suffix);let a=[];if(n.length>1){const s=new ny((l,d)=>l.score<d.score);let i={token:n[0],bias:0,prev:null,next:null},o=i;for(let l=1;l<n.length;++l){const d={bias:l/n.length,token:n[l],prev:o,next:null};o.next=d,this._add_node(s,o),o=d}for(;!s.isEmpty();){const l=s.pop();if(l.deleted||!l.next||l.next.deleted)continue;if(l.deleted=!0,l.next.deleted=!0,l.prev){const c={...l.prev};l.prev.deleted=!0,l.prev=c,c.prev?c.prev.next=c:i=c}const d={token:l.token+l.next.token,bias:l.bias,prev:l.prev,next:l.next.next};d.prev?(d.prev.next=d,this._add_node(s,d.prev)):i=d,d.next&&(d.next.prev=d,this._add_node(s,d))}for(let l=i;l!==null;l=l.next)a.push(l.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(t,a),a}_add_node(t,r){const n=this.bpe_ranks.get(r.token+this.BPE_SPLIT_TOKEN+r.next.token);n!==void 0&&(r.score=n+r.bias,t.push(r))}encode(t){const r=[];for(const n of t){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 Ny extends Yn{constructor(t,r){super(t),this.tokens_to_ids=vo(r.target_lang?t.vocab[r.target_lang]:t.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(t){return t}}class It extends Tt{constructor(t){super(),this.config=t}static fromConfig(t){if(t===null)return null;switch(t.type){case"BertNormalizer":return new Ky(t);case"Precompiled":return new hw(t);case"Sequence":return new jy(t);case"Replace":return new Fy(t);case"NFC":return new Ly(t);case"NFKC":return new Uy(t);case"NFKD":return new Wy(t);case"Strip":return new Vy(t);case"StripAccents":return new Gy(t);case"Lowercase":return new Hy(t);case"Prepend":return new qy(t);default:throw new Error(`Unknown Normalizer type: ${t.type}`)}}normalize(t){throw Error("normalize should be implemented in subclass.")}_call(t){return this.normalize(t)}}class Fy extends It{normalize(t){const r=ui(this.config.pattern);return r===null?t:t.replaceAll(r,this.config.content)}}class Ly extends It{normalize(t){return t=t.normalize("NFC"),t}}class Uy extends It{normalize(t){return t=t.normalize("NFKC"),t}}class Wy extends It{normalize(t){return t=t.normalize("NFKD"),t}}class Vy extends It{normalize(t){return this.config.strip_left&&this.config.strip_right?t=t.trim():(this.config.strip_left&&(t=t.trimStart()),this.config.strip_right&&(t=t.trimEnd())),t}}class Gy extends It{normalize(t){return t=Kf(t),t}}class Hy extends It{normalize(t){return t=t.toLowerCase(),t}}class qy extends It{normalize(t){return t=this.config.prepend+t,t}}class jy extends It{constructor(t){super(t),this.normalizers=t.normalizers.map(r=>It.fromConfig(r))}normalize(t){return this.normalizers.reduce((r,n)=>n.normalize(r),t)}}class Ky extends It{_tokenize_chinese_chars(t){const r=[];for(let n=0;n<t.length;++n){const a=t[n],s=a.charCodeAt(0);Yf(s)?(r.push(" "),r.push(a),r.push(" ")):r.push(a)}return r.join("")}stripAccents(t){return t.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(t){switch(t){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(t)}}_clean_text(t){const r=[];for(const n of t){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(t){return this.config.clean_text&&(t=this._clean_text(t)),this.config.handle_chinese_chars&&(t=this._tokenize_chinese_chars(t)),this.config.lowercase?(t=t.toLowerCase(),this.config.strip_accents!==!1&&(t=this.stripAccents(t))):this.config.strip_accents&&(t=this.stripAccents(t)),t}}class Nt extends Tt{static fromConfig(t){if(t===null)return null;switch(t.type){case"BertPreTokenizer":return new Yy(t);case"Sequence":return new fw(t);case"Whitespace":return new mw(t);case"WhitespaceSplit":return new gw(t);case"Metaspace":return new Zf(t);case"ByteLevel":return new Qy(t);case"Split":return new Xy(t);case"Punctuation":return new Jy(t);case"Digits":return new Zy(t);case"Replace":return new _w(t);default:throw new Error(`Unknown PreTokenizer type: ${t.type}`)}}pre_tokenize_text(t,r){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(t,r){return(Array.isArray(t)?t.map(n=>this.pre_tokenize_text(n,r)):this.pre_tokenize_text(t,r)).flat()}_call(t,r){return this.pre_tokenize(t,r)}}class Yy extends Nt{constructor(t){super(),this.pattern=new RegExp(`[^\\s${Kn}]+|[${Kn}]`,"gu")}pre_tokenize_text(t,r){return t.trim().match(this.pattern)||[]}}class Qy extends Nt{constructor(t){super(),this.config=t,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=Qf,this.text_encoder=new TextEncoder}pre_tokenize_text(t,r){return this.add_prefix_space&&!t.startsWith(" ")&&(t=" "+t),(this.use_regex?t.match(this.pattern)||[]:[t]).map(a=>Array.from(this.text_encoder.encode(a),s=>this.byte_encoder[s]).join(""))}}class Xy extends Nt{constructor(t){super(),this.config=t,this.pattern=ui(this.config.pattern,this.config.invert)}pre_tokenize_text(t,r){return this.pattern===null?[]:this.config.invert?t.match(this.pattern)||[]:Ty(t,this.pattern)}}class Jy extends Nt{constructor(t){super(),this.config=t,this.pattern=new RegExp(`[^${Kn}]+|[${Kn}]+`,"gu")}pre_tokenize_text(t,r){return t.match(this.pattern)||[]}}class Zy extends Nt{constructor(t){super(),this.config=t;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(t,r){return t.match(this.pattern)||[]}}class li extends Tt{constructor(t){super(),this.config=t}static fromConfig(t){if(t===null)return null;switch(t.type){case"TemplateProcessing":return new ew(t);case"ByteLevel":return new tw(t);case"RobertaProcessing":return new Jf(t);case"BertProcessing":return new Xf(t);default:throw new Error(`Unknown PostProcessor type: ${t.type}`)}}post_process(t,...r){throw Error("post_process should be implemented in subclass.")}_call(t,...r){return this.post_process(t,...r)}}class Xf extends li{constructor(t){super(t),this.cls=t.cls[0],this.sep=t.sep[0]}post_process(t,r=null,{add_special_tokens:n=!0}={}){n&&(t=it([this.cls],t,[this.sep]));let a=new Array(t.length).fill(0);if(r!==null){const s=n&&this instanceof Jf?[this.sep]:[],i=n?[this.sep]:[];t=it(t,s,r,i),a=it(a,new Array(r.length+s.length+i.length).fill(1))}return{tokens:t,token_type_ids:a}}}class Jf extends Xf{}class ew extends li{constructor(t){super(t),this.single=t.single,this.pair=t.pair}post_process(t,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=it(s,t),i=it(i,new Array(t.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(s=it(s,r),i=it(i,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:s,token_type_ids:i}}}class tw extends li{post_process(t,r=null){return r&&(t=it(t,r)),{tokens:t}}}class At extends Tt{constructor(t){super(),this.config=t,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=t.trim_offsets}static fromConfig(t){if(t===null)return null;switch(t.type){case"WordPiece":return new sw(t);case"Metaspace":return new pw(t);case"ByteLevel":return new ow(t);case"Replace":return new rw(t);case"ByteFallback":return new nw(t);case"Fuse":return new aw(t);case"Strip":return new iw(t);case"Sequence":return new lw(t);case"CTC":return new uw(t);case"BPEDecoder":return new dw(t);default:throw new Error(`Unknown Decoder type: ${t.type}`)}}_call(t){return this.decode(t)}decode(t){return this.decode_chain(t).join("")}decode_chain(t){throw Error("`decode_chain` should be implemented in subclass.")}}class rw extends At{decode_chain(t){const r=ui(this.config.pattern);return r===null?t:t.map(n=>n.replaceAll(r,this.config.content))}}class nw extends At{constructor(t){super(t),this.text_decoder=new TextDecoder}decode_chain(t){const r=[];let n=[];for(const a of t){let s=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const i=parseInt(a.slice(3,5),16);isNaN(i)||(s=i)}if(s!==null)n.push(s);else{if(n.length>0){const i=this.text_decoder.decode(Uint8Array.from(n));r.push(i),n=[]}r.push(a)}}if(n.length>0){const a=this.text_decoder.decode(Uint8Array.from(n));r.push(a),n=[]}return r}}class aw extends At{decode_chain(t){return[t.join("")]}}class iw extends At{constructor(t){super(t),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(t){return t.map(r=>{let n=0;for(let s=0;s<this.start&&r[s]===this.content;++s){n=s+1;continue}let a=r.length;for(let s=0;s<this.stop;++s){const i=r.length-s-1;if(r[i]===this.content){a=i;continue}else break}return r.slice(n,a)})}}class sw extends At{constructor(t){super(t),this.cleanup=t.cleanup}decode_chain(t){return t.map((r,n)=>(n!==0&&(r.startsWith(this.config.prefix)?r=r.replace(this.config.prefix,""):r=" "+r),this.cleanup&&(r=$o(r)),r))}}class ow extends At{constructor(t){super(t),this.byte_decoder=By,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(t){const r=t.join(""),n=new Uint8Array([...r].map(s=>this.byte_decoder[s]));return this.text_decoder.decode(n)}decode_chain(t){const r=[];let n=[];for(const a of t)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 uw extends At{constructor(t){super(t),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(t){if(t.length===0)return"";const r=[t[0]];for(let s=1;s<t.length;++s)t[s]!==r.at(-1)&&r.push(t[s]);let a=r.filter(s=>s!==this.pad_token).join("");return this.cleanup&&(a=$o(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(t){return[this.convert_tokens_to_string(t)]}}class lw extends At{constructor(t){super(t),this.decoders=t.decoders.map(r=>At.fromConfig(r))}decode_chain(t){return this.decoders.reduce((r,n)=>n.decode_chain(r),t)}}class dw extends At{constructor(t){super(t),this.suffix=this.config.suffix}decode_chain(t){return t.map((r,n)=>r.replaceAll(this.suffix,n===t.length-1?"":" "))}}class cw extends At{decode_chain(t){let r="";for(let n=1;n<t.length;n+=2)r+=t[n];return[r]}}class Zf extends Nt{constructor(t){super(),this.addPrefixSpace=t.add_prefix_space,this.replacement=t.replacement,this.strRep=t.str_rep||this.replacement,this.prepend_scheme=t.prepend_scheme??"always"}pre_tokenize_text(t,{section_index:r=void 0}={}){let n=t.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 pw extends At{constructor(t){super(t),this.addPrefixSpace=t.add_prefix_space,this.replacement=t.replacement}decode_chain(t){const r=[];for(let n=0;n<t.length;++n){let a=t[n].replaceAll(this.replacement," ");this.addPrefixSpace&&n==0&&a.startsWith(" ")&&(a=a.substring(1)),r.push(a)}return r}}class hw extends It{constructor(t){super(t),this.charsmap=t.precompiled_charsmap}normalize(t){return t=t.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),t=t.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),t.includes("～")?t=t.split("～").map(n=>n.normalize("NFKC")).join("～"):t=t.normalize("NFKC"),t}}class fw extends Nt{constructor(t){super(),this.tokenizers=t.pretokenizers.map(r=>Nt.fromConfig(r))}pre_tokenize_text(t,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[t])}}class mw extends Nt{constructor(t){super()}pre_tokenize_text(t,r){return t.match(/\w+|[^\w\s]+/g)||[]}}class gw extends Nt{constructor(t){super()}pre_tokenize_text(t,r){return Oy(t)}}class _w extends Nt{constructor(t){super(),this.config=t,this.pattern=ui(this.config.pattern),this.content=this.config.content}pre_tokenize_text(t,r){return this.pattern===null?[t]:[t.replaceAll(this.pattern,this.config.content)]}}const yw=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function ww(e,t,r,n){for(const a of Object.keys(e)){const s=t-e[a].length,i=r(a),o=new Array(s).fill(i);e[a]=n==="right"?it(e[a],o):it(o,e[a])}}function bw(e,t){for(const r of Object.keys(e))e[r].length=t}class ke extends Tt{constructor(r,n){super();W(this,"return_token_type_ids",!1);W(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 %}`);W(this,"padding_side","right");this._tokenizer_config=n,this.normalizer=It.fromConfig(r.normalizer),this.pre_tokenizer=Nt.fromConfig(r.pre_tokenizer),this.model=Yn.fromConfig(r.model,n),this.post_processor=li.fromConfig(r.post_processor),this.decoder=At.fromConfig(r.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const a of r.added_tokens){const s=new zy(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*":""}(${eu(a.content)})${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:l=null}={}){const d=await qf(r,{progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,legacy:l});return new this(...d)}_call(r,{text_pair:n=null,add_special_tokens:a=!0,padding:s=!1,truncation:i=null,max_length:o=null,return_tensor:l=!0}={}){const d=Array.isArray(r);let c;if(d){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((g,y)=>this._encode_plus(g,{text_pair:n[y],add_special_tokens:a}))}else c=r.map(g=>this._encode_plus(g,{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=Ji(c.map(g=>g.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 g=0;g<c.length;++g)c[g].input_ids.length!==o&&(c[g].input_ids.length>o?i&&bw(c[g],o):s&&ww(c[g],o,y=>y==="input_ids"?this.pad_token_id:0,this.padding_side));const f={};if(l){if(!(s&&i)&&c.some(y=>{var p;for(const v of Object.keys(y))if(y[v].length!==((p=c[0][v])==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 g=[c.length,c[0].input_ids.length];for(const y of Object.keys(c[0]))f[y]=new Ce("int64",BigInt64Array.from(c.flatMap(p=>p[y]).map(BigInt)),g)}else{for(const g of Object.keys(c[0]))f[g]=c.map(y=>y[g]);if(!d)for(const g of Object.keys(f))f[g]=f[g][0]}return f}_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(l=>l.content===s)!==void 0)return s;{if(this.remove_space===!0&&(s=s.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(s=Iy(s)),this.normalizer!==null&&(s=this.normalizer(s)),s.length===0)return[];const l=this.pre_tokenizer!==null?this.pre_tokenizer(s,{section_index:i}):[s];return this.model(l)}}).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),l={input_ids:o,attention_mask:new Array(o.length).fill(1)};return this.return_token_type_ids&&i&&(l.token_type_ids=i),l}_tokenize_helper(r,{pair:n=null,add_special_tokens:a=!1}={}){const s=this._encode_text(r),i=this._encode_text(n);return this.post_processor?this.post_processor(s,i,{add_special_tokens:a}):{tokens:it(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 Ce&&(r=r.tolist()),r.map(a=>this.decode(a,n))}decode(r,n={}){if(r instanceof Ce&&(r=jf(r)),!Array.isArray(r)||r.length===0||!a0(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=$o(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:l=null,return_tensor:d=!0,return_dict:c=!1,tokenizer_kwargs:f={},...g}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const S=this.chat_template??this.default_chat_template;if(n!==null&&Object.hasOwn(S,n))n=S[n];else if(n===null&&"default"in S)n=S.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(S).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 y=this._compiled_template_cache.get(n);y===void 0&&(y=new Cy(n),this._compiled_template_cache.set(n,y));const p=Object.create(null);for(const S of yw){const E=this.getToken(S);E&&(p[S]=E)}const v=y.render({messages:r,add_generation_prompt:a,...p,...g});if(s){const S=this._call(v,{add_special_tokens:!1,padding:i,truncation:o,max_length:l,return_tensor:d,...f});return c?S:S.input_ids}return v}}class vw extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class $w extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class xw extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class Sw extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class kw extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class Ew extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class Cw extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class Tw extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class Iw extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class Aw extends ke{}class Ow extends ke{}class Mw extends ke{constructor(r,n){super(r,n);W(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class zw extends ke{constructor(){super(...arguments);W(this,"return_token_type_ids",!0)}}class Rw extends ke{}class em extends ke{constructor(){super(...arguments);W(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}}class Pw extends ke{}class tm extends ke{constructor(t,r){super(t,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(t,r,n){return xo(this,t,r,n)}}class Bw extends tm{}class Dw extends ke{}class Nw extends em{constructor(t,r){var s,i;const n=".,!?…。，、।۔،",a=(i=(s=t.pre_tokenizer)==null?void 0:s.pretokenizers[0])==null?void 0:i.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(t,r)}}const di="▁";class rm extends ke{constructor(r,n){super(r,n);W(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 %}`);W(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.`);W(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 Zf({replacement:di,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(di+r.replaceAll(di," "));return n.length>1&&n[0]===di&&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 Fw extends rm{}class Lw extends ke{}class Uw extends ke{}class Ww extends ke{}class Vw extends ke{}class Gw extends ke{}class Hw extends ke{}class qw extends ke{constructor(){super(...arguments);W(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 jw extends ke{}function xo(e,t,r,n){if(!("language_codes"in e)||!Array.isArray(e.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in e)||!(e.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in e)||typeof e.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const a=n.src_lang,s=n.tgt_lang;if(!e.language_codes.includes(s))throw new Error(`Target language code "${s}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);if(a!==void 0){if(!e.language_codes.includes(a))throw new Error(`Source language code "${a}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);for(const i of e.post_processor.config.single)if("SpecialToken"in i&&e.languageRegex.test(i.SpecialToken.id)){i.SpecialToken.id=e.lang_to_token(a);break}}return n.forced_bos_token_id=e.model.convert_tokens_to_ids([e.lang_to_token(s)])[0],e._call(t,r)}class Kw extends ke{constructor(t,r){super(t,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(t,r,n){return xo(this,t,r,n)}}class Yw extends ke{constructor(t,r){super(t,r),this.languageRegex=/^__[a-z]{2,3}__$/,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(t,r,n){return xo(this,t,r,n)}}const nm=[["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"]],ci=new Map(nm),Qw=new Map([...nm.map(([e,t])=>[t,e]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class Xw extends ke{constructor(){super(...arguments);W(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 l=n==="word";function d(){return{language:o,timestamp:[null,null],text:""}}const c=[];let f=d(),g=0;const y=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let p=[],v=[],S=!1,E=null;const k=new Set(this.all_special_ids);for(const T of r){const F=T.tokens,B=l?T.token_timestamps:null;let G=null,H=y;if("stride"in T){const[oe,ne,de]=T.stride;if(g-=ne,E=oe-de,ne&&(H=ne/s+y),de)for(let D=F.length-1;D>=0;--D){const z=F[D];if(z>=y){if(G!==null&&(z-y)*s<E)break;G=z}}}let Z=[],pe=[];for(let oe=0;oe<F.length;++oe){const ne=F[oe];if(k.has(ne)){const de=this.decode([ne]),D=ci.get(de.slice(2,-2));if(D!==void 0){if(o!==null&&D!==o&&!n){p.push(Z);const z=this.findLongestCommonSequence(p)[0],q=this.decode(z);f.text=q,c.push(f),p=[],Z=[],f=d()}o=f.language=D}}else if(ne>=y){const de=(ne-y)*s+g,D=za(de,2);if(G!==null&&ne>=G)S=!0;else if(S||p.length>0&&ne<H)S=!1;else if(f.timestamp[0]===null)f.timestamp[0]=D;else if(D!==f.timestamp[0]){f.timestamp[1]=D,p.push(Z),l&&v.push(pe);const[z,q]=this.findLongestCommonSequence(p,v),te=this.decode(z);f.text=te,l&&(f.words=this.collateWordTimestamps(z,q,o)),c.push(f),p=[],Z=[],v=[],pe=[],f=d()}}else if(Z.push(ne),l){let de=za(B[oe]+g,2),D;oe+1<B.length?D=za(B[oe+1]+g,2):D=null,pe.push([de,D])}}if("stride"in T){const[oe,ne,de]=T.stride;g+=oe-de}Z.length>0?(p.push(Z),l&&v.push(pe)):p.every(oe=>oe.length===0)&&(f=d(),p=[],Z=[],v=[],pe=[])}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[T,F]=this.findLongestCommonSequence(p,v),B=this.decode(T);f.text=B,l&&(f.words=this.collateWordTimestamps(T,F,o)),c.push(f)}let O=Object.create(null);const I=c.map(T=>T.text).join("");if(n||a){for(let T=0;T<c.length;++T){const F=c[T];n||delete F.timestamp,a||delete F.language}if(l){const T=[];for(const F of c)for(const B of F.words)T.push(B);O={chunks:T}}else O={chunks:c}}return[I,O]}findLongestCommonSequence(r,n=null){let a=r[0],s=a.length,i=[];const o=Array.isArray(n)&&n.length>0;let l=o?[]:null,d=o?n[0]:null;for(let c=1;c<r.length;++c){const f=r[c];let g=0,y=[s,s,0,0];const p=f.length;for(let T=1;T<s+p;++T){const F=T/1e4,B=Math.max(0,s-T),G=Math.min(s,s+p-T),H=a.slice(B,G),Z=Math.max(0,T-s),pe=Math.min(p,T),oe=f.slice(Z,pe);if(H.length!==oe.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const ne=H.filter((D,z)=>D===oe[z]).length,de=ne/T+F;ne>1&&de>g&&(g=de,y=[B,G,Z,pe])}const[v,S,E,k]=y,O=Math.floor((S+v)/2),I=Math.floor((k+E)/2);i.push(...a.slice(0,O)),a=f.slice(I),s=a.length,o&&(l.push(...d.slice(0,O)),d=n[c].slice(I))}return i.push(...a),o?(l.push(...d),[i,l]):[i,[]]}collateWordTimestamps(r,n,a){const[s,i,o]=this.combineTokensIntoWords(r,a),l=[];for(let d=0;d<s.length;++d){const c=o[d];l.push({text:s[d],timestamp:[n[c.at(0)][0],n[c.at(-1)][1]]})}return l}combineTokensIntoWords(r,n,a=`"'“¡¿([{-`,s=`"'.。,，!！?？:：”)]}、`){n=n??"english";let i,o,l;return["chinese","japanese","thai","lao","myanmar"].includes(n)?[i,o,l]=this.splitTokensOnUnicode(r):[i,o,l]=this.splitTokensOnSpaces(r),this.mergePunctuations(i,o,l,a,s)}decode(r,n){let a;return n&&n.decode_with_timestamps?(r instanceof Ce&&(r=jf(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 l=za((Number(o)-s)*a,2);i.push(`<|${l}|>`),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 l=[],d=[],c=0;for(let f=0;f<r.length;++f){const g=r[f];l.push(g),d.push(f);const y=this.decode(l,{decode_with_timestamps:!0});(!y.includes(a)||n[c+y.indexOf(a)]===a)&&(s.push(y),i.push(l),o.push(d),l=[],d=[],c+=y.length)}return[s,i,o]}splitTokensOnSpaces(r){const[n,a,s]=this.splitTokensOnUnicode(r),i=[],o=[],l=[],d=new RegExp(`^[${Kn}]$`,"gu");for(let c=0;c<n.length;++c){const f=n[c],g=a[c],y=s[c],p=g[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),v=f.startsWith(" "),S=f.trim(),E=d.test(S);if(p||v||E||i.length===0)i.push(f),o.push(g),l.push(y);else{const k=i.length-1;i[k]+=f,o[k].push(...g),l[k].push(...y)}}return[i,o,l]}mergePunctuations(r,n,a,s,i){const o=structuredClone(r),l=structuredClone(n),d=structuredClone(a);let c=o.length-2,f=o.length-1;for(;c>=0;)o[c].startsWith(" ")&&s.includes(o[c].trim())?(o[f]=o[c]+o[f],l[f]=it(l[c],l[f]),d[f]=it(d[c],d[f]),o[c]="",l[c]=[],d[c]=[]):f=c,--c;for(c=0,f=1;f<o.length;)!o[c].endsWith(" ")&&i.includes(o[f])?(o[c]+=o[f],l[c]=it(l[c],l[f]),d[c]=it(d[c],d[f]),o[f]="",l[f]=[],d[f]=[]):c=f,++f;return[o.filter(g=>g),l.filter(g=>g.length>0),d.filter(g=>g.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=Qw.get(r);if(i===void 0)if(ci.has(r))i=r;else{const d=r.length===2?ci.keys():ci.values();throw new Error(`Language "${r}" is not supported. Must be one of: ${JSON.stringify(d)}`)}const o=this.model.tokens_to_ids.get(`<|${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 Jw extends ke{}class Zw extends ke{}class eb extends ke{}class tb extends ke{constructor(t,r){super(t,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(t){if(t===null)return null;const[r,...n]=t.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: ${JSON.stringify(this.supported_language_codes)}`),it([a],super._encode_text(s))}}}class rb extends ke{}class am extends ke{constructor(){super(...arguments);W(this,"_default_chat_template","{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}")}}class nb extends am{}class ab extends ke{}class ib extends ke{}class sb extends ke{constructor(t,r){super(t,r),this.decoder=new cw({})}}class ob extends ke{}class im{static async from_pretrained(t,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",legacy:o=null}={}){var g;const[l,d]=await qf(t,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,legacy:o}),c=((g=d.tokenizer_class)==null?void 0:g.replace(/Fast$/,""))??"PreTrainedTokenizer";let f=this.TOKENIZER_CLASS_MAPPING[c];return f||(console.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),f=ke),new f(l,d)}}W(im,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:Rw,DistilBertTokenizer:Aw,CamembertTokenizer:Ow,DebertaTokenizer:kw,DebertaV2Tokenizer:Ew,BertTokenizer:vw,HerbertTokenizer:Cw,ConvBertTokenizer:Tw,RoFormerTokenizer:Iw,XLMTokenizer:Mw,ElectraTokenizer:zw,MobileBertTokenizer:xw,SqueezeBertTokenizer:Sw,AlbertTokenizer:$w,GPT2Tokenizer:em,BartTokenizer:Pw,MBartTokenizer:tm,MBart50Tokenizer:Bw,RobertaTokenizer:Dw,WhisperTokenizer:Xw,CodeGenTokenizer:Jw,CLIPTokenizer:Zw,SiglipTokenizer:eb,MarianTokenizer:tb,BloomTokenizer:Nw,NllbTokenizer:Kw,M2M100Tokenizer:Yw,LlamaTokenizer:rm,CodeLlamaTokenizer:Fw,XLMRobertaTokenizer:Lw,MPNetTokenizer:Uw,FalconTokenizer:Ww,GPTNeoXTokenizer:Vw,EsmTokenizer:Gw,Wav2Vec2CTCTokenizer:rb,BlenderbotTokenizer:am,BlenderbotSmallTokenizer:nb,SpeechT5Tokenizer:ab,NougatTokenizer:ib,VitsTokenizer:sb,Qwen2Tokenizer:Hw,GemmaTokenizer:qw,Grok1Tokenizer:jw,CohereTokenizer:ob,PreTrainedTokenizer:ke});async function ub(e,t){return await Mr(e,"config.json",!0,t)}function pi(e){const t={};let r={};switch(e.model_type){case"llava":r=pi(e.text_config);break;case"moondream1":r=pi(e.phi_config);break;case"musicgen":r=pi(e.decoder);break;case"gpt2":case"gptj":case"codegen":case"gpt_bigcode":t.num_heads="n_head",t.num_layers="n_layer",t.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"phi":case"phi3":case"falcon":t.num_heads="num_attention_heads",t.num_layers="num_hidden_layers",t.hidden_size="hidden_size";break;case"llama":case"mistral":case"starcoder2":case"qwen2":t.num_heads="num_key_value_heads",t.num_layers="num_hidden_layers",t.hidden_size="hidden_size",t.num_attention_heads="num_attention_heads";break;case"openelm":t.num_heads="num_kv_heads",t.num_layers="num_transformer_layers",t.dim_kv="head_dim";break;case"gpt_neo":t.num_heads="num_heads",t.num_layers="num_layers",t.hidden_size="hidden_size";break;case"bloom":t.num_heads="n_head",t.num_layers="n_layer",t.hidden_size="hidden_size";break;case"mpt":t.num_heads="n_heads",t.num_layers="n_layers",t.hidden_size="d_model";break;case"t5":case"mt5":case"longt5":t.num_decoder_layers="num_decoder_layers",t.num_decoder_heads="num_heads",t.decoder_dim_kv="d_kv",t.num_encoder_layers="num_layers",t.num_encoder_heads="num_heads",t.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":t.num_decoder_layers="decoder_layers",t.num_decoder_heads="decoder_attention_heads",t.decoder_hidden_size="d_model",t.num_encoder_layers="encoder_layers",t.num_encoder_heads="encoder_attention_heads",t.encoder_hidden_size="d_model";break;case"speecht5":t.num_decoder_layers="decoder_layers",t.num_decoder_heads="decoder_attention_heads",t.decoder_hidden_size="hidden_size",t.num_encoder_layers="encoder_layers",t.num_encoder_heads="encoder_attention_heads",t.encoder_hidden_size="hidden_size";break;case"trocr":t.num_encoder_layers=t.num_decoder_layers="decoder_layers",t.num_encoder_heads=t.num_decoder_heads="decoder_attention_heads",t.encoder_hidden_size=t.decoder_hidden_size="d_model";break;case"musicgen_decoder":t.num_encoder_layers=t.num_decoder_layers="num_hidden_layers",t.num_encoder_heads=t.num_decoder_heads="num_attention_heads",t.encoder_hidden_size=t.decoder_hidden_size="hidden_size";break}const n={...r,...Or(e,["model_type","multi_query","is_encoder_decoder"])};for(const a in t)n[a]=e[t[a]];return n}function sm(e,{prefix:t="past_key_values",encoder_add_pkv:r=!0}={}){const n={},a=e.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,l=[s,a.num_encoder_heads,0,i],d=[s,a.num_decoder_heads,0,o];for(let c=0;c<a.num_decoder_layers;++c)n[`${t}.${c}.encoder.key`]=l,n[`${t}.${c}.encoder.value`]=l,n[`${t}.${c}.decoder.key`]=d,n[`${t}.${c}.decoder.value`]=d}else{const i=a.num_heads,o=a.num_layers,l=a.dim_kv??a.hidden_size/(a.num_attention_heads??i);if(a.model_type==="falcon"){const d=[s*i,0,l];for(let c=0;c<o;++c)n[`${t}.${c}.key`]=d,n[`${t}.${c}.value`]=d}else if(a.multi_query){const d=[s*i,0,2*l];for(let c=0;c<o;++c)n[`${t}.${c}.key_value`]=d}else if(a.model_type==="bloom"){const d=[s*i,l,0],c=[s*i,0,l];for(let f=0;f<o;++f)n[`${t}.${f}.key`]=d,n[`${t}.${f}.value`]=c}else if(a.model_type==="openelm")for(let d=0;d<o;++d){const c=[s,i[d],0,l];n[`${t}.${d}.key`]=c,n[`${t}.${d}.value`]=c}else{const d=[s,i,0,l];for(let c=0;c<o;++c)n[`${t}.${c}.key`]=d,n[`${t}.${c}.value`]=d}}return n}class hi{constructor(t){W(this,"max_position_embeddings");this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,t),this.normalized_config=pi(this)}static async from_pretrained(t,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main"}={}){n&&!(n instanceof hi)&&(n=new hi(n));const o=n??await ub(t,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i});return new this(o)}}class om{static async from_pretrained(...t){return hi.from_pretrained(...t)}}const fi=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),lb=function(){let e;return async function(){if(e===void 0)if(Ar.IS_NODE_ENV)e=!0;else if(!Ar.IS_WEBGPU_AVAILABLE)e=!1;else try{e=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{e=!1}return e}}(),vt=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),db=Object.freeze({[fi.cpu]:vt.q8,[fi.gpu]:vt.fp32,[fi.wasm]:vt.q8,[fi.webgpu]:vt.fp32}),um=Object.freeze({[vt.fp32]:"",[vt.fp16]:"_fp16",[vt.int8]:"_int8",[vt.uint8]:"_uint8",[vt.q8]:"_quantized",[vt.q4]:"_q4",[vt.q4f16]:"_q4f16",[vt.bnb4]:"_bnb4"});class gr extends Tt{_call(t,r){throw Error("`_call` should be implemented in a subclass")}}class So extends Tt{_call(t,r){throw Error("`_call` should be implemented in a subclass")}}class lm extends Tt{constructor(){super(),this.processors=[]}push(t){this.processors.push(t)}extend(t){this.processors.push(...t)}_call(t,r){let n=r;for(const a of this.processors)n=a(t,n);return n}[Symbol.iterator](){return this.processors.values()}}class cb extends gr{constructor(t){super(),this.bos_token_id=t}_call(t,r){for(let n=0;n<t.length;++n)if(t[n].length===1){const a=r[n];a.data.fill(-1/0),a.data[this.bos_token_id]=0}return r}}class pb extends gr{constructor(t,r){super(),this.max_length=t,this.forced_eos_token_id=r}_call(t,r){}}class hb extends gr{constructor(t,r){super(),this.begin_suppress_tokens=t,this.begin_index=r}_call(t,r){for(let n=0;n<t.length;++n)if(t[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 fb extends gr{constructor(t){super(),this.no_repeat_ngram_size=t}getNgrams(t){const r=t.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(t[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),l=a.get(o)??[];l.push(s[s.length-1]),a.set(o,l)}return a}getGeneratedNgrams(t,r){const n=r.slice(r.length+1-this.no_repeat_ngram_size,r.length);return t.get(JSON.stringify(n))??[]}calcBannedNgramTokens(t){const r=[];if(t.length+1<this.no_repeat_ngram_size)return r;{const n=this.getNgrams(t);return this.getGeneratedNgrams(n,t)}}_call(t,r){for(let n=0;n<t.length;++n){const a=r[n],s=this.calcBannedNgramTokens(t[n]);for(const i of s)a.data[i]=-1/0}return r}}class mb extends gr{constructor(t){super(),this.penalty=t}_call(t,r){for(let n=0;n<t.length;++n){const a=r[n];for(const s of t[n])a.data[s]<0?a.data[s]*=this.penalty:a.data[s]/=this.penalty}return r}}class gb extends gr{constructor(t,r){super(),this.min_length=t,this.eos_token_id=Array.isArray(r)?r:[r]}_call(t,r){for(let n=0;n<t.length;++n)if(t[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 _b extends gr{constructor(t,r,n){super(),this.prompt_length_to_skip=t,this.min_new_tokens=r,this.eos_token_id=Array.isArray(n)?n:[n]}_call(t,r){for(let n=0;n<t.length;++n)if(t[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 yb extends gr{constructor(t,r){super(),this.bad_words_ids=t,this.eos_token_id=Array.isArray(r)?r:[r]}_call(t,r){for(let n=0;n<t.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<t[o].length;++o)if(s.at(-o-1)!==t[o].at(-o)){i=!1;break}i&&(a[s.at(-1)]=-1/0)}}return r}}class wb extends gr{constructor(t){if(super(),t<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${t}.`);this.guidance_scale=t}_call(t,r){if(r.dims[0]!==2*t.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${r.dims[0]} for the logits and ${t.length} for the input ids.`);const n=t.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 bb extends So{constructor(t){super(),this.temperature=t}_call(t,r){const n=r.data;for(let a=0;a<n.length;++a)n[a]/=this.temperature;return r}}class vb extends So{constructor(t,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),t<0||t>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${t}`);if(!Number.isInteger(n)||n<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${n}`);this.top_p=t,this.filter_value=r,this.min_tokens_to_keep=n}}class $b extends So{constructor(t,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),!Number.isInteger(t)||t<0)throw new Error(`\`top_k\` must be a positive integer, but is ${t}`);this.top_k=Math.max(t,n),this.filter_value=r}}class xb{constructor(t){W(this,"max_length",20);W(this,"max_new_tokens",null);W(this,"min_length",0);W(this,"min_new_tokens",null);W(this,"early_stopping",!1);W(this,"max_time",null);W(this,"do_sample",!1);W(this,"num_beams",1);W(this,"num_beam_groups",1);W(this,"penalty_alpha",null);W(this,"use_cache",!0);W(this,"temperature",1);W(this,"top_k",50);W(this,"top_p",1);W(this,"typical_p",1);W(this,"epsilon_cutoff",0);W(this,"eta_cutoff",0);W(this,"diversity_penalty",0);W(this,"repetition_penalty",1);W(this,"encoder_repetition_penalty",1);W(this,"length_penalty",1);W(this,"no_repeat_ngram_size",0);W(this,"bad_words_ids",null);W(this,"force_words_ids",null);W(this,"renormalize_logits",!1);W(this,"constraints",null);W(this,"forced_bos_token_id",null);W(this,"forced_eos_token_id",null);W(this,"remove_invalid_values",!1);W(this,"exponential_decay_length_penalty",null);W(this,"suppress_tokens",null);W(this,"begin_suppress_tokens",null);W(this,"forced_decoder_ids",null);W(this,"guidance_scale",null);W(this,"num_return_sequences",1);W(this,"output_attentions",!1);W(this,"output_hidden_states",!1);W(this,"output_scores",!1);W(this,"return_dict_in_generate",!1);W(this,"pad_token_id",null);W(this,"bos_token_id",null);W(this,"eos_token_id",null);W(this,"encoder_no_repeat_ngram_size",0);W(this,"decoder_start_token_id",null);W(this,"generation_kwargs",{});Object.assign(this,Or(t,Object.getOwnPropertyNames(this)))}}class mi extends Tt{_call(t,r){throw Error("StoppingCriteria needs to be subclassed")}}class ko extends Tt{constructor(){super(),this.criteria=[]}push(t){this.criteria.push(t)}extend(t){t instanceof ko?t=t.criteria:t instanceof mi&&(t=[t]),this.criteria.push(...t)}_call(t,r){const n=new Array(t.length).fill(!1);for(const a of this.criteria){const s=a(t,r);for(let i=0;i<n.length;++i)n[i]||(n[i]=s[i])}return n}[Symbol.iterator](){return this.criteria.values()}}class Sb extends mi{constructor(t,r=null){super(),this.max_length=t,this.max_position_embeddings=r}_call(t){return t.map(r=>r.length>=this.max_length)}}class kb extends mi{constructor(t){super(),Array.isArray(t)||(t=[t]),this.eos_token_id=t}_call(t,r){return t.map(n=>{const a=n.at(-1);return this.eos_token_id.some(s=>a==s)})}}class gi extends Tt{constructor(t){super(),this.generation_config=t}_call(t,r=-1){return this.sample(t,r)}sample(t,r){throw Error("sample should be implemented in subclasses.")}getLogits(t,r){let n=t.dims.at(-1),a=t.data;if(r===-1)a=a.slice(-n);else{let s=r*n;a=a.slice(s,s+n)}return a}randomSelect(t){let r=t.reduce((a,s)=>a+s,0),n=Math.random()*r;for(let a=0;a<t.length;++a)if(n-=t[a],n<=0)return a;return 0}static getSampler(t){if(t.do_sample)return new Cb(t);if(t.num_beams>1)return new Tb(t);if(t.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${t.num_return_sequences}.`);return new Eb(t)}}class Eb extends gi{sample(t,r=-1){let n=this.getLogits(t,r);return[[Ji(n)[1],0]]}}class Cb extends gi{sample(t,r=-1){let n=t.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(t,r),s=au(a,n),i=nu(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 Tb extends gi{sample(t,r=-1){let n=t.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(t,r),s=au(a,n),i=nu(s.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},(o,l)=>[s[l][0],Math.log(i[l])])}}const ve={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},_i=new Map,dm=new Map,Qn=new Map;async function Ib(e,t,r){let n=r.device;n&&typeof n!="string"&&(n.hasOwnProperty(t)?n=n[t]:(console.warn(`Device not specified for ${t}. Using the default device.`),n=null));const a=G_(n);let s=r.dtype;if(typeof s!="string"&&(s&&s.hasOwnProperty(t)?s=s[t]:(s=db[a[0]],console.warn(`Dtype not specified for ${t}. Using the default dtype: ${s}.`))),um.hasOwnProperty(s)){if(s===vt.fp16&&!await lb())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${s}. Should be one of: ${Object.keys(vt).join(", ")}`);const i=um[s],o=`${r.subfolder??""}/${t}${i}.onnx`,l={...r.session_options};l.executionProviders??(l.executionProviders=a);const d=Oa(e,o,!0,r);let c=[];if(r.use_external_data_format){if(Ar.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const g=`${t}${i}.onnx_data`,y=`${r.subfolder??""}/${g}`;c.push(new Promise(async(p,v)=>{const S=await Oa(e,y,!0,r);p({path:g,data:S})}))}else l.externalData!==void 0&&(c=l.externalData.map(async g=>{if(typeof g.data=="string"){const y=await Oa(e,g.data,!0,r);return{...g,data:y}}return g}));if(c.length>0&&(l.externalData=await Promise.all(c)),n==="webgpu"){const g=sm(r.config,{prefix:"present"}),y={};for(const p in g)y[p]="gpu-buffer";l.preferredOutputLocation=y}return{buffer:await d,session_options:l}}async function jr(e,t,r){const n=Object.keys(t),a=await Promise.all(n.map(async i=>Ib(e,t[i],r))),s={};for(let i=0;i<n.length;++i){const{buffer:o,session_options:l}=a[i],d=await H_(o,l);s[n[i]]=d}return s}function Ab(e,t){const r=Object.create(null),n=[];for(const i of e.inputNames){const o=t[i];if(!(o instanceof Ce)){n.push(i);continue}r[i]=q_()?o.clone():o}if(n.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${n.join(", ")}.`);const a=Object.keys(t).length,s=e.inputNames.length;if(a>s){let i=Object.keys(t).filter(o=>!e.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${s}). The following inputs will be ignored: "${i.join(", ")}".`)}return r}async function kr(e,t){const r=Ab(e,t);try{const n=Object.fromEntries(Object.entries(r).map(([s,i])=>[s,i.ort_tensor]));let a=await e.run(n);return a=cm(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function cm(e){for(let t in e)Rf(e[t])?e[t]=new Ce(e[t]):typeof e[t]=="object"&&cm(e[t]);return e}function Ob(e){if(e instanceof Ce)return e;if(e.length===0)throw Error("items must be non-empty");if(Array.isArray(e[0])){if(e.some(t=>t.length!==e[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new Ce("int64",BigInt64Array.from(e.flat().map(t=>BigInt(t))),[e.length,e[0].length])}else return new Ce("int64",BigInt64Array.from(e.map(t=>BigInt(t))),[1,e.length])}function pm(e){return new Ce("bool",[e],[1])}async function hm(e,t){let{encoder_outputs:r,past_key_values:n}=t;if(!r){const l=Or(t,e.sessions.model.inputNames);r=(await Xn(e,l)).last_hidden_state}const{input_ids:a,decoder_input_ids:s,...i}=t;return i.input_ids=s,i.encoder_hidden_states=r,e.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(i.encoder_attention_mask=t.attention_mask),await Eo(e,i,!0)}async function Xn(e,t){const r=e.sessions.model,n=Object.create(null);for(const a of r.inputNames)n[a]=t[a];return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new Ce("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await kr(r,n)}async function Eo(e,t,r=!1){const n=e.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...s}=t;n.inputNames.includes("use_cache_branch")&&(s.use_cache_branch=pm(!!a)),n.inputNames.includes("position_ids")&&s.attention_mask&&!s.position_ids&&(s.position_ids=zb(s,a)),e.addPastKeyValues(s,a);const i=Or(s,n.inputNames);return await kr(n,i)}async function Mb(e,{input_ids:t=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:l=null,...d}){if(!s){if(s=await e.encode_text({input_ids:t}),n&&t.dims[1]!==1){const f=await e.encode_image({pixel_values:n});({inputs_embeds:s,inputs_embeds:s,attention_mask:r}=e._merge_input_ids_with_image_features({image_features:f,inputs_embeds:s,input_ids:t,attention_mask:r}))}else if(i&&n&&t.dims[1]===1){const f=t.dims[1],g=Object.values(i)[0].dims.at(-2);r=hr([ii([t.dims[0],g]),r.slice(null,[r.dims[1]-f,r.dims[1]])],1)}}return await Eo(e,{inputs_embeds:s,past_key_values:i,attention_mask:r,position_ids:a,generation_config:o,logits_processor:l},!0)}function zb(e,t=null){const{input_ids:r,inputs_embeds:n,attention_mask:a}=e,[s,i]=a.dims,o=new BigInt64Array(a.data.length);for(let d=0;d<s;++d){const c=d*i;let f=BigInt(0);for(let g=0;g<i;++g){const y=c+g;a.data[y]===0n?o[y]=BigInt(1):(o[y]=f,f+=a.data[y])}}let l=new Ce("int64",o,a.dims);if(t){const d=-(r??n).dims.at(1);l=l.slice(null,[d,null])}return l}function fm(e,t,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;i&&i.dims[1]>s.dims[1]?-(i.dims[1]-a):a<s.dims[1]?r.input_ids=s.slice(null,[a,null]):e.config.image_token_index!=null&&s.data.some(o=>o==e.config.image_token_index)&&(r.input_ids=s.slice(null,[-1,null]))}return r}function Rb(e,t,r,n){const{...a}=r;return r.past_key_values&&(t=t.map(i=>[i.at(-1)])),a.decoder_input_ids=Ob(t),a}class ee extends Tt{constructor(r,n){super();W(this,"main_input_name","input_ids");W(this,"forward_params",["input_ids","attention_mask"]);this.config=r,this.sessions=n;const a=Qn.get(this.constructor),s=_i.get(a);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,s===ve.DecoderOnly?(this.can_generate=!0,this._forward=Eo,this._prepare_inputs_for_generation=fm):s===ve.Seq2Seq||s===ve.Vision2Seq||s===ve.Musicgen?(this.can_generate=!0,this._forward=hm,this._prepare_inputs_for_generation=Rb):s===ve.EncoderDecoder?this._forward=hm:s===ve.ImageTextToText?(this.can_generate=!0,this._forward=Mb,this._prepare_inputs_for_generation=fm):this._forward=Xn,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:l=null,subfolder:d="onnx",device:c=null,dtype:f=null,use_external_data_format:g=null,session_options:y={}}={}){let p={progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,model_file_name:l,subfolder:d,device:c,dtype:f,use_external_data_format:g,session_options:y};const v=Qn.get(this),S=_i.get(v);p.config=await om.from_pretrained(r,p);let E;return S===ve.DecoderOnly?E=await Promise.all([jr(r,{model:p.model_file_name??"model"},p),Mr(r,"generation_config.json",!1,p)]):S===ve.Seq2Seq||S===ve.Vision2Seq?E=await Promise.all([jr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p),Mr(r,"generation_config.json",!1,p)]):S===ve.MaskGeneration?E=await Promise.all([jr(r,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},p)]):S===ve.EncoderDecoder?E=await Promise.all([jr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p)]):S===ve.ImageTextToText?E=await Promise.all([jr(r,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},p),Mr(r,"generation_config.json",!1,p)]):S===ve.Musicgen?E=await Promise.all([jr(r,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},p),Mr(r,"generation_config.json",!1,p)]):(S!==ve.EncoderOnly&&console.warn(`Model type for '${v??(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.`),E=await Promise.all([jr(r,{model:p.model_file_name??"model"},p)])),new this(p.config,...E)}async _call(r){return await this.forward(r)}async forward(r){return await this._forward(this,r)}_get_logits_warper(r){const n=new lm;return r.temperature!==null&&r.temperature!==1&&n.push(new bb(r.temperature)),r.top_k!==null&&r.top_k!==0&&n.push(new $b(r.top_k)),r.top_p!==null&&r.top_p<1&&n.push(new vb(r.top_p)),n}_get_logits_processor(r,n,a=null){const s=new lm;if(r.repetition_penalty!==null&&r.repetition_penalty!==1&&s.push(new mb(r.repetition_penalty)),r.no_repeat_ngram_size!==null&&r.no_repeat_ngram_size>0&&s.push(new fb(r.no_repeat_ngram_size)),r.bad_words_ids!==null&&s.push(new yb(r.bad_words_ids,r.eos_token_id)),r.min_length!==null&&r.eos_token_id!==null&&r.min_length>0&&s.push(new gb(r.min_length,r.eos_token_id)),r.min_new_tokens!==null&&r.eos_token_id!==null&&r.min_new_tokens>0&&s.push(new _b(n,r.min_new_tokens,r.eos_token_id)),r.forced_bos_token_id!==null&&s.push(new cb(r.forced_bos_token_id)),r.forced_eos_token_id!==null&&s.push(new pb(r.max_length,r.forced_eos_token_id)),r.begin_suppress_tokens!==null){let i=n>1||r.forced_bos_token_id===null?n:n+1;r.forced_decoder_ids!==null&&(i+=r.forced_decoder_ids[r.forced_decoder_ids.length-1][0]),s.push(new hb(r.begin_suppress_tokens,i))}return r.guidance_scale!==null&&r.guidance_scale>1&&s.push(new wb(r.guidance_scale)),a!==null&&s.extend(a),s}_prepare_generation_config(r,n){const a=new xb(this.config);return"generation_config"in this&&Object.assign(a,this.generation_config),r&&Object.assign(a,r),n&&Object.assign(a,Or(n,Object.getOwnPropertyNames(a))),a}_get_stopping_criteria(r,n=null){const a=new ko;return r.max_length!==null&&a.push(new Sb(r.max_length,this.config.max_position_embeddings??null)),r.eos_token_id!==null&&a.push(new kb(r.eos_token_id)),n&&a.extend(n),a}_validate_model_class(){if(!this.can_generate){const r=[Mo,mg,fg,hg],n=Qn.get(this.constructor),a=new Set,s=this.config.model_type;for(const o of r){const l=o.get(s);l&&a.add(l[0])}let i=`The current model class (${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 Ce("int64",r.flat(),[r.length,1]),s||(a.attention_mask=hr([a.attention_mask,ii([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=Or(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=Or(n,this.sessions.model.inputNames);let{last_hidden_state:o}=await Xn(this,i);return s.guidance_scale!==null&&s.guidance_scale>1&&(o=hr([o,Z_(o,0)],0),"attention_mask"in n&&(n.attention_mask=hr([n.attention_mask,ry(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 l;if(this.config.model_type==="musicgen")l=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}`);l=s}else l=new Array(r).fill(s);const c=new Ce("int64",l,[l.length,1]);return a.decoder_attention_mask=ey(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:l,model_inputs:d,model_input_name:c}=this._prepare_model_inputs({inputs:r,model_kwargs:o});const f=this.config.is_encoder_decoder;f&&("encoder_outputs"in d||(d=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:l,model_inputs:d,model_input_name:c,generation_config:n})));let g;f?{input_ids:g,model_inputs:d}=this._prepare_decoder_input_ids_for_generation({batch_size:d[c].dims.at(0),model_input_name:c,model_kwargs:d,decoder_start_token_id:n.decoder_start_token_id,bos_token_id:n.bos_token_id,generation_config:n}):g=d[c];let y=g.dims.at(-1);n.max_new_tokens!==null&&(n.max_length=y+n.max_new_tokens);const p=this._get_logits_processor(n,y,a),v=this._get_stopping_criteria(n,s),S=d[c].dims.at(0),E=gi.getSampler(n),k=new Array(S).fill(0),O=g.tolist();i&&i.put(O);let I=null;for(;;){d=this.prepare_inputs_for_generation(O,d,n);const F=await this.forward(d),B=F.logits.slice(null,-1,null),G=p(O,B),H=[];for(let pe=0;pe<G.dims.at(0);++pe){const oe=G[pe],ne=E(oe);for(const[de,D]of ne){const z=BigInt(de);k[pe]+=D,O[pe].push(z),H.push([z])}}if(i&&i.put(H),v(O).every(pe=>pe)){n.return_dict_in_generate&&(I=this.getPastKeyValues(F,d.past_key_values,!1));break}d=this._update_model_kwargs_for_generation({generated_input_ids:H,outputs:F,model_inputs:d,is_encoder_decoder:f})}i&&i.end();const T=new Ce("int64",O.flat(),[O.length,O[0].length]);return n.return_dict_in_generate?{sequences:T,past_key_values:I}:T}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 l=n[o];l.location==="gpu-buffer"&&l.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=sm(this.config,{encoder_add_pkv:this.add_encoder_pkv??!0});for(const o in i)r[o]=new Ce(a,s,i[o])}}}class Ft{}class Jn extends ee{}class Pb extends Jn{}class Bb extends Jn{async _call(t){return new ct(await super._call(t))}}class Db extends Jn{async _call(t){return new Pe(await super._call(t))}}class Nb extends Jn{async _call(t){return new dt(await super._call(t))}}class Fb extends Jn{async _call(t){return new _t(await super._call(t))}}class Lb extends ee{}class Ub extends Lb{}class Zn extends ee{}class Wb extends Zn{}class Vb extends Zn{async _call(t){return new ct(await super._call(t))}}class Gb extends Zn{async _call(t){return new Pe(await super._call(t))}}class Hb extends Zn{async _call(t){return new dt(await super._call(t))}}class qb extends Zn{async _call(t){return new _t(await super._call(t))}}class ea extends ee{}class jb extends ea{}class Kb extends ea{async _call(t){return new ct(await super._call(t))}}class Yb extends ea{async _call(t){return new Pe(await super._call(t))}}class Qb extends ea{async _call(t){return new dt(await super._call(t))}}class Xb extends ea{async _call(t){return new _t(await super._call(t))}}class ta extends ee{}class Jb extends ta{}class Zb extends ta{async _call(t){return new ct(await super._call(t))}}class ev extends ta{async _call(t){return new Pe(await super._call(t))}}class tv extends ta{async _call(t){return new dt(await super._call(t))}}class rv extends ta{async _call(t){return new _t(await super._call(t))}}class ra extends ee{}class nv extends ra{}class av extends ra{async _call(t){return new ct(await super._call(t))}}class iv extends ra{async _call(t){return new Pe(await super._call(t))}}class sv extends ra{async _call(t){return new dt(await super._call(t))}}class ov extends ra{async _call(t){return new _t(await super._call(t))}}class na extends ee{}class uv extends na{}class lv extends na{async _call(t){return new ct(await super._call(t))}}class dv extends na{async _call(t){return new Pe(await super._call(t))}}class cv extends na{async _call(t){return new dt(await super._call(t))}}class pv extends na{async _call(t){return new _t(await super._call(t))}}class aa extends ee{}class hv extends aa{}class fv extends aa{async _call(t){return new ct(await super._call(t))}}class mv extends aa{async _call(t){return new Pe(await super._call(t))}}class gv extends aa{async _call(t){return new dt(await super._call(t))}}class _v extends aa{async _call(t){return new _t(await super._call(t))}}class ia extends ee{}class yv extends ia{}class wv extends ia{async _call(t){return new Pe(await super._call(t))}}class bv extends ia{async _call(t){return new dt(await super._call(t))}}class vv extends ia{async _call(t){return new _t(await super._call(t))}}class $v extends ia{async _call(t){return new ct(await super._call(t))}}class yi extends ee{}class xv extends yi{}class Sv extends yi{async _call(t){return new ct(await super._call(t))}}class kv extends yi{async _call(t){return new Pe(await super._call(t))}}class Ev extends yi{async _call(t){return new dt(await super._call(t))}}class wi extends ee{}class Cv extends wi{}class Tv extends wi{async _call(t){return new ct(await super._call(t))}}class Iv extends wi{async _call(t){return new Pe(await super._call(t))}}class Av extends wi{async _call(t){return new _t(await super._call(t))}}class sa extends ee{}class Ov extends sa{}class Mv extends sa{async _call(t){return new ct(await super._call(t))}}class zv extends sa{async _call(t){return new Pe(await super._call(t))}}class Rv extends sa{async _call(t){return new dt(await super._call(t))}}class Pv extends sa{async _call(t){return new _t(await super._call(t))}}class bi extends ee{}class Bv extends bi{}class Dv extends bi{async _call(t){return new ct(await super._call(t))}}class Nv extends bi{async _call(t){return new Pe(await super._call(t))}}class Fv extends bi{async _call(t){return new _t(await super._call(t))}}class vi extends ee{}class Lv extends vi{}class Uv extends vi{async _call(t){return new Pe(await super._call(t))}}class Wv extends vi{async _call(t){return new _t(await super._call(t))}}class Vv extends vi{async _call(t){return new ct(await super._call(t))}}class mm extends ee{constructor(r,n,a){super(r,n);W(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class Gv extends mm{}class Hv extends mm{}class gm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class qv extends gm{}class jv extends gm{}class _m extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class Kv extends _m{}class Yv extends _m{}class Co extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class Qv extends Co{}class Xv extends Co{}class Jv extends Co{async _call(t){return new Pe(await super._call(t))}}class $i extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class Zv extends $i{}class e2 extends $i{}class t2 extends $i{async _call(t){return new Pe(await super._call(t))}}class r2 extends $i{}class ym extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class n2 extends ym{}class a2 extends ym{}class wm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class i2 extends wm{}class s2 extends wm{}class oa extends ee{}class o2 extends oa{}class u2 extends oa{async _call(t){return new ct(await super._call(t))}}class l2 extends oa{async _call(t){return new Pe(await super._call(t))}}class d2 extends oa{async _call(t){return new dt(await super._call(t))}}class c2 extends oa{async _call(t){return new _t(await super._call(t))}}class ua extends ee{}class p2 extends ua{}class h2 extends ua{async _call(t){return new ct(await super._call(t))}}class f2 extends ua{async _call(t){return new Pe(await super._call(t))}}class m2 extends ua{async _call(t){return new dt(await super._call(t))}}class g2 extends ua{async _call(t){return new _t(await super._call(t))}}class la extends ee{}class _2 extends la{}class y2 extends la{async _call(t){return new ct(await super._call(t))}}class w2 extends la{async _call(t){return new Pe(await super._call(t))}}class b2 extends la{async _call(t){return new dt(await super._call(t))}}class v2 extends la{async _call(t){return new _t(await super._call(t))}}class bm extends ee{}class $2 extends bm{}class x2 extends bm{}class vm extends ee{constructor(r,n,a){super(r,n);W(this,"requires_attention_mask",!1);W(this,"main_input_name","input_features");W(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class S2 extends vm{}class k2 extends vm{_retrieve_init_tokens(t){throw t.decoder_start_token_id,new Error("Not implemented yet")}async generate({inputs:t=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(t,r,n=null,a=.02){if(!t.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let 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=t.cross_attentions.map(d=>{let c=Array.from({length:this.config.decoder_layers},(S,E)=>hr(d.map(k=>k[E]),2)),f=go(r.map(([S,E])=>n?c[S].slice(null,E,null,[0,n]):c[S].slice(null,E)));f=f.transpose(1,0,2,3);let[g,y]=Y_(f,-2,0,!0),p=f.clone();for(let S=0;S<p.dims[0];++S){let E=p[S];for(let k=0;k<E.dims[0];++k){let O=E[k];const I=g[S][k][0],T=y[S][k][0];for(let F=0;F<O.dims[0];++F){let B=O[F];for(let G=0;G<B.data.length;++G)B.data[G]=(B.data[G]-T.data[G])/I.data[G];B.data.set(c0(B.data,s))}}}return _o(p,1)}),o=[t.sequences.length,t.sequences[0].length],l=new Ce("float32",new Float32Array(o[0]*o[1]),o);for(let d=0;d<o[0];++d){const c=i[d].neg().squeeze_(0);let[f,g]=Q_(c),y=Array.from({length:f.length-1},(S,E)=>f[E+1]-f[E]),p=it([1],y).map(S=>!!S),v=[];for(let S=0;S<p.length;++S)p[S]&&v.push(g[S]*a);l[d].data.set(v,1)}return l}}class E2 extends ee{constructor(r,n,a){super(r,n);W(this,"main_input_name","pixel_values");throw this.generation_config=a,new Error("Not implemented yet")}}class C2 extends ee{constructor(r,n,a){super(r,n);W(this,"forward_params",["input_ids","past_key_values","pixel_values","attention_mask","position_ids"]);this.generation_config=a}}class $m extends C2{async encode_image({pixel_values:t}){return(await kr(this.sessions.vision_encoder,{pixel_values:t})).image_features}async encode_text({input_ids:t}){return(await kr(this.sessions.embed_tokens,{input_ids:t})).inputs_embeds}_merge_input_ids_with_image_features({inputs_embeds:t,image_features:r,input_ids:n,attention_mask:a}){const s=this.config.image_token_index,o=n.tolist().map(g=>g.findIndex(y=>y==s)),l=o.every(g=>g===-1),d=o.every(g=>g!==-1);if(!l&&!d)throw new Error("Every input should contain either 0 or 1 image token.");if(l)return{inputs_embeds:t,attention_mask:a};const c=[],f=[];for(let g=0;g<o.length;++g){const y=o[g],p=t[g],v=r[g],S=a[g];c.push(hr([p.slice([0,y]),v,p.slice([y+1,p.dims[0]])],0)),f.push(hr([S.slice([0,y]),ii([v.dims[0]]),S.slice([y+1,S.dims[0]])],0))}return{inputs_embeds:go(c,0),attention_mask:go(f,0)}}}class T2 extends $m{}class xi extends ee{}class I2 extends xi{}class A2 extends xi{static async from_pretrained(t,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(t,r)}}class O2 extends xi{static async from_pretrained(t,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(t,r)}}class xm extends ee{}class M2 extends xm{}class z2 extends xm{static async from_pretrained(t,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(t,r)}}class R2 extends xi{static async from_pretrained(t,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(t,r)}}class P2 extends ee{}class B2 extends P2{}class Sm extends ee{}class D2 extends Sm{}class N2 extends Sm{}class km extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class F2 extends km{}class L2 extends km{}class Em extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class U2 extends Em{}class W2 extends Em{}class Cm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class V2 extends Cm{}class G2 extends Cm{}class Tm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class H2 extends Tm{}class q2 extends Tm{}class Im extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class j2 extends Im{}class K2 extends Im{}class Am extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class Y2 extends Am{}class Q2 extends Am{}class Om extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class X2 extends Om{}class J2 extends Om{}class Mm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class Z2 extends Mm{}class e$ extends Mm{}class zm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class t$ extends zm{}class r$ extends zm{}class Rm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class n$ extends Rm{}class a$ extends Rm{}class Pm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class i$ extends Pm{}class s$ extends Pm{}class Bm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class o$ extends Bm{}class u$ extends Bm{}class Dm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class l$ extends Dm{}class d$ extends Dm{}class Nm extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class c$ extends Nm{}class p$ extends Nm{}class Fm extends ee{}class h$ extends Fm{}class f$ extends Fm{async _call(t){return new Pe(await super._call(t))}}class m$ extends ee{}class g$ extends m${async _call(t){return new kx(await super._call(t))}}class Lm extends ee{}class _$ extends Lm{}class y$ extends Lm{async _call(t){return new Pe(await super._call(t))}}class Um extends ee{}class w$ extends Um{}class b$ extends Um{}class Wm extends ee{}class v$ extends Wm{}class $$ extends Wm{}class Vm extends ee{}class x$ extends Vm{}class S$ extends Vm{async _call(t){return new Pe(await super._call(t))}}class To extends ee{}class k$ extends To{}class E$ extends To{async _call(t){return new Gm(await super._call(t))}}class C$ extends To{async _call(t){return new T$(await super._call(t))}}class Gm extends Ft{constructor({logits:t,pred_boxes:r}){super(),this.logits=t,this.pred_boxes=r}}class T$ extends Ft{constructor({logits:t,pred_boxes:r,pred_masks:n}){super(),this.logits=t,this.pred_boxes=r,this.pred_masks=n}}class Hm extends ee{}class I$ extends Hm{}class A$ extends Hm{async _call(t){return new O$(await super._call(t))}}class O$ extends Gm{}class qm extends ee{}class M$ extends qm{}class z$ extends qm{async _call(t){return new Pe(await super._call(t))}}class jm extends ee{}class R$ extends jm{}class P$ extends jm{async _call(t){return new Pe(await super._call(t))}}class Km extends ee{}class B$ extends Km{}class D$ extends Km{async _call(t){return new Pe(await super._call(t))}}class Ym extends ee{}class N$ extends Ym{}class F$ extends Ym{}class Qm extends ee{}class L$ extends Qm{}class U$ extends Qm{}class W$ extends ee{}class V$ extends W${}class Xm extends ee{}class G$ extends Xm{}class H$ extends Xm{}class q$ extends ee{}class j$ extends q${}class Jm extends ee{}class K$ extends Jm{}class Y$ extends Jm{async _call(t){return new Pe(await super._call(t))}}class Zm extends ee{}class Q$ extends Zm{}class X$ extends Zm{async _call(t){return new Pe(await super._call(t))}}class eg extends ee{}class J$ extends eg{}class Z$ extends eg{async _call(t){return new Pe(await super._call(t))}}class tg extends ee{}class e1 extends tg{}class t1 extends tg{async _call(t){return new r1(await super._call(t))}}class r1 extends Ft{constructor({logits:t,pred_boxes:r}){super(),this.logits=t,this.pred_boxes=r}}class n1 extends ee{}class a1 extends n1{async get_image_embeddings({pixel_values:t}){return await Xn(this,{pixel_values:t})}async forward(t){if((!t.image_embeddings||!t.image_positional_embeddings)&&(t={...t,...await this.get_image_embeddings(t)}),!t.input_labels&&t.input_points){const n=t.input_points.dims.slice(0,-1),a=n.reduce((s,i)=>s*i,1);t.input_labels=new Ce("int64",new BigInt64Array(a).fill(1n),n)}const r={image_embeddings:t.image_embeddings,image_positional_embeddings:t.image_positional_embeddings};return t.input_points&&(r.input_points=t.input_points),t.input_labels&&(r.input_labels=t.input_labels),t.input_boxes&&(r.input_boxes=t.input_boxes),await kr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(t){return new i1(await super._call(t))}}class i1 extends Ft{constructor({iou_scores:t,pred_masks:r}){super(),this.iou_scores=t,this.pred_masks=r}}class rg extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class s1 extends rg{}class o1 extends rg{}class ng extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class u1 extends ng{}class l1 extends ng{}class Kr extends ee{}class d1 extends Kr{}class c1 extends Kr{async _call(t){return new cn(await super._call(t))}}class p1 extends Kr{async _call(t){return new Pe(await super._call(t))}}class h1 extends Kr{async _call(t){return new dt(await super._call(t))}}class Io extends ee{}class f1 extends Io{}class m1 extends Io{async _call(t){return new cn(await super._call(t))}}class g1 extends Io{async _call(t){return new Pe(await super._call(t))}}class Si extends ee{}class _1 extends Si{}class y1 extends Si{async _call(t){return new cn(await super._call(t))}}class w1 extends Si{async _call(t){return new Pe(await super._call(t))}}class b1 extends Si{async _call(t){return new dt(await super._call(t))}}class Ao extends ee{}class v1 extends Ao{}class $1 extends Ao{async _call(t){return new cn(await super._call(t))}}class x1 extends Ao{async _call(t){return new Pe(await super._call(t))}}class S1 extends Kr{}class k1 extends Kr{async _call(t){return new cn(await super._call(t))}}class E1 extends Kr{async _call(t){return new Pe(await super._call(t))}}class da extends ee{}class C1 extends da{}class T1 extends da{async _call(t){return new cn(await super._call(t))}}class I1 extends da{async _call(t){return new Pe(await super._call(t))}}class A1 extends da{async _call(t){return new Sx(await super._call(t))}}class O1 extends da{async _call(t){return new dt(await super._call(t))}}class ag extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class M1 extends ag{}class z1 extends ag{async generate_speech(t,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:s=20,vocoder:i=null}={}){const o={input_ids:t},{encoder_outputs:l,encoder_attention_mask:d}=await Xn(this,o),c=l.dims[1]/this.config.reduction_factor,f=Math.floor(c*s),g=Math.floor(c*a),y=this.config.num_mel_bins;let p=[],v=null,S=null,E=0;for(;;){++E;const I=pm(!!S);let T;S?T=S.output_sequence_out:T=new Ce("float32",new Float32Array(y),[1,1,y]);let F={use_cache_branch:I,output_sequence:T,encoder_attention_mask:d,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(F,v),S=await kr(this.sessions.decoder_model_merged,F),v=this.getPastKeyValues(S,v);const{prob:B,spectrum:G}=S;if(p.push(G),E>=g&&(Array.from(B.data).filter(H=>H>=n).length>0||E>=f))break}const k=hr(p),{waveform:O}=await kr(i.sessions.model,{spectrogram:k});return{spectrogram:k,waveform:O}}}class R1 extends ee{constructor(){super(...arguments);W(this,"main_input_name","spectrogram")}}class P1 extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class B1 extends P1{}class ig extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class D1 extends ig{}class N1 extends ig{}class sg extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class F1 extends sg{}class L1 extends sg{}class og extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class U1 extends og{}class W1 extends og{}class Oo extends ee{}class V1 extends Oo{}class G1 extends Oo{static async from_pretrained(t,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(t,r)}}class H1 extends Oo{static async from_pretrained(t,r={}){return r.model_file_name??(r.model_file_name="audio_model"),super.from_pretrained(t,r)}}class q1 extends ee{}class ug extends q1{async _call(t){return new Ex(await super._call(t))}}class lg extends ee{}class j1 extends lg{}class K1 extends lg{}class dg extends ee{constructor(t,r,n){super(t,r),this.generation_config=n}}class Y1 extends dg{}class Q1 extends dg{}class cg extends ee{}class X1 extends cg{}class J1 extends cg{async _call(t){return new Pe(await super._call(t))}}class pg extends ee{constructor(r,n,a){super(r,n);W(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 f=c%a,g=Math.floor(c/a)%s,y=f-g;y>0&&y<=i&&(r.data[o++]=r.data[c])}const l=Math.floor(n/s),d=o/(l*s);return new Ce(r.type,r.data.slice(0,o),[l,s,d])}prepare_inputs_for_generation(r,n,a){let s=structuredClone(r);for(let o=0;o<s.length;++o)for(let l=0;l<s[o].length;++l)o%this.config.decoder.num_codebooks>=l&&(s[o][l]=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 kr(this.sessions.encodec_decode,{audio_codes:a});return s}}class ki{static async from_pretrained(t,{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:l="onnx",device:d=null,dtype:c=null,use_external_data_format:f=null,session_options:g={}}={}){let y={progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,model_file_name:o,subfolder:l,device:d,dtype:c,use_external_data_format:f,session_options:g};if(y.config=await om.from_pretrained(t,y),!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 v=p.get(y.config.model_type);if(v)return await v[1].from_pretrained(t,y)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${y.config.model_type}", attempting to construct from base class.`),await ee.from_pretrained(t,y);throw Error(`Unsupported model type: ${y.config.model_type}`)}}W(ki,"MODEL_CLASS_MAPPINGS",null),W(ki,"BASE_IF_FAIL",!1);const Z1=new Map([["bert",["BertModel",Pb]],["nomic_bert",["NomicBertModel",Ub]],["roformer",["RoFormerModel",Wb]],["electra",["ElectraModel",Jb]],["esm",["EsmModel",xv]],["convbert",["ConvBertModel",jb]],["camembert",["CamembertModel",nv]],["deberta",["DebertaModel",uv]],["deberta-v2",["DebertaV2Model",hv]],["mpnet",["MPNetModel",Ov]],["albert",["AlbertModel",Lv]],["distilbert",["DistilBertModel",yv]],["roberta",["RobertaModel",o2]],["xlm",["XLMModel",p2]],["xlm-roberta",["XLMRobertaModel",_2]],["clap",["ClapModel",V1]],["clip",["CLIPModel",I2]],["clipseg",["CLIPSegModel",D2]],["chinese_clip",["ChineseCLIPModel",B2]],["siglip",["SiglipModel",M2]],["mobilebert",["MobileBertModel",Cv]],["squeezebert",["SqueezeBertModel",Bv]],["wav2vec2",["Wav2Vec2Model",d1]],["wav2vec2-bert",["Wav2Vec2BertModel",v1]],["unispeech",["UniSpeechModel",f1]],["unispeech-sat",["UniSpeechSatModel",_1]],["hubert",["HubertModel",S1]],["wavlm",["WavLMModel",C1]],["audio-spectrogram-transformer",["ASTModel",$2]],["vits",["VitsModel",ug]],["detr",["DetrModel",k$]],["table-transformer",["TableTransformerModel",I$]],["vit",["ViTModel",h$]],["mobilevit",["MobileViTModel",_$]],["owlvit",["OwlViTModel",w$]],["owlv2",["Owlv2Model",v$]],["beit",["BeitModel",x$]],["deit",["DeiTModel",M$]],["convnext",["ConvNextModel",K$]],["convnextv2",["ConvNextV2Model",Q$]],["dinov2",["Dinov2Model",J$]],["resnet",["ResNetModel",R$]],["swin",["SwinModel",B$]],["swin2sr",["Swin2SRModel",N$]],["donut-swin",["DonutSwinModel",j$]],["yolos",["YolosModel",e1]],["dpt",["DPTModel",L$]],["glpn",["GLPNModel",G$]],["hifigan",["SpeechT5HifiGan",R1]],["efficientnet",["EfficientNetModel",X1]]]),ex=new Map([["t5",["T5Model",Gv]],["longt5",["LongT5Model",qv]],["mt5",["MT5Model",Kv]],["bart",["BartModel",Qv]],["mbart",["MBartModel",Zv]],["marian",["MarianModel",s1]],["whisper",["WhisperModel",S2]],["m2m_100",["M2M100Model",u1]],["blenderbot",["BlenderbotModel",n2]],["blenderbot-small",["BlenderbotSmallModel",i2]]]),tx=new Map([["bloom",["BloomModel",o$]],["gpt2",["GPT2Model",F2]],["gptj",["GPTJModel",H2]],["gpt_bigcode",["GPTBigCodeModel",j2]],["gpt_neo",["GPTNeoModel",U2]],["gpt_neox",["GPTNeoXModel",V2]],["codegen",["CodeGenModel",Y2]],["llama",["LlamaModel",X2]],["openelm",["OpenELMModel",Z2]],["qwen2",["Qwen2Model",t$]],["phi",["PhiModel",n$]],["phi3",["Phi3Model",i$]],["mpt",["MptModel",l$]],["opt",["OPTModel",c$]],["mistral",["MistralModel",D1]],["starcoder2",["Starcoder2Model",F1]],["falcon",["FalconModel",U1]],["stablelm",["StableLmModel",Y1]]]),hg=new Map([["speecht5",["SpeechT5ForSpeechToText",M1]],["whisper",["WhisperForConditionalGeneration",k2]]]),rx=new Map([["speecht5",["SpeechT5ForTextToSpeech",z1]]]),nx=new Map([["vits",["VitsModel",ug]],["musicgen",["MusicgenForConditionalGeneration",pg]]]),ax=new Map([["bert",["BertForSequenceClassification",Db]],["roformer",["RoFormerForSequenceClassification",Gb]],["electra",["ElectraForSequenceClassification",ev]],["esm",["EsmForSequenceClassification",kv]],["convbert",["ConvBertForSequenceClassification",Yb]],["camembert",["CamembertForSequenceClassification",iv]],["deberta",["DebertaForSequenceClassification",dv]],["deberta-v2",["DebertaV2ForSequenceClassification",mv]],["mpnet",["MPNetForSequenceClassification",zv]],["albert",["AlbertForSequenceClassification",Uv]],["distilbert",["DistilBertForSequenceClassification",wv]],["roberta",["RobertaForSequenceClassification",l2]],["xlm",["XLMForSequenceClassification",f2]],["xlm-roberta",["XLMRobertaForSequenceClassification",w2]],["bart",["BartForSequenceClassification",Jv]],["mbart",["MBartForSequenceClassification",t2]],["mobilebert",["MobileBertForSequenceClassification",Iv]],["squeezebert",["SqueezeBertForSequenceClassification",Nv]]]),ix=new Map([["bert",["BertForTokenClassification",Nb]],["roformer",["RoFormerForTokenClassification",Hb]],["electra",["ElectraForTokenClassification",tv]],["esm",["EsmForTokenClassification",Ev]],["convbert",["ConvBertForTokenClassification",Qb]],["camembert",["CamembertForTokenClassification",sv]],["deberta",["DebertaForTokenClassification",cv]],["deberta-v2",["DebertaV2ForTokenClassification",gv]],["mpnet",["MPNetForTokenClassification",Rv]],["distilbert",["DistilBertForTokenClassification",bv]],["roberta",["RobertaForTokenClassification",d2]],["xlm",["XLMForTokenClassification",m2]],["xlm-roberta",["XLMRobertaForTokenClassification",b2]]]),fg=new Map([["t5",["T5ForConditionalGeneration",Hv]],["longt5",["LongT5ForConditionalGeneration",jv]],["mt5",["MT5ForConditionalGeneration",Yv]],["bart",["BartForConditionalGeneration",Xv]],["mbart",["MBartForConditionalGeneration",e2]],["marian",["MarianMTModel",o1]],["m2m_100",["M2M100ForConditionalGeneration",l1]],["blenderbot",["BlenderbotForConditionalGeneration",a2]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",s2]]]),Mo=new Map([["bloom",["BloomForCausalLM",u$]],["gpt2",["GPT2LMHeadModel",L2]],["gptj",["GPTJForCausalLM",q2]],["gpt_bigcode",["GPTBigCodeForCausalLM",K2]],["gpt_neo",["GPTNeoForCausalLM",W2]],["gpt_neox",["GPTNeoXForCausalLM",G2]],["codegen",["CodeGenForCausalLM",Q2]],["llama",["LlamaForCausalLM",J2]],["openelm",["OpenELMForCausalLM",e$]],["qwen2",["Qwen2ForCausalLM",r$]],["phi",["PhiForCausalLM",a$]],["phi3",["Phi3ForCausalLM",s$]],["mpt",["MptForCausalLM",d$]],["opt",["OPTForCausalLM",p$]],["mbart",["MBartForCausalLM",r2]],["mistral",["MistralForCausalLM",N1]],["starcoder2",["Starcoder2ForCausalLM",L1]],["falcon",["FalconForCausalLM",W1]],["trocr",["TrOCRForCausalLM",B1]],["stablelm",["StableLmForCausalLM",Q1]]]),sx=new Map([["bert",["BertForMaskedLM",Bb]],["roformer",["RoFormerForMaskedLM",Vb]],["electra",["ElectraForMaskedLM",Zb]],["esm",["EsmForMaskedLM",Sv]],["convbert",["ConvBertForMaskedLM",Kb]],["camembert",["CamembertForMaskedLM",av]],["deberta",["DebertaForMaskedLM",lv]],["deberta-v2",["DebertaV2ForMaskedLM",fv]],["mpnet",["MPNetForMaskedLM",Mv]],["albert",["AlbertForMaskedLM",Vv]],["distilbert",["DistilBertForMaskedLM",$v]],["roberta",["RobertaForMaskedLM",u2]],["xlm",["XLMWithLMHeadModel",h2]],["xlm-roberta",["XLMRobertaForMaskedLM",y2]],["mobilebert",["MobileBertForMaskedLM",Tv]],["squeezebert",["SqueezeBertForMaskedLM",Dv]]]),ox=new Map([["bert",["BertForQuestionAnswering",Fb]],["roformer",["RoFormerForQuestionAnswering",qb]],["electra",["ElectraForQuestionAnswering",rv]],["convbert",["ConvBertForQuestionAnswering",Xb]],["camembert",["CamembertForQuestionAnswering",ov]],["deberta",["DebertaForQuestionAnswering",pv]],["deberta-v2",["DebertaV2ForQuestionAnswering",_v]],["mpnet",["MPNetForQuestionAnswering",Pv]],["albert",["AlbertForQuestionAnswering",Wv]],["distilbert",["DistilBertForQuestionAnswering",vv]],["roberta",["RobertaForQuestionAnswering",c2]],["xlm",["XLMForQuestionAnswering",g2]],["xlm-roberta",["XLMRobertaForQuestionAnswering",v2]],["mobilebert",["MobileBertForQuestionAnswering",Av]],["squeezebert",["SqueezeBertForQuestionAnswering",Fv]]]),mg=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",E2]]]),ux=new Map([["llava",["LlavaForConditionalGeneration",$m]],["moondream1",["Moondream1ForConditionalGeneration",T2]]]),lx=new Map([["vit",["ViTForImageClassification",f$]],["mobilevit",["MobileViTForImageClassification",y$]],["beit",["BeitForImageClassification",S$]],["deit",["DeiTForImageClassification",z$]],["convnext",["ConvNextForImageClassification",Y$]],["convnextv2",["ConvNextV2ForImageClassification",X$]],["dinov2",["Dinov2ForImageClassification",Z$]],["resnet",["ResNetForImageClassification",P$]],["swin",["SwinForImageClassification",D$]],["segformer",["SegformerForImageClassification",j1]],["efficientnet",["EfficientNetForImageClassification",J1]]]),dx=new Map([["detr",["DetrForObjectDetection",E$]],["table-transformer",["TableTransformerForObjectDetection",A$]],["yolos",["YolosForObjectDetection",t1]]]),cx=new Map([["owlvit",["OwlViTForObjectDetection",b$]],["owlv2",["Owlv2ForObjectDetection",$$]]]),px=new Map([["detr",["DetrForSegmentation",C$]],["clipseg",["CLIPSegForImageSegmentation",N2]]]),hx=new Map([["segformer",["SegformerForSemanticSegmentation",K1]]]),fx=new Map([["sam",["SamModel",a1]]]),mx=new Map([["wav2vec2",["Wav2Vec2ForCTC",c1]],["wav2vec2-bert",["Wav2Vec2BertForCTC",$1]],["unispeech",["UniSpeechForCTC",m1]],["unispeech-sat",["UniSpeechSatForCTC",y1]],["wavlm",["WavLMForCTC",T1]],["hubert",["HubertForCTC",k1]]]),gx=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",p1]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",x1]],["unispeech",["UniSpeechForSequenceClassification",g1]],["unispeech-sat",["UniSpeechSatForSequenceClassification",w1]],["wavlm",["WavLMForSequenceClassification",I1]],["hubert",["HubertForSequenceClassification",E1]],["audio-spectrogram-transformer",["ASTForAudioClassification",x2]]]),_x=new Map([["wavlm",["WavLMForXVector",A1]]]),yx=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",b1]],["wavlm",["WavLMForAudioFrameClassification",O1]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",h1]]]),wx=new Map([["vitmatte",["VitMatteForImageMatting",g$]]]),bx=new Map([["swin2sr",["Swin2SRForImageSuperResolution",F$]]]),vx=new Map([["dpt",["DPTForDepthEstimation",U$]],["depth_anything",["DepthAnythingForDepthEstimation",V$]],["glpn",["GLPNForDepthEstimation",H$]]]),$x=new Map([["clip",["CLIPVisionModelWithProjection",O2]],["siglip",["SiglipVisionModel",R2]]]),gg=[[Z1,ve.EncoderOnly],[ex,ve.EncoderDecoder],[tx,ve.DecoderOnly],[ax,ve.EncoderOnly],[ix,ve.EncoderOnly],[fg,ve.Seq2Seq],[hg,ve.Seq2Seq],[Mo,ve.DecoderOnly],[sx,ve.EncoderOnly],[ox,ve.EncoderOnly],[mg,ve.Vision2Seq],[ux,ve.ImageTextToText],[lx,ve.EncoderOnly],[px,ve.EncoderOnly],[hx,ve.EncoderOnly],[wx,ve.EncoderOnly],[bx,ve.EncoderOnly],[vx,ve.EncoderOnly],[dx,ve.EncoderOnly],[cx,ve.EncoderOnly],[fx,ve.MaskGeneration],[mx,ve.EncoderOnly],[gx,ve.EncoderOnly],[rx,ve.Seq2Seq],[nx,ve.EncoderOnly],[_x,ve.EncoderOnly],[yx,ve.EncoderOnly],[$x,ve.EncoderOnly]];for(const[e,t]of gg)for(const[r,n]of e.values())_i.set(r,t),Qn.set(n,r),dm.set(r,n);const xx=[["MusicgenForConditionalGeneration",pg,ve.Musicgen],["CLIPTextModelWithProjection",A2,ve.EncoderOnly],["SiglipTextModel",z2,ve.EncoderOnly],["ClapTextModelWithProjection",G1,ve.EncoderOnly],["ClapAudioModelWithProjection",H1,ve.EncoderOnly]];for(const[e,t,r]of xx)_i.set(e,r),Qn.set(t,e),dm.set(e,t);class _g extends ki{}W(_g,"MODEL_CLASS_MAPPINGS",gg.map(t=>t[0])),W(_g,"BASE_IF_FAIL",!0);class yg extends ki{}W(yg,"MODEL_CLASS_MAPPINGS",[Mo]);class Pe extends Ft{constructor({logits:t}){super(),this.logits=t}}class Sx extends Ft{constructor({logits:t,embeddings:r}){super(),this.logits=t,this.embeddings=r}}class dt extends Ft{constructor({logits:t}){super(),this.logits=t}}class ct extends Ft{constructor({logits:t}){super(),this.logits=t}}class _t extends Ft{constructor({start_logits:t,end_logits:r}){super(),this.start_logits=t,this.end_logits=r}}class cn extends Ft{constructor({logits:t}){super(),this.logits=t}}class kx extends Ft{constructor({alphas:t}){super(),this.alphas=t}}class Ex extends Ft{constructor({waveform:t,spectrogram:r}){super(),this.waveform=t,this.spectrogram=r}}if(!(typeof self<"u")){if(!Ve)throw new Error("Unable to load image processing library.")}class Cx{put(t){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const wg=Ar.IS_PROCESS_AVAILABLE?e=>process.stdout.write(e):e=>console.log(e);class Tx extends Cx{constructor(t,{skip_prompt:r=!1,...n}={}){super(),this.tokenizer=t,this.skip_prompt=r,this.decode_kwargs=n,this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(t){if(t.length>1)throw Error("TextStreamer only supports batch size of 1");const r=t[0];if(this.skip_prompt&&this.next_tokens_are_prompt){this.next_tokens_are_prompt=!1;return}this.token_cache=it(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&&Yf(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 t;this.token_cache.length>0?(t=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):t="",this.next_tokens_are_prompt=!0,this.on_finalized_text(t,!0)}on_finalized_text(t,r){wg(t),r&&wg(`
`)}}class Ix extends Tx{constructor(t,r){super(t,{skip_prompt:!0,skip_special_tokens:!0}),this.cb=r}on_finalized_text(t){this.cb(t)}}class Ax extends mi{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(t,r){return new Array(t.length).fill(this.interrupted)}}const Ei=new Ax;class pn{static async getInstance(t=null){return this.model_id="Xenova/Phi-3-mini-4k-instruct-hf",this.tokenizer??(this.tokenizer=im.from_pretrained(this.model_id,{legacy:!0,progress_callback:t})),this.model??(this.model=yg.from_pretrained(this.model_id,{device:"webgpu",dtype:"q4",use_external_data_format:!0,progress_callback:t})),Promise.all([this.tokenizer,this.model])}}W(pn,"model_id",null),W(pn,"model",null),W(pn,"tokenizer",null),W(pn,"streamer",null);let zo=null;async function Ox(e){const[t,r]=await pn.getInstance(),n=t.apply_chat_template(e,{add_generation_prompt:!0,return_dict:!0});let a,s,i=0;const o=g=>{a??(a=performance.now());let y;i++>0?y=i/(performance.now()-a)*1e3:console.log("Time to first token:",a-s),self.postMessage({status:"update",output:g,tps:y,numTokens:i})},l=new Ix(t,o);self.postMessage({status:"start"}),s=performance.now();const{past_key_values:d,sequences:c}=await r.generate({...n,past_key_values:zo,max_new_tokens:512,streamer:l,stopping_criteria:Ei,return_dict_in_generate:!0});zo=d;const f=t.batch_decode(c,{skip_special_tokens:!1});self.postMessage({status:"complete",output:f})}async function Mx(){self.postMessage({status:"loading",data:"Loading model..."});const[e,t]=await pn.getInstance(n=>{self.postMessage(n)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const r=e("a");await t.generate({...r,max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async e=>{const{type:t,data:r}=e.data;switch(t){case"load":Mx();break;case"generate":Ei.reset(),Ox(r);break;case"interrupt":Ei.interrupt();break;case"reset":console.log("reset"),zo=null,Ei.reset();break}})})();