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    ${i}
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    let aIndices = perm(indices);

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

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

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

  ${rd(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];
    }

    ${nd(i,f)}
  }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c}),getShaderSource:p}},ad=(t,e)=>{let r=t.inputs,n=r[0].dims,a=K.normalizeAxis(e.axis,n.length);td(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);t.compute(Xa(i,a,s,r[0].dataType),{inputs:i})},id=t=>He({axis:t.axis})}),od,ud,ld,dd,Ja,cd,pd,hd=Z(()=>{$e(),xs(),Te(),sd(),od=(t,e)=>{let r=t[0],n=t[1],a=t[2],s=t[3],i=t[4],o=t[5];if(i&&o)throw new Error("Attention cannot have both past and relative_position_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let 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(e.qkvHiddenSizes.length>0){if(e.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let A of e.qkvHiddenSizes)if(A%e.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");f=e.qkvHiddenSizes[0],g=e.qkvHiddenSizes[1],y=e.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]!==e.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==g/e.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');e.pastPresentShareBuffer||(v=i.dims[3])}let x=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:x,maxSequenceLength:E,inputHiddenSize:c,hiddenSize:f,vHiddenSize:y,headSize:Math.floor(f/e.numHeads),vHeadSize:Math.floor(y/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:k,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},ud=(t,e,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:e.dataType,data:1/n},{type:12,data:i},{type:12,data:o}],d=lt(e.dataType,a),c=wt(1,a),f=g=>{let y=fe("x",e.dataType,e.dims,a),p=[{name:"d_inv",type:wt(e.dataType)},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${s}>;
  var<workgroup> thread_sum: array<f32, ${s}>;
  ${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})}},ld=(t,e,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"],x=E=>{let k=Y("q",e.dataType,e.dims,c),A=Y("key",r.dataType,r.dims,c),I=[k,A];n&&I.push(Y("relative_position_bias",n.dataType,n.dims));let T=fe("output",e.dataType,l),P=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 = ${P}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
        tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      workgroupBarrier();

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

      workgroupBarrier();
    }

    let headOffset = headIdx * uniforms.M * uniforms.N;
    if (global_id.y < uniforms.M && global_id.x < uniforms.N) {
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(c){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${c}`)}})()};
        output[outputIdx] = ${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:e.dataType,gpuDataType:0}],dispatchGroup:y,programUniforms:p}),getShaderSource:x}},dd=(t,e,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:e.dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:d}),getShaderSource:c=>{let f=Y("probs",e.dataType,e.dims),g=Y("v",r.dataType,r.dims),y=fe("output",e.dataType,i),p=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`
  const TILE_SIZE = ${o}u;
  var<workgroup> tileQ: array<${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;
   }
  }`}}},Ja=(t,e,r,n,a,s,i,o,l,d,c)=>{let f=t.outputCount>1,g=t.outputCount>2,y=f&&g?d.pastSequenceLength:0,p=y+d.kvSequenceLength,v=[d.batchSize,d.numHeads,p,d.headSize],x=i?[i,r]:[r],E=f?t.compute(Xa(x,2,v,r.dataType),{inputs:x,outputs:[1]})[0]:r,k=[d.batchSize,d.numHeads,p,d.headSize],A=o?[o,n]:[n],I=g?t.compute(Xa(A,2,k,n.dataType),{inputs:A,outputs:[2]})[0]:n,T=[e,E];l&&T.push(l);let P=t.compute(ld(t,e,E,l,d,c,y),{inputs:T,outputs:[-1]})[0];t.compute(ud(t,P,d.batchSize*d.numHeads*d.sequenceLength,p),{inputs:[P],outputs:[]});let B=[P,I];t.compute(dd(t,P,I,d,y),{inputs:B,outputs:[0]})},cd=(t,e)=>{let r=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],n=e.sequenceLength,a=e.inputHiddenSize,s=e.headSize,i=12,o={x:Math.ceil(e.headSize/i),y:Math.ceil(e.sequenceLength/i),z:e.batchSize*e.numHeads},l=[t.inputs[0],t.inputs[1],t.inputs[2]],d=[{type:12,data:n},{type:12,data:a},{type:12,data:s},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],c=f=>{let g=fe("output_q",l[0].dataType,r),y=fe("output_k",l[0].dataType,r),p=fe("output_v",l[0].dataType,r),v=Y("input",l[0].dataType,l[0].dims),x=Y("weight",l[1].dataType,l[1].dims),E=Y("bias",l[2].dataType,l[2].dims),k=v.type.storage,A=[{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(A).declareVariables(v,x,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 t.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:d}),getShaderSource:c},{inputs:l,outputs:[-1,-1,-1]})},pd=(t,e)=>{let r=od(t.inputs,e),[n,a,s]=cd(t,r);return Ja(t,n,a,s,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),fd,md,gd,_d,d_=Z(()=>{Vt(),$e(),Ae(),st(),Te(),fd=(t,e)=>{if(!t||t.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,a,s)=>{let i=a.length;if(i!==n.length)throw new Error(`${s}: num dimensions != ${i}`);a.forEach((o,l)=>{if(o!==n[l])throw new Error(`${s}: dim[${l}] do not match`)})};if(t[0].dims.length>1){let n=e.format==="NHWC"?e.spatial?t[0].dims.slice(-1):t[0].dims.slice(-1).concat(t[0].dims.slice(1,t[0].dims.length-1)):t[0].dims.slice(1,e.spatial?2:void 0);r(t[1].dims,n,"Invalid input scale"),r(t[2].dims,n,"Invalid input B"),r(t[3].dims,n,"Invalid input mean"),r(t[4].dims,n,"Invalid input var")}else r(t[1].dims,[1],"Invalid input scale"),r(t[2].dims,[1],"Invalid input B"),r(t[3].dims,[1],"Invalid input mean"),r(t[4].dims,[1],"Invalid input var")},md=(t,e)=>{let{epsilon:r,spatial:n,format:a}=e,s=t[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",t[0].dataType,t[0].dims,i),g=Y("scale",t[1].dataType,t[1].dims,o),y=Y("bias",t[2].dataType,t[2].dims,o),p=Y("inputMean",t[3].dataType,t[3].dims,o),v=Y("inputVar",t[4].dataType,t[4].dims,o),x=fe("y",t[0].dataType,c,i),E=()=>{let A="";if(n)A=`let cOffset = ${s.length===1?"0u":a==="NHWC"?`outputIndices[${s.length-1}] / ${i}`:"outputIndices[1]"};`;else if(a==="NCHW")A=`
            ${x.indicesSet("outputIndices","0","0")}
            let cOffset = ${x.indicesToOffset("outputIndices")};`;else{A=`var cIndices = ${g.type.indices}(0);
                       cIndices[0] = outputIndices[${s.length-1}];`;for(let I=1;I<g.rank;I++)A+=`cIndices[${I}] = outputIndices[${I}];`;A+=`let cOffset = ${g.indicesToOffset("cIndices")};`}return A},k=A=>`
  const epsilon = ${r};
  ${A.registerUniform("outputSize","u32").declareVariables(f,g,y,p,v,x)}
  ${A.mainStart()}
  ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${x.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;
    ${x.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${e.epsilon}_${e.format}_${n}_${i}`,inputDependencies:d?["rank","type","type","type","type"]:void 0},getShaderSource:k,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d?[{type:12,data:l},..._e(s)]:[{type:12,data:l}]})}},gd=t=>He(t),_d=(t,e)=>{let{inputs:r,outputCount:n}=t,a=gd({...e,outputCount:n});if(Ue.webgpu.validateInputContent&&fd(r,a),e.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");t.compute(md(r,a))}}),yd,wd,bd,c_=Z(()=>{Ae(),Te(),yd=t=>{if(t[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(t[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(t[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw new Error("last dimension of input and bias are not the same")},wd=t=>{let e=t[0].dims,r=t[0].dims[2],n=K.size(e)/4,a=t[0].dataType,s=Y("input",a,e,4),i=Y("bias",a,[r],4),o=Y("residual",a,e,4),l=fe("output",a,e,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:e,dataType:t[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")};
    ${l.setByOffset("global_idx","value")}
  }`}},bd=t=>{yd(t.inputs),t.compute(wd(t.inputs))}}),vd,De,$d,xd,Sd,kd,Ed,Cd,Td,Id,Ad,Od,Md,zd,Rd,Pd,Za,Bd,ei,Dd,Nd,Fd,Ld,Ud,Wd,Vd,Gd,Hd,qd,jd,Kd,Yd,Qd,Xd,Jd,Ps,Zd,Bs,Ds,ec,tc,rc,Ns=Z(()=>{$e(),Ae(),st(),Te(),vd=(t,e,r,n,a,s)=>{let i=Math.ceil(e/4),o="";typeof a=="string"?o=`${a}(a)`:o=a("a");let l=Y("inputData",r,[i],4),d=fe("outputData",n,[i],4);return`
      ${t.registerUniform("vec_size","u32").declareVariables(l,d)}

  ${s??""}

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

    let a = ${l.getByOffset("global_idx")};
    ${d.setByOffset("global_idx",o)}
  }`},De=(t,e,r,n,a,s=t.dataType)=>({name:e,shaderCache:{hint:a,inputDependencies:["type"]},getShaderSource:i=>vd(i,K.size(t.dims),t.dataType,s,r,n),getRunData:i=>({outputs:[{dims:t.dims,dataType:s}],dispatchGroup:{x:Math.ceil(K.size(i[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(K.size(t.dims)/4)}]})}),$d=t=>{t.compute(De(t.inputs[0],"Abs","abs"))},xd=t=>{t.compute(De(t.inputs[0],"Acos","acos"))},Sd=t=>{t.compute(De(t.inputs[0],"Acosh","acosh"))},kd=t=>{t.compute(De(t.inputs[0],"Asin","asin"))},Ed=t=>{t.compute(De(t.inputs[0],"Asinh","asinh"))},Cd=t=>{t.compute(De(t.inputs[0],"Atan","atan"))},Td=t=>{t.compute(De(t.inputs[0],"Atanh","atanh"))},Id=t=>He(t),Ad=(t,e)=>{let r;switch(e.to){case 10:r="vec4<f16>";break;case 1:r="vec4<f32>";break;case 12:r="vec4<u32>";break;case 6:r="vec4<i32>";break;case 9:r="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${e.to}`)}t.compute(De(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},Od=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:Cs,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:Ts;return He({min:e,max:r})},Md=(t,e)=>{let r=t.inputs.length===1?e:Od(t.inputs),n=wt(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Clip",a=>`clamp(${a}, clip_min_, clip_max_)`,`
    const clip_min_: vec4<${n}> = vec4(${n}(${r.min}));
    const clip_max_: vec4<${n}> = vec4(${n}(${r.max}));
`,r.cacheKey),{inputs:[0]})},zd=t=>{t.compute(De(t.inputs[0],"Ceil","ceil"))},Rd=t=>{t.compute(De(t.inputs[0],"Cos","cos"))},Pd=t=>{t.compute(De(t.inputs[0],"Cosh","cosh"))},Za=t=>He(t),Bd=(t,e)=>{let r=wt(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Elu",n=>`elu_vf32(${n})`,`
  const elu_alpha_ = ${r}(${e.alpha});

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

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

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

fn tanh_v(v: vec4<${t}>) -> vec4<${t}> {
  return ${Ps("v")};
}
`,Ds=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,ec=t=>{let e=wt(t.inputs[0].dataType);t.compute(De(t.inputs[0],"FastGelu",Ds,Bs(e),void 0,t.inputs[0].dataType))},tc=(t,e)=>{let r=wt(t.inputs[0].dataType);return t.compute(De(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},rc=t=>{t.compute(De(t.inputs[0],"Log","log"))}}),nc,ac,ic,p_=Z(()=>{Ae(),Te(),Ns(),nc=t=>{if(t[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(t[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(t[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw new Error("last dimension of input and bias are not the same")},ac=t=>{let e=t[0].dims.slice();e[2]=e[2]/2;let r=Y("input",t[0].dataType,t[0].dims,4),n=Y("bias",t[0].dataType,[t[0].dims[2]],4),a=fe("output",t[0].dataType,e,4),s=K.size(e)/4,i=lt(t[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:o=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${t[0].dims[2]/4/2}u;

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

  ${ei(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);

    ${a.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},ic=t=>{nc(t.inputs),t.compute(ac(t.inputs))}}),sc,oc,Kt,uc,lc,dc,cc,pc,hc,fc,mc,gc,_c,h_=Z(()=>{$e(),Ae(),Te(),sc=(t,e,r,n,a,s,i,o,l,d,c,f)=>{let g,y;typeof o=="string"?g=y=(k,A)=>`${o}((${k}),(${A}))`:typeof o=="function"?g=y=o:(g=o.scalar,y=o.vector);let p=fe("outputData",c,n.length,4),v=Y("aData",l,e.length,4),x=Y("bData",d,r.length,4),E;if(a)if(s){let k=K.size(e)===1,A=K.size(r)===1,I=e.length>0&&e[e.length-1]%4===0,T=r.length>0&&r[r.length-1]%4===0;k||A?E=p.setByOffset("global_idx",y(k?`${v.type.value}(${v.getByOffset("0")}.x)`:v.getByOffset("global_idx"),A?`${x.type.value}(${x.getByOffset("0")}.x)`:x.getByOffset("global_idx"))):E=`
            let outputIndices = ${p.offsetToIndices("global_idx * 4u")};
            let offsetA = ${v.broadcastedIndicesToOffset("outputIndices",p)};
            let offsetB = ${x.broadcastedIndicesToOffset("outputIndices",p)};
            ${p.setByOffset("global_idx",y(i||I?v.getByOffset("offsetA / 4u"):`${v.type.value}(${v.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||T?x.getByOffset("offsetB / 4u"):`${x.type.value}(${x.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else E=p.setByOffset("global_idx",y(v.getByOffset("global_idx"),x.getByOffset("global_idx")));else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let k=(A,I,T="")=>{let P=`aData[indexA${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} = ${x.broadcastedIndicesToOffset(`outputIndices${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;
            ${A}[${I}] = ${T}(${g(P,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`
        ${t.registerUniform("vec_size","u32").declareVariables(v,x,p)}

        ${f??""}

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

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

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

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

          ${bc(a,g)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},Vs=(t,e)=>t?`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              kStart + inputRow,
              globalRowStart + inputCol${e?", batchIndices":""});
            `:`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              globalRowStart + inputRow,
              kStart + inputCol${e?", batchIndices":""});
            `,vc=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",ri=(t,e,r="f32",n,a=!1,s=32,i=!1,o=32,l=!1)=>{let d=t[1]*e[1],c=t[0]*e[0],f=a?d:s,g=a?s:d;if(!(g%e[1]===0&&f%e[0]===0&&s%e[1]===0))throw new Error(`tileAHight ${g} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${f} must be divisible by workgroupSize[0]${e[0]}, tileInner ${s} must be divisible by workgroupSize[1]${e[1]}`);let y=g/e[1],p=f/e[0],v=s/e[1],x=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 + ${e[1]}) {
        for (var inputCol = localCol; inputCol < ${f}; inputCol = inputCol + ${e[0]}) {
          ${Vs(a,n)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${e[1]}) {
            for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {
          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
            kStart + inputRow,
            globalColStart + inputCol${n?", batchIndices":""});
        }
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

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

let globalRow = i32(globalId.y) * rowPerThread;
let globalCol = i32(globalId.x) * colPerThread;
let globalRowStart = i32(workgroupId.y) * ${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;
      ${Vs(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) {
      ${vc(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 = ${t[1]};
  const colPerThread = ${t[0]};
  const tileInner = ${s};

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

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

    // Without this initialization strange values show up in acc.
    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = 0.0;
      }
    }
    ${x}
  }
`},$c=(t,e,r,n,a,s=!1)=>{let[i,o,l]=a,[d,c,f,g]=n,y=Ln(i,l),p=Ln(o,l),v=lt(n[0].type.tensor),x=()=>{let k=c.rank,A=d.rank,I=`var aIndices: ${c.type.indices};`;for(let T=k-2-1,P=A-1;T>=0;T--,P--)I+=`
aIndices[${T}] = ${A>1?`batchIndices[${P}]`:"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,A=d.rank,I=`var bIndices: ${f.type.indices};`;for(let T=k-2-1,P=A-1;T>=0;T--,P--)I+=`
bIndices[${T}] = ${A>1?`batchIndices[${P}]`:"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(t,v)} {
      var value = ${mt(t,v)}(0.0);
      let col = colIn * ${t};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${x()}
        value = ${c.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${mt(t,v)} {
      var value = ${mt(t,v)}(0.0);
      let col = colIn * ${t};
      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(t,v)}) {
      let col = colIn * ${t};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${e?`value = value + ${s?"bias[colIn]":`${mt(t,v)}(bias[row])`};`:""}
        ${r}
        ${g.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},Gs=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s.slice(0,-2),l=i.slice(0,-2),d=n?n.slice(0,-2):r.slice(0,-2),c=K.size(d),f=s[s.length-2],g=s[s.length-1],y=i[i.length-1],p=g%4===0&&y%4===0,v=f<=8?[4,1,1]:[4,4,1],x=[8,8,1],E=[Math.ceil(y/x[0]/v[0]),Math.ceil(f/x[1]/v[1]),Math.ceil(c/x[2]/v[2])],k=p?4:1,A=[...o,f,g/k],I=A.length,T=[...l,g,y/k],P=T.length,B=[c,f,y/k],G=[{type:6,data:f},{type:6,data:y},{type:6,data:g}];Lr(e,G),G.push(..._e(d,A,T));let q=["rank","rank"],J=t.length>2;J&&(G.push(..._e(t[2].dims)),q.push("rank")),G.push(..._e(B));let me=re=>{let ne=d.length,de=Is("batchDims",t[0].dataType,ne,1),N=lt(t[0].dataType),M=Y("a",t[0].dataType,I,k),H=Y("b",t[1].dataType,P,k),te=fe("result",t[0].dataType,B.length,k),ye=[M,H];if(J){let Oe=a?k:1;ye.push(Y("bias",t[2].dataType,t[2].dims.length,Oe))}let pe=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ur(e,pe);let R=lt(te.type.tensor),se=Fr(e,te.type.value,R),le=$c(k,J,se,[de,M,H,te],[o,l,d],a);return`
  ${re.registerUniforms(pe).registerInternalVariables(de).declareVariables(...ye,te)}
  ${le}
  ${p?ti(v,x,N,de):ri(v,x,N,de)}
                   `};return{name:"MatMul",shaderCache:{hint:`${v};${e.activation};${p};${a}`,inputDependencies:q},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:G}),getShaderSource:me}}}),xc,Sc,f_=Z(()=>{$e(),Nr(),Te(),Wr(),Us(),yc(),ni(),xc=(t,e,r,n,a=!1,s,i=4,o=4,l=4,d="f32")=>{let c=q=>{switch(q){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 ${q} is not supported.`)}},f=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.`)}},g=t?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,y=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,p=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",v=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",x=t?"row":"col",E=t?"col":"row",k=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${x} / outWidth;
    let outCol = ${x} % 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;`,A=t?e&&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),P=mt(t?i:o,d),B=mt(t?o:i,d),G=Fr(s,T,d);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${P} {
      ${t?A:I}
    }

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

    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 = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${y}
      ${Ls(a)}
      ${G}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Sc=(t,e,r,n,a,s,i,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[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,x=l?f*g:y,E=[8,8,1],k=n<=8?[4,1,1]:[4,4,1],A=[Math.ceil(v/E[0]/k[0]),Math.ceil(x/E[1]/k[1]),Math.ceil(c/E[2]/k[2])];Xe("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${A}`);let I=p?l&&d%4!==0?3:4:1,T=E[1]*k[1],P=E[0]*k[0],B=Math.max(E[0]*I,E[1]),G=n%T===0,q=a%P===0,J=s%B===0,me=p?[I,4,4]:[1,1,1],re=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];Lr(e,re),re.push(..._e(t[0].dims,t[1].dims));let ne=["rank","rank"];i&&(re.push(..._e(t[2].dims)),ne.push("rank")),re.push(..._e(r));let de=N=>{let M=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Ur(e,M);let H=p?4:1,te=lt(t[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);
      }`,pe=Y("x",t[0].dataType,t[0].dims.length,I===3?1:I),R=Y("w",t[1].dataType,t[1].dims.length,H),se=[pe,R],le=fe("result",t[0].dataType,r.length,H);if(i){let Oe=Y("bias",t[2].dataType,t[2].dims.length,H);se.push(Oe),ye+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${p?`vec4<${te}>`:te} {
          return bias[coords.${l?"w":"y"}${p?"/ 4":""}];
        }`}return`
        ${Ws("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 };
        ${N.registerUniforms(M).declareVariables(...se,le)}
        ${ye}
        ${xc(l,G,q,J,i,e,me[0],me[1],me[2],te)}
        ${p?ti(k,E,te,void 0,!l,B):ri(k,E,te,void 0,!l,B,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${I};${p};${G};${q};${J};${T};${P};${B}`,inputDependencies:ne},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:re}),getShaderSource:de}}}),Hs,kc,m_=Z(()=>{$e(),Ae(),Te(),Mc(),Wr(),Hs=(t,e,r)=>{let n=t.length>2,a=n?"value += b[output_channel];":"",s=t[0].dims,i=t[1].dims,o=i[0]/e.group,l=e.format==="NHWC",d=ai(s,i,e.dilations,e.pads,e.strides,l),c=K.size(d),f=[{type:12,data:c},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];Lr(e,f),f.push(..._e(s,i));let g=["rank","rank"];n&&(f.push(..._e(t[2].dims)),g.push("rank")),f.push(..._e(d));let y=p=>{let v=fe("output",t[0].dataType,d.length),x=lt(v.type.tensor),E=Fr(e,v.type.value,x),k=Y("x",t[0].dataType,s.length),A=Y("w",t[1].dataType,i.length),I=[k,A];n&&I.push(Y("b",t[2].dataType,t[2].dims.length));let T=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:e.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Ur(e,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 = ${A.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${a}
    ${E}
    ${v.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:g},getRunData:()=>({outputs:[{dims:r?r(d):d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:f}),getShaderSource:y}},kc=(t,e,r)=>{let n=t.length>2,a=et(r[3]),s=et(r[2]),i=K.size(r)/a/s,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],l=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],d=[r[0],r[1],r[2],r[3]/a],c=[{type:12,data:i},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];Lr(e,c),c.push(..._e(o,l,d));let f=(s-1)*e.strides[1]+l[1],g=y=>{let p=fe("output",t[0].dataType,d.length,a),v=lt(p.type.tensor),x=Fr(e,p.type.value,v),E=Y("x",t[0].dataType,o.length,a),k=Y("w",t[1].dataType,l.length,a),A=[E,k];n&&A.push(Y("b",t[2].dataType,t[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(e,T),`
  ${y.registerUniforms(T).declareVariables(...A,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}
      ${x}
      ${p.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${s};${f};${l[0]};${l[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:c}),getShaderSource:g}}}),qs,Ec,Cc,Tc=Z(()=>{$e(),Ae(),ni(),Te(),Wr(),qs=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[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=t.length>2,v=n?n.slice(0,-2):r.slice(0,-2),x=[K.size(v),o,l],E=[{type:12,data:y},{type:12,data:o},{type:12,data:l},{type:12,data:d}];Lr(e,E),E.push(..._e(v,s,i)),p&&E.push(..._e(t[2].dims)),E.push(..._e(x));let k=A=>{let I=Is("batch_dims",t[0].dataType,v.length),T=Y("a",t[0].dataType,s.length,f),P=Y("b",t[1].dataType,i.length,c),B=fe("output",t[0].dataType,x.length,c),G=lt(B.type.tensor),q=Fr(e,B.type.value,G),J=[T,P],me="";if(p){let ye=a?c:1;J.push(Y("bias",t[2].dataType,t[2].dims.length,ye)),me=`${a?`value += bias[col / ${ye}];`:`value += ${B.type.value}(bias[row + i]);`}`}let re=s.slice(0,-2),ne=i.slice(0,-2),de=Ln(re,v),N=Ln(ne,v),M=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Ur(e,M);let H=(ye,pe)=>{let R=ye.rank,se=ye.name;if(R===2)return`var ${se}_indices = ${ye.type.indices}(0u, 0u);`;let le=I.rank,Oe=`var ${se}_indices: ${ye.type.indices};`;for(let ze=R-2-1,rt=le-1;ze>=0;ze--,rt--)Oe+=`
${se}_indices[${ze}] = ${le>1?`batch_indices[${rt}]`:"batch_indices"};`;return pe.forEach(ze=>{Oe+=`
${se}_indices[${ze}] = 0;`}),Oe+=`${se}_indices[${R-2}] = 0u;
                     ${se}_indices[${R-1}] = 0u;`,Oe},te=()=>{let ye=`var a_data: ${T.type.value};`;for(let pe=0;pe<f;pe++)ye+=`
              let b_data${pe} = b[(b_offset + (k + ${pe}) * uniforms.N + col) / ${c}];`;for(let pe=0;pe<g;pe++){ye+=`a_data = a[(a_offset + (row + ${pe}) * uniforms.K + k) / ${f}];`;for(let R=0;R<f;R++)ye+=`
            values[${pe}] = fma(${P.type.value}(a_data${f===1?"":`[${R}]`}), b_data${R}, values[${pe}]);
`}return ye};return`
  ${A.registerUniforms(M).registerInternalVariables(I).declareVariables(...J,B)}
  ${A.mainStart()}
    ${A.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")};`}
    ${H(T,de)}
    let a_offset = ${T.indicesToOffset("a_indices")};
    ${H(P,N)}
    let b_offset = ${P.indicesToOffset("b_indices")};
    var values: array<${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];
      ${me}
      ${q}
      let cur_indices = ${B.type.indices}(batch, row + i, col);
      let offset = ${B.indicesToOffset("cur_indices")};
      ${B.setByOffset(`offset / ${c}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${c};${f};${g};${a}`,inputDependencies:p?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:E}),getShaderSource:k}},Ec=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.")},Cc=t=>{Ec(t.inputs);let e=an.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!e)throw new Error("Can't use matmul on the given tensors");let r=e[e.length-1],n=t.inputs[0].dims[t.inputs[0].dims.length-1];r<8&&n<8?t.compute(qs(t.inputs,{activation:""},e)):t.compute(Gs(t.inputs,{activation:""},e))}}),ai,ii,Ic,js,Ks,Ac,Oc,Ys,Mc=Z(()=>{Ae(),f_(),ni(),m_(),Wr(),Tc(),Un(),ai=(t,e,r,n,a,s)=>{let i=t[0],o=t.slice(s?1:2,s?3:4),l=o.length,d=e[0],c=e.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},ii=[2,3,1,0],Ic=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[1]*e.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let a=t[0].dims.length-2;if(e.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(e.strides.length!==a)throw new Error(`strides should be ${a}D`);if(e.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},js=(t,e)=>{let r=t.kernelShape.slice();for(let s=2;s<e[1].dims.length;++s)r[s-2]===0&&(r[s-2]=e[1].dims[s]);let n=t.pads.slice();ja.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,r,n,t.format==="NHWC",t.autoPad);let a=Object.assign({},t);return Object.assign(a,{kernelShape:r,pads:n}),a},Ks=t=>{let e=Fs(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,s=t.group,i=t.kernel_shape,o=t.pads,l=t.strides,d=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:s,kernelShape:i,pads:o,strides:l,wIsConst:d,...e,cacheKey:`${t.format};${e.activation};`}},Ac=(t,e,r)=>{let n=js(r,e),a=r.format==="NHWC";if(r.group!==1){if(!t.adapterInfo.isArchitecture("ampere")&&a&&e[1].dims[0]===r.group&&e[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let P=ai(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),B=t.kernelCustomData.wT??t.compute(gr(e[1],ii),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=B);let G=[e[0],B];e.length===3&&G.push(e[2]),t.compute(kc(G,n,P),{inputs:G})}else t.compute(Hs(e,n));return}let s=e.length===3,i=e[0].dims[a?1:2],o=e[0].dims[a?2:3],l=e[0].dims[a?3:1],d=e[1].dims[2],c=e[1].dims[3],f=ai(e[0].dims,e[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 P=f[0],B,G,q,J=[];if(a){let ne=t.kernelCustomData.wT??t.compute(gr(e[1],ii),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=ne),v){let de=i*o*l;B=e[0].reshape([1,P,de]),G=ne.reshape([1,de,p]),q=[1,P,p]}else B=e[0].reshape([P,i*o,l]),G=ne.reshape([1,l,p]),q=[P,g*y,p];J.push(B),J.push(G)}else B=e[0].reshape([P,l,i*o]),G=e[1].reshape([1,p,l]),q=[P,p,g*y],J.push(G),J.push(B);s&&J.push(e[2]);let me=q[2],re=J[0].dims[J[0].dims.length-1];me<8&&re<8?t.compute(qs(J,n,f,q,a),{inputs:J}):t.compute(Gs(J,n,f,q,a),{inputs:J});return}let x=!0,E=t.kernelCustomData.wT??t.compute(gr(e[1],ii),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=E);let k=[e[0],E];s&&k.push(e[2]);let A=a?g*y:p,I=a?p:g*y,T=d*c*l;t.compute(Sc(k,n,f,A,I,T,s,x),{inputs:k})},Oc=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&n.push(t.inputs[2]);let a=[0,e.pads[0],0,e.pads[1]],s=[1].concat(e.strides),i=[1].concat(e.dilations),o=[1].concat(e.kernelShape),l=js({...e,pads:a,strides:s,dilations:i,kernelShape:o},n);t.compute(Hs(n,l,d=>r?[d[0],d[2],d[3]]:[]))},Ys=(t,e)=>{Ic(t.inputs,e),t.inputs[0].dims.length===3?Oc(t,e):Ac(t,t.inputs,e)}}),zc,Rc,g_=Z(()=>{$e(),Nr(),Te(),Wr(),Us(),yc(),ni(),zc=(t,e=!1,r,n,a=4)=>{let s=x=>{switch(x){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 ${x} is not supported.`)}},i=t?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,o=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,l=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",d=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",c=t?"row":"col",f=t?"col":"row",g=`
      let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${c} / outWidth;
      let outCol = ${c} % outWidth;

      let WRow = ${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=t?`
      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 = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);
      let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];
      if (${t?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"}  && coordX >= 0 && coordY >= 0) {
        let rowInner = row % inChannels;
        let coord = vec4<i32>(coordX, coordY, col, rowInner);
        ${s(a)}
      }
      return ${n}(0.0);
      `,v=Fr(r,n);return`
  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} {
    ${t?y:p}
  }

  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} {
    ${t?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 = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${o}
      ${Ls(e)}
      ${v}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${a}] = value;
    }
  }`},Rc=(t,e,r,n,a,s,i,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[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,x=l?f*g:y,E=[8,8,1],k=n<=8?[4,1,1]:[4,4,1],A=[Math.ceil(v/E[0]/k[0]),Math.ceil(x/E[1]/k[1]),Math.ceil(c/E[2]/k[2])];Xe("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${A}`);let I=p?4:1,T=Math.max(E[0]*I,E[1]),P=p?4:1,B=[e.kernelShape[l?1:2],e.kernelShape[l?2:3]],G=[B[0]+(e.dilations[0]<=1?0:(B[0]-1)*(e.dilations[0]-1)),B[1]+(e.dilations[1]<=1?0:(B[1]-1)*(e.dilations[1]-1))],q=[G[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),G[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],J=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:e.strides},{type:6,data:e.dilations},{type:6,data:B},{type:6,data:q}];Lr(e,J),J.push(..._e(t[0].dims,t[1].dims));let me=["rank","rank"];i&&(J.push(..._e(t[2].dims)),me.push("rank")),J.push(..._e(r));let re=ne=>{let de=Y("x",t[0].dataType,t[0].dims.length,P),N=Y("w",t[1].dataType,t[1].dims.length,1),M=fe("result",t[0].dataType,r.length,P),H=[de,N],te="";if(i){let R=Y("bias",t[2].dataType,t[2].dims.length,P);H.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:q.length}];Ur(e,ye);let pe=lt(t[0].dataType,1);if(pe!=="f16"&&pe!=="f32")throw new Error(`elemType ${pe} is not supported.`);return`
        ${Ws("uniforms.result_strides")}
        ${ne.registerUniforms(ye).declareVariables(...H,M)};
        ${te}
        ${zc(l,i,e,de.type.value,I)}
        ${p?ti(k,E,pe,void 0,!l,T):ri(k,E,pe,void 0,!l,T,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${k};${E};${p}`,inputDependencies:me},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:J}),getShaderSource:re}}}),Pc,Qs,__=Z(()=>{$e(),Nr(),Ae(),Te(),Pc=(t,e,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",e[1].dataType,e[1].dims.length,p),x=Y("Dy",e[0].dataType,e[0].dims.length,p),E=[x,v];n&&E.push(Y("bias",e[2].dataType,[r[f]].length,p));let k=fe("result",e[0].dataType,r.length,p),A=`{
        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 = ${x.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${i}>(dot(xValue, wValue0),
                                      dot(xValue, wValue1),
                                      dot(xValue, wValue2),
                                      dot(xValue, wValue3));
                dotProd[0] = dotProd[0] + tmpval;

                xValue =  ${x.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 = ${x.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${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 = ${x.get("batch","idyR","idyC2","d2")};
                let tmpval = vec4<${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?x.get("batch","idyR","idyC","inputChannel"):x.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`
  ${t.registerUniforms(o).declareVariables(...E,k)}
  ${y}

    ${t.mainStart()}
    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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      let x_in = x + bias;
      ${d.setByOffset("global_idx",Ds("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${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)}})}},cp=t=>{t.inputs.length<2||K.size(t.inputs[1].dims)===0?ec(t):t.compute(dp(t.inputs))}}),pp,hp,fp,mp,S_=Z(()=>{$e(),Ae(),st(),Te(),pp=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},hp=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,s=K.normalizeAxis(e.axis,a),i=r.slice(0);i.splice(s,1,...n);let o=r[s],l=t[0].dataType===9?4:1,d=Math.ceil(K.size(i)/l),c=[{type:12,data:d},{type:6,data:o},{type:12,data:s},..._e(t[0].dims,t[1].dims,i)],f=g=>{let y=Y("data",t[0].dataType,t[0].dims.length,l),p=Y("inputIndices",t[1].dataType,t[1].dims.length),v=fe("output",t[0].dataType,i.length,l),x=k=>{let A=n.length,I=`var indicesIndices${k}  = ${p.type.indices}(0);`;for(let T=0;T<A;T++)I+=`${A>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} : ${y.type.indices};
        `;for(let T=0,P=0;T<a;T++)T===s?(I+=`${a>1?`dataIndices${k}[${T}]`:`dataIndices${k}`} = u32(idx${k});`,P+=A):(I+=`${a>1?`dataIndices${k}[${T}]`:`dataIndices${k}`} = ${i.length>1?`outputIndices${k}[${P}]`:`outputIndices${k}`};`,P++);return I},E;if(t[0].dataType===9){let k=(A,I,T="")=>`
          let outputIndices${I} = ${v.offsetToIndices(`outputOffset + ${I}u`)};
          ${x(I)};
          let offset${I} = ${y.indicesToOffset(`dataIndices${I}`)};
          let index${I} = offset${I} / 4u;
          let component${I} = offset${I} % 4u;
          ${A}[${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")};
      ${x("")};
      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")}
        ${E}
      }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:f}},fp=t=>He({axis:t.axis}),mp=(t,e)=>{let r=t.inputs;pp(r),t.compute(hp(t.inputs,e))}}),gp,_p,yp,wp,k_=Z(()=>{$e(),Ae(),st(),Te(),gp=t=>{if(!t||t.length!==2)throw new Error("GatherElements requires 2 inputs.");if(t[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(t[0].dims.length!==t[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},_p=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,s=t[1].dims,i=t[1].dataType,o=K.normalizeAxis(e.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=fe("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:t[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")};

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

  ${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) * ${k.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:f}},$p=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},xp=(t,e)=>{bp(t.inputs),t.compute(vp(t.inputs,e))}}),Sp,kp,Ep,Cp,C_=Z(()=>{$e(),Ae(),Te(),Sp=(t,e)=>{let r=t[0].dims,n=r,a=2,s=K.sizeToDimension(r,a),i=K.sizeFromDimension(r,a),o=et(i),l=i/o,d=[r[0],r[1],l],c=["rank","type","type"],f=[{type:12,data:i},{type:12,data:l}];f.push(..._e(d,d));let g=y=>{let p=Y("x",t[0].dataType,d.length,o),v=Y("scale",t[1].dataType,t[1].dims),x=Y("bias",t[2].dataType,t[2].dims),E=fe("output",t[0].dataType,d.length,o),k=[p,v,x,E],A=p.type.value,I=o===1?"f32":`vec${o}<f32>`,T=64,P=[{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(P).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 = ${kr("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 = ${kr("workgroupShared[0]",o)};
    }
    workgroupBarrier();

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

  ${P.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 = ${mr("f32",l)};
    var squaredSum = ${mr("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] = ${g("sum","squaredSum")};
  }`},k=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:v},getRunData:()=>({outputs:[{dims:[a,i,d,2],dataType:1}],dispatchGroup:{x:a*i/l},programUniforms:x}),getShaderSource:E},{inputs:[e],outputs:[-1]})[0],A=[{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=P=>{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;

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

    let offset = currentImageNumber * uniforms.image_size;
    var sum = ${mr("f32",l)};
    var squaredSum = ${mr("f32",l)};
    for (var i: u32 = 0; i < min(${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] = ${g("channelScale","channelShift")};
  }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${o}`,inputDependencies:I},getRunData:()=>({outputs:[{dims:[a,i,2],dataType:1}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:A}),getShaderSource:T},{inputs:[k,r,n],outputs:[-1]})[0]},Ep=(t,e,r)=>{let n=e[0].dims,a=n,s=n[0],i=n[n.length-1],o=K.sizeFromDimension(n,1)/i,l=et(i),d=K.size(a)/l,c=[{type:12,data:o},{type:12,data:Math.floor(i/l)}],f=["type","type"],g=kp(t,e[0],e[1],e[2],s,o,i,r.epsilon),y=p=>{let v=lt(e[0].dataType),x=l===1?"vec2f":`mat2x${l}f`,E=l===1?v:`vec${l}<${v}>`,k=Y("input",e[0].dataType,e[0].dims,l),A=fe("output",e[0].dataType,a,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${k.type.storage}>;
  @group(0) @binding(1) var<storage, read> scaleInput : array<${x}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${A.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]), ${E}(scale[1]));
  }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:y},{inputs:[e[0],g]})},Cp=(t,e)=>{e.format==="NHWC"?Ep(t,t.inputs,e):t.compute(Sp(t.inputs,e))}}),Tp,Ip,Ap,T_=Z(()=>{$e(),Ae(),Te(),Tp=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Ip=(t,e,r)=>{let n=e.simplified,a=t[0].dims,s=t[1],i=!n&&t[2],o=a,l=K.normalizeAxis(e.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"],x=[{type:12,data:d},{type:1,data:c},{type:12,data:Math.floor(c/p)},{type:1,data:e.epsilon}];i&&v.push("type");let E=r>1,k=r>2,A=T=>{let P=lt(t[0].dataType),B=[Y("x",t[0].dataType,t[0].dims,p),Y("scale",s.dataType,s.dims,p)];i&&B.push(Y("bias",i.dataType,i.dims,p)),B.push(fe("output",t[0].dataType,o,p)),E&&B.push(fe("mean_data_output",1,y)),k&&B.push(fe("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 = ${mr("f32",p)};
    var mean_square_vector = ${mr("f32",p)};

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

    ${E?"mean_data_output[global_idx] = mean":""};
    ${k?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},I=[{dims:o,dataType:t[0].dataType}];return E&&I.push({dims:y,dataType:1}),k&&I.push({dims:y,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${p};${r};${n}`,inputDependencies:v},getRunData:()=>({outputs:I,dispatchGroup:{x:Math.ceil(d/64)},programUniforms:x}),getShaderSource:A}},Ap=(t,e)=>{Tp(t.inputs),t.compute(Ip(t.inputs,e,t.outputCount))}}),Op,Mp,zp,Rp,I_=Z(()=>{$e(),Ae(),st(),Te(),Op=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((e.k+e.blockSize-1)/e.blockSize),s=e.blockSize/8*e.bits,i=t[1];if(!K.areEqual(i.dims,[e.n,a,s]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(K.size(o)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let l=t[3].dims,d=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(K.size(l)!==d)throw new Error("zeroPoints input size error.")}},Mp=(t,e,r,n)=>{let a=t[0].dims,s=a.length,i=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=a[s-2],l=e.k,d=e.n,c=a.slice(0,s-2),f=K.size(c),g=e.blockSize/8*e.bits/4,y=t[0].dataType,p=et(o),v=et(e.k),x=et(g),E=Fn(y),k=o*i*E,A=Math.floor(n/k),I=i<=r[0]&&A>0,T=!I||A>=4?et(d):A>=2&&et(d)>=2?2:1,P=c.concat([o,d]),B=K.size(P)/T/p,G=I?[]:[{type:12,data:B},{type:12,data:e.blockSize}],q=[f,o,l/v],J=K.convertShape(t[1].dims).slice();J.splice(-1,1,g/x),G.push(..._e(q)),G.push(..._e(J)),G.push(..._e(t[2].dims)),t.length===4&&G.push(..._e(K.convertShape(t[3].dims)));let me=[f,o,d/T];G.push(..._e(me));let re=ne=>{let de=q.length,N=Y("a",t[0].dataType,de,v),M=Y("b",12,J.length,x),H=Y("scales",t[2].dataType,t[2].dims.length),te=[N,M,H],ye=t.length===4?Y("zero_points",12,t[3].dims.length):void 0;ye&&te.push(ye);let pe=me.length,R=fe("output",t[0].dataType,pe,T),se=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],le=lt(t[0].dataType),Oe=(()=>{switch(v){case 1:return`array<${le}, 8>`;case 2:return`mat4x2<${le}>`;case 4:return`mat2x4<${le}>`;default:throw new Error(`${v}-component is not supported.`)}})(),ze=`
        for (var word: u32 = 0; word < ${g}; word += ${x}) {
          ${M.indicesSet("b_indices","2","word")};
          let b_data = ${M.getByIndices("b_indices")};
          for (var i: u32 = 0; i < ${x}; i++) {
            let b_value: u32 = ${x===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 = ${Oe}(${Array.from({length:4},($t,nt)=>`${le}(b_value_lower[${nt}]), ${le}(b_value_upper[${nt}])`).join(", ")});
            let b_dequantized_values = ${v===1?`${Oe}(${Array.from({length:8},($t,nt)=>`(b_quantized_values[${nt}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Oe}(${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++) {
              ${N.indicesSet("a_indices",de-2,I?"m":`row * ${p} + m`)};
              ${N.indicesSet("a_indices",de-1,"word_offset")};
              var input_offset = ${N.indicesToOffset("a_indices")};
              var a_data: ${Oe};
              for (var j: u32 = 0; j < ${8/v}; j++) {
                a_data[j] = ${N.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: ${N.type.indices};
          var block = local_id.x;
          var col = workgroup_id.y;
          var batch = workgroup_id.z;
          ${N.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: ${M.type.indices};
            ${M.indicesSet("b_indices","0","col_times_components_plus_c")};
            // The scale and zero points are computed per block.
            var scales_index = col_times_components_plus_c * ${i} + block;
            let scale = ${H.getByOffset("scales_index")};
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${le}(${ye?"(zero_point_word) & 0xFu":8});
            ${M.indicesSet("b_indices","1","block")};
            var word_offset: u32 = block * ${e.blockSize/v};
            var workgroup_shared_offset: u32 = block * ${o};
            ${ze}
          }
          workgroupBarrier();
          if (local_id.x == 0u) {
            var output_indices: ${R.type.indices};
            ${R.indicesSet("output_indices","0","batch")};
            ${R.indicesSet("output_indices",pe-1,"col")};
            ${R.indicesSet("output_indices",pe-2,"0")};
            var output_offset = ${R.indicesToOffset("output_indices")};
            for (var m: u32 = 0u; m < ${o}u; m++) {
              var output_value: ${R.type.value} = ${R.type.value}(0);
              var workgroup_shared_offset: u32 = m;
              for (var b: u32 = 0u; b < ${i}u; b++) {
                output_value += workgroup_shared[workgroup_shared_offset];
                workgroup_shared_offset += ${o};
              }
              ${R.setByOffset("output_offset","output_value")};
              output_offset += ${d/T};
            }
          }
        }`:`
        ${ne.registerUniforms(se).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",pe-1)};
          var row = ${R.indicesGet("output_indices",pe-2)};
          var a_indices: ${N.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: ${M.type.indices};
          for (var c: u32 = 0; c < ${T}; c++) {
            ${M.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 = ${H.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});
              ${M.indicesSet("b_indices","1","block")};
              var word_offset: u32 = block_offset;
              ${ze}
              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++) {
              ${R.indicesSet("output_indices",pe-2,`${p} * row + k`)};
              ${R.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:I?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${y};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:P,dataType:y}],name:I?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:I?{x:1,y:Math.ceil(d/T),z:f}:{x:Math.ceil(B/64)},programUniforms:G}),getShaderSource:re}},zp=(t,e)=>{Op(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(Mp(t.inputs,e,r,n))},Rp=t=>He(t)}),gt,Pp,Bp,to,Dp,oi,Np,A_=Z(()=>{$e(),Ae(),st(),xs(),hd(),Te(),Un(),gt=(t,e)=>t.length>e&&t[e].dims.length>0&&K.size(t[e].dims)>0?t[e]:void 0,Pp=(t,e)=>{let r=t[0],n=gt(t,1),a=gt(t,2),s=gt(t,3),i=gt(t,4),o=gt(t,5),l=gt(t,6),d=gt(t,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let c=!1,f=r.dims[0],g=r.dims[1],y=r.dims.length===3?c?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],p=g,v=0,x=0,E=Math.floor(y/e.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]!==e.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]!==e.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],x=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]!==e.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]!==e.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]!==e.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');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 A=0;if(i){A=8;let G=i.dims;throw G.length===1?G[0]===f?A=1:G[0]===3*f+2&&(A=3):G.length===2&&G[0]===f&&G[1]===p&&(A=5),A===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 P=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]!==e.numHeads||o.dims[2]!==g||o.dims[3]!==P)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:P,maxSequenceLength:x,inputHiddenSize:0,hiddenSize:y,vHiddenSize:T,headSize:E,vHeadSize:Math.floor(T/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:A,scale:e.scale,broadcastResPosBias:B,passPastInKv:I,qkvFormat:k}},Bp=t=>He({...t}),to=He({perm:[0,2,1,3]}),Dp=(t,e,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=fe("qkv_with_bias",e.dataType,o),y=Y("qkv",e.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 t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:c},{inputs:[e,r],outputs:[-1]})[0]},oi=(t,e,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=Dp(t,s,i,e,n,r*a,o),l=l.reshape([e,n,r,a]),t.compute(gr(l,to.perm),{inputs:[l],outputs:[-1]})[0]}else return s.dims.length===3&&(l=s.reshape([e,n,r,a])),t.compute(gr(l,to.perm),{inputs:[l],outputs:[-1]})[0]},Np=(t,e)=>{let r=Pp(t.inputs,e),n=t.inputs[0],a=gt(t.inputs,1),s=gt(t.inputs,2),i=gt(t.inputs,3),o=gt(t.inputs,4),l=gt(t.inputs,5),d=gt(t.inputs,6),c=gt(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((a==null?void 0:a.dims.length)===5)throw new Error("Packed KV is not implemented");let f=a&&s&&a.dims.length===4&&s.dims.length===4,g=oi(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(f)return Ja(t,g,a,s,o,void 0,d,c,l,r,e);if(!a||!s)throw new Error("key and value must be provided");let y=oi(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,i,r.hiddenSize),p=oi(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,s,i,2*r.hiddenSize);Ja(t,g,y,p,o,void 0,d,c,l,r,e)}}),Fp,Lp,Up,Wp,Vp,Gp,Hp,qp,jp,O_=Z(()=>{$e(),Ae(),Te(),Fp=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1&&t[0].dataType!==10)throw new Error("Input type must be float or float16.");if(t.length>=2){let e=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(e=t[3].dims[0]*2===t[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Lp=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
            k = i32(${t.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${xe("uniforms.x_shape",a,e)})) {
              break;
            }
            offset += k * i32(${xe("uniforms.x_strides",a,e)});
        `;return`
          value = ${t.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${n}
            value = x[offset];
          }
      `},Up=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${xe("uniforms.x_shape",a,e)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${xe("uniforms.x_shape",a,e)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${xe("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Wp=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${xe("uniforms.x_shape",a,e)})) {
                  k = i32(${xe("uniforms.x_shape",a,e)}) - 1;
                }
                offset += k * i32(${xe("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Vp=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,r)};
                if (k < 0)  {
                  k += i32(${xe("uniforms.x_shape",a,e)}]);
                }
                if (k >= i32(${xe("uniforms.x_shape",a,e)})) {
                  k -= i32(${xe("uniforms.x_shape",a,e)});
                }
                offset += k * i32(${xe("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Gp=(t,e,r)=>{switch(r.mode){case 0:return Lp(t,e,r.pads.length);case 1:return Up(t,e,r.pads.length);case 2:return Wp(t,e,r.pads.length);case 3:return Vp(t,e,r.pads.length);default:throw new Error("Invalid mode")}},Hp=(t,e)=>{let r=K.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=K.size(r),s=[{type:12,data:a},{type:6,data:e.pads}];e.mode===0&&s.push({type:t[0].dataType,data:e.value}),s.push(..._e(t[0].dims,r));let i=["rank"],o=l=>{let d=fe("output",t[0].dataType,r.length),c=Y("x",t[0].dataType,n.length),f=c.type.value,g=Gp(d,n.length,e),y=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.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);
            ${g}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(K.size(r)/64)},programUniforms:s}),getShaderSource:o}},qp=(t,e)=>{if(t.length>1){let r=t[1].getBigInt64Array(),n=t.length>=3&&t[2].data?t[2].getFloat32Array()[0]:0,a=t[0].dims.length,s=new Int32Array(2*a).fill(0);if(t.length>=4){let o=t[3].getBigInt64Array();for(let 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:e.mode,value:n,pads:i}}else return e},jp=(t,e)=>{Fp(t.inputs);let r=qp(t.inputs,e);t.compute(Hp(t.inputs,r),{inputs:[0]})}}),Vn,ro,no,ao,io,Kp,Yp,so,oo,Qp,Xp,uo,Jp,Zp,lo,eh,th,rh,nh,M_=Z(()=>{Vt(),$e(),Ae(),Te(),Vn=t=>{if(Ue.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},ro=(t,e,r)=>{let n=e.format==="NHWC",a=t.dims.slice();n&&a.splice(1,0,a.pop());let s=Object.hasOwnProperty.call(e,"dilations"),i=e.kernelShape.slice(),o=e.strides.slice(),l=s?e.dilations.slice():[],d=e.pads.slice();ja.adjustPoolAttributes(r,a,i,o,l,d);let c=ja.computePoolOutputShape(r,a,o,l,i,d,e.autoPad),f=Object.assign({},e);s?Object.assign(f,{kernelShape:i,strides:o,pads:d,dilations:l,cacheKey:e.cacheKey}):Object.assign(f,{kernelShape:i,strides:o,pads:d,cacheKey:e.cacheKey});let g=c.slice();return g.push(g.splice(1,1)[0]),[f,n?g:c]},no=(t,e)=>{let r=e.format==="NHWC",n=K.size(t),a=K.size(e.kernelShape),s=[{type:12,data:n},{type:12,data:a}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let o=e.kernelShape[e.kernelShape.length-1],l=e.strides[e.strides.length-1],d=e.pads[e.pads.length/2-1],c=e.pads[e.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(e.kernelShape.length===2){let y=e.kernelShape[e.kernelShape.length-2],p=e.strides[e.strides.length-2],v=e.pads[e.pads.length/2-2],x=e.pads[e.pads.length-2];g=!!(v+x),s.push({type:12,data:y},{type:12,data:p},{type:12,data:v},{type:12,data:x}),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(e.kernelShape);s.push({type:12,data:o},{type:12,data:e.pads},{type:12,data:e.strides}),i.push({name:"kernelStrides",type:"u32",length:o.length},{name:"pads",type:"u32",length:e.pads.length},{name:"strides",type:"u32",length:e.strides.length});let l=e.pads.reduce((d,c)=>d+c);return[s,i,!!l,!1,!1]}},ao=(t,e,r,n,a,s,i,o,l,d,c,f)=>{let g=a.format==="NHWC",y=e.type.value,p=fe("output",e.type.tensor,n);if(a.kernelShape.length<=2){let v="",x="",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[${e.indicesToOffset("xIndices")}];
                  ${s}
                }`:v=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${k}] = indices[${k}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${s}
                }`,a.kernelShape.length===2){let A=r-(g?3:2);f?x=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${A}] < 0 || xIndices[${A}] >= uniforms.x_shape[${A}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:x=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;
                `,E=`
              }
            `}return`
            ${t.registerUniforms(l).declareVariables(e,p)}

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

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

              var value = ${y}(${o});
              var pad = 0;
              ${x}
              ${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,x=a.pads.length,E="";return d?E=`
                if (xIndices[j] >= uniforms.x_shape[j]) {
                  pad++;
                  isPad = true;
                  break;
                }
              }
              if (!isPad) {
                let x_val = x[${e.indicesToOffset("xIndices")}];
                ${s}
              }`:E=`
              }
              let x_val = x[${e.indicesToOffset("xIndices")}];
              ${s}
            `,`
            ${t.registerUniforms(l).declareVariables(e,p)}

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

              var offsets: array<u32, ${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",x)};
                  ${E}
              }
              ${i}

              output[global_idx] = value;
            }`}},io=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,Kp=t=>`${io(t)};${t.countIncludePad}`,Yp=t=>`${io(t)};${t.storageOrder};${t.dilations}`,so=t=>({format:t.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],ceilMode:t.ceil_mode,kernelShape:t.kernel_shape,strides:t.strides,pads:t.pads}),oo=(t,e,r,n)=>{let[a,s]=ro(e,n,r),i=Y("x",e.dataType,e.dims.length),o=i.type.value,l="value += x_val;",d="";a.countIncludePad?d+=`value /= ${o}(uniforms.kernelSize);`:d+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[c,f,g,y,p]=no(s,a);c.push(..._e(e.dims,s));let v=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${g};${y};${p}`,inputDependencies:v},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(K.size(s)/64)},programUniforms:c}),getShaderSource:x=>ao(x,i,e.dims.length,s.length,a,l,d,0,f,g,y,p)}},Qp=t=>{let e=t.count_include_pad!==0,r=so(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:Kp(n)}},Xp=(t,e)=>{Vn(t.inputs),t.compute(oo("AveragePool",t.inputs[0],!1,e))},uo={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Jp=t=>{let e=t.format;return{format:e,...uo,cacheKey:e}},Zp=(t,e)=>{Vn(t.inputs),t.compute(oo("GlobalAveragePool",t.inputs[0],!0,e))},lo=(t,e,r,n)=>{let[a,s]=ro(e,n,r),i=`
      value = max(x_val, value);
    `,o="",l=Y("x",e.dataType,e.dims.length),d=["rank"],[c,f,g,y,p]=no(s,a);return c.push(..._e(e.dims,s)),{name:t,shaderCache:{hint:`${n.cacheKey};${g};${y};${p}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(K.size(s)/64)},programUniforms:c}),getShaderSource:v=>ao(v,l,e.dims.length,s.length,a,i,o,e.dataType===10?-65504:-1e5,f,g,y,p)}},eh=(t,e)=>{Vn(t.inputs),t.compute(lo("MaxPool",t.inputs[0],!1,e))},th=t=>{let e=t.storage_order,r=t.dilations,n=so(t);if(e!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let a={storageOrder:e,dilations:r,...n,cacheKey:""};return{...a,cacheKey:Yp(a)}},rh=t=>{let e=t.format;return{format:e,...uo,cacheKey:e}},nh=(t,e)=>{Vn(t.inputs),t.compute(lo("GlobalMaxPool",t.inputs[0],!0,e))}}),ah,ih,sh,z_=Z(()=>{Vt(),$e(),Te(),ah=(t,e,r)=>{let n=t===e,a=t<e&&r<0,s=t>e&&r>0;if(n||a||s)throw new Error("Range these inputs' contents are invalid.")},ih=(t,e,r,n)=>{let a=Math.abs(Math.ceil((e-t)/r)),s=[a],i=a,o=[{type:12,data:i},{type:n,data:t},{type:n,data:r},..._e(s)],l=d=>{let c=fe("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})}},sh=t=>{let e=0,r=0,n=0;t.inputs[0].dataType===6?(e=t.inputs[0].getInt32Array()[0],r=t.inputs[1].getInt32Array()[0],n=t.inputs[2].getInt32Array()[0]):t.inputs[0].dataType===1&&(e=t.inputs[0].getFloat32Array()[0],r=t.inputs[1].getFloat32Array()[0],n=t.inputs[2].getFloat32Array()[0]),Ue.webgpu.validateInputContent&&ah(e,r,n),t.compute(ih(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),oh,uh,lh,dh,ch,ph,hh,fh,mh,gh,_h,co,yh,wh,bh,vh,$h,xh,Sh,R_=Z(()=>{$e(),Ae(),st(),Te(),oh=(t,e)=>{if(t.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),t.length>0){if(e.mode==="linear"){if(!(t.length===2||t.length===3||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1||t.length===5&&t[0]===1&&t[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(e.mode==="cubic"&&!(t.length===2||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},uh=(t,e,r)=>{e.every(a=>a>=0&&a<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return e.forEach((a,s)=>n[a]=t[s]),n},lh=(t,e,r,n,a,s)=>{let[i,o,l]=r>10?[1,2,3]:[-1,t.length>1?1:-1,-1],d=t[0].dims.length;if(i>0&&t.length>i&&t[i].dims.length>0)t[i].getFloat32Array().forEach(c=>s.push(c));else if(e.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&t.length>o&&t[o].dims.length>0){if(t[o].getFloat32Array().forEach(c=>n.push(c)),n.length!==0&&n.length!==d&&r>=18&&n.length!==e.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");oh(n,e),e.axes.length>0&&uh(n,e.axes,d).forEach((c,f)=>n[f]=c)}if(l>0&&t.length>l&&(t[l].getBigInt64Array().forEach(c=>a.push(Number(c))),a.length!==d||r>=18&&a.length===e.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(e.axes.length>0){if(n.length!==e.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(a.length!==e.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof a<"u"&&n.length>0&&a.length>d)throw new Error("Resize requires only of scales or sizes to be specified")},dh=(t,e)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${e} { `+(()=>{switch(t){case"asymmetric":return`return ${e}(xResized) / ${e}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${e}(xResized) + 0.5) / ${e}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${e}(xResized) + 0.5) / ${e}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating
                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
                    // offset-by-one error later in floor().
                    let whole = ${e}(xResized * (lengthOriginal - 1) / (lengthResized - 1));
                    let fract =
                        ${e}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${e}(lengthResized - 1);
                    return whole + fract;
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${e}(roiStart) * ${e}(lengthOriginal - 1) +
                        (${e}(xResized) * ${e}(roiEnd - roiStart) * ${e}(lengthOriginal - 1)) /
                        ${e}(lengthResized - 1);
                  } else {
                    return 0.5 * ${e}(roiStart + roiEnd) * ${e}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${e}xScale * ${e}(lengthResized);
                  const adjustment = ${e}(lengthResized) / outputWidth;
                  const center = ${e}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;case"half_pixel":return`return ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${t} is not supported`)}})()+"}",ch=(t,e,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(t){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(e<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${t} is not supported`)}})()+"}",ph=(t,e,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),a=t.length===0?n:t.slice();return e.length>0?(e.forEach((s,i)=>{n[s]=a[i],n[i+r]=a[e.length+i]}),n):a},hh=(t,e,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(t.forEach(s=>a.push(s)),Math.max(...n)>t.length)throw new Error("axes is out of bound");n.forEach((s,i)=>a[s]=r[i])}else r.forEach(s=>a.push(s));else{if(e.length===0)throw new Error("Resize requires either scales or sizes.");a=t.map((s,i)=>Math.round(s*e[i]))}return a},fh=(t,e,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>e[s]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>e[s]),Number.MIN_VALUE):Math.max(...e,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();e.fill(1,0,e.length);let a=t.slice();return r.axes.length>0?(r.axes.forEach(s=>e[s]=n),r.axes.forEach(s=>a[s]=Math.round(t[s]*e[s]))):(e.fill(n,0,e.length),a.forEach((s,i)=>a[i]=Math.round(s*e[i]))),a},mh=(t,e,r,n,a)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> array<${t.type.value}, ${r.length}> {
      var original_indices: array<${t.type.value}, ${r.length}>;
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var output_index = ${t.indicesGet("output_indices","i")};
        var scale = ${xe("uniforms.scales","i",n)};
        var roi_low = ${xe("uniforms.roi","i",a)};
        var roi_hi = ${xe("uniforms.roi",`i + ${e.length}`,a)};
        if (scale == 1.0) {
          original_indices[i] = ${t.type.value}(output_index);
        } else {
          var input_shape_i = ${xe("uniforms.input_shape","i",e.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;
    }`,gh=(t,e,r,n,a,s,i)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} {
      var input_indices: ${t.type.indices};
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${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 < ${e.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${e.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${t.indicesSet("input_indices","i"," input_index")}
      }
      return input_indices;
    }`,_h=(t,e)=>`
    fn checkInputIndices(input_indices: ${t.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${e.length}; i++) {
        var input_index = ${t.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${xe("uniforms.input_shape","i",e.length)}) {
          return false;
        }
      }
      return true;
    }`,co=(t,e,r,n)=>t.rank>n?`
    ${t.indicesSet("input_indices",e,"channel")};
    ${t.indicesSet("input_indices",r,"batch")};
`:"",yh=(t,e,r,n,a)=>{let[s,i,o,l]=r.length===2?[-1,0,1,-1]:[0,2,3,1],d=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",i,`max(0, min(row, ${r[i]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)};
      ${co(t,l,s,2)}
      return ${t.getByIndices("input_indices")};
    }

    fn bilinearInterpolation(output_indices: ${e.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);
    }`},wh=(t,e,r,n,a,s,i,o,l,d)=>{let c=r.length===2,[f,g]=c?[0,1]:[2,3],y=t.type.value,p=v=>{let x=v===f?"row":"col";return`
      fn ${x}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${y} {
        var output_index = ${e.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 ${x}: ${y} = originalIdx + ${y}(i);
          if (${x} < 0 || ${x} >= ${r[v]}) {
            ${d?`coefs[i + 1] = 0.0;
                        continue;`:o?`return ${l};`:`${x} = max(0, min(${x}, ${r[v]} - 1));`};
          }
        var input_indices_copy: ${t.type.indices} = input_indices;
          ${t.indicesSet("input_indices_copy",v,`u32(${x})`)};
          data[i + 1] = ${v===f?t.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: ${e.type.indices}) -> ${y} {
    var input_indices: ${t.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},bh=(t,e,r,n,a)=>{let[s,i,o,l,d]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],c=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${c} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",i,`max(0, min(depth, ${r[i]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(height, ${r[o]} - 1))`)};
      ${t.indicesSet("input_indices",l,`max(0, min(width, ${r[l]} - 1))`)};
      ${co(t,d,s,3)}
      return ${t.getByIndices("input_indices")};
    }

    fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${c} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var depth:${c} = originalIndices[${i}];
      var height:${c} = originalIndices[${o}];
      var width:${c} = originalIndices[${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);
    }`},vh=(t,e,r,n,a,s)=>{let i=t.dims,o=ph(s,e.axes,i.length),l=hh(i,n,a,e.axes),d=n.slice();n.length===0&&(d=i.map((k,A)=>k===0?1:l[A]/k),e.keepAspectRatioPolicy!=="stretch"&&(l=fh(i,d,e)));let c=fe("output",t.dataType,l.length),f=Y("input",t.dataType,i.length),g=K.size(l),y=i.length===l.length&&i.every((k,A)=>k===l[A]),p=e.coordinateTransformMode==="tf_crop_and_resize",v=e.extrapolationValue,x=f.type.value,E=k=>`
      ${y?"":`
      ${dh(e.coordinateTransformMode,x)};
      ${(()=>{switch(e.mode){case"nearest":return`
              ${_h(f,i)};
              ${ch(e.nearestMode,r,x)};
              ${gh(f,c,i,l,d.length,o.length,p)};
              `;case"linear":return`
              ${mh(c,i,l,d.length,o.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${yh(f,c,i,p,v)}`;if(i.length===3||i.length===5)return`${bh(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`
            ${(()=>{if(i.length===2||i.length===4)return`${wh(f,c,i,l,d,o,e.cubicCoeffA,p,e.extrapolationValue,e.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${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(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${f.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${e.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${e.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${e.cacheKey}|${r}|${d.length>0?d:""}|${a.length>0?a:""}|${o.length>0?o:""}|${y}|${i}`,inputDependencies:["rank"]},getShaderSource:E,getRunData:()=>({outputs:[{dims:l,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:12,data:g},{type:1,data:d},{type:1,data:o},..._e(i,l)]})}},$h=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},xh=(t,e)=>{let r=[],n=[],a=[],s=$h(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");lh(t.inputs,e,s,r,n,a),t.compute(vh(t.inputs[0],e,s,r,n,a),{inputs:[0]})},Sh=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,a=t.cubicCoeffA,s=t.excludeOutside!==0,i=t.extrapolationValue,o=t.keepAspectRatioPolicy,l=t.mode,d=t.nearestMode===""?"simple":t.nearestMode;return He({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:s,extrapolationValue:i,keepAspectRatioPolicy:o,mode:l,nearestMode:d})}}),kh,Eh,Ch,P_=Z(()=>{$e(),Ae(),st(),Te(),kh=(t,e)=>{let[r,n,a,s]=t,{numHeads:i,rotaryEmbeddingDim:o}=e;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!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")},Eh=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:s}=e,i=t[0].dims[0],o=K.sizeFromDimension(t[0].dims,1),l=t[0].dims[t[0].dims.length-2],d=o/l,c=t[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},...t[0].dims.length===3?new Array({type:12,data:[o,d,f,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,f,l*f,1]}):[],..._e(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],v=x=>{let E=Y("input",t[0].dataType,t[0].dims.length),k=Y("position_ids",t[1].dataType,t[1].dims.length),A=Y("cos_cache",t[2].dataType,t[2].dims.length),I=Y("sin_cache",t[3].dataType,t[3].dims.length),T=fe("output",t[0].dataType,t[0].dims.length);return x.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}]),`
        ${x.declareVariables(E,k,A,I,T)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${k.broadcastedIndicesToOffset("bsnh.xy",fe("",k.type.tensor,2))};
            let position_id =
                u32(${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")} * ${A.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")} * ${A.get("position_id","bsnh[3]")};
            ${T.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${T.setByOffset("k",E.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:He({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:v,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(K.size(g)/sn)},programUniforms:p})}},Ch=(t,e)=>{kh(t.inputs,e),t.compute(Eh(t.inputs,e))}}),Th,Ih,Ah,B_=Z(()=>{$e(),Ae(),Te(),Th=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let a=e.dims[e.dims.length-1],s=e.dims[e.dims.length-2];if(r.dims[r.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==a)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let i=t[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let i=t[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},Ih=(t,e,r,n)=>{let a=e.simplified,s=t[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&&t.length>3,g=t.length>4,y=n&&r>1,p=n&&r>2,v=r>3,x=et(d),E=[{type:12,data:l},{type:12,data:x},{type:12,data:d},{type:1,data:e.epsilon}],k=I=>{let T=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],P=[Y("x",t[0].dataType,t[0].dims,x),Y("skip",t[1].dataType,t[1].dims,x),Y("gamma",t[2].dataType,t[2].dims,x)];f&&P.push(Y("beta",t[3].dataType,t[3].dims,x)),g&&P.push(Y("bias",t[4].dataType,t[4].dims,x)),P.push(fe("output",t[0].dataType,o,x)),y&&P.push(fe("mean_output",1,c)),p&&P.push(fe("inv_std_output",1,c)),v&&P.push(fe("input_skip_bias_sum",t[0].dataType,o,x));let B=lt(t[0].dataType);return`

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

      ${I.mainStart()}
        ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}
        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
        let offset = global_idx * hidden_size_vectorized;
        var sum = ${mr("f32",x)};
        var squareSum = ${mr("f32",x)};
        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {
          let skip_value = skip[offset + i];
          let bias_value = ${g?"bias[i]":B+"(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(B,x,"value")};
          sum += f32_value;
          squareSum += f32_value * f32_value;
        }
        let mean = ${kr("sum",x)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${kr("squareSum",x)} / 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 < hidden_size_vectorized; i++) {
          output[offset + i] = (output[offset + i] ${a?"":`- ${B}(mean)`}) * ${B}(inv_std_dev) * gamma[i] ${f?"+ beta[i]":""};
        }
      }`},A=[{dims:o,dataType:t[0].dataType}];return r>1&&A.push({dims:c,dataType:1}),r>2&&A.push({dims:c,dataType:1}),r>3&&A.push({dims:s,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${x};${y};${p};${v}`,inputDependencies:t.map((I,T)=>"type")},getShaderSource:k,getRunData:()=>({outputs:A,dispatchGroup:{x:Math.ceil(l/d/64)},programUniforms:E})}},Ah=(t,e)=>{Th(t.inputs);let r=[0];t.outputCount>1&&r.push(-3),t.outputCount>2&&r.push(-3),t.outputCount>3&&r.push(3),t.compute(Ih(t.inputs,e,t.outputCount,!1),{outputs:r})}}),Oh,Gn,Mh,po,zh,Rh,Ph,Bh,D_=Z(()=>{$e(),Ae(),st(),Te(),Oh=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");if(e.axes.length!==0){if(e.axes.length!==e.starts.length||e.axes.length!==e.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(e.starts.length!==e.ends.length)throw new Error("starts and ends must have the same length");t.slice(1).forEach((r,n)=>{if(t[n+1].dataType!==6&&t[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},Gn=(t,e)=>{let r=[];if(t.length>e)if(t[e].dataType===7)t[e].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(t[e].dataType===6)t[e].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${e} must be an array of int32 or int64`);return r},Mh=(t,e)=>{if(t.length>1){let r=Gn(t,1),n=Gn(t,2),a=Gn(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),He({starts:r,ends:n,axes:a})}else return e},po=(t,e,r,n,a)=>{let s=t;return t<0&&(s+=r[n[e]]),a[e]<0?Math.max(0,Math.min(s,r[n[e]]-1)):Math.max(0,Math.min(s,r[n[e]]))},zh=(t,e,r)=>`fn calculateInputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} {
          var input_indices: ${t.type.indices};
          var carry = 0u;
          for (var i = ${r.length}; i >= 0; i--) {
            let input_shape_i = ${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 = ${e.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${t.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,Rh=(t,e)=>{let r=t[0].dims,n=K.size(r),a=e.axes.length>0?K.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],s=Gn(t,4);s.forEach(E=>E!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(a.length).fill(1));let i=e.starts.map((E,k)=>po(E,k,r,a,s)),o=e.ends.map((E,k)=>po(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,A)=>{if(E<0){let I=(o[k]-i[k])/E,T=i[k],P=T+I*s[k];i[k]=P,o[k]=T,A[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:t[0].dataType},f=fe("output",t[0].dataType,d.length),g=Y("input",t[0].dataType,t[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(t[0].dims,d)],x=E=>`
      ${E.registerUniforms(p).declareVariables(g,f)}
        ${zh(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:x,getRunData:()=>({outputs:[c],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:v})}},Ph=(t,e)=>{Oh(t.inputs,e);let r=Mh(t.inputs,e);t.compute(Rh(t.inputs,r),{inputs:[0]})},Bh=t=>{let e=t.starts,r=t.ends,n=t.axes;return He({starts:e,ends:r,axes:n})}}),Dh,Nh,Fh,Lh,N_=Z(()=>{$e(),Ae(),st(),Te(),Dh=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},Nh=(t,e)=>{let r=t.dims,n=K.size(r),a=64,s=e.axis;if(s<0&&(s=r.length+s),s<r.length-1)throw new Error("softmax only supports last axis for now.");let i=r[s],o=n/i,l=et(i),d=i/l,c=(x,E)=>E===4?`max(max(${x}.x, ${x}.y), max(${x}.z, ${x}.w))`:E===2?`max(${x}.x, ${x}.y)`:E===3?`max(max(${x}.x, ${x}.y), ${x}.z)`:x,f=Y("x",t.dataType,t.dims,l),g=fe("result",t.dataType,t.dims,l),y=f.type.value,p=lt(t.dataType)==="f32"?`var threadMax = ${y}(-3.402823e+38f);`:`var threadMax = ${y}(-65504.0h);`,v=x=>`
      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;
      }
      ${x.registerUniform("packedCols","i32").declareVariables(f,g)}
      ${x.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 = ${y}(${kr("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:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:6,data:d}]}),getShaderSource:v}},Fh=(t,e)=>{Dh(t.inputs),t.compute(Nh(t.inputs[0],e))},Lh=t=>He({axis:t.axis})}),Uh,Wh,Vh,Gh,Hh,qh,jh,F_=Z(()=>{$e(),Ae(),st(),Te(),Uh=t=>{if(!t||t.length<1)throw new Error("too few inputs")},Wh=(t,e)=>{let r=[],n=e.numOutputs;return t[1].dims[0]>0&&(t[1].getBigInt64Array().forEach(a=>r.push(Number(a))),n=r.length),He({numOutputs:n,axis:e.axis,splitSizes:r})},Vh=t=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${t}u; i += 1u ) {
    if (index < ${xe("uniforms.size_in_split_axis","i",t)}) {
        return i;
    }
    }
    return ${t}u;
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      fn writeBufferData(output_number: u32, indices: ${t[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},Hh=(t,e)=>{let r=t[0].dims,n=K.size(r),a=t[0].dataType,s=K.normalizeAxis(e.axis,r.length),i=new Array(e.numOutputs),o=Y("input",a,r.length),l=new Array(e.numOutputs),d=[],c=[],f=0,g=[{type:12,data:n}];for(let p=0;p<e.numOutputs;p++){f+=e.splitSizes[p],l[p]=f;let v=r.slice();v[e.axis]=e.splitSizes[p],c.push(v),i[p]=fe(`output${p}`,a,v.length),d.push({dims:c[p],dataType:t[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)}
  ${Vh(l.length)}
  ${Gh(i)}

  ${p.mainStart()}
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    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);
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      const inputShape = ${i.indices(...e)};
      ${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 < ${e.length}; i++) {
        let input_dim_i = ${i.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${o.indicesGet("output_indices","i")}  % input_dim_i;

        ${i.indicesSet("input_indices","i","input_dim_value")}
      }
      ${o.setByOffset("global_idx",i.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},..._e(t[0].dims,n)]}),getShaderSource:l}},Xh=t=>{Kh(t.inputs),t.compute(Qh(t.inputs),{inputs:[0]})}}),Jh,Zh,ef,U_=Z(()=>{$e(),Ae(),Te(),Jh=(t,e,r,n,a)=>{let s=fe("output_data",a,r.length,4),i=Y("a_data",e[1].dataType,e[1].dims.length,4),o=Y("b_data",e[2].dataType,e[2].dims.length,4),l=Y("c_data",e[0].dataType,e[0].dims.length,4),d,c=(f,g,y)=>`select(${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${y}]`,x=`b_data[index_b${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,x,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`
        ${t.registerUniform("vec_size","u32").declareVariables(l,i,o,s)}
        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${d}
      }`},Zh=t=>{let e=t[1].dims,r=t[2].dims,n=t[0].dims,a=t[1].dataType,s=!(K.areEqual(e,r)&&K.areEqual(r,n)),i=e,o=K.size(e);if(s){let d=an.calcShape(an.calcShape(e,r,!1),n,!1);if(!d)throw new Error("Can't perform where op on the given tensors");i=d,o=K.size(i)}let l=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:d=>Jh(d,t,i,s,a),getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:l},..._e(n,e,r,i)]})}},ef=t=>{t.compute(Zh(t.inputs))}}),tf,W_=Z(()=>{l_(),hd(),d_(),c_(),p_(),h_(),sd(),Mc(),y_(),w_(),b_(),v_(),$_(),x_(),S_(),k_(),E_(),C_(),T_(),Tc(),I_(),A_(),O_(),M_(),z_(),Ms(),R_(),P_(),B_(),D_(),N_(),F_(),L_(),Un(),Ns(),U_(),tf=new Map([["Abs",[$d]],["Acos",[xd]],["Acosh",[Sd]],["Add",[uc]],["ArgMax",[ed,Rs]],["ArgMin",[Zl,Rs]],["Asin",[kd]],["Asinh",[Ed]],["Atan",[Cd]],["Atanh",[Td]],["Attention",[pd]],["AveragePool",[Xp,Qp]],["BatchNormalization",[_d]],["BiasAdd",[bd]],["BiasSplitGelu",[ic]],["Cast",[Ad,Id]],["Ceil",[zd]],["Clip",[Md]],["Concat",[ad,id]],["Conv",[Ys,Ks]],["ConvTranspose",[Gc,Fc]],["Cos",[Rd]],["Cosh",[Pd]],["CumSum",[qc,jc]],["DepthToSpace",[Xc,Jc]],["Div",[lc]],["Einsum",[ap,ip]],["Elu",[Bd,Za]],["Equal",[dc]],["Erf",[Dd]],["Exp",[Nd]],["Expand",[lp]],["FastGelu",[cp]],["Floor",[Fd]],["FusedConv",[Ys,Ks]],["Gather",[mp,fp]],["GatherElements",[wp,yp]],["Gelu",[Ld]],["Gemm",[xp,$p]],["GlobalAveragePool",[Zp,Jp]],["GlobalMaxPool",[nh,rh]],["Greater",[fc]],["GreaterOrEqual",[gc]],["HardSigmoid",[Kd,jd]],["InstanceNormalization",[Cp]],["LayerNormalization",[Ap]],["LeakyRelu",[Ud,Za]],["Less",[mc]],["LessOrEqual",[_c]],["Log",[rc]],["MatMul",[Cc]],["MatMulNBits",[zp,Rp]],["MaxPool",[eh,th]],["Mul",[cc]],["MultiHeadAttention",[Np,Bp]],["Neg",[Vd]],["Not",[Wd]],["Pad",[jp]],["Pow",[pc]],["Range",[sh]],["Reciprocal",[Gd]],["ReduceMin",[Kl]],["ReduceMean",[Vl]],["ReduceMax",[jl]],["ReduceSum",[Ql]],["ReduceProd",[Yl]],["ReduceL1",[Gl]],["ReduceL2",[Hl]],["ReduceLogSum",[Jl]],["ReduceLogSumExp",[ql]],["ReduceSumSquare",[Xl]],["Relu",[Hd]],["Resize",[xh,Sh]],["RotaryEmbedding",[Ch]],["Sigmoid",[qd]],["Sin",[Yd]],["Sinh",[Qd]],["Slice",[Ph,Bh]],["SkipLayerNormalization",[Ah]],["Split",[qh,jh]],["Sqrt",[Xd]],["Softmax",[Fh,Lh]],["Sub",[hc]],["Tan",[Jd]],["Tanh",[Zd]],["ThresholdedRelu",[tc,Za]],["Tile",[Xh]],["Transpose",[dl,cl]],["Where",[ef]]])}),rf,V_=Z(()=>{Vt(),Nr(),Te(),rf=class{constructor(t){this.backend=t,this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,e){this.repo.set(t,e)}run(t,e,r,n,a){Wt(t.programInfo.name);let s=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let o=[];for(let d of e)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:t.computePipeline.getBindGroupLayout(0),entries:o,label:t.programInfo.name});if(this.backend.sessionStatus==="capturing"){let d={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:l,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(d)}i.setPipeline(t.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(),Nt(t.programInfo.name)}dispose(){}build(t,e){Wt(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=sl(e,this.backend.device.limits),s=t.getShaderSource(a),i=`${n.join(`
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(should not happen)");let t=this.kernelCustomData.get(this.currentKernelId);return t||(t={},this.kernelCustomData.set(this.currentKernelId,t)),t}async initialize(t,e){this.env=t;let r=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:e.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:e.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:e.limits.maxStorageBufferBindingSize,maxBufferSize:e.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:e.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:e.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:e.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:e.limits.maxComputeWorkgroupSizeZ},requiredFeatures:r};e.features.has("chromium-experimental-timestamp-query-inside-passes")?r.push("chromium-experimental-timestamp-query-inside-passes"):e.features.has("timestamp-query")&&r.push("timestamp-query"),e.features.has("shader-f16")&&r.push("shader-f16"),this.device=await e.requestDevice(n),this.adapterInfo=new sf(await e.requestAdapterInfo()),this.gpuDataManager=tl(this),this.programManager=new rf(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Yu(t.logLevel,!!t.debug),this.device.onuncapturederror=a=>{a.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${a.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:e,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let t=this.getCommandEncoder(),e={};this.queryType==="at-passes"&&(e.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=t.beginComputePass(e)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Wt(),this.endComputePass();let t;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&t.mapAsync(GPUMapMode.READ).then(()=>{var n;let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let a=0;a<e.length/2;a++){let s=r[a],i=s.kernelId,o=this.kernels.get(i),l=o.kernelType,d=o.kernelName,c=s.programName,f=s.inputTensorViews,g=s.outputTensorViews,y=e[a*2],p=e[a*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=y);let v=Number(y-this.queryTimeBase),x=Number(p-this.queryTimeBase);if(!Number.isSafeInteger(v)||!Number.isSafeInteger(x))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:Dr(E.dataType)})),outputsMetadata:g.map(E=>({dims:E.dims,dataType:Dr(E.dataType)})),kernelId:i,kernelType:l,kernelName:d,programName:c,startTime:v,endTime:x});else{let E="";f.forEach((A,I)=>{E+=`input[${I}]: [${A.dims}] | ${Dr(A.dataType)}, `});let k="";g.forEach((A,I)=>{k+=`output[${I}]: [${A.dims}] | ${Dr(A.dataType)}, `}),console.log(`[profiling] kernel "${i}|${l}|${d}|${c}" ${E}${k}execution time: ${x-v} ns`)}Bn("GPU",`${c}::${y}::${p}`)}t.unmap(),this.pendingQueries.delete(t)}),Nt()}run(t,e,r,n,a,s){Wt(t.name);let i=[];for(let k=0;k<e.length;++k){let A=e[k].data;if(A===0)continue;let I=this.gpuDataManager.get(A);if(!I)throw new Error(`no GPU data for input: ${A}`);i.push(I)}let{outputs:o,dispatchGroup:l,programUniforms:d}=t.getRunData(e),c=r.length===0?o.map((k,A)=>A):r;if(c.length!==o.length)throw new Error(`Output size ${c.length} must be equal to ${o.length}.`);let f=[],g=[];for(let k=0;k<o.length;++k){if(!Number.isInteger(c[k])||c[k]<-3||c[k]>=s)throw new Error(`Invalid output index: ${c[k]}`);if(c[k]===-3)continue;let A=c[k]===-1,I=c[k]===-2,T=A||I?a(o[k].dataType,o[k].dims):n(c[k],o[k].dataType,o[k].dims);if(f.push(T),T.data===0)continue;let P=this.gpuDataManager.get(T.data);if(!P)throw new Error(`no GPU data for output: ${T.data}`);if(A&&this.temporaryData.push(P),I){let B=this.kernelPersistentData.get(this.currentKernelId);B||(B=[],this.kernelPersistentData.set(this.currentKernelId,B)),B.push(P)}g.push(P)}if(i.length!==e.length||g.length!==f.length){if(g.length===0)return Nt(t.name),f;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. 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P=this.gpuDataManager.create(k,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(P.buffer,0,T,0,k),this.gpuDataManager.release(P.id),y={offset:0,size:k,buffer:P.buffer}}let p=this.programManager.normalizeDispatchGroupSize(l),v=p[1]===1&&p[2]===1,x=af(t,e,v),E=this.programManager.getArtifact(x);if(E||(E=this.programManager.build(t,p),this.programManager.setArtifact(x,E),Xe("info",()=>`[artifact] key: ${x}, programName: ${t.name}`)),d&&E.uniformVariablesInfo){if(d.length!==E.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${E.uniformVariablesInfo.length}, got ${d.length} in program "${E.programInfo.name}".`);for(let k=0;k<d.length;k++){let A=d[k],I=A.type,T=typeof A.data=="number"?1:A.data.length,[P,B]=E.uniformVariablesInfo[k];if(I!==P||T!==B)throw new Error(`Uniform variable ${k} mismatch: expect type ${P} with size ${B}, got type ${I} with size ${T} in program "${E.programInfo.name}".`)}}if(Xe("info",()=>`[ProgramManager] run "${t.name}" (key=${x}) with ${p[0]}x${p[1]}x${p[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let k={kernelId:this.currentKernelId,programName:E.programInfo.name,inputTensorViews:e,outputTensorViews:f};this.pendingKernels.push(k),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(k)}return this.programManager.run(E,i,g,p,y),Nt(t.name),f}upload(t,e){this.gpuDataManager.upload(t,e)}memcpy(t,e){this.gpuDataManager.memcpy(t,e)}async download(t,e){await this.gpuDataManager.download(t,e)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,e,r,n){let a=tf.get(t);if(!a)throw new Error(`kernel not implemented: ${t}`);let s={kernelType:t,kernelName:n,kernelEntry:a[0],attributes:[a[1],r]};this.kernels.set(e,s)}releaseKernel(t){let e=this.kernelPersistentData.get(t);if(e){for(let r of e)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,e,r){let n=this.kernels.get(t);if(!n)throw new Error(`kernel not created: ${t}`);let a=n.kernelType,s=n.kernelName,i=n.kernelEntry,o=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${a}] ${s}" is not allowed to be called recursively`);this.currentKernelId=t,o[0]&&(o[1]=o[0](o[1]),o[0]=void 0),Xe("info",()=>`[WebGPU] Start to run kernel "[${a}] ${s}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),i(e,o[1]),0}catch(d){return r.push(Promise.resolve(`[WebGPU] Kernel "[${a}] ${s}" failed. ${d}`)),1}finally{l&&r.push(this.device.popErrorScope().then(d=>d?`GPU validation error for kernel "[${a}] ${s}": ${d.message}`:null));for(let d of this.temporaryData)this.gpuDataManager.release(d.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,e,r,n){let a=this.sessionExternalDataMapping.get(t);a||(a=new Map,this.sessionExternalDataMapping.set(t,a));let s=a.get(e),i=this.gpuDataManager.registerExternalBuffer(r,n,s==null?void 0:s[1]);return a.set(e,[i,r]),i}unregisterBuffers(t){let e=this.sessionExternalDataMapping.get(t);e&&(e.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[1])),this.sessionExternalDataMapping.delete(t))}getBuffer(t){let e=this.gpuDataManager.get(t);if(!e)throw new Error(`no GPU data for buffer: ${t}`);return e.buffer}createDownloader(t,e,r){return async()=>{let n=await Es(this,t,e);return Xu(n.buffer,r)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){var t;this.queryType="none",(((t=this.env.webgpu.profiling)==null?void 0:t.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){Xe("info","captureBegin"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus="capturing"}captureEnd(){Xe("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){Xe("info","replay"),this.sessionStatus="replaying";let t=this.capturedCommandList.get(this.currentSessionId),e=this.capturedPendingKernels.get(this.currentSessionId),r=t.length;this.pendingKernels=[];for(let n=0;n<r;n++){let a=this.getComputePassEncoder(),s=t[n];this.writeTimestamp(this.pendingDispatchNumber*2),a.setPipeline(s.computePipeline),a.setBindGroup(0,s.bindGroup),a.dispatchWorkgroups(...s.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(e[n]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onReleaseSession(t){this.unregisterBuffers(t),this.capturedCommandList.has(t)&&this.capturedCommandList.delete(t),this.capturedPendingKernels.has(t)&&this.capturedPendingKernels.delete(t),this.gpuDataManager.onReleaseSession(t)}onRunStart(t){this.currentSessionId=t,this.setQueryType()}}}),uf={};nn(uf,{init:()=>df});var ui,lf,df,H_=Z(()=>{$e(),G_(),Nr(),Ae(),ui=class 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s=n[a++];this.outputCount=n[a++],this.customDataOffset=n[a++],this.customDataSize=n[a++];let i=[];for(let o=0;o<s;o++){let l=n[a++],d=n[a++],c=n[a++],f=[];for(let g=0;g<c;g++)f.push(n[a++]);i.push(new ui(t,l,d,f))}this.inputs=i}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}getMaxComputeWorkgroupSizes(){return[this.backend.device.limits.maxComputeWorkgroupSizeX,this.backend.device.limits.maxComputeWorkgroupSizeY,this.backend.device.limits.maxComputeWorkgroupSizeZ]}getMaxComputeWorkgroupStoragesize(){return this.backend.device.limits.maxComputeWorkgroupStorageSize}compute(t,e){var i;let r=((i=e==null?void 0:e.inputs)==null?void 0:i.map(o=>typeof o=="number"?this.inputs[o]:o))??this.inputs,n=(e==null?void 0:e.outputs)??[],a=(o,l,d)=>new ui(this.module,l,this.output(o,d),d),s=(o,l)=>{let d=Fn(o);if(!d)throw new Error(`Unsupported data type: 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}`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${u};${h};${i}`},getShaderSource:y,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:p})}},rc=(e,t,r,o,i,u,a)=>{let c=a+i.kvSequenceLength,p=[i.batchSize,i.numHeads,i.sequenceLength,c],h=u.scale===0?1/Math.sqrt(i.headSize):u.scale,d=Me(i.headSize),y=i.headSize/d,w=12,_={x:Math.ceil(c/w),y:Math.ceil(i.sequenceLength/w),z:i.batchSize*i.numHeads},v=[{type:12,data:i.sequenceLength},{type:12,data:y},{type:12,data:c},{type:12,data:i.numHeads},{type:1,data:h}],S=o?["type","type","type"]:["type","type"],A=I=>{let x=U("q",t.dataType,t.dims,d),E=U("key",r.dataType,r.dims,d),P=[x,E];o&&P.push(U("relative_position_bias",o.dataType,o.dims));let O=j("output",t.dataType,p),R=et(1,d),L=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return`\n  const TILE_SIZE = ${w}u;\n\n  var<workgroup> tileQ: array<${x.type.storage}, ${w*w}>;\n  var<workgroup> tileK: array<${x.type.storage}, ${w*w}>;\n  ${I.registerUniforms(L).declareVariables(...P,O)}\n  ${I.mainStart([w,w,1])}\n    // x holds the N and y holds the M\n    let headIdx = workgroup_id.z;\n    let m = workgroup_id.y * TILE_SIZE;\n    let n = workgroup_id.x * TILE_SIZE;\n    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n    let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;\n\n    var value = ${R}(0);\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {\n        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n        tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      workgroupBarrier();\n\n      for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {\n        value += ${R}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = headIdx * uniforms.M * uniforms.N;\n    if (global_id.y < uniforms.M && global_id.x < uniforms.N) {\n      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;\n      var sum: f32 = ${(()=>{switch(d){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${d}`)}})()};\n        output[outputIdx] = ${O.type.value} (sum * uniforms.alpha) + ${o?"relative_position_bias[outputIdx]":"0.0"};\n    }\n  }`};return{name:"AttentionProbs",shaderCache:{hint:`${d}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:p,dataType:t.dataType,gpuDataType:0}],dispatchGroup:_,programUniforms:v}),getShaderSource:A}},nc=(e,t,r,o,i)=>{let u=i+o.kvSequenceLength,a=[o.batchSize,o.sequenceLength,o.vHiddenSize],c=12,p={x:Math.ceil(o.vHeadSize/c),y:Math.ceil(o.sequenceLength/c),z:o.batchSize*o.numHeads},h=[{type:12,data:o.sequenceLength},{type:12,data:u},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType,gpuDataType:0}],dispatchGroup:p,programUniforms:h}),getShaderSource:w=>{let _=U("probs",t.dataType,t.dims),v=U("v",r.dataType,r.dims),S=j("output",t.dataType,a),A=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`\n  const TILE_SIZE = ${c}u;\n  var<workgroup> tileQ: array<${_.type.value}, ${c*c}>;\n  var<workgroup> tileK: array<${_.type.value}, ${c*c}>;\n  ${w.registerUniforms(A).declareVariables(_,v,S)}\n  ${w.mainStart([c,c,1])}\n   let headIdx = workgroup_id.z;\n   let m = global_id.y;\n   let n = global_id.x;\n\n   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n   let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n   var value = ${_.type.storage}(0);\n   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n     if (m < uniforms.M && w + local_id.x < uniforms.K) {\n       tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n     }\n     if (n < uniforms.N && w + local_id.y < uniforms.K) {\n       tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n     }\n     workgroupBarrier();\n     for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {\n       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];\n     }\n     workgroupBarrier();\n   }\n\n   // we need to transpose output from BNSH_v to BSND_v\n   let batchIdx = workgroup_id.z / uniforms.num_heads;\n   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;\n   if (m < uniforms.M && n < uniforms.N) {\n     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size\n       + currentBatchHeadNumber * uniforms.N + n;\n     output[outputIdx] = value;\n   }\n  }`}}},Pn=(e,t,r,o,i,u,a,c,p,h,d)=>{let y=e.outputCount>1,w=e.outputCount>2,_=y&&w?h.pastSequenceLength:0,v=_+h.kvSequenceLength,S=[h.batchSize,h.numHeads,v,h.headSize],A=a?[a,r]:[r],I=y?e.compute(En(A,2,S,r.dataType),{inputs:A,outputs:[1]})[0]:r,x=[h.batchSize,h.numHeads,v,h.headSize],E=c?[c,o]:[o],P=w?e.compute(En(E,2,x,o.dataType),{inputs:E,outputs:[2]})[0]:o,O=[t,I];p&&O.push(p);let R=e.compute(rc(e,t,I,p,h,d,_),{inputs:O,outputs:[-1]})[0];e.compute(tc(e,R,h.batchSize*h.numHeads*h.sequenceLength,v),{inputs:[R],outputs:[]});let L=[R,P];e.compute(nc(e,R,P,h,_),{inputs:L,outputs:[0]})},oc=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,i=t.inputHiddenSize,u=t.headSize,a=12,c={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:o},{type:12,data:i},{type:12,data:u},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],d=y=>{let w=j("output_q",p[0].dataType,r),_=j("output_k",p[0].dataType,r),v=j("output_v",p[0].dataType,r),S=U("input",p[0].dataType,p[0].dims),A=U("weight",p[1].dataType,p[1].dims),I=U("bias",p[2].dataType,p[2].dims),x=S.type.storage,E=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`\n  const TILE_SIZE = ${a}u;\n  var<workgroup> tileInput: array<${x}, ${a*a}>;\n  var<workgroup> tileWeightQ: array<${x}, ${a*a}>;\n  var<workgroup> tileWeightK: array<${x}, ${a*a}>;\n  var<workgroup> tileWeightV: array<${x}, ${a*a}>;\n  ${y.registerUniforms(E).declareVariables(S,A,I,w,_,v)}\n  ${y.mainStart([a,a,1])}\n    let batchIndex = workgroup_id.z / uniforms.num_heads;\n    let headNumber = workgroup_id.z % uniforms.num_heads;\n    let m = global_id.y;\n    let n = global_id.x;\n\n    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;\n    let biasOffsetQ = headNumber * uniforms.head_size;\n    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;\n    let biasOffsetV = uniforms.hidden_size + biasOffsetK;\n\n    var valueQ = ${x}(0);\n    var valueK = ${x}(0);\n    var valueV = ${x}(0);\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (m < uniforms.M && w + local_id.x < uniforms.K) {\n        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n      }\n      if (n < uniforms.N && w + local_id.y < uniforms.K) {\n        let offset = n + (w + local_id.y) * uniforms.ldb;\n        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n      }\n      workgroupBarrier();\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        let inputTileOffset = TILE_SIZE * local_id.y + k;\n        let weightTileOffset = TILE_SIZE * k + local_id.x;\n        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];\n        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];\n        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = (m * uniforms.N + n) % uniforms.head_size;\n    valueQ += bias[headOffset + biasOffsetQ];\n    valueK += bias[headOffset + biasOffsetK];\n    valueV += bias[headOffset + biasOffsetV];\n\n    let offset = workgroup_id.z * uniforms.M * uniforms.N;\n    if (m < uniforms.M && n < uniforms.N) {\n      let outputIdx = offset + m * uniforms.N + n;\n      output_q[outputIdx] = valueQ;\n      output_k[outputIdx] = valueK;\n      output_v[outputIdx] = valueV;\n    }\n  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:c,programUniforms:h}),getShaderSource:d},{inputs:p,outputs:[-1,-1,-1]})},Xa=(e,t)=>{let r=ec(e.inputs,t),[o,i,u]=oc(e,r);return Pn(e,o,i,u,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var ic,ac,sc,Qa,Ja=Y(()=>{"use strict";$r();ye();Se();Ze();_e();ic=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(o,i,u)=>{let a=i.length;if(a!==o.length)throw new Error(`${u}: num dimensions != ${a}`);i.forEach((c,p)=>{if(c!==o[p])throw new Error(`${u}: dim[${p}] do not match`)})};if(e[0].dims.length>1){let o=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,o,"Invalid input scale"),r(e[2].dims,o,"Invalid input B"),r(e[3].dims,o,"Invalid input mean"),r(e[4].dims,o,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input 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${E.registerUniform("outputSize","u32").declareVariables(y,w,_,v,S,A)}\n  ${E.mainStart()}\n  ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    var outputIndices = ${A.offsetToIndices(`global_idx * ${a}`)};\n    ${I()}\n    let scale = ${w.getByOffset("cOffset")};\n    let bias = ${_.getByOffset("cOffset")};\n    let inputMean = ${v.getByOffset("cOffset")};\n    let inputVar = ${S.getByOffset("cOffset")};\n    let x = ${y.getByOffset("global_idx")};\n    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;\n    ${A.setByOffset("global_idx","value")}\n  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${o}_${a}`,inputDependencies:h?["rank","type","type","type","type"]:void 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t=e[0].dims,r=e[0].dims[2],o=M.size(t)/4,i=e[0].dataType,u=U("input",i,t,4),a=U("bias",i,[r],4),c=U("residual",i,t,4),p=j("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:d=>`\n  const channels = ${r}u / 4;\n  ${d.declareVariables(u,a,c,p)}\n\n  ${d.mainStart()}\n    ${d.guardAgainstOutOfBoundsWorkgroupSizes(o)}\n    let value = ${u.getByOffset("global_idx")}\n      + ${a.getByOffset("global_idx % channels")} + ${c.getByOffset("global_idx")};\n    ${p.setByOffset("global_idx","value")}\n  }`}},es=e=>{uc(e.inputs),e.compute(dc(e.inputs))}});var lc,ke,rs,ns,os,is,as,ss,us,ds,ls,cc,cs,ps,ms,fs,kn,hs,On,gs,ys,bs,ws,vs,$s,_s,Ss,xs,Cs,As,Is,Ts,Es,Ps,ks,Os,Rs,Bo,Do,Bs,Ds,zs,Rn=Y(()=>{"use strict";ye();Se();Ze();_e();lc=(e,t,r,o,i,u)=>{let a=Math.ceil(t/4),c="";typeof i=="string"?c=`${i}(a)`:c=i("a");let p=U("inputData",r,[a],4),h=j("outputData",o,[a],4);return`\n      ${e.registerUniform("vec_size","u32").declareVariables(p,h)}\n\n  ${u??""}\n\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n\n    let a = ${p.getByOffset("global_idx")};\n    ${h.setByOffset("global_idx",c)}\n  }`},ke=(e,t,r,o,i,u=e.dataType)=>({name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:a=>lc(a,M.size(e.dims),e.dataType,u,r,o),getRunData:a=>({outputs:[{dims:e.dims,dataType:u}],dispatchGroup:{x:Math.ceil(M.size(a[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(e.dims)/4)}]})}),rs=e=>{e.compute(ke(e.inputs[0],"Abs","abs"))},ns=e=>{e.compute(ke(e.inputs[0],"Acos","acos"))},os=e=>{e.compute(ke(e.inputs[0],"Acosh","acosh"))},is=e=>{e.compute(ke(e.inputs[0],"Asin","asin"))},as=e=>{e.compute(ke(e.inputs[0],"Asinh","asinh"))},ss=e=>{e.compute(ke(e.inputs[0],"Atan","atan"))},us=e=>{e.compute(ke(e.inputs[0],"Atanh","atanh"))},ds=e=>ve(e),ls=(e,t)=>{let r;switch(t.to){case 10:r="vec4<f16>";break;case 1:r="vec4<f32>";break;case 12:r="vec4<u32>";break;case 6:r="vec4<i32>";break;case 9:r="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute \'to\' from \'Cast\' operator): ${t.to}`)}e.compute(ke(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},cc=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:xn,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:Cn;return ve({min:t,max:r})},cs=(e,t)=>{let r=e.inputs.length===1?t:cc(e.inputs),o=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Clip",i=>`clamp(${i}, clip_min_, clip_max_)`,`\n    const clip_min_: vec4<${o}> = vec4(${o}(${r.min}));\n    const clip_max_: vec4<${o}> = vec4(${o}(${r.max}));\n`,r.cacheKey),{inputs:[0]})},ps=e=>{e.compute(ke(e.inputs[0],"Ceil","ceil"))},ms=e=>{e.compute(ke(e.inputs[0],"Cos","cos"))},fs=e=>{e.compute(ke(e.inputs[0],"Cosh","cosh"))},kn=e=>ve(e),hs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Elu",o=>`elu_vf32(${o})`,`\n  const elu_alpha_ 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0.7071067811865475))`,On(t)))},vs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"LeakyRelu",o=>`select(leaky_relu_alpha_ * ${o}, ${o}, ${o} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},$s=e=>{e.compute(ke(e.inputs[0],"Not",t=>`!${t}`))},_s=e=>{e.compute(ke(e.inputs[0],"Neg",t=>`-${t}`))},Ss=e=>{e.compute(ke(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},xs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Cs=e=>{e.compute(ke(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},As=e=>ve(e),Is=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"HardSigmoid",o=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${o} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Ts=e=>{e.compute(ke(e.inputs[0],"Sin","sin"))},Es=e=>{e.compute(ke(e.inputs[0],"Sinh","sinh"))},Ps=e=>{e.compute(ke(e.inputs[0],"Sqrt","sqrt"))},ks=e=>{e.compute(ke(e.inputs[0],"Tan","tan"))},Os=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Rs=e=>{e.compute(ke(e.inputs[0],"Tanh",Os))},Bo=(e="f32")=>`\nconst fast_gelu_a: ${e} = 0.5;\nconst fast_gelu_b: ${e} = 0.7978845608028654;\nconst fast_gelu_c: ${e} = 0.035677408136300125;\n\nfn tanh_v(v: vec4<${e}>) -> vec4<${e}> {\n  return ${Os("v")};\n}\n`,Do=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Bs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"FastGelu",Do,Bo(t),void 0,e.inputs[0].dataType))},Ds=(e,t)=>{let r=et(e.inputs[0].dataType);return e.compute(ke(e.inputs[0],"ThresholdedRelu",o=>`select(vec4<${r}>(0.0), ${o}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = 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${p.guardAgainstOutOfBoundsWorkgroupSizes(u)}\n    let biasIdx = global_idx % halfChannels;\n    let batchIndex = global_idx / halfChannels;\n    let inputOffset = biasIdx + batchIndex * halfChannels * 2;\n    let valueLeft = input[inputOffset] + bias[biasIdx];\n    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];\n    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);\n\n    ${i.setByOffset("global_idx","valueLeft * geluRight")}\n  }`}},Us=e=>{pc(e.inputs),e.compute(mc(e.inputs))}});var fc,hc,Ot,Ws,Ns,Gs,Hs,Ls,Fs,qs,js,Ks,Ys,Zs=Y(()=>{"use strict";ye();Se();_e();fc=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w,_;typeof c=="string"?w=_=(x,E)=>`${c}((${x}),(${E}))`:typeof c=="function"?w=_=c:(w=c.scalar,_=c.vector);let v=j("outputData",d,o.length,4),S=U("aData",p,t.length,4),A=U("bData",h,r.length,4),I;if(i)if(u){let x=M.size(t)===1,E=M.size(r)===1,P=t.length>0&&t[t.length-1]%4===0,O=r.length>0&&r[r.length-1]%4===0;x||E?I=v.setByOffset("global_idx",_(x?`${S.type.value}(${S.getByOffset("0")}.x)`:S.getByOffset("global_idx"),E?`${A.type.value}(${A.getByOffset("0")}.x)`:A.getByOffset("global_idx"))):I=`\n            let outputIndices = ${v.offsetToIndices("global_idx * 4u")};\n            let offsetA = ${S.broadcastedIndicesToOffset("outputIndices",v)};\n            let offsetB = ${A.broadcastedIndicesToOffset("outputIndices",v)};\n            ${v.setByOffset("global_idx",_(a||P?S.getByOffset("offsetA / 4u"):`${S.type.value}(${S.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||O?A.getByOffset("offsetB / 4u"):`${A.type.value}(${A.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n          `}else I=v.setByOffset("global_idx",_(S.getByOffset("global_idx"),A.getByOffset("global_idx")));else{if(!u)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let x=(E,P,O="")=>{let R=`aData[indexA${P}][componentA${P}]`,L=`bData[indexB${P}][componentB${P}]`;return`\n            let outputIndices${P} = ${v.offsetToIndices(`global_idx * 4u + ${P}u`)};\n            let offsetA${P} = ${S.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n            let offsetB${P} = ${A.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n            let indexA${P} = offsetA${P} / 4u;\n            let indexB${P} = offsetB${P} / 4u;\n            let componentA${P} = offsetA${P} % 4u;\n            let componentB${P} = offsetB${P} % 4u;\n            ${E}[${P}] = ${O}(${w(R,L)});\n          `};d===9?I=`\n            var data = vec4<u32>(0);\n            ${x("data",0,"u32")}\n            ${x("data",1,"u32")}\n            ${x("data",2,"u32")}\n            ${x("data",3,"u32")}\n            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:I=`\n            ${x("outputData[global_idx]",0)}\n            ${x("outputData[global_idx]",1)}\n            ${x("outputData[global_idx]",2)}\n            ${x("outputData[global_idx]",3)}\n          `}return`\n        ${e.registerUniform("vec_size","u32").declareVariables(S,A,v)}\n\n        ${y??""}\n\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n        ${I}\n      }`},hc=(e,t,r,o,i,u,a=r.dataType)=>{let c=!M.areEqual(r.dims,o.dims),p=r.dims,h=M.size(r.dims),d=!1,y=!1,w=[c];if(c){let _=It.calcShape(r.dims,o.dims,!1);if(!_)throw new Error("Can\'t perform binary op on the given tensors");p=_,h=M.size(p);let v=M.size(r.dims)===1,S=M.size(o.dims)===1,A=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,I=o.dims.length>0&&o.dims[o.dims.length-1]%4===0;w.push(v),w.push(S),w.push(A),w.push(I);let x=1;for(let E=1;E<p.length;E++){let P=r.dims[r.dims.length-E]??1,O=o.dims[o.dims.length-E]??1;if(P===O)x*=P;else break}x%4===0?(y=!0,d=!0):(v||S||A||I)&&(d=!0)}else d=!0;return w.push(d),{name:e,shaderCache:{hint:t+w.map(_=>_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>fc(_,r.dims,o.dims,p,d,c,y,i,r.dataType,o.dataType,a,u),getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(h/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(p)/4)},...Z(r.dims,o.dims,p)]})}},Ot=(e,t,r,o,i,u)=>{e.compute(hc(t,i??"",e.inputs[0],e.inputs[1],r,o,u))},Ws=e=>{Ot(e,"Add",(t,r)=>`${t}+${r}`)},Ns=e=>{Ot(e,"Div",(t,r)=>`${t}/${r}`)},Gs=e=>{Ot(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},Hs=e=>{Ot(e,"Mul",(t,r)=>`${t}*${r}`)},Ls=e=>{let t=U("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Ot(e,"Pow",{scalar:(o,i)=>`pow_custom(${o},${i})`,vector:(o,i)=>`pow_vector_custom(${o},${i})`},`\n    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {\n      if (b == ${t}(0.0)) {\n        return ${t}(1.0);\n      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {\n        return ${t}(pow(f32(a), f32(b))); // NaN\n      }\n      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));\n    }\n    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {\n      // TODO: implement vectorized pow\n      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));\n    }\n      `)},Fs=e=>{Ot(e,"Sub",(t,r)=>`${t}-${r}`)},qs=e=>{Ot(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},js=e=>{Ot(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},Ks=e=>{Ot(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},Ys=e=>{Ot(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}});var St,xt,Ct,Bn,Ft=Y(()=>{"use strict";ye();Se();St=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},xt=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Ct=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Bn=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[r,o]=e?.activation_params||[.2,.5];return{activation:t,alpha:r,beta:o}}else if(t==="Clip"){let[r,o]=e?.activation_params||[xn,Cn];return{activation:t,clipMax:o,clipMin:r}}else if(t==="LeakyRelu"){let[r]=e?.activation_params||[.01];return{activation:t,alpha:r}}return{activation:t}}});var tt,Dn,zn=Y(()=>{"use strict";tt=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw 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innerElementSize][localRow];\n        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n        }`:`\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n        }`,Hr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32)=>{let p=t[1]*e[1],h=t[0]*e[0],d=i?p:u,y=i?u:p,w=d/t[0],_=u/t[1];if(!((i&&w===4&&e[1]===4||!i&&(w===3||w===4))&&d%t[0]===0&&u%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${w} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${w} must be 3 or 4.\n  tileAWidth ${d} must be divisible by workgroupSize[0]${t[0]}. tileInner ${u} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${w}<${r}>, ${d/w}>, ${y}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${r}>, ${h/e[0]}>, ${u}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${w};\nconst tileInner = ${u};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n  let localRow = i32(localId.y);\n  let tileRow = localRow * rowPerThread;\n  let tileCol = i32(localId.x);\n\n  let globalRow =i32(globalId.y) * rowPerThread;\n  let globalCol = i32(globalId.x);\n  let batch = ${a?"0":"i32(globalId.z)"};\n  ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n  let globalRowStart = i32(workgroupId.y) * ${p};\n\n  let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n  var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};\n\n  var acc: array<vec4<${r}>, rowPerThread>;\n\n  // Loop over shared dimension.\n  let tileRowB = localRow * ${_};\n  for (var t = 0; t < num_tiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let inputRow = tileRow + innerRow;\n          let inputCol = tileCol;\n          ${yc(i,o)}\n      }\n\n      // Load one tile of B into local memory.\n      for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n          let inputRow = tileRowB + innerRow;\n          let inputCol = tileCol;\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${o?", batchIndices":""});\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n          ${w===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n          ${bc(i,w)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},Xs=(e,t)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${t?", batchIndices":""});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${t?", batchIndices":""});\n            `,wc=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Lr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32,p=!1)=>{let h=e[1]*t[1],d=e[0]*t[0],y=i?h:u,w=i?u:h;if(!(w%t[1]===0&&y%t[0]===0&&u%t[1]===0))throw new Error(`tileAHight ${w} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${y} must be divisible by workgroupSize[0]${t[0]}, tileInner ${u} must be divisible by workgroupSize[1]${t[1]}`);let _=w/t[1],v=y/t[0],S=u/t[1],A=p?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${h};\n    let globalColStart = i32(workgroupId.x) * ${d};\n\n    // Loop over shared dimension.\n    for (var t = 0; t < num_tiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var inputRow = localRow; inputRow < ${w}; inputRow = inputRow + ${t[1]}) {\n        for (var inputCol = localCol; inputCol < ${y}; inputCol = inputCol + ${t[0]}) {\n          ${Xs(i,o)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${u}; inputRow = inputRow + ${t[1]}) {\n            for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${t[0]}) {\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n            kStart + inputRow,\n            globalColStart + inputCol${o?", batchIndices":""});\n        }\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      var BCached : array<${r}, colPerThread>;\n      for (var k = 0; k < tileInner; k = k + 1) {\n        for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n        }\n        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n            acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n                ACached * BCached[innerCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        let gCol = globalColStart + localCol + innerCol * ${t[0]};\n        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n      }\n    }\n    `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${h};\n\nlet tileRowA = i32(localId.y) * ${_};\nlet tileColA = i32(localId.x) * ${v};\nlet tileRowB = i32(localId.y) * ${S};\n// Loop over shared dimension.\nfor (var t = 0; t < num_tiles; t = t + 1) {\n  // Load one tile of A into local memory.\n  for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < ${v}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${Xs(i,o)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${S}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n      let inputRow = tileRowB + innerRow;\n      let inputCol = tileCol + innerCol;\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n        kStart + inputRow,\n        globalCol + innerCol${o?", batchIndices":""});\n    }\n  }\n  kStart = kStart + tileInner;\n  workgroupBarrier();\n\n  // Compute acc values for a single thread.\n  var BCached : array<${r}, colPerThread>;\n  for (var k = 0; k < tileInner; k = k + 1) {\n    for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n      BCached[inner] = mm_Bsub[k][tileCol + inner];\n    }\n\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      ${wc(i)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${r}, ${y}>, ${w}>;\n  var<workgroup> mm_Bsub : array<array<${r}, ${d}>, ${u}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${u};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n    let batch = ${a?"0":"i32(globalId.z)"};\n    ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n    let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n    var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};\n\n    var acc : array<array<${r}, colPerThread>, rowPerThread>;\n\n    // Without this initialization strange values show up in acc.\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = 0.0;\n      }\n    }\n    ${A}\n  }\n`},vc=(e,t,r,o,i,u=!1)=>{let[a,c,p]=i,[h,d,y,w]=o,_=_r(a,p),v=_r(c,p),S=De(o[0].type.tensor),A=()=>{let E=d.rank,P=h.rank,O=`var aIndices: ${d.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\naIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return _.forEach(R=>{O+=`\naIndices[${R}] = 0;`}),O+=`\naIndices[${E-2}] = u32(row);\n                   aIndices[${E-1}] = u32(colIn);`,O},I=()=>{let E=y.rank,P=h.rank,O=`var bIndices: ${y.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\nbIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return v.forEach(R=>{O+=`\nbIndices[${R}] = 0;`}),O+=`\nbIndices[${E-2}] = u32(row);\n                   bIndices[${E-1}] = u32(colIn);`,O};return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n      var value = ${tt(e,S)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n      {\n        ${A()}\n        value = ${d.getByIndices("aIndices")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n      var value = ${tt(e,S)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n      {\n        ${I()}\n        value = ${y.getByIndices("bIndices")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${tt(e,S)}) {\n      let col = colIn * ${e};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n        var value = valueIn;\n        let coords = vec3<i32>(batch, row, colIn);\n        ${t?`value = value + ${u?"bias[colIn]":`${tt(e,S)}(bias[row])`};`:""}\n        ${r}\n        ${w.setByIndices("vec3<u32>(coords)","value")}\n      }\n    }\n    `},Un=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u.slice(0,-2),p=a.slice(0,-2),h=o?o.slice(0,-2):r.slice(0,-2),d=M.size(h),y=u[u.length-2],w=u[u.length-1],_=a[a.length-1],v=w%4===0&&_%4===0,S=y<=8?[4,1,1]:[4,4,1],A=[8,8,1],I=[Math.ceil(_/A[0]/S[0]),Math.ceil(y/A[1]/S[1]),Math.ceil(d/A[2]/S[2])],x=v?4:1,E=[...c,y,w/x],P=E.length,O=[...p,w,_/x],R=O.length,L=[d,y,_/x],N=[{type:6,data:y},{type:6,data:_},{type:6,data:w}];xt(t,N),N.push(...Z(h,E,O));let K=["rank","rank"],Q=e.length>2;Q&&(N.push(...Z(e[2].dims)),K.push("rank")),N.push(...Z(L));let he=W=>{let se=h.length,Ce=An("batchDims",e[0].dataType,se,1),We=De(e[0].dataType),ee=U("a",e[0].dataType,P,x),ae=U("b",e[1].dataType,R,x),Ae=j("result",e[0].dataType,L.length,x),me=[ee,ae];if(Q){let G=i?x:1;me.push(U("bias",e[2].dataType,e[2].dims.length,G))}let ie=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ct(t,ie);let ue=De(Ae.type.tensor),le=St(t,Ae.type.value,ue),qe=vc(x,Q,le,[Ce,ee,ae,Ae],[c,p,h],i);return`\n  ${W.registerUniforms(ie).registerInternalVariables(Ce).declareVariables(...me,Ae)}\n  ${qe}\n  ${v?Hr(S,A,We,Ce):Lr(S,A,We,Ce)}\n                   `};return{name:"MatMul",shaderCache:{hint:`${S};${t.activation};${v};${i}`,inputDependencies:K},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:I[0],y:I[1],z:I[2]},programUniforms:N}),getShaderSource:he}}});var $c,Qs,Js=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();$c=(e,t,r,o,i=!1,u,a=4,c=4,p=4,h="f32")=>{let d=Q=>{switch(Q){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Q} is not supported.`)}},y=Q=>{switch(Q){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${Q} is not supported.`)}},w=e?`\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    `:`\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    `,_=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,v=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",S=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",A=e?"row":"col",I=e?"col":"row",x=`\n    let inChannels = i32(uniforms.w_shape[2]);\n    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n    let outRow = ${A} / outWidth;\n    let outCol = ${A} % outWidth;\n\n    let WRow = ${I} / (i32(uniforms.w_shape[1]) * inChannels);\n    let WCol = ${I} / inChannels % i32(uniforms.w_shape[1]);\n    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n    let xCh = ${I} % inChannels;\n    var resData = ${tt(a,h)}(0.0);\n    // The bounds checking is always needed since we use it to pad zero for\n    // the \'same\' padding type.\n    if (xRow >= 0 && xRow < ${v} && xCol >= 0 && xCol < ${S}) {\n      ${w}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${d(a)}\n    }\n    return resData;`,E=e?t&&o?`\n    let col = colIn * ${a};\n    ${x}`:`\n    let col = colIn * ${a};\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n      ${x}\n    }\n    return ${tt(a,h)}(0.0);`:o&&r?`\n    let col = colIn * ${a};\n    ${x}`:`\n    let col = colIn * ${a};\n    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n      ${x}\n    }\n    return ${tt(a,h)}(0.0);`,P=`${y(c)}`,O=tt(p,h),R=e?tt(a,h):tt(c,h),L=e?tt(c,h):tt(a,h),N=St(u,O,h);return`\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {\n      ${e?E:P}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${L} {\n      ${e?P:E}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${O}) {\n      let col = colIn * ${p};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)\n      {\n      var value = valueIn;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${_}\n      ${Dn(i)}\n      ${N}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},Qs=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&(h%4===0||h%3===0)&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let P=v?p&&h%4!==0?3:4:1,O=I[1]*x[1],R=I[0]*x[0],L=Math.max(I[0]*P,I[1]),N=o%O===0,K=i%R===0,Q=u%L===0,he=v?[P,4,4]:[1,1,1],W=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];xt(t,W),W.push(...Z(e[0].dims,e[1].dims));let se=["rank","rank"];a&&(W.push(...Z(e[2].dims)),se.push("rank")),W.push(...Z(r));let Ce=We=>{let ee=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Ct(t,ee);let ae=v?4:1,Ae=De(e[0].dataType),me=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n        result[flatIndex] = ${v?`vec4<${Ae}>`:Ae}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${v?"/ 4":""}, value);\n      }`,ie=U("x",e[0].dataType,e[0].dims.length,P===3?1:P),ue=U("w",e[1].dataType,e[1].dims.length,ae),le=[ie,ue],qe=j("result",e[0].dataType,r.length,ae);if(a){let G=U("bias",e[2].dataType,e[2].dims.length,ae);le.push(G),me+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${v?`vec4<${Ae}>`:Ae} {\n          return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n        }`}return`\n        ${Mn("uniforms.result_strides")}\n        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,\n        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,\n        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n        ${We.registerUniforms(ee).declareVariables(...le,qe)}\n        ${me}\n        ${$c(p,N,K,Q,a,t,he[0],he[1],he[2],Ae)}\n        ${v?Hr(x,I,Ae,void 0,!p,L):Lr(x,I,Ae,void 0,!p,L,!1,void 0,c)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${P};${v};${N};${K};${Q};${O};${R};${L}`,inputDependencies:se},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:W}),getShaderSource:Ce}}});var Mo,eu,tu=Y(()=>{"use strict";ye();Se();_e();Uo();Ft();Mo=(e,t,r)=>{let o=e.length>2,i=o?"value += b[output_channel];":"",u=e[0].dims,a=e[1].dims,c=a[0]/t.group,p=t.format==="NHWC",h=Vn(u,a,t.dilations,t.pads,t.strides,p),d=M.size(h),y=[{type:12,data:d},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:c}];xt(t,y),y.push(...Z(u,a));let w=["rank","rank"];o&&(y.push(...Z(e[2].dims)),w.push("rank")),y.push(...Z(h));let _=v=>{let S=j("output",e[0].dataType,h.length),A=De(S.type.tensor),I=St(t,S.type.value,A),x=U("x",e[0].dataType,u.length),E=U("w",e[1].dataType,a.length),P=[x,E];o&&P.push(U("b",e[2].dataType,e[2].dims.length));let O=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Ct(t,O),`\n  ${v.registerUniforms(O).declareVariables(...P,S)}\n\n  ${v.mainStart()}\n    ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n    let outputIndices = ${S.offsetToIndices("global_idx")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${p?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${p?1:2}], outputIndices[${p?2:3}]) * uniforms.strides - uniforms.pads;\n    let group_id: u32 = output_channel / uniforms.output_channels_per_group;\n\n    var value: ${S.type.value} = ${S.type.value}(0);\n    for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\n      let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\n      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\n        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\n\n        if (xHeight < 0u || xHeight >= uniforms.x_shape[${p?1:2}]) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];\n          if (xWidth < 0u || xWidth >= uniforms.x_shape[${p?2:3}]) {\n            continue;\n          }\n\n          let xVal = ${p?x.get("batch","xHeight","xWidth","input_channel"):x.get("batch","input_channel","xHeight","xWidth")};\n          let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${i}\n    ${I}\n    ${S.setByOffset("global_idx","value")}\n  }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:w},getRunData:()=>({outputs:[{dims:r?r(h):h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:y}),getShaderSource:_}},eu=(e,t,r)=>{let o=e.length>2,i=Me(r[3]),u=Me(r[2]),a=M.size(r)/i/u,c=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/i],p=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/i],h=[r[0],r[1],r[2],r[3]/i],d=[{type:12,data:a},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];xt(t,d),d.push(...Z(c,p,h));let y=(u-1)*t.strides[1]+p[1],w=_=>{let v=j("output",e[0].dataType,h.length,i),S=De(v.type.tensor),A=St(t,v.type.value,S),I=U("x",e[0].dataType,c.length,i),x=U("w",e[1].dataType,p.length,i),E=[I,x];o&&E.push(U("b",e[2].dataType,e[2].dims,i));let P=o?"value += b[output_channel];":"",O=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Ct(t,O),`\n  ${_.registerUniforms(O).declareVariables(...E,v)}\n  ${_.mainStart()}\n    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let width0 = uniforms.output_shape[3];\n    let output_channel = global_idx % width0;\n    var index1 = global_idx / width0;\n    let width1 = uniforms.output_shape[2] / ${u}u;\n    let col = (index1 % width1) * ${u}u;\n    index1 = index1 / width1;\n    let row = index1 % uniforms.output_shape[1];\n    let batch = index1 / uniforms.output_shape[1];\n\n    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;\n\n    var x_vals: array<${I.type.value}, ${y}>;\n    var values: array<${v.type.value}, ${u}>;\n    let input_channel = output_channel;\n    // Use constant instead of uniform can give better performance for w\'s height/width.\n    for (var w_height: u32 = 0u; w_height < ${p[0]}; w_height++) {\n      let x_height = x_corner.x + i32(w_height);\n      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {\n        for (var i = 0; i < ${y}; i++) {\n          let x_width = x_corner.y + i;\n          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {\n            x_vals[i] = ${I.get("batch","u32(x_height)","u32(x_width)","input_channel")};\n          } else {\n            x_vals[i] = ${I.type.value}(0);\n          }\n        }\n        for (var w_width: u32 = 0u; w_width < ${p[1]}; w_width++) {\n          let w_val = ${x.get("w_height","w_width","0","output_channel")};\n          for (var i = 0u; i < ${u}u; i++) {\n            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);\n          }\n        }\n      }\n    }\n\n    for (var i = 0u; i < ${u}u; i++) {\n      var value = values[i];\n      ${P}\n      ${A}\n      ${v.set("batch","row","col + i","output_channel","value")};\n    }\n  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${i};${u};${y};${p[0]};${p[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:w}}});var Vo,_c,ru,Wo=Y(()=>{"use strict";ye();Se();Fr();_e();Ft();Vo=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u[u.length-2],p=a[a.length-1],h=u[u.length-1],d=Me(p),y=Me(h),w=Me(c),_=M.size(r)/d/w,v=e.length>2,S=o?o.slice(0,-2):r.slice(0,-2),I=[M.size(S),c,p],x=[{type:12,data:_},{type:12,data:c},{type:12,data:p},{type:12,data:h}];xt(t,x),x.push(...Z(S,u,a)),v&&x.push(...Z(e[2].dims)),x.push(...Z(I));let E=P=>{let O=An("batch_dims",e[0].dataType,S.length),R=U("a",e[0].dataType,u.length,y),L=U("b",e[1].dataType,a.length,d),N=j("output",e[0].dataType,I.length,d),K=De(N.type.tensor),Q=St(t,N.type.value,K),he=[R,L],W="";if(v){let ie=i?d:1;he.push(U("bias",e[2].dataType,e[2].dims.length,ie)),W=`${i?`value += bias[col / ${ie}];`:`value += ${N.type.value}(bias[row + i]);`}`}let se=u.slice(0,-2),Ce=a.slice(0,-2),We=_r(se,S),ee=_r(Ce,S),ae=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Ct(t,ae);let Ae=(ie,ue)=>{let le=ie.rank,qe=ie.name;if(le===2)return`var ${qe}_indices = ${ie.type.indices}(0u, 0u);`;let G=O.rank,ne=`var ${qe}_indices: ${ie.type.indices};`;for(let xe=le-2-1,Ke=G-1;xe>=0;xe--,Ke--)ne+=`\n${qe}_indices[${xe}] = ${G>1?`batch_indices[${Ke}]`:"batch_indices"};`;return ue.forEach(xe=>{ne+=`\n${qe}_indices[${xe}] = 0;`}),ne+=`${qe}_indices[${le-2}] = 0u;\n                     ${qe}_indices[${le-1}] = 0u;`,ne},me=()=>{let ie=`var a_data: ${R.type.value};`;for(let ue=0;ue<y;ue++)ie+=`\n              let b_data${ue} = b[(b_offset + (k + ${ue}) * uniforms.N + col) / ${d}];`;for(let ue=0;ue<w;ue++){ie+=`a_data = a[(a_offset + (row + ${ue}) * uniforms.K + k) / ${y}];`;for(let le=0;le<y;le++)ie+=`\n            values[${ue}] = fma(${L.type.value}(a_data${y===1?"":`[${le}]`}), b_data${le}, values[${ue}]);\n`}return ie};return`\n  ${P.registerUniforms(ae).registerInternalVariables(O).declareVariables(...he,N)}\n  ${P.mainStart()}\n    ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let col = (global_idx % (uniforms.N / ${d})) * ${d};\n    var index1 = global_idx / (uniforms.N / ${d});\n    let stride1 = uniforms.M / ${w};\n    let row = (index1 % stride1) * ${w};\n    let batch = index1 / stride1;\n\n    ${r.length===2?"":`let batch_indices = ${O.offsetToIndices("batch")};`}\n    ${Ae(R,We)}\n    let a_offset = ${R.indicesToOffset("a_indices")};\n    ${Ae(L,ee)}\n    let b_offset = ${L.indicesToOffset("b_indices")};\n    var values: array<${N.type.value}, ${w}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${y}) {\n      ${me()}\n    }\n    for (var i = 0u; i < ${w}u; i++) {\n      var value = values[i];\n      ${W}\n      ${Q}\n      let cur_indices = ${N.type.indices}(batch, row + i, col);\n      let offset = ${N.indicesToOffset("cur_indices")};\n      ${N.setByOffset(`offset / ${d}`,"value")};\n    }\n  }\n  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${d};${y};${w};${i}`,inputDependencies:v?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:x}),getShaderSource:E}},_c=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},ru=e=>{_c(e.inputs);let t=It.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can\'t use matmul on the given tensors");let r=t[t.length-1],o=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&o<8?e.compute(Vo(e.inputs,{activation:""},t)):e.compute(Un(e.inputs,{activation:""},t))}});var Vn,No,Sc,nu,Go,xc,Cc,Ho,Uo=Y(()=>{"use strict";Se();Js();Fr();tu();Ft();Wo();Sr();Vn=(e,t,r,o,i,u)=>{let a=e[0],c=e.slice(u?1:2,u?3:4),p=c.length,h=t[0],y=t.slice(2).map((v,S)=>v+(v-1)*(r[S]-1)),_=c.map((v,S)=>v+o[S]+o[S+p]).map((v,S)=>Math.floor((v-y[S]+i[S])/i[S]));return _.splice(0,0,a),_.splice(u?3:1,0,h),_},No=[2,3,1,0],Sc=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[1]*t.group;if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let i=e[0].dims.length-2;if(t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},nu=(e,t)=>{let r=e.kernelShape.slice();for(let u=2;u<t[1].dims.length;++u)r[u-2]===0&&(r[u-2]=t[1].dims[u]);let o=e.pads.slice();nr.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,o,e.format==="NHWC",e.autoPad);let i=Object.assign({},e);return Object.assign(i,{kernelShape:r,pads:o}),i},Go=e=>{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,u=e.group,a=e.kernel_shape,c=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},xc=(e,t,r)=>{let o=nu(r,t),i=r.format==="NHWC";if(r.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&i&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let L=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),N=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=N);let K=[t[0],N];t.length===3&&K.push(t[2]),e.compute(eu(K,o,L),{inputs:K})}else e.compute(Mo(t,o));return}let u=t.length===3,a=t[0].dims[i?1:2],c=t[0].dims[i?2:3],p=t[0].dims[i?3:1],h=t[1].dims[2],d=t[1].dims[3],y=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),w=y[i?1:2],_=y[i?2:3],v=y[i?3:1],S=i&&h===a&&d===c&&r.pads[0]===0&&r.pads[1]===0;if(S||h===1&&d===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let R=y[0],L,N,K,Q=[];if(i){let se=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=se),S){let Ce=a*c*p;L=t[0].reshape([1,R,Ce]),N=se.reshape([1,Ce,v]),K=[1,R,v]}else L=t[0].reshape([R,a*c,p]),N=se.reshape([1,p,v]),K=[R,w*_,v];Q.push(L),Q.push(N)}else L=t[0].reshape([R,p,a*c]),N=t[1].reshape([1,v,p]),K=[R,v,w*_],Q.push(N),Q.push(L);u&&Q.push(t[2]);let he=K[2],W=Q[0].dims[Q[0].dims.length-1];he<8&&W<8?e.compute(Vo(Q,o,y,K,i),{inputs:Q}):e.compute(Un(Q,o,y,K,i),{inputs:Q});return}let A=!0,I=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=I);let x=[t[0],I];u&&x.push(t[2]);let E=i?w*_:v,P=i?v:w*_,O=h*d*p;e.compute(Qs(x,o,y,E,P,O,u,A),{inputs:x})},Cc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=[0,t.pads[0],0,t.pads[1]],u=[1].concat(t.strides),a=[1].concat(t.dilations),c=[1].concat(t.kernelShape),p=nu({...t,pads:i,strides:u,dilations:a,kernelShape:c},o);e.compute(Mo(o,p,h=>r?[h[0],h[2],h[3]]:[]))},Ho=(e,t)=>{Sc(e.inputs,t),e.inputs[0].dims.length===3?Cc(e,t):xc(e,e.inputs,t)}});var Ac,ou,iu=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();Ac=(e,t=!1,r,o,i=4)=>{let u=I=>{switch(I){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];\n            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];\n            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];\n            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];\n            return ${o}(v0, v1, v2, v3);\n            `;default:throw new Error(`innerElementSize ${I} is not supported.`)}},a=e?`\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      `,c=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,p=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",h=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",d=e?"row":"col",y=e?"col":"row",w=`\n      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      let outRow = ${d} / outWidth;\n      let outCol = ${d} % outWidth;\n\n      let WRow = ${y} / (uniforms.filter_dims[1] * inChannels);\n      let WCol = ${y} / inChannels % uniforms.filter_dims[1];\n      let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n      let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n      if (xR < 0.0 || xR >= f32(${p}) || fract(xR) > 0.0) {\n        return ${o}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${h}) || fract(xC) > 0.0) {\n        return ${o}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${y} % inChannels;\n      ${a}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${i}];`,_=e?`\n      let col = colIn * ${i};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n        ${w}\n      }\n      return ${o}(0.0);`:`\n      let col = colIn * ${i};\n      if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n        ${w}\n      }\n      return ${o}(0.0);`,v=`\n      let col = colIn * ${i};\n      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n      let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);\n      let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];\n      if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"}  && coordX >= 0 && coordY >= 0) {\n        let rowInner = row % inChannels;\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\n        ${u(i)}\n      }\n      return ${o}(0.0);\n      `,S=St(r,o);return`\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${o} {\n    ${e?_:v}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${o} {\n    ${e?v:_}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${o}) {\n    let col = colIn * ${i};\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n      var value = valueInput;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${c}\n      ${Dn(t)}\n      ${S}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${i}] = value;\n    }\n  }`},ou=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&h%4===0&&h%3&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let P=v?4:1,O=Math.max(I[0]*P,I[1]),R=v?4:1,L=[t.kernelShape[p?1:2],t.kernelShape[p?2:3]],N=[L[0]+(t.dilations[0]<=1?0:(L[0]-1)*(t.dilations[0]-1)),L[1]+(t.dilations[1]<=1?0:(L[1]-1)*(t.dilations[1]-1))],K=[N[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),N[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Q=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:L},{type:6,data:K}];xt(t,Q),Q.push(...Z(e[0].dims,e[1].dims));let he=["rank","rank"];a&&(Q.push(...Z(e[2].dims)),he.push("rank")),Q.push(...Z(r));let W=se=>{let Ce=U("x",e[0].dataType,e[0].dims.length,R),We=U("w",e[1].dataType,e[1].dims.length,1),ee=j("result",e[0].dataType,r.length,R),ae=[Ce,We],Ae="";if(a){let ue=U("bias",e[2].dataType,e[2].dims.length,R);ae.push(ue),Ae+=`\n          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${ue.type.value} {\n            return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n          }`}let me=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:L.length},{name:"pads",type:"i32",length:K.length}];Ct(t,me);let ie=De(e[0].dataType,1);if(ie!=="f16"&&ie!=="f32")throw new Error(`elemType ${ie} is not supported.`);return`\n        ${Mn("uniforms.result_strides")}\n        ${se.registerUniforms(me).declareVariables(...ae,ee)};\n        ${Ae}\n        ${Ac(p,a,t,Ce.type.value,P)}\n        ${v?Hr(x,I,ie,void 0,!p,O):Lr(x,I,ie,void 0,!p,O,!1,void 0,c)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${x};${I};${v}`,inputDependencies:he},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Q}),getShaderSource:W}}});var Ic,Lo,au=Y(()=>{"use strict";ye();Lt();Se();_e();Ic=(e,t,r,o,i,u=!1,a,c,p=!1)=>{let h=p?1:2,d=p?2:3,y=p?3:1,w=u?2:1,_=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${u?`vec4<${a}>`:a}) {\n    result[flatIndex] = ${u?`vec4<${a}>`:a}(value);\n  }`;o&&(_+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${u?`vec4<${a}>`:a} {\n      return bias[coords.${p?"w":"y"}${u?"/ 4":""}];\n    }`);let v=u?4:1,S=U("W",t[1].dataType,t[1].dims.length,v),A=U("Dy",t[0].dataType,t[0].dims.length,v),I=[A,S];o&&I.push(U("bias",t[2].dataType,[r[y]].length,v));let x=j("result",t[0].dataType,r.length,v),E=`{\n        let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];\n        let r = ${i?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];\n        let c = ${i?"global_id.y":"workgroup_id.y"} * ${w};\n        let d1: u32 = ${i?"global_id.x":"workgroup_id.x"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(uniforms.pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${a}>, ${w}>;\n        for (var i = 0; i < ${w}; i++) {\n          dotProd[i] = vec4<${a}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n          var dyR = (${a}(dyCorner.x) + ${a}(wR)) / ${a}(uniforms.strides.x);\n          let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n            let dyC = (${a}(dyCorner.y) + ${a}(wC)) / ${a}(uniforms.strides.y);\n            let dyC2 = (${a}(dyCorner.y) + 1.0 + ${a}(wC)) / ${a}(uniforms.strides.y);\n            let wCPerm = uniforms.filter_dims[1] - 1 - wC;\n            if (wCPerm < 0) {\n              continue;\n            }\n            var bDyCVal = true;\n            var bDyCVal2 = true;\n            if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${a}(uniforms.Dy_shape[2]) ||\n                fract(dyC2) > 0.0) {\n              bDyCVal2 = false;\n            }\n\n            let idyC: u32 = u32(dyC);\n            let idyC2: u32 = u32(dyC2);\n            if (bDyCVal && bDyCVal2) {\n              let d2Length = uniforms.Dy_shape[3];\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${A.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${a}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${A.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${a}>(dot(xValue, wValue0),\n                                                    dot(xValue, wValue1),\n                                                    dot(xValue, wValue2),\n                                                    dot(xValue, wValue3));\n              }\n            } else if (bDyCVal) {\n              let d2Length = uniforms.Dy_shape[${y}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${A.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${a}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n              }\n            } else if (bDyCVal2) {\n              let d2Length = uniforms.Dy_shape[3];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${A.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${a}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[1] = dotProd[1] + tmpval;\n              }\n            }\n          }\n        }\n\n        for (var i: u32 = 0; i < ${w}; i = i + 1) {\n          let value = dotProd[i] + ${o?"bias[c+i]":`vec4<${a}>(0.0)`};\n          ${x.set("batch","r","c + i","d1","value")};\n        }\n      }`,P=`\n          let outputIndices = ${x.offsetToIndices("global_idx")};\n          let batch = ${x.indicesGet("outputIndices",0)};\n          let d1 = ${x.indicesGet("outputIndices",y)};\n          let r = ${x.indicesGet("outputIndices",h)};\n          let c = ${x.indicesGet("outputIndices",d)};\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;\n          let dyRCorner = dyCorner.x;\n          let dyCCorner = dyCorner.y;\n          let groupId = d1 / uniforms.output_channels_per_group;\n          let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;\n          // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n          // ? = to be determined. : = across all values in that axis.\n          var dotProd = ${a}(0.0);\n          for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n            if (wR % uniforms.dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${a}(dyRCorner) + ${a}(wR)) / ${a}(uniforms.strides[0]);\n            let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n            if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[${h}]) || fract(dyR) > 0.0 ||\n                wRPerm < 0) {\n              continue;\n            }\n            let idyR: u32 = u32(dyR);\n\n            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {\n              if (wC % uniforms.dilations.y != 0) {\n                continue;\n              }\n              let dyC = (${a}(dyCCorner) + ${a}(wC)) / ${a}(uniforms.strides.y);\n              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n              if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[${d}]) ||\n                  fract(dyC) > 0.0 || wCPerm < 0) {\n                continue;\n              }\n              let idyC: u32 = u32(dyC);\n              var inputChannel = groupId * uniforms.input_channels_per_group;\n              for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {\n                let xValue = ${p?A.get("batch","idyR","idyC","inputChannel"):A.get("batch","inputChannel","idyR","idyC")};\n                let wValue = ${S.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${o?"bias[d1]":`${a}(0.0)`};\n          ${x.setByOffset("global_idx","value")};\n        `;return`\n  ${e.registerUniforms(c).declareVariables(...I,x)}\n  ${_}\n\n    ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};\n  ${u?E:P}}`},Lo=(e,t,r)=>{let o=e.length>2,i=t.outputShape,u=M.size(i),a=[Math.ceil(u/64),1,1];Ve("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${a}`);let c=t.format==="NHWC",p=["rank","rank"],h=[t.strides[0],t.strides[1]],d=[t.kernelShape[c?1:2],t.kernelShape[c?2:3]],y=[t.dilations[0],t.dilations[1]],w=[d[0]+(t.dilations[0]<=1?0:(t.kernelShape[c?1:2]-1)*(t.dilations[0]-1)),d[1]+(t.dilations[1]<=1?0:(t.kernelShape[c?2:3]-1)*(t.dilations[1]-1))],_=[w[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),w[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],v=!1,S=t.group,A=e[1].dims,I=A[0]/S,x=A[1],E=[{type:12,data:u},{type:12,data:h},{type:12,data:d},{type:12,data:y},{type:12,data:w},{type:6,data:_},{type:12,data:I},{type:12,data:x},...Z(e[0].dims,e[1].dims)];o&&(E.push(...Z(e[2].dims)),p.push("rank")),E.push(...Z(i));let P=a[1]===1&&a[2]===1,O=R=>{let L=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:h.length},{name:"filter_dims",type:"u32",length:d.length},{name:"dilations",type:"u32",length:d.length},{name:"effective_filter_dims",type:"u32",length:w.length},{name:"pads",type:"i32",length:_.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],N=De(e[0].dataType);return`${Ic(R,e,i,o,P,v,N,L,c)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:p},getRunData:()=>({dispatchGroup:{x:a[0],y:a[1],z:a[2]},outputs:[{dims:r?r(i):i,dataType:e[0].dataType}],programUniforms:E}),getShaderSource:O}}});var Tc,Ec,Pc,su,uu,kc,Oc,Rc,Bc,du,lu=Y(()=>{"use strict";iu();au();Ft();Sr();Tc=(e,t,r,o,i,u)=>(e-1)*t+r+(o-1)*i+1-u,Ec=(e,t,r,o,i)=>{let u=Math.floor(e/2);t==="SAME_UPPER"?(r[o]=u,r[i]=e-u):t==="SAME_LOWER"&&(r[o]=e-u,r[i]=u)},Pc=(e,t,r,o,i,u,a,c,p,h)=>{let d=e.length-2,y=h.length===0;if(p.length===0)for(let v=0;v<d;++v)p.push(0);let w=e[0],_=t[c?3:1]*i;for(let v=0,S=e.length-d-(c?1:0);v<d;++v,++S){let A=e[S],I=y?A*a[v]:h[v],x=Tc(A,a[v],u[v],t[S],r[v],I);Ec(x,o,u,v,v+d),y&&h.push(a[v]*(A-1)+p[v]+(t[S]-1)*r[v]+1-u[v]-u[v+d])}h.splice(0,0,w),h.splice(c?3:1,0,_)},su=(e,t)=>{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((y,w)=>y*w,1)===0){r.length=0;for(let y=2;y<t[1].dims.length;++y)r.push(t[1].dims[y])}let o=e.format==="NHWC";r.splice(0,0,t[1].dims[0]),r.splice(o?3:1,0,t[1].dims[1]);let i=e.pads.slice(),u=e.outputShape.slice(),a=e.outputPadding.slice(),c=t[0].dims,p=e.dilations.slice();if(p.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;p=new Array(y).fill(1)}let h=e.strides.slice();if(h.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;h=new Array(y).fill(1)}Pc(c,r,p,e.autoPad,e.group,i,h,o,a,u);let d=Object.assign({},e);return Object.assign(d,{kernelShape:r,pads:i,outputPadding:a,outputShape:u,dilations:p,strides:h}),d},uu=e=>{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,u=e.group,a=e.kernelShape,c=e.pads,p=e.strides,h=e.wIsConst(),d=e.outputPadding,y=e.outputShape;return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,outputPadding:d,outputShape:y,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},kc=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[0];if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let i=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==i))throw new Error("invalid bias");let u=e[0].dims.length-2;if(t.dilations.reduce((d,y)=>d+y,0)>0&&t.dilations.length!==u)throw new Error(`dilations should be ${u}D`);if(t.strides.reduce((d,y)=>d+y,0)>0&&t.strides.length!==u)throw new Error(`strides should be ${u}D`);if(t.pads.reduce((d,y)=>d+y,0)>0&&t.pads.length!==u*2)throw new Error(`pads should be ${u*2}D`);if(t.outputPadding.length!==u&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${u}D`);if(t.kernelShape.reduce((d,y)=>d+y,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Oc=[2,3,1,0],Rc=(e,t,r)=>{let o=su(r,t),i=r.format==="NHWC",u=o.outputShape,a=u[i?3:1],c=t[0].dims[i?3:1];if(o.group!==1||a===1&&c===1){e.compute(Lo(t,o));return}let p=u[i?1:2],h=u[i?2:3],d=t[1].dims[2],y=t[1].dims[3],w=i?p*h:a,_=i?a:p*h,v=d*y*c,S=!0,A=e.kernelCustomData.wT??e.compute(yt(t[1],Oc),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=A);let I=[t[0],A],x=t.length===3;x&&(!i&&t[2].dims.length===1?I.push(t[2].reshape([t[2].dims[0],1,1])):I.push(t[2])),e.compute(ou(I,o,u,w,_,v,x,S),{inputs:I})},Bc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let u=t.dilations;(u.length===0||u[0]===0)&&(u=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let c=t.pads;c.length===0&&(c=[0,0]),c=[0,c[0],0,c[1]],a=[1].concat(a),u=[1].concat(u),i=[1].concat(i);let p=su({...t,pads:c,strides:a,dilations:u,kernelShape:i},o);e.compute(Lo(o,p,h=>r?[h[0],h[2],h[3]]:[h[0],h[1],h[3]]))},du=(e,t)=>{kc(e.inputs,t),e.inputs[0].dims.length===3?Bc(e,t):Rc(e,e.inputs,t)}});var Dc,cu,pu,mu=Y(()=>{"use strict";ye();Se();Ze();_e();Dc=(e,t,r,o)=>{let i=M.size(t),u=t.length,a=U("input",e,u),c=j("output",e,u),p=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),h=M.normalizeAxis(p,u),d=y=>{let w=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,_=fe("uniforms.input_shape","uniforms.axis",u),v=o.reverse?w+(o.exclusive?" + 1":""):"0",S=o.reverse?_:w+(o.exclusive?"":" + 1");return`\n                ${y.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(a,c)}\n                ${y.mainStart()}\n                  ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n                  var inputIndices = ${c.offsetToIndices("global_idx")};\n                  var sum = ${c.type.value}(0);\n                 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r=e[0].dims,o=r,i=2,u=M.sizeToDimension(r,i),a=M.sizeFromDimension(r,i),c=Me(a),p=a/c,h=[r[0],r[1],p],d=["rank","type","type"],y=[{type:12,data:a},{type:12,data:p}];y.push(...Z(h,h));let w=_=>{let v=U("x",e[0].dataType,h.length,c),S=U("scale",e[1].dataType,e[1].dims),A=U("bias",e[2].dataType,e[2].dims),I=j("output",e[0].dataType,h.length,c),x=[v,S,A,I],E=v.type.value,P=c===1?"f32":`vec${c}<f32>`,O=64,R=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n  var<workgroup> meanShared : f32;\n  var<workgroup> squaredNormShared : f32;\n  var<workgroup> workgroupShared : array<${P}, ${O}>;\n  const workgroupSize = ${O}u;\n  ${_.registerUniforms(R).declareVariables(...x)}\n  ${_.mainStart(O)}\n    let norm = global_idx / workgroupSize;\n    let batch = norm / uniforms.x_shape[1];\n    let channel = norm % uniforms.x_shape[1];\n    let localIndex = local_id.x;\n\n    // initialize workgroup memory\n    var initial = ${P}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      initial = initial + ${P}(${v.get("batch","channel","h")});\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the mean of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      meanShared = ${_t("workgroupShared[0]",c)} / f32(uniforms.normSize);\n    }\n    workgroupBarrier();\n\n    // reinitialize workgroup memory.\n    initial = ${P}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let deviation =  ${P}(${v.get("batch","channel","h")}) - ${P}(meanShared);\n      initial = initial + deviation * deviation;\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the sum of square of deviation of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      squaredNormShared = ${_t("workgroupShared[0]",c)};\n    }\n    workgroupBarrier();\n\n    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n    let channelScale = invStdDev * f32(${S.getByOffset("channel")});\n    let channelShift = f32(${A.getByOffset("channel")}) - meanShared * channelScale;\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let value = ${v.get("batch","channel","h")} * ${E}(${P}(channelScale)) + ${E}(${P}(channelShift));\n      ${I.set("batch","channel","h","value")};\n    }\n  }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${c}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:u},programUniforms:y}),getShaderSource:w}},Jc=(e,t,r,o,i,u,a,c)=>{let p=Me(a),h=64,d=p===1?"vec2f":`mat2x${p}f`,y=p===1?"f32":`vec${p}f`,w=(R,L)=>`${d}(${R}, ${L})`,_=i*a/p,v=Math.ceil(u/h),S=["type"],A=[{type:12,data:v},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(u*a/p)}],I=R=>{let L=U("input",t.dataType,t.dims,p);return`\n  ${R.declareVariables(L)}\n  @group(0) @binding(1) var<storage, read_write> output : array<${d}>;\n  struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n  @group(0) @binding(2) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart(h)}\n    let currentImageNumber = global_idx / ${h} / uniforms.C;\n    let currentChannelNumber = (global_idx / ${h}) % uniforms.C;\n    let wgOffset = local_id.x * uniforms.wg_size;\n    if (wgOffset >= uniforms.H) {\n        return;\n    }\n    let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);\n\n    let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;\n    var sum = ${$t("f32",p)};\n    var squaredSum = ${$t("f32",p)};\n    for (var i: u32 = wgOffset; i < wgMax; i++) {\n        let value = ${y}(input[offset + i * uniforms.C]);\n        sum += value;\n        squaredSum += value * value;\n    }\n    output[global_idx] = ${w("sum","squaredSum")};\n  }`},x=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${p}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:[i,a,h,2],dataType:1}],dispatchGroup:{x:i*a/p},programUniforms:A}),getShaderSource:I},{inputs:[t],outputs:[-1]})[0],E=[{type:12,data:_},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(h*a/p)}],P=["type","type","type"],O=R=>{let L=U("scale",r.dataType,r.dims,p),N=U("bias",o.dataType,o.dims,p);return`\n  @group(0) @binding(0) var<storage, read> input : array<${d}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${L.type.storage}>;\n  @group(0) @binding(2) var<storage, read> bias : array<${N.type.storage}>;\n  @group(0) @binding(3) var<storage, read_write> output : array<${d}>;\n  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n  @group(0) @binding(4) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart()}\n    ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n    let currentImageNumber = global_idx / uniforms.C;\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let offset = currentImageNumber * uniforms.image_size;\n    var sum = ${$t("f32",p)};\n    var squaredSum = ${$t("f32",p)};\n    for (var i: u32 = 0; i < min(${h}, uniforms.H); i++) {\n        let value = input[offset + i + currentChannelNumber * ${h}];\n        sum += value[0];\n        squaredSum += value[1];\n    }\n    sum = sum / f32(uniforms.H);\n    squaredSum = squaredSum / f32(uniforms.H);\n    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${c}));\n    let channelScale = invStdDev * ${y}(scale[currentChannelNumber]);\n    let channelShift = ${y}(bias[currentChannelNumber]) - sum * channelScale;\n\n    output[global_idx] = ${w("channelScale","channelShift")};\n  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${p};${c}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:[i,a,2],dataType:1}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:E}),getShaderSource:O},{inputs:[x,r,o],outputs:[-1]})[0]},ep=(e,t,r)=>{let o=t[0].dims,i=o,u=o[0],a=o[o.length-1],c=M.sizeFromDimension(o,1)/a,p=Me(a),h=M.size(i)/p,d=[{type:12,data:c},{type:12,data:Math.floor(a/p)}],y=["type","type"],w=Jc(e,t[0],t[1],t[2],u,c,a,r.epsilon),_=v=>{let S=De(t[0].dataType),A=p===1?"vec2f":`mat2x${p}f`,I=p===1?S:`vec${p}<${S}>`,x=U("input",t[0].dataType,t[0].dims,p),E=j("output",t[0].dataType,i,p);return`\n  @group(0) @binding(0) var<storage, read> input : array<${x.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${A}>;\n  @group(0) @binding(2) var<storage, read_write> output : array<${E.type.storage}>;\n  struct Uniforms {H: u32, C : u32};\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\n\n  ${v.mainStart()}\n    let currentImageNumber = global_idx / (uniforms.C * uniforms.H);\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;\n    let scale = scaleInput[scaleOffset];\n    output[global_idx] = fma(input[global_idx], ${I}(scale[0]), ${I}(scale[1]));\n  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:d}),getShaderSource:_},{inputs:[t[0],w]})},zu=(e,t)=>{t.format==="NHWC"?ep(e,e.inputs,t):e.compute(Qc(e.inputs,t))}});var tp,rp,Uu,Vu=Y(()=>{"use strict";ye();Se();_e();tp=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},rp=(e,t,r)=>{let o=t.simplified,i=e[0].dims,u=e[1],a=!o&&e[2],c=i,p=M.normalizeAxis(t.axis,i.length),h=M.sizeToDimension(i,p),d=M.sizeFromDimension(i,p),y=M.size(u.dims),w=a?M.size(a.dims):0;if(y!==d||a&&w!==d)throw new Error(`Size of X.shape()[axis:] == ${d}.\n       Size of scale and bias (if provided) must match this.\n       Got scale size of ${y} and bias size of ${w}`);let _=[];for(let O=0;O<i.length;++O)O<p?_.push(i[O]):_.push(1);let v=Me(d),S=["type","type"],A=[{type:12,data:h},{type:1,data:d},{type:12,data:Math.floor(d/v)},{type:1,data:t.epsilon}];a&&S.push("type");let I=r>1,x=r>2,E=O=>{let R=De(e[0].dataType),L=[U("x",e[0].dataType,e[0].dims,v),U("scale",u.dataType,u.dims,v)];a&&L.push(U("bias",a.dataType,a.dims,v)),L.push(j("output",e[0].dataType,c,v)),I&&L.push(j("mean_data_output",1,_)),x&&L.push(j("inv_std_output",1,_));let N=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n  ${O.registerUniforms(N).declareVariables(...L)}\n  ${O.mainStart()}\n    ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var mean_vector = ${$t("f32",v)};\n    var mean_square_vector = ${$t("f32",v)};\n\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n      let value = ${ir(R,v,"x[h + offset]")};\n      mean_vector += value;\n      mean_square_vector += value * value;\n    }\n    let mean = ${_t("mean_vector",v)} / uniforms.norm_size;\n    let inv_std_dev = inverseSqrt(${_t("mean_square_vector",v)} / uniforms.norm_size ${o?"":"- mean * mean"} + uniforms.epsilon);\n\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n      let f32input = ${ir(R,v,"x[j + offset]")};\n      let f32scale = ${ir(R,v,"scale[j]")};\n      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i=Math.floor((t.k+t.blockSize-1)/t.blockSize),u=t.blockSize/8*t.bits,a=e[1];if(!M.areEqual(a.dims,[t.n,i,u]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let p=e[2].dims;if(M.size(p)!==t.n*i)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,y=t.bits>4?t.n*i:t.n*Math.floor((i+1)/2);if(M.size(d)!==y)throw new Error("zeroPoints input size error.")}},op=(e,t,r,o)=>{let i=e[0].dims,u=i.length,a=Math.floor((t.k+t.blockSize-1)/t.blockSize),c=i[u-2],p=t.k,h=t.n,d=i.slice(0,u-2),y=M.size(d),_=t.blockSize/8*t.bits/4,v=e[0].dataType,S=Me(c),A=Me(t.k),I=Me(_),x=tr(v),E=c*a*x,P=Math.floor(o/E),O=a<=r[0]&&P>0,R=!O||P>=4?Me(h):P>=2&&Me(h)>=2?2:1,L=d.concat([c,h]),N=M.size(L)/R/S,K=O?[]:[{type:12,data:N},{type:12,data:t.blockSize}],Q=[y,c,p/A],he=M.convertShape(e[1].dims).slice();he.splice(-1,1,_/I),K.push(...Z(Q)),K.push(...Z(he)),K.push(...Z(e[2].dims)),e.length===4&&K.push(...Z(M.convertShape(e[3].dims)));let W=[y,c,h/R];K.push(...Z(W));let se=Ce=>{let We=Q.length,ee=U("a",e[0].dataType,We,A),ae=U("b",12,he.length,I),Ae=U("scales",e[2].dataType,e[2].dims.length),me=[ee,ae,Ae],ie=e.length===4?U("zero_points",12,e[3].dims.length):void 0;ie&&me.push(ie);let ue=W.length,le=j("output",e[0].dataType,ue,R),qe=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],G=De(e[0].dataType),ne=(()=>{switch(A){case 1:return`array<${G}, 8>`;case 2:return`mat4x2<${G}>`;case 4:return`mat2x4<${G}>`;default:throw new Error(`${A}-component is not supported.`)}})(),xe=`\n        for (var word: u32 = 0; word < ${_}; word += ${I}) {\n          ${ae.indicesSet("b_indices","2","word")};\n          let b_data = ${ae.getByIndices("b_indices")};\n          for (var i: u32 = 0; i < ${I}; i++) {\n            let b_value: u32 = ${I===1?"b_data":"b_data[word + i]"};\n            let b_mask: u32 = 0x0F0F0F0Fu;\n            let b_value_lower: vec4<u32> = unpack4xU8(b_value & b_mask);\n            let b_value_upper: vec4<u32> = unpack4xU8((b_value >> 4) & b_mask);\n            let b_quantized_values = ${ne}(${Array.from({length:4},(Be,Ge)=>`${G}(b_value_lower[${Ge}]), ${G}(b_value_upper[${Ge}])`).join(", ")});\n            let b_dequantized_values = ${(()=>A===1?`${ne}(${Array.from({length:8},(Be,Ge)=>`(b_quantized_values[${Ge}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${ne}(${Array(8).fill("zero_point").join(",")})) * scale;`)()};\n            // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\n            for (var m: u32 = 0; m < ${O?c:S}u; m++) {\n              ${ee.indicesSet("a_indices",We-2,O?"m":`row * ${S} + m`)};\n              ${ee.indicesSet("a_indices",We-1,"word_offset")};\n              var input_offset = ${ee.indicesToOffset("a_indices")};\n              var a_data: ${ne};\n              for (var j: u32 = 0; j < ${8/A}; j++) {\n                a_data[j] = ${ee.getByOffset("input_offset")};\n                input_offset++;\n              }\n              ${O?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${R>1?"[c]":""} += ${Array.from({length:8/A},(Be,Ge)=>`${A===1?`a_data[${Ge}] * b_dequantized_values[${Ge}]`:`dot(a_data[${Ge}], b_dequantized_values[${Ge}])`}`).join(" + ")};\n            }\n            word_offset += ${8/A};\n          }\n        }`,Ke=ie?`\n          zero_point_offset += 4;\n          if (zero_point_offset == 32) {\n            zero_point_offset = 0;\n            zero_point_index++;\n            zero_point_word = ${ie.getByOffset("zero_point_index")};\n          }`:"";return O?`\n        var<workgroup> workgroup_shared: array<${le.type.value}, ${c*a}>;\n        ${Ce.declareVariables(...me,le)}\n        ${Ce.mainStart([a,1,1])}\n          var a_indices: ${ee.type.indices};\n          var block = local_id.x;\n          var col = workgroup_id.y;\n          var batch = workgroup_id.z;\n          ${ee.indicesSet("a_indices","0","batch")};\n          // Two zero points are packed into one byte when uniforms.bits is 4.\n          for (var c: u32 = 0; c < ${R}; c++) {\n            let col_times_components_plus_c = col * ${R} + c;\n              ${ie?`\n            var zero_point_bytes_per_col: u32 = (${a} + 1) / 2;\n            var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u);\n            var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u;\n            var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u;\n            var zero_point_nibble_offset: u32 = block & 0x1u;\n            var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);\n            var zero_point_word: u32 = ${ie.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}\n            var b_indices: ${ae.type.indices};\n            ${ae.indicesSet("b_indices","0","col_times_components_plus_c")};\n            // The scale and zero points are computed per block.\n            var scales_index = col_times_components_plus_c * ${a} + block;\n            let scale = ${Ae.getByOffset("scales_index")};\n            // The default zero point is 8 for unsigned 4-bit quantization.\n            let zero_point = ${G}(${ie?"(zero_point_word) & 0xFu":8});\n            ${ae.indicesSet("b_indices","1","block")};\n            var word_offset: u32 = block * ${t.blockSize/A};\n            var workgroup_shared_offset: u32 = block * ${c};\n            ${xe}\n          }\n          workgroupBarrier();\n          if (local_id.x == 0u) {\n            var output_indices: ${le.type.indices};\n            ${le.indicesSet("output_indices","0","batch")};\n            ${le.indicesSet("output_indices",ue-1,"col")};\n            ${le.indicesSet("output_indices",ue-2,"0")};\n            var output_offset = ${le.indicesToOffset("output_indices")};\n            for (var m: u32 = 0u; m < ${c}u; m++) {\n              var output_value: ${le.type.value} = ${le.type.value}(0);\n              var workgroup_shared_offset: u32 = m;\n              for (var b: u32 = 0u; b < ${a}u; b++) {\n                output_value += workgroup_shared[workgroup_shared_offset];\n                workgroup_shared_offset += ${c};\n              }\n              ${le.setByOffset("output_offset","output_value")};\n              output_offset += ${h/R};\n            }\n          }\n        }`:`\n        ${Ce.registerUniforms(qe).declareVariables(...me,le)}\n        ${Ce.mainStart()}\n          ${Ce.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n          var output_values: array<${le.type.value}, ${S}>;\n          var output_indices = ${le.offsetToIndices("global_idx")};\n          var col = ${le.indicesGet("output_indices",ue-1)};\n          var row = ${le.indicesGet("output_indices",ue-2)};\n          var a_indices: ${ee.type.indices} = output_indices;\n          // Two zero points are packed into one byte because uniforms.bits <= 4.\n          // zero_point_offset is either 0 or 4. It is bit offset within one byte.\n          // TODO support zero_point_offset for bits > 4\n          ${ie?`\n          var zero_point_abs_offset = col * ${R} * ((${a} + 1) / 2);\n          var zero_point_index: u32 = zero_point_abs_offset / 4;\n          var zero_point_word: u32 = ${ie.getByOffset("zero_point_index")};\n          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}\n          var scale_index = col * ${a*R};\n          var b_indices: ${ae.type.indices};\n          for (var c: u32 = 0; c < ${R}; c++) {\n            ${ae.indicesSet("b_indices","0",`col * ${R} + c`)};\n            var block_offset: u32 = 0;\n            for (var block: u32 = 0; block < ${a}; block++) {\n              // The scale and zero points are computed per block.\n              let scale = ${Ae.getByOffset("scale_index")};\n              // The default zero point is 8 for unsigned 4-bit quantization.\n              let zero_point = ${G}(${ie?"extractBits(zero_point_word, zero_point_offset, 4)":8});\n              ${ae.indicesSet("b_indices","1","block")};\n              var word_offset: u32 = block_offset;\n              ${xe}\n              scale_index++;\n              ${Ke}\n              block_offset += uniforms.block_size / ${A};\n            }\n            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.\n            ${ie?`if (zero_point_offset % 8 > 0) {\n                ${Ke}\n              }`:""}\n            }\n            for (var k: u32 = 0u; k < ${S}u; k++) {\n              ${le.indicesSet("output_indices",ue-2,`${S} * row + k`)};\n              ${le.setByIndices("output_indices","output_values[k]")}\n            }\n        }`};return{name:O?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${c};${v};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:L,dataType:v}],name:O?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:O?{x:1,y:Math.ceil(h/R),z:y}:{x:Math.ceil(N/64)},programUniforms:K}),getShaderSource:se}},Wu=(e,t)=>{np(e.inputs,t);let r=e.getMaxComputeWorkgroupSizes(),o=e.getMaxComputeWorkgroupStoragesize();e.compute(op(e.inputs,t,r,o))},Nu=e=>ve(e)});var it,ip,Lu,Hu,ap,Ko,Fu,qu=Y(()=>{"use strict";ye();Se();Ze();_n();Ro();_e();Sr();it=(e,t)=>e.length>t&&e[t].dims.length>0&&M.size(e[t].dims)>0?e[t]:void 0,ip=(e,t)=>{let r=e[0],o=it(e,1),i=it(e,2),u=it(e,3),a=it(e,4),c=it(e,5),p=it(e,6),h=it(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let d=!1,y=r.dims[0],w=r.dims[1],_=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],v=w,S=0,A=0,I=Math.floor(_/t.numHeads);if(p&&h){if(p.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims[0]!==y||p.dims[1]!==t.numHeads||p.dims[3]!==I)throw new Error(\'Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(h.dims[0]!==y||h.dims[1]!==t.numHeads||h.dims[3]!==I)throw new Error(\'Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(p.dims[2]!==h.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)\');if(h.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');S=p.dims[2],A=p.dims[2]}else if(p||h)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let x;if(o){if(r.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(o.dims.length<3||o.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(r.dims[0]!==o.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(o.dims.length===3){if(o.dims[2]!==r.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');x=2,v=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==I)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(i)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');x=5,v=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==I)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');x=0,v=o.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error(\'Input "query" is expected to have 3 or 5 dimensions when key is empty\');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error(\'Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\');x=3}if(u){if(u.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(i&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(a){E=8;let N=a.dims;throw N.length===1?N[0]===y?E=1:N[0]===3*y+2&&(E=3):N.length===2&&N[0]===y&&N[1]===v&&(E=5),E===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let P=!1,O=_;if(i){if(i.dims.length!==3&&i.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(r.dims[0]!==i.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(i.dims.length===3){if(v!==i.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');O=i.dims[2]}else{if(v!==i.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');O=i.dims[1]*i.dims[3],P=!0}}let R=S+v,L=!1;if(a)throw new Error("Key padding mask is not supported");if(c){if(c.dims.length!==4)throw new Error(\'Input "relative_position_bias" is expected to have 4 dimensions\');if(c.dims[0]!==y&&c.dims[0]!==1||c.dims[1]!==t.numHeads||c.dims[2]!==w||c.dims[3]!==R)throw new Error(\'Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)\')}return{batchSize:y,sequenceLength:w,pastSequenceLength:S,kvSequenceLength:v,totalSequenceLength:R,maxSequenceLength:A,inputHiddenSize:0,hiddenSize:_,vHiddenSize:O,headSize:I,vHeadSize:Math.floor(O/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:L,passPastInKv:P,qkvFormat:x}},Lu=e=>ve({...e}),Hu=ve({perm:[0,2,1,3]}),ap=(e,t,r,o,i,u,a)=>{let c=[o,i,u],p=M.size(c),h=[{type:12,data:p},{type:12,data:a},{type:12,data:u}],d=y=>{let w=j("qkv_with_bias",t.dataType,c),_=U("qkv",t.dataType,c),v=U("bias",r.dataType,c),S=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n  ${y.registerUniforms(S).declareVariables(_,v,w)}\n  ${y.mainStart()}\n    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:c,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:d},{inputs:[t,r],outputs:[-1]})[0]},Ko=(e,t,r,o,i,u,a,c)=>{let p=u;if(a){if(o===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return p=ap(e,u,a,t,o,r*i,c),p=p.reshape([t,o,r,i]),e.compute(yt(p,Hu.perm),{inputs:[p],outputs:[-1]})[0]}else return u.dims.length===3&&(p=u.reshape([t,o,r,i])),e.compute(yt(p,Hu.perm),{inputs:[p],outputs:[-1]})[0]},Fu=(e,t)=>{let r=ip(e.inputs,t),o=e.inputs[0],i=it(e.inputs,1),u=it(e.inputs,2),a=it(e.inputs,3),c=it(e.inputs,4),p=it(e.inputs,5),h=it(e.inputs,6),d=it(e.inputs,7);if(o.dims.length===5)throw new Error("Packed QKV is not implemented");if(i?.dims.length===5)throw new Error("Packed KV is not implemented");let y=i&&u&&i.dims.length===4&&u.dims.length===4,w=Ko(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,o,a,0);if(y)return Pn(e,w,i,u,c,void 0,h,d,p,r,t);if(!i||!u)throw new Error("key and value must be provided");let _=Ko(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,i,a,r.hiddenSize),v=Ko(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,u,a,2*r.hiddenSize);Pn(e,w,_,v,c,void 0,h,d,p,r,t)}});var sp,up,dp,lp,cp,pp,mp,fp,ju,Ku=Y(()=>{"use strict";ye();Se();_e();sp=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},up=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n            k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n            if (k < 0) {\n              break;\n            }\n            if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n              break;\n            }\n            offset += k * i32(${fe("uniforms.x_strides",i,t)});\n        `;return`\n          value = ${e.type.value}(uniforms.constant_value);\n          for (var i = 0; i < 1; i++) {\n            var offset = 0;\n            var k = 0;\n            ${o}\n            value = x[offset];\n          }\n      `},dp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n                if (k < 0) {\n                  k = -k;\n                }\n                {\n                  let _2n_1 = 2 * (i32(${fe("uniforms.x_shape",i,t)}) - 1);\n                  k = k % _2n_1;\n                  if(k >= i32(${fe("uniforms.x_shape",i,t)})) {\n                    k = _2n_1 - k;\n                  }\n                }\n                offset += k * i32(${fe("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},lp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n                if (k < 0) {\n                  k = 0;\n                }\n                if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n                  k = i32(${fe("uniforms.x_shape",i,t)}) - 1;\n                }\n                offset += k * i32(${fe("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},cp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n                if (k < 0)  {\n                  k += i32(${fe("uniforms.x_shape",i,t)}]);\n                }\n                if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n                  k -= i32(${fe("uniforms.x_shape",i,t)});\n                }\n                offset += k * i32(${fe("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},pp=(e,t,r)=>{switch(r.mode){case 0:return up(e,t,r.pads.length);case 1:return dp(e,t,r.pads.length);case 2:return lp(e,t,r.pads.length);case 3:return cp(e,t,r.pads.length);default:throw new Error("Invalid mode")}},mp=(e,t)=>{let r=M.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,i=M.size(r),u=[{type:12,data:i},{type:6,data:t.pads}];t.mode===0&&u.push({type:e[0].dataType,data:t.value}),u.push(...Z(e[0].dims,r));let a=["rank"],c=p=>{let h=j("output",e[0].dataType,r.length),d=U("x",e[0].dataType,o.length),y=d.type.value,w=pp(h,o.length,t),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&_.push({name:"constant_value",type:y}),`\n            ${p.registerUniforms(_).declareVariables(d,h)}\n            ${p.mainStart()}\n            ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n            let indices = ${h.offsetToIndices("global_idx")};\n\n            var value = ${y}(0);\n            ${w}\n            output[global_idx] = value;\n        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(r)/64)},programUniforms:u}),getShaderSource:c}},fp=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,i=e[0].dims.length,u=new Int32Array(2*i).fill(0);if(e.length>=4){let c=e[3].getBigInt64Array();for(let p=0;p<c.length;p++)u[Number(c[p])]=Number(r[p]),u[Number(c[p])+i]=Number(r[p+c.length])}else r.forEach((c,p)=>u[Number(p)]=Number(c));let a=[];return u.forEach(c=>a.push(c)),{mode:t.mode,value:o,pads:a}}else return t},ju=(e,t)=>{sp(e.inputs);let r=fp(e.inputs,t);e.compute(mp(e.inputs,r),{inputs:[0]})}});var Nn,Yu,Zu,Xu,Qu,hp,gp,Ju,ed,td,rd,nd,od,id,ad,sd,ud,dd,ld,cd=Y(()=>{"use strict";$r();ye();Se();_e();Nn=e=>{if(vr.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Yu=(e,t,r)=>{let o=t.format==="NHWC",i=e.dims.slice();o&&i.splice(1,0,i.pop());let u=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),c=t.strides.slice(),p=u?t.dilations.slice():[],h=t.pads.slice();nr.adjustPoolAttributes(r,i,a,c,p,h);let d=nr.computePoolOutputShape(r,i,c,p,a,h,t.autoPad),y=Object.assign({},t);u?Object.assign(y,{kernelShape:a,strides:c,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(y,{kernelShape:a,strides:c,pads:h,cacheKey:t.cacheKey});let w=d.slice();return w.push(w.splice(1,1)[0]),[y,o?w:d]},Zu=(e,t)=>{let r=t.format==="NHWC",o=M.size(e),i=M.size(t.kernelShape),u=[{type:12,data:o},{type:12,data:i}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let c=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],d=t.pads[t.pads.length-1],y=!!(h+d);u.push({type:12,data:c},{type:12,data:p},{type:12,data:h},{type:12,data:d}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let w=!1;if(t.kernelShape.length===2){let _=t.kernelShape[t.kernelShape.length-2],v=t.strides[t.strides.length-2],S=t.pads[t.pads.length/2-2],A=t.pads[t.pads.length-2];w=!!(S+A),u.push({type:12,data:_},{type:12,data:v},{type:12,data:S},{type:12,data:A}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[u,a,!0,y,w]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let c=M.computeStrides(t.kernelShape);u.push({type:12,data:c},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:c.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,d)=>h+d);return[u,a,!!p,!1,!1]}},Xu=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w=i.format==="NHWC",_=t.type.value,v=j("output",t.type.tensor,o);if(i.kernelShape.length<=2){let S="",A="",I="",x=r-(w?2:1);if(d?S=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  if (xIndices[${x}] < 0 || xIndices[${x}]\n                      >= uniforms.x_shape[${x}]) {\n                    pad++;\n                    continue;\n                  }\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${u}\n                }`:S=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${u}\n                }`,i.kernelShape.length===2){let P=r-(w?3:2);y?A=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${P}] = indices[${P}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${P}] < 0 || xIndices[${P}] >= uniforms.x_shape[${P}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:A=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${P}] = indices[${P}] * uniforms.sh - uniforms.phStart + j;\n                `,I=`\n              }\n            `}return`\n            ${e.registerUniforms(p).declareVariables(t,v)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n              let indices = ${v.offsetToIndices("global_idx")};\n              var xIndices = ${v.offsetToIndices("global_idx")};\n\n              var value = ${_}(${c});\n              var pad = 0;\n              ${A}\n              ${S}\n              ${I}\n              ${a}\n\n              output[global_idx] = value;\n            }`}else{if(w)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let S=i.kernelShape.length,A=i.pads.length,I="";return h?I=`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset("xIndices")}];\n                ${u}\n              }`:I=`\n              }\n              let x_val = x[${t.indicesToOffset("xIndices")}];\n              ${u}\n            `,`\n            ${e.registerUniforms(p).declareVariables(t,v)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n              let indices = ${v.offsetToIndices("global_idx")};\n              var xIndices = ${v.offsetToIndices("global_idx")};\n\n              var offsets: array<u32, ${S}>;\n\n              var value = ${_}(${c});\n              var pad = 0;\n              var isPad = false;\n\n              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n                var offset = i;\n                for (var j = 0u; j < ${S-1}u; j++) {\n                  offsets[j] = offset / ${fe("uniforms.kernelStrides","j",S)};\n                  offset -= offsets[j] * ${fe("uniforms.kernelStrides","j",S)};\n                }\n                offsets[${S-1}] = offset;\n\n                isPad = false;\n                for (var j = ${r-S}u; j < ${r}u; j++) {\n                  xIndices[j] = indices[j] * ${fe("uniforms.strides",`j - ${r-S}u`,S)}\n                    + offsets[j - ${r-S}u] - ${fe("uniforms.pads","j - 2u",A)};\n                  ${I}\n              }\n              ${a}\n\n              output[global_idx] = value;\n            }`}},Qu=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,hp=e=>`${Qu(e)};${e.countIncludePad}`,gp=e=>`${Qu(e)};${e.storageOrder};${e.dilations}`,Ju=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),ed=(e,t,r,o)=>{let[i,u]=Yu(t,o,r),a=U("x",t.dataType,t.dims.length),c=a.type.value,p="value += x_val;",h="";i.countIncludePad?h+=`value /= ${c}(uniforms.kernelSize);`:h+=`value /= ${c}(i32(uniforms.kernelSize) - pad);`;let[d,y,w,_,v]=Zu(u,i);d.push(...Z(t.dims,u));let S=["rank"];return{name:e,shaderCache:{hint:`${o.cacheKey};${w};${_};${v}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(M.size(u)/64)},programUniforms:d}),getShaderSource:A=>Xu(A,a,t.dims.length,u.length,i,p,h,0,y,w,_,v)}},td=e=>{let t=e.count_include_pad!==0,r=Ju(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let o={countIncludePad:t,...r,cacheKey:""};return{...o,cacheKey:hp(o)}},rd=(e,t)=>{Nn(e.inputs),e.compute(ed("AveragePool",e.inputs[0],!1,t))},nd={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},od=e=>{let t=e.format;return{format:t,...nd,cacheKey:t}},id=(e,t)=>{Nn(e.inputs),e.compute(ed("GlobalAveragePool",e.inputs[0],!0,t))},ad=(e,t,r,o)=>{let[i,u]=Yu(t,o,r),a=`\n      value = max(x_val, value);\n    `,c="",p=U("x",t.dataType,t.dims.length),h=["rank"],[d,y,w,_,v]=Zu(u,i);return d.push(...Z(t.dims,u)),{name:e,shaderCache:{hint:`${o.cacheKey};${w};${_};${v}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(M.size(u)/64)},programUniforms:d}),getShaderSource:S=>Xu(S,p,t.dims.length,u.length,i,a,c,t.dataType===10?-65504:-1e5,y,w,_,v)}},sd=(e,t)=>{Nn(e.inputs),e.compute(ad("MaxPool",e.inputs[0],!1,t))},ud=e=>{let t=e.storage_order,r=e.dilations,o=Ju(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(o.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:t,dilations:r,...o,cacheKey:""};return{...i,cacheKey:gp(i)}},dd=e=>{let t=e.format;return{format:t,...nd,cacheKey:t}},ld=(e,t)=>{Nn(e.inputs),e.compute(ad("GlobalMaxPool",e.inputs[0],!0,t))}});var bp,wp,pd,md=Y(()=>{"use strict";$r();ye();_e();bp=(e,t,r)=>{let o=e===t,i=e<t&&r<0,u=e>t&&r>0;if(o||i||u)throw new Error("Range these inputs\' contents are invalid.")},wp=(e,t,r,o)=>{let i=Math.abs(Math.ceil((t-e)/r)),u=[i],a=i,c=[{type:12,data:a},{type:o,data:e},{type:o,data:r},...Z(u)],p=h=>{let d=j("output",o,u.length),y=d.type.value,w=[{name:"outputSize",type:"u32"},{name:"start",type:y},{name:"delta",type:y}];return`\n        ${h.registerUniforms(w).declareVariables(d)}\n        ${h.mainStart()}\n        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        output[global_idx] = uniforms.start + ${y}(global_idx) * uniforms.delta;\n      }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:u,dataType:o}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c})}},pd=e=>{let t=0,r=0,o=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],o=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],o=e.inputs[2].getFloat32Array()[0]),vr.webgpu.validateInputContent&&bp(t,r,o),e.compute(wp(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var vp,$p,_p,Sp,xp,Cp,Ap,Ip,Tp,Ep,Pp,fd,kp,Op,Rp,Bp,Dp,hd,gd,yd=Y(()=>{"use strict";ye();Se();Ze();_e();vp=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},$p=(e,t,r)=>{t.every(i=>i>=0&&i<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let o=new Array(r).fill(1);return t.forEach((i,u)=>o[i]=e[u]),o},_p=(e,t,r,o,i,u)=>{let[a,c,p]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(d=>u.push(d));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(c>0&&e.length>c&&e[c].dims.length>0){if(e[c].getFloat32Array().forEach(d=>o.push(d)),o.length!==0&&o.length!==h&&r>=18&&o.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");vp(o,t),t.axes.length>0&&$p(o,t.axes,h).forEach((d,y)=>o[y]=d)}if(p>0&&e.length>p&&(e[p].getBigInt64Array().forEach(d=>i.push(Number(d))),i.length!==h||r>=18&&i.length===t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(o.length!==t.axes.length)throw new Error(\'Resize requires "scales" input size to be of axes rank when axes attributes is specified\');if(i.length!==t.axes.length)throw new Error(\'Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified\')}if(typeof o<"u"&&typeof i<"u"&&o.length>0&&i.length>h)throw new Error("Resize requires only of scales or sizes to be specified")},Sp=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;\n                  } else {\n                    return 0.0;\n                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n                    return 0.0;\n                  } else {\n                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n                    // offset-by-one error later in floor().\n                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n                    let fract =\n                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);\n                    return whole + fract;\n                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +\n                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /\n                        ${t}(lengthResized - 1);\n                  } else {\n                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);\n                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);\n                  const adjustment = ${t}(lengthResized) / outputWidth;\n                  const center = ${t}(lengthOriginal) / 2;\n                  const offset = center * (1 - adjustment);\n                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",xp=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Cp=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),i=e.length===0?o:e.slice();return t.length>0?(t.forEach((u,a)=>{o[u]=i[a],o[a+r]=i[t.length+a]}),o):i},Ap=(e,t,r,o)=>{let i=[];if(r.length>0)if(o.length>0){if(e.forEach(u=>i.push(u)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((u,a)=>i[u]=r[a])}else r.forEach(u=>i.push(u));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((u,a)=>Math.round(u*t[a]))}return i},Ip=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(u=>t[u]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(u=>t[u]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let i=e.slice();return r.axes.length>0?(r.axes.forEach(u=>t[u]=o),r.axes.forEach(u=>i[u]=Math.round(e[u]*t[u]))):(t.fill(o,0,t.length),i.forEach((u,a)=>i[a]=Math.round(u*t[a]))),i},Tp=(e,t,r,o,i)=>`\n    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {\n      var original_indices: array<${e.type.value}, ${r.length}>;\n      for (var i:u32 = 0; i < ${r.length}; i++) {\n        var output_index = ${e.indicesGet("output_indices","i")};\n        var scale = ${fe("uniforms.scales","i",o)};\n        var roi_low = ${fe("uniforms.roi","i",i)};\n        var roi_hi = ${fe("uniforms.roi",`i + ${t.length}`,i)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${fe("uniforms.input_shape","i",t.length)};\n          var output_shape_i = ${fe("uniforms.output_shape","i",r.length)};\n          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                           input_shape_i, roi_low, roi_hi);\n        }\n      }\n      return original_indices;\n    }`,Ep=(e,t,r,o,i,u,a)=>`\n    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n      var input_indices: ${e.type.indices};\n      for (var i:u32 = 0; i < ${o.length}; i++) {\n        var output_index = ${t.indicesGet("output_indices","i")};\n        var input_index: u32;\n        var scale = ${fe("uniforms.scales","i",i)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${fe("uniforms.roi","i",u)};\n          var roi_hi = ${fe("uniforms.roi",`i + ${r.length}`,u)};\n          var input_shape_i = ${fe("uniforms.input_shape","i",r.length)};\n          var output_shape_i = ${fe("uniforms.output_shape","i",o.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${a} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {\n            if (original_idx < 0) {\n              input_index = 0;\n            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {\n              input_index = input_shape_i - 1;\n            } else {\n              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n            }\n          } else {\n            input_index = u32(original_idx);\n          }\n        }\n        ${e.indicesSet("input_indices","i"," input_index")}\n      }\n      return input_indices;\n    }`,Pp=(e,t)=>`\n    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n      for (var i:u32 = 0; i < ${t.length}; i++) {\n        var input_index = ${e.indicesGet("input_indices","i")};\n        if (input_index < 0 || input_index >= ${fe("uniforms.input_shape","i",t.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,fd=(e,t,r,o)=>e.rank>o?`\n    ${e.indicesSet("input_indices",t,"channel")};\n    ${e.indicesSet("input_indices",r,"batch")};\n`:"",kp=(e,t,r,o,i)=>{let[a,c,p,h]=r.length===2?[-1,0,1,-1]:[0,2,3,1],d=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",c,`max(0, min(row, ${r[c]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(col, ${r[p]} - 1))`)};\n      ${fd(e,h,a,2)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${d} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${d} = originalIndices[${c}];\n      var col:${d} = originalIndices[${p}];\n      ${o?`if (row < 0 || row > (${r[c]} - 1) || col < 0 || col > (${r[p]} - 1)) {\n        return ${i};\n      }`:""};\n      row = max(0, min(row, ${r[c]} - 1));\n      col = max(0, min(col, ${r[p]} - 1));\n      var row1: u32 = u32(row);\n      var col1: u32 = u32(col);\n      var row2: u32 = u32(row + 1);\n      var col2: u32 = u32(col + 1);\n      var channel: u32 = ${r.length>2?`u32(originalIndices[${h}])`:"0"};\n      var batch: u32 =  ${r.length>2?`u32(originalIndices[${a}])`:"0"};\n      var x11: ${d} = getInputValue(batch, channel, row1, col1);\n      var x12: ${d} = getInputValue(batch, channel, row1, col2);\n      var x21: ${d} = getInputValue(batch, channel, row2, col1);\n      var x22: ${d} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${d} = abs(row - ${d}(row1));\n      var dx2: ${d} = abs(${d}(row2) - row);\n      var dy1: ${d} = abs(col - ${d}(col1));\n      var dy2: ${d} = abs(${d}(col2) - col);\n      if (row1 == row2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (col1 == col2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n    }`},Op=(e,t,r,o,i,u,a,c,p,h)=>{let d=r.length===2,y=!0,[w,_]=d?[0,1]:y?[2,3]:[1,2],v=e.type.value,S=A=>{let I=A===w?"row":"col";return`\n      fn ${I}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${v} {\n        var output_index = ${t.indicesGet("output_indices",A)};\n        var originalIdx: ${v} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[A]},\n        ${o[A]}, ${r[A]}, ${u[A]}, ${u[A]} + ${r.length});\n        var fractOriginalIdx: ${v} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${c} && (originalIdx < 0 || originalIdx > (${r[A]} - 1))) {\n          return ${p};\n        }\n        var data: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${I}: ${v} = originalIdx + ${v}(i);\n          if (${I} < 0 || ${I} >= ${r[A]}) {\n            ${(()=>h?`coefs[i + 1] = 0.0;\n                        continue;`:c?`return ${p};`:`${I} = max(0, min(${I}, ${r[A]} - 1));`)()};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",A,`u32(${I})`)};\n          data[i + 1] = ${A===w?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${S(w)};\n    ${S(_)};\n  fn getCubicInterpolationCoefs(s: ${v}) -> array<${v}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${v} = 1.0 - absS;\n    var twoMinusAbsS: ${v} = 2.0 - absS;\n    var onePlusAbsS: ${v} = 1.0 + absS;\n    coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a};\n    coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${v}, 4>, coefs: array<${v}, 4>) -> ${v} {\n    var coefsSum: ${v} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n  }\n\n  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${v} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},Rp=(e,t,r,o,i)=>{let[a,c,p,h,d]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],y=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${y} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",c,`max(0, min(depth, ${r[c]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(height, ${r[p]} - 1))`)};\n      ${e.indicesSet("input_indices",h,`max(0, min(width, ${r[h]} - 1))`)};\n      ${fd(e,d,a,3)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${y} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${y} = originalIndices[${c}];\n      var height:${y} = originalIndices[${p}];\n      var width:${y} = originalIndices[${h}];\n      ${o?`if (depth < 0 || depth > (${r[c]} - 1) || height < 0 || height > (${r[p]} - 1) || width < 0 || (width > ${r[h]} - 1)) {\n      return ${i};\n        }`:""};\n\n    depth = max(0, min(depth, ${r[c]} - 1));\n      height = max(0, min(height, ${r[p]} - 1));\n      width = max(0, min(width, ${r[h]} - 1));\n      var depth1: u32 = u32(depth);\n      var height1: u32 = u32(height);\n      var width1: u32 = u32(width);\n      var depth2: u32 = u32(depth + 1);\n      var height2: u32 = u32(height + 1);\n      var width2: u32 = u32(width + 1);\n      var channel: u32 = ${r.length>3?`u32(originalIndices[${d}])`:"0"};\n      var batch: u32 =  ${r.length>3?`u32(originalIndices[${a}])`:"0"};\n\n      var x111: ${y} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${y} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${y} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${y} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${y} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${y} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${y} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${y} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${y} = abs(depth - ${y}(depth1));\n      var dx2: ${y} = abs(${y}(depth2) - depth);\n      var dy1: ${y} = abs(height - ${y}(height1));\n      var dy2: ${y} = abs(${y}(height2) - height);\n      var dz1: ${y} = abs(width - ${y}(width1));\n      var dz2: ${y} = abs(${y}(width2) - width);\n      if (depth1 == depth2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (height1 == height2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      if (width1 == width2) {\n        dz1 = 0.5;\n        dz2 = 0.5;\n      }\n      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n    }`},Bp=(e,t,r,o,i,u)=>{let a=e.dims,c=Cp(u,t.axes,a.length),p=Ap(a,o,i,t.axes),h=o.slice();o.length===0&&(h=a.map((x,E)=>x===0?1:p[E]/x),t.keepAspectRatioPolicy!=="stretch"&&(p=Ip(a,h,t)));let d=j("output",e.dataType,p.length),y=U("input",e.dataType,a.length),w=M.size(p),_=a.length===p.length&&a.every((x,E)=>x===p[E]),v=t.coordinateTransformMode==="tf_crop_and_resize",S=t.extrapolationValue,A=y.type.value,I=x=>`\n      ${_?"":`\n      ${Sp(t.coordinateTransformMode,A)};\n      ${(()=>{switch(t.mode){case"nearest":return`\n              ${Pp(y,a)};\n              ${xp(t.nearestMode,r,A)};\n              ${Ep(y,d,a,p,h.length,c.length,v)};\n              `;case"linear":return`\n              ${Tp(d,a,p,h.length,c.length)};\n              ${(()=>{if(a.length===2||a.length===4)return`${kp(y,d,a,v,S)}`;if(a.length===3||a.length===5)return`${Rp(y,d,a,v,S)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n            `;case"cubic":return`\n            ${(()=>{if(a.length===2||a.length===4)return`${Op(y,d,a,p,h,c,t.cubicCoeffA,v,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n            `;default:throw Error("Invalid resize mode")}})()};\n      `}\n      ${x.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",c.length).declareVariables(y,d)}\n      ${x.mainStart()}\n        ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n        ${_?"output[global_idx] = input[global_idx];":`\n        let output_indices = ${d.offsetToIndices("global_idx")};\n        var input_indices: ${y.type.indices};\n        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n                if (checkInputIndices(input_indices)) {\n                  output[global_idx] = ${y.getByIndices("input_indices")};\n                } else {\n                  output[global_idx] = ${t.extrapolationValue};\n                }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};\n`}\n      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${h.length>0?h:""}|${i.length>0?i:""}|${c.length>0?c:""}|${_}|${a}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:[{type:12,data:w},{type:1,data:h},{type:1,data:c},...Z(a,p)]})}},Dp=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},hd=(e,t)=>{let r=[],o=[],i=[],u=Dp(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");_p(e.inputs,t,u,r,o,i),e.compute(Bp(e.inputs[0],t,u,r,o,i),{inputs:[0]})},gd=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,i=e.cubicCoeffA,u=e.excludeOutside!==0,a=e.extrapolationValue,c=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return ve({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:i,excludeOutside:u,extrapolationValue:a,keepAspectRatioPolicy:c,mode:p,nearestMode:h})}});var zp,Mp,bd,wd=Y(()=>{"use strict";ye();Se();Ze();_e();zp=(e,t)=>{let[r,o,i,u]=e,{numHeads:a,rotaryEmbeddingDim:c}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input \'x\' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!M.areEqual(o.dims,[])&&!M.areEqual(o.dims,[1])&&o.dims.length!==2)throw new Error(`Input \'position_ids\' is expected to have 0, 1, or 2 dimensions, got ${o.dims.length}`);if(i.dims.length!==2)throw new Error(`Input \'cos_cache\' is expected to have 2 dimensions, got ${i.dims.length}`);if(u.dims.length!==2)throw new Error(`Input \'sin_cache\' is expected to have 2 dimensions, got ${u.dims.length}`);if(!M.areEqual(i.dims,u.dims))throw new Error("Inputs \'cos_cache\' and \'sin_cache\' are expected to have the same shape");if(c>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=r.dims[0],h=r.dims[r.dims.length-2],d=i.dims[0],y=M.sizeFromDimension(r.dims,1)/h,w=c===0?i.dims[1]*2:y/a;if(c>w)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(o.dims.length===2){if(p!==o.dims[0])throw new Error(`Input \'position_ids\' dimension 0 should be of size batch_size, got ${o.dims[0]}`);if(h!==o.dims[1])throw new Error(`Input \'position_ids\' dimension 1 should be of size sequence_length, got ${o.dims[1]}`)}if(w/2!==i.dims[1]&&c/2!==i.dims[1])throw new Error(`Input \'cos_cache\' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${i.dims[1]}`);if(h>d)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Mp=(e,t)=>{let{interleaved:r,numHeads:o,rotaryEmbeddingDim:i,scale:u}=t,a=e[0].dims[0],c=M.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=c/p,d=e[2].dims[1],y=i===0?d*2:h/o,w=new Array(a,p,h/y,y-d),_=M.computeStrides(w),v=[{type:1,data:u},{type:12,data:w},{type:12,data:_},...e[0].dims.length===3?new Array({type:12,data:[c,h,y,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[c,y,p*y,1]}):[],...Z(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],S=A=>{let I=U("input",e[0].dataType,e[0].dims.length),x=U("position_ids",e[1].dataType,e[1].dims.length),E=U("cos_cache",e[2].dataType,e[2].dims.length),P=U("sin_cache",e[3].dataType,e[3].dims.length),O=j("output",e[0].dataType,e[0].dims.length);return A.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:w.length},{name:"global_strides",type:"u32",length:_.length},{name:"input_output_strides",type:"u32",length:_.length}]),`\n        ${A.declareVariables(I,x,E,P,O)}\n\n        ${A.mainStart(or)}\n          let half_rotary_emb_dim = uniforms.${E.name}_shape[1];\n          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;\n          let size = uniforms.global_shape[0] * uniforms.global_strides[0];\n          ${A.guardAgainstOutOfBoundsWorkgroupSizes("size")}\n\n          if (bsnh[3] < half_rotary_emb_dim) {\n            let position_ids_idx =\n                ${x.broadcastedIndicesToOffset("bsnh.xy",j("",x.type.tensor,2))};\n            let position_id =\n                u32(${x.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);\n            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r});\n            let j = i + select(half_rotary_emb_dim, 1, ${r});\n            let re = ${I.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} -\n                ${I.getByOffset("j")} * ${P.get("position_id","bsnh[3]")};\n            ${O.setByOffset("i","re")}\n            let im = ${I.getByOffset("i")} * ${P.get("position_id","bsnh[3]")} +\n                ${I.getByOffset("j")} * ${E.get("position_id","bsnh[3]")};\n            ${O.setByOffset("j","im")}\n          } else {\n            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;\n            ${O.setByOffset("k",I.getByOffset("k"))}\n          }\n        }`};return{name:"RotaryEmbedding",shaderCache:{hint:ve({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:S,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(w)/or)},programUniforms:v})}},bd=(e,t)=>{zp(e.inputs,t),e.compute(Mp(e.inputs,t))}});var Up,Vp,vd,$d=Y(()=>{"use strict";ye();Se();_e();Up=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],o=e[2];if(t.dataType!==r.dataType||t.dataType!==o.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let i=t.dims[t.dims.length-1],u=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==u)throw new Error("Skip must have the same sequence length as input");if(o.dims.length!==1)throw new Error("Gamma must be 1D");if(o.dims[o.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},Vp=(e,t,r,o)=>{let i=t.simplified,u=e[0].dims,a=M.size(u),c=u,p=a,h=u.slice(-1)[0],d=o?u.slice(0,-1).concat(1):[],y=!i&&e.length>3,w=e.length>4,_=o&&r>1,v=o&&r>2,S=r>3,A=Me(h),I=[{type:12,data:p},{type:12,data:A},{type:12,data:h},{type:1,data:t.epsilon}],x=P=>{let O=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],R=[U("x",e[0].dataType,e[0].dims,A),U("skip",e[1].dataType,e[1].dims,A),U("gamma",e[2].dataType,e[2].dims,A)];y&&R.push(U("beta",e[3].dataType,e[3].dims,A)),w&&R.push(U("bias",e[4].dataType,e[4].dims,A)),R.push(j("output",e[0].dataType,c,A)),_&&R.push(j("mean_output",1,d)),v&&R.push(j("inv_std_output",1,d)),S&&R.push(j("input_skip_bias_sum",e[0].dataType,c,A));let L=De(e[0].dataType);return`\n\n      ${P.registerUniforms(O).declareVariables(...R)}\n\n      ${P.mainStart()}\n        ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}\n        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;\n        let offset = global_idx * hidden_size_vectorized;\n        var sum = ${$t("f32",A)};\n        var squareSum = ${$t("f32",A)};\n        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n          let skip_value = skip[offset + i];\n          let bias_value = ${w?"bias[i]":L+"(0.0)"};\n          let input_value = x[offset + i];\n          let value = input_value + skip_value + bias_value;\n          ${S?"input_skip_bias_sum[offset + i] = value;":""}\n          output[offset + i] = value;\n          let f32_value = ${ir(L,A,"value")};\n          sum += f32_value;\n          squareSum += f32_value * f32_value;\n        }\n        let mean = ${_t("sum",A)} / f32(uniforms.hidden_size);\n        let inv_std_dev = inverseSqrt(${_t("squareSum",A)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon);\n        ${_?"mean_output[global_idx] = mean;":""}\n        ${v?"inv_std_output[global_idx] = inv_std_dev;":""}\n        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n          output[offset + i] = (output[offset + i] ${i?"":`- ${L}(mean)`}) * ${L}(inv_std_dev) * gamma[i] ${y?"+ beta[i]":""};\n        }\n      }`},E=[{dims:c,dataType:e[0].dataType}];return r>1&&E.push({dims:d,dataType:1}),r>2&&E.push({dims:d,dataType:1}),r>3&&E.push({dims:u,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${A};${_};${v};${S}`,inputDependencies:e.map((P,O)=>"type")},getShaderSource:x,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(p/h/64)},programUniforms:I})}},vd=(e,t)=>{Up(e.inputs);let o=[0];e.outputCount>1&&o.push(-3),e.outputCount>2&&o.push(-3),e.outputCount>3&&o.push(3),e.compute(Vp(e.inputs,t,e.outputCount,!1),{outputs:o})}});var Wp,Gn,Np,_d,Gp,Hp,Sd,xd,Cd=Y(()=>{"use strict";ye();Se();Ze();_e();Wp=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,o)=>{if(e[o+1].dataType!==6&&e[o+1].dataType!==7)throw new Error(`Input ${o} must be an array of int32 or int64`)})},Gn=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(o=>r.push(Number(o)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(o=>r.push(Number(o)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Np=(e,t)=>{if(e.length>1){let r=Gn(e,1),o=Gn(e,2),i=Gn(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),ve({starts:r,ends:o,axes:i})}else return t},_d=(e,t,r,o,i)=>{let u=e;return e<0&&(u+=r[o[t]]),i[t]<0?Math.max(0,Math.min(u,r[o[t]]-1)):Math.max(0,Math.min(u,r[o[t]]))},Gp=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n          var input_indices: ${e.type.indices};\n          var carry = 0u;\n          for (var i = ${r.length}; i >= 0; i--) {\n            let input_shape_i = ${fe("uniforms.input_shape","i",r.length)};\n            let steps_i = ${fe("uniforms.steps","i",r.length)};\n            let signs_i = ${fe("uniforms.signs","i",r.length)};\n            let starts_i = ${fe("uniforms.starts","i",r.length)};\n            var output_index = ${t.indicesGet("output_indices","i")};\n            var input_index = output_index * steps_i + starts_i + carry;\n            carry = input_index / input_shape_i;\n            input_index = input_index % input_shape_i;\n            if (signs_i < 0) {\n              input_index = input_shape_i - input_index - 1u + starts_i;\n            }\n            ${e.indicesSet("input_indices","i","input_index")};\n          }\n          return input_indices;\n      }`,Hp=(e,t)=>{let r=e[0].dims,o=M.size(r),i=t.axes.length>0?M.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],u=Gn(e,4);u.forEach(I=>I!==0||(()=>{throw new Error("step cannot be 0")})),u.length===0&&(u=Array(i.length).fill(1));let a=t.starts.map((I,x)=>_d(I,x,r,i,u)),c=t.ends.map((I,x)=>_d(I,x,r,i,u));if(i.length!==a.length||i.length!==c.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==r.length)for(let I=0;I<r.length;++I)i.includes(I)||(a.splice(I,0,0),c.splice(I,0,r[I]),u.splice(I,0,1));let p=u.map(I=>Math.sign(I));u.forEach((I,x,E)=>{if(I<0){let P=(c[x]-a[x])/I,O=a[x],R=O+P*u[x];a[x]=R,c[x]=O,E[x]=-I}});let h=r.slice(0);i.forEach((I,x)=>{h[I]=Math.ceil((c[I]-a[I])/u[I])});let 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n'}),Cr,bt,un,Hn,qn,go,li,Vr,Gr,bf,jn,vf,$f,xf,Sf,kf,Ef,Cf,Tf=Z(()=>{var t;Vt(),q_(),Nn(),Cr=()=>!!Ue.wasm.proxy&&typeof document<"u",un=!1,Hn=!1,qn=!1,li=new Map,Vr=(e,r)=>{let n=li.get(e);n?n.push(r):li.set(e,[r])},Gr=()=>{if(un||!Hn||qn||!bt)throw new Error("worker not 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Nt(),c}startProfiling(){}endProfiling(){Cf(this.sessionId)}}}),Of,Mf,Y_=Z(()=>{Vt(),Tf(),K_(),Of=()=>{if((typeof Ue.wasm.initTimeout!="number"||Ue.wasm.initTimeout<0)&&(Ue.wasm.initTimeout=0),typeof Ue.wasm.simd!="boolean"&&(Ue.wasm.simd=!0),typeof Ue.wasm.proxy!="boolean"&&(Ue.wasm.proxy=!1),typeof Ue.wasm.trace!="boolean"&&(Ue.wasm.trace=!1),typeof Ue.wasm.numThreads!="number"||!Number.isInteger(Ue.wasm.numThreads)||Ue.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(Ue.wasm.numThreads=1);let t=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;Ue.wasm.numThreads=Math.min(4,Math.ceil((t||1)/2))}},Mf=class{async init(t){Of(),await vf(),await $f(t)}async createInferenceSessionHandler(t,e){let r=new Af;return await r.loadModel(t,e),Promise.resolve(r)}}}),zf={};nn(zf,{wasmBackend:()=>Rf});var Rf,Q_=Z(()=>{Y_(),Rf=new Mf});Vt(),Vt(),Vt();var X_="1.18.0",J_=xu;{let t=(Q_(),Rr(zf)).wasmBackend;Pr("webgpu",t,5),Pr("webnn",t,5),Pr("cpu",t,10),Pr("wasm",t,10)}Object.defineProperty(Ue.versions,"web",{value:X_,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 Z_=Object.freeze({__proto__:null,get InferenceSession(){return ps},get TRACE(){return Bn},get TRACE_FUNC_BEGIN(){return Wt},get TRACE_FUNC_END(){return Nt},get Tensor(){return ft},get TrainingSession(){return hs},default:J_,get env(){return Ue},get registerBackend(){return Pr}});const ey=(t,e)=>{const r=typeof document<"u"?document.createElement("canvas"):new OffscreenCanvas(1,1);r.width=t.dims[3],r.height=t.dims[2];const n=r.getContext("2d");if(n!=null){let a,s;(e==null?void 0:e.tensorLayout)!==void 0&&e.tensorLayout==="NHWC"?(a=t.dims[2],s=t.dims[3]):(a=t.dims[3],s=t.dims[2]);const i=(e==null?void 0:e.format)!==void 0?e.format:"RGB",o=e==null?void 0:e.norm;let 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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v=y.getImageData(0,0,g.width,g.height);o.height=g.height,o.width=g.width,c(yo(v.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(i!==void 0)return yo(i,o);throw new Error("Input data provided is not supported - aborted tensor creation")},ny=(t,e)=>{const{width:r,height:n,download:a,dispose:s}=e,i=[1,n,r,4];return new sr({location:"texture",type:"float32",texture:t,dims:i,download:a,dispose:s})},ay=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:s}=e;return new sr({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:s})},iy=(t,e,r)=>new sr({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),ln=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),di=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let Pf=!1;const sy=()=>{if(!Pf){Pf=!0;const t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=typeof Float16Array<"u"&&Float16Array.from;t&&(ln.set("int64",BigInt64Array),di.set(BigInt64Array,"int64")),e&&(ln.set("uint64",BigUint64Array),di.set(BigUint64Array,"uint64")),r?(ln.set("float16",Float16Array),di.set(Float16Array,"float16")):ln.set("float16",Uint16Array)}},oy=t=>{let e=1;for(let r=0;r<t.length;r++){const n=t[r];if(typeof n!="number"||!Number.isSafeInteger(n))throw new TypeError(`dims[${r}] must be an integer, got: ${n}`);if(n<0)throw new RangeError(`dims[${r}] must be a non-negative integer, got: ${n}`);e*=n}return e},uy=(t,e)=>{switch(t.location){case"cpu":return new sr(t.type,t.data,e);case"cpu-pinned":return new sr({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new sr({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new sr({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let sr=class{constructor(e,r,n){sy();let a,s;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,a=e.type,s=e.dims,e.location){case"cpu-pinned":{const o=ln.get(a);if(!o)throw new TypeError(`unsupported type "${a}" to create tensor from pinned buffer`);if(!(e.data instanceof o))throw new TypeError(`buffer should be of type ${o.name}`);this.cpuData=e.data;break}case"texture":{if(a!=="float32")throw new TypeError(`unsupported type "${a}" to create tensor from 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Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":{if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;const r=await this.downloader();return this.downloader=void 0,this.dataLocation="cpu",this.cpuData=r,e&&this.disposer&&(this.disposer(),this.disposer=void 0),r}finally{this.isDownloading=!1}}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return uy(this,e)}};const ly=sr,Kn=[];let wo,Hr;xr.IS_NODE_ENV?(Hr=Ve??pr,Kn.push("cpu"),wo=["cpu"]):(Hr=Z_,xr.IS_WEBGPU_AVAILABLE&&Kn.push("webgpu"),Kn.push("wasm"),wo=["wasm"]);const dy=Hr.InferenceSession;function cy(t){let e=wo;if(t){if(!Kn.includes(t))throw new Error(`Unsupported device: "${t}". Should be one of: ${Kn.join(", ")}.`);e=[t]}return e}async function Bf(t,e){return await dy.create(t,e)}function Df(t){return t 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@1.18.0/dist/",Yt.wasm.proxy=!xr.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 py(){var t;return(t=Yt==null?void 0:Yt.wasm)==null?void 0:t.proxy}Ct.backends.onnx=Yt;const dn=async(t,e,r)=>{const n=await Bf(new Uint8Array(t),e);return async a=>{const s=Object.fromEntries(Object.entries(a).map(([o,l])=>[o,l.ort_tensor])),i=await n.run(s);return Array.isArray(r)?r.map(o=>new Ce(i[o])):new Ce(i[r])}};class Nf{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=dn([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=dn([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=dn([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=dn([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=dn([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=dn([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}}D(Nf,"session_options",{});const Ff=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(...e){D(this,"ort_tensor");return Df(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new ly(e[0],e[1],e[2]),new Proxy(this,{get:(r,n)=>{if(typeof n=="string"){let a=Number(n);if(Number.isInteger(a))return r._getitem(a)}return r[n]},set:(r,n,a)=>r[n]=a})}get dims(){return this.ort_tensor.dims}set dims(e){this.ort_tensor.dims=e}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...r]=this.dims;if(r.length>0){const n=r.reduce((a,s)=>a*s);for(let a=0;a<e;++a)yield this._subarray(a,n,r)}else yield*this.data}_getitem(e){const[r,...n]=this.dims;if(e=or(e,r),n.length>0){const a=n.reduce((s,i)=>s*i);return this._subarray(e,a,n)}else return new Ce(this.type,[this.data[e]],n)}indexOf(e){const r=this.data;for(let n=0;n<r.length;++n)if(r[n]==e)return n;return-1}_subarray(e,r,n){const a=e*r,s=(e+1)*r,i="subarray"in this.data?this.data.subarray(a,s):this.data.slice(a,s);return new Ce(this.type,i,n)}item(){const e=this.data;if(e.length!==1)throw new Error(`a Tensor with ${e.length} elements cannot be converted to Scalar`);return e[0]}tolist(){return hy(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=1/(1+Math.exp(-e[r]));return this}mul(e){return this.clone().mul_(e)}mul_(e){const r=this.data;for(let n=0;n<r.length;++n)r[n]*=e;return this}div(e){return this.clone().div_(e)}div_(e){const r=this.data;for(let n=0;n<r.length;++n)r[n]/=e;return this}add(e){return this.clone().add_(e)}add_(e){const r=this.data;for(let n=0;n<r.length;++n)r[n]+=e;return this}clone(){return new Ce(this.type,this.data.slice(),this.dims.slice())}slice(...e){const r=[],n=[];for(let d=0;d<this.dims.length;++d){let c=e[d];if(c==null)n.push([0,this.dims[d]]),r.push(this.dims[d]);else if(typeof c=="number")c=or(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:or(f,this.dims[d],d,!1),g=g===null?this.dims[d]:or(g,this.dims[d],d,!1),f>g)throw new Error(`Invalid slice: ${c}`);const 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}`)}const a=n.map(([d,c])=>c-d),s=a.reduce((d,c)=>d*c),i=this.data,o=new i.constructor(s),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(...e){return fy(this,e)}transpose(...e){return this.permute(...e)}sum(e=null,r=!1){return this.norm(1,e,r)}norm(e="fro",r=null,n=!1){if(e==="fro")e=2;else if(typeof e=="string")throw Error(`Unsupported norm: ${e}`);const a=this.data;if(r===null){let o=a.reduce((l,d)=>l+d**e,0)**(1/e);return new Ce(this.type,[o],[])}r=or(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]**e}if(e!==1)for(let o=0;o<i.length;++o)i[o]=i[o]**(1/e);return n||s.splice(r,1),new Ce(this.type,i,s)}normalize_(e=2,r=1){r=or(r,this.dims.length);const n=this.norm(e,r,!0),a=this.data,s=n.data;for(let i=0;i<a.length;++i){let o=0;for(let l=this.dims.length-1,d=i,c=1;l>=0;--l){const f=this.dims[l];if(l!==r){const g=d%f;o+=g*c,c*=this.dims[l]}d=Math.floor(d/f)}a[i]/=s[o]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return gy(this.dims)}squeeze(e=null){return new Ce(this.type,this.data,Uf(this.dims,e))}squeeze_(e=null){return this.dims=Uf(this.dims,e),this}unsqueeze(e=null){return new Ce(this.type,this.data,Wf(this.dims,e))}unsqueeze_(e=null){return this.dims=Wf(this.dims,e),this}flatten_(e=0,r=-1){r=(r+this.dims.length)%this.dims.length;let n=this.dims.slice(0,e),a=this.dims.slice(e,r+1),s=this.dims.slice(r+1);return this.dims=[...n,a.reduce((i,o)=>i*o,1),...s],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let a=0;a<e.length;++a)if(e[a]===-1){if(r!==-1)throw new Error("Only one dimension can be inferred");r=a}const n=this.data;if(r!==-1){const a=e.reduce((s,i,o)=>o!==r?s*i:s,1);e[r]=n.length/a}return new Ce(this.type,n,e)}neg_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=-e[r];return this}neg(){return this.clone().neg_()}clamp_(e,r){const n=this.data;for(let a=0;a<n.length;++a)n[a]=Math.min(Math.max(n[a],e),r);return this}clamp(e,r){return this.clone().clamp_(e,r)}round_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=Math.round(e[r]);return this}round(){return this.clone().round_()}mean(e=null,r=!1){return vo(this,e,r)}to(e){if(this.type===e)return this;if(!Ff.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);return new Ce(e,Ff[e].from(this.data),this.dims)}}function hy(t,e){const r=t.length,n=e.reduce((s,i)=>s*i);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let a=t;for(let s=e.length-1;s>=0;s--)a=a.reduce((i,o)=>{let l=i[i.length-1];return l.length<e[s]?l.push(o):i.push([o]),i},[[]]);return a[0]}function fy(t,e){const[r,n]=T0(t.data,t.dims,e);return new Ce(t.type,r,n)}async function Lf(t,e){const r=await Nf.top_k;return e===null?e=t.dims.at(-1):e=Math.min(e,t.dims.at(-1)),await r({x:t,k:new Ce("int64",[BigInt(e)],[1])})}function Uf(t,e){return t=t.slice(),e===null?t=t.filter(r=>r!==1):typeof e=="number"?t[e]===1&&t.splice(e,1):Array.isArray(e)&&(t=t.filter((r,n)=>r!==1||!e.includes(n))),t}function Wf(t,e){return e=or(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function or(t,e,r=null,n=!0){if(n&&(t<-e||t>=e))throw new Error(`IndexError: index ${t} is out of bounds for dimension${r===null?"":" "+r} with size ${e}`);return t<0&&(t=(t%e+e)%e),t}function Qt(t,e=0){e=or(e,t[0].dims.length);const r=t[0].dims.slice();r[e]=t.reduce((i,o)=>i+o.dims[e],0);const n=r.reduce((i,o)=>i*o,1),a=new t[0].data.constructor(n),s=t[0].type;if(e===0){let i=0;for(const o of t){const l=o.data;a.set(l,i),i+=l.length}}else{let i=0;for(let o=0;o<t.length;++o){const{data:l,dims:d}=t[o];for(let c=0;c<l.length;++c){let f=0;for(let g=d.length-1,y=c,p=1;g>=0;--g){const v=d[g];let x=y%v;g===e&&(x+=i),f+=x*p,p*=r[g],y=Math.floor(y/v)}a[f]=l[c]}i+=d[e]}}return new Ce(s,a,r)}function bo(t,e=0){return Qt(t.map(r=>r.unsqueeze(e)),e)}function my(t,e=null,r=1,n=!1){const a=t.data,s=t.dims;if(e===null){const g=a.reduce((x,E)=>x+E,0)/a.length,y=Math.sqrt(a.reduce((x,E)=>x+(E-g)**2,0)/(a.length-r)),p=new Ce(t.type,[g],[]);return[new Ce(t.type,[y],[]),p]}e=or(e,s.length);const i=vo(t,e,n),o=i.data,l=s.slice();l[e]=1;const d=new a.constructor(a.length/s[e]);for(let f=0;f<a.length;++f){let g=0;for(let y=s.length-1,p=f,v=1;y>=0;--y){const x=s[y];if(y!==e){const E=p%x;g+=E*v,v*=l[y]}p=Math.floor(p/x)}d[g]+=(a[f]-o[g])**2}for(let f=0;f<d.length;++f)d[f]=Math.sqrt(d[f]/(s[e]-r));return n||l.splice(e,1),[new Ce(t.type,d,l),i]}function vo(t,e=null,r=!1){const n=t.data;if(e===null){const o=n.reduce((l,d)=>l+d,0);return new Ce(t.type,[o/n.length],[])}const a=t.dims;e=or(e,a.length);const s=a.slice();s[e]=1;const i=new n.constructor(n.length/a[e]);for(let o=0;o<n.length;++o){let l=0;for(let d=a.length-1,c=o,f=1;d>=0;--d){const g=a[d];if(d!==e){const y=c%g;l+=y*f,f*=s[d]}c=Math.floor(c/g)}i[l]+=n[o]}if(a[e]!==1)for(let o=0;o<i.length;++o)i[o]=i[o]/a[e];return r||s.splice(e,1),new Ce(t.type,i,s)}function gy(t){const e=new Array(t.length);for(let r=t.length-1,n=1;r>=0;--r)e[r]=n,n*=t[r];return e}function $o(t,e,r,n){const a=t.reduce((s,i)=>s*i,1);return new Ce(r,new n(a).fill(e),t)}function _y(t,e){let r,n;return r="float32",n=Float32Array,$o(t,e,r,n)}function yy(t,e){return _y(t.dims,e)}function cn(t){return $o(t,1n,"int64",BigInt64Array)}function wy(t){return cn(t.dims)}function by(t){return $o(t,0n,"int64",BigInt64Array)}function vy(t){return by(t.dims)}class $y{constructor(e=(n,a)=>n>a,r=1/0){this._heap=[],this._comparator=e,this._maxSize=r}get size(){return this._heap.length}isEmpty(){return this.size===0}peek(){return this._heap[0]}push(...e){return this.extend(e)}extend(e){for(const r of e)if(this.size<this._maxSize)this._heap.push(r),this._siftUp();else{const n=this._smallest();this._comparator(r,this._heap[n])&&(this._heap[n]=r,this._siftUpFrom(n))}return this.size}pop(){const e=this.peek(),r=this.size-1;return r>0&&this._swap(0,r),this._heap.pop(),this._siftDown(),e}replace(e){const r=this.peek();return this._heap[0]=e,this._siftDown(),r}_parent(e){return(e+1>>>1)-1}_left(e){return(e<<1)+1}_right(e){return e+1<<1}_greater(e,r){return this._comparator(this._heap[e],this._heap[r])}_swap(e,r){const n=this._heap[e];this._heap[e]=this._heap[r],this._heap[r]=n}_siftUp(){this._siftUpFrom(this.size-1)}_siftUpFrom(e){for(;e>0&&this._greater(e,this._parent(e));)this._swap(e,this._parent(e)),e=this._parent(e)}_siftDown(){let e=0;for(;this._left(e)<this.size&&this._greater(this._left(e),e)||this._right(e)<this.size&&this._greater(this._right(e),e);){const r=this._right(e)<this.size&&this._greater(this._right(e),this._left(e))?this._right(e):this._left(e);this._swap(e,r),e=r}}_smallest(){return 2**Math.floor(Math.log2(this.size))-1}}class xy{constructor(){this.root=ci.default()}extend(e){for(let r of e)this.push(r)}push(e){let r=this.root;for(let n of e){let a=r.children.get(n);a===void 0&&(a=ci.default(),r.children.set(n,a)),r=a}r.isLeaf=!0}*commonPrefixSearch(e){let r=this.root,n="";for(let a=0;a<e.length&&r!==void 0;++a){const s=e[a];n+=s,r=r.children.get(s),r!==void 0&&r.isLeaf&&(yield n)}}}class ci{constructor(e,r){this.isLeaf=e,this.children=r}static default(){return new ci(!1,new Map)}}class Sy{constructor(e,r,n){this.sentence=e,this.len=e.length,this.bosTokenId=r,this.eosTokenId=n,this.nodes=[],this.beginNodes=Array.from({length:this.len+1},()=>[]),this.endNodes=Array.from({length:this.len+1},()=>[]);const a=new Yn(this.bosTokenId,0,0,0,0),s=new 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d=r.value.filter(c=>{const f=c.value.get(s.value);return f?l(f,o):!1});return new Ye(d)}}throw new Error(`Unknown ArrayValue filter: ${a}`)}else throw new Error(`Cannot apply filter "${a}" to type: ${r.type}`)}throw new Error(`Unknown filter: ${t.filter.type}`)}evaluateTestExpression(t,e){const r=this.evaluate(t.operand,e),n=e.tests.get(t.test.value);if(!n)throw new Error(`Unknown test: ${t.test.value}`);const a=n(r);return new Je(t.negate?!a:a)}evaluateUnaryExpression(t,e){const r=this.evaluate(t.argument,e);switch(t.operator.value){case"not":return new Je(!r.value);default:throw new SyntaxError(`Unknown operator: ${t.operator.value}`)}}evalProgram(t,e){return this.evaluateBlock(t.body,e)}evaluateBlock(t,e){let r="";for(const n of t){const a=this.evaluate(n,e);a.type!=="NullValue"&&a.type!=="UndefinedValue"&&(r+=a.value)}return new Pe(r)}evaluateIdentifier(t,e){return e.lookupVariable(t.value)}evaluateCallExpression(t,e){const r=[],n=new Map;for(const s of t.args)if(s.type==="KeywordArgumentExpression"){const i=s;n.set(i.key.value,this.evaluate(i.value,e))}else r.push(this.evaluate(s,e));n.size>0&&r.push(new Xt(n));const a=this.evaluate(t.callee,e);if(a.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${a.type}`);return a.value(r,e)}evaluateSliceExpression(t,e,r){if(!(t instanceof Ye||t instanceof Pe))throw new Error("Slice object must be an array or string");const n=this.evaluate(e.start,r),a=this.evaluate(e.stop,r),s=this.evaluate(e.step,r);if(!(n instanceof qe||n instanceof Jt))throw new Error("Slice start must be numeric or undefined");if(!(a instanceof qe||a instanceof Jt))throw new Error("Slice stop must be numeric or undefined");if(!(s instanceof qe||s instanceof Jt))throw new Error("Slice step must be numeric or undefined");return t instanceof Ye?new Ye(Yf(t.value,n.value,a.value,s.value)):new Pe(Yf(Array.from(t.value),n.value,a.value,s.value).join(""))}evaluateMemberExpression(t,e){const r=this.evaluate(t.object,e);let n;if(t.computed){if(t.property.type==="SliceExpression")return this.evaluateSliceExpression(r,t.property,e);n=this.evaluate(t.property,e)}else n=new Pe(t.property.value);let a;if(r instanceof Xt){if(!(n instanceof Pe))throw new Error(`Cannot access property with non-string: got ${n.type}`);a=r.value.get(n.value)??r.builtins.get(n.value)}else if(r instanceof Ye||r instanceof Pe)if(n instanceof qe)a=r.value.at(n.value),r instanceof Pe&&(a=new Pe(r.value.at(n.value)));else if(n instanceof Pe)a=r.builtins.get(n.value);else throw new Error(`Cannot access property with non-string/non-number: got ${n.type}`);else{if(!(n instanceof Pe))throw new Error(`Cannot access property with non-string: got ${n.type}`);a=r.builtins.get(n.value)}return a instanceof _r?a:new Jt}evaluateSet(t,e){const r=this.evaluate(t.value,e);if(t.assignee.type==="Identifier"){const n=t.assignee.value;e.setVariable(n,r)}else if(t.assignee.type==="MemberExpression"){const n=t.assignee,a=this.evaluate(n.object,e);if(!(a instanceof Xt))throw new Error("Cannot assign to member of non-object");if(n.property.type!=="Identifier")throw new Error("Cannot assign to member with non-identifier property");a.value.set(n.property.value,r)}else throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(t.assignee)}`);return new Jn}evaluateIf(t,e){const r=this.evaluate(t.test,e);return this.evaluateBlock(r.__bool__().value?t.body:t.alternate,e)}evaluateFor(t,e){const r=new So(e),n=this.evaluate(t.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 Jt],["nextitem",s<n.value.length-1?n.value[s+1]:new Jt]]);r.setVariable("loop",new Xt(i));const o=n.value[s];if(t.loopvar.type==="Identifier")r.setVariable(t.loopvar.value,o);else if(t.loopvar.type==="TupleLiteral"){const d=t.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(t.body,r);a+=l.value}return new Pe(a)}evaluate(t,e){if(t===void 0)return new Jt;switch(t.type){case"Program":return this.evalProgram(t,e);case"Set":return this.evaluateSet(t,e);case"If":return this.evaluateIf(t,e);case"For":return this.evaluateFor(t,e);case"NumericLiteral":return new qe(Number(t.value));case"StringLiteral":return new Pe(t.value);case"BooleanLiteral":return new Je(t.value);case"ArrayLiteral":return new Ye(t.value.map(r=>this.evaluate(r,e)));case"TupleLiteral":return new Gy(t.value.map(r=>this.evaluate(r,e)));case"ObjectLiteral":{const r=new Map;for(const[n,a]of t.value){const s=this.evaluate(n,e);if(!(s instanceof Pe))throw new Error(`Object keys must be strings: got ${s.type}`);r.set(s.value,this.evaluate(a,e))}return new Xt(r)}case"Identifier":return this.evaluateIdentifier(t,e);case"CallExpression":return this.evaluateCallExpression(t,e);case"MemberExpression":return this.evaluateMemberExpression(t,e);case"UnaryExpression":return this.evaluateUnaryExpression(t,e);case"BinaryExpression":return this.evaluateBinaryExpression(t,e);case"FilterExpression":return this.evaluateFilterExpression(t,e);case"TestExpression":return this.evaluateTestExpression(t,e);default:throw new SyntaxError(`Unknown node type: ${t.type}`)}}};function pi(t){switch(typeof t){case"number":return new qe(t);case"string":return new Pe(t);case"boolean":return new Je(t);case"object":return t===null?new Jn:Array.isArray(t)?new Ye(t.map(pi)):new Xt(new Map(Object.entries(t).map(([e,r])=>[e,pi(r)])));case"function":return new yr((e,r)=>{const n=t(...e.map(a=>a.value))??null;return pi(n)});default:throw new Error(`Cannot convert to runtime value: ${t}`)}}var qy=class{constructor(t){D(this,"parsed");const e=Ty(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=Wy(e)}render(t){const e=new So;e.set("false",!1),e.set("true",!0),e.set("raise_exception",a=>{throw new Error(a)}),e.set("range",Vy);for(const[a,s]of Object.entries(t))e.set(a,s);return new Hy(e).run(this.parsed).value}};const Xf=[["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"]],hi=new Map(Xf),jy=new Map([...Xf.map(([t,e])=>[e,t]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function Jf(t){t=t.toLowerCase();let e=jy.get(t);if(e===void 0)if(hi.has(t))e=t;else{const n=t.length===2?hi.keys():hi.values();throw new Error(`Language "${t}" is not supported. 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Use `tokenizer.batch_decode(...)` for batched inputs.");return t.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${e.length}.`)}}function Co(t){return t.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function tm(t){return t.replace(/[\u0300-\u036f]/g,"")}function Yy(t){return tm(t.toLowerCase())}function rm(t){return t>=19968&&t<=40959||t>=13312&&t<=19903||t>=131072&&t<=173791||t>=173824&&t<=177983||t>=177984&&t<=178207||t>=178208&&t<=183983||t>=63744&&t<=64255||t>=194560&&t<=195103}function Qy(t,e,r){const n=[];let a=0;for(;a<t.length;){if(n.push(t[a]),(r.get(t[a])??e)!==e){++a;continue}for(;a<t.length&&(r.get(t[a])??e)===e;)++a}return n}function Xy(t){return t.match(/\S+/g)||[]}const fn="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",Jy=new RegExp(`^[${fn}]+$`,"gu"),Zy=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class ew{constructor(e){this.content=e.content,this.id=e.id,this.single_word=e.single_word??!1,this.lstrip=e.lstrip??!1,this.rstrip=e.rstrip??!1,this.special=e.special??!1,this.normalized=e.normalized??null}}class Zn extends Tt{constructor(e){super(),this.config=e,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(e,...r){switch(e.type){case"WordPiece":return new tw(e);case"Unigram":return new rw(e,...r);case"BPE":return new aw(e);default:if(e.vocab)return new iw(e,...r);throw new Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){let r=this.encode(e);return this.fuse_unk&&(r=Qy(r,this.unk_token_id,this.tokens_to_ids)),r}encode(e){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(e){return e.map(r=>this.tokens_to_ids.get(r)??this.unk_token_id)}convert_ids_to_tokens(e){return e.map(r=>this.vocab[r]??this.unk_token)}}class tw extends Zn{constructor(e){super(e),this.tokens_to_ids=Eo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.max_input_chars_per_word=e.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r}encode(e){const r=[];for(const n of e){const a=[...n];if(a.length>this.max_input_chars_per_word){r.push(this.unk_token);continue}let s=!1,i=0;const o=[];for(;i<a.length;){let 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 rw extends Zn{constructor(e,r){super(e);const n=e.vocab.length;this.vocab=new Array(n),this.scores=new Array(n);for(let a=0;a<n;++a){const s=e.vocab[a];this.vocab[a]=s[0],this.scores[a]=s[1]}this.unk_token_id=e.unk_id,this.unk_token=this.vocab[e.unk_id],this.tokens_to_ids=new Map(this.vocab.map((a,s)=>[a,s])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=r.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=A0(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new xy,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(e){const r=e.sentence,n=r.length;let a=0;for(;a<n;){let i=!1;for(let o of this.trie.commonPrefixSearch(r.slice(a))){const l=this.tokens_to_ids.get(o),d=this.scores[l],c=o.length;e.insert(a,c,d,l),!i&&c===1&&(i=!0)}i||e.insert(a,1,this.unkScore,this.unk_token_id),a+=1}}tokenize(e){const r=new Sy(e,this.bosTokenId,this.eosTokenId);return this.populateNodes(r),r.tokens()}encode(e){const r=[];for(const n of e){const a=this.tokenize(n);r.push(...a)}return r}}const nm=(()=>{const t=[...Array.from({length:94},(a,s)=>s+33),...Array.from({length:12},(a,s)=>s+161),...Array.from({length:82},(a,s)=>s+174)],e=t.slice();let r=0;for(let a=0;a<256;++a)t.includes(a)||(t.push(a),e.push(256+r),r+=1);const n=e.map(a=>String.fromCharCode(a));return Object.fromEntries(t.map((a,s)=>[a,n[s]]))})(),nw=$0(nm);class aw extends Zn{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=Eo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r;this.bpe_ranks=new Map(e.merges.map((r,n)=>[r,n])),this.merges=e.merges.map(r=>r.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(e){if(e.length===0)return[];const r=this.cache.get(e);if(r!==void 0)return r;const n=Array.from(e);this.end_of_word_suffix&&(n[n.length-1]+=this.end_of_word_suffix);let a=[];if(n.length>1){const s=new $y((l,d)=>l.score<d.score);let i={token:n[0],bias:0,prev:null,next:null},o=i;for(let l=1;l<n.length;++l){const d={bias:l/n.length,token:n[l],prev:o,next:null};o.next=d,this._add_node(s,o),o=d}for(;!s.isEmpty();){const l=s.pop();if(l.deleted||!l.next||l.next.deleted)continue;if(l.deleted=!0,l.next.deleted=!0,l.prev){const c={...l.prev};l.prev.deleted=!0,l.prev=c,c.prev?c.prev.next=c:i=c}const d={token:l.token+l.next.token,bias:l.bias,prev:l.prev,next:l.next.next};d.prev?(d.prev.next=d,this._add_node(s,d.prev)):i=d,d.next&&(d.next.prev=d,this._add_node(s,d))}for(let l=i;l!==null;l=l.next)a.push(l.token)}else a=n;if(this.continuing_subword_suffix)for(let s=0;s<a.length-1;++s)a[s]+=this.continuing_subword_suffix;return this.cache.set(e,a),a}_add_node(e,r){const n=this.bpe_ranks.get(r.token+this.BPE_SPLIT_TOKEN+r.next.token);n!==void 0&&(r.score=n+r.bias,e.push(r))}encode(e){const r=[];for(const n of e){if(this.ignore_merges&&this.tokens_to_ids.has(n)){r.push(n);continue}const a=this.bpe(n);for(const s of a)this.tokens_to_ids.has(s)?r.push(s):this.byte_fallback?r.push(...Array.from(this.text_encoder.encode(s)).map(i=>`<0x${i.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class iw extends Zn{constructor(e,r){super(e),this.tokens_to_ids=Eo(r.target_lang?e.vocab[r.target_lang]:e.vocab),this.bos_token=r.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=r.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=r.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=r.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[n,a]of this.tokens_to_ids)this.vocab[a]=n}encode(e){return e}}class It extends Tt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new mw(e);case"Precompiled":return new Rw(e);case"Sequence":return new fw(e);case"Replace":return new sw(e);case"NFC":return new ow(e);case"NFKC":return new uw(e);case"NFKD":return new lw(e);case"Strip":return new dw(e);case"StripAccents":return new cw(e);case"Lowercase":return new pw(e);case"Prepend":return new hw(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class sw extends It{normalize(e){const r=fi(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class ow extends It{normalize(e){return e=e.normalize("NFC"),e}}class uw extends It{normalize(e){return e=e.normalize("NFKC"),e}}class lw extends It{normalize(e){return e=e.normalize("NFKD"),e}}class dw extends It{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class cw extends It{normalize(e){return e=tm(e),e}}class pw extends It{normalize(e){return e=e.toLowerCase(),e}}class hw extends It{normalize(e){return e=this.config.prepend+e,e}}class fw extends It{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>It.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class mw extends It{_tokenize_chinese_chars(e){const r=[];for(let n=0;n<e.length;++n){const a=e[n],s=a.charCodeAt(0);rm(s)?(r.push(" "),r.push(a),r.push(" ")):r.push(a)}return r.join("")}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(e){switch(e){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(e)}}_clean_text(e){const r=[];for(const n of e){const a=n.charCodeAt(0);a===0||a===65533||this._is_control(n)||(/^\s$/.test(n)?r.push(" "):r.push(n))}return r.join("")}normalize(e){return this.config.clean_text&&(e=this._clean_text(e)),this.config.handle_chinese_chars&&(e=this._tokenize_chinese_chars(e)),this.config.lowercase?(e=e.toLowerCase(),this.config.strip_accents!==!1&&(e=this.stripAccents(e))):this.config.strip_accents&&(e=this.stripAccents(e)),e}}class Ft extends Tt{static fromConfig(e){if(e===null)return null;switch(e.type){case"BertPreTokenizer":return new gw(e);case"Sequence":return new Pw(e);case"Whitespace":return new Bw(e);case"WhitespaceSplit":return new Dw(e);case"Metaspace":return new om(e);case"ByteLevel":return new _w(e);case"Split":return new yw(e);case"Punctuation":return new ww(e);case"Digits":return new bw(e);case"Replace":return new Nw(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,r){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,r){return(Array.isArray(e)?e.map(n=>this.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class gw extends Ft{constructor(e){super(),this.pattern=new RegExp(`[^\\s${fn}]+|[${fn}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class _w extends Ft{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=nm,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),s=>this.byte_encoder[s]).join(""))}}class yw extends Ft{constructor(e){super(),this.config=e,this.pattern=fi(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:Ky(e,this.pattern)}}class ww extends Ft{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${fn}]+|[${fn}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class bw extends Ft{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class mn extends Tt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new vw(e);case"ByteLevel":return new sm(e);case"RobertaProcessing":return new im(e);case"BertProcessing":return new am(e);case"Sequence":return new $w(e);default:throw new Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...r){throw Error("post_process should be implemented in subclass.")}_call(e,...r){return this.post_process(e,...r)}}class am extends mn{constructor(e){super(e),this.cls=e.cls[0],this.sep=e.sep[0]}post_process(e,r=null,{add_special_tokens:n=!0}={}){n&&(e=it([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const s=n&&this instanceof im?[this.sep]:[],i=n?[this.sep]:[];e=it(e,s,r,i),a=it(a,new Array(r.length+s.length+i.length).fill(1))}return{tokens:e,token_type_ids:a}}}class im extends am{}class vw extends mn{constructor(e){super(e),this.single=e.single,this.pair=e.pair}post_process(e,r=null,{add_special_tokens:n=!0}={}){const a=r===null?this.single:this.pair;let s=[],i=[];for(const o of a)"SpecialToken"in o?n&&(s.push(o.SpecialToken.id),i.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(s=it(s,e),i=it(i,new Array(e.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 sm extends mn{post_process(e,r=null){return r&&(e=it(e,r)),{tokens:e}}}class $w extends mn{constructor(e){super(e),this.processors=e.processors.map(r=>mn.fromConfig(r))}post_process(e,r=null,n={}){let a;for(const s of this.processors)if(s instanceof sm)e=s.post_process(e).tokens,r&&(r=s.post_process(r).tokens);else{const i=s.post_process(e,r,n);e=i.tokens,a=i.token_type_ids}return{tokens:e,token_type_ids:a}}}class At extends Tt{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=e.trim_offsets}static fromConfig(e){if(e===null)return null;switch(e.type){case"WordPiece":return new Cw(e);case"Metaspace":return new zw(e);case"ByteLevel":return new Tw(e);case"Replace":return new xw(e);case"ByteFallback":return new Sw(e);case"Fuse":return new kw(e);case"Strip":return new Ew(e);case"Sequence":return new Aw(e);case"CTC":return new Iw(e);case"BPEDecoder":return new Ow(e);default:throw new Error(`Unknown Decoder type: ${e.type}`)}}_call(e){return this.decode(e)}decode(e){return this.decode_chain(e).join("")}decode_chain(e){throw Error("`decode_chain` should be implemented in subclass.")}}class xw extends At{decode_chain(e){const r=fi(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class Sw extends At{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const r=[];let n=[];for(const a of e){let s=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const i=parseInt(a.slice(3,5),16);isNaN(i)||(s=i)}if(s!==null)n.push(s);else{if(n.length>0){const i=this.text_decoder.decode(Uint8Array.from(n));r.push(i),n=[]}r.push(a)}}if(n.length>0){const a=this.text_decoder.decode(Uint8Array.from(n));r.push(a),n=[]}return r}}class kw extends At{decode_chain(e){return[e.join("")]}}class Ew extends At{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map(r=>{let n=0;for(let s=0;s<this.start&&r[s]===this.content;++s){n=s+1;continue}let a=r.length;for(let s=0;s<this.stop;++s){const i=r.length-s-1;if(r[i]===this.content){a=i;continue}else break}return r.slice(n,a)})}}class Cw extends At{constructor(e){super(e),this.cleanup=e.cleanup}decode_chain(e){return e.map((r,n)=>(n!==0&&(r.startsWith(this.config.prefix)?r=r.replace(this.config.prefix,""):r=" "+r),this.cleanup&&(r=Co(r)),r))}}class Tw extends At{constructor(e){super(e),this.byte_decoder=nw,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(e){const r=e.join(""),n=new Uint8Array([...r].map(s=>this.byte_decoder[s]));return this.text_decoder.decode(n)}decode_chain(e){const r=[];let n=[];for(const a of e)this.added_tokens.find(s=>s.content===a)!==void 0?(n.length>0&&(r.push(this.convert_tokens_to_string(n)),n=[]),r.push(a)):n.push(a);return n.length>0&&r.push(this.convert_tokens_to_string(n)),r}}class Iw extends At{constructor(e){super(e),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(e){if(e.length===0)return"";const r=[e[0]];for(let s=1;s<e.length;++s)e[s]!==r.at(-1)&&r.push(e[s]);let a=r.filter(s=>s!==this.pad_token).join("");return this.cleanup&&(a=Co(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class Aw extends At{constructor(e){super(e),this.decoders=e.decoders.map(r=>At.fromConfig(r))}decode_chain(e){return this.decoders.reduce((r,n)=>n.decode_chain(r),e)}}class Ow extends At{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map((r,n)=>r.replaceAll(this.suffix,n===e.length-1?"":" "))}}class Mw extends At{decode_chain(e){let r="";for(let n=1;n<e.length;n+=2)r+=e[n];return[r]}}class om extends Ft{constructor(e){super(),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement,this.strRep=e.str_rep||this.replacement,this.prepend_scheme=e.prepend_scheme??"always"}pre_tokenize_text(e,{section_index:r=void 0}={}){let n=e.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!n.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&r===0)&&(n=this.strRep+n),[n]}}class zw extends At{constructor(e){super(e),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement}decode_chain(e){const r=[];for(let n=0;n<e.length;++n){let a=e[n].replaceAll(this.replacement," ");this.addPrefixSpace&&n==0&&a.startsWith(" ")&&(a=a.substring(1)),r.push(a)}return r}}class Rw extends It{constructor(e){super(e),this.charsmap=e.precompiled_charsmap}normalize(e){return e=e.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),e=e.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),e.includes("～")?e=e.split("～").map(n=>n.normalize("NFKC")).join("～"):e=e.normalize("NFKC"),e}}class Pw extends Ft{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>Ft.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class Bw extends Ft{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class Dw extends Ft{constructor(e){super()}pre_tokenize_text(e,r){return Xy(e)}}class Nw extends Ft{constructor(e){super(),this.config=e,this.pattern=fi(this.config.pattern),this.content=this.config.content}pre_tokenize_text(e,r){return this.pattern===null?[e]:[e.replaceAll(this.pattern,this.config.content)]}}const Fw=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Lw(t,e,r,n){for(const a of Object.keys(t)){const s=e-t[a].length,i=r(a),o=new Array(s).fill(i);t[a]=n==="right"?it(t[a],o):it(o,t[a])}}function Uw(t,e){for(const r of Object.keys(t))t[r].length=e}class ke extends Tt{constructor(r,n){super();D(this,"return_token_type_ids",!1);D(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 %}`);D(this,"padding_side","right");this._tokenizer_config=n,this.normalizer=It.fromConfig(r.normalizer),this.pre_tokenizer=Ft.fromConfig(r.pre_tokenizer),this.model=Zn.fromConfig(r.model,n),this.post_processor=mn.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 ew(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.toSorted((a,s)=>s.content.length-a.content.length).map(a=>`${a.lstrip?"\\s*":""}(${au(a.content)})${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 Zf(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,return_token_type_ids:d=null}={}){const c=Array.isArray(r);let f;if(c){if(r.length===0)throw Error("text array must be non-empty");if(n!==null){if(Array.isArray(n)){if(r.length!==n.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");f=r.map((y,p)=>this._encode_plus(y,{text_pair:n[p],add_special_tokens:a,return_token_type_ids:d}))}else f=r.map(y=>this._encode_plus(y,{add_special_tokens:a,return_token_type_ids:d}))}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).");f=[this._encode_plus(r,{text_pair:n,add_special_tokens:a,return_token_type_ids:d})]}if(o===null?s==="max_length"?o=this.model_max_length:o=Na(f.map(y=>y.input_ids.length))[0]:i||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),o=Math.min(o,this.model_max_length??1/0),s||i)for(let y=0;y<f.length;++y)f[y].input_ids.length!==o&&(f[y].input_ids.length>o?i&&Uw(f[y],o):s&&Lw(f[y],o,p=>p==="input_ids"?this.pad_token_id:0,this.padding_side));const g={};if(l){if(!(s&&i)&&f.some(p=>{var v;for(const x of Object.keys(p))if(p[x].length!==((v=f[0][x])==null?void 0:v.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 y=[f.length,f[0].input_ids.length];for(const p of Object.keys(f[0]))g[p]=new Ce("int64",BigInt64Array.from(f.flatMap(v=>v[p]).map(BigInt)),y)}else{for(const y of Object.keys(f[0]))g[y]=f.map(p=>p[y]);if(!c)for(const y of Object.keys(g))g[y]=g[y][0]}return g}_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=Yy(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,return_token_type_ids:s=null}={}){const{tokens:i,token_type_ids:o}=this._tokenize_helper(r,{pair:n,add_special_tokens:a}),l=this.model.convert_tokens_to_ids(i),d={input_ids:l,attention_mask:new Array(l.length).fill(1)};return(s??this.return_token_type_ids)&&o&&(d.token_type_ids=o),d}_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_token_type_ids:s=null}={}){return this._encode_plus(r,{text_pair:n,add_special_tokens:a,return_token_type_ids:s}).input_ids}batch_decode(r,n={}){return r instanceof Ce&&(r=r.tolist()),r.map(a=>this.decode(a,n))}decode(r,n={}){if(r instanceof Ce&&(r=em(r)),!Array.isArray(r)||r.length===0||!x0(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=Co(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 x=this.chat_template??this.default_chat_template;if(n!==null&&Object.hasOwn(x,n))n=x[n];else if(n===null&&"default"in x)n=x.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(x).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 ${typeof n}`);let y=this._compiled_template_cache.get(n);y===void 0&&(y=new qy(n),this._compiled_template_cache.set(n,y));const p=Object.create(null);for(const x of Fw){const E=this.getToken(x);E&&(p[x]=E)}const v=y.render({messages:r,add_generation_prompt:a,...p,...g});if(s){const x=this._call(v,{add_special_tokens:!1,padding:i,truncation:o,max_length:l,return_tensor:d,...f});return c?x:x.input_ids}return v}}class Ww extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class Vw extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class Gw extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class Hw extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class qw extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class jw extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class Kw extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class Yw extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class Qw extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class Xw extends ke{}class Jw extends ke{}class Zw extends ke{constructor(r,n){super(r,n);D(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class eb extends ke{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class tb extends ke{}class um extends ke{constructor(){super(...arguments);D(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}}class rb extends ke{}class lm extends ke{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return To(this,e,r,n)}}class nb extends lm{}class ab extends ke{}class ib extends um{constructor(e,r){var s,i;const n=".,!?…。，、।۔،",a=(i=(s=e.pre_tokenizer)==null?void 0:s.pretokenizers[0])==null?void 0:i.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const mi="▁";class dm extends ke{constructor(r,n){super(r,n);D(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 %}`);D(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.`);D(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 om({replacement:mi,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(mi+r.replaceAll(mi," "));return n.length>1&&n[0]===mi&&this.special_tokens.includes(n[1])&&(n=n.slice(1)),n}get default_chat_template(){return super.default_chat_template.replaceAll("USE_DEFAULT_PROMPT",this.use_default_system_prompt?"true":"false").replaceAll("DEFAULT_SYSTEM_MESSAGE",this.DEFAULT_SYSTEM_PROMPT.replaceAll(`
`,"\\n").replaceAll("'","\\'"))}}class sb extends dm{}class ob extends ke{}class ub extends ke{}class lb extends ke{}class db extends ke{}class cb extends ke{}class pb extends ke{}class hb extends ke{constructor(){super(...arguments);D(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 fb extends ke{}function To(t,e,r,n){if(!("language_codes"in t)||!Array.isArray(t.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in t)||!(t.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in t)||typeof t.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const a=n.src_lang,s=n.tgt_lang;if(!t.language_codes.includes(s))throw new Error(`Target language code "${s}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);if(a!==void 0){if(!t.language_codes.includes(a))throw new Error(`Source language code "${a}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);for(const i of t.post_processor.config.single)if("SpecialToken"in i&&t.languageRegex.test(i.SpecialToken.id)){i.SpecialToken.id=t.lang_to_token(a);break}}return n.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(s)])[0],t._call(e,r)}class mb extends ke{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return To(this,e,r,n)}}class gb extends ke{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)).map(n=>n.slice(2,-2)),this.lang_to_token=n=>`__${n}__`}_build_translation_inputs(e,r,n){return To(this,e,r,n)}}class _b extends ke{constructor(){super(...arguments);D(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(r,{return_timestamps:n=!1,return_language:a=!1,time_precision:s=null,force_full_sequences:i=!0}={}){if(s===null)throw Error("Must specify time_precision");let o=null;const l=n==="word";function d(){return{language:o,timestamp:[null,null],text:""}}const c=[];let f=d(),g=0;const y=this.timestamp_begin;let p=[],v=[],x=!1,E=null;const k=new Set(this.all_special_ids);for(const T of r){const P=T.tokens,B=l?T.token_timestamps:null;let G=null,q=y;if("stride"in T){const[re,ne,de]=T.stride;if(g-=ne,E=re-de,ne&&(q=ne/s+y),de)for(let N=P.length-1;N>=0;--N){const M=Number(P[N]);if(M>=y){if(G!==null&&(M-y)*s<E)break;G=M}}}let J=[],me=[];for(let re=0;re<P.length;++re){const ne=Number(P[re]);if(k.has(ne)){const de=this.decode([ne]),N=hi.get(de.slice(2,-2));if(N!==void 0){if(o!==null&&N!==o&&!n){p.push(J);const M=this.findLongestCommonSequence(p)[0],H=this.decode(M);f.text=H,c.push(f),p=[],J=[],f=d()}o=f.language=N}}else if(ne>=y){const de=(ne-y)*s+g,N=Fa(de,2);if(G!==null&&ne>=G)x=!0;else if(x||p.length>0&&ne<q)x=!1;else if(f.timestamp[0]===null)f.timestamp[0]=N;else if(N!==f.timestamp[0]){f.timestamp[1]=N,p.push(J),l&&v.push(me);const[M,H]=this.findLongestCommonSequence(p,v),te=this.decode(M);f.text=te,l&&(f.words=this.collateWordTimestamps(M,H,o)),c.push(f),p=[],J=[],v=[],me=[],f=d()}}else if(J.push(ne),l){let de=Fa(B[re]+g,2),N;if(re+1<B.length){N=Fa(B[re+1]+g,2);const M=this.decode([ne]);Jy.test(M)&&(N=Fa(Math.min(de+s,N),2))}else N=null;me.push([de,N])}}if("stride"in T){const[re,ne,de]=T.stride;g+=re-de}J.length>0?(p.push(J),l&&v.push(me)):p.every(re=>re.length===0)&&(f=d(),p=[],J=[],v=[],me=[])}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,P]=this.findLongestCommonSequence(p,v),B=this.decode(T);f.text=B,l&&(f.words=this.collateWordTimestamps(T,P,o)),c.push(f)}let A=Object.create(null);const I=c.map(T=>T.text).join("");if(n||a){for(let T=0;T<c.length;++T){const P=c[T];n||delete P.timestamp,a||delete P.language}if(l){const T=[];for(const P of c)for(const B of P.words)T.push(B);A={chunks:T}}else A={chunks:c}}return[I,A]}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 P=Math.max(0,s-T),B=Math.min(s,s+p-T),G=a.slice(P,B),q=Math.max(0,T-s),J=Math.min(p,T),me=f.slice(q,J);if(G.length!==me.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let re;o?re=G.filter((N,M)=>N===me[M]&&d[P+M]<=n[c][q+M]).length:re=G.filter((N,M)=>N===me[M]).length;const ne=T/1e4,de=re/T+ne;re>1&&de>g&&(g=de,y=[P,B,q,J])}const[v,x,E,k]=y,A=Math.floor((x+v)/2),I=Math.floor((k+E)/2);i.push(...a.slice(0,A)),a=f.slice(I),s=a.length,o&&(l.push(...d.slice(0,A)),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!=null&&n.decode_with_timestamps?(r instanceof Ce&&(r=em(r)),a=this.decodeWithTimestamps(r,n)):a=super.decode(r,n),a}decodeWithTimestamps(r,n){const a=(n==null?void 0:n.time_precision)??.02,s=Array.from(this.all_special_ids).at(-1)+1;let i=[[]];for(let o of r)if(o=Number(o),o>=s){const l=((o-s)*a).toFixed(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(`^[${fn}]$`,"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(" "),x=f.trim(),E=d.test(x);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){const i=Jf(r),o=this.model.tokens_to_ids.get(`<|${i}|>`);if(o===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at ${ko}.`);s.push(o)}else s.push(null);if(n){if(n=n.toLowerCase(),n!=="transcribe"&&n!=="translate")throw new Error(`Task "${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 ${ko}.`);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 ${ko}.`);s.push(i)}return s.map((i,o)=>[o+1,i]).filter(i=>i[1]!==null)}}class yb extends ke{}class wb extends ke{}class bb extends ke{}class vb extends ke{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(n=>this.languageRegex.test(n)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[r,...n]=e.trim().split(this.languageRegex);if(n.length===0)return super._encode_text(r);if(n.length===2){const[a,s]=n;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),it([a],super._encode_text(s))}}}class $b extends ke{}class cm extends ke{constructor(){super(...arguments);D(this,"_default_chat_template","{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}")}}class xb extends cm{}class Sb extends ke{}class kb extends ke{}class Eb extends ke{constructor(e,r){super(e,r),this.decoder=new Mw({})}}class Cb extends ke{}class pm{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",legacy:o=null}={}){var g;const[l,d]=await Zf(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,legacy:o}),c=((g=d.tokenizer_class)==null?void 0:g.replace(/Fast$/,""))??"PreTrainedTokenizer";let f=this.TOKENIZER_CLASS_MAPPING[c];return f||(console.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),f=ke),new f(l,d)}}D(pm,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:tb,DistilBertTokenizer:Xw,CamembertTokenizer:Jw,DebertaTokenizer:qw,DebertaV2Tokenizer:jw,BertTokenizer:Ww,HerbertTokenizer:Kw,ConvBertTokenizer:Yw,RoFormerTokenizer:Qw,XLMTokenizer:Zw,ElectraTokenizer:eb,MobileBertTokenizer:Gw,SqueezeBertTokenizer:Hw,AlbertTokenizer:Vw,GPT2Tokenizer:um,BartTokenizer:rb,MBartTokenizer:lm,MBart50Tokenizer:nb,RobertaTokenizer:ab,WhisperTokenizer:_b,CodeGenTokenizer:yb,CLIPTokenizer:wb,SiglipTokenizer:bb,MarianTokenizer:vb,BloomTokenizer:ib,NllbTokenizer:mb,M2M100Tokenizer:gb,LlamaTokenizer:dm,CodeLlamaTokenizer:sb,XLMRobertaTokenizer:ob,MPNetTokenizer:ub,FalconTokenizer:lb,GPTNeoXTokenizer:db,EsmTokenizer:cb,Wav2Vec2CTCTokenizer:$b,BlenderbotTokenizer:cm,BlenderbotSmallTokenizer:xb,SpeechT5Tokenizer:Sb,NougatTokenizer:kb,VitsTokenizer:Eb,Qwen2Tokenizer:pb,GemmaTokenizer:hb,Grok1Tokenizer:fb,CohereTokenizer:Cb,PreTrainedTokenizer:ke});async function Tb(t,e){return await zr(t,"config.json",!0,e)}function ea(t){const e={};let r={};switch(t.model_type){case"llava":case"paligemma":case"florence2":r=ea(t.text_config);break;case"moondream1":r=ea(t.phi_config);break;case"musicgen":r=ea(t.decoder);break;case"gpt2":case"gptj":case"codegen":case"gpt_bigcode":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"phi":case"phi3":case"falcon":e.num_heads="num_attention_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size";break;case"llama":case"cohere":case"mistral":case"starcoder2":case"qwen2":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size",e.num_attention_heads="num_attention_heads";break;case"gemma":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="head_dim";break;case"openelm":e.num_heads="num_kv_heads",e.num_layers="num_transformer_layers",e.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":e.num_heads="num_heads",e.num_layers="num_layers",e.hidden_size="hidden_size";break;case"bloom":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="hidden_size";break;case"mpt":e.num_heads="n_heads",e.num_layers="n_layers",e.hidden_size="d_model";break;case"t5":case"mt5":case"longt5":e.num_decoder_layers="num_decoder_layers",e.num_decoder_heads="num_heads",e.decoder_dim_kv="d_kv",e.num_encoder_layers="num_layers",e.num_encoder_heads="num_heads",e.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="d_model",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="d_model";break;case"speecht5":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="hidden_size",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="hidden_size";break;case"trocr":e.num_encoder_layers=e.num_decoder_layers="decoder_layers",e.num_encoder_heads=e.num_decoder_heads="decoder_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="d_model";break;case"musicgen_decoder":e.num_encoder_layers=e.num_decoder_layers="num_hidden_layers",e.num_encoder_heads=e.num_decoder_heads="num_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const a=ea(t.decoder),s="num_decoder_layers"in a,i=hr(t,["model_type","is_encoder_decoder"]);return s?(i.num_decoder_layers=a.num_decoder_layers,i.num_decoder_heads=a.num_decoder_heads,i.decoder_hidden_size=a.decoder_hidden_size,i.num_encoder_layers=a.num_encoder_layers,i.num_encoder_heads=a.num_encoder_heads,i.encoder_hidden_size=a.encoder_hidden_size):(i.num_layers=a.num_layers,i.num_heads=a.num_heads,i.hidden_size=a.hidden_size),i}const n={...r,...hr(t,["model_type","multi_query","is_encoder_decoder"])};for(const a in e)n[a]=t[e[a]];return n}function hm(t,{prefix:e="past_key_values"}={}){const r={},n=t.normalized_config,a=1;if(n.is_encoder_decoder&&"num_encoder_heads"in n&&"num_decoder_heads"in n){const s=n.encoder_dim_kv??n.encoder_hidden_size/n.num_encoder_heads,i=n.decoder_dim_kv??n.decoder_hidden_size/n.num_decoder_heads,o=[a,n.num_encoder_heads,0,s],l=[a,n.num_decoder_heads,0,i];for(let d=0;d<n.num_decoder_layers;++d)r[`${e}.${d}.encoder.key`]=o,r[`${e}.${d}.encoder.value`]=o,r[`${e}.${d}.decoder.key`]=l,r[`${e}.${d}.decoder.value`]=l}else{const s=n.num_heads,i=n.num_layers,o=n.dim_kv??n.hidden_size/(n.num_attention_heads??s);if(n.model_type==="falcon"){const l=[a*s,0,o];for(let d=0;d<i;++d)r[`${e}.${d}.key`]=l,r[`${e}.${d}.value`]=l}else if(n.multi_query){const l=[a*s,0,2*o];for(let d=0;d<i;++d)r[`${e}.${d}.key_value`]=l}else if(n.model_type==="bloom"){const l=[a*s,o,0],d=[a*s,0,o];for(let c=0;c<i;++c)r[`${e}.${c}.key`]=l,r[`${e}.${c}.value`]=d}else if(n.model_type==="openelm")for(let l=0;l<i;++l){const d=[a,s[l],0,o];r[`${e}.${l}.key`]=d,r[`${e}.${l}.value`]=d}else{const l=[a,s,0,o];for(let d=0;d<i;++d)r[`${e}.${d}.key`]=l,r[`${e}.${d}.value`]=l}}return r}class gi{constructor(e){D(this,"max_position_embeddings");this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e),this.normalized_config=ea(this)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main"}={}){n&&!(n instanceof gi)&&(n=new gi(n));const o=n??await Tb(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i});return new this(o)}}class fm{static async from_pretrained(...e){return gi.from_pretrained(...e)}}const _i=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),Ib=function(){let t;return async function(){if(t===void 0)if(xr.IS_NODE_ENV)t=!0;else if(!xr.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}}(),vt=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),Ab=Object.freeze({[_i.cpu]:vt.q8,[_i.gpu]:vt.fp32,[_i.wasm]:vt.q8,[_i.webgpu]:vt.fp32}),mm=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 lr extends Tt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class Io extends Tt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class yi extends Tt{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,r){let n=r;for(const a of this.processors)n=a(e,n);return n}[Symbol.iterator](){return this.processors.values()}}class Ob extends lr{constructor(e){super(),this.bos_token_id=e}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===1){const a=r[n];a.data.fill(-1/0),a.data[this.bos_token_id]=0}return r}}class Mb extends lr{constructor(e,r){super(),this.max_length=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===this.max_length-1){const a=r[n];a.data.fill(-1/0);for(const s of this.eos_token_id)a.data[s]=0}return r}}class gm extends lr{constructor(e,r){super(),this.begin_suppress_tokens=e,this.begin_index=r}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===this.begin_index){const a=r[n];for(const s of this.begin_suppress_tokens)a.data[s]=-1/0}return r}}class zb extends lr{constructor(e,r){super(),this.eos_token_id=Array.isArray(e.eos_token_id)?e.eos_token_id[0]:e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=r.length,r.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,r){for(let n=0;n<e.length;++n){const s=r[n].data;if(s[this.no_timestamps_token_id]=-1/0,e[n].length===this.begin_index-1){s.fill(-1/0),s[this.timestamp_begin]=0;continue}const i=e[n].slice(this.begin_index),o=i.length>=1&&i[i.length-1]>=this.timestamp_begin,l=i.length<2||i[i.length-2]>=this.timestamp_begin;if(o&&(l?s.subarray(this.timestamp_begin).fill(-1/0):s.subarray(0,this.eos_token_id).fill(-1/0)),e[n].length===this.begin_index&&this.max_initial_timestamp_index!==null){const g=this.timestamp_begin+this.max_initial_timestamp_index;s.subarray(g+1).fill(-1/0)}const d=I0(s),c=Math.log(d.subarray(this.timestamp_begin).map(Math.exp).reduce((g,y)=>g+y)),f=Na(d.subarray(0,this.timestamp_begin))[0];c>f&&s.subarray(0,this.timestamp_begin).fill(-1/0)}return r}}class Rb extends lr{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const r=e.length,n=[];for(let s=0;s<r+1-this.no_repeat_ngram_size;++s){const i=[];for(let o=0;o<this.no_repeat_ngram_size;++o)i.push(e[s+o]);n.push(i.map(Number))}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(e,r){const n=r.slice(r.length+1-this.no_repeat_ngram_size,r.length);return e.get(JSON.stringify(n.map(Number)))??[]}calcBannedNgramTokens(e){const r=[];if(e.length+1<this.no_repeat_ngram_size)return r;{const n=this.getNgrams(e);return this.getGeneratedNgrams(n,e)}}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n],s=this.calcBannedNgramTokens(e[n]);for(const i of s)a.data[i]=-1/0}return r}}class Pb extends lr{constructor(e){super(),this.penalty=e}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n];for(const s of e[n])a.data[s]<0?a.data[s]*=this.penalty:a.data[s]/=this.penalty}return r}}class Bb extends lr{constructor(e,r){super(),this.min_length=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length<this.min_length){const a=r[n];for(const s of this.eos_token_id)a.data[s]=-1/0}return r}}class Db extends lr{constructor(e,r,n){super(),this.prompt_length_to_skip=e,this.min_new_tokens=r,this.eos_token_id=Array.isArray(n)?n:[n]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length-this.prompt_length_to_skip<this.min_new_tokens){const s=r[n];for(const i of this.eos_token_id)s[i]=-1/0}return r}}class Nb extends lr{constructor(e,r){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n];for(const s of this.bad_words_ids){let i=!0;for(let o=1;o<=s.length-1&&s.length<e[o].length;++o)if(s.at(-o-1)!==Number(e[o].at(-o))){i=!1;break}i&&(a[s.at(-1)]=-1/0)}}return r}}class Fb extends lr{constructor(e){if(super(),e<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,r){if(r.dims[0]!==2*e.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${r.dims[0]} for the logits and ${e.length} for the input ids.`);const n=e.length,a=r.slice([0,n],null),s=r.slice([n,r.dims[0]],null);for(let i=0;i<s.data.length;++i)s.data[i]+=(a.data[i]-s.data[i])*this.guidance_scale;return s}}class Lb extends Io{constructor(e){super(),this.temperature=e}_call(e,r){const n=r.data;for(let a=0;a<n.length;++a)n[a]/=this.temperature;return r}}class Ub extends Io{constructor(e,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),e<0||e>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${e}`);if(!Number.isInteger(n)||n<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${n}`);this.top_p=e,this.filter_value=r,this.min_tokens_to_keep=n}}class Wb extends Io{constructor(e,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),!Number.isInteger(e)||e<0)throw new Error(`\`top_k\` must be a positive integer, but is ${e}`);this.top_k=Math.max(e,n),this.filter_value=r}}class _m{constructor(e){D(this,"max_length",20);D(this,"max_new_tokens",null);D(this,"min_length",0);D(this,"min_new_tokens",null);D(this,"early_stopping",!1);D(this,"max_time",null);D(this,"do_sample",!1);D(this,"num_beams",1);D(this,"num_beam_groups",1);D(this,"penalty_alpha",null);D(this,"use_cache",!0);D(this,"temperature",1);D(this,"top_k",50);D(this,"top_p",1);D(this,"typical_p",1);D(this,"epsilon_cutoff",0);D(this,"eta_cutoff",0);D(this,"diversity_penalty",0);D(this,"repetition_penalty",1);D(this,"encoder_repetition_penalty",1);D(this,"length_penalty",1);D(this,"no_repeat_ngram_size",0);D(this,"bad_words_ids",null);D(this,"force_words_ids",null);D(this,"renormalize_logits",!1);D(this,"constraints",null);D(this,"forced_bos_token_id",null);D(this,"forced_eos_token_id",null);D(this,"remove_invalid_values",!1);D(this,"exponential_decay_length_penalty",null);D(this,"suppress_tokens",null);D(this,"begin_suppress_tokens",null);D(this,"forced_decoder_ids",null);D(this,"guidance_scale",null);D(this,"num_return_sequences",1);D(this,"output_attentions",!1);D(this,"output_hidden_states",!1);D(this,"output_scores",!1);D(this,"return_dict_in_generate",!1);D(this,"pad_token_id",null);D(this,"bos_token_id",null);D(this,"eos_token_id",null);D(this,"encoder_no_repeat_ngram_size",0);D(this,"decoder_start_token_id",null);D(this,"generation_kwargs",{});Object.assign(this,hr(e,Object.getOwnPropertyNames(this)))}}class wi extends Tt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class Ao extends Tt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof Ao?e=e.criteria:e instanceof wi&&(e=[e]),this.criteria.push(...e)}_call(e,r){const n=new Array(e.length).fill(!1);for(const a of this.criteria){const s=a(e,r);for(let i=0;i<n.length;++i)n[i]||(n[i]=s[i])}return n}[Symbol.iterator](){return this.criteria.values()}}class Vb extends wi{constructor(e,r=null){super(),this.max_length=e,this.max_position_embeddings=r}_call(e){return e.map(r=>r.length>=this.max_length)}}class Gb extends wi{constructor(e){super(),Array.isArray(e)||(e=[e]),this.eos_token_id=e}_call(e,r){return e.map(n=>{const a=n.at(-1);return this.eos_token_id.some(s=>a==s)})}}class Hb extends wi{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(e,r){return new Array(e.length).fill(this.interrupted)}}class bi extends Tt{constructor(e){super(),this.generation_config=e}async _call(e){return this.sample(e)}async sample(e){throw Error("sample should be implemented in subclasses.")}getLogits(e,r){let n=e.dims.at(-1),a=e.data;if(r===-1)a=a.slice(-n);else{let s=r*n;a=a.slice(s,s+n)}return a}randomSelect(e){let r=0;for(let a=0;a<e.length;++a)r+=e[a];let n=Math.random()*r;for(let a=0;a<e.length;++a)if(n-=e[a],n<=0)return a;return 0}static getSampler(e){if(e.do_sample)return new jb(e);if(e.num_beams>1)return new Kb(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new qb(e)}}class qb extends bi{async sample(e){const r=Na(e.data)[1];return[[BigInt(r),0]]}}class jb extends bi{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await Lf(e,r),s=ns(n.data);return Array.from({length:this.generation_config.num_beams},()=>{const i=this.randomSelect(s);return[a.data[i],Math.log(s[i])]})}}class Kb extends bi{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await Lf(e,r),s=ns(n.data);return Array.from({length:this.generation_config.num_beams},(i,o)=>[a.data[o],Math.log(s[o])])}}class Yb extends _m{constructor(){super(...arguments);D(this,"return_timestamps",null);D(this,"return_token_timestamps",null);D(this,"num_frames",null);D(this,"alignment_heads",null);D(this,"task",null);D(this,"language",null);D(this,"no_timestamps_token_id",null);D(this,"prompt_ids",null);D(this,"is_multilingual",null);D(this,"lang_to_id",null);D(this,"task_to_id",null);D(this,"max_initial_timestamp_index",1)}}const ve={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},vi=new Map,ym=new Map,ta=new Map;async function Qb(t,e,r){let n=r.device;n&&typeof n!="string"&&(n.hasOwnProperty(e)?n=n[e]:(console.warn(`device not specified for "${e}". Using the default device.`),n=null));const a=cy(n);let s=r.dtype;if(typeof s!="string"&&(s&&s.hasOwnProperty(e)?s=s[e]:(s=Ab[a[0]],console.warn(`dtype not specified for "${e}". Using the default dtype for this device (${s}).`))),mm.hasOwnProperty(s)){if(s===vt.fp16&&!await Ib())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${s}. Should be one of: ${Object.keys(vt).join(", ")}`);const i=mm[s],o=`${r.subfolder??""}/${e}${i}.onnx`,l={...r.session_options};l.executionProviders??(l.executionProviders=a);const d=Ba(t,o,!0,r);let c=[];if(r.use_external_data_format&&(r.use_external_data_format===!0||typeof r.use_external_data_format=="object"&&r.use_external_data_format.hasOwnProperty(e)&&r.use_external_data_format[e]===!0)){if(xr.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const g=`${e}${i}.onnx_data`,y=`${r.subfolder??""}/${g}`;c.push(new Promise(async(p,v)=>{const x=await Ba(t,y,!0,r);p({path:g,data:x})}))}else l.externalData!==void 0&&(c=l.externalData.map(async g=>{if(typeof g.data=="string"){const y=await Ba(t,g.data,!0,r);return{...g,data:y}}return g}));if(c.length>0&&(l.externalData=await Promise.all(c)),n==="webgpu"){const g=hm(r.config,{prefix:"present"});if(Object.keys(g).length>0){const y={};for(const p in g)y[p]="gpu-buffer";l.preferredOutputLocation=y}}return{buffer:await d,session_options:l}}async function jr(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async i=>Qb(t,e[i],r))),s={};for(let i=0;i<n.length;++i){const{buffer:o,session_options:l}=a[i],d=await Bf(o,l);s[n[i]]=d}return s}function Xb(t,e){const r=Object.create(null),n=[];for(const i of t.inputNames){const o=e[i];if(!(o instanceof Ce)){n.push(i);continue}r[i]=py()?o.clone():o}if(n.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${n.join(", ")}.`);const a=Object.keys(e).length,s=t.inputNames.length;if(a>s){let i=Object.keys(e).filter(o=>!t.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${s}). The following inputs will be ignored: "${i.join(", ")}".`)}return r}async function Tr(t,e){const r=Xb(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([s,i])=>[s,i.ort_tensor]));let a=await t.run(n);return a=wm(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function wm(t){for(let e in t)Df(t[e])?t[e]=new Ce(t[e]):typeof t[e]=="object"&&wm(t[e]);return t}function bm(t){if(t instanceof Ce)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new Ce("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new Ce("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function vm(t){return new Ce("bool",[t],[1])}async function $m(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const l=hr(e,t.sessions.model.inputNames);r=(await gn(t,l)).last_hidden_state}const{input_ids:a,decoder_input_ids:s,...i}=e;return i.input_ids=s,i.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(i.encoder_attention_mask=e.attention_mask),await $i(t,i,!0)}async function gn(t,e){const r=t.sessions.model,n=hr(e,r.inputNames);if(r.inputNames.includes("inputs_embeds")&&!n.inputs_embeds){if(!e.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");n.inputs_embeds=await t.encode_text({input_ids:e.input_ids})}return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new Ce("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await Tr(r,n)}async function $i(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...s}=e;n.inputNames.includes("use_cache_branch")&&(s.use_cache_branch=vm(!!a)),n.inputNames.includes("position_ids")&&s.attention_mask&&!s.position_ids&&(s.position_ids=Zb(s,a)),t.addPastKeyValues(s,a);const i=hr(s,n.inputNames);return await Tr(n,i)}async function Jb(t,{input_ids:e=null,attention_mask:r=null,pixel_values:n=null,position_ids:a=null,inputs_embeds:s=null,past_key_values:i=null,generation_config:o=null,logits_processor:l=null,...d}){if(!s){if(s=await t.encode_text({input_ids:e}),n&&e.dims[1]!==1){const f=await t.encode_image({pixel_values:n});({inputs_embeds:s,attention_mask:r}=t._merge_input_ids_with_image_features({image_features:f,inputs_embeds:s,input_ids:e,attention_mask:r}))}else if(i&&n&&e.dims[1]===1){const f=e.dims[1],g=Object.values(i)[0].dims.at(-2);r=Qt([cn([e.dims[0],g]),r.slice(null,[r.dims[1]-f,r.dims[1]])],1)}}return await $i(t,{inputs_embeds:s,past_key_values:i,attention_mask:r,position_ids:a,generation_config:o,logits_processor:l},!0)}function Zb(t,e=null){const{input_ids:r,inputs_embeds:n,attention_mask:a}=t,[s,i]=a.dims,o=new BigInt64Array(a.data.length);for(let d=0;d<s;++d){const c=d*i;let f=BigInt(0);for(let g=0;g<i;++g){const y=c+g;a.data[y]===0n?o[y]=BigInt(1):(o[y]=f,f+=a.data[y])}}let l=new Ce("int64",o,a.dims);if(e){const d=-(r??n).dims.at(1);l=l.slice(null,[d,null])}return l}function xm(t,e,r,n){if(r.past_key_values){const a=Object.values(r.past_key_values)[0].dims.at(-2),{input_ids:s,attention_mask:i}=r;if(!(i&&i.dims[1]>s.dims[1])){if(a<s.dims[1])r.input_ids=s.slice(null,[a,null]);else if(t.config.image_token_index!=null&&s.data.some(o=>o==t.config.image_token_index)){const o=t.config.num_image_tokens;if(!o)throw new Error("`num_image_tokens` is missing in the model configuration.");const l=s.dims[1]-(a-o);r.input_ids=s.slice(null,[-l,null]),r.attention_mask=cn([1,a+l])}}}return r}function Sm(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(i=>[i.at(-1)])),a.decoder_input_ids=bm(e),a}function ev(t,...e){return t.config.is_encoder_decoder?Sm(t,...e):xm(t,...e)}class Q extends Tt{constructor(r,n){super();D(this,"main_input_name","input_ids");D(this,"forward_params",["input_ids","attention_mask"]);this.config=r,this.sessions=n;const a=ta.get(this.constructor),s=vi.get(a);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,s){case ve.DecoderOnly:this.can_generate=!0,this._forward=$i,this._prepare_inputs_for_generation=xm;break;case ve.Seq2Seq:case ve.Vision2Seq:case ve.Musicgen:this.can_generate=!0,this._forward=$m,this._prepare_inputs_for_generation=Sm;break;case ve.EncoderDecoder:this._forward=$m;break;case ve.ImageTextToText:this.can_generate=!0,this._forward=Jb,this._prepare_inputs_for_generation=ev;break;default:this._forward=gn;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var n;const r=[];for(const a of Object.values(this.sessions))(n=a==null?void 0:a.handler)!=null&&n.dispose&&r.push(a.handler.dispose());return await Promise.all(r)}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:s=null,local_files_only:i=!1,revision:o="main",model_file_name: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=ta.get(this),x=vi.get(v);a=p.config=await fm.from_pretrained(r,p);let E;if(x===ve.DecoderOnly)E=await Promise.all([jr(r,{model:p.model_file_name??"model"},p),zr(r,"generation_config.json",!1,p)]);else if(x===ve.Seq2Seq||x===ve.Vision2Seq)E=await Promise.all([jr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p),zr(r,"generation_config.json",!1,p)]);else if(x===ve.MaskGeneration)E=await Promise.all([jr(r,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},p)]);else if(x===ve.EncoderDecoder)E=await Promise.all([jr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p)]);else if(x===ve.ImageTextToText){const k={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};a.is_encoder_decoder&&(k.model="encoder_model"),E=await Promise.all([jr(r,k,p),zr(r,"generation_config.json",!1,p)])}else x===ve.Musicgen?E=await Promise.all([jr(r,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},p),zr(r,"generation_config.json",!1,p)]):(x!==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)]));return new this(a,...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 yi;return r.temperature!==null&&r.temperature!==1&&n.push(new Lb(r.temperature)),r.top_k!==null&&r.top_k!==0&&n.push(new Wb(r.top_k)),r.top_p!==null&&r.top_p<1&&n.push(new Ub(r.top_p)),n}_get_logits_processor(r,n,a=null){const s=new yi;if(r.repetition_penalty!==null&&r.repetition_penalty!==1&&s.push(new Pb(r.repetition_penalty)),r.no_repeat_ngram_size!==null&&r.no_repeat_ngram_size>0&&s.push(new Rb(r.no_repeat_ngram_size)),r.bad_words_ids!==null&&s.push(new Nb(r.bad_words_ids,r.eos_token_id)),r.min_length!==null&&r.eos_token_id!==null&&r.min_length>0&&s.push(new Bb(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 Db(n,r.min_new_tokens,r.eos_token_id)),r.forced_bos_token_id!==null&&s.push(new Ob(r.forced_bos_token_id)),r.forced_eos_token_id!==null&&s.push(new Mb(r.max_length,r.forced_eos_token_id)),r.begin_suppress_tokens!==null){const i=n>1||r.forced_bos_token_id===null?n:n+1;s.push(new gm(r.begin_suppress_tokens,i))}return r.guidance_scale!==null&&r.guidance_scale>1&&s.push(new Fb(r.guidance_scale)),a!==null&&s.extend(a),s}_prepare_generation_config(r,n,a=_m){const s={...this.config};for(const o of["decoder","generator","text_config"])o in s&&Object.assign(s,s[o]);const i=new a(s);return"generation_config"in this&&Object.assign(i,this.generation_config),r&&Object.assign(i,r),n&&Object.assign(i,hr(n,Object.getOwnPropertyNames(i))),i}_get_stopping_criteria(r,n=null){const a=new Ao;return r.max_length!==null&&a.push(new Vb(r.max_length,this.config.max_position_embeddings??null)),r.eos_token_id!==null&&a.push(new Gb(r.eos_token_id)),n&&a.extend(n),a}_validate_model_class(){if(!this.can_generate){const r=[Bo,Rg,zg,Mg],n=ta.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=Qt([a.attention_mask,cn([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=hr(a,this.forward_params),i=this.main_input_name;if(i in s){if(r)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else s[i]=r;return{inputs_tensor:s[i],model_inputs:s,model_input_name:i}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:r,model_inputs:n,model_input_name:a,generation_config:s}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!n.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:o,pixel_values:l,attention_mask:d,...c}=n,f=await this._prepare_inputs_embeds(n);n={...c,...hr(f,["inputs_embeds","attention_mask"])}}let{last_hidden_state:i}=await gn(this,n);return s.guidance_scale!==null&&s.guidance_scale>1&&(i=Qt([i,yy(i,0)],0),"attention_mask"in n&&(n.attention_mask=Qt([n.attention_mask,vy(n.attention_mask)],0))),n.encoder_outputs=i,n}_prepare_decoder_input_ids_for_generation({batch_size:r,model_input_name:n,model_kwargs:a,decoder_start_token_id:s,bos_token_id:i,generation_config:o}){let{decoder_input_ids:l,...d}=a;if(l)Array.isArray(l[0])||(l=Array.from({length:r},()=>l));else if(s??(s=i),this.config.model_type==="musicgen")l=Array.from({length:r*this.config.decoder.num_codebooks},()=>[s]);else if(Array.isArray(s)){if(s.length!==r)throw new Error(`\`decoder_start_token_id\` expcted to have length ${r} but got ${s.length}`);l=s}else l=Array.from({length:r},()=>[s]);return l=bm(l),a.decoder_attention_mask=wy(l),{input_ids:l,model_inputs:d}}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),x=d[c].dims.at(0),E=bi.getSampler(n),k=new Array(x).fill(0),A=g.tolist();i&&i.put(A);let I=null,T={};for(;;){d=this.prepare_inputs_for_generation(A,d,n);const B=await this.forward(d);if(n.output_attentions&&n.return_dict_in_generate){const re=this.getAttentions(B);for(const ne in re)ne in T||(T[ne]=[]),T[ne].push(re[ne])}const G=B.logits.slice(null,-1,null),q=p(A,G),J=[];for(let re=0;re<q.dims.at(0);++re){const ne=q[re],de=await E(ne);for(const[N,M]of de){const H=BigInt(N);k[re]+=M,A[re].push(H),J.push([H]);break}}if(i&&i.put(J),v(A).every(re=>re)){n.return_dict_in_generate&&(I=this.getPastKeyValues(B,d.past_key_values,!1));break}d=this._update_model_kwargs_for_generation({generated_input_ids:J,outputs:B,model_inputs:d,is_encoder_decoder:f})}i&&i.end();const P=new Ce("int64",A.flat(),[A.length,A[0].length]);return n.return_dict_in_generate?{sequences:P,past_key_values:I,...T}:P}getPastKeyValues(r,n,a=!0){const s=Object.create(null);for(const i in r)if(i.startsWith("present")){const 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={};for(const a of["cross_attentions","encoder_attentions","decoder_attentions"])for(const s in r)s.startsWith(a)&&(a in n||(n[a]=[]),n[a].push(r[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=hm(this.config);for(const o in i)r[o]=new Ce(a,s,i[o])}}async encode_image({pixel_values:r}){const n=(await Tr(this.sessions.vision_encoder,{pixel_values:r})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${n.dims[1]}).`),this.config.num_image_tokens=n.dims[1]),n}async encode_text({input_ids:r}){return(await Tr(this.sessions.embed_tokens,{input_ids:r})).inputs_embeds}}class Ot{}class ra extends Q{}class tv extends ra{}class rv extends ra{async _call(e){return new ct(await super._call(e))}}class nv extends ra{async _call(e){return new Ie(await super._call(e))}}class av extends ra{async _call(e){return new dt(await super._call(e))}}class iv extends ra{async _call(e){return new _t(await super._call(e))}}class sv extends Q{}class ov extends sv{}class na extends Q{}class uv extends na{}class lv extends na{async _call(e){return new ct(await super._call(e))}}class dv extends na{async _call(e){return new Ie(await super._call(e))}}class cv extends na{async _call(e){return new dt(await super._call(e))}}class pv extends na{async _call(e){return new _t(await super._call(e))}}class aa extends Q{}class hv extends aa{}class fv extends aa{async _call(e){return new ct(await super._call(e))}}class mv extends aa{async _call(e){return new Ie(await super._call(e))}}class gv extends aa{async _call(e){return new dt(await super._call(e))}}class _v extends aa{async _call(e){return new _t(await super._call(e))}}class ia extends Q{}class yv extends ia{}class wv extends ia{async _call(e){return new ct(await super._call(e))}}class bv extends ia{async _call(e){return new Ie(await super._call(e))}}class vv extends ia{async _call(e){return new dt(await super._call(e))}}class $v extends ia{async _call(e){return new _t(await super._call(e))}}class sa extends Q{}class xv extends sa{}class Sv extends sa{async _call(e){return new ct(await super._call(e))}}class kv extends sa{async _call(e){return new Ie(await super._call(e))}}class Ev extends sa{async _call(e){return new dt(await super._call(e))}}class Cv extends sa{async _call(e){return new _t(await super._call(e))}}class oa extends Q{}class Tv extends oa{}class Iv extends oa{async _call(e){return new ct(await super._call(e))}}class Av extends oa{async _call(e){return new Ie(await super._call(e))}}class Ov extends oa{async _call(e){return new dt(await super._call(e))}}class Mv extends oa{async _call(e){return new _t(await super._call(e))}}class ua extends Q{}class zv extends ua{}class Rv extends ua{async _call(e){return new ct(await super._call(e))}}class Pv extends ua{async _call(e){return new Ie(await super._call(e))}}class Bv extends ua{async _call(e){return new dt(await super._call(e))}}class Dv extends ua{async _call(e){return new _t(await super._call(e))}}class la extends Q{}class Nv extends la{}class Fv extends la{async _call(e){return new Ie(await super._call(e))}}class Lv extends la{async _call(e){return new dt(await super._call(e))}}class Uv extends la{async _call(e){return new _t(await super._call(e))}}class Wv extends la{async _call(e){return new ct(await super._call(e))}}class xi extends Q{}class Vv extends xi{}class Gv extends xi{async _call(e){return new ct(await super._call(e))}}class Hv extends xi{async _call(e){return new Ie(await super._call(e))}}class qv extends xi{async _call(e){return new dt(await super._call(e))}}class Si extends Q{}class jv extends Si{}class Kv extends Si{async _call(e){return new ct(await super._call(e))}}class Yv extends Si{async _call(e){return new Ie(await super._call(e))}}class Qv extends Si{async _call(e){return new _t(await super._call(e))}}class da extends Q{}class Xv extends da{}class Jv extends da{async _call(e){return new ct(await super._call(e))}}class Zv extends da{async _call(e){return new Ie(await super._call(e))}}class e2 extends da{async _call(e){return new dt(await super._call(e))}}class t2 extends da{async _call(e){return new _t(await super._call(e))}}class ki extends Q{}class r2 extends ki{}class n2 extends ki{async _call(e){return new ct(await super._call(e))}}class a2 extends ki{async _call(e){return new Ie(await super._call(e))}}class i2 extends ki{async _call(e){return new _t(await super._call(e))}}class Ei extends Q{}class s2 extends Ei{}class o2 extends Ei{async _call(e){return new Ie(await super._call(e))}}class u2 extends Ei{async _call(e){return new _t(await super._call(e))}}class l2 extends Ei{async _call(e){return new ct(await super._call(e))}}class km extends Q{constructor(r,n,a){super(r,n);D(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class d2 extends km{}class c2 extends km{}class Em extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class p2 extends Em{}class h2 extends Em{}class Cm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class f2 extends Cm{}class m2 extends Cm{}class Oo extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class g2 extends Oo{}class _2 extends Oo{}class y2 extends Oo{async _call(e){return new Ie(await super._call(e))}}class Ci extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class w2 extends Ci{}class b2 extends Ci{}class v2 extends Ci{async _call(e){return new Ie(await super._call(e))}}class $2 extends Ci{}class Tm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class x2 extends Tm{}class S2 extends Tm{}class Im extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class k2 extends Im{}class E2 extends Im{}class ca extends Q{}class C2 extends ca{}class T2 extends ca{async _call(e){return new ct(await super._call(e))}}class I2 extends ca{async _call(e){return new Ie(await super._call(e))}}class A2 extends ca{async _call(e){return new dt(await super._call(e))}}class O2 extends ca{async _call(e){return new _t(await super._call(e))}}class pa extends Q{}class M2 extends pa{}class z2 extends pa{async _call(e){return new ct(await super._call(e))}}class R2 extends pa{async _call(e){return new Ie(await super._call(e))}}class P2 extends pa{async _call(e){return new dt(await super._call(e))}}class B2 extends pa{async _call(e){return new _t(await super._call(e))}}class ha extends Q{}class D2 extends ha{}class N2 extends ha{async _call(e){return new ct(await super._call(e))}}class F2 extends ha{async _call(e){return new Ie(await super._call(e))}}class L2 extends ha{async _call(e){return new dt(await super._call(e))}}class U2 extends ha{async _call(e){return new _t(await super._call(e))}}class Am extends Q{}class W2 extends Am{}class V2 extends Am{}class Om extends Q{constructor(r,n,a){super(r,n);D(this,"requires_attention_mask",!1);D(this,"main_input_name","input_features");D(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class G2 extends Om{}class H2 extends Om{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,Yb)}_retrieve_init_tokens(e){const r=[e.decoder_start_token_id];let n=e.language;const a=e.task;if(e.is_multilingual){n||(console.warn("No language specified - defaulting to English (en)."),n="en");const i=`<|${Jf(n)}|>`;r.push(e.lang_to_id[i]),r.push(e.task_to_id[a??"transcribe"])}else if(n||a)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!e.return_timestamps&&e.no_timestamps_token_id&&r.at(-1)!==e.no_timestamps_token_id?r.push(e.no_timestamps_token_id):e.return_timestamps&&r.at(-1)===e.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),r.pop()),r.filter(s=>s!=null)}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,...s}){r=this._prepare_generation_config(r,s);const i=this._retrieve_init_tokens(r);if(r.return_timestamps&&(n??(n=new yi),n.push(new zb(r,i))),r.begin_suppress_tokens&&(n??(n=new yi),n.push(new gm(r.begin_suppress_tokens,i.length))),r.return_token_timestamps){if(!r.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");r.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),r.output_attentions=!0,r.return_dict_in_generate=!0}const o=await super.generate({inputs:e,generation_config:r,logits_processor:n,decoder_input_ids:i,...s});return r.return_token_timestamps&&(o.token_timestamps=this._extract_token_timestamps(o,r.alignment_heads,r.num_frames)),o}_extract_token_timestamps(e,r,n=null,a=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");n==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let s=this.config.median_filter_width;s===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),s=7);const i=e.cross_attentions,o=Array.from({length:this.config.decoder_layers},(v,x)=>Qt(i.map(E=>E[x]),2)),l=bo(r.map(([v,x])=>{if(v>=o.length)throw new Error(`Layer index ${v} is out of bounds for cross attentions (length ${o.length}).`);return n?o[v].slice(null,x,null,[0,n]):o[v].slice(null,x)})).transpose(1,0,2,3),[d,c]=my(l,-2,0,!0),f=l.clone();for(let v=0;v<f.dims[0];++v){const x=f[v];for(let E=0;E<x.dims[0];++E){const k=x[E],A=d[v][E][0].data,I=c[v][E][0].data;for(let T=0;T<k.dims[0];++T){let P=k[T].data;for(let B=0;B<P.length;++B)P[B]=(P[B]-I[B])/A[B];P.set(O0(P,s))}}}const g=[vo(f,1)],y=e.sequences.dims,p=new Ce("float32",new Float32Array(y[0]*y[1]),y);for(let v=0;v<y[0];++v){const x=g[v].neg().squeeze_(0),[E,k]=M0(x.tolist()),A=Array.from({length:E.length-1},(P,B)=>E[B+1]-E[B]),I=it([1],A).map(P=>!!P),T=[];for(let P=0;P<I.length;++P)I[P]&&T.push(k[P]*a);p[v].data.set(T,1)}return p}}class q2 extends Q{constructor(r,n,a){super(r,n);D(this,"main_input_name","pixel_values");D(this,"forward_params",["pixel_values","input_ids","encoder_hidden_states","past_key_values"]);this.generation_config=a}}class j2 extends Q{constructor(r,n,a){super(r,n);D(this,"forward_params",["input_ids","pixel_values","attention_mask","position_ids","past_key_values"]);this.generation_config=a}}class Mm extends j2{_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){const s=this.config.image_token_index,o=n.tolist().map(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:e,attention_mask:a};const c=[],f=[];for(let g=0;g<o.length;++g){const y=o[g],p=e[g],v=r[g],x=a[g];c.push(Qt([p.slice([0,y]),v,p.slice([y+1,p.dims[0]])],0)),f.push(Qt([x.slice([0,y]),cn([v.dims[0]]),x.slice([y+1,x.dims[0]])],0))}return{inputs_embeds:bo(c,0),attention_mask:bo(f,0)}}}class K2 extends Mm{}class Y2 extends Q{constructor(r,n,a){super(r,n);D(this,"forward_params",["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"]);D(this,"main_input_name","inputs_embeds");this.generation_config=a}}class Q2 extends Y2{_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){return{inputs_embeds:Qt([r,e],1),attention_mask:Qt([cn(r.dims.slice(0,2)),a],1)}}async _prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:n,attention_mask:a}){if(!e&&!r)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let s,i;return e&&(s=await this.encode_text({input_ids:e})),r&&(i=await this.encode_image({pixel_values:r})),s&&i?{inputs_embeds:n,attention_mask:a}=this._merge_input_ids_with_image_features({inputs_embeds:s,image_features:i,input_ids:e,attention_mask:a}):n=s||i,{inputs_embeds:n,attention_mask:a}}async forward({input_ids:e,pixel_values:r,attention_mask:n,decoder_input_ids:a,decoder_attention_mask:s,encoder_outputs:i,past_key_values:o,inputs_embeds:l,decoder_inputs_embeds:d}){if(l||({inputs_embeds:l,attention_mask:n}=await this._prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:l,attention_mask:n})),!i){let{last_hidden_state:g}=await gn(this,{inputs_embeds:l,attention_mask:n});i=g}if(!d){if(!a)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");d=await this.encode_text({input_ids:a})}return await $i(this,{inputs_embeds:d,attention_mask:s,encoder_attention_mask:n,encoder_hidden_states:i,past_key_values:o},!0)}}class Ti extends Q{}class X2 extends Ti{}class J2 extends Ti{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class Z2 extends Ti{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(e,r)}}class zm extends Q{}class e1 extends zm{}class t1 extends zm{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class r1 extends Ti{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(e,r)}}class n1 extends Q{}class a1 extends n1{}class Rm extends Q{}class i1 extends Rm{}class s1 extends Rm{}class Pm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class o1 extends Pm{}class u1 extends Pm{}class Bm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class l1 extends Bm{}class d1 extends Bm{}class Dm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class c1 extends Dm{}class p1 extends Dm{}class Nm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class h1 extends Nm{}class f1 extends Nm{}class Fm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class m1 extends Fm{}class g1 extends Fm{}class Lm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class _1 extends Lm{}class y1 extends Lm{}class Um extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class w1 extends Um{}class b1 extends Um{}class Wm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class v1 extends Wm{}class $1 extends Wm{}class Vm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class x1 extends Vm{}class S1 extends Vm{}class Gm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class k1 extends Gm{}class E1 extends Gm{}class Hm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class C1 extends Hm{}class T1 extends Hm{}class qm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class I1 extends qm{}class A1 extends qm{}class jm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class O1 extends jm{}class M1 extends jm{}class Km extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class z1 extends Km{}class R1 extends Km{}class Ym extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class P1 extends Ym{}class B1 extends Ym{}class Qm extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class D1 extends Qm{}class N1 extends Qm{}class Xm extends Q{}class F1 extends Xm{}class L1 extends Xm{async _call(e){return new Ie(await super._call(e))}}class Jm extends Q{}class U1 extends Jm{}class W1 extends Jm{async _call(e){return new Ie(await super._call(e))}}class V1 extends Q{}class G1 extends V1{async _call(e){return new pS(await super._call(e))}}class Zm extends Q{}class H1 extends Zm{}class q1 extends Zm{async _call(e){return new Ie(await super._call(e))}}class eg extends Q{}class j1 extends eg{}class K1 extends eg{async _call(e){return new Ie(await super._call(e))}}class tg extends Q{}class Y1 extends tg{}class Q1 extends tg{}class rg extends Q{}class X1 extends rg{}class J1 extends rg{}class ng extends Q{}class Z1 extends ng{}class e$ extends ng{async _call(e){return new Ie(await super._call(e))}}class Mo extends Q{}class t$ extends Mo{}class r$ extends Mo{async _call(e){return new ag(await super._call(e))}}class n$ extends Mo{async _call(e){return new a$(await super._call(e))}}class ag extends Ot{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class a$ extends Ot{constructor({logits:e,pred_boxes:r,pred_masks:n}){super(),this.logits=e,this.pred_boxes=r,this.pred_masks=n}}class ig extends Q{}class i$ extends ig{}class s$ extends ig{async _call(e){return new o$(await super._call(e))}}class o$ extends Ot{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class sg extends Q{}class u$ extends sg{}class l$ extends sg{async _call(e){return new d$(await super._call(e))}}class d$ extends ag{}class og extends Q{}class c$ extends og{}class p$ extends og{async _call(e){return new Ie(await super._call(e))}}class ug extends Q{}class h$ extends ug{}class f$ extends ug{async _call(e){return new Ie(await super._call(e))}}class lg extends Q{}class m$ extends lg{}class g$ extends lg{async _call(e){return new Ie(await super._call(e))}}class dg extends Q{}class _$ extends dg{}class y$ extends dg{}class cg extends Q{}class w$ extends cg{}class b$ extends cg{}class v$ extends Q{}class $$ extends v${}class pg extends Q{}class x$ extends pg{}class S$ extends pg{}class k$ extends Q{}class E$ extends k${}class hg extends Q{}class C$ extends hg{}class T$ extends hg{async _call(e){return new Ie(await super._call(e))}}class fg extends Q{}class I$ extends fg{}class A$ extends fg{async _call(e){return new Ie(await super._call(e))}}class mg extends Q{}class O$ extends mg{}class M$ extends mg{async _call(e){return new Ie(await super._call(e))}}class gg extends Q{}class z$ extends gg{}class R$ extends gg{async _call(e){return new P$(await super._call(e))}}class P$ extends Ot{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class B$ extends Q{}class D$ extends B${async get_image_embeddings({pixel_values:e}){return await gn(this,{pixel_values:e})}async forward(e){if((!e.image_embeddings||!e.image_positional_embeddings)&&(e={...e,...await this.get_image_embeddings(e)}),!e.input_labels&&e.input_points){const n=e.input_points.dims.slice(0,-1),a=n.reduce((s,i)=>s*i,1);e.input_labels=new Ce("int64",new BigInt64Array(a).fill(1n),n)}const r={image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings};return e.input_points&&(r.input_points=e.input_points),e.input_labels&&(r.input_labels=e.input_labels),e.input_boxes&&(r.input_boxes=e.input_boxes),await Tr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new N$(await super._call(e))}}class N$ extends Ot{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class _g extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class F$ extends _g{}class L$ extends _g{}class yg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class U$ extends yg{}class W$ extends yg{}class Kr extends Q{}class V$ extends Kr{}class G$ extends Kr{async _call(e){return new _n(await super._call(e))}}class H$ extends Kr{async _call(e){return new Ie(await super._call(e))}}class q$ extends Kr{async _call(e){return new dt(await super._call(e))}}class zo extends Q{}class j$ extends zo{}class K$ extends zo{async _call(e){return new _n(await super._call(e))}}class Y$ extends zo{async _call(e){return new Ie(await super._call(e))}}class Ii extends Q{}class Q$ extends Ii{}class X$ extends Ii{async _call(e){return new _n(await super._call(e))}}class J$ extends Ii{async _call(e){return new Ie(await super._call(e))}}class Z$ extends Ii{async _call(e){return new dt(await super._call(e))}}class Ro extends Q{}class ex extends Ro{}class tx extends Ro{async _call(e){return new _n(await super._call(e))}}class rx extends Ro{async _call(e){return new Ie(await super._call(e))}}class nx extends Kr{}class ax extends Kr{async _call(e){return new _n(await super._call(e))}}class ix extends Kr{async _call(e){return new Ie(await super._call(e))}}class fa extends Q{}class sx extends fa{}class ox extends fa{async _call(e){return new _n(await super._call(e))}}class ux extends fa{async _call(e){return new Ie(await super._call(e))}}class lx extends fa{async _call(e){return new cS(await super._call(e))}}class dx extends fa{async _call(e){return new dt(await super._call(e))}}class wg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class cx extends wg{}class px extends wg{async generate_speech(e,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:s=20,vocoder:i=null}={}){const o={input_ids:e},{encoder_outputs:l,encoder_attention_mask:d}=await gn(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,x=null,E=0;for(;;){++E;const I=vm(!!x);let T;x?T=x.output_sequence_out:T=new Ce("float32",new Float32Array(y),[1,1,y]);let P={use_cache_branch:I,output_sequence:T,encoder_attention_mask:d,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(P,v),x=await Tr(this.sessions.decoder_model_merged,P),v=this.getPastKeyValues(x,v);const{prob:B,spectrum:G}=x;if(p.push(G),E>=g&&(Array.from(B.data).filter(q=>q>=n).length>0||E>=f))break}const k=Qt(p),{waveform:A}=await Tr(i.sessions.model,{spectrogram:k});return{spectrogram:k,waveform:A}}}class hx extends Q{constructor(){super(...arguments);D(this,"main_input_name","spectrogram")}}class fx extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class mx extends fx{}class bg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class gx extends bg{}class _x extends bg{}class vg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class yx extends vg{}class wx extends vg{}class $g extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class bx extends $g{}class vx extends $g{}class Po extends Q{}class $x extends Po{}class xx extends Po{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class Sx extends Po{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="audio_model"),super.from_pretrained(e,r)}}class kx extends Q{}class xg extends kx{async _call(e){return new hS(await super._call(e))}}class Sg extends Q{}class Ex extends Sg{}class Cx extends Sg{}class kg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class Tx extends kg{}class Ix extends kg{}class Eg extends Q{}class Ax extends Eg{}class Ox extends Eg{async _call(e){return new Ie(await super._call(e))}}class Cg extends Q{constructor(r,n,a){super(r,n);D(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 Tr(this.sessions.encodec_decode,{audio_codes:a});return s}}class Tg extends Q{}class Mx extends Tg{}class zx extends Tg{async _call(e){return new Ie(await super._call(e))}}class Ig extends Q{}class Rx extends Ig{}class Px extends Ig{async _call(e){return new Ie(await super._call(e))}}class Ag extends Q{}class Bx extends Ag{}class Dx extends Ag{async _call(e){return new Ie(await super._call(e))}}class Og extends Q{}class Nx extends Og{}class Fx extends Og{async _call(e){return new Ie(await super._call(e))}}class Ai{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",model_file_name:o=null,subfolder: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 fm.from_pretrained(e,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(e,y)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${y.config.model_type}", attempting to construct from base class.`),await Q.from_pretrained(e,y);throw Error(`Unsupported model type: ${y.config.model_type}`)}}D(Ai,"MODEL_CLASS_MAPPINGS",null),D(Ai,"BASE_IF_FAIL",!1);const Lx=new Map([["bert",["BertModel",tv]],["nomic_bert",["NomicBertModel",ov]],["roformer",["RoFormerModel",uv]],["electra",["ElectraModel",yv]],["esm",["EsmModel",Vv]],["convbert",["ConvBertModel",hv]],["camembert",["CamembertModel",xv]],["deberta",["DebertaModel",Tv]],["deberta-v2",["DebertaV2Model",zv]],["mpnet",["MPNetModel",Xv]],["albert",["AlbertModel",s2]],["distilbert",["DistilBertModel",Nv]],["roberta",["RobertaModel",C2]],["xlm",["XLMModel",M2]],["xlm-roberta",["XLMRobertaModel",D2]],["clap",["ClapModel",$x]],["clip",["CLIPModel",X2]],["clipseg",["CLIPSegModel",i1]],["chinese_clip",["ChineseCLIPModel",a1]],["siglip",["SiglipModel",e1]],["mobilebert",["MobileBertModel",jv]],["squeezebert",["SqueezeBertModel",r2]],["wav2vec2",["Wav2Vec2Model",V$]],["wav2vec2-bert",["Wav2Vec2BertModel",ex]],["unispeech",["UniSpeechModel",j$]],["unispeech-sat",["UniSpeechSatModel",Q$]],["hubert",["HubertModel",nx]],["wavlm",["WavLMModel",sx]],["audio-spectrogram-transformer",["ASTModel",W2]],["vits",["VitsModel",xg]],["detr",["DetrModel",t$]],["rt_detr",["RTDetrModel",i$]],["table-transformer",["TableTransformerModel",u$]],["vit",["ViTModel",F1]],["fastvit",["FastViTModel",U1]],["mobilevit",["MobileViTModel",H1]],["mobilevitv2",["MobileViTV2Model",j1]],["owlvit",["OwlViTModel",Y1]],["owlv2",["Owlv2Model",X1]],["beit",["BeitModel",Z1]],["deit",["DeiTModel",c$]],["convnext",["ConvNextModel",C$]],["convnextv2",["ConvNextV2Model",I$]],["dinov2",["Dinov2Model",O$]],["resnet",["ResNetModel",h$]],["swin",["SwinModel",m$]],["swin2sr",["Swin2SRModel",_$]],["donut-swin",["DonutSwinModel",E$]],["yolos",["YolosModel",z$]],["dpt",["DPTModel",w$]],["glpn",["GLPNModel",x$]],["hifigan",["SpeechT5HifiGan",hx]],["efficientnet",["EfficientNetModel",Ax]],["mobilenet_v1",["MobileNetV1Model",Mx]],["mobilenet_v2",["MobileNetV2Model",Rx]],["mobilenet_v3",["MobileNetV3Model",Bx]],["mobilenet_v4",["MobileNetV4Model",Nx]]]),Ux=new Map([["t5",["T5Model",d2]],["longt5",["LongT5Model",p2]],["mt5",["MT5Model",f2]],["bart",["BartModel",g2]],["mbart",["MBartModel",w2]],["marian",["MarianModel",F$]],["whisper",["WhisperModel",G2]],["m2m_100",["M2M100Model",U$]],["blenderbot",["BlenderbotModel",x2]],["blenderbot-small",["BlenderbotSmallModel",k2]]]),Wx=new Map([["bloom",["BloomModel",z1]],["gpt2",["GPT2Model",o1]],["gptj",["GPTJModel",h1]],["gpt_bigcode",["GPTBigCodeModel",m1]],["gpt_neo",["GPTNeoModel",l1]],["gpt_neox",["GPTNeoXModel",c1]],["codegen",["CodeGenModel",_1]],["llama",["LlamaModel",w1]],["cohere",["CohereModel",v1]],["gemma",["GemmaModel",x1]],["openelm",["OpenELMModel",k1]],["qwen2",["Qwen2Model",C1]],["phi",["PhiModel",I1]],["phi3",["Phi3Model",O1]],["mpt",["MptModel",P1]],["opt",["OPTModel",D1]],["mistral",["MistralModel",gx]],["starcoder2",["Starcoder2Model",yx]],["falcon",["FalconModel",bx]],["stablelm",["StableLmModel",Tx]]]),Mg=new Map([["speecht5",["SpeechT5ForSpeechToText",cx]],["whisper",["WhisperForConditionalGeneration",H2]]]),Vx=new Map([["speecht5",["SpeechT5ForTextToSpeech",px]]]),Gx=new Map([["vits",["VitsModel",xg]],["musicgen",["MusicgenForConditionalGeneration",Cg]]]),Hx=new Map([["bert",["BertForSequenceClassification",nv]],["roformer",["RoFormerForSequenceClassification",dv]],["electra",["ElectraForSequenceClassification",bv]],["esm",["EsmForSequenceClassification",Hv]],["convbert",["ConvBertForSequenceClassification",mv]],["camembert",["CamembertForSequenceClassification",kv]],["deberta",["DebertaForSequenceClassification",Av]],["deberta-v2",["DebertaV2ForSequenceClassification",Pv]],["mpnet",["MPNetForSequenceClassification",Zv]],["albert",["AlbertForSequenceClassification",o2]],["distilbert",["DistilBertForSequenceClassification",Fv]],["roberta",["RobertaForSequenceClassification",I2]],["xlm",["XLMForSequenceClassification",R2]],["xlm-roberta",["XLMRobertaForSequenceClassification",F2]],["bart",["BartForSequenceClassification",y2]],["mbart",["MBartForSequenceClassification",v2]],["mobilebert",["MobileBertForSequenceClassification",Yv]],["squeezebert",["SqueezeBertForSequenceClassification",a2]]]),qx=new Map([["bert",["BertForTokenClassification",av]],["roformer",["RoFormerForTokenClassification",cv]],["electra",["ElectraForTokenClassification",vv]],["esm",["EsmForTokenClassification",qv]],["convbert",["ConvBertForTokenClassification",gv]],["camembert",["CamembertForTokenClassification",Ev]],["deberta",["DebertaForTokenClassification",Ov]],["deberta-v2",["DebertaV2ForTokenClassification",Bv]],["mpnet",["MPNetForTokenClassification",e2]],["distilbert",["DistilBertForTokenClassification",Lv]],["roberta",["RobertaForTokenClassification",A2]],["xlm",["XLMForTokenClassification",P2]],["xlm-roberta",["XLMRobertaForTokenClassification",L2]]]),zg=new Map([["t5",["T5ForConditionalGeneration",c2]],["longt5",["LongT5ForConditionalGeneration",h2]],["mt5",["MT5ForConditionalGeneration",m2]],["bart",["BartForConditionalGeneration",_2]],["mbart",["MBartForConditionalGeneration",b2]],["marian",["MarianMTModel",L$]],["m2m_100",["M2M100ForConditionalGeneration",W$]],["blenderbot",["BlenderbotForConditionalGeneration",S2]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",E2]]]),Bo=new Map([["bloom",["BloomForCausalLM",R1]],["gpt2",["GPT2LMHeadModel",u1]],["gptj",["GPTJForCausalLM",f1]],["gpt_bigcode",["GPTBigCodeForCausalLM",g1]],["gpt_neo",["GPTNeoForCausalLM",d1]],["gpt_neox",["GPTNeoXForCausalLM",p1]],["codegen",["CodeGenForCausalLM",y1]],["llama",["LlamaForCausalLM",b1]],["cohere",["CohereForCausalLM",$1]],["gemma",["GemmaForCausalLM",S1]],["openelm",["OpenELMForCausalLM",E1]],["qwen2",["Qwen2ForCausalLM",T1]],["phi",["PhiForCausalLM",A1]],["phi3",["Phi3ForCausalLM",M1]],["mpt",["MptForCausalLM",B1]],["opt",["OPTForCausalLM",N1]],["mbart",["MBartForCausalLM",$2]],["mistral",["MistralForCausalLM",_x]],["starcoder2",["Starcoder2ForCausalLM",wx]],["falcon",["FalconForCausalLM",vx]],["trocr",["TrOCRForCausalLM",mx]],["stablelm",["StableLmForCausalLM",Ix]]]),jx=new Map([["bert",["BertForMaskedLM",rv]],["roformer",["RoFormerForMaskedLM",lv]],["electra",["ElectraForMaskedLM",wv]],["esm",["EsmForMaskedLM",Gv]],["convbert",["ConvBertForMaskedLM",fv]],["camembert",["CamembertForMaskedLM",Sv]],["deberta",["DebertaForMaskedLM",Iv]],["deberta-v2",["DebertaV2ForMaskedLM",Rv]],["mpnet",["MPNetForMaskedLM",Jv]],["albert",["AlbertForMaskedLM",l2]],["distilbert",["DistilBertForMaskedLM",Wv]],["roberta",["RobertaForMaskedLM",T2]],["xlm",["XLMWithLMHeadModel",z2]],["xlm-roberta",["XLMRobertaForMaskedLM",N2]],["mobilebert",["MobileBertForMaskedLM",Kv]],["squeezebert",["SqueezeBertForMaskedLM",n2]]]),Kx=new Map([["bert",["BertForQuestionAnswering",iv]],["roformer",["RoFormerForQuestionAnswering",pv]],["electra",["ElectraForQuestionAnswering",$v]],["convbert",["ConvBertForQuestionAnswering",_v]],["camembert",["CamembertForQuestionAnswering",Cv]],["deberta",["DebertaForQuestionAnswering",Mv]],["deberta-v2",["DebertaV2ForQuestionAnswering",Dv]],["mpnet",["MPNetForQuestionAnswering",t2]],["albert",["AlbertForQuestionAnswering",u2]],["distilbert",["DistilBertForQuestionAnswering",Uv]],["roberta",["RobertaForQuestionAnswering",O2]],["xlm",["XLMForQuestionAnswering",B2]],["xlm-roberta",["XLMRobertaForQuestionAnswering",U2]],["mobilebert",["MobileBertForQuestionAnswering",Qv]],["squeezebert",["SqueezeBertForQuestionAnswering",i2]]]),Rg=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",q2]]]),Yx=new Map([["llava",["LlavaForConditionalGeneration",Mm]],["moondream1",["Moondream1ForConditionalGeneration",K2]],["florence2",["Florence2ForConditionalGeneration",Q2]]]),Qx=new Map([["vit",["ViTForImageClassification",L1]],["fastvit",["FastViTForImageClassification",W1]],["mobilevit",["MobileViTForImageClassification",q1]],["mobilevitv2",["MobileViTV2ForImageClassification",K1]],["beit",["BeitForImageClassification",e$]],["deit",["DeiTForImageClassification",p$]],["convnext",["ConvNextForImageClassification",T$]],["convnextv2",["ConvNextV2ForImageClassification",A$]],["dinov2",["Dinov2ForImageClassification",M$]],["resnet",["ResNetForImageClassification",f$]],["swin",["SwinForImageClassification",g$]],["segformer",["SegformerForImageClassification",Ex]],["efficientnet",["EfficientNetForImageClassification",Ox]],["mobilenet_v1",["MobileNetV1ForImageClassification",zx]],["mobilenet_v2",["MobileNetV2ForImageClassification",Px]],["mobilenet_v3",["MobileNetV3ForImageClassification",Dx]],["mobilenet_v4",["MobileNetV4ForImageClassification",Fx]]]),Xx=new Map([["detr",["DetrForObjectDetection",r$]],["rt_detr",["RTDetrForObjectDetection",s$]],["table-transformer",["TableTransformerForObjectDetection",l$]],["yolos",["YolosForObjectDetection",R$]]]),Jx=new Map([["owlvit",["OwlViTForObjectDetection",Q1]],["owlv2",["Owlv2ForObjectDetection",J1]]]),Zx=new Map([["detr",["DetrForSegmentation",n$]],["clipseg",["CLIPSegForImageSegmentation",s1]]]),eS=new Map([["segformer",["SegformerForSemanticSegmentation",Cx]]]),tS=new Map([["sam",["SamModel",D$]]]),rS=new Map([["wav2vec2",["Wav2Vec2ForCTC",G$]],["wav2vec2-bert",["Wav2Vec2BertForCTC",tx]],["unispeech",["UniSpeechForCTC",K$]],["unispeech-sat",["UniSpeechSatForCTC",X$]],["wavlm",["WavLMForCTC",ox]],["hubert",["HubertForCTC",ax]]]),nS=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",H$]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",rx]],["unispeech",["UniSpeechForSequenceClassification",Y$]],["unispeech-sat",["UniSpeechSatForSequenceClassification",J$]],["wavlm",["WavLMForSequenceClassification",ux]],["hubert",["HubertForSequenceClassification",ix]],["audio-spectrogram-transformer",["ASTForAudioClassification",V2]]]),aS=new Map([["wavlm",["WavLMForXVector",lx]]]),iS=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Z$]],["wavlm",["WavLMForAudioFrameClassification",dx]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",q$]]]),sS=new Map([["vitmatte",["VitMatteForImageMatting",G1]]]),oS=new Map([["swin2sr",["Swin2SRForImageSuperResolution",y$]]]),uS=new Map([["dpt",["DPTForDepthEstimation",b$]],["depth_anything",["DepthAnythingForDepthEstimation",$$]],["glpn",["GLPNForDepthEstimation",S$]]]),lS=new Map([["clip",["CLIPVisionModelWithProjection",Z2]],["siglip",["SiglipVisionModel",r1]]]),Pg=[[Lx,ve.EncoderOnly],[Ux,ve.EncoderDecoder],[Wx,ve.DecoderOnly],[Hx,ve.EncoderOnly],[qx,ve.EncoderOnly],[zg,ve.Seq2Seq],[Mg,ve.Seq2Seq],[Bo,ve.DecoderOnly],[jx,ve.EncoderOnly],[Kx,ve.EncoderOnly],[Rg,ve.Vision2Seq],[Yx,ve.ImageTextToText],[Qx,ve.EncoderOnly],[Zx,ve.EncoderOnly],[eS,ve.EncoderOnly],[sS,ve.EncoderOnly],[oS,ve.EncoderOnly],[uS,ve.EncoderOnly],[Xx,ve.EncoderOnly],[Jx,ve.EncoderOnly],[tS,ve.MaskGeneration],[rS,ve.EncoderOnly],[nS,ve.EncoderOnly],[Vx,ve.Seq2Seq],[Gx,ve.EncoderOnly],[aS,ve.EncoderOnly],[iS,ve.EncoderOnly],[lS,ve.EncoderOnly]];for(const[t,e]of Pg)for(const[r,n]of t.values())vi.set(r,e),ta.set(n,r),ym.set(r,n);const dS=[["MusicgenForConditionalGeneration",Cg,ve.Musicgen],["CLIPTextModelWithProjection",J2,ve.EncoderOnly],["SiglipTextModel",t1,ve.EncoderOnly],["ClapTextModelWithProjection",xx,ve.EncoderOnly],["ClapAudioModelWithProjection",Sx,ve.EncoderOnly]];for(const[t,e,r]of dS)vi.set(t,r),ta.set(e,t),ym.set(t,e);class Bg extends Ai{}D(Bg,"MODEL_CLASS_MAPPINGS",Pg.map(e=>e[0])),D(Bg,"BASE_IF_FAIL",!0);class Dg extends Ai{}D(Dg,"MODEL_CLASS_MAPPINGS",[Bo]);class Ie extends Ot{constructor({logits:e}){super(),this.logits=e}}class cS extends Ot{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class dt extends Ot{constructor({logits:e}){super(),this.logits=e}}class ct extends Ot{constructor({logits:e}){super(),this.logits=e}}class _t extends Ot{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class _n extends Ot{constructor({logits:e}){super(),this.logits=e}}class pS extends Ot{constructor({alphas:e}){super(),this.alphas=e}}class hS extends Ot{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}if(!(typeof self<"u")){if(!Ve)throw new Error("Unable to load image processing library.")}class fS{put(e){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const Ng=xr.IS_PROCESS_AVAILABLE?t=>process.stdout.write(t):t=>console.log(t);class mS extends fS{constructor(e,{skip_prompt:r=!1,callback_function:n=null,token_callback_function:a=null,decode_kwargs:s={},...i}={}){super(),this.tokenizer=e,this.skip_prompt=r,this.callback_function=n??Ng,this.token_callback_function=a,this.decode_kwargs={...s,...i},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(e){var s;if(e.length>1)throw Error("TextStreamer only supports batch size of 1");const r=e[0];if((s=this.token_callback_function)==null||s.call(this,r),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&&rm(n.charCodeAt(n.length-1))?(a=n.slice(this.print_len),this.print_len+=a.length):(a=n.slice(this.print_len,n.lastIndexOf(" ")+1),this.print_len+=a.length),this.on_finalized_text(a,!1)}end(){let e;this.token_cache.length>0?(e=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):e="",this.next_tokens_are_prompt=!0,this.on_finalized_text(e,!0)}on_finalized_text(e,r){var n,a;e.length>0&&((n=this.callback_function)==null||n.call(this,e)),r&&this.callback_function===Ng&&xr.IS_PROCESS_AVAILABLE&&((a=this.callback_function)==null||a.call(this,`
`))}}const Oi=new Hb;class ma{static async getInstance(e=null){return this.tokenizer??(this.tokenizer=pm.from_pretrained(this.model_id,{progress_callback:e})),this.model??(this.model=Dg.from_pretrained(this.model_id,{dtype:"q4",device:"webgpu",progress_callback:e})),Promise.all([this.tokenizer,this.model])}}D(ma,"model_id","HuggingFaceTB/SmolLM-360M-Instruct"),D(ma,"model",null),D(ma,"tokenizer",null);let Do=null;async function gS(t){const[e,r]=await ma.getInstance(),n=e.apply_chat_template(t,{add_generation_prompt:!0,return_dict:!0});let a,s=0,i;const o=y=>{a??(a=performance.now()),s++>0&&(i=s/(performance.now()-a)*1e3)},l=y=>{self.postMessage({status:"update",output:y,tps:i,numTokens:s})},d=new mS(e,{skip_prompt:!0,skip_special_tokens:!0,callback_function:l,token_callback_function:o});self.postMessage({status:"start"});const{past_key_values:c,sequences:f}=await r.generate({...n,past_key_values:Do,do_sample:!0,top_k:20,temperature:.6,max_new_tokens:512,streamer:d,stopping_criteria:Oi,return_dict_in_generate:!0});Do=c;const g=e.batch_decode(f,{skip_special_tokens:!0});self.postMessage({status:"complete",output:g})}async function _S(){self.postMessage({status:"loading",data:"Loading model..."});const[t,e]=await ma.getInstance(n=>{self.postMessage(n)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const r=t("a");await e.generate({...r,max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async t=>{const{type:e,data:r}=t.data;switch(e){case"load":_S();break;case"generate":Oi.reset(),gS(r);break;case"interrupt":Oi.interrupt();break;case"reset":Do=null,Oi.reset();break}})})();