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

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

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

         var reduceSize = min(Length, ${m}u);
         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
             currentSize = reduceSize / 2u) {
           let interval = DIV_CEIL(reduceSize, 2u);
           if (local_idx < currentSize) {
            let candidate = aBestValues[local_idx + interval];
            bestValue = ${bl[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}(${$l[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:u},programUniforms:[{type:12,data:l}]})}},Kt=(t,e,r,n)=>{let a=t.inputs.length===1?r:_o(t.inputs,r),i=a.axes;i.length===0&&!a.noopWithEmptyAxes&&(i=t.inputs[0].dims.map((c,y)=>y));let s=Y.normalizeAxes(i,t.inputs[0].dims.length),o=s,u=t.inputs[0],l=Cl(o,t.inputs[0].dims.length);l.length>0&&(u=t.compute(vr(t.inputs[0],l),{inputs:[0],outputs:[-1]})[0],o=xl(o.length,u.dims.length));let[p,f]=Sl(u.dims,o),m=p;a.keepDims&&(m=kl(p,s)),t.compute(Tl(e,{hint:a.cacheKey,inputDependencies:["type"]},[u],n,t.inputs[0].dataType,m,f),{inputs:[u]})},_h=(t,e)=>{Kt(t,"ReduceMeanShared",e,"mean")},yh=(t,e)=>{Kt(t,"ReduceL1Shared",e,"l1")},wh=(t,e)=>{Kt(t,"ReduceL2Shared",e,"l2")},bh=(t,e)=>{Kt(t,"ReduceLogSumExpShared",e,"logSumExp")},vh=(t,e)=>{Kt(t,"ReduceMaxShared",e,"max")},$h=(t,e)=>{Kt(t,"ReduceMinShared",e,"min")},xh=(t,e)=>{Kt(t,"ReduceProdShared",e,"prod")},Sh=(t,e)=>{Kt(t,"ReduceSumShared",e,"sum")},kh=(t,e)=>{Kt(t,"ReduceSumSquareShared",e,"sumSquare")},Eh=(t,e)=>{Kt(t,"ReduceLogSumShared",e,"logSum")}}),Yt,Il,Li,_o,Xt,Al,Ml,Ol,zl,Pl,Rl,Bl,Dl,Nl,Fl,Qt,Ch,Th,Ih,Ah,Mh,Oh,zh,Ph,Rh,Bh,Yo=Z(()=>{$e(),Ae(),lt(),Te(),Z_(),Yt=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.")},Il=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],Li=(t,e,r,n,a,i,s=!1,o=!1)=>{let u=[],l=r[0].dims,p=l.length,f=Y.normalizeAxes(a,p),m=!o&&f.length===0;l.forEach((w,v)=>{m||f.indexOf(v)>=0?s&&u.push(1):u.push(w)});let c=u.length,y=Y.size(u);return{name:t,shaderCache:e,getShaderSource:w=>{let v=[],k=Q("_A",r[0].dataType,p),$=ge("output",i,c),C=n(k,$,f),T=C[2];for(let A=0,B=0;A<p;A++)m||f.indexOf(A)>=0?(s&&B++,T=`for(var j${A}: u32 = 0; j${A} < ${l[A]}; j${A}++) {
                  ${C[2].includes("last_index")?`let last_index = j${A};`:""}
                  ${k.indicesSet("input_indices",A,`j${A}`)}
                  ${T}
                }`):(v.push(`${k.indicesSet("input_indices",A,$.indicesGet("output_indices",B))};`),B++);return`

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

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

          ${v.join(`
`)}
          ${C[0]}       // init ops for reduce max/min
          ${C[1]}
          ${T}
          ${C[3]}
          ${C.length===4?$.setByOffset("global_idx","value"):C.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:u,dataType:i}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:[{type:12,data:y},...ye(l,u)]})}},_o=(t,e)=>{let r=[];return t[1].dims[0]>0&&t[1].getBigInt64Array().forEach(n=>r.push(Number(n))),qe({axes:r,keepDims:e.keepDims,noopWithEmptyAxes:e.noopWithEmptyAxes})},Xt=(t,e,r,n)=>{let a=t.inputs,i=a.length===1?r:_o(a,r);t.compute(Li(e,{hint:i.cacheKey,inputDependencies:["rank"]},[a[0]],i.noopWithEmptyAxes&&i.axes.length===0?Il:n,i.axes,a[0].dataType,i.keepDims,i.noopWithEmptyAxes),{inputs:[0]})},Al=(t,e)=>{Yt(t.inputs),Xt(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},Ml=(t,e)=>{Yt(t.inputs),Xt(t,"ReduceL1",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},Ol=(t,e)=>{Yt(t.inputs),Xt(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);"])},zl=(t,e)=>{Yt(t.inputs),Xt(t,"ReduceLogSumExp",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},Pl=(t,e)=>{Yt(t.inputs),Xt(t,"ReduceMax",e,(r,n,a)=>{let i=[];for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&i.push(r.indicesSet("input_indices",s,0));return[`${i.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = max(value, ${r.getByIndices("input_indices")});`,""]})},Rl=(t,e)=>{Yt(t.inputs),Xt(t,"ReduceMean",e,(r,n,a)=>{let i=1;for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&(i*=t.inputs[0].dims[s]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${i});`]})},Bl=(t,e)=>{Yt(t.inputs),Xt(t,"ReduceMin",e,(r,n,a)=>{let i=[];for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&i.push(`input_indices[${s}] = 0;`);return[`${i.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},Dl=(t,e)=>{Yt(t.inputs),Xt(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},Nl=(t,e)=>{Yt(t.inputs),Xt(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},Fl=(t,e)=>{Yt(t.inputs),Xt(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},Qt=(t,e,r)=>{if(e.length===0)return r;let n=1,a=1;for(let i=0;i<e.length;i++)e.indexOf(i)===-1?n*=t[i]:a*=t[i];return a<32&&n>1024},Ch=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Rl(t,e):_h(t,e)},Th=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Ml(t,e):yh(t,e)},Ih=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Ol(t,e):wh(t,e)},Ah=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?zl(t,e):bh(t,e)},Mh=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Pl(t,e):vh(t,e)},Oh=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Bl(t,e):$h(t,e)},zh=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Dl(t,e):xh(t,e)},Ph=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Nl(t,e):Sh(t,e)},Rh=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Fl(t,e):kh(t,e)},Bh=(t,e)=>{Qt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Al(t,e):Eh(t,e)}}),zs,Dh,Nh,yo,ey=Z(()=>{$e(),lt(),Yo(),zs=t=>{if(!t||t.length===0||t.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(t[0].dataType!==1)throw new Error("Invalid input type.")},Dh=(t,e)=>{zs(t.inputs);let r=(n,a,i)=>{let s=[];for(let o=0;o<n.rank;o++)(i.indexOf(o)>=0||i.length===0)&&s.push(`input_indices[${o}] = 0;`);return[`${s.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?"<=":"<"} value) {
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         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(Li("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Nh=(t,e)=>{zs(t.inputs);let r=(n,a,i)=>{let s=[];for(let o=0;o<n.rank;o++)(i.indexOf(o)>=0||i.length===0)&&s.push(`input_indices[${o}] = 0;`);return[`${s.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?">=":">"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(Li("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},yo=t=>qe(t)}),Ll,Ul,Wl,Ui,Fh,Lh,Uh=Z(()=>{$e(),Ae(),lt(),Te(),Ll=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");let r=0,n=t[r],a=n.dataType,i=n.dims.length;t.forEach((s,o)=>{if(o!==r){if(s.dataType!==a)throw new Error("input tensors should be one type");if(s.dims.length!==i)throw new Error("input tensors should have the same shape");s.dims.forEach((u,l)=>{if(l!==e&&u!==n.dims[l])throw new Error("non concat dimensions must match")})}})},Ul=(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;
  }`,Wl=(t,e)=>{let r=t.length,n=[];for(let a=0;a<r;++a){let i=e.setByOffset("global_idx",t[a].getByIndices("indices"));r===1?n.push(i):a===0?n.push(`if (inputIndex == ${a}u) { ${i} }`):a===r-1?n.push(`else { ${i} }`):n.push(`else if (inputIndex == ${a}) { ${i} }`)}return n.join(`
`)},Ui=(t,e,r,n)=>{let a=Y.size(r),i=new Array(t.length),s=new Array(t.length),o=0,u=[],l=[],p=[{type:12,data:a}];for(let w=0;w<t.length;++w)o+=t[w].dims[e],i[w]=o,l.push(t[w].dims.length),s[w]=Q(`input${w}`,n,l[w]),u.push("rank"),p.push({type:12,data:i[w]});for(let w=0;w<t.length;++w)p.push(...ye(t[w].dims));p.push(...ye(r));let f=ge("output",n,r.length),m=f.indicesGet("indices",e),c=Array.from(Array(i.length).keys()).map(w=>`uniforms.sizeInConcatAxis${w}`).join(","),y=w=>`

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

  ${Ul(i.length,c)}

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

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

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

    ${Wl(s,f)}
  }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:y}},Fh=(t,e)=>{let r=t.inputs,n=r[0].dims,a=Y.normalizeAxis(e.axis,n.length);Ll(r,a);let i=n.slice();i[a]=r.reduce((o,u)=>o+(u.dims.length>a?u.dims[a]:0),0);let s=r.filter(o=>Y.size(o.dims)>0);t.compute(Ui(s,a,i,r[0].dataType),{inputs:s})},Lh=t=>qe({axis:t.axis})}),Vl,Gl,Hl,jl,Wi,ql,Wh,Vh=Z(()=>{$e(),Ho(),Te(),Uh(),Vl=(t,e)=>{let r=t[0],n=t[1],a=t[2],i=t[3],s=t[4],o=t[5];if(s&&o)throw new Error("Attention cannot have both past and relative_position_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let u=r.dims[0],l=r.dims[1],p=r.dims[2];if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(a.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let f=a.dims[0]/3,m=f,c=m;if(e.qkvHiddenSizes.length>0){if(e.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let C of e.qkvHiddenSizes)if(C%e.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");f=e.qkvHiddenSizes[0],m=e.qkvHiddenSizes[1],c=e.qkvHiddenSizes[2]}let y=l;if(f!==m)throw new Error("qkv_hidden_sizes first element should be same as the second");if(a.dims[0]!==f+m+c)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let w=0;if(s){if(m!==c)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(s.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(s.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(s.dims[1]!==u)throw new Error('Input "past" second dimension must be batch_size');if(s.dims[2]!==e.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(s.dims[4]!==m/e.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');e.pastPresentShareBuffer||(w=s.dims[3])}let v=y+w,k=-1,$=0;if(i)throw new Error("Mask not supported");if(s)throw new Error("past is not supported");return{batchSize:u,sequenceLength:l,pastSequenceLength:w,kvSequenceLength:y,totalSequenceLength:v,maxSequenceLength:k,inputHiddenSize:p,hiddenSize:f,vHiddenSize:c,headSize:Math.floor(f/e.numHeads),vHeadSize:Math.floor(c/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:$,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Gl=(t,e,r,n)=>{let a=it(n),i=64,s=n/a;s<i?i=1:s/8<64&&(i=Math.ceil(s/8));let o=Math.ceil(n/a/i),u=[{type:e.dataType,data:1/n},{type:12,data:s},{type:12,data:o}],l=pt(e.dataType,a),p=Tt(1,a),f=m=>{let c=ge("x",e.dataType,e.dims,a),y=[{name:"d_inv",type:Tt(e.dataType)},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${i}>;
  var<workgroup> thread_sum: array<f32, ${i}>;
  ${m.registerUniforms(y).declareVariables(c)}
  ${m.mainStart([i,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 = ${p}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
      thread_max_vector = max(${p}(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 < ${i}; i++) {
      max_value = max(thread_max[i], max_value);
    }

    var sum_vector = ${p}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
      sum_vector += exp(${p}(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 < ${i}; 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] = ${c.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 = ${p}(x[offset + i]);
        x[offset + i] = ${c.type.value}(exp(f32input - max_value) / sum);
      }
    }
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${i};${l};${a}`},getShaderSource:f,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:u})}},Hl=(t,e,r,n,a,i,s)=>{let o=s+a.kvSequenceLength,u=[a.batchSize,a.numHeads,a.sequenceLength,o],l=i.scale===0?1/Math.sqrt(a.headSize):i.scale,p=it(a.headSize),f=a.headSize/p,m=12,c={x:Math.ceil(o/m),y:Math.ceil(a.sequenceLength/m),z:a.batchSize*a.numHeads},y=[{type:12,data:a.sequenceLength},{type:12,data:f},{type:12,data:o},{type:12,data:a.numHeads},{type:1,data:l}],w=n?["type","type","type"]:["type","type"],v=k=>{let $=Q("q",e.dataType,e.dims,p),C=Q("key",r.dataType,r.dims,p),T=[$,C];n&&T.push(Q("relative_position_bias",n.dataType,n.dims));let A=ge("output",e.dataType,u),B=Tt(1,p),R=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return`
  const TILE_SIZE = ${m}u;

  var<workgroup> tileQ: array<${$.type.storage}, ${m*m}>;
  var<workgroup> tileK: array<${$.type.storage}, ${m*m}>;
  ${k.registerUniforms(R).declareVariables(...T,A)}
  ${k.mainStart([m,m,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;
    let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;

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

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

      workgroupBarrier();
    }

    let headOffset = headIdx * uniforms.M * uniforms.N;
    if (global_id.y < uniforms.M && global_id.x < uniforms.N) {
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(p){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: ${p}`)}})()};
        output[outputIdx] = ${A.type.value} (sum * uniforms.alpha) + ${n?"relative_position_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${p}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:u,dataType:e.dataType,gpuDataType:0}],dispatchGroup:c,programUniforms:y}),getShaderSource:v}},jl=(t,e,r,n,a)=>{let i=a+n.kvSequenceLength,s=[n.batchSize,n.sequenceLength,n.vHiddenSize],o=12,u={x:Math.ceil(n.vHeadSize/o),y:Math.ceil(n.sequenceLength/o),z:n.batchSize*n.numHeads},l=[{type:12,data:n.sequenceLength},{type:12,data:i},{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:s,dataType:e.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:l}),getShaderSource:p=>{let f=Q("probs",e.dataType,e.dims),m=Q("v",r.dataType,r.dims),c=ge("output",e.dataType,s),y=[{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}>;
  ${p.registerUniforms(y).declareVariables(f,m,c)}
  ${p.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;
   }
  }`}}},Wi=(t,e,r,n,a,i,s,o,u,l,p)=>{let f=t.outputCount>1,m=t.outputCount>2,c=f&&m?l.pastSequenceLength:0,y=c+l.kvSequenceLength,w=[l.batchSize,l.numHeads,y,l.headSize],v=s?[s,r]:[r],k=f?t.compute(Ui(v,2,w,r.dataType),{inputs:v,outputs:[1]})[0]:r,$=[l.batchSize,l.numHeads,y,l.headSize],C=o?[o,n]:[n],T=m?t.compute(Ui(C,2,$,n.dataType),{inputs:C,outputs:[2]})[0]:n,A=[e,k];u&&A.push(u);let B=t.compute(Hl(t,e,k,u,l,p,c),{inputs:A,outputs:[-1]})[0];t.compute(Gl(t,B,l.batchSize*l.numHeads*l.sequenceLength,y),{inputs:[B],outputs:[]});let R=[B,T];t.compute(jl(t,B,T,l,c),{inputs:R,outputs:[0]})},ql=(t,e)=>{let r=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],n=e.sequenceLength,a=e.inputHiddenSize,i=e.headSize,s=12,o={x:Math.ceil(e.headSize/s),y:Math.ceil(e.sequenceLength/s),z:e.batchSize*e.numHeads},u=[t.inputs[0],t.inputs[1],t.inputs[2]],l=[{type:12,data:n},{type:12,data:a},{type:12,data:i},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],p=f=>{let m=ge("output_q",u[0].dataType,r),c=ge("output_k",u[0].dataType,r),y=ge("output_v",u[0].dataType,r),w=Q("input",u[0].dataType,u[0].dims),v=Q("weight",u[1].dataType,u[1].dims),k=Q("bias",u[2].dataType,u[2].dims),$=w.type.storage,C=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
  const TILE_SIZE = ${s}u;
  var<workgroup> tileInput: array<${$}, ${s*s}>;
  var<workgroup> tileWeightQ: array<${$}, ${s*s}>;
  var<workgroup> tileWeightK: array<${$}, ${s*s}>;
  var<workgroup> tileWeightV: array<${$}, ${s*s}>;
  ${f.registerUniforms(C).declareVariables(w,v,k,m,c,y)}
  ${f.mainStart([s,s,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 = ${$}(0);
    var valueK = ${$}(0);
    var valueV = ${$}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return t.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:l}),getShaderSource:p},{inputs:u,outputs:[-1,-1,-1]})},Wh=(t,e)=>{let r=Vl(t.inputs,e),[n,a,i]=ql(t,r);return Wi(t,n,a,i,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),Kl,Yl,Xl,Gh,ty=Z(()=>{ar(),$e(),Ae(),lt(),Te(),Kl=(t,e)=>{if(!t||t.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,a,i)=>{let s=a.length;if(s!==n.length)throw new Error(`${i}: num dimensions != ${s}`);a.forEach((o,u)=>{if(o!==n[u])throw new Error(`${i}: dim[${u}] do not match`)})};if(t[0].dims.length>1){let n=e.format==="NHWC"?e.spatial?t[0].dims.slice(-1):t[0].dims.slice(-1).concat(t[0].dims.slice(1,t[0].dims.length-1)):t[0].dims.slice(1,e.spatial?2:void 0);r(t[1].dims,n,"Invalid input scale"),r(t[2].dims,n,"Invalid input B"),r(t[3].dims,n,"Invalid input mean"),r(t[4].dims,n,"Invalid input var")}else r(t[1].dims,[1],"Invalid input scale"),r(t[2].dims,[1],"Invalid input B"),r(t[3].dims,[1],"Invalid input mean"),r(t[4].dims,[1],"Invalid input var")},Yl=(t,e)=>{let{epsilon:r,spatial:n,format:a}=e,i=t[0].dims,s=n?it(i[i.length-1]):1,o=a==="NHWC"&&i.length>1?s:1,u=Y.size(i)/s,l=n,p=l?i.length:i,f=Q("x",t[0].dataType,t[0].dims,s),m=Q("scale",t[1].dataType,t[1].dims,o),c=Q("bias",t[2].dataType,t[2].dims,o),y=Q("inputMean",t[3].dataType,t[3].dims,o),w=Q("inputVar",t[4].dataType,t[4].dims,o),v=ge("y",t[0].dataType,p,s),k=()=>{let C="";if(n)C=`let cOffset = ${i.length===1?"0u":a==="NHWC"?`outputIndices[${i.length-1}] / ${s}`:"outputIndices[1]"};`;else if(a==="NCHW")C=`
            ${v.indicesSet("outputIndices","0","0")}
            let cOffset = ${v.indicesToOffset("outputIndices")};`;else{C=`var cIndices = ${m.type.indices}(0);
                       cIndices[0] = outputIndices[${i.length-1}];`;for(let T=1;T<m.rank;T++)C+=`cIndices[${T}] = outputIndices[${T}];`;C+=`let cOffset = ${m.indicesToOffset("cIndices")};`}return C},$=C=>`
  const epsilon = ${r};
  ${C.registerUniform("outputSize","u32").declareVariables(f,m,c,y,w,v)}
  ${C.mainStart()}
  ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${v.offsetToIndices(`global_idx * ${s}`)};
    ${k()}
    let scale = ${m.getByOffset("cOffset")};
    let bias = ${c.getByOffset("cOffset")};
    let inputMean = ${y.getByOffset("cOffset")};
    let inputVar = ${w.getByOffset("cOffset")};
    let x = ${f.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${v.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${e.epsilon}_${e.format}_${n}_${s}`,inputDependencies:l?["rank","type","type","type","type"]:void 0},getShaderSource:$,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l?[{type:12,data:u},...ye(i)]:[{type:12,data:u}]})}},Xl=t=>qe(t),Gh=(t,e)=>{let{inputs:r,outputCount:n}=t,a=Xl({...e,outputCount:n});if(Ue.webgpu.validateInputContent&&Kl(r,a),e.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");t.compute(Yl(r,a))}}),Ql,Jl,Hh,ry=Z(()=>{Ae(),Te(),Ql=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")},Jl=t=>{let e=t[0].dims,r=t[0].dims[2],n=Y.size(e)/4,a=t[0].dataType,i=Q("input",a,e,4),s=Q("bias",a,[r],4),o=Q("residual",a,e,4),u=ge("output",a,e,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:l=>`
  const channels = ${r}u / 4;
  ${l.declareVariables(i,s,o,u)}

  ${l.mainStart()}
    ${l.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${i.getByOffset("global_idx")}
      + ${s.getByOffset("global_idx % channels")} + ${o.getByOffset("global_idx")};
    ${u.setByOffset("global_idx","value")}
  }`}},Hh=t=>{Ql(t.inputs),t.compute(Jl(t.inputs))}}),Zl,Re,jh,qh,Kh,Yh,Xh,Qh,Jh,Zh,ef,ed,tf,rf,nf,af,Ci,sf,Ti,of,uf,lf,df,cf,pf,hf,ff,mf,gf,_f,yf,wf,bf,vf,$f,Ps,xf,wo,bo,Sf,kf,Ef,Xo=Z(()=>{$e(),Ae(),lt(),Te(),Zl=(t,e,r,n,a,i)=>{let s=Math.ceil(e/4),o="";typeof a=="string"?o=`${a}(a)`:o=a("a");let u=Q("inputData",r,[s],4),l=ge("outputData",n,[s],4);return`
      ${t.registerUniform("vec_size","u32").declareVariables(u,l)}

  ${i??""}

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

    let a = ${u.getByOffset("global_idx")};
    ${l.setByOffset("global_idx",o)}
  }`},Re=(t,e,r,n,a,i=t.dataType)=>({name:e,shaderCache:{hint:a,inputDependencies:["type"]},getShaderSource:s=>Zl(s,Y.size(t.dims),t.dataType,i,r,n),getRunData:s=>({outputs:[{dims:t.dims,dataType:i}],dispatchGroup:{x:Math.ceil(Y.size(s[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(Y.size(t.dims)/4)}]})}),jh=t=>{t.compute(Re(t.inputs[0],"Abs","abs"))},qh=t=>{t.compute(Re(t.inputs[0],"Acos","acos"))},Kh=t=>{t.compute(Re(t.inputs[0],"Acosh","acosh"))},Yh=t=>{t.compute(Re(t.inputs[0],"Asin","asin"))},Xh=t=>{t.compute(Re(t.inputs[0],"Asinh","asinh"))},Qh=t=>{t.compute(Re(t.inputs[0],"Atan","atan"))},Jh=t=>{t.compute(Re(t.inputs[0],"Atanh","atanh"))},Zh=t=>qe(t),ef=(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): ${e.to}`)}t.compute(Re(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},ed=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:jo,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:qo;return qe({min:e,max:r})},tf=(t,e)=>{let r=t.inputs.length===1?e:ed(t.inputs),n=Tt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"Clip",a=>`clamp(${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]})},rf=t=>{t.compute(Re(t.inputs[0],"Ceil","ceil"))},nf=t=>{t.compute(Re(t.inputs[0],"Cos","cos"))},af=t=>{t.compute(Re(t.inputs[0],"Cosh","cosh"))},Ci=t=>qe(t),sf=(t,e)=>{let r=Tt(t.inputs[0].dataType);t.compute(Re(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))},Ti=(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));
}`,of=t=>{let e=Tt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,Ti(e)))},uf=t=>{t.compute(Re(t.inputs[0],"Exp","exp"))},lf=t=>{t.compute(Re(t.inputs[0],"Floor","floor"))},df=t=>{let e=Tt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,Ti(e)))},cf=(t,e)=>{let r=Tt(t.inputs[0].dataType);t.compute(Re(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))},pf=t=>{t.compute(Re(t.inputs[0],"Not",e=>`!${e}`))},hf=t=>{t.compute(Re(t.inputs[0],"Neg",e=>`-${e}`))},ff=t=>{t.compute(Re(t.inputs[0],"Reciprocal",e=>`1.0/${e}`))},mf=t=>{let e=Tt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"Relu",r=>`select(vec4<${e}>(0.0), ${r}, ${r} > vec4<${e}>(0.0))`))},gf=t=>{t.compute(Re(t.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},_f=t=>qe(t),yf=(t,e)=>{let r=Tt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${e.alpha} * ${n} + vec4<${r}>(${e.beta})))`,void 0,e.cacheKey))},wf=t=>{t.compute(Re(t.inputs[0],"Sin","sin"))},bf=t=>{t.compute(Re(t.inputs[0],"Sinh","sinh"))},vf=t=>{t.compute(Re(t.inputs[0],"Sqrt","sqrt"))},$f=t=>{t.compute(Re(t.inputs[0],"Tan","tan"))},Ps=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,xf=t=>{t.compute(Re(t.inputs[0],"Tanh",Ps))},wo=(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 ${Ps("v")};
}
`,bo=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,Sf=t=>{let e=Tt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"FastGelu",bo,wo(e),void 0,t.inputs[0].dataType))},kf=(t,e)=>{let r=Tt(t.inputs[0].dataType);return t.compute(Re(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},Ef=t=>{t.compute(Re(t.inputs[0],"Log","log"))}}),td,rd,Cf,ny=Z(()=>{Ae(),Te(),Xo(),td=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")},rd=t=>{let e=t[0].dims.slice();e[2]=e[2]/2;let r=Q("input",t[0].dataType,t[0].dims,4),n=Q("bias",t[0].dataType,[t[0].dims[2]],4),a=ge("output",t[0].dataType,e,4),i=Y.size(e)/4,s=pt(t[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)}}),getShaderSource:o=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${t[0].dims[2]/4/2}u;

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

  ${Ti(s)}

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

    ${a.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Cf=t=>{td(t.inputs),t.compute(rd(t.inputs))}}),nd,ad,Jt,Tf,If,Af,Mf,Of,zf,Pf,Rf,Bf,Df,ay=Z(()=>{$e(),Ae(),Te(),nd=(t,e,r,n,a,i,s,o,u,l,p,f)=>{let m,c;typeof o=="string"?m=c=($,C)=>`${o}((${$}),(${C}))`:typeof o=="function"?m=c=o:(m=o.scalar,c=o.vector);let y=ge("outputData",p,n.length,4),w=Q("aData",u,e.length,4),v=Q("bData",l,r.length,4),k;if(a)if(i){let $=Y.size(e)===1,C=Y.size(r)===1,T=e.length>0&&e[e.length-1]%4===0,A=r.length>0&&r[r.length-1]%4===0;$||C?k=y.setByOffset("global_idx",c($?`${w.type.value}(${w.getByOffset("0")}.x)`:w.getByOffset("global_idx"),C?`${v.type.value}(${v.getByOffset("0")}.x)`:v.getByOffset("global_idx"))):k=`
            let outputIndices = ${y.offsetToIndices("global_idx * 4u")};
            let offsetA = ${w.broadcastedIndicesToOffset("outputIndices",y)};
            let offsetB = ${v.broadcastedIndicesToOffset("outputIndices",y)};
            ${y.setByOffset("global_idx",c(s||T?w.getByOffset("offsetA / 4u"):`${w.type.value}(${w.getByOffset("offsetA / 4u")}[offsetA % 4u])`,s||A?v.getByOffset("offsetB / 4u"):`${v.type.value}(${v.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else k=y.setByOffset("global_idx",c(w.getByOffset("global_idx"),v.getByOffset("global_idx")));else{if(!i)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let $=(C,T,A="")=>{let B=`aData[indexA${T}][componentA${T}]`,R=`bData[indexB${T}][componentB${T}]`;return`
            let outputIndices${T} = ${y.offsetToIndices(`global_idx * 4u + ${T}u`)};
            let offsetA${T} = ${w.broadcastedIndicesToOffset(`outputIndices${T}`,y)};
            let offsetB${T} = ${v.broadcastedIndicesToOffset(`outputIndices${T}`,y)};
            let indexA${T} = offsetA${T} / 4u;
            let indexB${T} = offsetB${T} / 4u;
            let componentA${T} = offsetA${T} % 4u;
            let componentB${T} = offsetB${T} % 4u;
            ${C}[${T}] = ${A}(${m(B,R)});
          `};p===9?k=`
            var data = vec4<u32>(0);
            ${$("data",0,"u32")}
            ${$("data",1,"u32")}
            ${$("data",2,"u32")}
            ${$("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:k=`
            ${$("outputData[global_idx]",0)}
            ${$("outputData[global_idx]",1)}
            ${$("outputData[global_idx]",2)}
            ${$("outputData[global_idx]",3)}
          `}return`
        ${t.registerUniform("vec_size","u32").declareVariables(w,v,y)}

        ${f??""}

        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${k}
      }`},ad=(t,e,r,n,a,i,s=r.dataType)=>{let o=!Y.areEqual(r.dims,n.dims),u=r.dims,l=Y.size(r.dims),p=!1,f=!1,m=[o];if(o){let c=En.calcShape(r.dims,n.dims,!1);if(!c)throw new Error("Can't perform binary op on the given tensors");u=c,l=Y.size(u);let y=Y.size(r.dims)===1,w=Y.size(n.dims)===1,v=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,k=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;m.push(y),m.push(w),m.push(v),m.push(k);let $=1;for(let C=1;C<u.length;C++){let T=r.dims[r.dims.length-C]??1,A=n.dims[n.dims.length-C]??1;if(T===A)$*=T;else break}$%4===0?(f=!0,p=!0):(y||w||v||k)&&(p=!0)}else p=!0;return m.push(p),{name:t,shaderCache:{hint:e+m.map(c=>c.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:c=>nd(c,r.dims,n.dims,u,p,o,f,a,r.dataType,n.dataType,s,i),getRunData:()=>({outputs:[{dims:u,dataType:s}],dispatchGroup:{x:Math.ceil(l/64/4)},programUniforms:[{type:12,data:Math.ceil(Y.size(u)/4)},...ye(r.dims,n.dims,u)]})}},Jt=(t,e,r,n,a,i)=>{t.compute(ad(e,a??"",t.inputs[0],t.inputs[1],r,n,i))},Tf=t=>{Jt(t,"Add",(e,r)=>`${e}+${r}`)},If=t=>{Jt(t,"Div",(e,r)=>`${e}/${r}`)},Af=t=>{Jt(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4<u32>(${e}==${r})`},void 0,void 0,9)},Mf=t=>{Jt(t,"Mul",(e,r)=>`${e}*${r}`)},Of=t=>{let e=Q("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;Jt(t,"Pow",{scalar:(r,n)=>`pow_custom(${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));
    }
      `)},zf=t=>{Jt(t,"Sub",(e,r)=>`${e}-${r}`)},Pf=t=>{Jt(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4<u32>(${e}>${r})`},void 0,void 0,9)},Rf=t=>{Jt(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4<u32>(${e}<${r})`},void 0,void 0,9)},Bf=t=>{Jt(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4<u32>(${e}>=${r})`},void 0,void 0,9)},Df=t=>{Jt(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4<u32>(${e}<=${r})`},void 0,void 0,9)}}),en,tn,rn,Qo,an=Z(()=>{$e(),Ae(),en=(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 ${t.activation}`)}},tn=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},rn=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},Qo=t=>{let e=t?.activation||"";if(e==="HardSigmoid"){let[r,n]=t?.activation_params||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=t?.activation_params||[jo,qo];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=t?.activation_params||[.01];return{activation:e,alpha:r}}return{activation:e}}}),wt,Jo,Zo=Z(()=>{wt=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},Jo=t=>`
      ${t?"value = value + getBiasByOutputCoords(coords);":""}
      `}),eu,Nf=Z(()=>{eu=t=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${t}.x), i32(${t}.y), i32(${t}.z), 1));
}
`}),id,sd,Vi,Rs,od,Gi,ud,tu,Xi=Z(()=>{$e(),Ae(),Te(),an(),Zo(),id=(t,e)=>t?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${e?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${e?", batchIndices":""});
        `,sd=(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];"}
        }`,Vi=(t,e,r="f32",n,a=!1,i=32,s=!1,o=32)=>{let u=e[1]*t[1],l=e[0]*t[0],p=a?u:i,f=a?i:u,m=p/e[0],c=i/e[1];if(!((a&&m===4&&t[1]===4||!a&&(m===3||m===4))&&p%e[0]===0&&i%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${m} and workPerThread[1] ${t[1]} must be 4.
      Otherwise, innerElementSize ${m} must be 3 or 4.
  tileAWidth ${p} must be divisible by workgroupSize[0]${e[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${m}<${r}>, ${p/m}>, ${f}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${l/t[0]}>, ${i}>;

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

@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 = ${s?"0":"i32(globalId.z)"};
  ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
  let globalRowStart = i32(workgroupId.y) * ${u};

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

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

  // Loop over shared dimension.
  let tileRowB = localRow * ${c};
  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;
          ${id(a,n)}
      }

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

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

          ${sd(a,m)}
      }

      workgroupBarrier();
  }

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

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

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

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

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

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${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) {
      ${od(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}>, ${m}>;
  var<workgroup> mm_Bsub : array<array<${r}, ${p}>, ${i}>;
  const rowPerThread = ${t[1]};
  const colPerThread = ${t[0]};
  const tileInner = ${i};

@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 = ${s?"0":"i32(globalId.z)"};
    ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
    let num_tiles = ${s?`${Math.ceil(o/i)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
    var kStart = ${s?`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;
      }
    }
    ${v}
  }
`},ud=(t,e,r,n,a,i=!1)=>{let[s,o,u]=a,[l,p,f,m]=n,c=_a(s,u),y=_a(o,u),w=pt(n[0].type.tensor),v=()=>{let $=p.rank,C=l.rank,T=`var aIndices: ${p.type.indices};`;for(let A=$-2-1,B=C-1;A>=0;A--,B--)T+=`
aIndices[${A}] = ${C>1?`batchIndices[${B}]`:"batchIndices"};`;return c.forEach(A=>{T+=`
aIndices[${A}] = 0;`}),T+=`
aIndices[${$-2}] = u32(row);
                   aIndices[${$-1}] = u32(colIn);`,T},k=()=>{let $=f.rank,C=l.rank,T=`var bIndices: ${f.type.indices};`;for(let A=$-2-1,B=C-1;A>=0;A--,B--)T+=`
bIndices[${A}] = ${C>1?`batchIndices[${B}]`:"batchIndices"};`;return y.forEach(A=>{T+=`
bIndices[${A}] = 0;`}),T+=`
bIndices[${$-2}] = u32(row);
                   bIndices[${$-1}] = u32(colIn);`,T};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${l.type.indices}) -> ${wt(t,w)} {
      var value = ${wt(t,w)}(0.0);
      let col = colIn * ${t};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${v()}
        value = ${p.getByIndices("aIndices")};
      }
      return value;
    }

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${wt(t,w)}) {
      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 + ${i?"bias[colIn]":`${wt(t,w)}(bias[row])`};`:""}
        ${r}
        ${m.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},tu=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i.slice(0,-2),u=s.slice(0,-2),l=n?n.slice(0,-2):r.slice(0,-2),p=Y.size(l),f=i[i.length-2],m=i[i.length-1],c=s[s.length-1],y=m%4===0&&c%4===0,w=f<=8?[4,1,1]:[4,4,1],v=[8,8,1],k=[Math.ceil(c/v[0]/w[0]),Math.ceil(f/v[1]/w[1]),Math.ceil(p/v[2]/w[2])],$=y?4:1,C=[...o,f,m/$],T=C.length,A=[...u,m,c/$],B=A.length,R=[p,f,c/$],D=[{type:6,data:f},{type:6,data:c},{type:6,data:m}];tn(e,D),D.push(...ye(l,C,A));let K=["rank","rank"],j=t.length>2;j&&(D.push(...ye(t[2].dims)),K.push("rank")),D.push(...ye(R));let ie=te=>{let oe=l.length,re=Ko("batchDims",t[0].dataType,oe,1),M=pt(t[0].dataType),P=Q("a",t[0].dataType,T,$),H=Q("b",t[1].dataType,B,$),le=ge("result",t[0].dataType,R.length,$),G=[P,H];if(j){let Ce=a?$:1;G.push(Q("bias",t[2].dataType,t[2].dims.length,Ce))}let ne=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];rn(e,ne);let N=pt(le.type.tensor),ae=en(e,le.type.value,N),fe=ud($,j,ae,[re,P,H,le],[o,u,l],a);return`
  ${te.registerUniforms(ne).registerInternalVariables(re).declareVariables(...G,le)}
  ${fe}
  ${y?Vi(w,v,M,re):Gi(w,v,M,re)}
                   `};return{name:"MatMul",shaderCache:{hint:`${w};${e.activation};${y};${a}`,inputDependencies:K},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:k[0],y:k[1],z:k[2]},programUniforms:D}),getShaderSource:ie}}}),ld,Ff,iy=Z(()=>{$e(),nn(),Te(),an(),Zo(),Nf(),Xi(),ld=(t,e,r,n,a=!1,i,s=4,o=4,u=4,l="f32")=>{let p=K=>{switch(K){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${l}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${K} is not supported.`)}},f=K=>{switch(K){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 ${K} is not supported.`)}},m=t?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,c=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,y=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",v=t?"row":"col",k=t?"col":"row",$=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${v} / outWidth;
    let outCol = ${v} % outWidth;

    let WRow = ${k} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${k} / 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 = ${k} % inChannels;
    var resData = ${wt(s,l)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${y} && xCol >= 0 && xCol < ${w}) {
      ${m}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${p(s)}
    }
    return resData;`,C=t?e&&n?`
    let col = colIn * ${s};
    ${$}`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${$}
    }
    return ${wt(s,l)}(0.0);`:n&&r?`
    let col = colIn * ${s};
    ${$}`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${$}
    }
    return ${wt(s,l)}(0.0);`,T=`${f(o)}`,A=wt(u,l),B=wt(t?s:o,l),R=wt(t?o:s,l),D=en(i,A,l);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${B} {
      ${t?C:T}
    }

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${A}) {
      let col = colIn * ${u};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${c}
      ${Jo(a)}
      ${D}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Ff=(t,e,r,n,a,i,s,o)=>{let u=e.format==="NHWC",l=u?t[0].dims[3]:t[0].dims[1],p=r[0],f=u?r[2]:r[3],m=u?r[1]:r[2],c=u?r[3]:r[1],y=u&&(l%4===0||l%3===0)&&c%4===0,w=u?c:f*m,v=u?f*m:c,k=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],C=[Math.ceil(w/k[0]/$[0]),Math.ceil(v/k[1]/$[1]),Math.ceil(p/k[2]/$[2])];rt("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${C}`);let T=y?u&&l%4!==0?3:4:1,A=k[1]*$[1],B=k[0]*$[0],R=Math.max(k[0]*T,k[1]),D=n%A===0,K=a%B===0,j=i%R===0,ie=y?[T,4,4]:[1,1,1],te=[{type:6,data:n},{type:6,data:a},{type:6,data:i},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];tn(e,te),te.push(...ye(t[0].dims,t[1].dims));let oe=["rank","rank"];s&&(te.push(...ye(t[2].dims)),oe.push("rank")),te.push(...ye(r));let re=M=>{let P=[{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}];rn(e,P);let H=y?4:1,le=pt(t[0].dataType),G=`
      fn setOutputAtIndex(flatIndex : i32, value : ${y?`vec4<${le}>`:le}) {
        result[flatIndex] = ${y?`vec4<${le}>`:le}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${y?`vec4<${le}>`:le}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${y?"/ 4":""}, value);
      }`,ne=Q("x",t[0].dataType,t[0].dims.length,T===3?1:T),N=Q("w",t[1].dataType,t[1].dims.length,H),ae=[ne,N],fe=ge("result",t[0].dataType,r.length,H);if(s){let Ce=Q("bias",t[2].dataType,t[2].dims.length,H);ae.push(Ce),G+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${y?`vec4<${le}>`:le} {
          return bias[coords.${u?"w":"y"}${y?"/ 4":""}];
        }`}return`
        ${eu("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 };
        ${M.registerUniforms(P).declareVariables(...ae,fe)}
        ${G}
        ${ld(u,D,K,j,s,e,ie[0],ie[1],ie[2],le)}
        ${y?Vi($,k,le,void 0,!u,R):Gi($,k,le,void 0,!u,R,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${T};${y};${D};${K};${j};${A};${B};${R}`,inputDependencies:oe},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:te}),getShaderSource:re}}}),vo,Lf,sy=Z(()=>{$e(),Ae(),Te(),Vf(),an(),vo=(t,e,r)=>{let n=t.length>2,a=n?"value += b[output_channel];":"",i=t[0].dims,s=t[1].dims,o=s[0]/e.group,u=e.format==="NHWC",l=Ii(i,s,e.dilations,e.pads,e.strides,u),p=Y.size(l),f=[{type:12,data:p},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];tn(e,f),f.push(...ye(i,s));let m=["rank","rank"];n&&(f.push(...ye(t[2].dims)),m.push("rank")),f.push(...ye(l));let c=y=>{let w=ge("output",t[0].dataType,l.length),v=pt(w.type.tensor),k=en(e,w.type.value,v),$=Q("x",t[0].dataType,i.length),C=Q("w",t[1].dataType,s.length),T=[$,C];n&&T.push(Q("b",t[2].dataType,t[2].dims.length));let A=[{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 rn(e,A),`
  ${y.registerUniforms(A).declareVariables(...T,w)}

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

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

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

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

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

          let xVal = ${u?$.get("batch","xHeight","xWidth","input_channel"):$.get("batch","input_channel","xHeight","xWidth")};
          let wVal = ${C.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${a}
    ${k}
    ${w.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:m},getRunData:()=>({outputs:[{dims:r?r(l):l,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:f}),getShaderSource:c}},Lf=(t,e,r)=>{let n=t.length>2,a=it(r[3]),i=it(r[2]),s=Y.size(r)/a/i,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],u=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],l=[r[0],r[1],r[2],r[3]/a],p=[{type:12,data:s},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];tn(e,p),p.push(...ye(o,u,l));let f=(i-1)*e.strides[1]+u[1],m=c=>{let y=ge("output",t[0].dataType,l.length,a),w=pt(y.type.tensor),v=en(e,y.type.value,w),k=Q("x",t[0].dataType,o.length,a),$=Q("w",t[1].dataType,u.length,a),C=[k,$];n&&C.push(Q("b",t[2].dataType,t[2].dims,a));let T=n?"value += b[output_channel];":"",A=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return rn(e,A),`
  ${c.registerUniforms(A).declareVariables(...C,y)}
  ${c.mainStart()}
    ${c.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] / ${i}u;
    let col = (index1 % width1) * ${i}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<${k.type.value}, ${f}>;
    var values: array<${y.type.value}, ${i}>;
    let input_channel = output_channel;
    // Use constant instead of uniform can give better performance for w's height/width.
    for (var w_height: u32 = 0u; w_height < ${u[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${f}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${k.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${k.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${u[1]}; w_width++) {
          let w_val = ${$.get("w_height","w_width","0","output_channel")};
          for (var i = 0u; i < ${i}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${i}u; i++) {
      var value = values[i];
      ${T}
      ${v}
      ${y.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${i};${f};${u[0]};${u[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:p}),getShaderSource:m}}}),$o,dd,Uf,Wf=Z(()=>{$e(),Ae(),Xi(),Te(),an(),$o=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i[i.length-2],u=s[s.length-1],l=i[i.length-1],p=it(u),f=it(l),m=it(o),c=Y.size(r)/p/m,y=t.length>2,w=n?n.slice(0,-2):r.slice(0,-2),v=[Y.size(w),o,u],k=[{type:12,data:c},{type:12,data:o},{type:12,data:u},{type:12,data:l}];tn(e,k),k.push(...ye(w,i,s)),y&&k.push(...ye(t[2].dims)),k.push(...ye(v));let $=C=>{let T=Ko("batch_dims",t[0].dataType,w.length),A=Q("a",t[0].dataType,i.length,f),B=Q("b",t[1].dataType,s.length,p),R=ge("output",t[0].dataType,v.length,p),D=pt(R.type.tensor),K=en(e,R.type.value,D),j=[A,B],ie="";if(y){let G=a?p:1;j.push(Q("bias",t[2].dataType,t[2].dims.length,G)),ie=`${a?`value += bias[col / ${G}];`:`value += ${R.type.value}(bias[row + i]);`}`}let te=i.slice(0,-2),oe=s.slice(0,-2),re=_a(te,w),M=_a(oe,w),P=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];rn(e,P);let H=(G,ne)=>{let N=G.rank,ae=G.name;if(N===2)return`var ${ae}_indices = ${G.type.indices}(0u, 0u);`;let fe=T.rank,Ce=`var ${ae}_indices: ${G.type.indices};`;for(let Be=N-2-1,Ke=fe-1;Be>=0;Be--,Ke--)Ce+=`
${ae}_indices[${Be}] = ${fe>1?`batch_indices[${Ke}]`:"batch_indices"};`;return ne.forEach(Be=>{Ce+=`
${ae}_indices[${Be}] = 0;`}),Ce+=`${ae}_indices[${N-2}] = 0u;
                     ${ae}_indices[${N-1}] = 0u;`,Ce},le=()=>{let G=`var a_data: ${A.type.value};`;for(let ne=0;ne<f;ne++)G+=`
              let b_data${ne} = b[(b_offset + (k + ${ne}) * uniforms.N + col) / ${p}];`;for(let ne=0;ne<m;ne++){G+=`a_data = a[(a_offset + (row + ${ne}) * uniforms.K + k) / ${f}];`;for(let N=0;N<f;N++)G+=`
            values[${ne}] = fma(${B.type.value}(a_data${f===1?"":`[${N}]`}), b_data${N}, values[${ne}]);
`}return G};return`
  ${C.registerUniforms(P).registerInternalVariables(T).declareVariables(...j,R)}
  ${C.mainStart()}
    ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${p})) * ${p};
    var index1 = global_idx / (uniforms.N / ${p});
    let stride1 = uniforms.M / ${m};
    let row = (index1 % stride1) * ${m};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${T.offsetToIndices("batch")};`}
    ${H(A,re)}
    let a_offset = ${A.indicesToOffset("a_indices")};
    ${H(B,M)}
    let b_offset = ${B.indicesToOffset("b_indices")};
    var values: array<${R.type.value}, ${m}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${f}) {
      ${le()}
    }
    for (var i = 0u; i < ${m}u; i++) {
      var value = values[i];
      ${ie}
      ${K}
      let cur_indices = ${R.type.indices}(batch, row + i, col);
      let offset = ${R.indicesToOffset("cur_indices")};
      ${R.setByOffset(`offset / ${p}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${p};${f};${m};${a}`,inputDependencies:y?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:k}),getShaderSource:$}},dd=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.")},Uf=t=>{dd(t.inputs);let e=En.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!e)throw new Error("Can't use matmul on the given tensors");let r=e[e.length-1],n=t.inputs[0].dims[t.inputs[0].dims.length-1];r<8&&n<8?t.compute($o(t.inputs,{activation:""},e)):t.compute(tu(t.inputs,{activation:""},e))}}),Ii,gi,cd,Bs,xo,pd,hd,So,Vf=Z(()=>{Ae(),iy(),Xi(),sy(),an(),Wf(),Sa(),Ii=(t,e,r,n,a,i)=>{let s=t[0],o=t.slice(i?1:2,i?3:4),u=o.length,l=e[0],p=e.slice(2).map((m,c)=>m+(m-1)*(r[c]-1)),f=o.map((m,c)=>m+n[c]+n[c+u]).map((m,c)=>Math.floor((m-p[c]+a[c])/a[c]));return f.splice(0,0,s),f.splice(i?3:1,0,l),f},gi=[2,3,1,0],cd=(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 ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},Bs=(t,e)=>{let r=t.kernelShape.slice();for(let i=2;i<e[1].dims.length;++i)r[i-2]===0&&(r[i-2]=e[1].dims[i]);let n=t.pads.slice();Fi.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,r,n,t.format==="NHWC",t.autoPad);let a=Object.assign({},t);return Object.assign(a,{kernelShape:r,pads:n}),a},xo=t=>{let e=Qo(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,i=t.group,s=t.kernel_shape,o=t.pads,u=t.strides,l=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:i,kernelShape:s,pads:o,strides:u,wIsConst:l,...e,cacheKey:`${t.format};${e.activation};`}},pd=(t,e,r)=>{let n=Bs(r,e),a=r.format==="NHWC";if(r.group!==1){if(!t.adapterInfo.isArchitecture("ampere")&&a&&e[1].dims[0]===r.group&&e[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let B=Ii(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),R=t.kernelCustomData.wT??t.compute(vr(e[1],gi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=R);let D=[e[0],R];e.length===3&&D.push(e[2]),t.compute(Lf(D,n,B),{inputs:D})}else t.compute(vo(e,n));return}let i=e.length===3,s=e[0].dims[a?1:2],o=e[0].dims[a?2:3],u=e[0].dims[a?3:1],l=e[1].dims[2],p=e[1].dims[3],f=Ii(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),m=f[a?1:2],c=f[a?2:3],y=f[a?3:1],w=a&&l===s&&p===o&&r.pads[0]===0&&r.pads[1]===0;if(w||l===1&&p===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let B=f[0],R,D,K,j=[];if(a){let oe=t.kernelCustomData.wT??t.compute(vr(e[1],gi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=oe),w){let re=s*o*u;R=e[0].reshape([1,B,re]),D=oe.reshape([1,re,y]),K=[1,B,y]}else R=e[0].reshape([B,s*o,u]),D=oe.reshape([1,u,y]),K=[B,m*c,y];j.push(R),j.push(D)}else R=e[0].reshape([B,u,s*o]),D=e[1].reshape([1,y,u]),K=[B,y,m*c],j.push(D),j.push(R);i&&j.push(e[2]);let ie=K[2],te=j[0].dims[j[0].dims.length-1];ie<8&&te<8?t.compute($o(j,n,f,K,a),{inputs:j}):t.compute(tu(j,n,f,K,a),{inputs:j});return}let v=!0,k=t.kernelCustomData.wT??t.compute(vr(e[1],gi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=k);let $=[e[0],k];i&&$.push(e[2]);let C=a?m*c:y,T=a?y:m*c,A=l*p*u;t.compute(Ff($,n,f,C,T,A,i,v),{inputs:$})},hd=(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]],i=[1].concat(e.strides),s=[1].concat(e.dilations),o=[1].concat(e.kernelShape),u=Bs({...e,pads:a,strides:i,dilations:s,kernelShape:o},n);t.compute(vo(n,u,l=>r?[l[0],l[2],l[3]]:[]))},So=(t,e)=>{cd(t.inputs,e),t.inputs[0].dims.length===3?hd(t,e):pd(t,t.inputs,e)}}),fd,Gf,oy=Z(()=>{$e(),nn(),Te(),an(),Zo(),Nf(),Xi(),fd=(t,e=!1,r,n,a=4)=>{let i=v=>{switch(v){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 ${v} is not supported.`)}},s=t?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,o=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,u=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",l=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",p=t?"row":"col",f=t?"col":"row",m=`
      let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${p} / outWidth;
      let outCol = ${p} % outWidth;

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

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

  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}
      ${Jo(e)}
      ${w}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${a}] = value;
    }
  }`},Gf=(t,e,r,n,a,i,s,o)=>{let u=e.format==="NHWC",l=u?t[0].dims[3]:t[0].dims[1],p=r[0],f=u?r[2]:r[3],m=u?r[1]:r[2],c=u?r[3]:r[1],y=u&&l%4===0&&l%3&&c%4===0,w=u?c:f*m,v=u?f*m:c,k=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],C=[Math.ceil(w/k[0]/$[0]),Math.ceil(v/k[1]/$[1]),Math.ceil(p/k[2]/$[2])];rt("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${C}`);let T=y?4:1,A=Math.max(k[0]*T,k[1]),B=y?4:1,R=[e.kernelShape[u?1:2],e.kernelShape[u?2:3]],D=[R[0]+(e.dilations[0]<=1?0:(R[0]-1)*(e.dilations[0]-1)),R[1]+(e.dilations[1]<=1?0:(R[1]-1)*(e.dilations[1]-1))],K=[D[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),D[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],j=[{type:6,data:n},{type:6,data:a},{type:6,data:i},{type:6,data:e.strides},{type:6,data:e.dilations},{type:6,data:R},{type:6,data:K}];tn(e,j),j.push(...ye(t[0].dims,t[1].dims));let ie=["rank","rank"];s&&(j.push(...ye(t[2].dims)),ie.push("rank")),j.push(...ye(r));let te=oe=>{let re=Q("x",t[0].dataType,t[0].dims.length,B),M=Q("w",t[1].dataType,t[1].dims.length,1),P=ge("result",t[0].dataType,r.length,B),H=[re,M],le="";if(s){let N=Q("bias",t[2].dataType,t[2].dims.length,B);H.push(N),le+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${N.type.value} {
            return bias[coords.${u?"w":"y"}${y?"/ 4":""}];
          }`}let G=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:R.length},{name:"pads",type:"i32",length:K.length}];rn(e,G);let ne=pt(t[0].dataType,1);if(ne!=="f16"&&ne!=="f32")throw new Error(`elemType ${ne} is not supported.`);return`
        ${eu("uniforms.result_strides")}
        ${oe.registerUniforms(G).declareVariables(...H,P)};
        ${le}
        ${fd(u,s,e,re.type.value,T)}
        ${y?Vi($,k,ne,void 0,!u,A):Gi($,k,ne,void 0,!u,A,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${$};${k};${y}`,inputDependencies:ie},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:j}),getShaderSource:te}}}),md,ko,uy=Z(()=>{$e(),nn(),Ae(),Te(),md=(t,e,r,n,a,i=!1,s,o,u=!1)=>{let l=u?1:2,p=u?2:3,f=u?3:1,m=i?2:1,c=`
  fn setOutputAtIndex(flatIndex : u32, value : ${i?`vec4<${s}>`:s}) {
    result[flatIndex] = ${i?`vec4<${s}>`:s}(value);
  }`;n&&(c+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${i?`vec4<${s}>`:s} {
      return bias[coords.${u?"w":"y"}${i?"/ 4":""}];
    }`);let y=i?4:1,w=Q("W",e[1].dataType,e[1].dims.length,y),v=Q("Dy",e[0].dataType,e[0].dims.length,y),k=[v,w];n&&k.push(Q("bias",e[2].dataType,[r[f]].length,y));let $=ge("result",e[0].dataType,r.length,y),C=`{
        let batch: u32 = ${a?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];
        let r = ${a?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];
        let c = ${a?"global_id.y":"workgroup_id.y"} * ${m};
        let d1: u32 = ${a?"global_id.x":"workgroup_id.x"} * 4;

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

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        var dotProd: array<vec4<${s}>, ${m}>;
        for (var i = 0; i < ${m}; i++) {
          dotProd[i] = vec4<${s}>(0.0);
        }
        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {
          var dyR = (${s}(dyCorner.x) + ${s}(wR)) / ${s}(uniforms.strides.x);
          let wRPerm = uniforms.filter_dims[0] - 1 - wR;
          if (dyR < 0.0 || dyR >= ${s}(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 = (${s}(dyCorner.y) + ${s}(wC)) / ${s}(uniforms.strides.y);
            let dyC2 = (${s}(dyCorner.y) + 1.0 + ${s}(wC)) / ${s}(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 >= ${s}(uniforms.Dy_shape[2]) ||
                fract(dyC) > 0.0) {
              bDyCVal = false;
            }
            if (dyC2 < 0.0 || dyC2 >= ${s}(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 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

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

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

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

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

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

        for (var i: u32 = 0; i < ${m}; i = i + 1) {
          let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${s}>(0.0)`};
          ${$.set("batch","r","c + i","d1","value")};
        }
      }`,T=`
          let outputIndices = ${$.offsetToIndices("global_idx")};
          let batch = ${$.indicesGet("outputIndices",0)};
          let d1 = ${$.indicesGet("outputIndices",f)};
          let r = ${$.indicesGet("outputIndices",l)};
          let c = ${$.indicesGet("outputIndices",p)};
          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 = ${s}(0.0);
          for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
            if (wR % uniforms.dilations.x != 0) {
              continue;
            }
            let dyR = (${s}(dyRCorner) + ${s}(wR)) / ${s}(uniforms.strides[0]);
            let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
            if (dyR < 0.0 || dyR >= ${s}(uniforms.Dy_shape[${l}]) || fract(dyR) > 0.0 ||
                wRPerm < 0) {
              continue;
            }
            let idyR: u32 = u32(dyR);

            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
              if (wC % uniforms.dilations.y != 0) {
                continue;
              }
              let dyC = (${s}(dyCCorner) + ${s}(wC)) / ${s}(uniforms.strides.y);
              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
              if (dyC < 0.0 || dyC >= ${s}(uniforms.Dy_shape[${p}]) ||
                  fract(dyC) > 0.0 || wCPerm < 0) {
                continue;
              }
              let idyC: u32 = u32(dyC);
              var inputChannel = groupId * uniforms.input_channels_per_group;
              for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {
                let xValue = ${u?v.get("batch","idyR","idyC","inputChannel"):v.get("batch","inputChannel","idyR","idyC")};
                let wValue = ${w.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};
                dotProd = dotProd + xValue * wValue;
                inputChannel = inputChannel + 1;
              }
            }
          }
          let value = dotProd + ${n?"bias[d1]":`${s}(0.0)`};
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        `;return`
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  ${c}

    ${t.mainStart()}
    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
  ${i?C:T}}`},ko=(t,e,r)=>{let n=t.length>2,a=e.outputShape,i=Y.size(a),s=[Math.ceil(i/64),1,1];rt("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${s}`);let o=e.format==="NHWC",u=["rank","rank"],l=[e.strides[0],e.strides[1]],p=[e.kernelShape[o?1:2],e.kernelShape[o?2:3]],f=[e.dilations[0],e.dilations[1]],m=[p[0]+(e.dilations[0]<=1?0:(e.kernelShape[o?1:2]-1)*(e.dilations[0]-1)),p[1]+(e.dilations[1]<=1?0:(e.kernelShape[o?2:3]-1)*(e.dilations[1]-1))],c=[m[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),m[1]-1-Math.floor(e.pads[1]+e.pads[3])/2],y=!1,w=e.group,v=t[1].dims,k=v[0]/w,$=v[1],C=[{type:12,data:i},{type:12,data:l},{type:12,data:p},{type:12,data:f},{type:12,data:m},{type:6,data:c},{type:12,data:k},{type:12,data:$},...ye(t[0].dims,t[1].dims)];n&&(C.push(...ye(t[2].dims)),u.push("rank")),C.push(...ye(a));let T=s[1]===1&&s[2]===1,A=B=>{let 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k=[e[0],v],$=e.length===3;$&&(!a&&e[2].dims.length===1?k.push(e[2].reshape([e[2].dims[0],1,1])):k.push(e[2])),t.compute(Gf(k,n,i,m,c,y,$,w),{inputs:k})},$d=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&n.push(t.inputs[2]);let a=e.kernelShape;(a.length===0||a[0]===0)&&(a=[t.inputs[1].dims[2]]);let i=e.dilations;(i.length===0||i[0]===0)&&(i=[1]);let s=e.strides;(s.length===0||s[0]===0)&&(s=[1]);let o=e.pads;o.length===0&&(o=[0,0]),o=[0,o[0],0,o[1]],s=[1].concat(s),i=[1].concat(i),a=[1].concat(a);let u=Ds({...e,pads:o,strides:s,dilations:i,kernelShape:a},n);t.compute(ko(n,u,l=>r?[l[0],l[2],l[3]]:[l[0],l[1],l[3]]))},jf=(t,e)=>{wd(t.inputs,e),t.inputs[0].dims.length===3?$d(t,e):vd(t,t.inputs,e)}}),xd,qf,Kf,dy=Z(()=>{$e(),Ae(),lt(),Te(),xd=(t,e,r,n)=>{let a=Y.size(e),i=e.length,s=Q("input",t,i),o=ge("output",t,i),u=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),l=Y.normalizeAxis(u,i),p=f=>{let m=` i32(${s.indicesGet("inputIndices","uniforms.axis")}) `,c=Se("uniforms.input_shape","uniforms.axis",i),y=n.reverse?m+(n.exclusive?" + 1":""):"0",w=n.reverse?c:m+(n.exclusive?"":" + 1");return`
                ${f.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(s,o)}
                ${f.mainStart()}
                  ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
                  var inputIndices = ${o.offsetToIndices("global_idx")};
                  var sum = ${o.type.value}(0);
                  let first : i32 = ${y};
                  let last : i32 = ${w};
                  for (var i : i32 = first; i < last; i++) {
                    ${s.indicesSet("inputIndices","uniforms.axis","u32(i)")};
                    sum = sum + ${s.getByIndices("inputIndices")};
                  }
                  ${o.setByOffset("global_idx","sum")};
                }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:e,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},{type:12,data:l},...ye(e,e)]}),getShaderSource:p}},qf=(t,e)=>{let r=t.inputs[0].dims,n=t.inputs[0].dataType,a=t.inputs[1];t.compute(xd(n,r,a,e),{inputs:[0]})},Kf=t=>{let e=t.exclusive===1,r=t.reverse===1;return qe({exclusive:e,reverse:r})}}),Sd,kd,Ed,Yf,Xf,cy=Z(()=>{$e(),Ae(),lt(),Te(),Sd=t=>{if(!t||t.length!==1)throw new Error("DepthToSpace requires 1 input.");if(t[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},kd=(t,e,r,n)=>{let a=[];a.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
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`)},Ed=(t,e)=>{let r,n,a,i,s,o,u=e.format==="NHWC",l=e.blocksize,p=e.mode==="DCR";u?([r,n,a,i]=t.dims,s=p?[r,n,a,l,l,i/l**2]:[r,n,a,i/l**2,l,l],o=p?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([r,n,a,i]=[t.dims[0],t.dims[2],t.dims[3],t.dims[1]],s=p?[r,l,l,i/l**2,n,a]:[r,i/l**2,l,l,n,a],o=p?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let f=t.reshape(s),m=f.dims.length,c=t.dataType,y=Q("a",c,m),w=ge("output",c,m),v=k=>`
  ${k.registerUniform("output_size","u32").declareVariables(y,w)}

  ${kd(o,m,y,w)}

  ${k.mainStart()}
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    let indices = ${w.offsetToIndices("global_idx")};
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    ${w.setByOffset("global_idx",y.getByIndices("aIndices"))}
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            ${l.mainStart()}
            ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
            var outputIndices = ${s.offsetToIndices("global_idx")};
            ${a.map((T,A)=>`var input${A}Indices: ${a[A].type.indices};`).join(`
`)}
            ${C.join(`
`)};
            ${s.setByOffset("global_idx","sum")};
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          let outputIndices${w} = ${f.offsetToIndices(`outputOffset + ${w}u`)};
          let offset${w} = ${p.broadcastedIndicesToOffset(`outputIndices${w}`,f)};
          let index${w} = offset${w} / 4u;
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        let outputOffset = global_idx * ${i};
        var data = vec4<u32>(0);
        ${c("data",0,"u32")}
        ${c("data",1,"u32")}
        ${c("data",2,"u32")}
        ${c("data",3,"u32")}
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        let inputOffset = ${p.broadcastedIndicesToOffset("outputIndices",f)};
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    ${l.registerUniform("vec_size","u32").declareVariables(p,f)}
    ${l.mainStart()}
    ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
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      let x = ${o.getByOffset("global_idx")};
      ${m}
      let x_in = x + bias;
      ${l.setByOffset("global_idx",bo("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getShaderSource:i,getRunData:s=>({outputs:[{dims:s[0].dims,dataType:s[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/Cn/4)}})}},em=t=>{t.inputs.length<2||Y.size(t.inputs[1].dims)===0?Sf(t):t.compute(Rd(t.inputs))}}),Bd,Dd,tm,rm,my=Z(()=>{$e(),Ae(),lt(),Te(),Bd=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},Dd=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,i=Y.normalizeAxis(e.axis,a),s=r.slice(0);s.splice(i,1,...n);let o=r[i],u=t[0].dataType===9?4:1,l=Math.ceil(Y.size(s)/u),p=[{type:12,data:l},{type:6,data:o},{type:12,data:i},...ye(t[0].dims,t[1].dims,s)],f=m=>{let c=Q("data",t[0].dataType,t[0].dims.length,u),y=Q("inputIndices",t[1].dataType,t[1].dims.length),w=ge("output",t[0].dataType,s.length,u),v=$=>{let C=n.length,T=`var indicesIndices${$}  = ${y.type.indices}(0);`;for(let A=0;A<C;A++)T+=`${C>1?`indicesIndices${$}[${A}]`:`indicesIndices${$}`} = ${s.length>1?`outputIndices${$}[uniforms.axis + ${A}]`:`outputIndices${$}`};`;T+=`
          var idx${$} = ${y.getByIndices(`indicesIndices${$}`)};
          if (idx${$} < 0) {
            idx${$} = idx${$} + uniforms.axisDimLimit;
          }
          var dataIndices${$} : ${c.type.indices};
        `;for(let A=0,B=0;A<a;A++)A===i?(T+=`${a>1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = u32(idx${$});`,B+=C):(T+=`${a>1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = ${s.length>1?`outputIndices${$}[${B}]`:`outputIndices${$}`};`,B++);return T},k;if(t[0].dataType===9){let $=(C,T,A="")=>`
          let outputIndices${T} = ${w.offsetToIndices(`outputOffset + ${T}u`)};
          ${v(T)};
          let offset${T} = ${c.indicesToOffset(`dataIndices${T}`)};
          let index${T} = offset${T} / 4u;
          let component${T} = offset${T} % 4u;
          ${C}[${T}] = ${A}(${c.getByOffset(`index${T}`)}[component${T}]);
        `;k=`
        let outputOffset = global_idx * ${u};
        var value = vec4<u32>(0);
        ${$("value",0,"u32")}
        ${$("value",1,"u32")}
        ${$("value",2,"u32")}
        ${$("value",3,"u32")}
        ${w.setByOffset("global_idx","value")}
      `}else k=`
      let outputIndices = ${w.offsetToIndices("global_idx")};
      ${v("")};
      let value = ${c.getByIndices("dataIndices")};
      ${w.setByOffset("global_idx","value")};
      `;return`
      ${m.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(c,y,w)}
      ${m.mainStart()}
        ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${k}
      }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:p}),getShaderSource:f}},tm=t=>qe({axis:t.axis}),rm=(t,e)=>{let r=t.inputs;Bd(r),t.compute(Dd(t.inputs,e))}}),Nd,Fd,nm,am,gy=Z(()=>{$e(),Ae(),lt(),Te(),Nd=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.`)},Fd=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,i=t[1].dims,s=t[1].dataType,o=Y.normalizeAxis(e.axis,a),u=r[o],l=i.slice(0),p=Y.size(l),f=Q("input",n,a),m=Q("indicesInput",s,i.length),c=ge("output",n,l.length),y=[{type:12,data:p},{type:6,data:u},{type:12,data:o}];return y.push(...ye(r,i,l)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:l,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:y}),getShaderSource:w=>`
      ${w.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(f,m,c)}
      ${w.mainStart()}
      ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

      var idx = ${m.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")};

      ${c.setByOffset("global_idx","value")};
  }`}},nm=t=>qe({axis:t.axis}),am=(t,e)=>{let r=t.inputs;Nd(r),t.compute(Fd(t.inputs,e))}}),Ld,Ud,im,sm,_y=Z(()=>{$e(),Ae(),Te(),Ld=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")},Ud=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,i,s]=hh.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[a,i];if(!o)throw new Error("Can't use gemm on the given tensors");let u=Y.size(o),l=[{type:12,data:u},{type:12,data:a},{type:12,data:i},{type:12,data:s},{type:1,data:e.alpha},{type:1,data:e.beta}],p=["type","type"];t.length===3&&(l.push(...ye(t[2].dims)),p.push("rank")),l.push(...ye(o));let f=m=>{let c="";e.transA&&e.transB?c="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?c="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?c="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(c="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let y=e.alpha===1?"":"value *= uniforms.alpha;",w=Q("a",t[0].dataType,t[0].dims),v=Q("b",t[1].dataType,t[1].dims),k=w.type.value,$=null,C=[w,v];t.length===3&&($=Q("c",t[2].dataType,t[2].dims.length),C.push($));let T=ge("output",t[0].dataType,o.length);C.push(T);let A=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`
  ${m.registerUniforms(A).declareVariables(...C)}

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

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

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

    ${y}
    ${$!=null?`let cOffset = ${$.broadcastedIndicesToOffset("vec2(m, n)",T)}; value += ${k}(uniforms.beta) * ${$.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:f}},im=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},sm=(t,e)=>{Ld(t.inputs),t.compute(Ud(t.inputs,e))}}),Wd,Vd,Gd,om,yy=Z(()=>{$e(),Ae(),Te(),Wd=(t,e)=>{let r=t[0].dims,n=r,a=2,i=Y.sizeToDimension(r,a),s=Y.sizeFromDimension(r,a),o=it(s),u=s/o,l=[r[0],r[1],u],p=["rank","type","type"],f=[{type:12,data:s},{type:12,data:u}];f.push(...ye(l,l));let m=c=>{let y=Q("x",t[0].dataType,l.length,o),w=Q("scale",t[1].dataType,t[1].dims),v=Q("bias",t[2].dataType,t[2].dims),k=ge("output",t[0].dataType,l.length,o),$=[y,w,v,k],C=y.type.value,T=o===1?"f32":`vec${o}<f32>`,A=64,B=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`
  var<workgroup> meanShared : f32;
  var<workgroup> squaredNormShared : f32;
  var<workgroup> workgroupShared : array<${T}, ${A}>;
  const workgroupSize = ${A}u;
  ${c.registerUniforms(B).declareVariables(...$)}
  ${c.mainStart(A)}
    let norm = global_idx / workgroupSize;
    let batch = norm / uniforms.x_shape[1];
    let channel = norm % uniforms.x_shape[1];
    let localIndex = local_id.x;

    // initialize workgroup memory
    var initial = ${T}(0);
    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
      initial = initial + ${T}(${y.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 = ${Or("workgroupShared[0]",o)} / f32(uniforms.normSize);
    }
    workgroupBarrier();

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

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

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

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

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

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

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

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

  ${y.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], ${k}(scale[0]), ${k}(scale[1]));
  }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${u}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:p}),getShaderSource:c},{inputs:[e[0],m]})},om=(t,e)=>{e.format==="NHWC"?Gd(t,t.inputs,e):t.compute(Wd(t.inputs,e))}}),Hd,jd,um,wy=Z(()=>{$e(),Ae(),Te(),Hd=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},jd=(t,e,r)=>{let n=e.simplified,a=t[0].dims,i=t[1],s=!n&&t[2],o=a,u=Y.normalizeAxis(e.axis,a.length),l=Y.sizeToDimension(a,u),p=Y.sizeFromDimension(a,u),f=Y.size(i.dims),m=s?Y.size(s.dims):0;if(f!==p||s&&m!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${f} and bias size of ${m}`);let c=[];for(let A=0;A<a.length;++A)A<u?c.push(a[A]):c.push(1);let y=it(p),w=["type","type"],v=[{type:12,data:l},{type:1,data:p},{type:12,data:Math.floor(p/y)},{type:1,data:e.epsilon}];s&&w.push("type");let k=r>1,$=r>2,C=A=>{let B=pt(t[0].dataType),R=[Q("x",t[0].dataType,t[0].dims,y),Q("scale",i.dataType,i.dims,y)];s&&R.push(Q("bias",s.dataType,s.dims,y)),R.push(ge("output",t[0].dataType,o,y)),k&&R.push(ge("mean_data_output",1,c)),$&&R.push(ge("inv_std_output",1,c));let D=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${A.registerUniforms(D).declareVariables(...R)}
  ${A.mainStart()}
    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${wr("f32",y)};
    var mean_square_vector = ${wr("f32",y)};

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

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

    ${k?"mean_data_output[global_idx] = mean":""};
    ${$?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},T=[{dims:o,dataType:t[0].dataType}];return k&&T.push({dims:c,dataType:1}),$&&T.push({dims:c,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${y};${r};${n}`,inputDependencies:w},getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:v}),getShaderSource:C}},um=(t,e)=>{Hd(t.inputs),t.compute(jd(t.inputs,e,t.outputCount))}}),qd,Kd,lm,dm,by=Z(()=>{$e(),Ae(),lt(),Te(),qd=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((e.k+e.blockSize-1)/e.blockSize),i=e.blockSize/8*e.bits,s=t[1];if(!Y.areEqual(s.dims,[e.n,a,i]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(Y.size(o)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let u=t[3].dims,l=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(Y.size(u)!==l)throw new Error("zeroPoints input size error.")}},Kd=(t,e,r,n)=>{let a=t[0].dims,i=a.length,s=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=a[i-2],u=e.k,l=e.n,p=a.slice(0,i-2),f=Y.size(p),m=e.blockSize/8*e.bits/4,c=t[0].dataType,y=it(o),w=it(e.k),v=it(m),k=ga(c),$=o*s*k,C=Math.floor(n/$),T=s<=r[0]&&C>0,A=!T||C>=4?it(l):C>=2&&it(l)>=2?2:1,B=p.concat([o,l]),R=Y.size(B)/A/y,D=T?[]:[{type:12,data:R},{type:12,data:e.blockSize}],K=[f,o,u/w],j=Y.convertShape(t[1].dims).slice();j.splice(-1,1,m/v),D.push(...ye(K)),D.push(...ye(j)),D.push(...ye(t[2].dims)),t.length===4&&D.push(...ye(Y.convertShape(t[3].dims)));let ie=[f,o,l/A];D.push(...ye(ie));let te=oe=>{let re=K.length,M=Q("a",t[0].dataType,re,w),P=Q("b",12,j.length,v),H=Q("scales",t[2].dataType,t[2].dims.length),le=[M,P,H],G=t.length===4?Q("zero_points",12,t[3].dims.length):void 0;G&&le.push(G);let ne=ie.length,N=ge("output",t[0].dataType,ne,A),ae=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],fe=pt(t[0].dataType),Ce=(()=>{switch(w){case 1:return`array<${fe}, 8>`;case 2:return`mat4x2<${fe}>`;case 4:return`mat2x4<${fe}>`;default:throw new Error(`${w}-component is not supported.`)}})(),Be=`
        for (var word: u32 = 0; word < ${m}; word += ${v}) {
          ${P.indicesSet("b_indices","2","word")};
          let b_data = ${P.getByIndices("b_indices")};
          for (var i: u32 = 0; i < ${v}; i++) {
            let b_value: u32 = ${v===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 = ${Ce}(${Array.from({length:4},(Qe,We)=>`${fe}(b_value_lower[${We}]), ${fe}(b_value_upper[${We}])`).join(", ")});
            let b_dequantized_values = ${w===1?`${Ce}(${Array.from({length:8},(Qe,We)=>`(b_quantized_values[${We}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Ce}(${Array(8).fill("zero_point").join(",")})) * scale;`};
            // Number of B elements per 32-bit word is 32/bits = 32/4 = 8
            for (var m: u32 = 0; m < ${T?o:y}u; m++) {
              ${M.indicesSet("a_indices",re-2,T?"m":`row * ${y} + m`)};
              ${M.indicesSet("a_indices",re-1,"word_offset")};
              var input_offset = ${M.indicesToOffset("a_indices")};
              var a_data: ${Ce};
              for (var j: u32 = 0; j < ${8/w}; j++) {
                a_data[j] = ${M.getByOffset("input_offset")};
                input_offset++;
              }
              ${T?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${A>1?"[c]":""} += ${Array.from({length:8/w},(Qe,We)=>`${w===1?`a_data[${We}] * b_dequantized_values[${We}]`:`dot(a_data[${We}], b_dequantized_values[${We}])`}`).join(" + ")};
            }
            word_offset += ${8/w};
          }
        }`,Ke=G?`
          zero_point_offset += 4;
          if (zero_point_offset == 32) {
            zero_point_offset = 0;
            zero_point_index++;
            zero_point_word = ${G.getByOffset("zero_point_index")};
          }`:"";return T?`
        var<workgroup> workgroup_shared: array<${N.type.value}, ${o*s}>;
        ${oe.declareVariables(...le,N)}
        ${oe.mainStart([s,1,1])}
          var a_indices: ${M.type.indices};
          var block = local_id.x;
          var col = workgroup_id.y;
          var batch = workgroup_id.z;
          ${M.indicesSet("a_indices","0","batch")};
          // Two zero points are packed into one byte when uniforms.bits is 4.
          for (var c: u32 = 0; c < ${A}; c++) {
            let col_times_components_plus_c = col * ${A} + c;
              ${G?`
            var zero_point_bytes_per_col: u32 = (${s} + 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 = ${G.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}
            var b_indices: ${P.type.indices};
            ${P.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 * ${s} + block;
            let scale = ${H.getByOffset("scales_index")};
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${fe}(${G?"(zero_point_word) & 0xFu":8});
            ${P.indicesSet("b_indices","1","block")};
            var word_offset: u32 = block * ${e.blockSize/w};
            var workgroup_shared_offset: u32 = block * ${o};
            ${Be}
          }
          workgroupBarrier();
          if (local_id.x == 0u) {
            var output_indices: ${N.type.indices};
            ${N.indicesSet("output_indices","0","batch")};
            ${N.indicesSet("output_indices",ne-1,"col")};
            ${N.indicesSet("output_indices",ne-2,"0")};
            var output_offset = ${N.indicesToOffset("output_indices")};
            for (var m: u32 = 0u; m < ${o}u; m++) {
              var output_value: ${N.type.value} = ${N.type.value}(0);
              var workgroup_shared_offset: u32 = m;
              for (var b: u32 = 0u; b < ${s}u; b++) {
                output_value += workgroup_shared[workgroup_shared_offset];
                workgroup_shared_offset += ${o};
              }
              ${N.setByOffset("output_offset","output_value")};
              output_offset += ${l/A};
            }
          }
        }`:`
        ${oe.registerUniforms(ae).declareVariables(...le,N)}
        ${oe.mainStart()}
          ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var output_values: array<${N.type.value}, ${y}>;
          var output_indices = ${N.offsetToIndices("global_idx")};
          var col = ${N.indicesGet("output_indices",ne-1)};
          var row = ${N.indicesGet("output_indices",ne-2)};
          var a_indices: ${M.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
          ${G?`
          var zero_point_abs_offset = col * ${A} * ((${s} + 1) / 2);
          var zero_point_index: u32 = zero_point_abs_offset / 4;
          var zero_point_word: u32 = ${G.getByOffset("zero_point_index")};
          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}
          var scale_index = col * ${s*A};
          var b_indices: ${P.type.indices};
          for (var c: u32 = 0; c < ${A}; c++) {
            ${P.indicesSet("b_indices","0",`col * ${A} + c`)};
            var block_offset: u32 = 0;
            for (var block: u32 = 0; block < ${s}; 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 = ${fe}(${G?"extractBits(zero_point_word, zero_point_offset, 4)":8});
              ${P.indicesSet("b_indices","1","block")};
              var word_offset: u32 = block_offset;
              ${Be}
              scale_index++;
              ${Ke}
              block_offset += uniforms.block_size / ${w};
            }
            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.
            ${G?`if (zero_point_offset % 8 > 0) {
                ${Ke}
              }`:""}
            }
            for (var k: u32 = 0u; k < ${y}u; k++) {
              ${N.indicesSet("output_indices",ne-2,`${y} * row + k`)};
              ${N.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:T?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${c};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:B,dataType:c}],name:T?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:T?{x:1,y:Math.ceil(l/A),z:f}:{x:Math.ceil(R/64)},programUniforms:D}),getShaderSource:te}},lm=(t,e)=>{qd(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(Kd(t.inputs,e,r,n))},dm=t=>qe(t)}),yt,Yd,cm,Us,Xd,yi,pm,vy=Z(()=>{$e(),Ae(),lt(),Ho(),Vh(),Te(),Sa(),yt=(t,e)=>t.length>e&&t[e].dims.length>0&&Y.size(t[e].dims)>0?t[e]:void 0,Yd=(t,e)=>{let r=t[0],n=yt(t,1),a=yt(t,2),i=yt(t,3),s=yt(t,4),o=yt(t,5),u=yt(t,6),l=yt(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 p=!1,f=r.dims[0],m=r.dims[1],c=r.dims.length===3?p?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],y=m,w=0,v=0,k=Math.floor(c/e.numHeads);if(u&&l){if(u.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(u.dims[0]!==f||u.dims[1]!==e.numHeads||u.dims[3]!==k)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[0]!==f||l.dims[1]!==e.numHeads||l.dims[3]!==k)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[2]!==l.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(l.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');w=u.dims[2],v=u.dims[2]}else if(u||l)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let $;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');$=2,y=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==k)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.');$=5,y=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==k)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');$=0,y=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');$=3}if(i){if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(a&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let C=0;if(s){C=8;let D=s.dims;throw D.length===1?D[0]===f?C=1:D[0]===3*f+2&&(C=3):D.length===2&&D[0]===f&&D[1]===y&&(C=5),C===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let T=!1,A=c;if(a){if(a.dims.length!==3&&a.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==a.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(a.dims.length===3){if(y!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');A=a.dims[2]}else{if(y!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');A=a.dims[1]*a.dims[3],T=!0}}let B=w+y,R=!1;if(s)throw new Error("Key padding mask is not supported");if(o){if(o.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(o.dims[0]!==f&&o.dims[0]!==1||o.dims[1]!==e.numHeads||o.dims[2]!==m||o.dims[3]!==B)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:f,sequenceLength:m,pastSequenceLength:w,kvSequenceLength:y,totalSequenceLength:B,maxSequenceLength:v,inputHiddenSize:0,hiddenSize:c,vHiddenSize:A,headSize:k,vHeadSize:Math.floor(A/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:C,scale:e.scale,broadcastResPosBias:R,passPastInKv:T,qkvFormat:$}},cm=t=>qe({...t}),Us=qe({perm:[0,2,1,3]}),Xd=(t,e,r,n,a,i,s)=>{let o=[n,a,i],u=Y.size(o),l=[{type:12,data:u},{type:12,data:s},{type:12,data:i}],p=f=>{let m=ge("qkv_with_bias",e.dataType,o),c=Q("qkv",e.dataType,o),y=Q("bias",r.dataType,o),w=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${f.registerUniforms(w).declareVariables(c,y,m)}
  ${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(u/64)},programUniforms:l}),getShaderSource:p},{inputs:[e,r],outputs:[-1]})[0]},yi=(t,e,r,n,a,i,s,o)=>{let u=i;if(s){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return u=Xd(t,i,s,e,n,r*a,o),u=u.reshape([e,n,r,a]),t.compute(vr(u,Us.perm),{inputs:[u],outputs:[-1]})[0]}else return i.dims.length===3&&(u=i.reshape([e,n,r,a])),t.compute(vr(u,Us.perm),{inputs:[u],outputs:[-1]})[0]},pm=(t,e)=>{let r=Yd(t.inputs,e),n=t.inputs[0],a=yt(t.inputs,1),i=yt(t.inputs,2),s=yt(t.inputs,3),o=yt(t.inputs,4),u=yt(t.inputs,5),l=yt(t.inputs,6),p=yt(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if(a?.dims.length===5)throw new Error("Packed KV is not implemented");let f=a&&i&&a.dims.length===4&&i.dims.length===4,m=yi(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,s,0);if(f)return Wi(t,m,a,i,o,void 0,l,p,u,r,e);if(!a||!i)throw new Error("key and value must be provided");let c=yi(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,s,r.hiddenSize),y=yi(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,i,s,2*r.hiddenSize);Wi(t,m,c,y,o,void 0,l,p,u,r,e)}}),Qd,Jd,Zd,ec,tc,rc,nc,ac,hm,$y=Z(()=>{$e(),Ae(),Te(),Qd=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].")}},Jd=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
            k = i32(${t.indicesGet("indices",a)}) - ${Se("uniforms.pads",a,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${Se("uniforms.x_shape",a,e)})) {
              break;
            }
            offset += k * i32(${Se("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;
            ${n}
            value = x[offset];
          }
      `},Zd=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${Se("uniforms.pads",a,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${Se("uniforms.x_shape",a,e)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${Se("uniforms.x_shape",a,e)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${Se("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},ec=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${Se("uniforms.pads",a,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${Se("uniforms.x_shape",a,e)})) {
                  k = i32(${Se("uniforms.x_shape",a,e)}) - 1;
                }
                offset += k * i32(${Se("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},tc=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${Se("uniforms.pads",a,r)};
                if (k < 0)  {
                  k += i32(${Se("uniforms.x_shape",a,e)}]);
                }
                if (k >= i32(${Se("uniforms.x_shape",a,e)})) {
                  k -= i32(${Se("uniforms.x_shape",a,e)});
                }
                offset += k * i32(${Se("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},rc=(t,e,r)=>{switch(r.mode){case 0:return Jd(t,e,r.pads.length);case 1:return Zd(t,e,r.pads.length);case 2:return ec(t,e,r.pads.length);case 3:return tc(t,e,r.pads.length);default:throw new Error("Invalid mode")}},nc=(t,e)=>{let r=Y.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=Y.size(r),i=[{type:12,data:a},{type:6,data:e.pads}];e.mode===0&&i.push({type:t[0].dataType,data:e.value}),i.push(...ye(t[0].dims,r));let s=["rank"],o=u=>{let l=ge("output",t[0].dataType,r.length),p=Q("x",t[0].dataType,n.length),f=p.type.value,m=rc(l,n.length,e),c=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&c.push({name:"constant_value",type:f}),`
            ${u.registerUniforms(c).declareVariables(p,l)}
            ${u.mainStart()}
            ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${f}(0);
            ${m}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:s},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(Y.size(r)/64)},programUniforms:i}),getShaderSource:o}},ac=(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,i=new Int32Array(2*a).fill(0);if(t.length>=4){let o=t[3].getBigInt64Array();for(let u=0;u<o.length;u++)i[Number(o[u])]=Number(r[u]),i[Number(o[u])+a]=Number(r[u+o.length])}else r.forEach((o,u)=>i[Number(u)]=Number(o));let s=[];return i.forEach(o=>s.push(o)),{mode:e.mode,value:n,pads:s}}else return e},hm=(t,e)=>{Qd(t.inputs);let r=ac(t.inputs,e);t.compute(nc(t.inputs,r),{inputs:[0]})}}),ra,Ws,Vs,Gs,Hs,ic,sc,js,qs,fm,mm,Ks,gm,_m,Ys,ym,wm,bm,vm,xy=Z(()=>{ar(),$e(),Ae(),Te(),ra=t=>{if(Ue.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},Ws=(t,e,r)=>{let n=e.format==="NHWC",a=t.dims.slice();n&&a.splice(1,0,a.pop());let i=Object.hasOwnProperty.call(e,"dilations"),s=e.kernelShape.slice(),o=e.strides.slice(),u=i?e.dilations.slice():[],l=e.pads.slice();Fi.adjustPoolAttributes(r,a,s,o,u,l);let p=Fi.computePoolOutputShape(r,a,o,u,s,l,e.autoPad),f=Object.assign({},e);i?Object.assign(f,{kernelShape:s,strides:o,pads:l,dilations:u,cacheKey:e.cacheKey}):Object.assign(f,{kernelShape:s,strides:o,pads:l,cacheKey:e.cacheKey});let m=p.slice();return m.push(m.splice(1,1)[0]),[f,n?m:p]},Vs=(t,e)=>{let r=e.format==="NHWC",n=Y.size(t),a=Y.size(e.kernelShape),i=[{type:12,data:n},{type:12,data:a}],s=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let o=e.kernelShape[e.kernelShape.length-1],u=e.strides[e.strides.length-1],l=e.pads[e.pads.length/2-1],p=e.pads[e.pads.length-1],f=!!(l+p);i.push({type:12,data:o},{type:12,data:u},{type:12,data:l},{type:12,data:p}),s.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let m=!1;if(e.kernelShape.length===2){let c=e.kernelShape[e.kernelShape.length-2],y=e.strides[e.strides.length-2],w=e.pads[e.pads.length/2-2],v=e.pads[e.pads.length-2];m=!!(w+v),i.push({type:12,data:c},{type:12,data:y},{type:12,data:w},{type:12,data:v}),s.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,s,!0,f,m]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=Y.computeStrides(e.kernelShape);i.push({type:12,data:o},{type:12,data:e.pads},{type:12,data:e.strides}),s.push({name:"kernelStrides",type:"u32",length:o.length},{name:"pads",type:"u32",length:e.pads.length},{name:"strides",type:"u32",length:e.strides.length});let u=e.pads.reduce((l,p)=>l+p);return[i,s,!!u,!1,!1]}},Gs=(t,e,r,n,a,i,s,o,u,l,p,f)=>{let m=a.format==="NHWC",c=e.type.value,y=ge("output",e.type.tensor,n);if(a.kernelShape.length<=2){let w="",v="",k="",$=r-(m?2:1);if(p?w=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${$}] < 0 || xIndices[${$}]
                      >= uniforms.x_shape[${$}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`:w=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`,a.kernelShape.length===2){let C=r-(m?3:2);f?v=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${C}] = indices[${C}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${C}] < 0 || xIndices[${C}] >= uniforms.x_shape[${C}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:v=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${C}] = indices[${C}] * uniforms.sh - uniforms.phStart + j;
                `,k=`
              }
            `}return`
            ${t.registerUniforms(u).declareVariables(e,y)}

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

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

              var value = ${c}(${o});
              var pad = 0;
              ${v}
              ${w}
              ${k}
              ${s}

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

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

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

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

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

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

              output[global_idx] = value;
            }`}},Hs=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,ic=t=>`${Hs(t)};${t.countIncludePad}`,sc=t=>`${Hs(t)};${t.storageOrder};${t.dilations}`,js=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}),qs=(t,e,r,n)=>{let[a,i]=Ws(e,n,r),s=Q("x",e.dataType,e.dims.length),o=s.type.value,u="value += x_val;",l="";a.countIncludePad?l+=`value /= ${o}(uniforms.kernelSize);`:l+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[p,f,m,c,y]=Vs(i,a);p.push(...ye(e.dims,i));let w=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${m};${c};${y}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(Y.size(i)/64)},programUniforms:p}),getShaderSource:v=>Gs(v,s,e.dims.length,i.length,a,u,l,0,f,m,c,y)}},fm=t=>{let e=t.count_include_pad!==0,r=js(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:ic(n)}},mm=(t,e)=>{ra(t.inputs),t.compute(qs("AveragePool",t.inputs[0],!1,e))},Ks={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},gm=t=>{let e=t.format;return{format:e,...Ks,cacheKey:e}},_m=(t,e)=>{ra(t.inputs),t.compute(qs("GlobalAveragePool",t.inputs[0],!0,e))},Ys=(t,e,r,n)=>{let[a,i]=Ws(e,n,r),s=`
      value = max(x_val, value);
    `,o="",u=Q("x",e.dataType,e.dims.length),l=["rank"],[p,f,m,c,y]=Vs(i,a);return p.push(...ye(e.dims,i)),{name:t,shaderCache:{hint:`${n.cacheKey};${m};${c};${y}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(Y.size(i)/64)},programUniforms:p}),getShaderSource:w=>Gs(w,u,e.dims.length,i.length,a,s,o,e.dataType===10?-65504:-1e5,f,m,c,y)}},ym=(t,e)=>{ra(t.inputs),t.compute(Ys("MaxPool",t.inputs[0],!1,e))},wm=t=>{let e=t.storage_order,r=t.dilations,n=js(t);if(e!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let a={storageOrder:e,dilations:r,...n,cacheKey:""};return{...a,cacheKey:sc(a)}},bm=t=>{let e=t.format;return{format:e,...Ks,cacheKey:e}},vm=(t,e)=>{ra(t.inputs),t.compute(Ys("GlobalMaxPool",t.inputs[0],!0,e))}}),oc,uc,$m,Sy=Z(()=>{ar(),$e(),Te(),oc=(t,e,r)=>{let n=t===e,a=t<e&&r<0,i=t>e&&r>0;if(n||a||i)throw new Error("Range these inputs' contents are invalid.")},uc=(t,e,r,n)=>{let a=Math.abs(Math.ceil((e-t)/r)),i=[a],s=a,o=[{type:12,data:s},{type:n,data:t},{type:n,data:r},...ye(i)],u=l=>{let p=ge("output",n,i.length),f=p.type.value,m=[{name:"outputSize",type:"u32"},{name:"start",type:f},{name:"delta",type:f}];return`
        ${l.registerUniforms(m).declareVariables(p)}
        ${l.mainStart()}
        ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${f}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:u,getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:o})}},$m=t=>{let e=0,r=0,n=0;t.inputs[0].dataType===6?(e=t.inputs[0].getInt32Array()[0],r=t.inputs[1].getInt32Array()[0],n=t.inputs[2].getInt32Array()[0]):t.inputs[0].dataType===1&&(e=t.inputs[0].getFloat32Array()[0],r=t.inputs[1].getFloat32Array()[0],n=t.inputs[2].getFloat32Array()[0]),Ue.webgpu.validateInputContent&&oc(e,r,n),t.compute(uc(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),lc,dc,cc,pc,hc,fc,mc,gc,_c,yc,wc,Xs,bc,vc,$c,xc,Sc,xm,Sm,ky=Z(()=>{$e(),Ae(),lt(),Te(),lc=(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")}},dc=(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,i)=>n[a]=t[i]),n},cc=(t,e,r,n,a,i)=>{let[s,o,u]=r>10?[1,2,3]:[-1,t.length>1?1:-1,-1],l=t[0].dims.length;if(s>0&&t.length>s&&t[s].dims.length>0)t[s].getFloat32Array().forEach(p=>i.push(p));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(p=>n.push(p)),n.length!==0&&n.length!==l&&r>=18&&n.length!==e.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");lc(n,e),e.axes.length>0&&dc(n,e.axes,l).forEach((p,f)=>n[f]=p)}if(u>0&&t.length>u&&(t[u].getBigInt64Array().forEach(p=>a.push(Number(p))),a.length!==l||r>=18&&a.length===e.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(e.axes.length>0){if(n.length!==e.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(a.length!==e.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof a<"u"&&n.length>0&&a.length>l)throw new Error("Resize requires only of scales or sizes to be specified")},pc=(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`)}})()+"}",hc=(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`)}})()+"}",fc=(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((i,s)=>{n[i]=a[s],n[s+r]=a[e.length+s]}),n):a},mc=(t,e,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(t.forEach(i=>a.push(i)),Math.max(...n)>t.length)throw new Error("axes is out of bound");n.forEach((i,s)=>a[i]=r[s])}else r.forEach(i=>a.push(i));else{if(e.length===0)throw new Error("Resize requires either scales or sizes.");a=t.map((i,s)=>Math.round(i*e[s]))}return a},gc=(t,e,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(i=>e[i]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(i=>e[i]),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(i=>e[i]=n),r.axes.forEach(i=>a[i]=Math.round(t[i]*e[i]))):(e.fill(n,0,e.length),a.forEach((i,s)=>a[s]=Math.round(i*e[s]))),a},_c=(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 = ${Se("uniforms.scales","i",n)};
        var roi_low = ${Se("uniforms.roi","i",a)};
        var roi_hi = ${Se("uniforms.roi",`i + ${e.length}`,a)};
        if (scale == 1.0) {
          original_indices[i] = ${t.type.value}(output_index);
        } else {
          var input_shape_i = ${Se("uniforms.input_shape","i",e.length)};
          var output_shape_i = ${Se("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;
    }`,yc=(t,e,r,n,a,i,s)=>`
    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 = ${Se("uniforms.scales","i",a)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${Se("uniforms.roi","i",i)};
          var roi_hi = ${Se("uniforms.roi",`i + ${r.length}`,i)};
          var input_shape_i = ${Se("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${Se("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${s} || (original_idx >= 0 && original_idx < ${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;
    }`,wc=(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 >= ${Se("uniforms.input_shape","i",e.length)}) {
          return false;
        }
      }
      return true;
    }`,Xs=(t,e,r,n)=>t.rank>n?`
    ${t.indicesSet("input_indices",e,"channel")};
    ${t.indicesSet("input_indices",r,"batch")};
`:"",bc=(t,e,r,n,a)=>{let[i,s,o,u]=r.length===2?[-1,0,1,-1]:[0,2,3,1],l=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${l} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",s,`max(0, min(row, ${r[s]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)};
      ${Xs(t,u,i,2)}
      return ${t.getByIndices("input_indices")};
    }

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

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

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

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

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

      var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${p} = abs(depth - ${p}(depth1));
      var dx2: ${p} = abs(${p}(depth2) - depth);
      var dy1: ${p} = abs(height - ${p}(height1));
      var dy2: ${p} = abs(${p}(height2) - height);
      var dz1: ${p} = abs(width - ${p}(width1));
      var dz2: ${p} = abs(${p}(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);
    }`},xc=(t,e,r,n,a,i)=>{let s=t.dims,o=fc(i,e.axes,s.length),u=mc(s,n,a,e.axes),l=n.slice();n.length===0&&(l=s.map(($,C)=>$===0?1:u[C]/$),e.keepAspectRatioPolicy!=="stretch"&&(u=gc(s,l,e)));let p=ge("output",t.dataType,u.length),f=Q("input",t.dataType,s.length),m=Y.size(u),c=s.length===u.length&&s.every(($,C)=>$===u[C]),y=e.coordinateTransformMode==="tf_crop_and_resize",w=e.extrapolationValue,v=f.type.value,k=$=>`
      ${c?"":`
      ${pc(e.coordinateTransformMode,v)};
      ${(()=>{switch(e.mode){case"nearest":return`
              ${wc(f,s)};
              ${hc(e.nearestMode,r,v)};
              ${yc(f,p,s,u,l.length,o.length,y)};
              `;case"linear":return`
              ${_c(p,s,u,l.length,o.length)};
              ${(()=>{if(s.length===2||s.length===4)return`${bc(f,p,s,y,w)}`;if(s.length===3||s.length===5)return`${$c(f,p,s,y,w)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(s.length===2||s.length===4)return`${vc(f,p,s,u,l,o,e.cubicCoeffA,y,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")}})()};
      `}
      ${$.registerUniform("output_size","u32").registerUniform("scales","f32",l.length).registerUniform("roi","f32",o.length).declareVariables(f,p)}
      ${$.mainStart()}
        ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${c?"output[global_idx] = input[global_idx];":`
        let output_indices = ${p.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] = ${s.length===2||s.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${e.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${e.cacheKey}|${r}|${l.length>0?l:""}|${a.length>0?a:""}|${o.length>0?o:""}|${c}|${s}`,inputDependencies:["rank"]},getShaderSource:k,getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:[{type:12,data:m},{type:1,data:l},{type:1,data:o},...ye(s,u)]})}},Sc=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},xm=(t,e)=>{let r=[],n=[],a=[],i=Sc(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");cc(t.inputs,e,i,r,n,a),t.compute(xc(t.inputs[0],e,i,r,n,a),{inputs:[0]})},Sm=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,a=t.cubicCoeffA,i=t.excludeOutside!==0,s=t.extrapolationValue,o=t.keepAspectRatioPolicy,u=t.mode,l=t.nearestMode===""?"simple":t.nearestMode;return qe({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:i,extrapolationValue:s,keepAspectRatioPolicy:o,mode:u,nearestMode:l})}}),kc,Ec,km,Ey=Z(()=>{$e(),Ae(),lt(),Te(),kc=(t,e)=>{let[r,n,a,i]=t,{numHeads:s,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(!Y.areEqual(n.dims,[])&&!Y.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(i.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(!Y.areEqual(a.dims,i.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(o>0&&s===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let u=r.dims[0],l=r.dims[r.dims.length-2],p=a.dims[0],f=Y.sizeFromDimension(r.dims,1)/l,m=o===0?a.dims[1]*2:f/s;if(o>m)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(u!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(l!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(m/2!==a.dims[1]&&o/2!==a.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${a.dims[1]}`);if(l>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Ec=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:i}=e,s=t[0].dims[0],o=Y.sizeFromDimension(t[0].dims,1),u=t[0].dims[t[0].dims.length-2],l=o/u,p=t[2].dims[1],f=a===0?p*2:l/n,m=new Array(s,u,l/f,f-p),c=Y.computeStrides(m),y=[{type:1,data:i},{type:12,data:m},{type:12,data:c},...t[0].dims.length===3?new Array({type:12,data:[o,l,f,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,f,u*f,1]}):[],...ye(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],w=v=>{let k=Q("input",t[0].dataType,t[0].dims.length),$=Q("position_ids",t[1].dataType,t[1].dims.length),C=Q("cos_cache",t[2].dataType,t[2].dims.length),T=Q("sin_cache",t[3].dataType,t[3].dims.length),A=ge("output",t[0].dataType,t[0].dims.length);return v.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:m.length},{name:"global_strides",type:"u32",length:c.length},{name:"input_output_strides",type:"u32",length:c.length}]),`
        ${v.declareVariables(k,$,C,T,A)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${$.broadcastedIndicesToOffset("bsnh.xy",ge("",$.type.tensor,2))};
            let position_id =
                u32(${$.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 = ${k.getByOffset("i")} * ${C.get("position_id","bsnh[3]")} -
                ${k.getByOffset("j")} * ${T.get("position_id","bsnh[3]")};
            ${A.setByOffset("i","re")}
            let im = ${k.getByOffset("i")} * ${T.get("position_id","bsnh[3]")} +
                ${k.getByOffset("j")} * ${C.get("position_id","bsnh[3]")};
            ${A.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${A.setByOffset("k",k.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:qe({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(Y.size(m)/Cn)},programUniforms:y})}},km=(t,e)=>{kc(t.inputs,e),t.compute(Ec(t.inputs,e))}}),Cc,Tc,Em,Cy=Z(()=>{$e(),Ae(),Te(),Cc=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],i=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]!==i)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 s=t[3];if(s.dims.length!==1)throw new Error("Beta must be 1D");if(s.dims[s.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let s=t[4];if(s.dims.length!==1)throw new Error("Bias must be 1D");if(s.dims[s.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},Tc=(t,e,r,n)=>{let a=e.simplified,i=t[0].dims,s=Y.size(i),o=i,u=s,l=i.slice(-1)[0],p=n?i.slice(0,-1).concat(1):[],f=!a&&t.length>3,m=t.length>4,c=n&&r>1,y=n&&r>2,w=r>3,v=it(l),k=[{type:12,data:u},{type:12,data:v},{type:12,data:l},{type:1,data:e.epsilon}],$=T=>{let A=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],B=[Q("x",t[0].dataType,t[0].dims,v),Q("skip",t[1].dataType,t[1].dims,v),Q("gamma",t[2].dataType,t[2].dims,v)];f&&B.push(Q("beta",t[3].dataType,t[3].dims,v)),m&&B.push(Q("bias",t[4].dataType,t[4].dims,v)),B.push(ge("output",t[0].dataType,o,v)),c&&B.push(ge("mean_output",1,p)),y&&B.push(ge("inv_std_output",1,p)),w&&B.push(ge("input_skip_bias_sum",t[0].dataType,o,v));let R=pt(t[0].dataType);return`

      ${T.registerUniforms(A).declareVariables(...B)}

      ${T.mainStart()}
        ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}
        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
        let offset = global_idx * hidden_size_vectorized;
        var sum = ${wr("f32",v)};
        var squareSum = ${wr("f32",v)};
        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {
          let skip_value = skip[offset + i];
          let bias_value = ${m?"bias[i]":R+"(0.0)"};
          let input_value = x[offset + i];
          let value = input_value + skip_value + bias_value;
          ${w?"input_skip_bias_sum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32_value = ${Sn(R,v,"value")};
          sum += f32_value;
          squareSum += f32_value * f32_value;
        }
        let mean = ${Or("sum",v)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Or("squareSum",v)} / f32(uniforms.hidden_size) ${a?"":"- mean * mean"} + uniforms.epsilon);
        ${c?"mean_output[global_idx] = mean;":""}
        ${y?"inv_std_output[global_idx] = inv_std_dev;":""}
        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {
          output[offset + i] = (output[offset + i] ${a?"":`- ${R}(mean)`}) * ${R}(inv_std_dev) * gamma[i] ${f?"+ beta[i]":""};
        }
      }`},C=[{dims:o,dataType:t[0].dataType}];return r>1&&C.push({dims:p,dataType:1}),r>2&&C.push({dims:p,dataType:1}),r>3&&C.push({dims:i,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${v};${c};${y};${w}`,inputDependencies:t.map((T,A)=>"type")},getShaderSource:$,getRunData:()=>({outputs:C,dispatchGroup:{x:Math.ceil(u/l/64)},programUniforms:k})}},Em=(t,e)=>{Cc(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(Tc(t.inputs,e,t.outputCount,!1),{outputs:r})}}),Ic,na,Ac,Qs,Mc,Oc,Cm,Tm,Ty=Z(()=>{$e(),Ae(),lt(),Te(),Ic=(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`)})},na=(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},Ac=(t,e)=>{if(t.length>1){let r=na(t,1),n=na(t,2),a=na(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),qe({starts:r,ends:n,axes:a})}else return e},Qs=(t,e,r,n,a)=>{let i=t;return t<0&&(i+=r[n[e]]),a[e]<0?Math.max(0,Math.min(i,r[n[e]]-1)):Math.max(0,Math.min(i,r[n[e]]))},Mc=(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 = ${Se("uniforms.input_shape","i",r.length)};
            let steps_i = ${Se("uniforms.steps","i",r.length)};
            let signs_i = ${Se("uniforms.signs","i",r.length)};
            let starts_i = ${Se("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;
            }
            ${t.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,Oc=(t,e)=>{let r=t[0].dims,n=Y.size(r),a=e.axes.length>0?Y.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],i=na(t,4);i.forEach(k=>k!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(a.length).fill(1));let s=e.starts.map((k,$)=>Qs(k,$,r,a,i)),o=e.ends.map((k,$)=>Qs(k,$,r,a,i));if(a.length!==s.length||a.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(a.length!==r.length)for(let k=0;k<r.length;++k)a.includes(k)||(s.splice(k,0,0),o.splice(k,0,r[k]),i.splice(k,0,1));let u=i.map(k=>Math.sign(k));i.forEach((k,$,C)=>{if(k<0){let T=(o[$]-s[$])/k,A=s[$],B=A+T*i[$];s[$]=B,o[$]=A,C[$]=-k}});let l=r.slice(0);a.forEach((k,$)=>{l[k]=Math.ceil((o[k]-s[k])/i[k])});let p={dims:l,dataType:t[0].dataType},f=ge("output",t[0].dataType,l.length),m=Q("input",t[0].dataType,t[0].dims.length),c=Y.size(l),y=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:s.length},{name:"signs",type:"i32",length:u.length},{name:"steps",type:"u32",length:i.length}],w=[{type:12,data:c},{type:12,data:s},{type:6,data:u},{type:12,data:i},...ye(t[0].dims,l)],v=k=>`
      ${k.registerUniforms(y).declareVariables(m,f)}
        ${Mc(m,f,r)}
        ${k.mainStart()}
          ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${f.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${f.setByOffset("global_idx",m.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${u.length}_${s.length}_${i.length}`,inputDependencies:["rank"]},getShaderSource:v,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:w})}},Cm=(t,e)=>{Ic(t.inputs,e);let r=Ac(t.inputs,e);t.compute(Oc(t.inputs,r),{inputs:[0]})},Tm=t=>{let e=t.starts,r=t.ends,n=t.axes;return qe({starts:e,ends:r,axes:n})}}),zc,Pc,Im,Am,Iy=Z(()=>{$e(),Ae(),lt(),Te(),zc=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},Pc=(t,e)=>{let r=t.dims,n=Y.size(r),a=64,i=e.axis;if(i<0&&(i=r.length+i),i<r.length-1)throw new Error("softmax only supports last axis for now.");let s=r[i],o=n/s,u=it(s),l=s/u,p=(v,k)=>k===4?`max(max(${v}.x, ${v}.y), max(${v}.z, ${v}.w))`:k===2?`max(${v}.x, ${v}.y)`:k===3?`max(max(${v}.x, ${v}.y), ${v}.z)`:v,f=Q("x",t.dataType,t.dims,u),m=ge("result",t.dataType,t.dims,u),c=f.type.value,y=pt(t.dataType)==="f32"?`var threadMax = ${c}(-3.402823e+38f);`:`var threadMax = ${c}(-65504.0h);`,w=v=>`
      var<workgroup> rowMaxShared : ${c};
      var<workgroup> rowSumShared : ${c};
      var<workgroup> threadShared : array<${c}, ${a}>;

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

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${c}) {
        let index = row * row_stride + col;
        result[index] = value;
      }
      ${v.registerUniform("packedCols","i32").declareVariables(f,m)}
      ${v.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
        ${y}
        for (var col = lindex; col < cols; col += wg) {
          let value = getValue(row, col, row_stride);
          threadMax = max(threadMax, value);
        }
        if (lindex < cols) {
          threadShared[lindex] = threadMax;
        }
        workgroupBarrier();

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

        // find the rows sum
        var threadSum = ${c}(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 = ${c}(${Or("threadShared[0]",u)});
        }
        workgroupBarrier();

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          setValue(row, col, row_stride, value);
        }
      }`;return{name:"Softmax",shaderCache:{hint:`${u}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:6,data:l}]}),getShaderSource:w}},Im=(t,e)=>{zc(t.inputs),t.compute(Pc(t.inputs[0],e))},Am=t=>qe({axis:t.axis})}),Rc,Bc,Dc,Nc,Fc,Mm,Om,Ay=Z(()=>{$e(),Ae(),lt(),Te(),Rc=t=>{if(!t||t.length<1)throw new Error("too few inputs")},Bc=(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),qe({numOutputs:n,axis:e.axis,splitSizes:r})},Dc=t=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${t}u; i += 1u ) {
    if (index < ${Se("uniforms.size_in_split_axis","i",t)}) {
        return i;
    }
    }
    return ${t}u;
}`,Nc=t=>{let e=t.length,r=[];for(let n=0;n<e;++n){let a=t[n].setByIndices("indices","input[global_idx]");e===1?r.push(a):n===0?r.push(`if (output_number == ${n}u) { ${a} }`):n===e-1?r.push(`else { ${a} }`):r.push(`else if (output_number == ${n}) { ${a} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${t[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},Fc=(t,e)=>{let r=t[0].dims,n=Y.size(r),a=t[0].dataType,i=Y.normalizeAxis(e.axis,r.length),s=new Array(e.numOutputs),o=Q("input",a,r.length),u=new Array(e.numOutputs),l=[],p=[],f=0,m=[{type:12,data:n}];for(let y=0;y<e.numOutputs;y++){f+=e.splitSizes[y],u[y]=f;let w=r.slice();w[e.axis]=e.splitSizes[y],p.push(w),s[y]=ge(`output${y}`,a,w.length),l.push({dims:p[y],dataType:t[0].dataType})}m.push({type:12,data:u},...ye(r,...p));let c=y=>`
  ${y.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",u.length).declareVariables(o,...s)}
  ${Dc(u.length)}
  ${Nc(s)}

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

    var indices = ${o.offsetToIndices("global_idx")};
    var index = ${o.indicesGet("indices",i)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${Se("uniforms.size_in_split_axis","output_number - 1u",u.length)};
      ${o.indicesSet("indices",i,"index")};
    }
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      const inputShape = ${s.indices(...e)};
      ${l.registerUniform("output_size","u32").declareVariables(s,o)}
      ${l.mainStart()}
      ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${o.offsetToIndices("global_idx")};
      var input_indices: ${s.type.indices};
      for (var i = 0; i < ${e.length}; i++) {
        let input_dim_i = ${s.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${o.indicesGet("output_indices","i")}  % input_dim_i;

        ${s.indicesSet("input_indices","i","input_dim_value")}
      }
      ${o.setByOffset("global_idx",s.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...ye(t[0].dims,n)]}),getShaderSource:u}},zm=t=>{Lc(t.inputs),t.compute(Wc(t.inputs),{inputs:[0]})}}),Vc,Gc,Pm,Oy=Z(()=>{$e(),Ae(),Te(),Vc=(t,e,r,n,a)=>{let i=ge("output_data",a,r.length,4),s=Q("a_data",e[1].dataType,e[1].dims.length,4),o=Q("b_data",e[2].dataType,e[2].dims.length,4),u=Q("c_data",e[0].dataType,e[0].dims.length,4),l,p=(f,m,c)=>`select(${m}, ${f}, ${c})`;if(!n)l=i.setByOffset("global_idx",p(s.getByOffset("global_idx"),o.getByOffset("global_idx"),u.getByOffset("global_idx")));else{let f=(m,c,y="")=>{let w=`a_data[index_a${c}][component_a${c}]`,v=`b_data[index_b${c}][component_b${c}]`,k=`bool(c_data[index_c${c}] & (0xffu << (component_c${c} * 8)))`;return`
            let output_indices${c} = ${i.offsetToIndices(`global_idx * 4u + ${c}u`)};
            let offset_a${c} = ${s.broadcastedIndicesToOffset(`output_indices${c}`,i)};
            let offset_b${c} = ${o.broadcastedIndicesToOffset(`output_indices${c}`,i)};
            let offset_c${c} = ${u.broadcastedIndicesToOffset(`output_indices${c}`,i)};
            let index_a${c} = offset_a${c} / 4u;
            let index_b${c} = offset_b${c} / 4u;
            let index_c${c} = offset_c${c} / 4u;
            let component_a${c} = offset_a${c} % 4u;
            let component_b${c} = offset_b${c} % 4u;
            let component_c${c} = offset_c${c} % 4u;
            ${m}[${c}] = ${y}(${p(w,v,k)});
          `};a===9?l=`
            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));`:l=`
            ${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(u,s,o,i)}
        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${l}
      }`},Gc=t=>{let e=t[1].dims,r=t[2].dims,n=t[0].dims,a=t[1].dataType,i=!(Y.areEqual(e,r)&&Y.areEqual(r,n)),s=e,o=Y.size(e);if(i){let l=En.calcShape(En.calcShape(e,r,!1),n,!1);if(!l)throw new Error("Can't perform where op on the given tensors");s=l,o=Y.size(s)}let u=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:l=>Vc(l,t,s,i,a),getRunData:()=>({outputs:[{dims:s,dataType:a}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:u},...ye(n,e,r,s)]})}},Pm=t=>{t.compute(Gc(t.inputs))}}),Rm,zy=Z(()=>{ey(),Vh(),ty(),ry(),ny(),ay(),Uh(),Vf(),ly(),dy(),cy(),py(),hy(),fy(),my(),gy(),_y(),yy(),wy(),Wf(),by(),vy(),$y(),xy(),Sy(),Yo(),ky(),Ey(),Cy(),Ty(),Iy(),Ay(),My(),Sa(),Xo(),Oy(),Rm=new Map([["Abs",[jh]],["Acos",[qh]],["Acosh",[Kh]],["Add",[Tf]],["ArgMax",[Nh,yo]],["ArgMin",[Dh,yo]],["Asin",[Yh]],["Asinh",[Xh]],["Atan",[Qh]],["Atanh",[Jh]],["Attention",[Wh]],["AveragePool",[mm,fm]],["BatchNormalization",[Gh]],["BiasAdd",[Hh]],["BiasSplitGelu",[Cf]],["Cast",[ef,Zh]],["Ceil",[rf]],["Clip",[tf]],["Concat",[Fh,Lh]],["Conv",[So,xo]],["ConvTranspose",[jf,Hf]],["Cos",[nf]],["Cosh",[af]],["CumSum",[qf,Kf]],["DepthToSpace",[Yf,Xf]],["Div",[If]],["Einsum",[Qf,Jf]],["Elu",[sf,Ci]],["Equal",[Af]],["Erf",[of]],["Exp",[uf]],["Expand",[Zf]],["FastGelu",[em]],["Floor",[lf]],["FusedConv",[So,xo]],["Gather",[rm,tm]],["GatherElements",[am,nm]],["Gelu",[df]],["Gemm",[sm,im]],["GlobalAveragePool",[_m,gm]],["GlobalMaxPool",[vm,bm]],["Greater",[Pf]],["GreaterOrEqual",[Bf]],["HardSigmoid",[yf,_f]],["InstanceNormalization",[om]],["LayerNormalization",[um]],["LeakyRelu",[cf,Ci]],["Less",[Rf]],["LessOrEqual",[Df]],["Log",[Ef]],["MatMul",[Uf]],["MatMulNBits",[lm,dm]],["MaxPool",[ym,wm]],["Mul",[Mf]],["MultiHeadAttention",[pm,cm]],["Neg",[hf]],["Not",[pf]],["Pad",[hm]],["Pow",[Of]],["Range",[$m]],["Reciprocal",[ff]],["ReduceMin",[Oh]],["ReduceMean",[Ch]],["ReduceMax",[Mh]],["ReduceSum",[Ph]],["ReduceProd",[zh]],["ReduceL1",[Th]],["ReduceL2",[Ih]],["ReduceLogSum",[Bh]],["ReduceLogSumExp",[Ah]],["ReduceSumSquare",[Rh]],["Relu",[mf]],["Resize",[xm,Sm]],["RotaryEmbedding",[km]],["Sigmoid",[gf]],["Sin",[wf]],["Sinh",[bf]],["Slice",[Cm,Tm]],["SkipLayerNormalization",[Em]],["Split",[Mm,Om]],["Sqrt",[vf]],["Softmax",[Im,Am]],["Sub",[zf]],["Tan",[$f]],["Tanh",[xf]],["ThresholdedRelu",[kf,Ci]],["Tile",[zm]],["Transpose",[mh,gh]],["Where",[Pm]]])}),Bm,Py=Z(()=>{ar(),nn(),Te(),Bm=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){nr(t.programInfo.name);let i=this.backend.device,s=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let o=[];for(let l of e)o.push({binding:o.length,resource:{buffer:l.buffer}});for(let l of r)o.push({binding:o.length,resource:{buffer:l.buffer}});a&&o.push({binding:o.length,resource:a});let u=i.createBindGroup({layout:t.computePipeline.getBindGroupLayout(0),entries:o,label:t.programInfo.name});if(this.backend.sessionStatus==="capturing"){let l={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:u,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(l)}s.setPipeline(t.computePipeline),s.setBindGroup(0,u),s.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(),Lt(t.programInfo.name)}dispose(){}build(t,e){nr(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=fh(e,this.backend.device.limits),i=t.getShaderSource(a),s=`${n.join(`
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e.requestDevice(n),this.adapterInfo=new qc(await e.requestAdapterInfo()),this.gpuDataManager=ph(this),this.programManager=new Bm(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,dh(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;nr(),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(()=>{let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let n=0;n<e.length/2;n++){let a=r[n],i=a.kernelId,s=this.kernels.get(i),o=s.kernelType,u=s.kernelName,l=a.programName,p=a.inputTensorViews,f=a.outputTensorViews,m=e[n*2],c=e[n*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=m);let y=Number(m-this.queryTimeBase),w=Number(c-this.queryTimeBase);if(!Number.isSafeInteger(y)||!Number.isSafeInteger(w))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:p.map(v=>({dims:v.dims,dataType:Kr(v.dataType)})),outputsMetadata:f.map(v=>({dims:v.dims,dataType:Kr(v.dataType)})),kernelId:i,kernelType:o,kernelName:u,programName:l,startTime:y,endTime:w});else{let v="";p.forEach(($,C)=>{v+=`input[${C}]: [${$.dims}] | ${Kr($.dataType)}, `});let k="";f.forEach(($,C)=>{k+=`output[${C}]: [${$.dims}] | ${Kr($.dataType)}, `}),console.log(`[profiling] kernel "${i}|${o}|${u}|${l}" ${v}${k}execution time: ${w-y} ns`)}ma("GPU",`${l}::${m}::${c}`)}t.unmap(),this.pendingQueries.delete(t)}),Lt()}run(t,e,r,n,a,i){nr(t.name);let s=[];for(let $=0;$<e.length;++$){let C=e[$].data;if(C===0)continue;let T=this.gpuDataManager.get(C);if(!T)throw new Error(`no GPU data for input: ${C}`);s.push(T)}let{outputs:o,dispatchGroup:u,programUniforms:l}=t.getRunData(e),p=r.length===0?o.map(($,C)=>C):r;if(p.length!==o.length)throw new Error(`Output size ${p.length} must be equal to ${o.length}.`);let f=[],m=[];for(let $=0;$<o.length;++$){if(!Number.isInteger(p[$])||p[$]<-3||p[$]>=i)throw new Error(`Invalid output index: ${p[$]}`);if(p[$]===-3)continue;let C=p[$]===-1,T=p[$]===-2,A=C||T?a(o[$].dataType,o[$].dims):n(p[$],o[$].dataType,o[$].dims);if(f.push(A),A.data===0)continue;let B=this.gpuDataManager.get(A.data);if(!B)throw new Error(`no GPU data for output: ${A.data}`);if(C&&this.temporaryData.push(B),T){let R=this.kernelPersistentData.get(this.currentKernelId);R||(R=[],this.kernelPersistentData.set(this.currentKernelId,R)),R.push(B)}m.push(B)}if(s.length!==e.length||m.length!==f.length){if(m.length===0)return Lt(t.name),f;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let c;if(l){let $=0,C=[];l.forEach(R=>{let D=typeof R.data=="number"?[R.data]:R.data;if(D.length===0)return;let K=R.type===10?2:4,j,ie;R.type===10?(ie=D.length>4?16:D.length>2?8:D.length*K,j=D.length>4?16:K*D.length):(ie=D.length<=2?D.length*K:16,j=16),$=Math.ceil($/ie)*ie,C.push($);let te=R.type===10?8:4;$+=D.length>4?Math.ceil(D.length/te)*j:D.length*K});let T=16;$=Math.ceil($/T)*T;let A=new ArrayBuffer($);l.forEach((R,D)=>{let K=C[D],j=typeof R.data=="number"?[R.data]:R.data;if(R.type===6)new Int32Array(A,K,j.length).set(j);else if(R.type===12)new Uint32Array(A,K,j.length).set(j);else if(R.type===10)new Uint16Array(A,K,j.length).set(j);else if(R.type===1)new Float32Array(A,K,j.length).set(j);else throw new Error(`Unsupported uniform type: ${Kr(R.type)}`)});let B=this.gpuDataManager.create($,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(B.buffer,0,A,0,$),this.gpuDataManager.release(B.id),c={offset:0,size:$,buffer:B.buffer}}let y=this.programManager.normalizeDispatchGroupSize(u),w=y[1]===1&&y[2]===1,v=jc(t,e,w),k=this.programManager.getArtifact(v);if(k||(k=this.programManager.build(t,y),this.programManager.setArtifact(v,k),rt("info",()=>`[artifact] key: ${v}, programName: ${t.name}`)),l&&k.uniformVariablesInfo){if(l.length!==k.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${k.uniformVariablesInfo.length}, got ${l.length} in program "${k.programInfo.name}".`);for(let $=0;$<l.length;$++){let C=l[$],T=C.type,A=typeof C.data=="number"?1:C.data.length,[B,R]=k.uniformVariablesInfo[$];if(T!==B||A!==R)throw new Error(`Uniform variable ${$} mismatch: expect type ${B} with size ${R}, got type ${T} with size ${A} in program "${k.programInfo.name}".`)}}if(rt("info",()=>`[ProgramManager] run "${t.name}" (key=${v}) with ${y[0]}x${y[1]}x${y[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let $={kernelId:this.currentKernelId,programName:k.programInfo.name,inputTensorViews:e,outputTensorViews:f};this.pendingKernels.push($),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push($)}return this.programManager.run(k,s,m,y,c),Lt(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=Rm.get(t);if(!a)throw new Error(`kernel not implemented: ${t}`);let i={kernelType:t,kernelName:n,kernelEntry:a[0],attributes:[a[1],r]};this.kernels.set(e,i)}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: ${t}`);let a=n.kernelType,i=n.kernelName,s=n.kernelEntry,o=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${a}] ${i}" is not allowed to be called recursively`);this.currentKernelId=t,o[0]&&(o[1]=o[0](o[1]),o[0]=void 0),rt("info",()=>`[WebGPU] Start to run kernel "[${a}] ${i}"...`);let u=this.env.debug;this.temporaryData=[];try{return u&&this.device.pushErrorScope("validation"),s(e,o[1]),0}catch(l){return r.push(Promise.resolve(`[WebGPU] Kernel "[${a}] ${i}" failed. ${l}`)),1}finally{u&&r.push(this.device.popErrorScope().then(l=>l?`GPU validation error for kernel "[${a}] ${i}": ${l.message}`:null));for(let l of this.temporaryData)this.gpuDataManager.release(l.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,e,r,n){let a=this.sessionExternalDataMapping.get(t);a||(a=new Map,this.sessionExternalDataMapping.set(t,a));let i=a.get(e),s=this.gpuDataManager.registerExternalBuffer(r,n,i?.[1]);return a.set(e,[s,r]),s}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 go(this,t,e);return ch(n.buffer,r)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){this.queryType="none",(this.env.webgpu.profiling?.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(){rt("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(){rt("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){rt("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(),i=t[n];this.writeTimestamp(this.pendingDispatchNumber*2),a.setPipeline(i.computePipeline),a.setBindGroup(0,i.bindGroup),a.dispatchWorkgroups(...i.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()}}}),Nm={};Mn(Nm,{init:()=>Fm});var wi,Kc,Fm,By=Z(()=>{$e(),Ry(),nn(),Ae(),wi=class Lm{constructor(e,r,n,a){this.module=e,this.dataType=r,this.data=n,this.dims=a}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let e=Y.size(this.dims);return e===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,e)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let e=Y.size(this.dims);return e===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,e)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let e=Y.size(this.dims);return e===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,e)}reshape(e){if(Y.size(e)!==Y.size(this.dims))throw new Error("Invalid new shape");return new Lm(this.module,this.dataType,this.data,e)}},Kc=class{constructor(t,e,r){this.module=t,this.backend=e,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=e.adapterInfo;let n=t.HEAPU32,a=r>>>2;this.opKernelContext=n[a++];let i=n[a++];this.outputCount=n[a++],this.customDataOffset=n[a++],this.customDataSize=n[a++];let s=[];for(let o=0;o<i;o++){let u=n[a++],l=n[a++],p=n[a++],f=[];for(let m=0;m<p;m++)f.push(n[a++]);s.push(new wi(t,u,l,f))}this.inputs=s}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){let r=e?.inputs?.map(s=>typeof s=="number"?this.inputs[s]:s)??this.inputs,n=e?.outputs??[],a=(s,o,u)=>new wi(this.module,o,this.output(s,u),u),i=(s,o)=>{let u=ga(s);if(!u)throw new Error(`Unsupported data type: ${s}`);let l=u*Y.size(o),p=l>0?this.backend.gpuDataManager.create(l).id:0;return new wi(this.module,s,p,o)};return this.backend.run(t,r,n,a,i,this.outputCount)}output(t,e){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+e.length)*4),a=n>>2;this.module.HEAPU32[a++]=e.length;for(let i=0;i<e.length;i++)this.module.HEAPU32[a++]=e[i];return this.module._JsepOutput(this.opKernelContext,t,n)}catch(n){throw new Error(`Failed to generate kernel's output[${t}] with dims [${e}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(r)}}},Fm=async(t,e,r,n)=>{let a=e.jsepInit;if(!a)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(t==="webgpu"){let i=new Dm;await i.initialize(r,n),a("webgpu",[i,s=>i.alloc(s),s=>i.free(s),(s,o,u,l=!1)=>{if(l)rt("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${s}, dst=${o}, size=${u}`),i.memcpy(s,o);else{rt("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${s}, gpuDataId=${o}, size=${u}`);let p=e.HEAPU8.subarray(s>>>0,(s>>>0)+u);i.upload(o,p)}},async(s,o,u)=>{rt("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${s}, dataOffset=${o}, size=${u}`),await i.download(s,()=>e.HEAPU8.subarray(o>>>0,(o>>>0)+u))},(s,o,u)=>i.createKernel(s,o,u,e.UTF8ToString(e._JsepGetNodeName(o))),s=>i.releaseKernel(s),(s,o,u,l)=>{rt("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${u}, kernel=${s}, contextDataOffset=${o}`);let p=new Kc(e,i,o);return i.computeKernel(s,p,l)},()=>i.captureBegin(),()=>i.captureEnd(),()=>i.replay()])}else a("webnn")}}),Yc,Um,Wm,Tr,Xc,Eo,Vm,Gm,Zs,Hm,jm,qm,Dy=Z(()=>{Y_(),X_(),$e(),xa(),Wo(),lh(),Yc=(t,e)=>{at()._OrtInit(t,e)!==0&&je("Can't initialize onnxruntime.")},Um=async t=>{Yc(t.wasm.numThreads,Di(t.logLevel))},Wm=async(t,e)=>{{let r=(By(),Jr(Nm)).init;if(e==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let n=t.webgpu.adapter;if(n){if(typeof n.limits!="object"||typeof n.features!="object"||typeof n.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let a=t.webgpu.powerPreference;if(a!==void 0&&a!=="low-power"&&a!=="high-performance")throw new Error(`Invalid powerPreference setting: "${a}"`);let i=t.webgpu.forceFallbackAdapter;if(i!==void 0&&typeof i!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${i}"`);if(n=await navigator.gpu.requestAdapter({powerPreference:a,forceFallbackAdapter:i}),!n)throw new Error('Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.')}if(!t.wasm.simd)throw new Error("Not supported for WebGPU=ON and SIMD=OFF. Please set `env.wasm.simd` to true when using `webgpu` EP");await r("webgpu",at(),t,n)}if(e==="webnn"){if(typeof navigator>"u"||!navigator.ml)throw new Error("WebNN is not supported in current environment");await r("webnn",at(),t)}}},Tr=new Map,Xc=t=>{let e=at(),r=e.stackSave();try{let n=e.stackAlloc(8);return e._OrtGetInputOutputCount(t,n,n+4)!==0&&je("Can't get session input/output count."),[e.HEAP32[n/4],e.HEAP32[n/4+1]]}finally{e.stackRestore(r)}},Eo=t=>{let e=at(),r=e._malloc(t.byteLength);if(r===0)throw new Error(`Can't create a session. failed to allocate a buffer of size ${t.byteLength}.`);return e.HEAPU8.set(t,r),[r,t.byteLength]},Vm=async(t,e)=>{let r,n,a=at();Array.isArray(t)?[r,n]=t:t.buffer===a.HEAPU8.buffer?[r,n]=[t.byteOffset,t.byteLength]:[r,n]=Eo(t);let i=0,s=0,o=0,u=[],l=[],p=[];try{if([s,u]=uh(e),e?.externalData&&a.mountExternalData){let $=[];for(let C of e.externalData){let T=typeof C=="string"?C:C.path;$.push(Ni(typeof C=="string"?C:C.data).then(A=>{a.mountExternalData(T,A)}))}await Promise.all($)}i=await a._OrtCreateSession(r,n,s),i===0&&je("Can't create a session.");let[f,m]=Xc(i),c=!!e?.enableGraphCapture,y=[],w=[],v=[];for(let $=0;$<f;$++){let C=a._OrtGetInputName(i,$);C===0&&je("Can't get an input name."),l.push(C),y.push(a.UTF8ToString(C))}for(let $=0;$<m;$++){let C=a._OrtGetOutputName(i,$);C===0&&je("Can't get an output name."),p.push(C);let T=a.UTF8ToString(C);w.push(T);{if(c&&e?.preferredOutputLocation===void 0){v.push("gpu-buffer");continue}let A=typeof e?.preferredOutputLocation=="string"?e.preferredOutputLocation:e?.preferredOutputLocation?.[T]??"cpu";if(A!=="cpu"&&A!=="cpu-pinned"&&A!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${A}.`);if(c&&A!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${A}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);v.push(A)}}let k=null;return v.some($=>$==="gpu-buffer")&&(o=a._OrtCreateBinding(i),o===0&&je("Can't create IO binding."),k={handle:o,outputPreferredLocations:v,outputPreferredLocationsEncoded:v.map($=>mo($))}),Tr.set(i,[i,l,p,k,c,!1]),[i,y,w]}catch(f){throw l.forEach(m=>a._OrtFree(m)),p.forEach(m=>a._OrtFree(m)),o!==0&&a._OrtReleaseBinding(o),i!==0&&a._OrtReleaseSession(i),f}finally{a._free(r),s!==0&&a._OrtReleaseSessionOptions(s),u.forEach(f=>a._free(f)),a.unmountExternalData?.()}},Gm=t=>{let e=at(),r=Tr.get(t);if(!r)throw new Error(`cannot release session. invalid session id: ${t}`);let[n,a,i,s,o]=r;s&&(o&&e._OrtClearBoundOutputs(s.handle),e._OrtReleaseBinding(s.handle)),e.jsepOnReleaseSession?.(t),a.forEach(u=>e._OrtFree(u)),i.forEach(u=>e._OrtFree(u)),e._OrtReleaseSession(n),Tr.delete(t)},Zs=(t,e,r,n,a,i=!1)=>{if(!t){e.push(0);return}let 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${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 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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 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= ${r}(${t.alpha});\n\n  fn elu_f32(a: ${r}) -> ${r} {\n  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);\n  }\n\n  fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {\n  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));\n  }`,t.cacheKey))},On=(e="f32")=>`\nconst r0: ${e} = 0.3275911;\nconst r1: ${e} = 0.254829592;\nconst r2: ${e} = -0.284496736;\nconst r3: ${e} = 1.421413741;\nconst r4: ${e} = -1.453152027;\nconst r5: ${e} = 1.061405429;\n\nfn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {\n  let absv = abs(v);\n  let x = 1.0 / (1.0 + r0 * absv);\n  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));\n}`,gs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,On(t)))},ys=e=>{e.compute(ke(e.inputs[0],"Exp","exp"))},bs=e=>{e.compute(ke(e.inputs[0],"Floor","floor"))},ws=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,On(t)))},vs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"LeakyRelu",o=>`select(leaky_relu_alpha_ * ${o}, ${o}, ${o} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},$s=e=>{e.compute(ke(e.inputs[0],"Not",t=>`!${t}`))},_s=e=>{e.compute(ke(e.inputs[0],"Neg",t=>`-${t}`))},Ss=e=>{e.compute(ke(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},xs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Cs=e=>{e.compute(ke(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},As=e=>ve(e),Is=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"HardSigmoid",o=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${o} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Ts=e=>{e.compute(ke(e.inputs[0],"Sin","sin"))},Es=e=>{e.compute(ke(e.inputs[0],"Sinh","sinh"))},Ps=e=>{e.compute(ke(e.inputs[0],"Sqrt","sqrt"))},ks=e=>{e.compute(ke(e.inputs[0],"Tan","tan"))},Os=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Rs=e=>{e.compute(ke(e.inputs[0],"Tanh",Os))},Bo=(e="f32")=>`\nconst fast_gelu_a: ${e} = 0.5;\nconst fast_gelu_b: ${e} = 0.7978845608028654;\nconst fast_gelu_c: ${e} = 0.035677408136300125;\n\nfn tanh_v(v: vec4<${e}>) -> vec4<${e}> {\n  return ${Os("v")};\n}\n`,Do=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Bs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"FastGelu",Do,Bo(t),void 0,e.inputs[0].dataType))},Ds=(e,t)=>{let r=et(e.inputs[0].dataType);return e.compute(ke(e.inputs[0],"ThresholdedRelu",o=>`select(vec4<${r}>(0.0), ${o}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},zs=e=>{e.compute(ke(e.inputs[0],"Log","log"))}});var pc,mc,Us,Vs=Y(()=>{"use strict";Se();_e();Rn();pc=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},mc=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=U("input",e[0].dataType,e[0].dims,4),o=U("bias",e[0].dataType,[e[0].dims[2]],4),i=j("output",e[0].dataType,t,4),u=M.size(t)/4,a=De(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)}}),getShaderSource:p=>`\n  const M_SQRT2 = sqrt(2.0);\n  const halfChannels = ${e[0].dims[2]/4/2}u;\n\n  ${p.declareVariables(r,o,i)}\n\n  ${On(a)}\n\n  ${p.mainStart()}\n    ${p.guardAgainstOutOfBoundsWorkgroupSizes(u)}\n    let biasIdx = global_idx % halfChannels;\n    let batchIndex = global_idx / halfChannels;\n    let inputOffset = biasIdx + batchIndex * halfChannels * 2;\n    let valueLeft = input[inputOffset] + bias[biasIdx];\n    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];\n    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);\n\n    ${i.setByOffset("global_idx","valueLeft * geluRight")}\n  }`}},Us=e=>{pc(e.inputs),e.compute(mc(e.inputs))}});var fc,hc,Ot,Ws,Ns,Gs,Hs,Ls,Fs,qs,js,Ks,Ys,Zs=Y(()=>{"use strict";ye();Se();_e();fc=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w,_;typeof c=="string"?w=_=(x,E)=>`${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'}),Ir,Dt,vn,aa,ia,eo,bi,Ur,Wr,Qc,sa,Km,Ym,Xm,Qm,Jm,Zm,eg,tg=Z(()=>{ar(),Dy(),xa(),Ir=()=>!!Ue.wasm.proxy&&typeof document<"u",vn=!1,aa=!1,ia=!1,bi=new Map,Ur=(t,e)=>{let r=bi.get(t);r?r.push(e):bi.set(t,[e])},Wr=()=>{if(vn||!aa||ia||!Dt)throw new Error("worker not 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t=(Uy(),Jr(ag)).wasmBackend;Xr("webgpu",t,5),Xr("webnn",t,5),Xr("cpu",t,10),Xr("wasm",t,10)}Object.defineProperty(Ue.versions,"web",{value:Wy,enumerable:!0});/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*//**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */const Gy=Object.freeze(Object.defineProperty({__proto__:null,get InferenceSession(){return No},get TRACE(){return ma},get TRACE_FUNC_BEGIN(){return nr},get TRACE_FUNC_END(){return Lt},get Tensor(){return bt},get TrainingSession(){return Fo},default:Vy,get env(){return Ue},get registerBackend(){return Xr}},Symbol.toStringTag,{value:"Module"})),Hy=(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,i;e?.tensorLayout!==void 0&&e.tensorLayout==="NHWC"?(a=t.dims[2],i=t.dims[3]):(a=t.dims[3],i=t.dims[2]);const s=e?.format!==void 0?e.format:"RGB",o=e?.norm;let u,l;o===void 0||o.mean===void 0?u=[255,255,255,255]:typeof o.mean=="number"?u=[o.mean,o.mean,o.mean,o.mean]:(u=[o.mean[0],o.mean[1],o.mean[2],0],o.mean[3]!==void 0&&(u[3]=o.mean[3])),o===void 0||o.bias===void 0?l=[0,0,0,0]:typeof 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Error("Can not access image data")}else if(n){let p,f;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(p=e.resizedHeight,f=e.resizedWidth):(p=t.height,f=t.width),e!==void 0&&(o=e),o.format="RGBA",o.height=p,o.width=f,e!==void 0){const m=u();m.width=f,m.height=p;const c=l(m);if(c!=null)c.putImageData(t,0,0),s=c.getImageData(0,0,f,p).data;else throw new Error("Can not access image data")}else s=t.data}else if(a){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const p=u();p.width=t.width,p.height=t.height;const f=l(p);if(f!=null){const m=t.height,c=t.width;return f.drawImage(t,0,0,c,m),s=f.getImageData(0,0,c,m).data,o.height=m,o.width=c,ro(s,o)}else throw new Error("Can not access image data")}else{if(i)return new Promise((p,f)=>{const m=u(),c=l(m);if(!t||!c)return f();const y=new Image;y.crossOrigin="Anonymous",y.src=t,y.onload=()=>{m.width=y.width,m.height=y.height,c.drawImage(y,0,0,m.width,m.height);const w=c.getImageData(0,0,m.width,m.height);o.height=m.height,o.width=m.width,p(ro(w.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return ro(s,o);throw new Error("Input data provided is not supported - aborted tensor creation")},Ky=(t,e)=>{const{width:r,height:n,download:a,dispose:i}=e,s=[1,n,r,4];return new lr({location:"texture",type:"float32",texture:t,dims:s,download:a,dispose:i})},Yy=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:i}=e;return new lr({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:i})},Xy=(t,e,r)=>new lr({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),kn=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),Ai=new 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lr({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new lr({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new lr({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let lr=class{constructor(e,r,n){Qy();let a,i;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,a=e.type,i=e.dims,e.location){case"cpu-pinned":{const o=kn.get(a);if(!o)throw new TypeError(`unsupported type "${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: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return Zy(this,e)}};const ew=lr,ca=[];let Co,ya;Zr.IS_NODE_ENV?(ya=Ze??n_,ca.push("cpu"),Co=["cpu"]):(ya=Gy,Zr.IS_WEBGPU_AVAILABLE&&ca.push("webgpu"),ca.push("wasm"),Co=["wasm"]);const tw=ya.InferenceSession;function rw(t){let e=Co;if(t){if(!ca.includes(t))throw new Error(`Unsupported device: "${t}". Should be one of: ${ca.join(", ")}.`);e=[t]}return e}async function sg(t,e){return await tw.create(t,e)}function og(t){return t instanceof ya.Tensor}const Yr=ya?.env;Yr?.wasm&&(Yr.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.18.0/dist/",Yr.wasm.proxy=!Zr.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(Yr.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(Yr.wasm.simd=!1));function nw(){return Yr?.wasm?.proxy}Ct.backends.onnx=Yr;const no=async(t,e,r)=>{const n=await sg(t,e);return async a=>{const i=Object.fromEntries(Object.entries(a).map(([o,u])=>[o,u.ort_tensor])),s=await n.run(i);return new pe(s[r])}};class tp{static session_options={};static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=no(new Uint8Array([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20]),this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=no(new Uint8Array([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20]),this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=no(new Uint8Array([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20]),this.session_options,"c")),this._matmul}}const rp=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 pe{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}ort_tensor;constructor(...e){return og(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new ew(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})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...r]=this.dims;if(r.length>0){const n=r.reduce((a,i)=>a*i);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=ur(e,r),n.length>0){const a=n.reduce((i,s)=>i*s);return this._subarray(e,a,n)}else return new pe(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,i=(e+1)*r,s="subarray"in this.data?this.data.subarray(a,i):this.data.slice(a,i);return new pe(this.type,s,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 aw(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 pe(this.type,this.data.slice(),this.dims.slice())}slice(...e){let r=[],n=[];for(let l=0;l<this.dims.length;++l){let p=e[l];if(p==null)n.push([0,this.dims[l]]),r.push(this.dims[l]);else if(typeof p=="number")p=ur(p,this.dims[l],l),n.push([p,p+1]);else if(Array.isArray(p)&&p.length===2){let[f,m]=p;if(f=f===null?0:ur(f,this.dims[l],l,!1),m=m===null?this.dims[l]:ur(m,this.dims[l],l,!1),f>m)throw new Error(`Invalid slice: ${p}`);let c=[Math.max(f,0),Math.min(m,this.dims[l])];n.push(c),r.push(c[1]-c[0])}else throw new Error(`Invalid slice: ${p}`)}let a=n.map(([l,p])=>p-l),i=a.reduce((l,p)=>l*p);const s=this.data;let o=new s.constructor(i);const u=this.stride();for(let l=0;l<i;++l){let p=0;for(let f=a.length-1,m=l;f>=0;--f){const c=a[f];p+=(m%c+n[f][0])*u[f],m=Math.floor(m/c)}o[l]=s[p]}return new pe(this.type,o,r)}permute(...e){return iw(this,e)}transpose(...e){return this.permute(...e)}sum(e=null,r=!1){return this.norm(1,e,r)}norm(e="fro",r=null,n=!1){if(e==="fro")e=2;else if(typeof e=="string")throw Error(`Unsupported norm: ${e}`);const a=this.data;if(r===null){let o=a.reduce((u,l)=>u+l**e,0)**(1/e);return new pe(this.type,[o],[])}r=ur(r,this.dims.length);const i=this.dims.slice();i[r]=1;const s=new a.constructor(a.length/this.dims[r]);for(let o=0;o<a.length;++o){let u=0;for(let l=this.dims.length-1,p=o,f=1;l>=0;--l){const m=this.dims[l];if(l!==r){const c=p%m;u+=c*f,f*=i[l]}p=Math.floor(p/m)}s[u]+=a[o]**e}if(e!==1)for(let o=0;o<s.length;++o)s[o]=s[o]**(1/e);return n||i.splice(r,1),new pe(this.type,s,i)}normalize_(e=2,r=1){r=ur(r,this.dims.length);const n=this.norm(e,r,!0),a=this.data;for(let i=0;i<a.length;++i){let s=0;for(let o=this.dims.length-1,u=i,l=1;o>=0;--o){const p=this.dims[o];if(o!==r){const f=u%p;s+=f*l,l*=this.dims[o]}u=Math.floor(u/p)}a[i]/=n.data[s]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return uw(this.dims)}squeeze(e=null){return new pe(this.type,this.data,ip(this.dims,e))}squeeze_(e=null){return this.dims=ip(this.dims,e),this}unsqueeze(e=null){return new pe(this.type,this.data,sp(this.dims,e))}unsqueeze_(e=null){return this.dims=sp(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),i=this.dims.slice(r+1);return this.dims=[...n,a.reduce((s,o)=>s*o,1),...i],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let n=0;n<e.length;++n)if(e[n]===-1){if(r!==-1)throw new Error("Only one dimension can be inferred");r=n}if(r!==-1){const n=e.reduce((a,i,s)=>s!==r?a*i:a,1);e[r]=this.data.length/n}return new pe(this.type,this.data,e)}neg_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=-e[r];return this}neg(){return this.clone().neg_()}clamp_(e,r){const n=this.data;for(let a=0;a<n.length;++a)n[a]=Math.min(Math.max(n[a],e),r);return this}clamp(e,r){return this.clone().clamp_(e,r)}round_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=Math.round(e[r]);return this}round(){return this.clone().round_()}mean(e=null,r=!1){return ru(this,e,r)}to(e){if(this.type===e)return this;if(!rp.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);return new pe(e,rp[e].from(this.data),this.dims)}}function aw(t,e){const r=t.length,n=e.reduce((i,s)=>i*s);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let a=t;for(let i=e.length-1;i>=0;i--)a=a.reduce((s,o)=>{let u=s[s.length-1];return u.length<e[i]?u.push(o):s.push([o]),s},[[]]);return a[0]}function iw(t,e){const[r,n]=__(t.data,t.dims,e);return new pe(t.type,r,n)}function ug(t,[e,r],n="bilinear",a=!1){const i=t.dims.at(-3)??1,s=t.dims.at(-2),o=t.dims.at(-1);let u=g_(t.data,[i,s,o],[e,r],n,a);return new pe(t.type,u,[i,e,r])}async function np(t,{size:e=null,mode:r="bilinear"}={}){if(t.dims.length!==4)throw new Error("`interpolate_4d` currently only supports 4D input.");if(!e)throw new Error("`interpolate_4d` requires a `size` argument.");let n;if(e.length===2)n=[...t.dims.slice(0,2),...e];else if(e.length===3)n=[t.dims[0],...e];else if(e.length===4)n=e;else throw new Error("`size` must be of length 2, 3, or 4.");let a;if(r==="bilinear")a=await tp.bilinear_interpolate_4d;else if(r==="bicubic")a=await tp.bicubic_interpolate_4d;else throw new Error(`Unsupported mode: ${r}`);const i=new pe("int64",new BigInt64Array(n.map(BigInt)),[n.length]);return await a({x:t,s:i})}function ap(t,e){let r=[t.dims[0],t.dims[2]],n=new t.data.constructor(r[0]*r[1]),[a,i,s]=t.dims,o=0;for(let u=0;u<a;++u){let l=u*s*i;for(let p=0;p<s;++p){let f=0,m=0,c=u*i,y=l+p;for(let v=0;v<i;++v){let k=Number(e.data[c+v]);m+=k,f+=t.data[y+v*s]*k}let w=f/m;n[o++]=w}}return new pe(t.type,n,r)}function sw(t,e,{eps:r=1e-5}={}){if(t.dims.length!==2)throw new Error("`layer_norm` currently only supports 2D input.");const[n,a]=t.dims;if(e.length!==1&&e[0]!==a)throw new Error("`normalized_shape` must be a 1D array with shape `[input.dims[1]]`.");const[i,s]=lg(t,1,0,!0),o=new t.data.constructor(t.data.length);for(let u=0;u<n;++u){const l=u*a;for(let p=0;p<a;++p){const f=l+p;o[f]=(t.data[f]-s.data[u])/(i.data[u]+r)}}return new pe(t.type,o,t.dims)}function ip(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 sp(t,e){return e=ur(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function ur(t,e,r=null,n=!0){if(n&&(t<-e||t>=e))throw new Error(`IndexError: index ${t} is out of bounds for dimension${r===null?"":" "+r} with size ${e}`);return t<0&&(t=(t%e+e)%e),t}function dr(t,e=0){e=ur(e,t[0].dims.length);const r=t[0].dims.slice();r[e]=t.reduce((s,o)=>s+o.dims[e],0);const n=r.reduce((s,o)=>s*o,1),a=new t[0].data.constructor(n),i=t[0].type;if(e===0){let s=0;for(let o of t)a.set(o.data,s),s+=o.data.length}else{let s=0;for(let o=0;o<t.length;++o){let u=t[o];for(let l=0;l<u.data.length;++l){let p=0;for(let f=u.dims.length-1,m=l,c=1;f>=0;--f){const y=u.dims[f];let w=m%y;f===e&&(w+=s),p+=w*c,c*=r[f],m=Math.floor(m/y)}a[p]=u.data[l]}s+=u.dims[e]}}return new pe(i,a,r)}function wa(t,e=0){return dr(t.map(r=>r.unsqueeze(e)),e)}function lg(t,e=null,r=1,n=!1){if(e===null){const l=t.data.reduce((c,y)=>c+y,0)/t.data.length,p=Math.sqrt(t.data.reduce((c,y)=>c+(y-l)**2,0)/(t.data.length-r)),f=new pe(t.type,[l],[]);return[new pe(t.type,[p],[]),f]}e=ur(e,t.dims.length);const a=ru(t,e,n),i=t.dims.slice();i[e]=1;const s=new t.data.constructor(t.data.length/t.dims[e]);for(let u=0;u<t.data.length;++u){let l=0;for(let p=t.dims.length-1,f=u,m=1;p>=0;--p){const c=t.dims[p];if(p!==e){const y=f%c;l+=y*m,m*=i[p]}f=Math.floor(f/c)}s[l]+=(t.data[u]-a.data[l])**2}for(let u=0;u<s.length;++u)s[u]=Math.sqrt(s[u]/(t.dims[e]-r));return n||i.splice(e,1),[new pe(t.type,s,i),a]}function ru(t,e=null,r=!1){if(e===null){let i=t.data.reduce((s,o)=>s+o,0);return new pe(t.type,[i/t.data.length],[])}e=ur(e,t.dims.length);const n=t.dims.slice();n[e]=1;const a=new t.data.constructor(t.data.length/t.dims[e]);for(let i=0;i<t.data.length;++i){let s=0;for(let o=t.dims.length-1,u=i,l=1;o>=0;--o){const p=t.dims[o];if(o!==e){const f=u%p;s+=f*l,l*=n[o]}u=Math.floor(u/p)}a[s]+=t.data[i]}if(t.dims[e]!==1)for(let i=0;i<a.length;++i)a[i]=a[i]/t.dims[e];return r||n.splice(e,1),new pe(t.type,a,n)}function ow(t){const[e,r]=t.dims,n=[e+1,r+1],a=new pe("float32",new Float32Array(n[0]*n[1]).fill(1/0),n),i=new pe("float32",new Float32Array(n[0]*n[1]).fill(-1),n);a[0].data[0]=0;for(let p=1;p<r+1;++p)for(let f=1;f<e+1;++f){const m=a[f-1][p-1].item(),c=a[f-1][p].item(),y=a[f][p-1].item();let w,v;m<c&&m<y?(w=m,v=0):c<m&&c<y?(w=c,v=1):(w=y,v=2),a[f].data[p]=t[f-1][p-1].item()+w,i[f].data[p]=v}let s=e,o=r;i.data.fill(2,0,n[1]);for(let p=0;p<n[0];++p)i[p].data[0]=1;let u=[],l=[];for(;s>0||o>0;)switch(u.push(s-1),l.push(o-1),i[s][o].item()){case 0:--s,--o;break;case 1:--s;break;case 2:--o;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${o}]. Please file a bug report.`)}return u.reverse(),l.reverse(),[u,l]}function uw(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 nu(t,e,r,n){const a=t.reduce((i,s)=>i*s,1);return new pe(r,new n(a).fill(e),t)}function lw(t,e){let r,n;return r="float32",n=Float32Array,nu(t,e,r,n)}function dw(t,e){return lw(t.dims,e)}function On(t){return nu(t,1n,"int64",BigInt64Array)}function cw(t){return On(t.dims)}function pw(t){return nu(t,0n,"int64",BigInt64Array)}function hw(t){return pw(t.dims)}class fw{constructor(e=(r,n)=>r>n){this._heap=[],this._comparator=e}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)this._heap.push(r),this._siftUp();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(){let e=this.size-1;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}}}class mw{constructor(){this.root=Hi.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=Hi.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 i=e[a];n+=i,r=r.children.get(i),r!==void 0&&r.isLeaf&&(yield n)}}}class Hi{constructor(e,r){this.isLeaf=e,this.children=r}static default(){return new Hi(!1,new Map)}}class gw{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 pa(this.bosTokenId,0,0,0,0),i=new pa(this.eosTokenId,1,this.len,0,0);this.nodes.push(a.clone()),this.nodes.push(i.clone()),this.beginNodes[this.len].push(i),this.endNodes[0].push(a)}insert(e,r,n,a){const i=this.nodes.length,s=new pa(a,i,e,r,n);this.beginNodes[e].push(s),this.endNodes[e+r].push(s),this.nodes.push(s)}viterbi(){const e=this.len;let r=0;for(;r<=e;){if(this.beginNodes[r].length==0)return[];for(let o of this.beginNodes[r]){o.prev=null;let u=0,l=null;for(let p of this.endNodes[r]){const f=p.backtraceScore+o.score;(l===null||f>u)&&(l=p.clone(),u=f)}if(l!==null)o.prev=l,o.backtraceScore=u;else return[]}++r}const 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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 et(t.negate?!a:a)}evaluateUnaryExpression(t,e){const r=this.evaluate(t.argument,e);switch(t.operator.value){case"not":return new et(!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 i of t.args)if(i.type==="KeywordArgumentExpression"){const s=i;n.set(s.key.value,this.evaluate(s.value,e))}else r.push(this.evaluate(i,e));n.size>0&&r.push(new er(n));const a=this.evaluate(t.callee,e);if(a.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${a.type}`);return a.value(r,e)}evaluateSliceExpression(t,e,r){if(!(t instanceof Je||t instanceof 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),i=this.evaluate(e.step,r);if(!(n instanceof He||n instanceof Zt))throw new Error("Slice start must be numeric or undefined");if(!(a instanceof He||a instanceof Zt))throw new Error("Slice stop must be numeric or undefined");if(!(i instanceof He||i instanceof Zt))throw new Error("Slice step must be numeric or undefined");return t instanceof Je?new Je(hp(t.value,n.value,a.value,i.value)):new Pe(hp(Array.from(t.value),n.value,a.value,i.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 er){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 Je||r instanceof Pe)if(n instanceof He)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 xr?a:new Zt}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 er))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 ha}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 To(e),n=this.evaluate(t.iterable,r);if(!(n instanceof Je))throw new Error(`Expected iterable type in for loop: got ${n.type}`);let a="";for(let i=0;i<n.value.length;++i){const s=new Map([["index",new He(i+1)],["index0",new He(i)],["revindex",new He(n.value.length-i)],["revindex0",new He(n.value.length-i-1)],["first",new et(i===0)],["last",new et(i===n.value.length-1)],["length",new He(n.value.length)],["previtem",i>0?n.value[i-1]:new Zt],["nextitem",i<n.value.length-1?n.value[i+1]:new Zt]]);r.setVariable("loop",new er(s));const o=n.value[i];if(t.loopvar.type==="Identifier")r.setVariable(t.loopvar.value,o);else if(t.loopvar.type==="TupleLiteral"){const l=t.loopvar;if(o.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${o.type}`);const p=o;if(l.value.length!==p.value.length)throw new Error(`Too ${l.value.length>p.value.length?"few":"many"} items to unpack`);for(let f=0;f<l.value.length;++f){if(l.value[f].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${l.value[f].type}`);r.setVariable(l.value[f].value,p.value[f])}}const u=this.evaluateBlock(t.body,r);a+=u.value}return new Pe(a)}evaluate(t,e){if(t===void 0)return new Zt;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 He(Number(t.value));case"StringLiteral":return new Pe(t.value);case"BooleanLiteral":return new et(t.value);case"ArrayLiteral":return new Je(t.value.map(r=>this.evaluate(r,e)));case"TupleLiteral":return new Bw(t.value.map(r=>this.evaluate(r,e)));case"ObjectLiteral":{const r=new Map;for(const[n,a]of t.value){const i=this.evaluate(n,e);if(!(i instanceof Pe))throw new Error(`Object keys must be strings: got ${i.type}`);r.set(i.value,this.evaluate(a,e))}return new er(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 Mi(t){switch(typeof t){case"number":return new He(t);case"string":return new Pe(t);case"boolean":return new et(t);case"object":return t===null?new ha:Array.isArray(t)?new Je(t.map(Mi)):new er(new Map(Object.entries(t).map(([e,r])=>[e,Mi(r)])));case"function":return new br((e,r)=>{const n=t(...e.map(a=>a.value))??null;return Mi(n)});default:throw new Error(`Cannot convert to runtime value: ${t}`)}}var Nw=class{parsed;constructor(t){const e=bw(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=Pw(e)}render(t){const e=new To;e.set("false",!1),e.set("true",!0),e.set("raise_exception",a=>{throw new Error(a)}),e.set("range",Rw);for(const[a,i]of Object.entries(t))e.set(a,i);return new Dw(e).run(this.parsed).value}};const cg=[["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"]],Oi=new Map(cg),Fw=new Map([...cg.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 pg(t){t=t.toLowerCase();let e=Fw.get(t);if(e===void 0)if(Oi.has(t))e=t;else{const n=t.length===2?Oi.keys():Oi.values();throw new Error(`Language "${t}" is not supported. Must be one of: ${JSON.stringify(n)}`)}return e}const io="https://github.com/xenova/transformers.js/issues/new/choose";async function hg(t,e){const r=await Promise.all([Mr(t,"tokenizer.json",!0,e),Mr(t,"tokenizer_config.json",!0,e)]);return e.legacy!==null&&(r[1].legacy=e.legacy),r}function Lw(t,e){const r=[];let n=0;for(const a of t.matchAll(e)){const i=a[0];n<a.index&&r.push(t.slice(n,a.index)),i.length>0&&r.push(i),n=a.index+i.length}return n<t.length&&r.push(t.slice(n)),r}function Qi(t,e=!0){if(t.Regex!==void 0){let r=t.Regex.replace(/\\([#&~])/g,"$1");for(const[n,a]of Hw)r=r.replaceAll(n,a);return new RegExp(r,"gu")}else if(t.String!==void 0){const r=Ap(t.String);return new RegExp(e?r:`(${r})`,"gu")}else return console.warn("Unknown pattern type:",t),null}function au(t){return new Map(Object.entries(t))}function fg(t){const e=t.dims;switch(e.length){case 1:return t.tolist();case 2:if(e[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return t.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${e.length}.`)}}function iu(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 mg(t){return t.replace(/[\u0300-\u036f]/g,"")}function Uw(t){return mg(t.toLowerCase())}function Ww(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 Vw(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 Gw(t){return t.match(/\S+/g)||[]}const ba="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",Hw=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class jw{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 Ea extends vt{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 qw(e);case"Unigram":return new Kw(e,...r);case"BPE":return new Xw(e);default:if(e.vocab)return new Qw(e,...r);throw new Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){let r=this.encode(e);return this.fuse_unk&&(r=Vw(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 qw extends Ea{constructor(e){super(e),this.tokens_to_ids=au(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 i=!1,s=0;const o=[];for(;s<a.length;){let u=a.length,l=null;for(;s<u;){let p=a.slice(s,u).join("");if(s>0&&(p=this.config.continuing_subword_prefix+p),this.tokens_to_ids.has(p)){l=p;break}--u}if(l===null){i=!0;break}o.push(l),s=u}i?r.push(this.unk_token):r.push(...o)}return r}}class Kw extends Ea{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 i=e.vocab[a];this.vocab[a]=i[0],this.scores[a]=i[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,i)=>[a,i])),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=Op(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new mw,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 s=!1;for(let o of this.trie.commonPrefixSearch(r.slice(a))){const u=this.tokens_to_ids.get(o),l=this.scores[u],p=o.length;e.insert(a,p,l,u),!s&&p===1&&(s=!0)}s||e.insert(a,1,this.unkScore,this.unk_token_id),a+=1}}tokenize(e){const r=new gw(e,this.bosTokenId,this.eosTokenId);return this.populateNodes(r),r.tokens()}encode(e){const r=[];for(const n of e){const a=this.tokenize(n);r.push(...a)}return r}}const gg=(()=>{const t=[...Array.from({length:94},(a,i)=>i+33),...Array.from({length:12},(a,i)=>i+161),...Array.from({length:82},(a,i)=>i+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,i)=>[a,n[i]]))})(),Yw=d_(gg);class Xw extends Ea{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=au(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 i=new fw((u,l)=>u.score<l.score);let s={token:n[0],bias:0,prev:null,next:null},o=s;for(let u=1;u<n.length;++u){const l={bias:u/n.length,token:n[u],prev:o,next:null};o.next=l,this._add_node(i,o),o=l}for(;!i.isEmpty();){const u=i.pop();if(u.deleted||!u.next||u.next.deleted)continue;if(u.deleted=!0,u.next.deleted=!0,u.prev){const p={...u.prev};u.prev.deleted=!0,u.prev=p,p.prev?p.prev.next=p:s=p}const l={token:u.token+u.next.token,bias:u.bias,prev:u.prev,next:u.next.next};l.prev?(l.prev.next=l,this._add_node(i,l.prev)):s=l,l.next&&(l.next.prev=l,this._add_node(i,l))}for(let u=s;u!==null;u=u.next)a.push(u.token)}else a=n;if(this.continuing_subword_suffix)for(let i=0;i<a.length-1;++i)a[i]+=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 i of a)this.tokens_to_ids.has(i)?r.push(i):this.byte_fallback?r.push(...Array.from(this.text_encoder.encode(i)).map(s=>`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class Qw extends Ea{constructor(e,r){super(e),this.tokens_to_ids=au(r.target_lang?e.vocab[r.target_lang]:e.vocab),this.bos_token=r.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=r.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=r.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=r.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[n,a]of this.tokens_to_ids)this.vocab[a]=n}encode(e){return e}}class Pt extends vt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new ob(e);case"Precompiled":return new Eb(e);case"Sequence":return new sb(e);case"Replace":return new Jw(e);case"NFC":return new Zw(e);case"NFKC":return new eb(e);case"NFKD":return new tb(e);case"Strip":return new rb(e);case"StripAccents":return new nb(e);case"Lowercase":return new ab(e);case"Prepend":return new ib(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class Jw extends Pt{normalize(e){const r=Qi(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class Zw extends Pt{normalize(e){return e=e.normalize("NFC"),e}}class eb extends Pt{normalize(e){return e=e.normalize("NFKC"),e}}class tb extends Pt{normalize(e){return e=e.normalize("NFKD"),e}}class rb extends Pt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class nb extends Pt{normalize(e){return e=mg(e),e}}class ab extends Pt{normalize(e){return e=e.toLowerCase(),e}}class ib extends Pt{normalize(e){return e=this.config.prepend+e,e}}class sb extends Pt{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>Pt.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class ob extends Pt{_tokenize_chinese_chars(e){const r=[];for(let n=0;n<e.length;++n){const a=e[n],i=a.charCodeAt(0);Ww(i)?(r.push(" "),r.push(a),r.push(" ")):r.push(a)}return r.join("")}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(e){switch(e){case"	":case`
`:case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/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 Ut extends vt{static fromConfig(e){if(e===null)return null;switch(e.type){case"BertPreTokenizer":return new ub(e);case"Sequence":return new Cb(e);case"Whitespace":return new Tb(e);case"WhitespaceSplit":return new Ib(e);case"Metaspace":return new bg(e);case"ByteLevel":return new lb(e);case"Split":return new db(e);case"Punctuation":return new cb(e);case"Digits":return new pb(e);case"Replace":return new Ab(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,r){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,r){return(Array.isArray(e)?e.map(n=>this.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class ub extends Ut{constructor(e){super(),this.pattern=new RegExp(`[^\\s${ba}]+|[${ba}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class lb extends Ut{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=gg,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),i=>this.byte_encoder[i]).join(""))}}class db extends Ut{constructor(e){super(),this.config=e,this.pattern=Qi(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:Lw(e,this.pattern)}}class cb extends Ut{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${ba}]+|[${ba}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class pb extends Ut{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Tn extends vt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new hb(e);case"ByteLevel":return new wg(e);case"RobertaProcessing":return new yg(e);case"BertProcessing":return new _g(e);case"Sequence":return new fb(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 _g extends Tn{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=ct([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const i=n&&this instanceof yg?[this.sep]:[],s=n?[this.sep]:[];e=ct(e,i,r,s),a=ct(a,new Array(r.length+i.length+s.length).fill(1))}return{tokens:e,token_type_ids:a}}}class yg extends _g{}class hb extends Tn{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 i=[],s=[];for(const o of a)"SpecialToken"in o?n&&(i.push(o.SpecialToken.id),s.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(i=ct(i,e),s=ct(s,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(i=ct(i,r),s=ct(s,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:i,token_type_ids:s}}}class wg extends Tn{post_process(e,r=null){return r&&(e=ct(e,r)),{tokens:e}}}class fb extends Tn{constructor(e){super(e),this.processors=e.processors.map(r=>Tn.fromConfig(r))}post_process(e,r=null,n={}){let a;for(const i of this.processors)if(i instanceof wg)e=i.post_process(e).tokens,r&&(r=i.post_process(r).tokens);else{const s=i.post_process(e,r,n);e=s.tokens,a=s.token_type_ids}return{tokens:e,token_type_ids:a}}}class Rt extends vt{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 wb(e);case"Metaspace":return new kb(e);case"ByteLevel":return new bb(e);case"Replace":return new mb(e);case"ByteFallback":return new gb(e);case"Fuse":return new _b(e);case"Strip":return new yb(e);case"Sequence":return new $b(e);case"CTC":return new vb(e);case"BPEDecoder":return new xb(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 mb extends Rt{decode_chain(e){const r=Qi(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class gb extends Rt{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const r=[];let n=[];for(const a of e){let i=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const s=parseInt(a.slice(3,5),16);isNaN(s)||(i=s)}if(i!==null)n.push(i);else{if(n.length>0){const s=this.text_decoder.decode(Uint8Array.from(n));r.push(s),n=[]}r.push(a)}}if(n.length>0){const a=this.text_decoder.decode(Uint8Array.from(n));r.push(a),n=[]}return r}}class _b extends Rt{decode_chain(e){return[e.join("")]}}class yb extends Rt{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map(r=>{let n=0;for(let i=0;i<this.start&&r[i]===this.content;++i){n=i+1;continue}let a=r.length;for(let i=0;i<this.stop;++i){const s=r.length-i-1;if(r[s]===this.content){a=s;continue}else break}return r.slice(n,a)})}}class wb extends Rt{constructor(e){super(e),this.cleanup=e.cleanup}decode_chain(e){return e.map((r,n)=>(n!==0&&(r.startsWith(this.config.prefix)?r=r.replace(this.config.prefix,""):r=" "+r),this.cleanup&&(r=iu(r)),r))}}class bb extends Rt{constructor(e){super(e),this.byte_decoder=Yw,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(i=>this.byte_decoder[i]));return this.text_decoder.decode(n)}decode_chain(e){const r=[];let n=[];for(const a of e)this.added_tokens.find(i=>i.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 vb extends Rt{constructor(e){super(e),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(e){if(e.length===0)return"";const r=[e[0]];for(let i=1;i<e.length;++i)e[i]!==r.at(-1)&&r.push(e[i]);let a=r.filter(i=>i!==this.pad_token).join("");return this.cleanup&&(a=iu(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class $b extends Rt{constructor(e){super(e),this.decoders=e.decoders.map(r=>Rt.fromConfig(r))}decode_chain(e){return this.decoders.reduce((r,n)=>n.decode_chain(r),e)}}class xb extends Rt{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map((r,n)=>r.replaceAll(this.suffix,n===e.length-1?"":" "))}}class Sb extends Rt{decode_chain(e){let r="";for(let n=1;n<e.length;n+=2)r+=e[n];return[r]}}class bg extends Ut{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 kb extends Rt{constructor(e){super(e),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement}decode_chain(e){const r=[];for(let n=0;n<e.length;++n){let a=e[n].replaceAll(this.replacement," ");this.addPrefixSpace&&n==0&&a.startsWith(" ")&&(a=a.substring(1)),r.push(a)}return r}}class Eb extends Pt{constructor(e){super(e),this.charsmap=e.precompiled_charsmap}normalize(e){return e=e.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),e=e.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),e.includes("～")?e=e.split("～").map(n=>n.normalize("NFKC")).join("～"):e=e.normalize("NFKC"),e}}class Cb extends Ut{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>Ut.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class Tb extends Ut{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class Ib extends Ut{constructor(e){super()}pre_tokenize_text(e,r){return Gw(e)}}class Ab extends Ut{constructor(e){super(),this.config=e,this.pattern=Qi(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 Mb=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ob(t,e,r,n){for(const a of Object.keys(t)){const i=e-t[a].length,s=r(a),o=new Array(i).fill(s);t[a]=n==="right"?ct(t[a],o):ct(o,t[a])}}function zb(t,e){for(const r of Object.keys(t))t[r].length=e}class Ee extends vt{return_token_type_ids=!1;_default_chat_template=`{% for message in messages %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}`;padding_side="right";constructor(e,r){super(),this._tokenizer_config=r,this.normalizer=Pt.fromConfig(e.normalizer),this.pre_tokenizer=Ut.fromConfig(e.pre_tokenizer),this.model=Ea.fromConfig(e.model,r),this.post_processor=Tn.fromConfig(e.post_processor),this.decoder=Rt.fromConfig(e.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const n of e.added_tokens){const a=new jw(n);this.added_tokens.push(a),this.model.tokens_to_ids.set(a.content,a.id),this.model.vocab[a.id]=a.content,a.special&&(this.special_tokens.push(a.content),this.all_special_ids.push(a.id))}if(this.additional_special_tokens=r.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.map(n=>`${n.lstrip?"\\s*":""}(${Ap(n.content)})${n.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=r.model_max_length,this.remove_space=r.remove_space,this.clean_up_tokenization_spaces=r.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=r.do_lowercase_and_remove_accent??!1,r.padding_side&&(this.padding_side=r.padding_side),this.legacy=!1,this.chat_template=r.chat_template??null,Array.isArray(this.chat_template)){const n=Object.create(null);for(const{name:a,template:i}of this.chat_template){if(typeof a!="string"||typeof i!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');n[a]=i}this.chat_template=n}this._compiled_template_cache=new Map}getToken(...e){for(const r of e){const n=this._tokenizer_config[r];if(n)if(typeof n=="object"){if(n.__type==="AddedToken")return n.content;throw Error(`Unknown token: ${n}`)}else return n}return null}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",legacy:o=null}={}){const u=await hg(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,legacy:o});return new this(...u)}_call(e,{text_pair:r=null,add_special_tokens:n=!0,padding:a=!1,truncation:i=null,max_length:s=null,return_tensor:o=!0,return_token_type_ids:u=null}={}){const l=Array.isArray(e);let p;if(l){if(e.length===0)throw Error("text array must be non-empty");if(r!==null){if(Array.isArray(r)){if(e.length!==r.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");p=e.map((m,c)=>this._encode_plus(m,{text_pair:r[c],add_special_tokens:n,return_token_type_ids:u}))}else p=e.map(m=>this._encode_plus(m,{add_special_tokens:n,return_token_type_ids:u}))}else{if(e==null)throw Error("text may not be null or undefined");if(Array.isArray(r))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");p=[this._encode_plus(e,{text_pair:r,add_special_tokens:n,return_token_type_ids:u})]}if(s===null?a==="max_length"?s=this.model_max_length:s=$r(p.map(m=>m.input_ids.length))[0]:i||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),s=Math.min(s,this.model_max_length),a||i)for(let m=0;m<p.length;++m)p[m].input_ids.length!==s&&(p[m].input_ids.length>s?i&&zb(p[m],s):a&&Ob(p[m],s,c=>c==="input_ids"?this.pad_token_id:0,this.padding_side));const f={};if(o){if(!(a&&i)&&p.some(c=>{for(const y of Object.keys(c))if(c[y].length!==p[0][y]?.length)return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const m=[p.length,p[0].input_ids.length];for(const c of Object.keys(p[0]))f[c]=new pe("int64",BigInt64Array.from(p.flatMap(y=>y[c]).map(BigInt)),m)}else{for(const m of Object.keys(p[0]))f[m]=p.map(c=>c[m]);if(!l)for(const m of Object.keys(f))f[m]=f[m][0]}return f}_encode_text(e){return e===null?null:(this.added_tokens_regex?e.split(this.added_tokens_regex).filter(a=>a):[e]).map((a,i)=>{if(this.added_tokens.find(o=>o.content===a)!==void 0)return a;{if(this.remove_space===!0&&(a=a.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(a=Uw(a)),this.normalizer!==null&&(a=this.normalizer(a)),a.length===0)return[];const o=this.pre_tokenizer!==null?this.pre_tokenizer(a,{section_index:i}):[a];return this.model(o)}}).flat()}_encode_plus(e,{text_pair:r=null,add_special_tokens:n=!0,return_token_type_ids:a=null}={}){const{tokens:i,token_type_ids:s}=this._tokenize_helper(e,{pair:r,add_special_tokens:n}),o=this.model.convert_tokens_to_ids(i),u={input_ids:o,attention_mask:new Array(o.length).fill(1)};return(a??this.return_token_type_ids)&&s&&(u.token_type_ids=s),u}_tokenize_helper(e,{pair:r=null,add_special_tokens:n=!1}={}){const a=this._encode_text(e),i=this._encode_text(r);return this.post_processor?this.post_processor(a,i,{add_special_tokens:n}):{tokens:ct(a??[],i??[])}}tokenize(e,{pair:r=null,add_special_tokens:n=!1}={}){return this._tokenize_helper(e,{pair:r,add_special_tokens:n}).tokens}encode(e,{text_pair:r=null,add_special_tokens:n=!0,return_token_type_ids:a=null}={}){return this._encode_plus(e,{text_pair:r,add_special_tokens:n,return_token_type_ids:a}).input_ids}batch_decode(e,r={}){return e instanceof pe&&(e=e.tolist()),e.map(n=>this.decode(n,r))}decode(e,r={}){if(e instanceof pe&&(e=fg(e)),!Array.isArray(e)||e.length===0||!c_(e[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(e,r)}decode_single(e,{skip_special_tokens:r=!1,clean_up_tokenization_spaces:n=null}){let a=this.model.convert_ids_to_tokens(e);r&&(a=a.filter(s=>!this.special_tokens.includes(s)));let i=this.decoder?this.decoder(a):a.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(i=i.replaceAll(this.decoder.end_of_word_suffix," "),r&&(i=i.trim())),(n??this.clean_up_tokenization_spaces)&&(i=iu(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(e,{chat_template:r=null,add_generation_prompt:n=!1,tokenize:a=!0,padding:i=!1,truncation:s=!1,max_length:o=null,return_tensor:u=!0,return_dict:l=!1,tokenizer_kwargs:p={},...f}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const w=this.chat_template??this.default_chat_template;if(r!==null&&Object.hasOwn(w,r))r=w[r];else if(r===null&&"default"in w)r=w.default;else if(r===null)throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(w).sort()}.`)}else r??=this.chat_template??this.default_chat_template;if(typeof r!="string")throw Error(`chat_template must be a string, but got ${typeof r}`);let m=this._compiled_template_cache.get(r);m===void 0&&(m=new Nw(r),this._compiled_template_cache.set(r,m));const c=Object.create(null);for(const w of Mb){const v=this.getToken(w);v&&(c[w]=v)}const y=m.render({messages:e,add_generation_prompt:n,...c,...f});if(a){const w=this._call(y,{add_special_tokens:!1,padding:i,truncation:s,max_length:o,return_tensor:u,...p});return l?w:w.input_ids}return y}}class Pb extends Ee{return_token_type_ids=!0}class Rb extends Ee{return_token_type_ids=!0}class Bb extends Ee{return_token_type_ids=!0}class Db extends Ee{return_token_type_ids=!0}class Nb extends Ee{return_token_type_ids=!0}class Fb extends Ee{return_token_type_ids=!0}class Lb extends Ee{return_token_type_ids=!0}class Ub extends Ee{return_token_type_ids=!0}class Wb extends Ee{return_token_type_ids=!0}class Vb extends Ee{}class Gb extends Ee{}class Hb extends Ee{return_token_type_ids=!0;constructor(e,r){super(e,r),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class jb extends Ee{return_token_type_ids=!0}class qb extends Ee{}class vg extends Ee{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}'}class Kb extends Ee{}class $g extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,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 su(this,e,r,n)}}class Yb extends $g{}class Xb extends Ee{}class Qb extends vg{constructor(e,r){const n=".,!?…。，、।۔،",a=e.pre_tokenizer?.pretokenizers[0]?.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const vi="▁";class xg extends Ee{_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 %}`;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.`;padding_side="left";constructor(e,r){super(e,r),this.use_default_system_prompt=r.use_default_system_prompt??!1,this.legacy=r.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new bg({replacement:vi,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(e){if(e===null)return null;if(this.legacy||e.length===0)return super._encode_text(e);let r=super._encode_text(vi+e.replaceAll(vi," "));return r.length>1&&r[0]===vi&&this.special_tokens.includes(r[1])&&(r=r.slice(1)),r}get default_chat_template(){return super.default_chat_template.replaceAll("USE_DEFAULT_PROMPT",this.use_default_system_prompt?"true":"false").replaceAll("DEFAULT_SYSTEM_MESSAGE",this.DEFAULT_SYSTEM_PROMPT.replaceAll(`
`,"\\n").replaceAll("'","\\'"))}}class Jb extends xg{}class Zb extends Ee{}class ev extends Ee{}class tv extends Ee{}class rv extends Ee{}class nv extends Ee{}class av extends Ee{}class iv extends Ee{_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 sv extends Ee{}function su(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,i=n.tgt_lang;if(!t.language_codes.includes(i))throw new Error(`Target language code "${i}" is not valid. Must be one of: {${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 s of t.post_processor.config.single)if("SpecialToken"in s&&t.languageRegex.test(s.SpecialToken.id)){s.SpecialToken.id=t.lang_to_token(a);break}}return n.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(i)])[0],t._call(e,r)}class ov extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return su(this,e,r,n)}}class uv extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,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 su(this,e,r,n)}}class lv extends Ee{_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(e,{return_timestamps:r=!1,return_language:n=!1,time_precision:a=null,force_full_sequences:i=!0}={}){if(a===null)throw Error("Must specify time_precision");let s=null;const o=r==="word";function u(){return{language:s,timestamp:[null,null],text:""}}const l=[];let p=u(),f=0;const m=this.timestamp_begin;let c=[],y=[],w=!1,v=null;const k=new Set(this.all_special_ids);for(const T of e){const A=T.tokens,B=o?T.token_timestamps:null;let R=null,D=m;if("stride"in T){const[ie,te,oe]=T.stride;if(f-=te,v=ie-oe,te&&(D=te/a+m),oe)for(let re=A.length-1;re>=0;--re){const M=Number(A[re]);if(M>=m){if(R!==null&&(M-m)*a<v)break;R=M}}}let K=[],j=[];for(let ie=0;ie<A.length;++ie){const te=Number(A[ie]);if(k.has(te)){const oe=this.decode([te]),re=Oi.get(oe.slice(2,-2));if(re!==void 0){if(s!==null&&re!==s&&!r){c.push(K);const M=this.findLongestCommonSequence(c)[0],P=this.decode(M);p.text=P,l.push(p),c=[],K=[],p=u()}s=p.language=re}}else if(te>=m){const oe=(te-m)*a+f,re=Ss(oe,2);if(R!==null&&te>=R)w=!0;else if(w||c.length>0&&te<D)w=!1;else if(p.timestamp[0]===null)p.timestamp[0]=re;else if(re!==p.timestamp[0]){p.timestamp[1]=re,c.push(K),o&&y.push(j);const[M,P]=this.findLongestCommonSequence(c,y),H=this.decode(M);p.text=H,o&&(p.words=this.collateWordTimestamps(M,P,s)),l.push(p),c=[],K=[],y=[],j=[],p=u()}}else if(K.push(te),o){let oe=Ss(B[ie]+f,2),re;ie+1<B.length?re=Ss(B[ie+1]+f,2):re=null,j.push([oe,re])}}if("stride"in T){const[ie,te,oe]=T.stride;f+=ie-oe}K.length>0?(c.push(K),o&&y.push(j)):c.every(ie=>ie.length===0)&&(p=u(),c=[],K=[],y=[],j=[])}if(c.length>0){if(i&&r)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[T,A]=this.findLongestCommonSequence(c,y),B=this.decode(T);p.text=B,o&&(p.words=this.collateWordTimestamps(T,A,s)),l.push(p)}let $=Object.create(null);const C=l.map(T=>T.text).join("");if(r||n){for(let T=0;T<l.length;++T){const A=l[T];r||delete A.timestamp,n||delete A.language}if(o){const T=[];for(const A of l)for(const B of A.words)T.push(B);$={chunks:T}}else $={chunks:l}}return[C,$]}findLongestCommonSequence(e,r=null){let n=e[0],a=n.length,i=[];const s=Array.isArray(r)&&r.length>0;let o=s?[]:null,u=s?r[0]:null;for(let l=1;l<e.length;++l){const p=e[l];let f=0,m=[a,a,0,0];const c=p.length;for(let T=1;T<a+c;++T){const A=T/1e4,B=Math.max(0,a-T),R=Math.min(a,a+c-T),D=n.slice(B,R),K=Math.max(0,T-a),j=Math.min(c,T),ie=p.slice(K,j);if(D.length!==ie.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const te=D.filter((re,M)=>re===ie[M]).length,oe=te/T+A;te>1&&oe>f&&(f=oe,m=[B,R,K,j])}const[y,w,v,k]=m,$=Math.floor((w+y)/2),C=Math.floor((k+v)/2);i.push(...n.slice(0,$)),n=p.slice(C),a=n.length,s&&(o.push(...u.slice(0,$)),u=r[l].slice(C))}return i.push(...n),s?(o.push(...u),[i,o]):[i,[]]}collateWordTimestamps(e,r,n){const[a,i,s]=this.combineTokensIntoWords(e,n),o=[];for(let u=0;u<a.length;++u){const l=s[u];o.push({text:a[u],timestamp:[r[l.at(0)][0],r[l.at(-1)][1]]})}return o}combineTokensIntoWords(e,r,n=`"'“¡¿([{-`,a=`"'.。,，!！?？:：”)]}、`){r=r??"english";let i,s,o;return["chinese","japanese","thai","lao","myanmar"].includes(r)?[i,s,o]=this.splitTokensOnUnicode(e):[i,s,o]=this.splitTokensOnSpaces(e),this.mergePunctuations(i,s,o,n,a)}decode(e,r){let n;return r?.decode_with_timestamps?(e instanceof pe&&(e=fg(e)),n=this.decodeWithTimestamps(e,r)):n=super.decode(e,r),n}decodeWithTimestamps(e,r){const n=r?.time_precision??.02,a=Array.from(this.all_special_ids).at(-1)+1;let i=[[]];for(let s of e)if(s=Number(s),s>=a){const o=((s-a)*n).toFixed(2);i.push(`<|${o}|>`),i.push([])}else i[i.length-1].push(s);return i=i.map(s=>typeof s=="string"?s:super.decode(s,r)),i.join("")}splitTokensOnUnicode(e){const r=this.decode(e,{decode_with_timestamps:!0}),n="�",a=[],i=[],s=[];let o=[],u=[],l=0;for(let p=0;p<e.length;++p){const f=e[p];o.push(f),u.push(p);const m=this.decode(o,{decode_with_timestamps:!0});(!m.includes(n)||r[l+m.indexOf(n)]===n)&&(a.push(m),i.push(o),s.push(u),o=[],u=[],l+=m.length)}return[a,i,s]}splitTokensOnSpaces(e){const[r,n,a]=this.splitTokensOnUnicode(e),i=[],s=[],o=[],u=new RegExp(`^[${ba}]$`,"gu");for(let l=0;l<r.length;++l){const p=r[l],f=n[l],m=a[l],c=f[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),y=p.startsWith(" "),w=p.trim(),v=u.test(w);if(c||y||v||i.length===0)i.push(p),s.push(f),o.push(m);else{const k=i.length-1;i[k]+=p,s[k].push(...f),o[k].push(...m)}}return[i,s,o]}mergePunctuations(e,r,n,a,i){const s=structuredClone(e),o=structuredClone(r),u=structuredClone(n);let l=s.length-2,p=s.length-1;for(;l>=0;)s[l].startsWith(" ")&&a.includes(s[l].trim())?(s[p]=s[l]+s[p],o[p]=ct(o[l],o[p]),u[p]=ct(u[l],u[p]),s[l]="",o[l]=[],u[l]=[]):p=l,--l;for(l=0,p=1;p<s.length;)!s[l].endsWith(" ")&&i.includes(s[p])?(s[l]+=s[p],o[l]=ct(o[l],o[p]),u[l]=ct(u[l],u[p]),s[p]="",o[p]=[],u[p]=[]):l=p,++p;return[s.filter(f=>f),o.filter(f=>f.length>0),u.filter(f=>f.length>0)]}get_decoder_prompt_ids({language:e=null,task:r=null,no_timestamps:n=!0}={}){const a=[];if(e){const i=pg(e),s=this.model.tokens_to_ids.get(`<|${i}|>`);if(s===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at ${io}.`);a.push(s)}else a.push(null);if(r){if(r=r.toLowerCase(),r!=="transcribe"&&r!=="translate")throw new Error(`Task "${r}" is not supported. Must be one of: ["transcribe", "translate"]`);const i=this.model.tokens_to_ids.get(`<|${r}|>`);if(i===void 0)throw new Error(`Unable to find task "${r}" in model vocabulary. Please report this issue at ${io}.`);a.push(i)}else a.push(null);if(n){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 ${io}.`);a.push(i)}return a.map((i,s)=>[s+1,i]).filter(i=>i[1]!==null)}}class dv extends Ee{}class cv extends Ee{}class pv extends Ee{}class hv extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(n=>this.languageRegex.test(n)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[r,...n]=e.trim().split(this.languageRegex);if(n.length===0)return super._encode_text(r);if(n.length===2){const[a,i]=n;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),ct([a],super._encode_text(i))}}}class fv extends Ee{}class Sg extends Ee{_default_chat_template="{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}"}class mv extends Sg{}class gv extends Ee{}class _v extends Ee{}class yv extends Ee{constructor(e,r){super(e,r),this.decoder=new Sb({})}}class wv extends Ee{}class bv{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:qb,DistilBertTokenizer:Vb,CamembertTokenizer:Gb,DebertaTokenizer:Nb,DebertaV2Tokenizer:Fb,BertTokenizer:Pb,HerbertTokenizer:Lb,ConvBertTokenizer:Ub,RoFormerTokenizer:Wb,XLMTokenizer:Hb,ElectraTokenizer:jb,MobileBertTokenizer:Bb,SqueezeBertTokenizer:Db,AlbertTokenizer:Rb,GPT2Tokenizer:vg,BartTokenizer:Kb,MBartTokenizer:$g,MBart50Tokenizer:Yb,RobertaTokenizer:Xb,WhisperTokenizer:lv,CodeGenTokenizer:dv,CLIPTokenizer:cv,SiglipTokenizer:pv,MarianTokenizer:hv,BloomTokenizer:Qb,NllbTokenizer:ov,M2M100Tokenizer:uv,LlamaTokenizer:xg,CodeLlamaTokenizer:Jb,XLMRobertaTokenizer:Zb,MPNetTokenizer:ev,FalconTokenizer:tv,GPTNeoXTokenizer:rv,EsmTokenizer:nv,Wav2Vec2CTCTokenizer:fv,BlenderbotTokenizer:Sg,BlenderbotSmallTokenizer:mv,SpeechT5Tokenizer:gv,NougatTokenizer:_v,VitsTokenizer:yv,Qwen2Tokenizer:av,GemmaTokenizer:iv,Grok1Tokenizer:sv,CohereTokenizer:wv,PreTrainedTokenizer:Ee};static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",legacy:o=null}={}){const[u,l]=await hg(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,legacy:o}),p=l.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let f=this.TOKENIZER_CLASS_MAPPING[p];return f||(console.warn(`Unknown tokenizer class "${p}", attempting to construct from base class.`),f=Ee),new f(u,l)}}async function vv(t,e){return await Mr(t,"config.json",!0,e)}function xn(t){const e={};let r={};switch(t.model_type){case"llava":case"paligemma":r=xn(t.text_config);break;case"moondream1":r=xn(t.phi_config);break;case"musicgen":r=xn(t.decoder);break;case"gpt2":case"gptj":case"codegen":case"gpt_bigcode":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"phi":case"phi3":case"falcon":e.num_heads="num_attention_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size";break;case"llama":case"mistral":case"starcoder2":case"qwen2":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size",e.num_attention_heads="num_attention_heads";break;case"gemma":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="head_dim";break;case"openelm":e.num_heads="num_kv_heads",e.num_layers="num_transformer_layers",e.dim_kv="head_dim";break;case"gpt_neo":e.num_heads="num_heads",e.num_layers="num_layers",e.hidden_size="hidden_size";break;case"bloom":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="hidden_size";break;case"mpt":e.num_heads="n_heads",e.num_layers="n_layers",e.hidden_size="d_model";break;case"t5":case"mt5":case"longt5":e.num_decoder_layers="num_decoder_layers",e.num_decoder_heads="num_heads",e.decoder_dim_kv="d_kv",e.num_encoder_layers="num_layers",e.num_encoder_heads="num_heads",e.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="d_model",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="d_model";break;case"speecht5":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="hidden_size",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="hidden_size";break;case"trocr":e.num_encoder_layers=e.num_decoder_layers="decoder_layers",e.num_encoder_heads=e.num_decoder_heads="decoder_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="d_model";break;case"musicgen_decoder":e.num_encoder_layers=e.num_decoder_layers="num_hidden_layers",e.num_encoder_heads=e.num_decoder_heads="num_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const a=xn(t.encoder),i=xn(t.decoder),s="num_decoder_layers"in i,o={};return s?(o.num_decoder_layers=i.num_layers,o.num_decoder_heads=i.num_heads,o.decoder_hidden_size=i.hidden_size,o.num_encoder_layers=a.num_layers,o.num_encoder_heads=a.num_heads,o.encoder_hidden_size=a.hidden_size):(o.num_layers=i.num_layers,o.num_heads=i.num_heads,o.hidden_size=i.hidden_size),o}const n={...r,...Qr(t,["model_type","multi_query","is_encoder_decoder"])};for(const a in e)n[a]=t[e[a]];return n}function kg(t,{prefix:e="past_key_values",encoder_add_pkv:r=!0}={}){const n={},a=t.normalized_config,i=1;if(a.is_encoder_decoder&&r){const s=a.encoder_dim_kv??a.encoder_hidden_size/a.num_encoder_heads,o=a.decoder_dim_kv??a.decoder_hidden_size/a.num_decoder_heads,u=[i,a.num_encoder_heads,0,s],l=[i,a.num_decoder_heads,0,o];for(let p=0;p<a.num_decoder_layers;++p)n[`${e}.${p}.encoder.key`]=u,n[`${e}.${p}.encoder.value`]=u,n[`${e}.${p}.decoder.key`]=l,n[`${e}.${p}.decoder.value`]=l}else{const s=a.num_heads,o=a.num_layers,u=a.dim_kv??a.hidden_size/(a.num_attention_heads??s);if(a.model_type==="falcon"){const l=[i*s,0,u];for(let p=0;p<o;++p)n[`${e}.${p}.key`]=l,n[`${e}.${p}.value`]=l}else if(a.multi_query){const l=[i*s,0,2*u];for(let p=0;p<o;++p)n[`${e}.${p}.key_value`]=l}else if(a.model_type==="bloom"){const l=[i*s,u,0],p=[i*s,0,u];for(let f=0;f<o;++f)n[`${e}.${f}.key`]=l,n[`${e}.${f}.value`]=p}else if(a.model_type==="openelm")for(let l=0;l<o;++l){const p=[i,s[l],0,u];n[`${e}.${l}.key`]=p,n[`${e}.${l}.value`]=p}else{const l=[i,s,0,u];for(let p=0;p<o;++p)n[`${e}.${p}.key`]=l,n[`${e}.${p}.value`]=l}}return n}class ji{max_position_embeddings;constructor(e){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e),this.normalized_config=xn(this)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main"}={}){n&&!(n instanceof ji)&&(n=new ji(n));const o=n??await vv(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s});return new this(o)}}class Eg{static async from_pretrained(...e){return ji.from_pretrained(...e)}}const $i=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),$v=function(){let t;return async function(){if(t===void 0)if(Zr.IS_NODE_ENV)t=!0;else if(!Zr.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}}(),It=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),xv=Object.freeze({[$i.cpu]:It.q8,[$i.gpu]:It.fp32,[$i.wasm]:It.q8,[$i.webgpu]:It.fp32}),fp=Object.freeze({[It.fp32]:"",[It.fp16]:"_fp16",[It.int8]:"_int8",[It.uint8]:"_uint8",[It.q8]:"_quantized",[It.q4]:"_q4",[It.q4f16]:"_q4f16",[It.bnb4]:"_bnb4"});class pr extends vt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class ou extends vt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class Io extends vt{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 Sv extends pr{constructor(e){super(),this.bos_token_id=e}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===1){const a=r[n];a.data.fill(-1/0),a.data[this.bos_token_id]=0}return r}}class kv extends pr{constructor(e,r){super(),this.max_length=e,this.forced_eos_token_id=r}_call(e,r){}}class Ev extends pr{constructor(e,r){super(),this.begin_suppress_tokens=e,this.begin_index=r}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===this.begin_index){const a=r[n];for(const i of this.begin_suppress_tokens)a.data[i]=-1/0}return r}}class Cv extends pr{constructor(e,r){super(),this.eos_token_id=Array.isArray(e.eos_token_id)?e.eos_token_id[0]:e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=r.length,r.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,r){for(let n=0;n<e.length;++n){const i=r[n].data;if(i[this.no_timestamps_token_id]=-1/0,e[n].length===this.begin_index-1){i.fill(-1/0),i[this.timestamp_begin]=0;continue}const s=e[n].slice(this.begin_index),o=s.length>=1&&s[s.length-1]>=this.timestamp_begin,u=s.length<2||s[s.length-2]>=this.timestamp_begin;if(o&&(u?i.subarray(this.timestamp_begin).fill(-1/0):i.subarray(0,this.eos_token_id).fill(-1/0)),e[n].length===this.begin_index&&this.max_initial_timestamp_index!==null){const m=this.timestamp_begin+this.max_initial_timestamp_index;i.subarray(m+1).fill(-1/0)}const l=y_(i),p=Math.log(l.subarray(this.timestamp_begin).map(Math.exp).reduce((m,c)=>m+c)),f=$r(l.subarray(0,this.timestamp_begin))[0];p>f&&i.subarray(0,this.timestamp_begin).fill(-1/0)}return r}}class Tv extends pr{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const r=e.length,n=[];for(let i=0;i<r+1-this.no_repeat_ngram_size;++i){const s=[];for(let o=0;o<this.no_repeat_ngram_size;++o)s.push(e[i+o]);n.push(s)}const a=new Map;for(const i of n){const s=i.slice(0,i.length-1),o=JSON.stringify(s),u=a.get(o)??[];u.push(i[i.length-1]),a.set(o,u)}return a}getGeneratedNgrams(e,r){const n=r.slice(r.length+1-this.no_repeat_ngram_size,r.length);return e.get(JSON.stringify(n))??[]}calcBannedNgramTokens(e){const r=[];if(e.length+1<this.no_repeat_ngram_size)return r;{const n=this.getNgrams(e);return this.getGeneratedNgrams(n,e)}}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n],i=this.calcBannedNgramTokens(e[n]);for(const s of i)a.data[s]=-1/0}return r}}class Iv extends pr{constructor(e){super(),this.penalty=e}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n];for(const i of e[n])a.data[i]<0?a.data[i]*=this.penalty:a.data[i]/=this.penalty}return r}}class Av extends pr{constructor(e,r){super(),this.min_length=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length<this.min_length){const a=r[n];for(const i of this.eos_token_id)a.data[i]=-1/0}return r}}class Mv extends pr{constructor(e,r,n){super(),this.prompt_length_to_skip=e,this.min_new_tokens=r,this.eos_token_id=Array.isArray(n)?n:[n]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length-this.prompt_length_to_skip<this.min_new_tokens){const i=r[n];for(const s of this.eos_token_id)i[s]=-1/0}return r}}class Ov extends pr{constructor(e,r){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n];for(const i of this.bad_words_ids){let s=!0;for(let o=1;o<=i.length-1&&i.length<e[o].length;++o)if(i.at(-o-1)!==e[o].at(-o)){s=!1;break}s&&(a[i.at(-1)]=-1/0)}}return r}}class zv extends pr{constructor(e){if(super(),e<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,r){if(r.dims[0]!==2*e.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${r.dims[0]} for the logits and ${e.length} for the input ids.`);const n=e.length,a=r.slice([0,n],null),i=r.slice([n,r.dims[0]],null);for(let s=0;s<i.data.length;++s)i.data[s]+=(a.data[s]-i.data[s])*this.guidance_scale;return i}}class Pv extends ou{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 Rv extends ou{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 Bv extends ou{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 Cg{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(e){Object.assign(this,Qr(e,Object.getOwnPropertyNames(this)))}}class uu extends vt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class lu extends vt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof lu?e=e.criteria:e instanceof uu&&(e=[e]),this.criteria.push(...e)}_call(e,r){const n=new Array(e.length).fill(!1);for(const a of this.criteria){const i=a(e,r);for(let s=0;s<n.length;++s)n[s]||=i[s]}return n}[Symbol.iterator](){return this.criteria.values()}}class Dv extends uu{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 Nv extends uu{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(i=>a==i)})}}class Ji extends vt{constructor(e){super(),this.generation_config=e}_call(e,r=-1){return this.sample(e,r)}sample(e,r){throw Error("sample should be implemented in subclasses.")}getLogits(e,r){let n=e.dims.at(-1),a=e.data;if(r===-1)a=a.slice(-n);else{let i=r*n;a=a.slice(i,i+n)}return a}randomSelect(e){let r=e.reduce((a,i)=>a+i,0),n=Math.random()*r;for(let a=0;a<e.length;++a)if(n-=e[a],n<=0)return a;return 0}static getSampler(e){if(e.do_sample)return new Lv(e);if(e.num_beams>1)return new Uv(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new Fv(e)}}class Fv extends Ji{sample(e,r=-1){let n=this.getLogits(e,r);return[[$r(n)[1],0]]}}class Lv extends Ji{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),i=Mp(a,n),s=In(i.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},()=>{const o=this.randomSelect(s);return[i[o][0],Math.log(s[o])]})}}class Uv extends Ji{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),i=Mp(a,n),s=In(i.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},(o,u)=>[i[u][0],Math.log(s[u])])}}class Wv extends Cg{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1}const ve={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},qi=new Map,Tg=new Map,fa=new Map;async function Vv(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=rw(n);let i=r.dtype;if(typeof i!="string"&&(i&&i.hasOwnProperty(e)?i=i[e]:(i=xv[a[0]],console.warn(`Dtype not specified for ${e}. Using the default dtype: ${i}.`))),fp.hasOwnProperty(i)){if(i===It.fp16&&!await $v())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${i}. Should be one of: ${Object.keys(It).join(", ")}`);const s=fp[i],o=`${r.subfolder??""}/${e}${s}.onnx`,u={...r.session_options};u.executionProviders??=a;const l=ki(t,o,!0,r);let p=[];if(r.use_external_data_format){if(Zr.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const m=`${e}${s}.onnx_data`,c=`${r.subfolder??""}/${m}`;p.push(new Promise(async(y,w)=>{const v=await ki(t,c,!0,r);y({path:m,data:v})}))}else u.externalData!==void 0&&(p=u.externalData.map(async m=>{if(typeof m.data=="string"){const c=await ki(t,m.data,!0,r);return{...m,data:c}}return m}));if(p.length>0&&(u.externalData=await Promise.all(p)),n==="webgpu"){const m=kg(r.config,{prefix:"present"});if(Object.keys(m).length>0){const c={};for(const y in m)c[y]="gpu-buffer";u.preferredOutputLocation=c}}return{buffer:await l,session_options:u}}async function Gr(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async s=>Vv(t,e[s],r))),i={};for(let s=0;s<n.length;++s){const{buffer:o,session_options:u}=a[s],l=await sg(o,u);i[n[s]]=l}return i}function Gv(t,e){const r=Object.create(null),n=[];for(const s of t.inputNames){const o=e[s];if(!(o instanceof pe)){n.push(s);continue}r[s]=nw()?o.clone():o}if(n.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${n.join(", ")}.`);const a=Object.keys(e).length,i=t.inputNames.length;if(a>i){let s=Object.keys(e).filter(o=>!t.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${i}). The following inputs will be ignored: "${s.join(", ")}".`)}return r}async function zr(t,e){const r=Gv(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([i,s])=>[i,s.ort_tensor]));let a=await t.run(n);return a=Ig(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Ig(t){for(let e in t)og(t[e])?t[e]=new pe(t[e]):typeof t[e]=="object"&&Ig(t[e]);return t}function Ag(t){if(t instanceof pe)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 pe("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new pe("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function Mg(t){return new pe("bool",[t],[1])}async function mp(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const u=Qr(e,t.sessions.model.inputNames);r=(await va(t,u)).last_hidden_state}const{input_ids:a,decoder_input_ids:i,...s}=e;return s.input_ids=i,s.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(s.encoder_attention_mask=e.attention_mask),await du(t,s,!0)}async function va(t,e){const r=t.sessions.model,n=Object.create(null);for(const a of r.inputNames)n[a]=e[a];return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new pe("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await zr(r,n)}async function du(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...i}=e;n.inputNames.includes("use_cache_branch")&&(i.use_cache_branch=Mg(!!a)),n.inputNames.includes("position_ids")&&i.attention_mask&&!i.position_ids&&(i.position_ids=jv(i,a)),t.addPastKeyValues(i,a);const s=Qr(i,n.inputNames);return await zr(n,s)}async function Hv(t,{input_ids:e=null,attention_mask:r=null,pixel_values:n=null,position_ids:a=null,inputs_embeds:i=null,past_key_values:s=null,generation_config:o=null,logits_processor:u=null,...l}){if(!i){if(i=await t.encode_text({input_ids:e}),n&&e.dims[1]!==1){const f=await t.encode_image({pixel_values:n});({inputs_embeds:i,attention_mask:r}=t._merge_input_ids_with_image_features({image_features:f,inputs_embeds:i,input_ids:e,attention_mask:r}))}else if(s&&n&&e.dims[1]===1){const f=e.dims[1],m=Object.values(s)[0].dims.at(-2);r=dr([On([e.dims[0],m]),r.slice(null,[r.dims[1]-f,r.dims[1]])],1)}}return await du(t,{inputs_embeds:i,past_key_values:s,attention_mask:r,position_ids:a,generation_config:o,logits_processor:u},!0)}function jv(t,e=null){const{input_ids:r,inputs_embeds:n,attention_mask:a}=t,[i,s]=a.dims,o=new BigInt64Array(a.data.length);for(let l=0;l<i;++l){const p=l*s;let f=BigInt(0);for(let m=0;m<s;++m){const c=p+m;a.data[c]===0n?o[c]=BigInt(1):(o[c]=f,f+=a.data[c])}}let u=new pe("int64",o,a.dims);if(e){const l=-(r??n).dims.at(1);u=u.slice(null,[l,null])}return u}function gp(t,e,r,n){if(r.past_key_values){const a=Object.values(r.past_key_values)[0].dims.at(-2),{input_ids:i,attention_mask:s}=r;if(!(s&&s.dims[1]>i.dims[1])){if(a<i.dims[1])r.input_ids=i.slice(null,[a,null]);else if(t.config.image_token_index!=null&&i.data.some(o=>o==t.config.image_token_index)){const o=t.config.num_image_tokens;if(!o)throw new Error("`num_image_tokens` is missing in the model configuration.");const u=i.dims[1]-(a-o);r.input_ids=i.slice(null,[-u,null]),r.attention_mask=On([1,a+u])}}}return r}function qv(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(s=>[s.at(-1)])),a.decoder_input_ids=Ag(e),a}class ee extends vt{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(e,r){super(),this.config=e,this.sessions=r;const n=fa.get(this.constructor),a=qi.get(n);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,a===ve.DecoderOnly?(this.can_generate=!0,this._forward=du,this._prepare_inputs_for_generation=gp):a===ve.Seq2Seq||a===ve.Vision2Seq||a===ve.Musicgen?(this.can_generate=!0,this._forward=mp,this._prepare_inputs_for_generation=qv):a===ve.EncoderDecoder?this._forward=mp:a===ve.ImageTextToText?(this.can_generate=!0,this._forward=Hv,this._prepare_inputs_for_generation=gp):this._forward=va,this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const e=[];for(const r of Object.values(this.sessions))r?.handler?.dispose&&e.push(r.handler.dispose());return await Promise.all(e)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",model_file_name:o=null,subfolder:u="onnx",device:l=null,dtype:p=null,use_external_data_format:f=null,session_options:m={}}={}){let c={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,model_file_name:o,subfolder:u,device:l,dtype:p,use_external_data_format:f,session_options:m};const y=fa.get(this),w=qi.get(y);c.config=await Eg.from_pretrained(e,c);let v;return w===ve.DecoderOnly?v=await Promise.all([Gr(e,{model:c.model_file_name??"model"},c),Mr(e,"generation_config.json",!1,c)]):w===ve.Seq2Seq||w===ve.Vision2Seq?v=await Promise.all([Gr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},c),Mr(e,"generation_config.json",!1,c)]):w===ve.MaskGeneration?v=await Promise.all([Gr(e,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},c)]):w===ve.EncoderDecoder?v=await Promise.all([Gr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},c)]):w===ve.ImageTextToText?v=await Promise.all([Gr(e,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},c),Mr(e,"generation_config.json",!1,c)]):w===ve.Musicgen?v=await Promise.all([Gr(e,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},c),Mr(e,"generation_config.json",!1,c)]):(w!==ve.EncoderOnly&&console.warn(`Model type for '${y??n?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),v=await Promise.all([Gr(e,{model:c.model_file_name??"model"},c)])),new this(c.config,...v)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_warper(e){const r=new Io;return e.temperature!==null&&e.temperature!==1&&r.push(new Pv(e.temperature)),e.top_k!==null&&e.top_k!==0&&r.push(new Bv(e.top_k)),e.top_p!==null&&e.top_p<1&&r.push(new Rv(e.top_p)),r}_get_logits_processor(e,r,n=null){const a=new Io;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&a.push(new Iv(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&a.push(new Tv(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&a.push(new Ov(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&a.push(new Av(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&a.push(new Mv(r,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&a.push(new Sv(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&a.push(new kv(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){const i=r>1||e.forced_bos_token_id===null?r:r+1;a.push(new Ev(e.begin_suppress_tokens,i))}return e.guidance_scale!==null&&e.guidance_scale>1&&a.push(new zv(e.guidance_scale)),n!==null&&a.extend(n),a}_prepare_generation_config(e,r,n=Cg){const a={...this.config};for(const s of["decoder","generator","text_config"])s in a&&Object.assign(a,a[s]);const i=new n(a);return"generation_config"in this&&Object.assign(i,this.generation_config),e&&Object.assign(i,e),r&&Object.assign(i,Qr(r,Object.getOwnPropertyNames(i))),i}_get_stopping_criteria(e,r=null){const n=new lu;return e.max_length!==null&&n.push(new Dv(e.max_length,this.config.max_position_embeddings??null)),e.eos_token_id!==null&&n.push(new Nv(e.eos_token_id)),r&&n.extend(r),n}_validate_model_class(){if(!this.can_generate){const e=[R0,B0,P0,z0],r=fa.get(this.constructor),n=new Set,a=this.config.model_type;for(const s of e){const o=s.get(a);o&&n.add(o[0])}let i=`The current model class (${r}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw n.size>0&&(i+=` Please use the following class instead: ${[...n].join(", ")}`),Error(i)}}prepare_inputs_for_generation(...e){return this._prepare_inputs_for_generation(this,...e)}_update_model_kwargs_for_generation({generated_input_ids:e,outputs:r,model_inputs:n,is_encoder_decoder:a}){return n.past_key_values=this.getPastKeyValues(r,n.past_key_values),n.input_ids=new pe("int64",e.flat(),[e.length,1]),a||(n.attention_mask=dr([n.attention_mask,On([n.attention_mask.dims[0],1])],1)),n.position_ids=null,n}_prepare_model_inputs({inputs:e,bos_token_id:r,model_kwargs:n}){const a=Qr(n,this.forward_params),i=this.main_input_name;if(i in a){if(e)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else a[i]=e;return{inputs_tensor:a[i],model_inputs:a,model_input_name:i}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:e,model_inputs:r,model_input_name:n,generation_config:a}){const i=Qr(r,this.sessions.model.inputNames);let{last_hidden_state:s}=await va(this,i);return a.guidance_scale!==null&&a.guidance_scale>1&&(s=dr([s,dw(s,0)],0),"attention_mask"in r&&(r.attention_mask=dr([r.attention_mask,hw(r.attention_mask)],0))),r.encoder_outputs=s,r}_prepare_decoder_input_ids_for_generation({batch_size:e,model_input_name:r,model_kwargs:n,decoder_start_token_id:a,bos_token_id:i,generation_config:s}){let{decoder_input_ids:o,...u}=n;if(!o)if(a??=i,this.config.model_type==="musicgen")o=Array.from({length:e*this.config.decoder.num_codebooks},()=>[a]);else if(Array.isArray(a)){if(a.length!==e)throw new Error(`\`decoder_start_token_id\` expcted to have length ${e} but got ${a.length}`);o=a}else o=Array.from({length:e},()=>[a]);return o=Ag(o),n.decoder_attention_mask=cw(o),{input_ids:o,model_inputs:u}}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,streamer:i=null,...s}){this._validate_model_class(),r=this._prepare_generation_config(r,s);let{inputs_tensor:o,model_inputs:u,model_input_name:l}=this._prepare_model_inputs({inputs:e,model_kwargs:s});const p=this.config.is_encoder_decoder;p&&("encoder_outputs"in u||(u=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:o,model_inputs:u,model_input_name:l,generation_config:r})));let f;p?{input_ids:f,model_inputs:u}=this._prepare_decoder_input_ids_for_generation({batch_size:u[l].dims.at(0),model_input_name:l,model_kwargs:u,decoder_start_token_id:r.decoder_start_token_id,bos_token_id:r.bos_token_id,generation_config:r}):f=u[l];let m=f.dims.at(-1);r.max_new_tokens!==null&&(r.max_length=m+r.max_new_tokens);const c=this._get_logits_processor(r,m,n),y=this._get_stopping_criteria(r,a),w=u[l].dims.at(0),v=Ji.getSampler(r),k=new Array(w).fill(0),$=f.tolist();i&&i.put($);let C=null;for(;;){u=this.prepare_inputs_for_generation($,u,r);const A=await this.forward(u),B=A.logits.slice(null,-1,null),R=c($,B),D=[];for(let j=0;j<R.dims.at(0);++j){const ie=R[j],te=v(ie);for(const[oe,re]of te){const M=BigInt(oe);k[j]+=re,$[j].push(M),D.push([M])}}if(i&&i.put(D),y($).every(j=>j)){r.return_dict_in_generate&&(C=this.getPastKeyValues(A,u.past_key_values,!1));break}u=this._update_model_kwargs_for_generation({generated_input_ids:D,outputs:A,model_inputs:u,is_encoder_decoder:p})}i&&i.end();const T=new pe("int64",$.flat(),[$.length,$[0].length]);return r.return_dict_in_generate?{sequences:T,past_key_values:C}:T}addAttentionsToBeam(e,r){if(this.config.is_encoder_decoder){if(!r.cross_attentions||r.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(r.cross_attentions)}if(!r.decoder_attentions||r.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(r.decoder_attentions)}groupBeams(e){const r=Object.create(null);for(const n of e)r[n.id]===void 0?r[n.id]=[n]:r[n.id].push(n);return Object.values(r)}getPastKeyValues(e,r,n=!0){const a=Object.create(null);for(const i in e)if(i.startsWith("present")){let s=i.replace("present","past_key_values");if(r&&i.includes("encoder"))a[s]=r[s];else{if(n&&r){const o=r[s];o.location==="gpu-buffer"&&o.dispose()}a[s]=e[i]}}return a}getAttentions(e){const r=Object.create(null);for(const n of["cross_attentions","decoder_attentions"]){const a=[];for(const i in e)if(i.startsWith(n)){const s=i.split(".").pop();a[s]=e[i]}r[n]=a}return r}addPastKeyValues(e,r){if(r)Object.assign(e,r);else{const n=this.custom_config.kv_cache_dtype??"float32",a=n==="float16"?new Uint16Array:[],i=kg(this.config);for(const s in i)e[s]=new pe(n,a,i[s])}}}class Wt{}class Ca extends ee{}class Kv extends Ca{}class Yv extends Ca{async _call(e){return new gt(await super._call(e))}}class Xv extends Ca{async _call(e){return new Me(await super._call(e))}}class Qv extends Ca{async _call(e){return new mt(await super._call(e))}}class Jv extends Ca{async _call(e){return new $t(await super._call(e))}}class Zv extends ee{}class e2 extends Zv{}class Ta extends ee{}class t2 extends Ta{}class r2 extends Ta{async _call(e){return new gt(await super._call(e))}}class n2 extends Ta{async _call(e){return new Me(await super._call(e))}}class a2 extends Ta{async _call(e){return new mt(await super._call(e))}}class i2 extends Ta{async _call(e){return new $t(await super._call(e))}}class Ia extends ee{}class s2 extends Ia{}class o2 extends Ia{async _call(e){return new gt(await super._call(e))}}class u2 extends Ia{async _call(e){return new Me(await super._call(e))}}class l2 extends Ia{async _call(e){return new mt(await super._call(e))}}class d2 extends Ia{async _call(e){return new $t(await super._call(e))}}class Aa extends ee{}class c2 extends Aa{}class p2 extends Aa{async _call(e){return new gt(await super._call(e))}}class h2 extends Aa{async _call(e){return new Me(await super._call(e))}}class f2 extends Aa{async _call(e){return new mt(await super._call(e))}}class m2 extends Aa{async _call(e){return new $t(await super._call(e))}}class Ma extends ee{}class g2 extends Ma{}class _2 extends Ma{async _call(e){return new gt(await super._call(e))}}class y2 extends Ma{async _call(e){return new Me(await super._call(e))}}class w2 extends Ma{async _call(e){return new mt(await super._call(e))}}class b2 extends Ma{async _call(e){return new $t(await super._call(e))}}class Oa extends ee{}class v2 extends Oa{}class $2 extends Oa{async _call(e){return new gt(await super._call(e))}}class x2 extends Oa{async _call(e){return new Me(await super._call(e))}}class S2 extends Oa{async _call(e){return new mt(await super._call(e))}}class k2 extends Oa{async _call(e){return new $t(await super._call(e))}}class za extends ee{}class E2 extends za{}class C2 extends za{async _call(e){return new gt(await super._call(e))}}class T2 extends za{async _call(e){return new Me(await super._call(e))}}class I2 extends za{async _call(e){return new mt(await super._call(e))}}class A2 extends za{async _call(e){return new $t(await super._call(e))}}class Pa extends ee{}class M2 extends Pa{}class O2 extends Pa{async _call(e){return new Me(await super._call(e))}}class z2 extends Pa{async _call(e){return new mt(await super._call(e))}}class P2 extends Pa{async _call(e){return new $t(await super._call(e))}}class R2 extends Pa{async _call(e){return new gt(await super._call(e))}}class Zi extends ee{}class B2 extends Zi{}class D2 extends Zi{async _call(e){return new gt(await super._call(e))}}class N2 extends Zi{async _call(e){return new Me(await super._call(e))}}class F2 extends Zi{async _call(e){return new mt(await super._call(e))}}class es extends ee{}class L2 extends es{}class U2 extends es{async _call(e){return new gt(await super._call(e))}}class W2 extends es{async _call(e){return new Me(await super._call(e))}}class V2 extends es{async _call(e){return new $t(await super._call(e))}}class Ra extends ee{}class G2 extends Ra{}class H2 extends Ra{async _call(e){return new gt(await super._call(e))}}class j2 extends Ra{async _call(e){return new Me(await super._call(e))}}class q2 extends Ra{async _call(e){return new mt(await super._call(e))}}class K2 extends Ra{async _call(e){return new $t(await super._call(e))}}class ts extends ee{}class Y2 extends ts{}class X2 extends ts{async _call(e){return new gt(await super._call(e))}}class Q2 extends ts{async _call(e){return new Me(await super._call(e))}}class J2 extends ts{async _call(e){return new $t(await super._call(e))}}class rs extends ee{}class Z2 extends rs{}class e1 extends rs{async _call(e){return new Me(await super._call(e))}}class t1 extends rs{async _call(e){return new $t(await super._call(e))}}class r1 extends rs{async _call(e){return new gt(await super._call(e))}}class Og extends ee{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class n1 extends Og{}class a1 extends Og{}class zg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class i1 extends zg{}class s1 extends zg{}class Pg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class o1 extends Pg{}class u1 extends Pg{}class cu extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class l1 extends cu{}class d1 extends cu{}class c1 extends cu{async _call(e){return new Me(await super._call(e))}}class ns extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class p1 extends ns{}class h1 extends ns{}class f1 extends ns{async _call(e){return new Me(await super._call(e))}}class m1 extends ns{}class Rg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class g1 extends Rg{}class _1 extends Rg{}class Bg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class y1 extends Bg{}class w1 extends Bg{}class Ba extends ee{}class b1 extends Ba{}class v1 extends Ba{async _call(e){return new gt(await super._call(e))}}class $1 extends Ba{async _call(e){return new Me(await super._call(e))}}class x1 extends Ba{async _call(e){return new mt(await super._call(e))}}class S1 extends Ba{async _call(e){return new $t(await super._call(e))}}class Da extends ee{}class k1 extends Da{}class E1 extends Da{async _call(e){return new gt(await super._call(e))}}class C1 extends Da{async _call(e){return new Me(await super._call(e))}}class T1 extends Da{async _call(e){return new mt(await super._call(e))}}class I1 extends Da{async _call(e){return new $t(await super._call(e))}}class Na extends ee{}class A1 extends Na{}class M1 extends Na{async _call(e){return new gt(await super._call(e))}}class O1 extends Na{async _call(e){return new Me(await super._call(e))}}class z1 extends Na{async _call(e){return new mt(await super._call(e))}}class P1 extends Na{async _call(e){return new $t(await super._call(e))}}class Dg extends ee{}class R1 extends Dg{}class B1 extends Dg{}class Ng extends ee{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class D1 extends Ng{}class N1 extends Ng{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,Wv)}_retrieve_init_tokens(e){const r=[e.decoder_start_token_id];let n=e.language;const a=e.task;if(e.is_multilingual){n||(console.warn("No language specified - defaulting to English (en)."),n="en");const s=`<|${pg(n)}|>`;r.push(e.lang_to_id[s]),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(i=>i!=null)}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,...i}){r=this._prepare_generation_config(r,i);const s=this._retrieve_init_tokens(r);return r.return_timestamps&&(n??=new Io,n.push(new Cv(r,s))),await super.generate({inputs:e,generation_config:r,logits_processor:n,decoder_input_ids:s,...i})}_extract_token_timestamps(e,r,n=null,a=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let i=this.config.median_filter_width;i===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),i=7);const s=e.cross_attentions.map(l=>{let p=Array.from({length:this.config.decoder_layers},(v,k)=>dr(l.map($=>$[k]),2)),f=wa(r.map(([v,k])=>n?p[v].slice(null,k,null,[0,n]):p[v].slice(null,k)));f=f.transpose(1,0,2,3);let[m,c]=lg(f,-2,0,!0),y=f.clone();for(let v=0;v<y.dims[0];++v){let k=y[v];for(let $=0;$<k.dims[0];++$){let C=k[$];const T=m[v][$][0],A=c[v][$][0];for(let B=0;B<C.dims[0];++B){let R=C[B];for(let D=0;D<R.data.length;++D)R.data[D]=(R.data[D]-A.data[D])/T.data[D];R.data.set($_(R.data,i))}}}return ru(y,1)}),o=[e.sequences.length,e.sequences[0].length],u=new pe("float32",new Float32Array(o[0]*o[1]),o);for(let l=0;l<o[0];++l){const p=s[l].neg().squeeze_(0);let[f,m]=ow(p),c=Array.from({length:f.length-1},(v,k)=>f[k+1]-f[k]),y=ct([1],c).map(v=>!!v),w=[];for(let v=0;v<y.length;++v)y[v]&&w.push(m[v]*a);u[l].data.set(w,1)}return u}}class F1 extends ee{main_input_name="pixel_values";forward_params=["pixel_values","input_ids","encoder_hidden_states","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class L1 extends ee{forward_params=["input_ids","pixel_values","attention_mask","position_ids","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class Fg extends L1{async encode_image({pixel_values:e}){const r=(await zr(this.sessions.vision_encoder,{pixel_values:e})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${r.dims[1]}).`),this.config.num_image_tokens=r.dims[1]),r}async encode_text({input_ids:e}){return(await zr(this.sessions.embed_tokens,{input_ids:e})).inputs_embeds}_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){const i=this.config.image_token_index,o=n.tolist().map(m=>m.findIndex(c=>c==i)),u=o.every(m=>m===-1),l=o.every(m=>m!==-1);if(!u&&!l)throw new Error("Every input should contain either 0 or 1 image token.");if(u)return{inputs_embeds:e,attention_mask:a};const p=[],f=[];for(let m=0;m<o.length;++m){const c=o[m],y=e[m],w=r[m],v=a[m];p.push(dr([y.slice([0,c]),w,y.slice([c+1,y.dims[0]])],0)),f.push(dr([v.slice([0,c]),On([w.dims[0]]),v.slice([c+1,v.dims[0]])],0))}return{inputs_embeds:wa(p,0),attention_mask:wa(f,0)}}}class U1 extends Fg{}class as extends ee{}class W1 extends as{}class V1 extends as{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class G1 extends as{static async from_pretrained(e,r={}){return r.model_file_name??="vision_model",super.from_pretrained(e,r)}}class Lg extends ee{}class H1 extends Lg{}class j1 extends Lg{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class q1 extends as{static async from_pretrained(e,r={}){return r.model_file_name??="vision_model",super.from_pretrained(e,r)}}class K1 extends ee{}class Y1 extends K1{}class Ug extends ee{}class X1 extends Ug{}class Q1 extends Ug{}class Wg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class J1 extends Wg{}class Z1 extends Wg{}class Vg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class e$ extends Vg{}class t$ extends Vg{}class Gg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class r$ extends Gg{}class n$ extends Gg{}class Hg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class a$ extends Hg{}class i$ extends Hg{}class jg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class s$ extends jg{}class o$ extends jg{}class qg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class u$ extends qg{}class l$ extends qg{}class Kg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class d$ extends Kg{}class c$ extends Kg{}class Yg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class p$ extends Yg{}class h$ extends Yg{}class Xg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class f$ extends Xg{}class m$ extends Xg{}class Qg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class g$ extends Qg{}class _$ extends Qg{}class Jg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class y$ extends Jg{}class w$ extends Jg{}class Zg extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class b$ extends Zg{}class v$ extends Zg{}class e0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class $$ extends e0{}class x$ extends e0{}class t0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class S$ extends t0{}class k$ extends t0{}class r0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class E$ extends r0{}class C$ extends r0{}class n0 extends ee{}class T$ extends n0{}class I$ extends n0{async _call(e){return new Me(await super._call(e))}}class a0 extends ee{}class A$ extends a0{}class M$ extends a0{async _call(e){return new Me(await super._call(e))}}class O$ extends ee{}class z$ extends O${async _call(e){return new HS(await super._call(e))}}class i0 extends ee{}class P$ extends i0{}class R$ extends i0{async _call(e){return new Me(await super._call(e))}}class s0 extends ee{}class B$ extends s0{}class D$ extends s0{async _call(e){return new Me(await super._call(e))}}class o0 extends ee{}class N$ extends o0{}class F$ extends o0{}class u0 extends ee{}class L$ extends u0{}class U$ extends u0{}class l0 extends ee{}class W$ extends l0{}class V$ extends l0{async _call(e){return new Me(await super._call(e))}}class pu extends ee{}class G$ extends pu{}class H$ extends pu{async _call(e){return new d0(await super._call(e))}}class j$ extends pu{async _call(e){return new q$(await super._call(e))}}class d0 extends Wt{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class q$ extends Wt{constructor({logits:e,pred_boxes:r,pred_masks:n}){super(),this.logits=e,this.pred_boxes=r,this.pred_masks=n}}class c0 extends ee{}class K$ extends c0{}class Y$ extends c0{async _call(e){return new X$(await super._call(e))}}class X$ extends d0{}class p0 extends ee{}class Q$ extends p0{}class J$ extends p0{async _call(e){return new Me(await super._call(e))}}class h0 extends ee{}class Z$ extends h0{}class ex extends h0{async _call(e){return new Me(await super._call(e))}}class f0 extends ee{}class tx extends f0{}class rx extends f0{async _call(e){return new Me(await super._call(e))}}class m0 extends ee{}class nx extends m0{}class ax extends m0{}class g0 extends ee{}class ix extends g0{}class sx extends g0{}class ox extends ee{}class ux extends ox{}class _0 extends ee{}class lx extends _0{}class dx extends _0{}class cx extends ee{}class px extends cx{}class y0 extends ee{}class hx extends y0{}class fx extends y0{async _call(e){return new Me(await super._call(e))}}class w0 extends ee{}class mx extends w0{}class gx extends w0{async _call(e){return new Me(await super._call(e))}}class b0 extends ee{}class _x extends b0{}class yx extends b0{async _call(e){return new Me(await super._call(e))}}class v0 extends ee{}class wx extends v0{}class bx extends v0{async _call(e){return new vx(await super._call(e))}}class vx extends Wt{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class $x extends ee{}class xx extends $x{async get_image_embeddings({pixel_values:e}){return await va(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((i,s)=>i*s,1);e.input_labels=new pe("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 zr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new Sx(await super._call(e))}}class Sx extends Wt{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class $0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class kx extends $0{}class Ex extends $0{}class x0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class Cx extends x0{}class Tx extends x0{}class sn extends ee{}class Ix extends sn{}class Ax extends sn{async _call(e){return new Pn(await super._call(e))}}class Mx extends sn{async _call(e){return new Me(await super._call(e))}}class Ox extends sn{async _call(e){return new mt(await super._call(e))}}class hu extends ee{}class zx extends hu{}class Px extends hu{async _call(e){return new Pn(await super._call(e))}}class Rx extends hu{async _call(e){return new Me(await super._call(e))}}class is extends ee{}class Bx extends is{}class Dx extends is{async _call(e){return new Pn(await super._call(e))}}class Nx extends is{async _call(e){return new Me(await super._call(e))}}class Fx extends is{async _call(e){return new mt(await super._call(e))}}class fu extends ee{}class Lx extends fu{}class Ux extends fu{async _call(e){return new Pn(await super._call(e))}}class Wx extends fu{async _call(e){return new Me(await super._call(e))}}class Vx extends sn{}class Gx extends sn{async _call(e){return new Pn(await super._call(e))}}class Hx extends sn{async _call(e){return new Me(await super._call(e))}}class Fa extends ee{}class jx extends Fa{}class qx extends Fa{async _call(e){return new Pn(await super._call(e))}}class Kx extends Fa{async _call(e){return new Me(await super._call(e))}}class Yx extends Fa{async _call(e){return new GS(await super._call(e))}}class Xx extends Fa{async _call(e){return new mt(await super._call(e))}}class S0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class Qx extends S0{}class Jx extends S0{async generate_speech(e,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:i=20,vocoder:s=null}={}){const o={input_ids:e},{encoder_outputs:u,encoder_attention_mask:l}=await va(this,o),p=u.dims[1]/this.config.reduction_factor,f=Math.floor(p*i),m=Math.floor(p*a),c=this.config.num_mel_bins;let y=[],w=null,v=null,k=0;for(;;){++k;const T=Mg(!!v);let A;v?A=v.output_sequence_out:A=new pe("float32",new Float32Array(c),[1,1,c]);let B={use_cache_branch:T,output_sequence:A,encoder_attention_mask:l,speaker_embeddings:r,encoder_hidden_states:u};this.addPastKeyValues(B,w),v=await zr(this.sessions.decoder_model_merged,B),w=this.getPastKeyValues(v,w);const{prob:R,spectrum:D}=v;if(y.push(D),k>=m&&(Array.from(R.data).filter(K=>K>=n).length>0||k>=f))break}const $=dr(y),{waveform:C}=await zr(s.sessions.model,{spectrogram:$});return{spectrogram:$,waveform:C}}}class Zx extends ee{main_input_name="spectrogram"}class eS extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class tS extends eS{}class k0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class rS extends k0{}class nS extends k0{}class E0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class aS extends E0{}class iS extends E0{}class C0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class sS extends C0{}class oS extends C0{}class mu extends ee{}class uS extends mu{}class lS extends mu{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class dS extends mu{static async from_pretrained(e,r={}){return r.model_file_name??="audio_model",super.from_pretrained(e,r)}}class cS extends ee{}class T0 extends cS{async _call(e){return new jS(await super._call(e))}}class I0 extends ee{}class pS extends I0{}class hS extends I0{}class A0 extends ee{constructor(e,r,n){super(e,r),this.generation_config=n}}class fS extends A0{}class mS extends A0{}class M0 extends ee{}class gS extends M0{}class _S extends M0{async _call(e){return new Me(await super._call(e))}}class O0 extends ee{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}_apply_and_filter_by_delay_pattern_mask(e){const[r,n]=e.dims,a=this.config.decoder.num_codebooks,i=n-a;let s=0;for(let l=0;l<e.size;++l){if(e.data[l]===this.config.decoder.pad_token_id)continue;const p=l%n,f=Math.floor(l/n)%a,m=p-f;m>0&&m<=i&&(e.data[s++]=e.data[l])}const o=Math.floor(r/a),u=s/(o*a);return new pe(e.type,e.data.slice(0,s),[o,a,u])}prepare_inputs_for_generation(e,r,n){let a=structuredClone(e);for(let s=0;s<a.length;++s)for(let o=0;o<a[s].length;++o)s%this.config.decoder.num_codebooks>=o&&(a[s][o]=BigInt(this.config.decoder.pad_token_id));return n.guidance_scale!==null&&n.guidance_scale>1&&(a=a.concat(a)),super.prepare_inputs_for_generation(a,r,n)}async generate(e){const r=await super.generate(e),n=this._apply_and_filter_by_delay_pattern_mask(r).unsqueeze_(0),{audio_values:a}=await zr(this.sessions.encodec_decode,{audio_codes:n});return a}}class yS{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",model_file_name:o=null,subfolder:u="onnx",device:l=null,dtype:p=null,use_external_data_format:f=null,session_options:m={}}={}){let c={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,model_file_name:o,subfolder:u,device:l,dtype:p,use_external_data_format:f,session_options:m};if(c.config=await Eg.from_pretrained(e,c),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let y of this.MODEL_CLASS_MAPPINGS){const w=y.get(c.config.model_type);if(w)return await w[1].from_pretrained(e,c)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${c.config.model_type}", attempting to construct from base class.`),await ee.from_pretrained(e,c);throw Error(`Unsupported model type: ${c.config.model_type}`)}}const wS=new Map([["bert",["BertModel",Kv]],["nomic_bert",["NomicBertModel",e2]],["roformer",["RoFormerModel",t2]],["electra",["ElectraModel",c2]],["esm",["EsmModel",B2]],["convbert",["ConvBertModel",s2]],["camembert",["CamembertModel",g2]],["deberta",["DebertaModel",v2]],["deberta-v2",["DebertaV2Model",E2]],["mpnet",["MPNetModel",G2]],["albert",["AlbertModel",Z2]],["distilbert",["DistilBertModel",M2]],["roberta",["RobertaModel",b1]],["xlm",["XLMModel",k1]],["xlm-roberta",["XLMRobertaModel",A1]],["clap",["ClapModel",uS]],["clip",["CLIPModel",W1]],["clipseg",["CLIPSegModel",X1]],["chinese_clip",["ChineseCLIPModel",Y1]],["siglip",["SiglipModel",H1]],["mobilebert",["MobileBertModel",L2]],["squeezebert",["SqueezeBertModel",Y2]],["wav2vec2",["Wav2Vec2Model",Ix]],["wav2vec2-bert",["Wav2Vec2BertModel",Lx]],["unispeech",["UniSpeechModel",zx]],["unispeech-sat",["UniSpeechSatModel",Bx]],["hubert",["HubertModel",Vx]],["wavlm",["WavLMModel",jx]],["audio-spectrogram-transformer",["ASTModel",R1]],["vits",["VitsModel",T0]],["detr",["DetrModel",G$]],["table-transformer",["TableTransformerModel",K$]],["vit",["ViTModel",T$]],["fastvit",["FastViTModel",A$]],["mobilevit",["MobileViTModel",P$]],["mobilevitv2",["MobileViTV2Model",B$]],["owlvit",["OwlViTModel",N$]],["owlv2",["Owlv2Model",L$]],["beit",["BeitModel",W$]],["deit",["DeiTModel",Q$]],["convnext",["ConvNextModel",hx]],["convnextv2",["ConvNextV2Model",mx]],["dinov2",["Dinov2Model",_x]],["resnet",["ResNetModel",Z$]],["swin",["SwinModel",tx]],["swin2sr",["Swin2SRModel",nx]],["donut-swin",["DonutSwinModel",px]],["yolos",["YolosModel",wx]],["dpt",["DPTModel",ix]],["glpn",["GLPNModel",lx]],["hifigan",["SpeechT5HifiGan",Zx]],["efficientnet",["EfficientNetModel",gS]]]),bS=new Map([["t5",["T5Model",n1]],["longt5",["LongT5Model",i1]],["mt5",["MT5Model",o1]],["bart",["BartModel",l1]],["mbart",["MBartModel",p1]],["marian",["MarianModel",kx]],["whisper",["WhisperModel",D1]],["m2m_100",["M2M100Model",Cx]],["blenderbot",["BlenderbotModel",g1]],["blenderbot-small",["BlenderbotSmallModel",y1]]]),vS=new Map([["bloom",["BloomModel",$$]],["gpt2",["GPT2Model",J1]],["gptj",["GPTJModel",a$]],["gpt_bigcode",["GPTBigCodeModel",s$]],["gpt_neo",["GPTNeoModel",e$]],["gpt_neox",["GPTNeoXModel",r$]],["codegen",["CodeGenModel",u$]],["llama",["LlamaModel",d$]],["gemma",["GemmaModel",p$]],["openelm",["OpenELMModel",f$]],["qwen2",["Qwen2Model",g$]],["phi",["PhiModel",y$]],["phi3",["Phi3Model",b$]],["mpt",["MptModel",S$]],["opt",["OPTModel",E$]],["mistral",["MistralModel",rS]],["starcoder2",["Starcoder2Model",aS]],["falcon",["FalconModel",sS]],["stablelm",["StableLmModel",fS]]]),z0=new Map([["speecht5",["SpeechT5ForSpeechToText",Qx]],["whisper",["WhisperForConditionalGeneration",N1]]]),$S=new Map([["speecht5",["SpeechT5ForTextToSpeech",Jx]]]),xS=new Map([["vits",["VitsModel",T0]],["musicgen",["MusicgenForConditionalGeneration",O0]]]),SS=new Map([["bert",["BertForSequenceClassification",Xv]],["roformer",["RoFormerForSequenceClassification",n2]],["electra",["ElectraForSequenceClassification",h2]],["esm",["EsmForSequenceClassification",N2]],["convbert",["ConvBertForSequenceClassification",u2]],["camembert",["CamembertForSequenceClassification",y2]],["deberta",["DebertaForSequenceClassification",x2]],["deberta-v2",["DebertaV2ForSequenceClassification",T2]],["mpnet",["MPNetForSequenceClassification",j2]],["albert",["AlbertForSequenceClassification",e1]],["distilbert",["DistilBertForSequenceClassification",O2]],["roberta",["RobertaForSequenceClassification",$1]],["xlm",["XLMForSequenceClassification",C1]],["xlm-roberta",["XLMRobertaForSequenceClassification",O1]],["bart",["BartForSequenceClassification",c1]],["mbart",["MBartForSequenceClassification",f1]],["mobilebert",["MobileBertForSequenceClassification",W2]],["squeezebert",["SqueezeBertForSequenceClassification",Q2]]]),kS=new Map([["bert",["BertForTokenClassification",Qv]],["roformer",["RoFormerForTokenClassification",a2]],["electra",["ElectraForTokenClassification",f2]],["esm",["EsmForTokenClassification",F2]],["convbert",["ConvBertForTokenClassification",l2]],["camembert",["CamembertForTokenClassification",w2]],["deberta",["DebertaForTokenClassification",S2]],["deberta-v2",["DebertaV2ForTokenClassification",I2]],["mpnet",["MPNetForTokenClassification",q2]],["distilbert",["DistilBertForTokenClassification",z2]],["roberta",["RobertaForTokenClassification",x1]],["xlm",["XLMForTokenClassification",T1]],["xlm-roberta",["XLMRobertaForTokenClassification",z1]]]),P0=new Map([["t5",["T5ForConditionalGeneration",a1]],["longt5",["LongT5ForConditionalGeneration",s1]],["mt5",["MT5ForConditionalGeneration",u1]],["bart",["BartForConditionalGeneration",d1]],["mbart",["MBartForConditionalGeneration",h1]],["marian",["MarianMTModel",Ex]],["m2m_100",["M2M100ForConditionalGeneration",Tx]],["blenderbot",["BlenderbotForConditionalGeneration",_1]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",w1]]]),R0=new Map([["bloom",["BloomForCausalLM",x$]],["gpt2",["GPT2LMHeadModel",Z1]],["gptj",["GPTJForCausalLM",i$]],["gpt_bigcode",["GPTBigCodeForCausalLM",o$]],["gpt_neo",["GPTNeoForCausalLM",t$]],["gpt_neox",["GPTNeoXForCausalLM",n$]],["codegen",["CodeGenForCausalLM",l$]],["llama",["LlamaForCausalLM",c$]],["gemma",["GemmaForCausalLM",h$]],["openelm",["OpenELMForCausalLM",m$]],["qwen2",["Qwen2ForCausalLM",_$]],["phi",["PhiForCausalLM",w$]],["phi3",["Phi3ForCausalLM",v$]],["mpt",["MptForCausalLM",k$]],["opt",["OPTForCausalLM",C$]],["mbart",["MBartForCausalLM",m1]],["mistral",["MistralForCausalLM",nS]],["starcoder2",["Starcoder2ForCausalLM",iS]],["falcon",["FalconForCausalLM",oS]],["trocr",["TrOCRForCausalLM",tS]],["stablelm",["StableLmForCausalLM",mS]]]),ES=new Map([["bert",["BertForMaskedLM",Yv]],["roformer",["RoFormerForMaskedLM",r2]],["electra",["ElectraForMaskedLM",p2]],["esm",["EsmForMaskedLM",D2]],["convbert",["ConvBertForMaskedLM",o2]],["camembert",["CamembertForMaskedLM",_2]],["deberta",["DebertaForMaskedLM",$2]],["deberta-v2",["DebertaV2ForMaskedLM",C2]],["mpnet",["MPNetForMaskedLM",H2]],["albert",["AlbertForMaskedLM",r1]],["distilbert",["DistilBertForMaskedLM",R2]],["roberta",["RobertaForMaskedLM",v1]],["xlm",["XLMWithLMHeadModel",E1]],["xlm-roberta",["XLMRobertaForMaskedLM",M1]],["mobilebert",["MobileBertForMaskedLM",U2]],["squeezebert",["SqueezeBertForMaskedLM",X2]]]),CS=new Map([["bert",["BertForQuestionAnswering",Jv]],["roformer",["RoFormerForQuestionAnswering",i2]],["electra",["ElectraForQuestionAnswering",m2]],["convbert",["ConvBertForQuestionAnswering",d2]],["camembert",["CamembertForQuestionAnswering",b2]],["deberta",["DebertaForQuestionAnswering",k2]],["deberta-v2",["DebertaV2ForQuestionAnswering",A2]],["mpnet",["MPNetForQuestionAnswering",K2]],["albert",["AlbertForQuestionAnswering",t1]],["distilbert",["DistilBertForQuestionAnswering",P2]],["roberta",["RobertaForQuestionAnswering",S1]],["xlm",["XLMForQuestionAnswering",I1]],["xlm-roberta",["XLMRobertaForQuestionAnswering",P1]],["mobilebert",["MobileBertForQuestionAnswering",V2]],["squeezebert",["SqueezeBertForQuestionAnswering",J2]]]),B0=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",F1]]]),TS=new Map([["llava",["LlavaForConditionalGeneration",Fg]],["moondream1",["Moondream1ForConditionalGeneration",U1]]]),IS=new Map([["vit",["ViTForImageClassification",I$]],["fastvit",["FastViTForImageClassification",M$]],["mobilevit",["MobileViTForImageClassification",R$]],["mobilevitv2",["MobileViTV2ForImageClassification",D$]],["beit",["BeitForImageClassification",V$]],["deit",["DeiTForImageClassification",J$]],["convnext",["ConvNextForImageClassification",fx]],["convnextv2",["ConvNextV2ForImageClassification",gx]],["dinov2",["Dinov2ForImageClassification",yx]],["resnet",["ResNetForImageClassification",ex]],["swin",["SwinForImageClassification",rx]],["segformer",["SegformerForImageClassification",pS]],["efficientnet",["EfficientNetForImageClassification",_S]]]),AS=new Map([["detr",["DetrForObjectDetection",H$]],["table-transformer",["TableTransformerForObjectDetection",Y$]],["yolos",["YolosForObjectDetection",bx]]]),MS=new Map([["owlvit",["OwlViTForObjectDetection",F$]],["owlv2",["Owlv2ForObjectDetection",U$]]]),OS=new Map([["detr",["DetrForSegmentation",j$]],["clipseg",["CLIPSegForImageSegmentation",Q1]]]),zS=new Map([["segformer",["SegformerForSemanticSegmentation",hS]]]),PS=new Map([["sam",["SamModel",xx]]]),RS=new Map([["wav2vec2",["Wav2Vec2ForCTC",Ax]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Ux]],["unispeech",["UniSpeechForCTC",Px]],["unispeech-sat",["UniSpeechSatForCTC",Dx]],["wavlm",["WavLMForCTC",qx]],["hubert",["HubertForCTC",Gx]]]),BS=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Mx]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Wx]],["unispeech",["UniSpeechForSequenceClassification",Rx]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Nx]],["wavlm",["WavLMForSequenceClassification",Kx]],["hubert",["HubertForSequenceClassification",Hx]],["audio-spectrogram-transformer",["ASTForAudioClassification",B1]]]),DS=new Map([["wavlm",["WavLMForXVector",Yx]]]),NS=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Fx]],["wavlm",["WavLMForAudioFrameClassification",Xx]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Ox]]]),FS=new Map([["vitmatte",["VitMatteForImageMatting",z$]]]),LS=new Map([["swin2sr",["Swin2SRForImageSuperResolution",ax]]]),US=new Map([["dpt",["DPTForDepthEstimation",sx]],["depth_anything",["DepthAnythingForDepthEstimation",ux]],["glpn",["GLPNForDepthEstimation",dx]]]),WS=new Map([["clip",["CLIPVisionModelWithProjection",G1]],["siglip",["SiglipVisionModel",q1]]]),D0=[[wS,ve.EncoderOnly],[bS,ve.EncoderDecoder],[vS,ve.DecoderOnly],[SS,ve.EncoderOnly],[kS,ve.EncoderOnly],[P0,ve.Seq2Seq],[z0,ve.Seq2Seq],[R0,ve.DecoderOnly],[ES,ve.EncoderOnly],[CS,ve.EncoderOnly],[B0,ve.Vision2Seq],[TS,ve.ImageTextToText],[IS,ve.EncoderOnly],[OS,ve.EncoderOnly],[zS,ve.EncoderOnly],[FS,ve.EncoderOnly],[LS,ve.EncoderOnly],[US,ve.EncoderOnly],[AS,ve.EncoderOnly],[MS,ve.EncoderOnly],[PS,ve.MaskGeneration],[RS,ve.EncoderOnly],[BS,ve.EncoderOnly],[$S,ve.Seq2Seq],[xS,ve.EncoderOnly],[DS,ve.EncoderOnly],[NS,ve.EncoderOnly],[WS,ve.EncoderOnly]];for(const[t,e]of D0)for(const[r,n]of t.values())qi.set(r,e),fa.set(n,r),Tg.set(r,n);const VS=[["MusicgenForConditionalGeneration",O0,ve.Musicgen],["CLIPTextModelWithProjection",V1,ve.EncoderOnly],["SiglipTextModel",j1,ve.EncoderOnly],["ClapTextModelWithProjection",lS,ve.EncoderOnly],["ClapAudioModelWithProjection",dS,ve.EncoderOnly]];for(const[t,e,r]of VS)qi.set(t,r),fa.set(e,t),Tg.set(t,e);class _p extends yS{static MODEL_CLASS_MAPPINGS=D0.map(e=>e[0]);static BASE_IF_FAIL=!0}class Me extends Wt{constructor({logits:e}){super(),this.logits=e}}class GS extends Wt{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class mt extends Wt{constructor({logits:e}){super(),this.logits=e}}class gt extends Wt{constructor({logits:e}){super(),this.logits=e}}class $t extends Wt{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class Pn extends Wt{constructor({logits:e}){super(),this.logits=e}}class HS extends Wt{constructor({alphas:e}){super(),this.alphas=e}}class jS extends Wt{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const Nt=typeof self<"u",qS=Nt&&self.constructor.name==="DedicatedWorkerGlobalScope";let Hr,N0,Ar;if(Nt)Hr=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},Ar=self.createImageBitmap,N0=self.ImageData;else if(Ze)Ar=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),i=new tr(new Uint8ClampedArray(n),a.width,a.height,a.channels);return r!==void 0&&r!==a.channels&&i.convert(r),i};else throw new Error("Unable to load image processing library.");const KS={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},YS=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class tr{constructor(e,r,n,a){this.data=e,this.width=r,this.height=n,this.channels=a}get size(){return[this.width,this.height]}static async read(e){if(e instanceof tr)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!Nt)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new tr(n,e.width,e.height,4)}static async fromURL(e){const r=await ho(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(Nt){const r=await Ar(e),n=Hr(r.width,r.height).getContext("2d");return n.drawImage(r,0,0),new this(n.getImageData(0,0,r.width,r.height).data,r.width,r.height,4)}else{const r=Ze(await e.arrayBuffer());return await Ar(r)}}static fromTensor(e,r="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(r==="CHW")e=e.transpose(1,2,0);else if(r!=="HWC")throw new Error(`Unsupported channel format: ${r}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new tr(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let r=0,n=0;r<this.data.length;r+=this.channels){const a=this.data[r],i=this.data[r+1],s=this.data[r+2];e[n++]=Math.round(.2989*a+.587*i+.114*s)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let r=0,n=0;r<this.data.length;++r)e[n++]=this.data[r],e[n++]=this.data[r],e[n++]=this.data[r];break;case 4:for(let r=0,n=0;r<this.data.length;r+=4)e[n++]=this.data[r],e[n++]=this.data[r+1],e[n++]=this.data[r+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let r=0,n=0;r<this.data.length;++r)e[n++]=this.data[r],e[n++]=this.data[r],e[n++]=this.data[r],e[n++]=255;break;case 3:for(let r=0,n=0;r<this.data.length;r+=3)e[n++]=this.data[r],e[n++]=this.data[r+1],e[n++]=this.data[r+2],e[n++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}async resize(e,r,{resample:n=2}={}){let a=KS[n]??n;if(Nt){const i=this.channels,s=this.toCanvas(),o=Hr(e,r).getContext("2d");return o.drawImage(s,0,0,e,r),new tr(o.getImageData(0,0,e,r).data,e,r,4).convert(i)}else{let i=this.toSharp();switch(a){case"box":case"hamming":(a==="box"||a==="hamming")&&(console.warn(`Resampling method ${a} is not yet supported. Using bilinear instead.`),a="bilinear");case"nearest":case"bilinear":case"bicubic":i=i.affine([e/this.width,0,0,r/this.height],{interpolator:a});break;case"lanczos":i=i.resize({width:e,height:r,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${a} is not supported.`)}return await Ar(i)}}async pad([e,r,n,a]){if(e=Math.max(e,0),r=Math.max(r,0),n=Math.max(n,0),a=Math.max(a,0),e===0&&r===0&&n===0&&a===0)return this;if(Nt){const i=this.channels,s=this.toCanvas(),o=this.width+e+r,u=this.height+n+a,l=Hr(o,u).getContext("2d");return l.drawImage(s,0,0,this.width,this.height,e,n,o,u),new tr(l.getImageData(0,0,o,u).data,o,u,4).convert(i)}else{const i=this.toSharp().extend({left:e,right:r,top:n,bottom:a});return await Ar(i)}}async crop([e,r,n,a]){if(e=Math.max(e,0),r=Math.max(r,0),n=Math.min(n,this.width-1),a=Math.min(a,this.height-1),e===0&&r===0&&n===this.width-1&&a===this.height-1)return this;const i=n-e+1,s=a-r+1;if(Nt){const o=this.channels,u=this.toCanvas(),l=Hr(i,s).getContext("2d");return l.drawImage(u,e,r,i,s,0,0,i,s),new tr(l.getImageData(0,0,i,s).data,i,s,4).convert(o)}else{const o=this.toSharp().extract({left:e,top:r,width:i,height:s});return await Ar(o)}}async center_crop(e,r){if(this.width===e&&this.height===r)return this;const n=(this.width-e)/2,a=(this.height-r)/2;if(Nt){const i=this.channels,s=this.toCanvas(),o=Hr(e,r).getContext("2d");let u=0,l=0,p=0,f=0;return n>=0?u=n:p=-n,a>=0?l=a:f=-a,o.drawImage(s,u,l,e,r,p,f,e,r),new tr(o.getImageData(0,0,e,r).data,e,r,4).convert(i)}else{let i=this.toSharp();if(n>=0&&a>=0)i=i.extract({left:Math.floor(n),top:Math.floor(a),width:e,height:r});else if(n<=0&&a<=0){const s=Math.floor(-a),o=Math.floor(-n);i=i.extend({top:s,left:o,right:e-this.width-o,bottom:r-this.height-s})}else{let s=[0,0],o=0;a<0?(s[0]=Math.floor(-a),s[1]=r-this.height-s[0]):o=Math.floor(a);let u=[0,0],l=0;n<0?(u[0]=Math.floor(-n),u[1]=e-this.width-u[0]):l=Math.floor(n),i=i.extend({top:s[0],bottom:s[1],left:u[0],right:u[1]}).extract({left:l,top:o,width:e,height:r})}return await Ar(i)}}async toBlob(e="image/png",r=1){if(!Nt)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:r})}toTensor(e="CHW"){let r=new pe("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(e!=="HWC")if(e==="CHW")r=r.permute(2,0,1);else throw new Error(`Unsupported channel format: ${e}`);return r}toCanvas(){if(!Nt)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=Hr(e.width,e.height),n=new N0(e.data,e.width,e.height);return r.getContext("2d").putImageData(n,0,0),r}_update(e,r,n,a=null){return this.data=e,this.width=r,this.height=n,a!==null&&(this.channels=a),this}clone(){return new tr(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(Nt){if(qS)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=YS.get(r)??"image/png",a=await this.toBlob(n),i=URL.createObjectURL(a),s=document.createElement("a");s.href=i,s.download=e,s.click(),s.remove()}else{if(Ct.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Nt)throw new Error("toSharp() is only supported in server-side environments.");return Ze(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}function yp(t){if(t<1)return new Float64Array;if(t===1)return new Float64Array([1]);const e=t-1,r=Math.PI/e,n=new Float64Array(t);for(let a=0;a<t;++a){const i=2*a-e;n[a]=.5+.5*Math.cos(r*i)}return n}const XS={htk:t=>2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,r=15,n=27/Math.log(6.4))=>t>=e?r+Math.log(t/e)*n:3*t/200};function so(t,e="htk"){const r=XS[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}const QS={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,r=15,n=Math.log(6.4)/27)=>t>=r?e*Math.exp(n*(t-r)):200*t/3};function JS(t,e="htk"){const r=QS[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}function ZS(t,e){const r=Float64Array.from({length:e.length-1},(s,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let s=0;s<t.length;++s){const o=n[s];for(let u=0;u<e.length;++u)o[u]=e[u]-t[s]}const a=e.length-2,i=Array.from({length:a},()=>new Array(t.length));for(let s=0;s<t.length;++s){const o=n[s];for(let u=0;u<a;++u){const l=-o[u]/r[u],p=o[u+2]/r[u+1];i[u][s]=Math.max(0,Math.min(l,p))}}return i}function wp(t,e,r){const n=(e-t)/(r-1);return Float64Array.from({length:r},(a,i)=>t+n*i)}function $a(t,e,r,n,a,i=null,s="htk",o=!1){if(i!==null&&i!=="slaney")throw new Error('norm must be one of null or "slaney"');const u=so(r,s),l=so(n,s),p=wp(u,l,e+2);let f=JS(p,s),m;if(o){const y=a/(t*2);m=so(Float64Array.from({length:t},(w,v)=>v*y),s),f=p}else m=wp(0,Math.floor(a/2),t);const c=ZS(m,f);if(i!==null&&i==="slaney")for(let y=0;y<e;++y){const w=c[y],v=2/(f[y+2]-f[y]);for(let k=0;k<t;++k)w[k]*=v}return c}function e3(t,e,r){const n=new t.constructor(t.length+e+r),a=t.length-1;for(let i=0;i<t.length;++i)n[e+i]=t[i];for(let i=1;i<=e;++i)n[e-i]=t[Ri(i,a)];for(let i=1;i<=r;++i)n[a+e+i]=t[Ri(a-i,a)];return n}function F0(t,e,r,n,a){if(r<=0)throw new Error("reference must be greater than zero");if(n<=0)throw new Error("min_value must be greater than zero");r=Math.max(n,r);const i=Math.log10(r);for(let s=0;s<t.length;++s)t[s]=e*Math.log10(Math.max(n,t[s])-i);if(a!==null){if(a<=0)throw new Error("db_range must be greater than zero");const s=$r(t)[0]-a;for(let o=0;o<t.length;++o)t[o]=Math.max(t[o],s)}return t}function t3(t,e=1,r=1e-5,n=null){return F0(t,20,e,r,n)}function r3(t,e=1,r=1e-10,n=null){return F0(t,10,e,r,n)}function ss(t,e,r,n,{fft_length:a=null,power:i=1,center:s=!0,pad_mode:o="reflect",onesided:u=!0,preemphasis:l=null,mel_filters:p=null,mel_floor:f=1e-10,log_mel:m=null,reference:c=1,min_value:y=1e-10,db_range:w=null,remove_dc_offset:v=null,max_num_frames:k=null,do_pad:$=!0,transpose:C=!1}={}){const T=e.length;if(a===null&&(a=r),r>a)throw Error(`frame_length (${r}) may not be larger than fft_length (${a})`);if(T!==r)throw new Error(`Length of the window (${T}) must equal frame_length (${r})`);if(n<=0)throw new Error("hop_length must be greater than zero");if(i===null&&p!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(s){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const P=Math.floor((a-1)/2)+1;t=e3(t,P,P)}const A=Math.floor(1+Math.floor((t.length-r)/n)),B=u?Math.floor(a/2)+1:a;let R=A,D=A;k!==null&&(k>A?$&&(D=k):D=R=k);const K=new v_(a),j=new Float64Array(a),ie=new Float64Array(K.outputBufferSize),te=new Array(R);for(let P=0;P<R;++P){const H=P*n;for(let G=0;G<r;++G)j[G]=t[H+G];if(v){let G=0;for(let N=0;N<r;++N)G+=j[N];const ne=G/r;for(let N=0;N<r;++N)j[N]-=ne}if(l!==null){for(let G=r-1;G>=1;--G)j[G]-=l*j[G-1];j[0]*=1-l}for(let G=0;G<e.length;++G)j[G]*=e[G];K.realTransform(ie,j);const le=new Array(B);for(let G=0;G<le.length;++G){const ne=G<<1;le[G]=ie[ne]**2+ie[ne+1]**2}te[P]=le}if(i!==null&&i!==2){const P=2/i;for(let H=0;H<te.length;++H){const le=te[H];for(let G=0;G<le.length;++G)le[G]**=P}}const oe=p.length,re=new Float32Array(oe*D),M=C?[D,oe]:[oe,D];for(let P=0;P<oe;++P){const H=p[P];for(let le=0;le<R;++le){const G=te[le];let ne=0;for(let N=0;N<B;++N)ne+=H[N]*G[N];re[C?le*oe+P:P*R+le]=Math.max(f,ne)}}if(i!==null&&m!==null){const P=Math.min(re.length,R*oe);switch(m){case"log":for(let H=0;H<P;++H)re[H]=Math.log(re[H]);break;case"log10":for(let H=0;H<P;++H)re[H]=Math.log10(re[H]);break;case"dB":if(i===1)t3(re,c,y,w);else if(i===2)r3(re,c,y,w);else throw new Error(`Cannot use log_mel option '${m}' with power ${i}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${m}'`)}}return{data:re,dims:M}}function os(t,e,{periodic:r=!0,frame_length:n=null,center:a=!0}={}){const i=r?t+1:t;let s;switch(e){case"boxcar":s=new Float64Array(i).fill(1);break;case"hann":case"hann_window":s=yp(i);break;case"povey":s=yp(i).map(o=>Math.pow(o,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(s=s.subarray(0,t)),n===null)return s;if(t>n)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${n})`);return s}function n3([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function gu(t,e=.5,r=null,n=!1){const a=t.logits,i=t.pred_boxes,[s,o,u]=a.dims;if(r!==null&&r.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let l=[];for(let p=0;p<s;++p){let f=r!==null?r[p]:null,m={boxes:[],classes:[],scores:[]},c=a[p],y=i[p];for(let w=0;w<o;++w){let v=c[w],k=[],$;if(n){$=v.sigmoid().data;for(let C=0;C<$.length;++C)$[C]>e&&k.push(C)}else{let C=$r(v.data)[1];if(C===u-1||($=In(v.data),$[C]<e))continue;k.push(C)}for(const C of k){let T=y[w].data;T=n3(T),f!==null&&(T=T.map((A,B)=>A*f[(B+1)%2])),m.boxes.push(T),m.classes.push(C),m.scores.push($[C])}}l.push(m)}return l}function La(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${t?.constructor?.name??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function bp(t,e,r=0,n=null){const a=t/e;let i=x_(a)*e;return n!==null&&i>n&&(i=Math.floor(a)*e),i<r&&(i=Math.ceil(a)*e),i}function oo([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}class on extends vt{constructor(e){super(),this.config=e}}class Xe extends on{constructor(e){super(e),this.image_mean=this.config.image_mean??this.config.mean,this.image_std=this.config.image_std??this.config.std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.size_divisibility=this.config.size_divisibility??this.config.size_divisor,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=this.config.do_flip_channel_order??!1}async thumbnail(e,r,n=2){const a=e.height,i=e.width,s=r.height,o=r.width;let u=Math.min(a,s),l=Math.min(i,o);return u===a&&l===i?e:(a>i?l=Math.floor(i*u/a):i>a&&(u=Math.floor(a*l/i)),await e.resize(l,u,{resample:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),a=Op(n.data)[0],s=$r(n.data)[0]-a;if(s===0)return e;const o=r/255;let u=n.width,l=n.height,p=0,f=0;const m=n.data;for(let c=0;c<n.height;++c){const y=c*n.width;for(let w=0;w<n.width;++w)(m[y+w]-a)/s<o&&(u=Math.min(u,w),l=Math.min(l,c),p=Math.max(p,w),f=Math.max(f,c))}return e=await e.crop([u,l,p,f]),e}pad_image(e,r,n,{mode:a="constant",center:i=!1,constant_values:s=0}={}){const[o,u,l]=r;let p,f;if(typeof n=="number"?(p=n,f=n):(p=n.width,f=n.height),p!==u||f!==o){const m=new Float32Array(p*f*l);if(Array.isArray(s))for(let w=0;w<m.length;++w)m[w]=s[w%l];else s!==0&&m.fill(s);const[c,y]=i?[Math.floor((p-u)/2),Math.floor((f-o)/2)]:[0,0];for(let w=0;w<o;++w){const v=(w+y)*p,k=w*u;for(let $=0;$<u;++$){const C=(v+$+c)*l,T=(k+$)*l;for(let A=0;A<l;++A)m[C+A]=e[T+A]}}if(a==="symmetric"){if(i)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const w=o-1,v=u-1;for(let k=0;k<f;++k){const $=k*p,C=Ri(k,w)*u;for(let T=0;T<p;++T){if(k<o&&T<u)continue;const A=($+T)*l,B=(C+Ri(T,v))*l;for(let R=0;R<l;++R)m[A+R]=e[B+R]}}}e=m,r=[f,p,l]}return[e,r]}rescale(e){for(let r=0;r<e.length;++r)e[r]=this.rescale_factor*e[r]}get_resize_output_image_size(e,r){const[n,a]=e.size;let i,s;if(this.do_thumbnail){const{height:o,width:u}=r;i=Math.min(o,u)}else Number.isInteger(r)?(i=r,s=this.config.max_size??i):r!==void 0&&(i=r.shortest_edge,s=r.longest_edge);if(i!==void 0||s!==void 0){const o=i===void 0?1:Math.max(i/n,i/a),u=n*o,l=a*o,p=s===void 0?1:Math.min(s/u,s/l);let f=Math.floor(Number((u*p).toFixed(2))),m=Math.floor(Number((l*p).toFixed(2)));return this.size_divisibility!==void 0&&([f,m]=oo([f,m],this.size_divisibility)),[f,m]}else if(r!==void 0&&r.width!==void 0&&r.height!==void 0){let o=r.width,u=r.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let l=u/a,p=o/n;Math.abs(1-p)<Math.abs(1-l)?l=p:p=l,u=bp(l*a,this.config.ensure_multiple_of),o=bp(p*n,this.config.ensure_multiple_of)}return[o,u]}else{if(this.size_divisibility!==void 0)return oo([n,a],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(r)}`)}}async resize(e){const[r,n]=this.get_resize_output_image_size(e,this.size);return await e.resize(r,n,{resample:this.resample})}async preprocess(e,{do_normalize:r=null,do_pad:n=null,do_convert_rgb:a=null,do_convert_grayscale:i=null,do_flip_channel_order:s=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[o,u]=e.size;if(a??this.do_convert_rgb?e=e.rgb():i&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let c,y;Number.isInteger(this.crop_size)?(c=this.crop_size,y=this.crop_size):(c=this.crop_size.width,y=this.crop_size.height),e=await e.center_crop(c,y)}const l=[e.height,e.width];let p=Float32Array.from(e.data),f=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(p),r??this.do_normalize){let c=this.image_mean;Array.isArray(this.image_mean)||(c=new Array(e.channels).fill(c));let y=this.image_std;if(Array.isArray(this.image_std)||(y=new Array(e.channels).fill(c)),c.length!==e.channels||y.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${c.length}) and \`image_std\` (${y.length}) must match the number of channels in the image (${e.channels}).`);for(let w=0;w<p.length;w+=e.channels)for(let v=0;v<e.channels;++v)p[w+v]=(p[w+v]-c[v])/y[v]}if(n??this.do_pad){if(this.pad_size)[p,f]=this.pad_image(p,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){const[c,y]=oo([f[1],f[0]],this.size_divisibility);[p,f]=this.pad_image(p,f,{width:c,height:y})}}if(s??this.do_flip_channel_order){if(f[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let c=0;c<p.length;c+=3){const y=p[c];p[c]=p[c+2],p[c+2]=y}}const m=new pe("float32",p,f).permute(2,0,1);return{original_size:[u,o],reshaped_input_size:l,pixel_values:m}}async _call(e,...r){Array.isArray(e)||(e=[e]);const n=await Promise.all(e.map(i=>this.preprocess(i)));return{pixel_values:wa(n.map(i=>i.pixel_values),0),original_sizes:n.map(i=>i.original_size),reshaped_input_sizes:n.map(i=>i.reshaped_input_size)}}}class a3 extends Xe{post_process_semantic_segmentation(e,r=null){const n=e.logits,a=n.dims[0];if(r!==null&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const i=[];for(let s=0;s<a;++s){const o=r!==null?r[s]:null;let u=n[s];o!==null&&(u=ug(u,o,"bilinear",!1));const[l,p]=o??u.dims.slice(-2),f=new pe("int32",new Int32Array(l*p),[l,p]),m=u[0].data,c=f.data;for(let k=1;k<u.dims[0];++k){const $=u[k].data;for(let C=0;C<$.length;++C)$[C]>m[C]&&(m[C]=$[C],c[C]=k)}const y=new Array(u.dims[0]),w=f.data;for(let k=0;k<w.length;++k){const $=w[k];y[$]=$}const v=y.filter(k=>k!==void 0);i.push({segmentation:f,labels:v})}return i}}class L0 extends Xe{}class i3 extends L0{}class s3 extends Xe{}class o3 extends Xe{}class U0 extends Xe{}class u3 extends U0{}class l3 extends Xe{}class d3 extends Xe{}class W0 extends Xe{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){const r=this.size?.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){const n=Math.floor(r/this.crop_pct),[a,i]=this.get_resize_output_image_size(e,{shortest_edge:n});e=await e.resize(a,i,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}}class c3 extends W0{}class p3 extends Xe{}class h3 extends Xe{}class f3 extends Xe{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(r=>r*r))}}class V0 extends Xe{}class m3 extends V0{}class G0 extends Xe{post_process_object_detection(...e){return gu(...e)}}class g3 extends G0{}class _3 extends Xe{}class y3 extends Xe{}class H0 extends Xe{pad_image(e,r,n,a={}){const[i,s,o]=r;let u=this.image_mean;Array.isArray(this.image_mean)||(u=new Array(o).fill(u));let l=this.image_std;Array.isArray(l)||(l=new Array(o).fill(u));const p=u.map((f,m)=>-f/l[m]);return super.pad_image(e,r,n,{center:!0,constant_values:p,...a})}}class w3 extends H0{}class b3 extends Xe{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new pe("int64",new BigInt64Array(n.reduce((i,s)=>i*s)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return gu(...e)}remove_low_and_no_objects(e,r,n,a){let i=[],s=[],o=[];for(let u=0;u<e.dims[0];++u){let l=e[u],p=r[u],f=$r(l.data)[1];if(f===a)continue;let c=In(l.data)[f];c>n&&(i.push(p),s.push(c),o.push(f))}return[i,s,o]}check_segment_validity(e,r,n,a=.5,i=.8){let s=[],o=0,u=0;const l=r[n].data;for(let f=0;f<e.length;++f)e[f]===n&&(s.push(f),++o),l[f]>=a&&++u;let p=o>0&&u>0;return p&&(p=o/u>i),[p,s]}compute_segments(e,r,n,a,i,s=null,o=null){let[u,l]=o??e[0].dims,p=new pe("int32",new Int32Array(u*l),[u,l]),f=[];if(o!==null)for(let v=0;v<e.length;++v)e[v]=ug(e[v],o,"bilinear",!1);let m=new Int32Array(e[0].data.length),c=new Float32Array(e[0].data.length);for(let v=0;v<e.length;++v){let k=r[v];const $=e[v].data;for(let C=0;C<$.length;++C)$[C]*=k,$[C]>c[C]&&(m[C]=v,c[C]=$[C])}let y=0;const w=p.data;for(let v=0;v<n.length;++v){let k=n[v],[$,C]=this.check_segment_validity(m,e,v,a,i);if($){++y;for(let T of C)w[T]=y;f.push({id:y,label_id:k,score:r[v]})}}return[p,f]}post_process_panoptic_segmentation(e,r=.5,n=.5,a=.8,i=null,s=null){i===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),i=new Set);const o=e.logits,l=e.pred_masks.sigmoid();let[p,f,m]=o.dims;if(m-=1,s!==null&&s.length!==p)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let c=[];for(let y=0;y<p;++y){let w=s!==null?s[y]:null,v=o[y],k=l[y],[$,C,T]=this.remove_low_and_no_objects(v,k,r,m);if(T.length===0){let[R,D]=w??k.dims.slice(-2),K=new pe("int32",new Int32Array(R*D).fill(-1),[R,D]);c.push({segmentation:K,segments_info:[]});continue}let[A,B]=this.compute_segments($,C,T,n,a,i,w);c.push({segmentation:A,segments_info:B})}return c}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class v3 extends Xe{post_process_object_detection(...e){return gu(...e)}}class $3 extends Xe{reshape_input_points(e,r,n,a=!1){e=structuredClone(e);let i=ju(e);if(i.length===3)a||(i=[1,...i]),e=[e];else if(i.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let s=0;s<e.length;++s){let o=r[s],u=n[s],l=[u[0]/o[0],u[1]/o[1]];for(let p=0;p<e[s].length;++p)for(let f=0;f<e[s][p].length;++f)for(let m=0;m<e[s][p][f].length;++m)e[s][p][f][m]*=l[m%2]}return new pe("float32",Float32Array.from(e.flat(1/0)),i)}add_input_labels(e,r){let n=ju(e);if(n.length===2)n=[1,...n],e=[e];else if(n.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(n.some((a,i)=>a!==r.dims[i]))throw Error(`The first ${n.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new pe("int64",e.flat(1/0).map(BigInt),n)}async _call(e,{input_points:r=null,input_labels:n=null,input_boxes:a=null}={}){const i=await super._call(e);if(r&&(i.input_points=this.reshape_input_points(r,i.original_sizes,i.reshaped_input_sizes)),n){if(!i.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");i.input_labels=this.add_input_labels(n,i.input_points)}return a&&(i.input_boxes=this.reshape_input_points(a,i.original_sizes,i.reshaped_input_sizes,!0)),i}async post_process_masks(e,r,n,{mask_threshold:a=0,binarize:i=!0,pad_size:s=null}={}){const o=[];s=s??this.pad_size;const u=[s.height,s.width];for(let l=0;l<r.length;++l){const p=r[l],f=n[l];let m=await np(e[l],{mode:"bilinear",size:u});if(m=m.slice(null,null,[0,f[0]],[0,f[1]]),m=await np(m,{mode:"bilinear",size:p}),i){const c=m.data,y=new Uint8Array(c.length);for(let w=0;w<c.length;++w)c[w]>a&&(y[w]=1);m=new pe("bool",y,m.dims)}o.push(m)}return o}generate_crop_boxes(e,r,{crop_n_layers:n=0,overlap_ratio:a=512/1500,points_per_crop:i=32,crop_n_points_downscale_factor:s=1}={}){}}class x3 extends Xe{pad_image(e,r,n,a={}){const[i,s,o]=r;return super.pad_image(e,r,{width:s+(n-s%n)%n,height:i+(n-i%n)%n},{mode:"symmetric",center:!1,constant_values:-1,...a})}}class S3 extends Xe{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);const n=await Promise.all(e.map(s=>this.preprocess(s))),a=await Promise.all(r.map(s=>this.preprocess(s,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:wa(n.map((s,o)=>dr([s.pixel_values,a[o].pixel_values],0)),0),original_sizes:n.map(s=>s.original_size),reshaped_input_sizes:n.map(s=>s.reshaped_input_size)}}}class k3 extends on{constructor(e){super(e),this.config.mel_filters??=$a(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=os(this.config.n_fft,"hann")}_extract_fbank_features(e){const{data:r,dims:n}=ss(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),a=$r(r)[0];for(let i=0;i<r.length;++i)r[i]=(Math.max(r[i],a-8)+4)/4;return{data:r,dims:n}}async _call(e){La(e,"WhisperFeatureExtractor");let r;e.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),r=e.slice(0,this.config.n_samples)):(r=new Float32Array(this.config.n_samples),r.set(e));const{data:n,dims:a}=this._extract_fbank_features(r);return{input_features:new pe("float32",n,[1,...a])}}}class E3 extends on{_zero_mean_unit_var_norm(e){const n=e.reduce((i,s)=>i+s,0)/e.length,a=e.reduce((i,s)=>i+(s-n)**2,0)/e.length;return e.map(i=>(i-n)/Math.sqrt(a+1e-7))}async _call(e){La(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=e;this.config.do_normalize&&(r=this._zero_mean_unit_var_norm(r));const n=[1,r.length];return{input_values:new pe("float32",r,n),attention_mask:new pe("int64",new BigInt64Array(r.length).fill(1n),n)}}}class C3 extends on{constructor(e){super(e);const r=this.config.sampling_rate,n=$a(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;a<n.length;++a)n[a].push(0);this.mel_filters=n,this.window=os(400,"povey",{periodic:!1})}_extract_fbank_features(e,r){return e=e.map(n=>n*32768),ss(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e,{padding:r=!0,pad_to_multiple_of:n=2,do_normalize_per_mel_bins:a=!0,return_attention_mask:i=!0}={}){La(e,"SeamlessM4TFeatureExtractor");let{data:s,dims:o}=this._extract_fbank_features(e,this.config.max_length);if(a){const[w,v]=o;for(let k=0;k<v;++k){let $=0;for(let B=0;B<w;++B)$+=s[B*v+k];const C=$/w;let T=0;for(let B=0;B<w;++B)T+=(s[B*v+k]-C)**2;T/=w-1;const A=Math.sqrt(T+1e-7);for(let B=0;B<w;++B){const R=B*v+k;s[R]=(s[R]-C)/A}}}let u;if(r){const[w,v]=o,k=w%n;if(k>0){const $=new Float32Array(v*(w+k));$.set(s),$.fill(this.config.padding_value,s.length);const C=w+k;s=$,o=[C,v],i&&(u=new pe("int64",new BigInt64Array(C),[1,C]),u.data.fill(1n,0,w))}}const[l,p]=o,f=this.config.stride;if(l%f!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${f}).`);const c=new pe("float32",s,o).view(1,Math.floor(l/f),p*f),y={input_features:c};if(i){const w=c.dims[1],v=new BigInt64Array(w);if(u){const k=u.data;for(let $=1,C=0;$<l;$+=f,++C)v[C]=k[$]}else v.fill(1n);y.attention_mask=new pe("int64",v,[1,w])}return y}}class T3 extends on{constructor(e){super(e);const r=this.config.sampling_rate,n=$a(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;a<n.length;++a)n[a].push(0);this.mel_filters=n,this.window=os(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(e,r){return ss(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e){La(e,"ASTFeatureExtractor");const r=this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){const n=this.std*2,a=r.data;for(let i=0;i<a.length;++i)a[i]=(a[i]-this.mean)/n}return{input_values:new pe("float32",r.data,[1,...r.dims])}}}class I3 extends on{constructor(e){super(e),this.mel_filters=$a(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=$a(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=os(this.config.fft_window_size,"hann")}_get_input_mel(e,r,n,a){let i,s=!1;const o=e.length-r;if(o>0)if(n==="rand_trunc"){s=!0;const u=Math.floor(Math.random()*(o+1));e=e.subarray(u,u+r),i=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),i.dims=[1,...i.dims]}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(o<0){let u=new Float64Array(r);if(u.set(e),a==="repeat")for(let l=e.length;l<r;l+=e.length)u.set(e.subarray(0,Math.min(e.length,r-l)),l);else if(a==="repeatpad")for(let l=e.length;l<-o;l+=e.length)u.set(e,l);e=u}if(n==="fusion")throw new Error(`Truncation strategy "${n}" not implemented`);i=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),i.dims=[1,...i.dims]}return{...i,longer:s}}_extract_fbank_features(e,r,n=null){return ss(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:r,log_mel:"dB",max_num_frames:n,do_pad:!1,transpose:!0})}async _call(e,{max_length:r=null}={}){La(e,"ClapFeatureExtractor");const n=this._get_input_mel(e,r??this.config.nb_max_samples,this.config.truncation,this.config.padding);return{input_features:new pe("float32",n.data,[1,...n.dims])}}}class A3 extends on{}class Rn extends vt{constructor(e){super(),this.feature_extractor=e}async _call(e,...r){return await this.feature_extractor(e,...r)}}class M3 extends Rn{async _call(...e){return await this.feature_extractor(...e)}post_process_masks(...e){return this.feature_extractor.post_process_masks(...e)}reshape_input_points(...e){return this.feature_extractor.reshape_input_points(...e)}}class O3 extends Rn{async _call(e){return await this.feature_extractor(e)}}class z3 extends Rn{async _call(e){return await this.feature_extractor(e)}}class P3 extends Rn{async _call(e){return await this.feature_extractor(e)}}class R3 extends Rn{}class B3{static 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