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

    ${s.setByOffset("global_idx",o.getByIndices("aIndices"))}
  }`;return{name:"Transpose",shaderCache:{hint:`${e}`,inputDependencies:["rank"]},getRunData:d=>{let c=Q.size(i);return{outputs:[{dims:i,dataType:d[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:[{type:12,data:c},...he(d[0].dims,i)]}},getShaderSource:l}},up=(t,e)=>{_l(t.inputs),t.compute(er(t.inputs[0],e.perm))},dp=t=>qe({perm:t.perm})}),bl,vl,$l,xl,Sl,El,kl,Cl,Tl,Il,Mt,cp,pp,hp,fp,mp,gp,_p,wp,yp,bp,N_=X(()=>{ye(),Te(),Ce(),no(),jn(),bl={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},vl={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},$l={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},xl={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},Sl=(t,e)=>{let r=[];for(let n=e-t;n<e;++n)r.push(n);return r},El=(t,e)=>{let r=[],n=t.length;for(let i=0;i<n;i++)e.indexOf(i)===-1&&r.push(t[i]);let a=e.map(i=>t[i]);return[r,a]},kl=(t,e)=>{let r=t.length+e.length,n=[],a=0;for(let i=0;i<r;i++)e.indexOf(i)===-1?n.push(t[a++]):n.push(1);return n},Cl=(t,e)=>{for(let r=0;r<t.length;++r)if(t[t.length-r-1]!==e-1-r)return!1;return!0},Tl=(t,e)=>{let r=[];if(!Cl(t,e)){for(let n=0;n<e;++n)t.indexOf(n)===-1&&r.push(n);t.forEach(n=>r.push(n))}return r},Il=(t,e,r,n,a,i,s)=>{let o=r[0].dims,l=Q.size(i),d=Q.size(s),c=Y("_A",r[0].dataType,o),u=pe("output",a,i),h=32,m=`
          var<workgroup> aBestValues : array<f32, ${h}>;
       `;return{name:t,shaderCache:e,getShaderSource:g=>`
        ${g.registerUniform("reduceSize","u32").declareVariables(c,u)}
        ${m}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${g.mainStart(h)}

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

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

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

         if (local_idx == 0u) {
          ${u.setByOffset("outputIndex",`${n==="mean"?`${u.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${u.type.storage}(${xl[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:l},programUniforms:[{type:12,data:d}]})}},Mt=(t,e,r,n)=>{let a=t.inputs.length===1?r:Es(t.inputs,r),i=a.axes;i.length===0&&!a.noopWithEmptyAxes&&(i=t.inputs[0].dims.map((m,g)=>g));let s=Q.normalizeAxes(i,t.inputs[0].dims.length),o=s,l=t.inputs[0],d=Tl(o,t.inputs[0].dims.length);d.length>0&&(l=t.compute(er(t.inputs[0],d),{inputs:[0],outputs:[-1]})[0],o=Sl(o.length,l.dims.length));let[c,u]=El(l.dims,o),h=c;a.keepDims&&(h=kl(c,s)),t.compute(Il(e,{hint:a.cacheKey,inputDependencies:["type"]},[l],n,t.inputs[0].dataType,h,u),{inputs:[l]})},cp=(t,e)=>{Mt(t,"ReduceMeanShared",e,"mean")},pp=(t,e)=>{Mt(t,"ReduceL1Shared",e,"l1")},hp=(t,e)=>{Mt(t,"ReduceL2Shared",e,"l2")},fp=(t,e)=>{Mt(t,"ReduceLogSumExpShared",e,"logSumExp")},mp=(t,e)=>{Mt(t,"ReduceMaxShared",e,"max")},gp=(t,e)=>{Mt(t,"ReduceMinShared",e,"min")},_p=(t,e)=>{Mt(t,"ReduceProdShared",e,"prod")},wp=(t,e)=>{Mt(t,"ReduceSumShared",e,"sum")},yp=(t,e)=>{Mt(t,"ReduceSumSquareShared",e,"sumSquare")},bp=(t,e)=>{Mt(t,"ReduceLogSumShared",e,"logSum")}}),Ot,Al,ai,Es,zt,Ml,Ol,zl,Pl,Rl,Bl,Dl,Nl,Ll,Ul,Pt,vp,$p,xp,Sp,Ep,kp,Cp,Tp,Ip,Ap,no=X(()=>{ye(),Te(),ut(),Ce(),N_(),Ot=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.")},Al=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],ai=(t,e,r,n,a,i,s=!1,o=!1)=>{let l=[],d=r[0].dims,c=d.length,u=Q.normalizeAxes(a,c),h=!o&&u.length===0;d.forEach((_,b)=>{h||u.indexOf(b)>=0?s&&l.push(1):l.push(_)});let m=l.length,g=Q.size(l);return{name:t,shaderCache:e,getShaderSource:_=>{let b=[],$=Y("_A",r[0].dataType,c),y=pe("output",i,m),E=n($,y,u),k=E[2];for(let T=0,A=0;T<c;T++)h||u.indexOf(T)>=0?(s&&A++,k=`for(var j${T}: u32 = 0; j${T} < ${d[T]}; j${T}++) {
                  ${E[2].includes("last_index")?`let last_index = j${T};`:""}
                  ${$.indicesSet("input_indices",T,`j${T}`)}
                  ${k}
                }`):(b.push(`${$.indicesSet("input_indices",T,y.indicesGet("output_indices",A))};`),A++);return`

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

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

          ${b.join(`
`)}
          ${E[0]}       // init ops for reduce max/min
          ${E[1]}
          ${k}
          ${E[3]}
          ${E.length===4?y.setByOffset("global_idx","value"):E.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:l,dataType:i}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:12,data:g},...he(d,l)]})}},Es=(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})},zt=(t,e,r,n)=>{let a=t.inputs,i=a.length===1?r:Es(a,r);t.compute(ai(e,{hint:i.cacheKey,inputDependencies:["rank"]},[a[0]],i.noopWithEmptyAxes&&i.axes.length===0?Al:n,i.axes,a[0].dataType,i.keepDims,i.noopWithEmptyAxes),{inputs:[0]})},Ml=(t,e)=>{Ot(t.inputs),zt(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},Ol=(t,e)=>{Ot(t.inputs),zt(t,"ReduceL1",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},zl=(t,e)=>{Ot(t.inputs),zt(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);"])},Pl=(t,e)=>{Ot(t.inputs),zt(t,"ReduceLogSumExp",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},Rl=(t,e)=>{Ot(t.inputs),zt(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, ${r.getByIndices("input_indices")});`,""]})},Bl=(t,e)=>{Ot(t.inputs),zt(t,"ReduceMean",e,(r,n,a)=>{let i=1;for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&(i*=t.inputs[0].dims[s]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${i});`]})},Dl=(t,e)=>{Ot(t.inputs),zt(t,"ReduceMin",e,(r,n,a)=>{let i=[];for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&i.push(`input_indices[${s}] = 0;`);return[`${i.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},Nl=(t,e)=>{Ot(t.inputs),zt(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},Ll=(t,e)=>{Ot(t.inputs),zt(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},Ul=(t,e)=>{Ot(t.inputs),zt(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},Pt=(t,e,r)=>{if(e.length===0)return r;let n=1,a=1;for(let i=0;i<e.length;i++)e.indexOf(i)===-1?n*=t[i]:a*=t[i];return a<32&&n>1024},vp=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Bl(t,e):cp(t,e)},$p=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Ol(t,e):pp(t,e)},xp=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?zl(t,e):hp(t,e)},Sp=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Pl(t,e):fp(t,e)},Ep=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Rl(t,e):mp(t,e)},kp=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Dl(t,e):gp(t,e)},Cp=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Nl(t,e):_p(t,e)},Tp=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Ll(t,e):wp(t,e)},Ip=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Ul(t,e):yp(t,e)},Ap=(t,e)=>{Pt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Ml(t,e):bp(t,e)}}),Gi,Mp,Op,ks,L_=X(()=>{ye(),ut(),no(),Gi=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.")},Mp=(t,e)=>{Gi(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 = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(ai("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Op=(t,e)=>{Gi(t.inputs);let r=(n,a,i)=>{let s=[];for(let o=0;o<n.rank;o++)(i.indexOf(o)>=0||i.length===0)&&s.push(`input_indices[${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(ai("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},ks=t=>qe(t)}),Fl,Wl,Gl,Vl,ii,Hl,zp,Pp=X(()=>{ye(),Js(),Ce(),Fl=(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 l=r.dims[0],d=r.dims[1],c=r.dims[2];if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==c)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(a.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let u=a.dims[0]/3,h=u,m=h;if(e.qkvHiddenSizes.length>0){if(e.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let E of e.qkvHiddenSizes)if(E%e.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");u=e.qkvHiddenSizes[0],h=e.qkvHiddenSizes[1],m=e.qkvHiddenSizes[2]}let g=d;if(u!==h)throw new Error("qkv_hidden_sizes first element should be same as the second");if(a.dims[0]!==u+h+m)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let _=0;if(s){if(h!==m)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]!==l)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]!==h/e.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');e.pastPresentShareBuffer||(_=s.dims[3])}let b=g+_,$=-1,y=0;if(i)throw new Error("Mask not supported");if(s)throw new Error("past is not supported");if(o)throw new Error("relativePositionBias is not supported");return{batchSize:l,sequenceLength:d,pastSequenceLength:_,kvSequenceLength:g,totalSequenceLength:b,maxSequenceLength:$,inputHiddenSize:c,hiddenSize:u,vHiddenSize:m,headSize:Math.floor(u/e.numHeads),vHeadSize:Math.floor(m/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:y,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Wl=(t,e,r,n)=>{let a=rt(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),l=[{type:e.dataType,data:1/n},{type:12,data:s},{type:12,data:o}],d=it(e.dataType,a),c=u=>{let h=pe("x",e.dataType,e.dims,a),m="thread_max_vector";a===2?m="max(thread_max_vector.x, thread_max_vector.y)":a===4&&(m="max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))");let g=Ct(e.dataType),_=[{name:"d_inv",type:g},{name:"d_comp",type:"u32"},{name:"elements_per_wg",type:"u32"}];return`
  var<workgroup> wgMax: array<f32, ${i}>;
  var<workgroup> wgSum: array<f32, ${i}>;
  ${u.registerUniforms(_).declareVariables(h)}
  ${u.mainStart([i,1,1])}
    let localOffset = local_idx * uniforms.elements_per_wg;
    let offset: u32 = workgroup_id.x * uniforms.d_comp + localOffset;

    var thread_max_vector = ${bt("f32",a,"-3.402823e+38f")};
    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
      thread_max_vector = max(${Zt(g,a,"x[offset + i]")}, thread_max_vector);
    }
    wgMax[local_idx] = ${m};
    workgroupBarrier();

    var maxValue = -3.402823e+38f;
    for (var i = 0u; i < ${i}; i++) {
      maxValue = max(wgMax[i], maxValue);
    }

    var sumVector = ${bt("f32",a,"0")};
    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
      sumVector += exp(${Zt(g,a,"x[offset + i]")} - maxValue);
    }
    wgSum[local_idx] = ${Kt("sumVector",a)};
    workgroupBarrier();

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

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
        x[offset + i] = ${bt(g,a,"uniforms.d_inv")};
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
        let f32input = ${Zt(g,a,"x[offset + i]")};
        x[offset + i] = ${h.type.value}(exp(f32input - maxValue) / sum);
      }
    }
  }`};t.compute({name:"AttentionProbsSoftmax",shaderCache:{hint:`${i};${d};${a}`},getShaderSource:c,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:l})},{inputs:[e],outputs:[]})},Gl=(t,e,r,n,a,i)=>{let s=[a.batchSize,a.numHeads,a.sequenceLength,a.kvSequenceLength+a.pastSequenceLength],o=i.scale===0?1/Math.sqrt(a.headSize):i.scale,l=rt(a.headSize),d=a.headSize/l,c=12,u={x:Math.ceil(a.totalSequenceLength/c),y:Math.ceil(a.sequenceLength/c),z:a.batchSize*a.numHeads},h=[{type:12,data:a.sequenceLength},{type:12,data:d},{type:12,data:a.totalSequenceLength},{type:12,data:a.kvSequenceLength},{type:e.dataType,data:o}],m=[e,r],g=b=>{let $=Y("q",e.dataType,e.dims,l),y=Y("key",r.dataType,r.dims,l),E=pe("output",e.dataType,s),k=it(e.dataType),T=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"alpha",type:k}];return`
  const beta: ${k} = 1.0;
  const TILE_SIZE = ${c}u;

  var<workgroup> tileQ: array<${$.type.storage}, ${c*c}>;
  var<workgroup> tileK: array<${$.type.storage}, ${c*c}>;
  ${b.registerUniforms(T).declareVariables($,y,E)}
  ${b.mainStart([c,c,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 lm = m + local_id.y;
    let ln = n + local_id.x;

    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;
    let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx + n * uniforms.K;

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

      workgroupBarrier();
    }

    let headOffset = headIdx * uniforms.M * uniforms.N;
    if (lm < uniforms.M && ln < uniforms.N) {
      let outputIdx = headOffset + lm * uniforms.N + ln;
      output[outputIdx] = ${Kt("value",l)} * uniforms.alpha;
    }
  }`},_=t.compute({name:"AttentionProbs",shaderCache:{hint:`${l}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:h}),getShaderSource:g},{inputs:m,outputs:[-1]})[0];return Wl(t,_,a.batchSize*a.numHeads*a.sequenceLength,a.totalSequenceLength),_},Vl=(t,e,r,n)=>{let a=[n.batchSize,n.sequenceLength,n.vHiddenSize],i=12,s={x:Math.ceil(n.vHeadSize/i),y:Math.ceil(n.sequenceLength/i),z:n.batchSize*n.numHeads},o=[{type:12,data:n.sequenceLength},{type:12,data:n.totalSequenceLength},{type:12,data:n.vHeadSize},{type:12,data:n.numHeads},{type:12,data:n.vHiddenSize}],l=d=>{let c=Y("probs",e.dataType,e.dims),u=Y("v",r.dataType,r.dims),h=pe("output",e.dataType,a),m=[{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 = ${i}u;
  var<workgroup> tileQ: array<${c.type.value}, ${i*i}>;
  var<workgroup> tileK: array<${c.type.value}, ${i*i}>;
  ${d.registerUniforms(m).declareVariables(c,u,h)}
  ${d.mainStart([i,i,1])}
   let headIdx = workgroup_id.z;
   let m = workgroup_id.y * TILE_SIZE + local_id.y;
   let n = workgroup_id.x * TILE_SIZE + local_id.x;

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

   var value = ${c.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;
   let headOffset = (batchIdx * uniforms.M * uniforms.num_heads + currentBatchHeadNumber) * uniforms.N;
   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;
   }
  }`};return t.compute({name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:e.dataType,gpuDataType:0}],dispatchGroup:s,programUniforms:o}),getShaderSource:l},{inputs:[e,r],outputs:[0]})[0]},ii=(t,e,r,n,a,i,s,o,l,d,c)=>{let u=Gl(t,e,r,l,d,c);Vl(t,u,n,d)},Hl=(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},l=[t.inputs[0],t.inputs[1],t.inputs[2]],d=[{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}],c=u=>{let h=pe("output_q",l[0].dataType,r),m=pe("output_k",l[0].dataType,r),g=pe("output_v",l[0].dataType,r),_=Y("input",l[0].dataType,l[0].dims),b=Y("weight",l[1].dataType,l[1].dims),$=Y("bias",l[2].dataType,l[2].dims),y=_.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`
  const TILE_SIZE = ${s}u;
  var<workgroup> tileInput: array<${y}, ${s*s}>;
  var<workgroup> tileWeightQ: array<${y}, ${s*s}>;
  var<workgroup> tileWeightK: array<${y}, ${s*s}>;
  var<workgroup> tileWeightV: array<${y}, ${s*s}>;
  ${u.registerUniforms(E).declareVariables(_,b,$,h,m,g)}
  ${u.mainStart([s,s,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = workgroup_id.y * TILE_SIZE + local_id.y;
    let n = workgroup_id.x * TILE_SIZE + local_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 = ${y}(0);
    var valueK = ${y}(0);
    var valueV = ${y}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return t.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:d}),getShaderSource:c},{inputs:l,outputs:[-1,-1,-1]})},zp=(t,e)=>{let r=Fl(t.inputs,e),[n,a,i]=Hl(t,r);return ii(t,n,a,i,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),ql,jl,Kl,Rp,U_=X(()=>{Ft(),ye(),Te(),ut(),Ce(),ql=(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 != ${s}`);a.forEach((o,l)=>{if(o!==n[l])throw new Error(`${i}: dim[${l}] do not match`)})};if(t[0].dims.length>1){let n=e.format==="NHWC"?e.spatial?t[0].dims.slice(-1):t[0].dims.slice(-1).concat(t[0].dims.slice(1,t[0].dims.length-1)):t[0].dims.slice(1,e.spatial?2:void 0);r(t[1].dims,n,"Invalid input scale"),r(t[2].dims,n,"Invalid input B"),r(t[3].dims,n,"Invalid input mean"),r(t[4].dims,n,"Invalid input var")}else r(t[1].dims,[1],"Invalid input scale"),r(t[2].dims,[1],"Invalid input B"),r(t[3].dims,[1],"Invalid input mean"),r(t[4].dims,[1],"Invalid input var")},jl=(t,e)=>{let{epsilon:r,spatial:n,format:a}=e,i=t[0].dims,s=n?rt(i[i.length-1]):1,o=a==="NHWC"&&i.length>1?s:1,l=Q.size(i)/s,d=n,c=d?i.length:i,u=Y("x",t[0].dataType,t[0].dims,s),h=Y("scale",t[1].dataType,t[1].dims,o),m=Y("bias",t[2].dataType,t[2].dims,o),g=Y("inputMean",t[3].dataType,t[3].dims,o),_=Y("inputVar",t[4].dataType,t[4].dims,o),b=pe("y",t[0].dataType,c,s),$=()=>{let E="";if(n)E=`let cOffset = ${i.length===1?"0u":a==="NHWC"?`outputIndices[${i.length-1}] / ${s}`:"outputIndices[1]"};`;else if(a==="NCHW")E=`
            ${b.indicesSet("outputIndices","0","0")}
            let cOffset = ${b.indicesToOffset("outputIndices")};`;else{E=`var cIndices = ${h.type.indices}(0);
                       cIndices[0] = outputIndices[${i.length-1}];`;for(let k=1;k<h.rank;k++)E+=`cIndices[${k}] = outputIndices[${k}];`;E+=`let cOffset = ${h.indicesToOffset("cIndices")};`}return E},y=E=>`
  const epsilon = ${r};
  ${E.registerUniform("outputSize","u32").declareVariables(u,h,m,g,_,b)}
  ${E.mainStart()}
  ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${b.offsetToIndices(`global_idx * ${s}`)};
    ${$()}
    let scale = ${h.getByOffset("cOffset")};
    let bias = ${m.getByOffset("cOffset")};
    let inputMean = ${g.getByOffset("cOffset")};
    let inputVar = ${_.getByOffset("cOffset")};
    let x = ${u.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${b.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${e.epsilon}_${e.format}_${n}_${s}`,inputDependencies:d?["rank","type","type","type","type"]:void 0},getShaderSource:y,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d?[{type:12,data:l},...he(i)]:[{type:12,data:l}]})}},Kl=t=>qe(t),Rp=(t,e)=>{let{inputs:r,outputCount:n}=t,a=Kl({...e,outputCount:n});if(De.webgpu.validateInputContent&&ql(r,a),e.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");t.compute(jl(r,a))}}),Yl,Xl,Bp,F_=X(()=>{Te(),Ce(),Yl=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")},Xl=t=>{let e=t[0].dims,r=t[0].dims[2],n=Q.size(e)/4,a=t[0].dataType,i=Y("input",a,e,4),s=Y("bias",a,[r],4),o=Y("residual",a,e,4),l=pe("output",a,e,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:d=>`
  const channels = ${r}u / 4;
  ${d.declareVariables(i,s,o,l)}

  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${i.getByOffset("global_idx")}
      + ${s.getByOffset("global_idx % channels")} + ${o.getByOffset("global_idx")};
    ${l.setByOffset("global_idx","value")}
  }`}},Bp=t=>{Yl(t.inputs),t.compute(Xl(t.inputs))}}),Ql,Oe,Dp,Np,Lp,Up,Fp,Wp,Gp,Vp,Hp,Zl,qp,jp,Kp,Yp,Ha,Xp,qa,Qp,Zp,Jp,eh,th,rh,nh,ah,ih,sh,oh,lh,uh,dh,ch,ph,Vi,hh,Cs,Ts,fh,mh,gh,ao=X(()=>{ye(),Te(),ut(),Ce(),Ql=(t,e,r,n,a,i)=>{let s=Math.ceil(e/4),o="";typeof a=="string"?o=`${a}(a)`:o=a("a");let l=Y("inputData",r,[s],4),d=pe("outputData",n,[s],4);return`
      ${t.registerUniform("vec_size","u32").declareVariables(l,d)}

  ${i??""}

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

    let a = ${l.getByOffset("global_idx")};
    ${d.setByOffset("global_idx",o)}
  }`},Oe=(t,e,r,n,a,i=t.dataType)=>({name:e,shaderCache:{hint:a,inputDependencies:["type"]},getShaderSource:s=>Ql(s,Q.size(t.dims),t.dataType,i,r,n),getRunData:s=>({outputs:[{dims:t.dims,dataType:i}],dispatchGroup:{x:Math.ceil(Q.size(s[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(Q.size(t.dims)/4)}]})}),Dp=t=>{t.compute(Oe(t.inputs[0],"Abs","abs"))},Np=t=>{t.compute(Oe(t.inputs[0],"Acos","acos"))},Lp=t=>{t.compute(Oe(t.inputs[0],"Acosh","acosh"))},Up=t=>{t.compute(Oe(t.inputs[0],"Asin","asin"))},Fp=t=>{t.compute(Oe(t.inputs[0],"Asinh","asinh"))},Wp=t=>{t.compute(Oe(t.inputs[0],"Atan","atan"))},Gp=t=>{t.compute(Oe(t.inputs[0],"Atanh","atanh"))},Vp=t=>qe(t),Hp=(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): ${e.to}`)}t.compute(Oe(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},Zl=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:eo,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:to;return qe({min:e,max:r})},qp=(t,e)=>{let r=t.inputs.length===1?e:Zl(t.inputs),n=Ct(t.inputs[0].dataType);t.compute(Oe(t.inputs[0],"Clip",a=>`clamp(${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]})},jp=t=>{t.compute(Oe(t.inputs[0],"Ceil","ceil"))},Kp=t=>{t.compute(Oe(t.inputs[0],"Cos","cos"))},Yp=t=>{t.compute(Oe(t.inputs[0],"Cosh","cosh"))},Ha=t=>qe(t),Xp=(t,e)=>{let r=Ct(t.inputs[0].dataType);t.compute(Oe(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))},qa=(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));
}`,Qp=t=>{let e=Ct(t.inputs[0].dataType);t.compute(Oe(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,qa(e)))},Zp=t=>{t.compute(Oe(t.inputs[0],"Exp","exp"))},Jp=t=>{t.compute(Oe(t.inputs[0],"Floor","floor"))},eh=t=>{let e=Ct(t.inputs[0].dataType);t.compute(Oe(t.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,qa(e)))},th=(t,e)=>{let r=Ct(t.inputs[0].dataType);t.compute(Oe(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))},rh=t=>{t.compute(Oe(t.inputs[0],"Not",e=>`!${e}`))},nh=t=>{t.compute(Oe(t.inputs[0],"Neg",e=>`-${e}`))},ah=t=>{t.compute(Oe(t.inputs[0],"Reciprocal",e=>`1.0/${e}`))},ih=t=>{let e=Ct(t.inputs[0].dataType);t.compute(Oe(t.inputs[0],"Relu",r=>`select(vec4<${e}>(0.0), ${r}, ${r} > vec4<${e}>(0.0))`))},sh=t=>{t.compute(Oe(t.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},oh=t=>qe(t),lh=(t,e)=>{let r=Ct(t.inputs[0].dataType);t.compute(Oe(t.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${e.alpha} * ${n} + vec4<${r}>(${e.beta})))`,void 0,e.cacheKey))},uh=t=>{t.compute(Oe(t.inputs[0],"Sin","sin"))},dh=t=>{t.compute(Oe(t.inputs[0],"Sinh","sinh"))},ch=t=>{t.compute(Oe(t.inputs[0],"Sqrt","sqrt"))},ph=t=>{t.compute(Oe(t.inputs[0],"Tan","tan"))},Vi=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,hh=t=>{t.compute(Oe(t.inputs[0],"Tanh",Vi))},Cs=(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 ${Vi("v")};
}
`,Ts=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,fh=t=>{let e=Ct(t.inputs[0].dataType);t.compute(Oe(t.inputs[0],"FastGelu",Ts,Cs(e),void 0,t.inputs[0].dataType))},mh=(t,e)=>{let r=Ct(t.inputs[0].dataType);return t.compute(Oe(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},gh=t=>{t.compute(Oe(t.inputs[0],"Log","log"))}}),Jl,eu,_h,W_=X(()=>{Te(),Ce(),ao(),Jl=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")},eu=t=>{let e=t[0].dims.slice();e[2]=e[2]/2;let r=Y("input",t[0].dataType,t[0].dims,4),n=Y("bias",t[0].dataType,[t[0].dims[2]],4),a=pe("output",t[0].dataType,e,4),i=Q.size(e)/4,s=it(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)}

  ${qa(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);

    ${a.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},_h=t=>{Jl(t.inputs),t.compute(eu(t.inputs))}}),tu,ru,Rt,wh,yh,bh,vh,$h,xh,Sh,Eh,kh,Ch,G_=X(()=>{ye(),Te(),Ce(),tu=(t,e,r,n,a,i,s,o,l,d,c,u)=>{let h,m;typeof o=="string"?h=m=(y,E)=>`${o}((${y}),(${E}))`:typeof o=="function"?h=m=o:(h=o.scalar,m=o.vector);let g=pe("outputData",c,n.length,4),_=Y("aData",l,e.length,4),b=Y("bData",d,r.length,4),$;if(a)if(i){let y=Q.size(e)===1,E=Q.size(r)===1,k=e.length>0&&e[e.length-1]%4===0,T=r.length>0&&r[r.length-1]%4===0;y||E?$=g.setByOffset("global_idx",m(y?`${_.type.value}(${_.getByOffset("0")}.x)`:_.getByOffset("global_idx"),E?`${b.type.value}(${b.getByOffset("0")}.x)`:b.getByOffset("global_idx"))):$=`
            let outputIndices = ${g.offsetToIndices("global_idx * 4u")};
            let offsetA = ${_.broadcastedIndicesToOffset("outputIndices",g)};
            let offsetB = ${b.broadcastedIndicesToOffset("outputIndices",g)};
            ${g.setByOffset("global_idx",m(s||k?_.getByOffset("offsetA / 4u"):`${_.type.value}(${_.getByOffset("offsetA / 4u")}[offsetA % 4u])`,s||T?b.getByOffset("offsetB / 4u"):`${b.type.value}(${b.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else $=g.setByOffset("global_idx",m(_.getByOffset("global_idx"),b.getByOffset("global_idx")));else{if(!i)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let y=(E,k,T="")=>{let A=`aData[indexA${k}][componentA${k}]`,R=`bData[indexB${k}][componentB${k}]`;return`
            let outputIndices${k} = ${g.offsetToIndices(`global_idx * 4u + ${k}u`)};
            let offsetA${k} = ${_.broadcastedIndicesToOffset(`outputIndices${k}`,g)};
            let offsetB${k} = ${b.broadcastedIndicesToOffset(`outputIndices${k}`,g)};
            let indexA${k} = offsetA${k} / 4u;
            let indexB${k} = offsetB${k} / 4u;
            let componentA${k} = offsetA${k} % 4u;
            let componentB${k} = offsetB${k} % 4u;
            ${E}[${k}] = ${T}(${h(A,R)});
          `};c===9?$=`
            var data = vec4<u32>(0);
            ${y("data",0,"u32")}
            ${y("data",1,"u32")}
            ${y("data",2,"u32")}
            ${y("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:$=`
            ${y("outputData[global_idx]",0)}
            ${y("outputData[global_idx]",1)}
            ${y("outputData[global_idx]",2)}
            ${y("outputData[global_idx]",3)}
          `}return`
        ${t.registerUniform("vec_size","u32").declareVariables(_,b,g)}

        ${u??""}

        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${$}
      }`},ru=(t,e,r,n,a,i,s=r.dataType)=>{let o=!Q.areEqual(r.dims,n.dims),l=r.dims,d=Q.size(r.dims),c=!1,u=!1,h=[o];if(o){let m=Xr.calcShape(r.dims,n.dims,!1);if(!m)throw new Error("Can't perform binary op on the given tensors");l=m,d=Q.size(l);let g=Q.size(r.dims)===1,_=Q.size(n.dims)===1,b=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,$=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;h.push(g),h.push(_),h.push(b),h.push($);let y=1;for(let E=1;E<l.length;E++){let k=r.dims[r.dims.length-E]??1,T=n.dims[n.dims.length-E]??1;if(k===T)y*=k;else break}y%4===0?(u=!0,c=!0):(g||_||b||$)&&(c=!0)}else c=!0;return h.push(c),{name:t,shaderCache:{hint:e+h.map(m=>m.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:m=>tu(m,r.dims,n.dims,l,c,o,u,a,r.dataType,n.dataType,s,i),getRunData:()=>({outputs:[{dims:l,dataType:s}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:Math.ceil(Q.size(l)/4)},...he(r.dims,n.dims,l)]})}},Rt=(t,e,r,n,a,i)=>{t.compute(ru(e,a??"",t.inputs[0],t.inputs[1],r,n,i))},wh=t=>{Rt(t,"Add",(e,r)=>`${e}+${r}`)},yh=t=>{Rt(t,"Div",(e,r)=>`${e}/${r}`)},bh=t=>{Rt(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4<u32>(${e}==${r})`},void 0,void 0,9)},vh=t=>{Rt(t,"Mul",(e,r)=>`${e}*${r}`)},$h=t=>{let e=Y("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;Rt(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));
    }
      `)},xh=t=>{Rt(t,"Sub",(e,r)=>`${e}-${r}`)},Sh=t=>{Rt(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4<u32>(${e}>${r})`},void 0,void 0,9)},Eh=t=>{Rt(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4<u32>(${e}<${r})`},void 0,void 0,9)},kh=t=>{Rt(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4<u32>(${e}>=${r})`},void 0,void 0,9)},Ch=t=>{Rt(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4<u32>(${e}<=${r})`},void 0,void 0,9)}}),nu,au,iu,su,Th,Ih,V_=X(()=>{ye(),Te(),ut(),Ce(),nu=(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((l,d)=>{if(d!==e&&l!==n.dims[d])throw new Error("non concat dimensions must match")})}})},au=(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;
  }`,iu=(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}) { ${i} }`)}return n.join(`
`)},su=(t,e,r,n)=>{let a=Q.size(r),i=new Array(t.length),s=new Array(t.length),o=0,l=[],d=[],c=[{type:12,data:a}];for(let _=0;_<t.length;++_)o+=t[_].dims[e],i[_]=o,d.push(t[_].dims.length),s[_]=Y(`input${_}`,n,d[_]),l.push("rank"),c.push({type:12,data:i[_]});for(let _=0;_<t.length;++_)c.push(...he(t[_].dims));c.push(...he(r));let u=pe("output",n,r.length),h=u.indicesGet("indices",e),m=Array.from(Array(i.length).keys()).map(_=>`uniforms.sizeInConcatAxis${_}`).join(","),g=_=>`

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

  ${au(i.length,m)}

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

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

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

    ${iu(s,u)}
  }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c}),getShaderSource:g}},Th=(t,e)=>{let r=t.inputs,n=r[0].dims,a=Q.normalizeAxis(e.axis,n.length);nu(r,a);let i=n.slice();i[a]=r.reduce((o,l)=>o+(l.dims.length>a?l.dims[a]:0),0);let s=r.filter(o=>Q.size(o.dims)>0);t.compute(su(s,a,i,r[0].dataType),{inputs:s})},Ih=t=>qe({axis:t.axis})}),Or,zr,Pr,io,Br=X(()=>{ye(),Te(),Or=(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 ${t.activation}`)}},zr=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},Pr=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},io=t=>{let e=t?.activation||"";if(e==="HardSigmoid"){let[r,n]=t?.activation_params||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=t?.activation_params||[eo,to];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=t?.activation_params||[.01];return{activation:e,alpha:r}}return{activation:e}}}),ht,so,oo=X(()=>{ht=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},so=t=>`
      ${t?"value = value + getBiasByOutputCoords(coords);":""}
      `}),lo,Ah=X(()=>{lo=t=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${t}.x), i32(${t}.y), i32(${t}.z), 1));
}
`}),ou,lu,si,Hi,uu,oi,du,uo,fi=X(()=>{ye(),Te(),Ce(),Br(),oo(),ou=(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":""});
        `,lu=(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];"}
        }`,si=(t,e,r="f32",n,a=!1,i=32,s=!1,o=32)=>{let l=e[1]*t[1],d=e[0]*t[0],c=a?l:i,u=a?i:l,h=c/e[0],m=i/e[1];if(!((a&&h===4&&t[1]===4||!a&&(h===3||h===4))&&c%e[0]===0&&i%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${h} and workPerThread[1] ${t[1]} must be 4.
      Otherwise, innerElementSize ${h} must be 3 or 4.
  tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${h}<${r}>, ${c/h}>, ${u}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${d/t[0]}>, ${i}>;

const rowPerThread = ${t[1]};
const colPerThread = ${t[0]};
const innerElementSize = ${h};
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) * ${l};

  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 * ${m};
  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;
          ${ou(a,n)}
      }

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

          ${lu(a,h)}
      }

      workgroupBarrier();
  }

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

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

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

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

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

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${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) {
      ${uu(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}, ${u}>, ${h}>;
  var<workgroup> mm_Bsub : array<array<${r}, ${c}>, ${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;
      }
    }
    ${b}
  }
`},du=(t,e,r,n,a,i=!1)=>{let[s,o,l]=a,[d,c,u,h]=n,m=Ln(s,l),g=Ln(o,l),_=it(n[0].type.tensor),b=()=>{let y=c.rank,E=d.rank,k=`var aIndices: ${c.type.indices};`;for(let T=y-2-1,A=E-1;T>=0;T--,A--)k+=`
aIndices[${T}] = ${E>1?`batchIndices[${A}]`:"batchIndices"};`;return m.forEach(T=>{k+=`
aIndices[${T}] = 0;`}),k+=`
aIndices[${y-2}] = u32(row);
                   aIndices[${y-1}] = u32(colIn);`,k},$=()=>{let y=u.rank,E=d.rank,k=`var bIndices: ${u.type.indices};`;for(let T=y-2-1,A=E-1;T>=0;T--,A--)k+=`
bIndices[${T}] = ${E>1?`batchIndices[${A}]`:"batchIndices"};`;return g.forEach(T=>{k+=`
bIndices[${T}] = 0;`}),k+=`
bIndices[${y-2}] = u32(row);
                   bIndices[${y-1}] = u32(colIn);`,k};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${ht(t,_)} {
      var value = ${ht(t,_)}(0.0);
      let col = colIn * ${t};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${b()}
        value = ${c.getByIndices("aIndices")};
      }
      return value;
    }

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${ht(t,_)}) {
      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 + ${i?"bias[colIn]":`${ht(t,_)}(bias[row])`};`:""}
        ${r}
        ${h.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},uo=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i.slice(0,-2),l=s.slice(0,-2),d=n?n.slice(0,-2):r.slice(0,-2),c=Q.size(d),u=i[i.length-2],h=i[i.length-1],m=s[s.length-1],g=h%4===0&&m%4===0,_=u<=8?[4,1,1]:[4,4,1],b=[8,8,1],$=[Math.ceil(m/b[0]/_[0]),Math.ceil(u/b[1]/_[1]),Math.ceil(c/b[2]/_[2])],y=g?4:1,E=[...o,u,h/y],k=E.length,T=[...l,h,m/y],A=T.length,R=[c,u,m/y],W=[{type:6,data:u},{type:6,data:m},{type:6,data:h}];zr(e,W),W.push(...he(d,E,T));let K=["rank","rank"],q=t.length>2;q&&(W.push(...he(t[2].dims)),K.push("rank")),W.push(...he(R));let ee=P=>{let J=d.length,te=ro("batchDims",t[0].dataType,J,1),F=it(t[0].dataType),O=Y("a",t[0].dataType,k,y),G=Y("b",t[1].dataType,A,y),ie=pe("result",t[0].dataType,R.length,y),j=[O,G];if(q){let Pe=a?y:1;j.push(Y("bias",t[2].dataType,t[2].dims.length,Pe))}let se=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Pr(e,se);let L=it(ie.type.tensor),ne=Or(e,ie.type.value,L),me=du(y,q,ne,[te,O,G,ie],[o,l,d],a);return`
  ${P.registerUniforms(se).registerInternalVariables(te).declareVariables(...j,ie)}
  ${me}
  ${g?si(_,b,F,te):oi(_,b,F,te)}
                   `};return{name:"MatMul",shaderCache:{hint:`${_};${e.activation};${g};${a}`,inputDependencies:K},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:$[0],y:$[1],z:$[2]},programUniforms:W}),getShaderSource:ee}}}),cu,Mh,H_=X(()=>{ye(),Rr(),Ce(),Br(),oo(),Ah(),fi(),cu=(t,e,r,n,a=!1,i,s=4,o=4,l=4,d="f32")=>{let c=K=>{switch(K){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${d}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${K} is not supported.`)}},u=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 ${K} is not supported.`)}},h=t?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,m=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,g=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",_=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",b=t?"row":"col",$=t?"col":"row",y=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${b} / outWidth;
    let outCol = ${b} % outWidth;

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

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${T}) {
      let col = colIn * ${l};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${m}
      ${so(a)}
      ${W}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Mh=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[0].dims[1],c=r[0],u=l?r[2]:r[3],h=l?r[1]:r[2],m=l?r[3]:r[1],g=l&&(d%4===0||d%3===0)&&m%4===0,_=l?m:u*h,b=l?u*h:m,$=[8,8,1],y=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(_/$[0]/y[0]),Math.ceil(b/$[1]/y[1]),Math.ceil(c/$[2]/y[2])];et("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let k=g?l&&d%4!==0?3:4:1,T=$[1]*y[1],A=$[0]*y[0],R=Math.max($[0]*k,$[1]),W=n%T===0,K=a%A===0,q=i%R===0,ee=g?[k,4,4]:[1,1,1],P=[{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}];zr(e,P),P.push(...he(t[0].dims,t[1].dims));let J=["rank","rank"];s&&(P.push(...he(t[2].dims)),J.push("rank")),P.push(...he(r));let te=F=>{let O=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Pr(e,O);let G=g?4:1,ie=it(t[0].dataType),j=`
      fn setOutputAtIndex(flatIndex : i32, value : ${g?`vec4<${ie}>`:ie}) {
        result[flatIndex] = ${g?`vec4<${ie}>`:ie}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${g?`vec4<${ie}>`:ie}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${g?"/ 4":""}, value);
      }`,se=Y("x",t[0].dataType,t[0].dims.length,k===3?1:k),L=Y("w",t[1].dataType,t[1].dims.length,G),ne=[se,L],me=pe("result",t[0].dataType,r.length,G);if(s){let Pe=Y("bias",t[2].dataType,t[2].dims.length,G);ne.push(Pe),j+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${g?`vec4<${ie}>`:ie} {
          return bias[coords.${l?"w":"y"}${g?"/ 4":""}];
        }`}return`
        ${lo("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 };
        ${F.registerUniforms(O).declareVariables(...ne,me)}
        ${j}
        ${cu(l,W,K,q,s,e,ee[0],ee[1],ee[2],ie)}
        ${g?si(y,$,ie,void 0,!l,R):oi(y,$,ie,void 0,!l,R,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${k};${g};${W};${K};${q};${T};${A};${R}`,inputDependencies:J},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:P}),getShaderSource:te}}}),Is,Oh,q_=X(()=>{ye(),Te(),Ce(),Rh(),Br(),Is=(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,l=e.format==="NHWC",d=ja(i,s,e.dilations,e.pads,e.strides,l),c=Q.size(d),u=[{type:12,data:c},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];zr(e,u),u.push(...he(i,s,d));let h=["rank","rank"];n&&(u.push(...he(t[2].dims)),h.push("rank")),u.push(...he(d));let m=g=>{let _=pe("output",t[0].dataType,d.length),b=it(_.type.tensor),$=Or(e,_.type.value,b),y=Y("x",t[0].dataType,i.length),E=Y("w",t[1].dataType,s.length),k=[y,E];n&&k.push(Y("b",t[2].dataType,t[2].dims));let T=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:e.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Pr(e,T),`
  ${g.registerUniforms(T).declareVariables(...k,_)}

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

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

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

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

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

          let xVal = ${l?y.get("batch","xHeight","xWidth","input_channel"):y.get("batch","input_channel","xHeight","xWidth")};
          let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${a}
    ${$}
    ${_.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:h},getRunData:()=>({outputs:[{dims:r?r(d):d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:u}),getShaderSource:m}},Oh=(t,e,r)=>{let n=t.length>2,a=rt(r[3]),i=rt(r[2]),s=Q.size(r)/a/i,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],l=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],d=[r[0],r[1],r[2],r[3]/a],c=[{type:12,data:s},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];zr(e,c),c.push(...he(o,l,d));let u=(i-1)*e.strides[1]+l[1],h=m=>{let g=pe("output",t[0].dataType,d.length,a),_=it(g.type.tensor),b=Or(e,g.type.value,_),$=Y("x",t[0].dataType,o.length,a),y=Y("w",t[1].dataType,l.length,a),E=[$,y];n&&E.push(Y("b",t[2].dataType,t[2].dims,a));let k=n?"value += b[output_channel];":"",T=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Pr(e,T),`
  ${m.registerUniforms(T).declareVariables(...E,g)}
  ${m.mainStart()}
    ${m.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<${$.type.value}, ${u}>;
    var values: array<${g.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 < ${l[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${u}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${$.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${$.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${l[1]}; w_width++) {
          let w_val = ${y.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];
      ${k}
      ${b}
      ${g.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${i};${u};${l[0]};${l[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:c}),getShaderSource:h}}}),As,pu,zh,Ph=X(()=>{ye(),Te(),fi(),Ce(),Br(),As=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i[i.length-2],l=s[s.length-1],d=i[i.length-1],c=rt(l),u=rt(d),h=rt(o),m=Q.size(r)/c/h,g=t.length>2,_=n?n.slice(0,-2):r.slice(0,-2),b=[Q.size(_),o,l],$=[{type:12,data:m},{type:12,data:o},{type:12,data:l},{type:12,data:d}];zr(e,$),$.push(...he(_,i,s)),g&&$.push(...he(t[2].dims)),$.push(...he(b));let y=E=>{let k=ro("batch_dims",t[0].dataType,_.length),T=Y("a",t[0].dataType,i.length,u),A=Y("b",t[1].dataType,s.length,c),R=pe("output",t[0].dataType,b.length,c),W=it(R.type.tensor),K=Or(e,R.type.value,W),q=[T,A],ee="";if(g){let j=a?c:1;q.push(Y("bias",t[2].dataType,t[2].dims.length,j)),ee=`${a?`value += bias[col / ${j}];`:`value += ${R.type.value}(bias[row + i]);`}`}let P=i.slice(0,-2),J=s.slice(0,-2),te=Ln(P,_),F=Ln(J,_),O=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Pr(e,O);let G=(j,se)=>{let L=j.rank,ne=j.name;if(L===2)return`var ${ne}_indices = ${j.type.indices}(0u, 0u);`;let me=k.rank,Pe=`var ${ne}_indices: ${j.type.indices};`;for(let Re=L-2-1,nt=me-1;Re>=0;Re--,nt--)Pe+=`
${ne}_indices[${Re}] = ${me>1?`batch_indices[${nt}]`:"batch_indices"};`;return se.forEach(Re=>{Pe+=`
${ne}_indices[${Re}] = 0;`}),Pe+=`${ne}_indices[${L-2}] = 0u;
                     ${ne}_indices[${L-1}] = 0u;`,Pe},ie=()=>{let j=`var a_data: ${T.type.value};`;for(let se=0;se<u;se++)j+=`
              let b_data${se} = b[(b_offset + (k + ${se}) * uniforms.N + col) / ${c}];`;for(let se=0;se<h;se++){j+=`a_data = a[(a_offset + (row + ${se}) * uniforms.K + k) / ${u}];`;for(let L=0;L<u;L++)j+=`
            values[${se}] = fma(${A.type.value}(a_data${u===1?"":`[${L}]`}), b_data${L}, values[${se}]);
`}return j};return`
  ${E.registerUniforms(O).registerInternalVariables(k).declareVariables(...q,R)}
  ${E.mainStart()}
    ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${c})) * ${c};
    var index1 = global_idx / (uniforms.N / ${c});
    let stride1 = uniforms.M / ${h};
    let row = (index1 % stride1) * ${h};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${k.offsetToIndices("batch")};`}
    ${G(T,te)}
    let a_offset = ${T.indicesToOffset("a_indices")};
    ${G(A,F)}
    let b_offset = ${A.indicesToOffset("b_indices")};
    var values: array<${R.type.value}, ${h}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${u}) {
      ${ie()}
    }
    for (var i = 0u; i < ${h}u; i++) {
      var value = values[i];
      ${ee}
      ${K}
      let cur_indices = ${R.type.indices}(batch, row + i, col);
      let offset = ${R.indicesToOffset("cur_indices")};
      ${R.setByOffset(`offset / ${c}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${c};${u};${h};${a}`,inputDependencies:g?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:$}),getShaderSource:y}},pu=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.")},zh=t=>{pu(t.inputs);let e=Xr.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(As(t.inputs,{activation:""},e)):t.compute(uo(t.inputs,{activation:""},e))}}),ja,Oa,hu,qi,Ms,fu,mu,Os,Rh=X(()=>{Te(),H_(),fi(),q_(),Br(),Ph(),jn(),ja=(t,e,r,n,a,i)=>{let s=t[0],o=t.slice(i?1:2,i?3:4),l=o.length,d=e[0],c=e.slice(2).map((h,m)=>h+(h-1)*(r[m]-1)),u=o.map((h,m)=>h+n[m]+n[m+l]).map((h,m)=>Math.floor((h-c[m]+a[m])/a[m]));return u.splice(0,0,s),u.splice(i?3:1,0,d),u},Oa=[2,3,1,0],hu=(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 ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},qi=(t,e)=>{let r=t.kernelShape.slice();for(let i=2;i<e[1].dims.length;++i)r[i-2]===0&&(r[i-2]=e[1].dims[i]);let n=t.pads.slice();ri.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,r,n,t.format==="NHWC",t.autoPad);let a=Object.assign({},t);return Object.assign(a,{kernelShape:r,pads:n}),a},Ms=t=>{let e=io(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,i=t.group,s=t.kernel_shape,o=t.pads,l=t.strides,d=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:i,kernelShape:s,pads:o,strides:l,wIsConst:d,...e,cacheKey:`${t.format};${e.activation};`}},fu=(t,e,r)=>{let n=qi(r,e),a=r.format==="NHWC";if(r.group!==1){if(!t.adapterInfo.isArchitecture("ampere")&&a&&e[1].dims[0]===r.group&&e[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let A=ja(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),R=t.kernelCustomData.wT??t.compute(er(e[1],Oa),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=R);let W=[e[0],R];e.length===3&&W.push(e[2]),t.compute(Oh(W,n,A),{inputs:W})}else t.compute(Is(e,n));return}let i=e.length===3,s=e[0].dims[a?1:2],o=e[0].dims[a?2:3],l=e[0].dims[a?3:1],d=e[1].dims[2],c=e[1].dims[3],u=ja(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),h=u[a?1:2],m=u[a?2:3],g=u[a?3:1],_=a&&d===s&&c===o&&r.pads[0]===0&&r.pads[1]===0;if(_||d===1&&c===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let A=u[0],R,W,K,q=[];if(a){let J=t.kernelCustomData.wT??t.compute(er(e[1],Oa),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=J),_){let te=s*o*l;R=e[0].reshape([1,A,te]),W=J.reshape([1,te,g]),K=[1,A,g]}else R=e[0].reshape([A,s*o,l]),W=J.reshape([1,l,g]),K=[A,h*m,g];q.push(R),q.push(W)}else R=e[0].reshape([A,l,s*o]),W=e[1].reshape([1,g,l]),K=[A,g,h*m],q.push(W),q.push(R);i&&q.push(e[2]);let ee=K[2],P=q[0].dims[q[0].dims.length-1];ee<8&&P<8?t.compute(As(q,n,u,K,a),{inputs:q}):t.compute(uo(q,n,u,K,a),{inputs:q});return}let b=!0,$=t.kernelCustomData.wT??t.compute(er(e[1],Oa),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=$);let y=[e[0],$];i&&y.push(e[2]);let E=a?h*m:g,k=a?g:h*m,T=d*c*l;t.compute(Mh(y,n,u,E,k,T,i,b),{inputs:y})},mu=(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),l=qi({...e,pads:a,strides:i,dilations:s,kernelShape:o},n);t.compute(Is(n,l,d=>r?[d[0],d[2],d[3]]:[]))},Os=(t,e)=>{hu(t.inputs,e),t.inputs[0].dims.length===3?mu(t,e):fu(t,t.inputs,e)}}),gu,Bh,j_=X(()=>{ye(),Rr(),Ce(),Br(),oo(),Ah(),fi(),gu=(t,e=!1,r,n,a=4)=>{let i=b=>{switch(b){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 ${b} 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);
    `,l=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",d=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",c=t?"row":"col",u=t?"col":"row",h=`
      let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${c} / outWidth;
      let outCol = ${c} % outWidth;

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

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

  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}
      ${so(e)}
      ${_}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${a}] = value;
    }
  }`},Bh=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[0].dims[1],c=r[0],u=l?r[2]:r[3],h=l?r[1]:r[2],m=l?r[3]:r[1],g=l?d%4===0&&m%4===0:u%4===0&&m%4===0,_=l?m:u*h,b=l?u*h:m,$=g?[8,8,1]:[_<=4||b<=4?4:16,_>4&&b<=4?4:16,1],y=g?[4,4,1]:[_<=4?1:4,_>4&&b<=4?1:4,1],E=[Math.ceil(_/$[0]/y[0]),Math.ceil(b/$[1]/y[1]),Math.ceil(c/$[2]/y[2])];et("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let k=g?4:1,T=Math.max($[0]*k,$[1]),A=g?4:1,R=[e.kernelShape[l?1:2],e.kernelShape[l?2:3]],W=[R[0]+(e.dilations[0]<=1?0:(R[0]-1)*(e.dilations[0]-1)),R[1]+(e.dilations[1]<=1?0:(R[1]-1)*(e.dilations[1]-1))],K=[W[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),W[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],q=[{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}];zr(e,q),q.push(...he(t[0].dims,t[1].dims));let ee=["rank","rank"];s&&(q.push(...he(t[2].dims)),ee.push("rank")),q.push(...he(r));let P=J=>{let te=Y("x",t[0].dataType,t[0].dims.length,A),F=Y("w",t[1].dataType,t[1].dims.length,1),O=pe("result",t[0].dataType,r.length,A),G=[te,F],ie="";if(s){let L=Y("bias",t[2].dataType,t[2].dims.length,A);G.push(L),ie+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${L.type.value} {
            return bias[coords.${l?"w":"y"}${g?"/ 4":""}];
          }`}let j=[{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}];Pr(e,j);let se=it(t[0].dataType,1);if(se!=="f16"&&se!=="f32")throw new Error(`elemType ${se} is not supported.`);return`
        ${lo("uniforms.result_strides")}
        ${J.registerUniforms(j).declareVariables(...G,O)};
        ${ie}
        ${gu(l,s,e,te.type.value,k)}
        ${g?si(y,$,se,void 0,!l,T):oi(y,$,se,void 0,!l,T,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${y};${$};${g}`,inputDependencies:ee},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:q}),getShaderSource:P}}}),_u,zs,K_=X(()=>{ye(),Rr(),Te(),Ce(),_u=(t,e,r,n,a,i=!1,s,o,l=!1)=>{let d=l?1:2,c=l?2:3,u=l?3:1,h=i?2:1,m=`
  fn setOutputAtIndex(flatIndex : u32, value : ${i?`vec4<${s}>`:s}) {
    result[flatIndex] = ${i?`vec4<${s}>`:s}(value);
  }`;n&&(m+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${i?`vec4<${s}>`:s} {
      return bias[coords.${l?"w":"y"}${i?"/ 4":""}];
    }`);let g=i?4:1,_=Y("W",e[1].dataType,e[1].dims.length,g),b=Y("Dy",e[0].dataType,e[0].dims.length,g),$=[b,_];n&&$.push(Y("bias",e[2].dataType,[r[u]].length,g));let y=pe("result",e[0].dataType,r.length,g),E=`{
        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"} * ${h};
        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}>, ${h}>;
        for (var i = 0; i < ${h}; i++) {
          dotProd[i] = vec4<${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 = ${_.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${_.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${_.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${_.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

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

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

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

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

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

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

            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
              if (wC % uniforms.dilations.y != 0) {
                continue;
              }
              let dyC = (${s}(dyCCorner) + ${s}(wC)) / ${s}(uniforms.strides.y);
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          let outputIndices${k} = ${_.offsetToIndices(`outputOffset + ${k}u`)};
          ${b(k)};
          let offset${k} = ${m.indicesToOffset(`dataIndices${k}`)};
          let index${k} = offset${k} / 4u;
          let component${k} = offset${k} % 4u;
          ${E}[${k}] = ${T}(${m.getByOffset(`index${k}`)}[component${k}]);
        `;$=`
        let outputOffset = global_idx * ${l};
        var value = vec4<u32>(0);
        ${y("value",0,"u32")}
        ${y("value",1,"u32")}
        ${y("value",2,"u32")}
        ${y("value",3,"u32")}
        ${_.setByOffset("global_idx","value")}
      `}else $=`
      let outputIndices = ${_.offsetToIndices("global_idx")};
      ${b("")};
      let value = ${m.getByIndices("dataIndices")};
      ${_.setByOffset("global_idx","value")};
      `;return`
      ${h.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(m,g,_)}
      ${h.mainStart()}
        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${$}
      }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:u}},Hh=t=>qe({axis:t.axis}),qh=(t,e)=>{let r=t.inputs;Ru(r),t.compute(Bu(t.inputs,e))}}),Du,Nu,jh,Kh,t0=X(()=>{ye(),Te(),ut(),Ce(),Du=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.`)},Nu=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,i=t[1].dims,s=t[1].dataType,o=Q.normalizeAxis(e.axis,a),l=r[o],d=i.slice(0),c=Q.size(d),u=Y("input",n,a),h=Y("indicesInput",s,i.length),m=pe("output",n,d.length),g=[{type:12,data:c},{type:6,data:l},{type:12,data:o}];return g.push(...he(r,i,d)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:_=>`
      ${_.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(u,h,m)}
      ${_.mainStart()}
      ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${m.setByOffset("global_idx","value")};
  }`}},jh=t=>qe({axis:t.axis}),Kh=(t,e)=>{let r=t.inputs;Du(r),t.compute(Nu(t.inputs,e))}}),Lu,Uu,Yh,Xh,r0=X(()=>{ye(),Te(),Ce(),Lu=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},Uu=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,i,s]=op.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[a,i];if(!o)throw new Error("Can't use gemm on the given tensors");let l=Q.size(o),d=[{type:12,data:l},{type:12,data:a},{type:12,data:i},{type:12,data:s},{type:1,data:e.alpha},{type:1,data:e.beta}],c=["type","type"];t.length===3&&(d.push(...he(t[2].dims)),c.push("rank")),d.push(...he(o));let u=h=>{let m="";e.transA&&e.transB?m="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?m="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?m="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(m="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let g=e.alpha===1?"":"value *= uniforms.alpha;",_=Y("a",t[0].dataType,t[0].dims),b=Y("b",t[1].dataType,t[1].dims),$=_.type.value,y=null,E=[_,b];t.length===3&&(y=Y("c",t[2].dataType,t[2].dims.length),E.push(y));let k=pe("output",t[0].dataType,o.length);E.push(k);let T=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`
  ${h.registerUniforms(T).declareVariables(...E)}

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

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

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

    ${g}
    ${y!=null?`let cOffset = ${y.broadcastedIndicesToOffset("vec2(m, n)",k)}; value += ${$}(uniforms.beta) * ${y.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:u}},Yh=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};${t.alpha===1}`}},Xh=(t,e)=>{Lu(t.inputs),t.compute(Uu(t.inputs,e))}}),Fu,Wu,Gu,Qh,n0=X(()=>{ye(),Te(),Ce(),Fu=(t,e)=>{let r=t[0].dims,n=r,a=2,i=Q.sizeToDimension(r,a),s=Q.sizeFromDimension(r,a),o=rt(s),l=s/o,d=[r[0],r[1],l],c=["rank","type","type"],u=[{type:12,data:s},{type:12,data:l}];u.push(...he(d,d));let h=m=>{let g=Y("x",t[0].dataType,d.length,o),_=Y("scale",t[1].dataType,t[1].dims),b=Y("bias",t[2].dataType,t[2].dims),$=pe("output",t[0].dataType,d.length,o),y=[g,_,b,$],E=g.type.value,k=o===1?"f32":`vec${o}<f32>`,T=64,A=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`
  var<workgroup> meanShared : f32;
  var<workgroup> squaredNormShared : f32;
  var<workgroup> workgroupShared : array<${k}, ${T}>;
  const workgroupSize = ${T}u;
  ${m.registerUniforms(A).declareVariables(...y)}
  ${m.mainStart(T)}
    let norm = global_idx / workgroupSize;
    let batch = norm / uniforms.x_shape[1];
    let channel = norm % uniforms.x_shape[1];
    let localIndex = local_id.x;

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

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

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

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

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

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

  ${g.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], ${$}(scale[0]), ${$}(scale[1]));
  }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:m},{inputs:[e[0],h]})},Qh=(t,e)=>{e.format==="NHWC"?Gu(t,t.inputs,e):t.compute(Fu(t.inputs,e))}}),Vu,Hu,Zh,a0=X(()=>{ye(),Te(),Ce(),Vu=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Hu=(t,e,r)=>{let n=t[0].dims,a=t[1],i=t[2],s=n,o=Q.normalizeAxis(e.axis,n.length),l=Q.sizeToDimension(n,o),d=Q.sizeFromDimension(n,o),c=Q.size(a.dims),u=i?Q.size(i.dims):0;if(c!==d||i&&u!==d)throw new Error(`Size of X.shape()[axis:] == ${d}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${c} and bias size of ${u}`);let h=[];for(let k=0;k<n.length;++k)k<o?h.push(n[k]):h.push(1);let m=rt(d),g=["type","type"],_=[{type:12,data:l},{type:1,data:d},{type:12,data:Math.floor(d/m)},{type:1,data:e.epsilon}];i&&g.push("type");let b=r>1,$=r>2,y=k=>{let T=it(t[0].dataType),A=[Y("x",t[0].dataType,t[0].dims,m),Y("scale",a.dataType,a.dims,m)];i&&A.push(Y("bias",i.dataType,i.dims,m)),A.push(pe("output",t[0].dataType,s,m)),b&&A.push(pe("mean_data_output",1,h)),$&&A.push(pe("inv_std_output",1,h));let R=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${k.registerUniforms(R).declareVariables(...A)}
  ${k.mainStart()}
    ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${bt("f32",m)};
    var mean_square_vector = ${bt("f32",m)};

    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
      let value = ${Zt(T,m,"x[h + offset]")};
      mean_vector += value;
      mean_square_vector += value * value;
    }
    let mean = ${Kt("mean_vector",m)} / uniforms.norm_size;
    let inv_std_dev = inverseSqrt(${Kt("mean_square_vector",m)} / uniforms.norm_size - mean * mean + uniforms.epsilon);

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

    ${b?"mean_data_output[global_idx] = mean":""};
    ${$?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},E=[{dims:s,dataType:t[0].dataType}];return b&&E.push({dims:h,dataType:1}),$&&E.push({dims:h,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${m};${r}`,inputDependencies:g},getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:_}),getShaderSource:y}},Zh=(t,e)=>{Vu(t.inputs),t.compute(Hu(t.inputs,e,t.outputCount))}}),qu,ju,Jh,ef,i0=X(()=>{ye(),Te(),ut(),Ce(),qu=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((e.k+e.blockSize-1)/e.blockSize),i=e.blockSize/8*e.bits,s=t[1];if(!Q.areEqual(s.dims,[e.n,a,i]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(Q.size(o)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let l=t[3].dims,d=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(Q.size(l)!==d)throw new Error("zeroPoints input size error.")}},ju=(t,e)=>{let r=t[0].dims,n=r.length,a=r.slice(0,n-1).concat(e.n),i=r[n-2],s=e.blockSize/8*e.bits/4,o=rt(i),l=rt(e.n),d=rt(e.k),c=rt(s),u=Q.size(a)/l/o,h=[{type:12,data:u},{type:12,data:e.k},{type:12,data:e.n},{type:12,data:e.accuracyLevel},{type:12,data:e.bits},{type:12,data:e.blockSize}],m=r.slice();m.splice(-1,1,e.k/d);let g=Q.convertShape(t[1].dims).slice();g.splice(-1,1,s/c),h.push(...he(m)),h.push(...he(g)),h.push(...he(t[2].dims)),t.length===4&&h.push(...he(Q.convertShape(t[3].dims)));let _=a.slice();_.splice(-1,1,e.n/l),h.push(...he(_));let b=$=>{let y=Y("a",t[0].dataType,m.length,d),E=Y("b",12,g.length,c),k=Y("scales",t[2].dataType,t[2].dims.length),T=[y,E,k],A=t.length===4?Y("zero_points",12,t[3].dims.length):void 0;A&&T.push(A);let R=pe("output",t[0].dataType,a.length,l),W=[{name:"output_size",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"accuracy_level",type:"u32"},{name:"bits",type:"u32"},{name:"block_size",type:"u32"}],K=Math.floor((e.k+e.blockSize-1)/e.blockSize),q=it(t[0].dataType),ee=(()=>{switch(d){case 1:return`array<${q}, 8>`;case 2:return`mat4x2<${q}>`;case 4:return`mat2x4<${q}>`;default:throw new Error(`${d}-component is not supported.`)}})(),P=`
        fn dequantize(quantized: ${ee}, zero_point: ${q}, scale: ${q}) -> ${ee} {
          ${d===1?`var dequantized = ${ee}(${Array.from({length:8},(F,O)=>`(quantized[${O}] - zero_point) * scale`).join(", ")});
              return dequantized;`:`var zero_points: ${ee} = ${ee}(${Array(8).fill("zero_point").join(",")});
              return (quantized - zero_points) * scale;`}
        }`,J=`
        fn ortUnpack8x4snorm(value: u32) -> ${ee} {
          var quantized: ${ee};
          var offset: u32 = 0;
          let count: u32 = 4;
          for (var i: u32 = 0; i < 8u; i++) {
            var result = ${q}(extractBits(value, offset, count));
            ${(()=>{switch(d){case 1:return"quantized[i] = result;";case 2:return"quantized[i / 2][i % 2] = result;";case 4:return"quantized[i / 4][i % 4] = result;";default:throw new Error(`${d}-component is not supported.`)}})()}
            offset += count;
          }
          return quantized;
        }`,te=A?`
          zero_point_offset += 4;
          if (zero_point_offset == 32) {
            zero_point_offset = 0;
            zero_point_index++;
            zero_point_word = ${A.getByOffset("zero_point_index")};
          }`:"";return`
        ${P};
        ${J};
        ${$.registerUniforms(W).declareVariables(...T,R)}
        ${$.mainStart()}
          ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var output_values: array<${R.type.value}, ${o}>;
          var output_indices = ${R.offsetToIndices("global_idx")};
          var n = ${R.indicesGet("output_indices",n-1)};
          var m = ${R.indicesGet("output_indices",n-2)};
          var a_indices: ${y.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
          ${A?`
          var zero_point_index: u32 = n * ${l} * ((${K} + 1) / 2) / 4;
          var zero_point_word: u32 = ${A.getByOffset("zero_point_index")};
          var zero_point_offset: u32 = 0;`:""}
          var scale_index = n * ${K*l};
          var b_indices: ${E.type.indices};
          for (var c: u32 = 0; c < ${l}; c++) {
            ${E.indicesSet("b_indices","0",`n * ${l} + c`)};
            var block_offset: u32 = 0;
            for (var block: u32 = 0; block < ${K}; block++) {
              // The scale and zero points are computed per block.
              let scale = ${k.getByOffset("scale_index")};
              // The default zero point is 8 for unsigned 4-bit quantization.
              let zero_point = ${q}(${A?"extractBits(zero_point_word, zero_point_offset, 4)":8});
              ${E.indicesSet("b_indices","1","block")};
              var word_offset: u32 = block_offset;
              for (var word: u32 = 0; word < ${s}; word += ${c}) {
                ${E.indicesSet("b_indices","2","word")};
                let b_data = ${E.getByIndices("b_indices")};
                for (var i: u32 = 0; i < ${c}; i++) {
                  let b_value = ${c===1?"b_data":"b_data[word + i]"};
                  let b_quantized_values: ${ee} = ortUnpack8x4snorm(b_value);
                  let b_dequantized_values = dequantize(b_quantized_values, zero_point, scale);
                  // Number of B elements per 32-bit word is 32/bits = 32/4 = 8
                  var offset: u32 = word_offset;
                  for (var j: u32 = 0; j < 8/${d}; j++) {
                    ${y.indicesSet("a_indices",n-1,`offset/${d}`)};
                    for (var k: u32 = 0; k < ${o}u; k++) {
                      ${y.indicesSet("a_indices",n-2,`m * ${o} + k`)};
                      let a_data = ${y.getByIndices("a_indices")};
                      output_values[k]${l>1?"[c]":""} += ${d===1?"a_data * b_dequantized_values[j]":"dot(a_data, b_dequantized_values[j])"};
                    }
                    offset += ${d};
                  }
                  word_offset += 8;
                }
              }
              scale_index++;
              ${te}
              block_offset += uniforms.block_size;
            }
            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.
            ${A?`if (zero_point_offset % 8 > 0) {
                ${te}
              }`:""}
            }
            for (var k: u32 = 0u; k < ${o}u; k++) {
              ${R.indicesSet("output_indices",n-2,`${o+" * m + k"}`)};
              ${R.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:a,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:h}),getShaderSource:b}},Jh=(t,e)=>{qu(t.inputs,e),t.compute(ju(t.inputs,e))},ef=t=>qe(t)}),Ku,tf,Qi,Yu,Pa,rf,s0=X(()=>{ye(),Te(),ut(),Js(),Pp(),Ce(),jn(),Ku=(t,e)=>{let r=t[0],n=t[1],a=t[2],i=t[3],s=t[4],o=t[5],l=t[6],d=t[7];if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let c=!1,u=r.dims[0],h=r.dims[1],m=r.dims.length===3?c?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],g=h,_=0,b=0,$=Math.floor(m/e.numHeads);if(l&&d){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');_=l.dims[2],b=l.dims[2]}else if(l||d)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let y;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)');y=2,g=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==$)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(a)throw new Error('Expect "value" be none when "key" has packed kv format.');y=5,g=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==$)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');y=0,g=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');y=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 E=0;if(s){E=8;let W=s.dims;throw W.length===1?W[0]===u?E=1:W[0]===3*u+2&&(E=3):W.length===2&&W[0]===u&&W[1]===g&&(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 k=!1,T=m;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(g!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');T=a.dims[2]}else{if(g!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');T=a.dims[1]*a.dims[3],k=!0}}let A=_+g,R=!1;if(s)throw new Error("Key padding mask is not supported");if(o)throw new Error("extraAddQk is not supported");if(l)throw new Error("pastKey is not supported");if(d)throw new Error("pastValue is not supported");return{batchSize:u,sequenceLength:h,pastSequenceLength:_,kvSequenceLength:g,totalSequenceLength:A,maxSequenceLength:b,inputHiddenSize:0,hiddenSize:m,vHiddenSize:T,headSize:$,vHeadSize:Math.floor(T/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:E,scale:e.scale,broadcastResPosBias:R,passPastInKv:k,qkvFormat:y}},tf=t=>qe({...t}),Qi=qe({perm:[0,2,1,3]}),Yu=(t,e,r,n,a,i,s)=>{let o=[n,a,i],l=Q.size(o),d=[{type:12,data:l},{type:12,data:s},{type:12,data:i}],c=u=>{let h=pe("qkv_with_bias",e.dataType,o),m=Y("qkv",e.dataType,o),g=Y("bias",r.dataType,o),_=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${u.registerUniforms(_).declareVariables(m,g,h)}
  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:c},{inputs:[e,r],outputs:[-1]})[0]},Pa=(t,e,r,n,a,i,s,o)=>{let l=i;if(s){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=Yu(t,i,s,e,n,r*a,o),l=l.reshape([e,n,r,a]),t.compute(er(l,Qi.perm),{inputs:[l],outputs:[-1]})[0]}else return i.dims.length===3&&(l=i.reshape([e,n,r,a])),t.compute(er(l,Qi.perm),{inputs:[l],outputs:[-1]})[0]},rf=(t,e)=>{let r=Ku(t.inputs,e);if(t.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(t.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let n=t.inputs[1]&&t.inputs[2]&&t.inputs[1].dims.length===4&&t.inputs[2].dims.length===4,a=Pa(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,t.inputs[0],t.inputs[3],0);if(n)return ii(t,a,t.inputs[1],t.inputs[2],t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e);let i=Pa(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,t.inputs[1],t.inputs[3],r.hiddenSize),s=Pa(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,t.inputs[2],t.inputs[3],2*r.hiddenSize);ii(t,a,i,s,t.inputs[4],void 0,t.inputs[6],t.inputs[7],t.inputs[5],r,e)}}),Xu,Qu,Zu,Ju,ed,td,rd,nd,nf,o0=X(()=>{ye(),Te(),Ce(),Xu=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].")}},Qu=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
            k = i32(${t.indicesGet("indices",a)}) - ${we("uniforms.pads",a,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${we("uniforms.x_shape",a,e)})) {
              break;
            }
            offset += k * i32(${we("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;
            ${n}
            value = x[offset];
          }
      `},Zu=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${we("uniforms.pads",a,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${we("uniforms.x_shape",a,e)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${we("uniforms.x_shape",a,e)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${we("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Ju=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${we("uniforms.pads",a,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${we("uniforms.x_shape",a,e)})) {
                  k = i32(${we("uniforms.x_shape",a,e)}) - 1;
                }
                offset += k * i32(${we("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},ed=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${we("uniforms.pads",a,r)};
                if (k < 0)  {
                  k += i32(${we("uniforms.x_shape",a,e)}]);
                }
                if (k >= i32(${we("uniforms.x_shape",a,e)})) {
                  k -= i32(${we("uniforms.x_shape",a,e)});
                }
                offset += k * i32(${we("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},td=(t,e,r)=>{switch(r.mode){case 0:return Qu(t,e,r.pads.length);case 1:return Zu(t,e,r.pads.length);case 2:return Ju(t,e,r.pads.length);case 3:return ed(t,e,r.pads.length);default:throw new Error("Invalid mode")}},rd=(t,e)=>{let r=Q.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=Q.size(r),i=[{type:12,data:a},{type:12,data:e.pads}];e.mode===0&&i.push({type:t[0].dataType,data:e.value}),i.push(...he(t[0].dims,r));let s=["rank"],o=l=>{let d=pe("output",t[0].dataType,r.length),c=Y("x",t[0].dataType,n.length),u=c.type.value,h=td(d,n.length,e),m=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&m.push({name:"constant_value",type:u}),`
            ${l.registerUniforms(m).declareVariables(c,d)}
            ${l.mainStart()}
            ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${u}(0);
            ${h}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:s},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(Q.size(r)/64)},programUniforms:i}),getShaderSource:o}},nd=(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 l=0;l<o.length;l++)i[Number(o[l])]=Number(r[l]),i[Number(o[l])+a]=Number(r[l+o.length])}else r.forEach((o,l)=>i[Number(l)]=Number(o));let s=[];return i.forEach(o=>s.push(o)),{mode:e.mode,value:n,pads:s}}else return e},nf=(t,e)=>{Xu(t.inputs);let r=nd(t.inputs,e);t.compute(rd(t.inputs,r),{inputs:[0]})}}),Sn,Zi,Ji,es,ts,ad,id,rs,ns,af,sf,as,of,lf,is,uf,df,cf,pf,l0=X(()=>{Ft(),ye(),Te(),Ce(),Sn=t=>{if(De.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},Zi=(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(),l=i?e.dilations.slice():[],d=e.pads.slice();ri.adjustPoolAttributes(r,a,s,o,l,d);let c=ri.computePoolOutputShape(r,a,o,l,s,d,e.autoPad),u=Object.assign({},e);i?Object.assign(u,{kernelShape:s,strides:o,pads:d,dilations:l,cacheKey:e.cacheKey}):Object.assign(u,{kernelShape:s,strides:o,pads:d,cacheKey:e.cacheKey});let h=c.slice();return h.push(h.splice(1,1)[0]),[u,n?h:c]},Ji=(t,e)=>{let r=e.format==="NHWC",n=Q.size(t),a=Q.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],l=e.strides[e.strides.length-1],d=e.pads[e.pads.length/2-1],c=e.pads[e.pads.length-1],u=!!(d+c);i.push({type:12,data:o},{type:12,data:l},{type:12,data:d},{type:12,data:c}),s.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let h=!1;if(e.kernelShape.length===2){let m=e.kernelShape[e.kernelShape.length-2],g=e.strides[e.strides.length-2],_=e.pads[e.pads.length/2-2],b=e.pads[e.pads.length-2];h=!!(_+b),i.push({type:12,data:m},{type:12,data:g},{type:12,data:_},{type:12,data:b}),s.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,s,!0,u,h]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=Q.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 l=e.pads.reduce((d,c)=>d+c);return[i,s,!!l,!1,!1]}},es=(t,e,r,n,a,i,s,o,l,d,c,u)=>{let h=a.format==="NHWC",m=e.type.value,g=pe("output",e.type.tensor,n);if(a.kernelShape.length<=2){let _="",b="",$="",y=r-(h?2:1);if(c?_=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${y}] = indices[${y}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${y}] < 0 || xIndices[${y}]
                      >= uniforms.x_shape[${y}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`:_=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${y}] = indices[${y}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`,a.kernelShape.length===2){let E=r-(h?3:2);u?b=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${E}] = indices[${E}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${E}] < 0 || xIndices[${E}] >= uniforms.x_shape[${E}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:b=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${E}] = indices[${E}] * uniforms.sh - uniforms.phStart + j;
                `,$=`
              }
            `}return`
            ${t.registerUniforms(l).declareVariables(e,g)}

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

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

              var value = ${m}(${o});
              var pad = 0;
              ${b}
              ${_}
              ${$}
              ${s}

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

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

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

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

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

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

              output[global_idx] = value;
            }`}},ts=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,ad=t=>`${ts(t)};${t.countIncludePad}`,id=t=>`${ts(t)};${t.storageOrder};${t.dilations}`,rs=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}),ns=(t,e,r,n)=>{let[a,i]=Zi(e,n,r),s=Y("x",e.dataType,e.dims.length),o=s.type.value,l="value += x_val;",d="";a.countIncludePad?d+=`value /= ${o}(uniforms.kernelSize);`:d+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[c,u,h,m,g]=Ji(i,a);c.push(...he(e.dims,i));let _=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${h};${m};${g}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(Q.size(i)/64)},programUniforms:c}),getShaderSource:b=>es(b,s,e.dims.length,i.length,a,l,d,0,u,h,m,g)}},af=t=>{let e=t.count_include_pad!==0,r=rs(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:ad(n)}},sf=(t,e)=>{Sn(t.inputs),t.compute(ns("AveragePool",t.inputs[0],!1,e))},as={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},of=t=>{let e=t.format;return{format:e,...as,cacheKey:e}},lf=(t,e)=>{Sn(t.inputs),t.compute(ns("GlobalAveragePool",t.inputs[0],!0,e))},is=(t,e,r,n)=>{let[a,i]=Zi(e,n,r),s=`
      value = max(x_val, value);
    `,o="",l=Y("x",e.dataType,e.dims.length),d=["rank"],[c,u,h,m,g]=Ji(i,a);return c.push(...he(e.dims,i)),{name:t,shaderCache:{hint:`${n.cacheKey};${h};${m};${g}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(Q.size(i)/64)},programUniforms:c}),getShaderSource:_=>es(_,l,e.dims.length,i.length,a,s,o,e.dataType===10?-65504:-1e5,u,h,m,g)}},uf=(t,e)=>{Sn(t.inputs),t.compute(is("MaxPool",t.inputs[0],!1,e))},df=t=>{let e=t.storage_order,r=t.dilations,n=rs(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:id(a)}},cf=t=>{let e=t.format;return{format:e,...as,cacheKey:e}},pf=(t,e)=>{Sn(t.inputs),t.compute(is("GlobalMaxPool",t.inputs[0],!0,e))}}),sd,od,hf,u0=X(()=>{Ft(),ye(),Ce(),sd=(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.")},od=(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},...he(i)],l=d=>{let c=pe("output",n,i.length),u=c.type.value,h=[{name:"outputSize",type:"u32"},{name:"start",type:u},{name:"delta",type:u}];return`
        ${d.registerUniforms(h).declareVariables(c)}
        ${d.mainStart()}
        ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${u}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:o})}},hf=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]),De.webgpu.validateInputContent&&sd(e,r,n),t.compute(od(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),ld,ud,dd,cd,pd,hd,fd,md,gd,_d,wd,ss,yd,bd,vd,$d,xd,ff,mf,d0=X(()=>{ye(),Te(),ut(),Ce(),ld=(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")}},ud=(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},dd=(t,e,r,n,a,i)=>{let[s,o,l]=r>10?[1,2,3]:[-1,t.length>1?1:-1,-1],d=t[0].dims.length;if(s>0&&t.length>s&&t[s].dims.length>0)t[s].getFloat32Array().forEach(c=>i.push(c));else if(e.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&t.length>o&&t[o].dims.length>0){if(t[o].getFloat32Array().forEach(c=>n.push(c)),n.length!==0&&n.length!==d&&r>=18&&n.length!==e.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");ld(n,e),e.axes.length>0&&ud(n,e.axes,d).forEach((c,u)=>n[u]=c)}if(l>0&&t.length>l&&(t[l].getBigInt64Array().forEach(c=>a.push(Number(c))),a.length!==d||r>=18&&a.length===e.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(e.axes.length>0){if(n.length!==e.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(a.length!==e.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof a<"u"&&n.length>0&&a.length>d)throw new Error("Resize requires only of scales or sizes to be specified")},cd=(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`)}})()+"}",pd=(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`)}})()+"}",hd=(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},fd=(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},md=(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},gd=(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 = ${we("uniforms.scales","i",n)};
        var roi_low = ${we("uniforms.roi","i",a)};
        var roi_hi = ${we("uniforms.roi",`i + ${e.length}`,a)};
        if (scale == 1.0) {
          original_indices[i] = ${t.type.value}(output_index);
        } else {
          var input_shape_i = ${we("uniforms.input_shape","i",e.length)};
          var output_shape_i = ${we("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;
    }`,_d=(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 = ${we("uniforms.scales","i",a)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${we("uniforms.roi","i",i)};
          var roi_hi = ${we("uniforms.roi",`i + ${r.length}`,i)};
          var input_shape_i = ${we("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${we("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${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;
    }`,wd=(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 >= ${we("uniforms.input_shape","i",e.length)}) {
          return false;
        }
      }
      return true;
    }`,ss=(t,e,r,n)=>t.rank>n?`
    ${t.indicesSet("input_indices",e,"channel")};
    ${t.indicesSet("input_indices",r,"batch")};
`:"",yd=(t,e,r,n,a)=>{let[i,s,o,l]=r.length===2?[-1,0,1,-1]:[0,2,3,1],d=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",s,`max(0, min(row, ${r[s]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)};
      ${ss(t,l,i,2)}
      return ${t.getByIndices("input_indices")};
    }

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

        if (${o} && (originalIdx < 0 || originalIdx > (${r[_]} - 1))) {
          return ${l};
        }
        var data: array<${m}, 4> = array<${m}, 4>(0.0, 0.0, 0.0, 0.0);
        for (var i: i32 = -1; i < 3; i++) {
          var ${b}: ${m} = originalIdx + ${m}(i);
          if (${b} < 0 || ${b} >= ${r[_]}) {
            ${d?`coefs[i + 1] = 0.0;
                        continue;`:o?`return ${l};`:`${b} = max(0, min(${b}, ${r[_]} - 1));`};
          }
        var input_indices_copy: ${t.type.indices} = input_indices;
          ${t.indicesSet("input_indices_copy",_,`u32(${b})`)};
          data[i + 1] = ${_===u?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
        }
        return cubicInterpolation1D(data, coefs);
      }`};return`
    ${g(u)};
    ${g(h)};
  fn getCubicInterpolationCoefs(s: ${m}) -> array<${m}, 4> {
    var absS = abs(s);
    var coeffs: array<${m}, 4> = array<${m}, 4>(0.0, 0.0, 0.0, 0.0);
    var oneMinusAbsS: ${m} = 1.0 - absS;
    var twoMinusAbsS: ${m} = 2.0 - absS;
    var onePlusAbsS: ${m} = 1.0 + absS;
    coeffs[0] = ((${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<${m}, 4>, coefs: array<${m}, 4>) -> ${m} {
    var coefsSum: ${m} = 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}) -> ${m} {
    var input_indices: ${t.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},vd=(t,e,r,n,a)=>{let[i,s,o,l,d]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],c=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${c} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",s,`max(0, min(depth, ${r[s]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(height, ${r[o]} - 1))`)};
      ${t.indicesSet("input_indices",l,`max(0, min(width, ${r[l]} - 1))`)};
      ${ss(t,d,i,3)}
      return ${t.getByIndices("input_indices")};
    }

    fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${c} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var depth:${c} = originalIndices[${s}];
      var height:${c} = originalIndices[${o}];
      var width:${c} = originalIndices[${l}];
      ${n?`if (depth < 0 || depth > (${r[s]} - 1) || height < 0 || height > (${r[o]} - 1) || width < 0 || (width > ${r[l]} - 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[l]} - 1));
      var depth1: u32 = u32(depth);
      var height1: u32 = u32(height);
      var width1: u32 = u32(width);
      var depth2: u32 = u32(depth + 1);
      var height2: u32 = u32(height + 1);
      var width2: u32 = u32(width + 1);
      var channel: u32 = ${r.length>3?`u32(originalIndices[${d}])`:"0"};
      var batch: u32 =  ${r.length>3?`u32(originalIndices[${i}])`:"0"};

      var x111: ${c} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${c} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${c} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${c} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${c} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${c} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${c} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${c} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${c} = abs(depth - ${c}(depth1));
      var dx2: ${c} = abs(${c}(depth2) - depth);
      var dy1: ${c} = abs(height - ${c}(height1));
      var dy2: ${c} = abs(${c}(height2) - height);
      var dz1: ${c} = abs(width - ${c}(width1));
      var dz2: ${c} = abs(${c}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},$d=(t,e,r,n,a,i)=>{let s=t.dims,o=hd(i,e.axes,s.length),l=fd(s,n,a,e.axes),d=n.slice();n.length===0&&(d=s.map((y,E)=>y===0?1:l[E]/y),e.keepAspectRatioPolicy!=="stretch"&&(l=md(s,d,e)));let c=pe("output",t.dataType,l.length),u=Y("input",t.dataType,s.length),h=Q.size(l),m=s.length===l.length&&s.every((y,E)=>y===l[E]),g=e.coordinateTransformMode==="tf_crop_and_resize",_=e.extrapolationValue,b=u.type.value,$=y=>`
      ${m?"":`
      ${cd(e.coordinateTransformMode,b)};
      ${(()=>{switch(e.mode){case"nearest":return`
              ${wd(u,s)};
              ${pd(e.nearestMode,r,b)};
              ${_d(u,c,s,l,d.length,o.length,g)};
              `;case"linear":return`
              ${gd(c,s,l,d.length,o.length)};
              ${(()=>{if(s.length===2||s.length===4)return`${yd(u,c,s,g,_)}`;if(s.length===3||s.length===5)return`${vd(u,c,s,g,_)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(s.length===2||s.length===4)return`${bd(u,c,s,l,d,o,e.cubicCoeffA,g,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")}})()};
      `}
      ${y.registerUniform("output_size","u32").registerUniform("scales","f32",d.length).registerUniform("roi","f32",o.length).declareVariables(u,c)}
      ${y.mainStart()}
        ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${m?"output[global_idx] = input[global_idx];":`
        let output_indices = ${c.offsetToIndices("global_idx")};
        var input_indices: ${u.type.indices};
        ${(()=>{switch(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${u.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}|${r}|${d.length>0?d:""}|${a.length>0?a:""}|${o.length>0?o:""}|${m}|${s}`,inputDependencies:["rank"]},getShaderSource:$,getRunData:()=>({outputs:[{dims:l,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:[{type:12,data:h},{type:1,data:d},{type:1,data:o},...he(s,l)]})}},xd=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},ff=(t,e)=>{let r=[],n=[],a=[],i=xd(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");dd(t.inputs,e,i,r,n,a),t.compute($d(t.inputs[0],e,i,r,n,a),{inputs:[0]})},mf=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,a=t.cubicCoeffA,i=t.excludeOutside!==0,s=t.extrapolationValue,o=t.keepAspectRatioPolicy,l=t.mode,d=t.nearestMode===""?"simple":t.nearestMode;return qe({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:i,extrapolationValue:s,keepAspectRatioPolicy:o,mode:l,nearestMode:d})}}),Sd,Ed,gf,c0=X(()=>{ye(),Te(),Ce(),Sd=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")}},Ed=(t,e,r,n)=>{let a=t[0].dims,i=Q.size(a),s=a,o=i,l=a.slice(-1)[0],d=n?a.slice(0,-1).concat(1):[],c=t.length>3,u=t.length>4,h=n&&r>1,m=n&&r>2,g=r>3,_=rt(l),b=[{type:12,data:o},{type:12,data:_},{type:12,data:l},{type:1,data:e.epsilon}],$=E=>{let k=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],T=[Y("x",t[0].dataType,t[0].dims,_),Y("skip",t[1].dataType,t[1].dims,_),Y("gamma",t[2].dataType,t[2].dims,_)];c&&T.push(Y("beta",t[3].dataType,t[3].dims,_)),u&&T.push(Y("bias",t[4].dataType,t[4].dims,_)),T.push(pe("output",t[0].dataType,s,_)),h&&T.push(pe("mean_output",1,d)),m&&T.push(pe("inv_std_output",1,d)),g&&T.push(pe("input_skip_bias_sum",t[0].dataType,s,_));let A=it(t[0].dataType);return`

      ${E.registerUniforms(k).declareVariables(...T)}

      ${E.mainStart()}
        ${E.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 = ${bt("f32",_)};
        var squareSum = ${bt("f32",_)};
        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {
          let skip_value = skip[offset + i];
          let bias_value = ${u?"bias[i]":"0.0"};
          let input_value = x[offset + i];
          let value = input_value + skip_value + bias_value;
          ${g?"input_skip_bias_sum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32_value = ${Zt(A,_,"value")};
          sum += f32_value;
          squareSum += f32_value * f32_value;
        }
        let mean = ${Kt("sum",_)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Kt("squareSum",_)} / f32(uniforms.hidden_size) - mean * mean + uniforms.epsilon);
        ${h?"mean_output[global_idx] = mean;":""}
        ${m?"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}(mean)) * ${A}(inv_std_dev) * gamma[i] + ${c?"beta[i]":"0.0"};
        }
      }`},y=[{dims:s,dataType:t[0].dataType}];return r>1&&y.push({dims:d,dataType:1}),r>2&&y.push({dims:d,dataType:1}),r>3&&y.push({dims:a,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${_};${h};${m};${g}`,inputDependencies:t.map((E,k)=>"type")},getShaderSource:$,getRunData:()=>({outputs:y,dispatchGroup:{x:Math.ceil(o/l/64)},programUniforms:b})}},gf=(t,e)=>{Sd(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(Ed(t.inputs,e,t.outputCount,!1),{outputs:r})}}),kd,En,Cd,os,Td,Id,_f,wf,p0=X(()=>{ye(),Te(),ut(),Ce(),kd=(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`)})},En=(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},Cd=(t,e)=>{if(t.length>1){let r=En(t,1),n=En(t,2),a=En(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),qe({starts:r,ends:n,axes:a})}else return e},os=(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]]))},Td=(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 = ${we("uniforms.input_shape","i",r.length)};
            let steps_i = ${we("uniforms.steps","i",r.length)};
            let signs_i = ${we("uniforms.signs","i",r.length)};
            let starts_i = ${we("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;
            }
            ${t.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,Id=(t,e)=>{let r=t[0].dims,n=Q.size(r),a=e.axes.length>0?Q.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],i=En(t,4);i.forEach($=>$!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(a.length).fill(1));let s=e.starts.map(($,y)=>os($,y,r,a,i)),o=e.ends.map(($,y)=>os($,y,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 $=0;$<r.length;++$)a.includes($)||(s.splice($,0,0),o.splice($,0,r[$]),i.splice($,0,1));let l=i.map($=>Math.sign($));i.forEach(($,y,E)=>{if($<0){let k=(o[y]-s[y])/$,T=s[y],A=T+k*i[y];s[y]=A,o[y]=T,E[y]=-$}});let d=r.slice(0);a.forEach(($,y)=>{d[$]=Math.ceil((o[$]-s[$])/i[$])});let c={dims:d,dataType:t[0].dataType},u=pe("output",t[0].dataType,d.length),h=Y("input",t[0].dataType,t[0].dims.length),m=Q.size(d),g=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:s.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:i.length}],_=[{type:12,data:m},{type:12,data:s},{type:6,data:l},{type:12,data:i},...he(t[0].dims,d)],b=$=>`
      ${$.registerUniforms(g).declareVariables(h,u)}
        ${Td(h,u,r)}
        ${$.mainStart()}
          ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${u.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${u.setByOffset("global_idx",h.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${s.length}_${i.length}`,inputDependencies:["rank"]},getShaderSource:b,getRunData:()=>({outputs:[c],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:_})}},_f=(t,e)=>{kd(t.inputs,e);let r=Cd(t.inputs,e);t.compute(Id(t.inputs,r),{inputs:[0]})},wf=t=>{let e=t.starts,r=t.ends,n=t.axes;return qe({starts:e,ends:r,axes:n})}}),Ad,Md,yf,bf,h0=X(()=>{ye(),Te(),ut(),Ce(),Ad=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},Md=(t,e)=>{let r=t.dims,n=Q.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,l=rt(s),d=s/l,c=(b,$)=>$===4?`max(max(${b}.x, ${b}.y), max(${b}.z, ${b}.w))`:$===2?`max(${b}.x, ${b}.y)`:$===3?`max(max(${b}.x, ${b}.y), ${b}.z)`:b,u=Y("x",t.dataType,t.dims,l),h=pe("result",t.dataType,t.dims,l),m=u.type.value,g=it(t.dataType)==="f32"?`var threadMax = ${m}(-3.402823e+38f);`:`var threadMax = ${m}(-65504.0h);`,_=b=>`
      var<workgroup> rowMaxShared : ${m};
      var<workgroup> rowSumShared : ${m};
      var<workgroup> threadShared : array<${m}, ${a}>;

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

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

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

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          setValue(row, col, row_stride, value);
        }
      }`;return{name:"Softmax",shaderCache:{hint:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:12,data:d}]}),getShaderSource:_}},yf=(t,e)=>{Ad(t.inputs),t.compute(Md(t.inputs[0],e))},bf=t=>qe({axis:t.axis})}),Od,zd,Pd,Rd,Bd,vf,$f,f0=X(()=>{ye(),Te(),ut(),Ce(),Od=t=>{if(!t||t.length<1)throw new Error("too few inputs")},zd=(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})},Pd=t=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${t}u; i += 1u ) {
    if (index < ${we("uniforms.size_in_split_axis","i",t)}) {
        return i;
    }
    }
    return ${t}u;
}`,Rd=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) {
        ${r.join(`
`)}
      }`},Bd=(t,e)=>{let r=t[0].dims,n=Q.size(r),a=t[0].dataType,i=Q.normalizeAxis(e.axis,r.length),s=new Array(e.numOutputs),o=Y("input",a,r.length),l=new Array(e.numOutputs),d=[],c=[],u=0,h=[{type:12,data:n}];for(let g=0;g<e.numOutputs;g++){u+=e.splitSizes[g],l[g]=u;let _=r.slice();_[e.axis]=e.splitSizes[g],c.push(_),s[g]=pe(`output${g}`,a,_.length),d.push({dims:c[g],dataType:t[0].dataType})}h.push({type:12,data:l},...he(r,...c));let m=g=>`
  ${g.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...s)}
  ${Pd(l.length)}
  ${Rd(s)}

  ${g.mainStart()}
    ${g.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 -= ${we("uniforms.size_in_split_axis","output_number - 1u",l.length)};
      ${o.indicesSet("indices",i,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:m,getRunData:()=>({outputs:d,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:h})}},vf=(t,e)=>{Od(t.inputs);let r=t.inputs.length===1?e:zd(t.inputs,e);t.compute(Bd(t.inputs,r),{inputs:[0]})},$f=t=>{let e=t.axis,r=t.splitSizes,n=t.numOutputs<0?r.length:t.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return qe({axis:e,numOutputs:n,splitSizes:r})}}),ls,Dd,Nd,Ld,xf,m0=X(()=>{ye(),Te(),Ce(),ls=t=>Array.from(t.getBigInt64Array(),Number),Dd=t=>{if(!t||t.length!==2)throw new Error("Tile requires 2 inputs.");if(t[0].dataType!==1&&t[0].dataType!==6&&t[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(t[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(t[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(ls(t[1]).length!==t[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Nd=(t,e)=>{let r=[];for(let n=0;n<t.length;++n)r.push(t[n]*e[n]);return r},Ld=t=>{let e=t[0].dims,r=ls(t[1]),n=Nd(e,r),a=Q.size(n),i=t[0].dataType,s=Y("input",i,e.length),o=pe("output",i,n.length),l=d=>`
      const inputShape = ${s.indices(...e)};
      ${d.registerUniform("output_size","u32").declareVariables(s,o)}
      ${d.mainStart()}
      ${d.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},...he(t[0].dims,n)]}),getShaderSource:l}},xf=t=>{Dd(t.inputs),t.compute(Ld(t.inputs),{inputs:[0]})}}),Ud,Fd,Sf,g0=X(()=>{ye(),Te(),Ce(),Ud=(t,e,r,n,a)=>{let i=pe("output_data",a,r.length,4),s=Y("a_data",e[1].dataType,e[1].dims.length,4),o=Y("b_data",e[2].dataType,e[2].dims.length,4),l=Y("c_data",e[0].dataType,e[0].dims.length,4),d,c=(u,h,m)=>`select(${h}, ${u}, ${m})`;if(!n)d=i.setByOffset("global_idx",c(s.getByOffset("global_idx"),o.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let u=(h,m,g="")=>{let _=`a_data[index_a${m}][component_a${m}]`,b=`b_data[index_b${m}][component_b${m}]`,$=`bool(c_data[index_c${m}] & (0xffu << (component_c${m} * 8)))`;return`
            let output_indices${m} = ${i.offsetToIndices(`global_idx * 4u + ${m}u`)};
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n=[o.batchSize,o.sequenceLength,o.vHiddenSize],s=12,u={x:Math.ceil(o.vHeadSize/s),y:Math.ceil(o.sequenceLength/s),z:o.batchSize*o.numHeads},l=[{type:12,data:o.sequenceLength},{type:12,data:o.totalSequenceLength},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.vHiddenSize}],a=p=>{let m=M("probs",t.dataType,t.dims),f=M("v",r.dataType,r.dims),b=F("output",t.dataType,n),_=[{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 = ${s}u;\n  var<workgroup> tileQ: array<${m.type.value}, ${s*s}>;\n  var<workgroup> tileK: array<${m.type.value}, ${s*s}>;\n  ${p.registerUniforms(_).declareVariables(m,f,b)}\n  ${p.mainStart([s,s,1])}\n   let headIdx = workgroup_id.z;\n   let m = workgroup_id.y * TILE_SIZE + local_id.y;\n   let n = workgroup_id.x * TILE_SIZE + local_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 = ${m.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   let headOffset = (batchIdx * uniforms.M * uniforms.num_heads + currentBatchHeadNumber) * uniforms.N;\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  }`};return e.compute({name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:t.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:l}),getShaderSource:a},{inputs:[t,r],outputs:[0]})[0]},Qr=(e,t,r,o,n,s,u,l,a,p,m)=>{let f=ol(e,t,r,a,p,m);al(e,f,o,p)},il=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,n=t.inputHiddenSize,s=t.headSize,u=12,l={x:Math.ceil(t.headSize/u),y:Math.ceil(t.sequenceLength/u),z:t.batchSize*t.numHeads},a=[e.inputs[0],e.inputs[1],e.inputs[2]],p=[{type:12,data:o},{type:12,data:n},{type:12,data:s},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],m=f=>{let b=F("output_q",a[0].dataType,r),_=F("output_k",a[0].dataType,r),y=F("output_v",a[0].dataType,r),$=M("input",a[0].dataType,a[0].dims),I=M("weight",a[1].dataType,a[1].dims),C=M("bias",a[2].dataType,a[2].dims),v=$.type.storage,A=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`\n  const TILE_SIZE = ${u}u;\n  var<workgroup> tileInput: array<${v}, ${u*u}>;\n  var<workgroup> tileWeightQ: array<${v}, ${u*u}>;\n  var<workgroup> tileWeightK: array<${v}, ${u*u}>;\n  var<workgroup> tileWeightV: array<${v}, ${u*u}>;\n  ${f.registerUniforms(A).declareVariables($,I,C,b,_,y)}\n  ${f.mainStart([u,u,1])}\n    let batchIndex = workgroup_id.z / uniforms.num_heads;\n    let headNumber = workgroup_id.z % uniforms.num_heads;\n    let m = workgroup_id.y * TILE_SIZE + local_id.y;\n    let n = workgroup_id.x * TILE_SIZE + local_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 = ${v}(0);\n    var valueK = ${v}(0);\n    var valueV = ${v}(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:l,programUniforms:p}),getShaderSource:m},{inputs:a,outputs:[-1,-1,-1]})},Xa=(e,t)=>{let r=rl(e.inputs,t),[o,n,s]=il(e,r);return Qr(e,o,n,s,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var sl,ul,dl,Ja,ei=q(()=>{"use strict";Kt();ie();_e();je();be();sl=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(o,n,s)=>{let u=n.length;if(u!==o.length)throw new Error(`${s}: num dimensions != ${u}`);n.forEach((l,a)=>{if(l!==o[a])throw new Error(`${s}: dim[${a}] do not match`)})};if(e[0].dims.length>1){let o=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,o,"Invalid input scale"),r(e[2].dims,o,"Invalid input B"),r(e[3].dims,o,"Invalid input mean"),r(e[4].dims,o,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input var")},ul=(e,t)=>{let{epsilon:r,spatial:o,format:n}=t,s=e[0].dims,u=o?Me(s[s.length-1]):1,l=n==="NHWC"&&s.length>1?u:1,a=z.size(s)/u,p=o,m=p?s.length:s,f=M("x",e[0].dataType,e[0].dims,u),b=M("scale",e[1].dataType,e[1].dims,l),_=M("bias",e[2].dataType,e[2].dims,l),y=M("inputMean",e[3].dataType,e[3].dims,l),$=M("inputVar",e[4].dataType,e[4].dims,l),I=F("y",e[0].dataType,m,u),C=()=>{let A="";if(o)A=`let cOffset = ${s.length===1?"0u":n==="NHWC"?`outputIndices[${s.length-1}] / ${u}`:"outputIndices[1]"};`;else if(n==="NCHW")A=`\n            ${I.indicesSet("outputIndices","0","0")}\n            let cOffset = ${I.indicesToOffset("outputIndices")};`;else{A=`var cIndices = ${b.type.indices}(0);\n                       cIndices[0] = outputIndices[${s.length-1}];`;for(let T=1;T<b.rank;T++)A+=`cIndices[${T}] = outputIndices[${T}];`;A+=`let cOffset = ${b.indicesToOffset("cIndices")};`}return A},v=A=>`\n  const epsilon = ${r};\n  ${A.registerUniform("outputSize","u32").declareVariables(f,b,_,y,$,I)}\n  ${A.mainStart()}\n  ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    var outputIndices = ${I.offsetToIndices(`global_idx * ${u}`)};\n    ${C()}\n    let scale = ${b.getByOffset("cOffset")};\n    let bias = ${_.getByOffset("cOffset")};\n    let inputMean = ${y.getByOffset("cOffset")};\n    let inputVar = ${$.getByOffset("cOffset")};\n    let x = ${f.getByOffset("global_idx")};\n    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;\n    ${I.setByOffset("global_idx","value")}\n  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${o}_${u}`,inputDependencies:p?["rank","type","type","type","type"]:void 0},getShaderSource:v,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p?[{type:12,data:a},...j(s)]:[{type:12,data:a}]})}},dl=e=>$e(e),Ja=(e,t)=>{let{inputs:r,outputCount:o}=e,n=dl({...t,outputCount:o});if(qt.webgpu.validateInputContent&&sl(r,n),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(ul(r,n))}});var ll,cl,ti,ri=q(()=>{"use strict";_e();be();ll=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},cl=e=>{let t=e[0].dims,r=e[0].dims[2],o=z.size(t)/4,n=e[0].dataType,s=M("input",n,t,4),u=M("bias",n,[r],4),l=M("residual",n,t,4),a=F("output",n,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:m=>`\n  const channels = ${r}u / 4;\n  ${m.declareVariables(s,u,l,a)}\n\n  ${m.mainStart()}\n    ${m.guardAgainstOutOfBoundsWorkgroupSizes(o)}\n    let value = ${s.getByOffset("global_idx")}\n      + ${u.getByOffset("global_idx % channels")} + ${l.getByOffset("global_idx")};\n    ${a.setByOffset("global_idx","value")}\n  }`}},ti=e=>{ll(e.inputs),e.compute(cl(e.inputs))}});var pl,Ee,ni,oi,ai,ii,si,ui,di,li,ci,ml,pi,mi,fi,hi,Xr,gi,Jr,yi,bi,wi,vi,$i,_i,xi,Si,Ci,Ii,Ai,Ti,Ei,Pi,Oi,ki,Ri,Bi,Ln,Fn,Di,Mi,zi,en=q(()=>{"use strict";ie();_e();je();be();pl=(e,t,r,o,n,s)=>{let u=Math.ceil(t/4),l="";typeof n=="string"?l=`${n}(a)`:l=n("a");let a=M("inputData",r,[u],4),p=F("outputData",o,[u],4);return`\n      ${e.registerUniform("vec_size","u32").declareVariables(a,p)}\n\n  ${s??""}\n\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n\n    let a = ${a.getByOffset("global_idx")};\n    ${p.setByOffset("global_idx",l)}\n  }`},Ee=(e,t,r,o,n,s=e.dataType)=>({name:t,shaderCache:{hint:n,inputDependencies:["type"]},getShaderSource:u=>pl(u,z.size(e.dims),e.dataType,s,r,o),getRunData:u=>({outputs:[{dims:e.dims,dataType:s}],dispatchGroup:{x:Math.ceil(z.size(u[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(z.size(e.dims)/4)}]})}),ni=e=>{e.compute(Ee(e.inputs[0],"Abs","abs"))},oi=e=>{e.compute(Ee(e.inputs[0],"Acos","acos"))},ai=e=>{e.compute(Ee(e.inputs[0],"Acosh","acosh"))},ii=e=>{e.compute(Ee(e.inputs[0],"Asin","asin"))},si=e=>{e.compute(Ee(e.inputs[0],"Asinh","asinh"))},ui=e=>{e.compute(Ee(e.inputs[0],"Atan","atan"))},di=e=>{e.compute(Ee(e.inputs[0],"Atanh","atanh"))},li=e=>$e(e),ci=(e,t)=>{let r;switch(t.to){case 10:r="vec4<f16>";break;case 1:r="vec4<f32>";break;case 12:r="vec4<u32>";break;case 6:r="vec4<i32>";break;case 9:r="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute \'to\' from \'Cast\' operator): ${t.to}`)}e.compute(Ee(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},ml=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:Fr,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:qr;return $e({min:t,max:r})},pi=(e,t)=>{let r=e.inputs.length===1?t:ml(e.inputs),o=et(e.inputs[0].dataType);e.compute(Ee(e.inputs[0],"Clip",n=>`clamp(${n}, clip_min_, clip_max_)`,`\n    const clip_min_: vec4<${o}> = vec4(${o}(${r.min}));\n    const clip_max_: vec4<${o}> = vec4(${o}(${r.max}));\n`,r.cacheKey),{inputs:[0]})},mi=e=>{e.compute(Ee(e.inputs[0],"Ceil","ceil"))},fi=e=>{e.compute(Ee(e.inputs[0],"Cos","cos"))},hi=e=>{e.compute(Ee(e.inputs[0],"Cosh","cosh"))},Xr=e=>$e(e),gi=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(Ee(e.inputs[0],"Elu",o=>`elu_vf32(${o})`,`\n  const elu_alpha_ = ${r}(${t.alpha});\n\n  fn elu_f32(a: ${r}) -> ${r} {\n  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);\n  }\n\n  fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {\n  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));\n  }`,t.cacheKey))},Jr=(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<${e}> {\n  let absv = abs(v);\n  let x = 1.0 / (1.0 + r0 * absv);\n  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));\n}`,yi=e=>{let t=et(e.inputs[0].dataType);e.compute(Ee(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,Jr(t)))},bi=e=>{e.compute(Ee(e.inputs[0],"Exp","exp"))},wi=e=>{e.compute(Ee(e.inputs[0],"Floor","floor"))},vi=e=>{let t=et(e.inputs[0].dataType);e.compute(Ee(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,Jr(t)))},$i=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(Ee(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))},_i=e=>{e.compute(Ee(e.inputs[0],"Not",t=>`!${t}`))},xi=e=>{e.compute(Ee(e.inputs[0],"Neg",t=>`-${t}`))},Si=e=>{e.compute(Ee(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Ci=e=>{let t=et(e.inputs[0].dataType);e.compute(Ee(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Ii=e=>{e.compute(Ee(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},Ai=e=>$e(e),Ti=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(Ee(e.inputs[0],"HardSigmoid",o=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${o} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Ei=e=>{e.compute(Ee(e.inputs[0],"Sin","sin"))},Pi=e=>{e.compute(Ee(e.inputs[0],"Sinh","sinh"))},Oi=e=>{e.compute(Ee(e.inputs[0],"Sqrt","sqrt"))},ki=e=>{e.compute(Ee(e.inputs[0],"Tan","tan"))},Ri=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Bi=e=>{e.compute(Ee(e.inputs[0],"Tanh",Ri))},Ln=(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 ${Ri("v")};\n}\n`,Fn=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Di=e=>{let t=et(e.inputs[0].dataType);e.compute(Ee(e.inputs[0],"FastGelu",Fn,Ln(t),void 0,e.inputs[0].dataType))},Mi=(e,t)=>{let r=et(e.inputs[0].dataType);return e.compute(Ee(e.inputs[0],"ThresholdedRelu",o=>`select(vec4<${r}>(0.0), ${o}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},zi=e=>{e.compute(Ee(e.inputs[0],"Log","log"))}});var fl,hl,Vi,Ni=q(()=>{"use strict";_e();be();en();fl=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")},hl=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=M("input",e[0].dataType,e[0].dims,4),o=M("bias",e[0].dataType,[e[0].dims[2]],4),n=F("output",e[0].dataType,t,4),s=z.size(t)/4,u=Pe(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:a=>`\n  const M_SQRT2 = sqrt(2.0);\n  const halfChannels = ${e[0].dims[2]/4/2}u;\n\n  ${a.declareVariables(r,o,n)}\n\n  ${Jr(u)}\n\n  ${a.mainStart()}\n    ${a.guardAgainstOutOfBoundsWorkgroupSizes(s)}\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    ${n.setByOffset("global_idx","valueLeft * geluRight")}\n  }`}},Vi=e=>{fl(e.inputs),e.compute(hl(e.inputs))}});var gl,yl,bt,Wi,Gi,Hi,Li,Fi,qi,Ki,ji,Yi,Zi,Qi=q(()=>{"use strict";ie();_e();be();gl=(e,t,r,o,n,s,u,l,a,p,m,f)=>{let b,_;typeof l=="string"?b=_=(v,A)=>`${l}((${v}),(${A}))`:typeof l=="function"?b=_=l:(b=l.scalar,_=l.vector);let y=F("outputData",m,o.length,4),$=M("aData",a,t.length,4),I=M("bData",p,r.length,4),C;if(n)if(s){let v=z.size(t)===1,A=z.size(r)===1,T=t.length>0&&t[t.length-1]%4===0,D=r.length>0&&r[r.length-1]%4===0;v||A?C=y.setByOffset("global_idx",_(v?`${$.type.value}(${$.getByOffset("0")}.x)`:$.getByOffset("global_idx"),A?`${I.type.value}(${I.getByOffset("0")}.x)`:I.getByOffset("global_idx"))):C=`\n            let outputIndices = ${y.offsetToIndices("global_idx * 4u")};\n            let offsetA = ${$.broadcastedIndicesToOffset("outputIndices",y)};\n            let offsetB = ${I.broadcastedIndicesToOffset("outputIndices",y)};\n            ${y.setByOffset("global_idx",_(u||T?$.getByOffset("offsetA / 4u"):`${$.type.value}(${$.getByOffset("offsetA / 4u")}[offsetA % 4u])`,u||D?I.getByOffset("offsetB / 4u"):`${I.type.value}(${I.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n          `}else C=y.setByOffset("global_idx",_($.getByOffset("global_idx"),I.getByOffset("global_idx")));else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let v=(A,T,D="")=>{let U=`aData[indexA${T}][componentA${T}]`,V=`bData[indexB${T}][componentB${T}]`;return`\n            let outputIndices${T} = ${y.offsetToIndices(`global_idx * 4u + ${T}u`)};\n            let offsetA${T} = ${$.broadcastedIndicesToOffset(`outputIndices${T}`,y)};\n            let offsetB${T} = ${I.broadcastedIndicesToOffset(`outputIndices${T}`,y)};\n            let indexA${T} = offsetA${T} / 4u;\n            let indexB${T} = offsetB${T} / 4u;\n            let componentA${T} = offsetA${T} % 4u;\n            let componentB${T} = offsetB${T} % 4u;\n            ${A}[${T}] = ${D}(${b(U,V)});\n          `};m===9?C=`\n            var data = vec4<u32>(0);\n            ${v("data",0,"u32")}\n            ${v("data",1,"u32")}\n            ${v("data",2,"u32")}\n            ${v("data",3,"u32")}\n            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:C=`\n            ${v("outputData[global_idx]",0)}\n            ${v("outputData[global_idx]",1)}\n            ${v("outputData[global_idx]",2)}\n            ${v("outputData[global_idx]",3)}\n          `}return`\n        ${e.registerUniform("vec_size","u32").declareVariables($,I,y)}\n\n        ${f??""}\n\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n        ${C}\n      }`},yl=(e,t,r,o,n,s,u=r.dataType)=>{let l=!z.areEqual(r.dims,o.dims),a=r.dims,p=z.size(r.dims),m=!1,f=!1,b=[l];if(l){let _=mt.calcShape(r.dims,o.dims,!1);if(!_)throw new Error("Can\'t perform binary op on the given tensors");a=_,p=z.size(a);let y=z.size(r.dims)===1,$=z.size(o.dims)===1,I=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,C=o.dims.length>0&&o.dims[o.dims.length-1]%4===0;b.push(y),b.push($),b.push(I),b.push(C);let v=1;for(let A=1;A<a.length;A++){let T=r.dims[r.dims.length-A]??1,D=o.dims[o.dims.length-A]??1;if(T===D)v*=T;else break}v%4===0?(f=!0,m=!0):(y||$||I||C)&&(m=!0)}else m=!0;return b.push(m),{name:e,shaderCache:{hint:t+b.map(_=>_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>gl(_,r.dims,o.dims,a,m,l,f,n,r.dataType,o.dataType,u,s),getRunData:()=>({outputs:[{dims:a,dataType:u}],dispatchGroup:{x:Math.ceil(p/64/4)},programUniforms:[{type:12,data:Math.ceil(z.size(a)/4)},...j(r.dims,o.dims,a)]})}},bt=(e,t,r,o,n,s)=>{e.compute(yl(t,n??"",e.inputs[0],e.inputs[1],r,o,s))},Wi=e=>{bt(e,"Add",(t,r)=>`${t}+${r}`)},Gi=e=>{bt(e,"Div",(t,r)=>`${t}/${r}`)},Hi=e=>{bt(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},Li=e=>{bt(e,"Mul",(t,r)=>`${t}*${r}`)},Fi=e=>{let t=M("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;bt(e,"Pow",{scalar:(o,n)=>`pow_custom(${o},${n})`,vector:(o,n)=>`pow_vector_custom(${o},${n})`},`\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      `)},qi=e=>{bt(e,"Sub",(t,r)=>`${t}-${r}`)},Ki=e=>{bt(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},ji=e=>{bt(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},Yi=e=>{bt(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},Zi=e=>{bt(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}});var wl,vl,$l,_l,Xi,Ji,es=q(()=>{"use strict";ie();_e();je();be();wl=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,o=e[r],n=o.dataType,s=o.dims.length;e.forEach((u,l)=>{if(l!==r){if(u.dataType!==n)throw new Error("input tensors should be one type");if(u.dims.length!==s)throw new Error("input tensors should have the same shape");u.dims.forEach((a,p)=>{if(p!==t&&a!==o.dims[p])throw new Error("non concat dimensions must match")})}})},vl=(e,t)=>`\n  fn calculateInputIndex(index: u32) -> u32 {\n    let sizeInConcatAxis = array<u32, ${e}u>(${t});\n    for (var i: u32 = 0u; i < ${e}; i += 1u ) {\n      if (index < sizeInConcatAxis[i]) {\n        return i;\n      }\n    }\n    return ${e}u;\n  }`,$l=(e,t)=>{let r=e.length,o=[];for(let n=0;n<r;++n){let s=t.setByOffset("global_idx",e[n].getByIndices("indices"));r===1?o.push(s):n===0?o.push(`if (inputIndex == ${n}u) { ${s} }`):n===r-1?o.push(`else { ${s} }`):o.push(`else if (inputIndex == ${n}) { ${s} }`)}return o.join(`\n`)},_l=(e,t,r,o)=>{let n=z.size(r),s=new Array(e.length),u=new Array(e.length),l=0,a=[],p=[],m=[{type:12,data:n}];for(let $=0;$<e.length;++$)l+=e[$].dims[t],s[$]=l,p.push(e[$].dims.length),u[$]=M(`input${$}`,o,p[$]),a.push("rank"),m.push({type:12,data:s[$]});for(let $=0;$<e.length;++$)m.push(...j(e[$].dims));m.push(...j(r));let f=F("output",o,r.length),b=f.indicesGet("indices",t),_=Array.from(Array(s.length).keys()).map($=>`uniforms.sizeInConcatAxis${$}`).join(","),y=$=>`\n\n  ${(()=>{$.registerUniform("outputSize","u32");for(let I=0;I<e.length;I++)$.registerUniform(`sizeInConcatAxis${I}`,"u32");return $.declareVariables(...u,f)})()}\n\n  ${vl(s.length,_)}\n\n  ${$.mainStart()}\n    ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n    var indices = ${f.offsetToIndices("global_idx")};\n\n    let inputIndex = calculateInputIndex(${b});\n    if (inputIndex != 0u) {\n      let sizeInConcatAxis = array<u32, ${s.length}u>(${_});\n      ${b} -= sizeInConcatAxis[inputIndex - 1u];\n    }\n\n    ${$l(u,f)}\n  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:r,dataType:o}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:m}),getShaderSource:y}},Xi=(e,t)=>{let r=e.inputs,o=r[0].dims,n=z.normalizeAxis(t.axis,o.length);wl(r,n);let s=o.slice();s[n]=r.reduce((l,a)=>l+(a.dims.length>n?a.dims[n]:0),0);let u=r.filter(l=>z.size(l.dims)>0);e.compute(_l(u,n,s,r[0].dataType),{inputs:u})},Ji=e=>$e({axis:e.axis})});var ut,dt,lt,tn,Ot=q(()=>{"use strict";ie();_e();ut=(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 ${e.activation}`)}},dt=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},lt=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},tn=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[r,o]=e?.activation_params||[.2,.5];return{activation:t,alpha:r,beta:o}}else if(t==="Clip"){let[r,o]=e?.activation_params||[Fr,qr];return{activation:t,clipMax:o,clipMin:r}}else if(t==="LeakyRelu"){let[r]=e?.activation_params||[.01];return{activation:t,alpha:r}}return{activation:t}}});var Ze,rn,nn=q(()=>{"use strict";Ze=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},rn=e=>`\n      ${e?"value = value + getBiasByOutputCoords(coords);":""}\n      `});var on,qn=q(()=>{"use strict";on=e=>`\nfn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var xl,Sl,gr,ts,Cl,yr,Il,an,br=q(()=>{"use strict";ie();_e();be();Ot();nn();xl=(e,t)=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `,Sl=(e,t)=>e?`\n        let ACached0 = mm_Asub[k * innerElementSize][localRow];\n        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n        }`:`\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n        }`,gr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32)=>{let a=t[1]*e[1],p=t[0]*e[0],m=n?a:s,f=n?s:a,b=m/t[0],_=s/t[1];if(!((n&&b===4&&e[1]===4||!n&&(b===3||b===4))&&m%t[0]===0&&s%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${n} is true, innerElementSize ${b} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${b} must be 3 or 4.\n  tileAWidth ${m} must be divisible by workgroupSize[0]${t[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${b}<${r}>, ${m/b}>, ${f}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${r}>, ${p/e[0]}>, ${s}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${b};\nconst tileInner = ${s};\n\n@compute @workgroup_size(${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 = ${u?"0":"i32(globalId.z)"};\n  ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n  let globalRowStart = i32(workgroupId.y) * ${a};\n\n  let num_tiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n  var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n  var acc: array<vec4<${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          ${xl(n,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          ${b===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n          ${Sl(n,b)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},ts=(e,t)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${t?", batchIndices":""});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${t?", batchIndices":""});\n            `,Cl=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",yr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32,a=!1)=>{let p=e[1]*t[1],m=e[0]*t[0],f=n?p:s,b=n?s:p;if(!(b%t[1]===0&&f%t[0]===0&&s%t[1]===0))throw new Error(`tileAHight ${b} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${f} must be divisible by workgroupSize[0]${t[0]}, tileInner ${s} must be divisible by workgroupSize[1]${t[1]}`);let _=b/t[1],y=f/t[0],$=s/t[1],I=a?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${p};\n    let globalColStart = i32(workgroupId.x) * ${m};\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 < ${b}; inputRow = inputRow + ${t[1]}) {\n        for (var inputCol = localCol; inputCol < ${f}; inputCol = inputCol + ${t[0]}) {\n          ${ts(n,o)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${t[1]}) {\n            for (var inputCol = localCol; inputCol < ${m}; 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 = ${n?`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) * ${p};\n\nlet tileRowA = i32(localId.y) * ${_};\nlet tileColA = i32(localId.x) * ${y};\nlet tileRowB = i32(localId.y) * ${$};\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 < ${y}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${ts(n,o)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${$}; 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      ${Cl(n)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${r}, ${f}>, ${b}>;\n  var<workgroup> mm_Bsub : array<array<${r}, ${m}>, ${s}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${s};\n\n@compute @workgroup_size(${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 = ${u?"0":"i32(globalId.z)"};\n    ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n    let num_tiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n    var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n    var acc : array<array<${r}, colPerThread>, rowPerThread>;\n\n    // Without this initialization strange values show up in acc.\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = 0.0;\n      }\n    }\n    ${I}\n  }\n`},Il=(e,t,r,o,n,s=!1)=>{let[u,l,a]=n,[p,m,f,b]=o,_=jt(u,a),y=jt(l,a),$=Pe(o[0].type.tensor),I=()=>{let A=m.rank,T=p.rank,D=`var aIndices: ${m.type.indices};`;for(let U=A-2-1,V=T-1;U>=0;U--,V--)D+=`\naIndices[${U}] = ${T>1?`batchIndices[${V}]`:"batchIndices"};`;return _.forEach(U=>{D+=`\naIndices[${U}] = 0;`}),D+=`\naIndices[${A-2}] = u32(row);\n                   aIndices[${A-1}] = u32(colIn);`,D},C=()=>{let A=f.rank,T=p.rank,D=`var bIndices: ${f.type.indices};`;for(let U=A-2-1,V=T-1;U>=0;U--,V--)D+=`\nbIndices[${U}] = ${T>1?`batchIndices[${V}]`:"batchIndices"};`;return y.forEach(U=>{D+=`\nbIndices[${U}] = 0;`}),D+=`\nbIndices[${A-2}] = u32(row);\n                   bIndices[${A-1}] = u32(colIn);`,D};return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ze(e,$)} {\n      var value = ${Ze(e,$)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n      {\n        ${I()}\n        value = ${m.getByIndices("aIndices")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ze(e,$)} {\n      var value = ${Ze(e,$)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n      {\n        ${C()}\n        value = ${f.getByIndices("bIndices")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ze(e,$)}) {\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 + ${s?"bias[colIn]":`${Ze(e,$)}(bias[row])`};`:""}\n        ${r}\n        ${b.setByIndices("vec3<u32>(coords)","value")}\n      }\n    }\n    `},an=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s.slice(0,-2),a=u.slice(0,-2),p=o?o.slice(0,-2):r.slice(0,-2),m=z.size(p),f=s[s.length-2],b=s[s.length-1],_=u[u.length-1],y=b%4===0&&_%4===0,$=f<=8?[4,1,1]:[4,4,1],I=[8,8,1],C=[Math.ceil(_/I[0]/$[0]),Math.ceil(f/I[1]/$[1]),Math.ceil(m/I[2]/$[2])],v=y?4:1,A=[...l,f,b/v],T=A.length,D=[...a,b,_/v],U=D.length,V=[m,f,_/v],H=[{type:6,data:f},{type:6,data:_},{type:6,data:b}];dt(t,H),H.push(...j(p,A,D));let R=["rank","rank"],L=e.length>2;L&&(H.push(...j(e[2].dims)),R.push("rank")),H.push(...j(V));let pe=Ie=>{let we=p.length,ne=jr("batchDims",e[0].dataType,we,1),ze=Pe(e[0].dataType),Q=M("a",e[0].dataType,T,v),xe=M("b",e[1].dataType,U,v),me=F("result",e[0].dataType,V.length,v),ue=[Q,xe];if(L){let G=n?v:1;ue.push(M("bias",e[2].dataType,e[2].dims.length,G))}let se=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];lt(t,se);let he=Pe(me.type.tensor),Ae=ut(t,me.type.value,he),He=Il(v,L,Ae,[ne,Q,xe,me],[l,a,p],n);return`\n  ${Ie.registerUniforms(se).registerInternalVariables(ne).declareVariables(...ue,me)}\n  ${He}\n  ${y?gr($,I,ze,ne):yr($,I,ze,ne)}\n                   `};return{name:"MatMul",shaderCache:{hint:`${$};${t.activation};${y};${n}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:H}),getShaderSource:pe}}});var Al,rs,ns=q(()=>{"use strict";ie();Pt();be();Ot();nn();qn();br();Al=(e,t,r,o,n=!1,s,u=4,l=4,a=4,p="f32")=>{let m=L=>{switch(L){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${p}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${L} is not supported.`)}},f=L=>{switch(L){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 ${L} is not supported.`)}},b=e?`\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    `:`\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    `,_=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,y=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",$=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",I=e?"row":"col",C=e?"col":"row",v=`\n    let inChannels = i32(uniforms.w_shape[2]);\n    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n    let outRow = ${I} / outWidth;\n    let outCol = ${I} % outWidth;\n\n    let WRow = ${C} / (i32(uniforms.w_shape[1]) * inChannels);\n    let WCol = ${C} / 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 = ${C} % inChannels;\n    var resData = ${Ze(u,p)}(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 < ${y} && xCol >= 0 && xCol < ${$}) {\n      ${b}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${m(u)}\n    }\n    return resData;`,A=e?t&&o?`\n    let col = colIn * ${u};\n    ${v}`:`\n    let col = colIn * ${u};\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n      ${v}\n    }\n    return ${Ze(u,p)}(0.0);`:o&&r?`\n    let col = colIn * ${u};\n    ${v}`:`\n    let col = colIn * ${u};\n    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n      ${v}\n    }\n    return ${Ze(u,p)}(0.0);`,T=`${f(l)}`,D=Ze(a,p),U=e?Ze(u,p):Ze(l,p),V=e?Ze(l,p):Ze(u,p),H=ut(s,D,p);return`\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${U} {\n      ${e?A:T}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${V} {\n      ${e?T:A}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${D}) {\n      let col = colIn * ${a};\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      ${rn(n)}\n      ${H}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},rs=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],m=r[0],f=a?r[2]:r[3],b=a?r[1]:r[2],_=a?r[3]:r[1],y=a&&(p%4===0||p%3===0)&&_%4===0,$=a?_:f*b,I=a?f*b:_,C=[8,8,1],v=o<=8?[4,1,1]:[4,4,1],A=[Math.ceil($/C[0]/v[0]),Math.ceil(I/C[1]/v[1]),Math.ceil(m/C[2]/v[2])];De("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${A}`);let T=y?a&&p%4!==0?3:4:1,D=C[1]*v[1],U=C[0]*v[0],V=Math.max(C[0]*T,C[1]),H=o%D===0,R=n%U===0,L=s%V===0,pe=y?[T,4,4]:[1,1,1],Ie=[{type:6,data:o},{type:6,data:n},{type:6,data:s},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];dt(t,Ie),Ie.push(...j(e[0].dims,e[1].dims));let we=["rank","rank"];u&&(Ie.push(...j(e[2].dims)),we.push("rank")),Ie.push(...j(r));let ne=ze=>{let Q=[{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}];lt(t,Q);let xe=y?4:1,me=Pe(e[0].dataType),ue=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${y?`vec4<${me}>`:me}) {\n        result[flatIndex] = ${y?`vec4<${me}>`:me}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${y?`vec4<${me}>`:me}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${y?"/ 4":""}, value);\n      }`,se=M("x",e[0].dataType,e[0].dims.length,T===3?1:T),he=M("w",e[1].dataType,e[1].dims.length,xe),Ae=[se,he],He=F("result",e[0].dataType,r.length,xe);if(u){let G=M("bias",e[2].dataType,e[2].dims.length,xe);Ae.push(G),ue+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${y?`vec4<${me}>`:me} {\n          return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n        }`}return`\n        ${on("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        ${ze.registerUniforms(Q).declareVariables(...Ae,He)}\n        ${ue}\n        ${Al(a,H,R,L,u,t,pe[0],pe[1],pe[2],me)}\n        ${y?gr(v,C,me,void 0,!a,V):yr(v,C,me,void 0,!a,V,!1,void 0,l)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${T};${y};${H};${R};${L};${D};${U};${V}`,inputDependencies:we},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:Ie}),getShaderSource:ne}}});var Kn,os,as=q(()=>{"use strict";ie();_e();be();jn();Ot();Kn=(e,t,r)=>{let o=e.length>2,n=o?"value += b[output_channel];":"",s=e[0].dims,u=e[1].dims,l=u[0]/t.group,a=t.format==="NHWC",p=sn(s,u,t.dilations,t.pads,t.strides,a),m=z.size(p),f=[{type:12,data:m},{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:l}];dt(t,f),f.push(...j(s,u,p));let b=["rank","rank"];o&&(f.push(...j(e[2].dims)),b.push("rank")),f.push(...j(p));let _=y=>{let $=F("output",e[0].dataType,p.length),I=Pe($.type.tensor),C=ut(t,$.type.value,I),v=M("x",e[0].dataType,s.length),A=M("w",e[1].dataType,u.length),T=[v,A];o&&T.push(M("b",e[2].dataType,e[2].dims));let D=[{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 lt(t,D),`\n  ${y.registerUniforms(D).declareVariables(...T,$)}\n\n  ${y.mainStart()}\n    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n    let outputIndices = ${$.offsetToIndices("global_idx")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${a?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${a?1:2}], outputIndices[${a?2:3}]) * uniforms.strides - uniforms.pads;\n    let group_id: u32 = output_channel / uniforms.output_channels_per_group;\n\n    var value: ${$.type.value} = ${$.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[${a?1:2}]) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];\n          if (xWidth < 0u || xWidth >= uniforms.x_shape[${a?2:3}]) {\n            continue;\n          }\n\n          let xVal = ${a?v.get("batch","xHeight","xWidth","input_channel"):v.get("batch","input_channel","xHeight","xWidth")};\n          let wVal = ${A.get("output_channel","wInChannel","wHeight","wWidth")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${n}\n    ${C}\n    ${$.setByOffset("global_idx","value")}\n  }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:b},getRunData:()=>({outputs:[{dims:r?r(p):p,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:f}),getShaderSource:_}},os=(e,t,r)=>{let o=e.length>2,n=Me(r[3]),s=Me(r[2]),u=z.size(r)/n/s,l=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/n],a=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/n],p=[r[0],r[1],r[2],r[3]/n],m=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];dt(t,m),m.push(...j(l,a,p));let f=(s-1)*t.strides[1]+a[1],b=_=>{let y=F("output",e[0].dataType,p.length,n),$=Pe(y.type.tensor),I=ut(t,y.type.value,$),C=M("x",e[0].dataType,l.length,n),v=M("w",e[1].dataType,a.length,n),A=[C,v];o&&A.push(M("b",e[2].dataType,e[2].dims,n));let T=o?"value += b[output_channel];":"",D=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return lt(t,D),`\n  ${_.registerUniforms(D).declareVariables(...A,y)}\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] / ${s}u;\n    let col = (index1 % width1) * ${s}u;\n    index1 = index1 / width1;\n    let row = index1 % uniforms.output_shape[1];\n    let batch = index1 / uniforms.output_shape[1];\n\n    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;\n\n    var x_vals: array<${C.type.value}, ${f}>;\n    var values: array<${y.type.value}, ${s}>;\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 < ${a[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 < ${f}; 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] = ${C.get("batch","u32(x_height)","u32(x_width)","input_channel")};\n          } else {\n            x_vals[i] = ${C.type.value}(0);\n          }\n        }\n        for (var w_width: u32 = 0u; w_width < ${a[1]}; w_width++) {\n          let w_val = ${v.get("w_height","w_width","0","output_channel")};\n          for (var i = 0u; i < ${s}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 < ${s}u; i++) {\n      var value = values[i];\n      ${T}\n      ${I}\n      ${y.set("batch","row","col + i","output_channel","value")};\n    }\n  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${n};${s};${f};${a[0]};${a[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:m}),getShaderSource:b}}});var Yn,Tl,is,Zn=q(()=>{"use strict";ie();_e();br();be();Ot();Yn=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s[s.length-2],a=u[u.length-1],p=s[s.length-1],m=Me(a),f=Me(p),b=Me(l),_=z.size(r)/m/b,y=e.length>2,$=o?o.slice(0,-2):r.slice(0,-2),C=[z.size($),l,a],v=[{type:12,data:_},{type:12,data:l},{type:12,data:a},{type:12,data:p}];dt(t,v),v.push(...j($,s,u)),y&&v.push(...j(e[2].dims)),v.push(...j(C));let A=T=>{let D=jr("batch_dims",e[0].dataType,$.length),U=M("a",e[0].dataType,s.length,f),V=M("b",e[1].dataType,u.length,m),H=F("output",e[0].dataType,C.length,m),R=Pe(H.type.tensor),L=ut(t,H.type.value,R),pe=[U,V],Ie="";if(y){let se=n?m:1;pe.push(M("bias",e[2].dataType,e[2].dims.length,se)),Ie=`${n?`value += bias[col / ${se}];`:`value += ${H.type.value}(bias[row + i]);`}`}let we=s.slice(0,-2),ne=u.slice(0,-2),ze=jt(we,$),Q=jt(ne,$),xe=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];lt(t,xe);let me=(se,he)=>{let Ae=se.rank,He=se.name;if(Ae===2)return`var ${He}_indices = ${se.type.indices}(0u, 0u);`;let G=D.rank,J=`var ${He}_indices: ${se.type.indices};`;for(let Se=Ae-2-1,Qe=G-1;Se>=0;Se--,Qe--)J+=`\n${He}_indices[${Se}] = ${G>1?`batch_indices[${Qe}]`:"batch_indices"};`;return he.forEach(Se=>{J+=`\n${He}_indices[${Se}] = 0;`}),J+=`${He}_indices[${Ae-2}] = 0u;\n                     ${He}_indices[${Ae-1}] = 0u;`,J},ue=()=>{let se=`var a_data: ${U.type.value};`;for(let he=0;he<f;he++)se+=`\n              let b_data${he} = b[(b_offset + (k + ${he}) * uniforms.N + col) / ${m}];`;for(let he=0;he<b;he++){se+=`a_data = a[(a_offset + (row + ${he}) * uniforms.K + k) / ${f}];`;for(let Ae=0;Ae<f;Ae++)se+=`\n            values[${he}] = fma(${V.type.value}(a_data${f===1?"":`[${Ae}]`}), b_data${Ae}, values[${he}]);\n`}return se};return`\n  ${T.registerUniforms(xe).registerInternalVariables(D).declareVariables(...pe,H)}\n  ${T.mainStart()}\n    ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let col = (global_idx % (uniforms.N / ${m})) * ${m};\n    var index1 = global_idx / (uniforms.N / ${m});\n    let stride1 = uniforms.M / ${b};\n    let row = (index1 % stride1) * ${b};\n    let batch = index1 / stride1;\n\n    ${r.length===2?"":`let batch_indices = ${D.offsetToIndices("batch")};`}\n    ${me(U,ze)}\n    let a_offset = ${U.indicesToOffset("a_indices")};\n    ${me(V,Q)}\n    let b_offset = ${V.indicesToOffset("b_indices")};\n    var values: array<${H.type.value}, ${b}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${f}) {\n      ${ue()}\n    }\n    for (var i = 0u; i < ${b}u; i++) {\n      var value = values[i];\n      ${Ie}\n      ${L}\n      let cur_indices = ${H.type.indices}(batch, row + i, col);\n      let offset = ${H.indicesToOffset("cur_indices")};\n      ${H.setByOffset(`offset / ${m}`,"value")};\n    }\n  }\n  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${m};${f};${b};${n}`,inputDependencies:y?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:v}),getShaderSource:A}},Tl=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.")},is=e=>{Tl(e.inputs);let t=mt.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(Yn(e.inputs,{activation:""},t)):e.compute(an(e.inputs,{activation:""},t))}});var sn,Qn,El,ss,Xn,Pl,Ol,Jn,jn=q(()=>{"use strict";_e();ns();br();as();Ot();Zn();Yt();sn=(e,t,r,o,n,s)=>{let u=e[0],l=e.slice(s?1:2,s?3:4),a=l.length,p=t[0],f=t.slice(2).map((y,$)=>y+(y-1)*(r[$]-1)),_=l.map((y,$)=>y+o[$]+o[$+a]).map((y,$)=>Math.floor((y-f[$]+n[$])/n[$]));return _.splice(0,0,u),_.splice(s?3:1,0,p),_},Qn=[2,3,1,0],El=(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 n=e[0].dims.length-2;if(t.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw new Error(`strides should be ${n}D`);if(t.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ss=(e,t)=>{let r=e.kernelShape.slice();for(let s=2;s<t[1].dims.length;++s)r[s-2]===0&&(r[s-2]=t[1].dims[s]);let o=e.pads.slice();zt.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,o,e.format==="NHWC",e.autoPad);let n=Object.assign({},e);return Object.assign(n,{kernelShape:r,pads:o}),n},Xn=e=>{let t=tn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],n=e.dilations,s=e.group,u=e.kernel_shape,l=e.pads,a=e.strides,p=e.w_is_const();return{autoPad:o,format:r,dilations:n,group:s,kernelShape:u,pads:l,strides:a,wIsConst:p,...t,cacheKey:`${e.format};${t.activation};`}},Pl=(e,t,r)=>{let o=ss(r,t),n=r.format==="NHWC";if(r.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&n&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let V=sn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,n),H=e.kernelCustomData.wT??e.compute(ot(t[1],Qn),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=H);let R=[t[0],H];t.length===3&&R.push(t[2]),e.compute(os(R,o,V),{inputs:R})}else e.compute(Kn(t,o));return}let s=t.length===3,u=t[0].dims[n?1:2],l=t[0].dims[n?2:3],a=t[0].dims[n?3:1],p=t[1].dims[2],m=t[1].dims[3],f=sn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,n),b=f[n?1:2],_=f[n?2:3],y=f[n?3:1],$=n&&p===u&&m===l&&r.pads[0]===0&&r.pads[1]===0;if($||p===1&&m===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let U=f[0],V,H,R,L=[];if(n){let we=e.kernelCustomData.wT??e.compute(ot(t[1],Qn),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=we),$){let ne=u*l*a;V=t[0].reshape([1,U,ne]),H=we.reshape([1,ne,y]),R=[1,U,y]}else V=t[0].reshape([U,u*l,a]),H=we.reshape([1,a,y]),R=[U,b*_,y];L.push(V),L.push(H)}else V=t[0].reshape([U,a,u*l]),H=t[1].reshape([1,y,a]),R=[U,y,b*_],L.push(H),L.push(V);s&&L.push(t[2]);let pe=R[2],Ie=L[0].dims[L[0].dims.length-1];pe<8&&Ie<8?e.compute(Yn(L,o,f,R,n),{inputs:L}):e.compute(an(L,o,f,R,n),{inputs:L});return}let I=!0,C=e.kernelCustomData.wT??e.compute(ot(t[1],Qn),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=C);let v=[t[0],C];s&&v.push(t[2]);let A=n?b*_:y,T=n?y:b*_,D=p*m*a;e.compute(rs(v,o,f,A,T,D,s,I),{inputs:v})},Ol=(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 n=[0,t.pads[0],0,t.pads[1]],s=[1].concat(t.strides),u=[1].concat(t.dilations),l=[1].concat(t.kernelShape),a=ss({...t,pads:n,strides:s,dilations:u,kernelShape:l},o);e.compute(Kn(o,a,p=>r?[p[0],p[2],p[3]]:[]))},Jn=(e,t)=>{El(e.inputs,t),e.inputs[0].dims.length===3?Ol(e,t):Pl(e,e.inputs,t)}});var kl,us,ds=q(()=>{"use strict";ie();Pt();be();Ot();nn();qn();br();kl=(e,t=!1,r,o,n=4)=>{let s=C=>{switch(C){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 ${C} is not supported.`)}},u=e?`\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      `,l=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    `,a=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",p=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",m=e?"row":"col",f=e?"col":"row",b=`\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 = ${m} / outWidth;\n      let outCol = ${m} % outWidth;\n\n      let WRow = ${f} / (uniforms.filter_dims[1] * inChannels);\n      let WCol = ${f} / inChannels % uniforms.filter_dims[1];\n      let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n      let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n      if (xR < 0.0 || xR >= f32(${a}) || fract(xR) > 0.0) {\n        return ${o}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${p}) || fract(xC) > 0.0) {\n        return ${o}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${f} % inChannels;\n      ${u}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${n}];`,_=e?`\n      let col = colIn * ${n};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n        ${b}\n      }\n      return ${o}(0.0);`:`\n      let col = colIn * ${n};\n      if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n        ${b}\n      }\n      return ${o}(0.0);`,y=`\n      let col = colIn * ${n};\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        ${s(n)}\n      }\n      return ${o}(0.0);\n      `,$=ut(r,o);return`\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${o} {\n    ${e?_:y}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${o} {\n    ${e?y:_}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${o}) {\n    let col = colIn * ${n};\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      ${l}\n      ${rn(t)}\n      ${$}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${n}] = value;\n    }\n  }`},us=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],m=r[0],f=a?r[2]:r[3],b=a?r[1]:r[2],_=a?r[3]:r[1],y=a?p%4===0&&_%4===0:f%4===0&&_%4===0,$=a?_:f*b,I=a?f*b:_,C=y?[8,8,1]:[$<=4||I<=4?4:16,$>4&&I<=4?4:16,1],v=y?[4,4,1]:[$<=4?1:4,$>4&&I<=4?1:4,1],A=[Math.ceil($/C[0]/v[0]),Math.ceil(I/C[1]/v[1]),Math.ceil(m/C[2]/v[2])];De("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${A}`);let T=y?4:1,D=Math.max(C[0]*T,C[1]),U=y?4:1,V=[t.kernelShape[a?1:2],t.kernelShape[a?2:3]],H=[V[0]+(t.dilations[0]<=1?0:(V[0]-1)*(t.dilations[0]-1)),V[1]+(t.dilations[1]<=1?0:(V[1]-1)*(t.dilations[1]-1))],R=[H[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),H[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],L=[{type:6,data:o},{type:6,data:n},{type:6,data:s},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:V},{type:6,data:R}];dt(t,L),L.push(...j(e[0].dims,e[1].dims));let pe=["rank","rank"];u&&(L.push(...j(e[2].dims)),pe.push("rank")),L.push(...j(r));let Ie=we=>{let ne=M("x",e[0].dataType,e[0].dims.length,U),ze=M("w",e[1].dataType,e[1].dims.length,1),Q=F("result",e[0].dataType,r.length,U),xe=[ne,ze],me="";if(u){let he=M("bias",e[2].dataType,e[2].dims.length,U);xe.push(he),me+=`\n          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${he.type.value} {\n            return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n          }`}let ue=[{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:V.length},{name:"pads",type:"i32",length:R.length}];lt(t,ue);let se=Pe(e[0].dataType,1);if(se!=="f16"&&se!=="f32")throw new Error(`elemType ${se} is not supported.`);return`\n        ${on("uniforms.result_strides")}\n        ${we.registerUniforms(ue).declareVariables(...xe,Q)};\n        ${me}\n        ${kl(a,u,t,ne.type.value,T)}\n        ${y?gr(v,C,se,void 0,!a,D):yr(v,C,se,void 0,!a,D,!1,void 0,l)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${v};${C};${y}`,inputDependencies:pe},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:L}),getShaderSource:Ie}}});var Rl,eo,ls=q(()=>{"use strict";ie();Pt();_e();be();Rl=(e,t,r,o,n,s=!1,u,l,a=!1)=>{let p=a?1:2,m=a?2:3,f=a?3:1,b=s?2:1,_=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${s?`vec4<${u}>`:u}) {\n    result[flatIndex] = ${s?`vec4<${u}>`:u}(value);\n  }`;o&&(_+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${s?`vec4<${u}>`:u} {\n      return bias[coords.${a?"w":"y"}${s?"/ 4":""}];\n    }`);let y=s?4:1,$=M("W",t[1].dataType,t[1].dims.length,y),I=M("Dy",t[0].dataType,t[0].dims.length,y),C=[I,$];o&&C.push(M("bias",t[2].dataType,[r[f]].length,y));let v=F("result",t[0].dataType,r.length,y),A=`{\n        let batch: u32 = ${n?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];\n        let r = ${n?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];\n        let c = ${n?"global_id.y":"workgroup_id.y"} * ${b};\n        let d1: u32 = ${n?"global_id.x":"workgroup_id.x"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(uniforms.pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${u}>, ${b}>;\n        for (var i = 0; i < ${b}; i++) {\n          dotProd[i] = vec4<${u}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n          var dyR = (${u}(dyCorner.x) + ${u}(wR)) / ${u}(uniforms.strides.x);\n          let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${u}(uniforms.Dy_shape[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n            let dyC = (${u}(dyCorner.y) + ${u}(wC)) / ${u}(uniforms.strides.y);\n            let dyC2 = (${u}(dyCorner.y) + 1.0 + ${u}(wC)) / ${u}(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 >= ${u}(uniforms.Dy_shape[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${u}(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 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${I.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${u}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${I.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${u}>(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[${f}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${I.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${u}>(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 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${$.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${I.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${u}>(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 < ${b}; i = i + 1) {\n          let value = dotProd[i] + ${o?"bias[c+i]":`vec4<${u}>(0.0)`};\n          ${v.set("batch","r","c + i","d1","value")};\n        }\n      }`,T=`\n          let outputIndices = ${v.offsetToIndices("global_idx")};\n          let batch = ${v.indicesGet("outputIndices",0)};\n          let d1 = ${v.indicesGet("outputIndices",f)};\n          let r = ${v.indicesGet("outputIndices",p)};\n          let c = ${v.indicesGet("outputIndices",m)};\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 = ${u}(0.0);\n          for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n            if (wR % uniforms.dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${u}(dyRCorner) + ${u}(wR)) / ${u}(uniforms.strides[0]);\n            let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n            if (dyR < 0.0 || dyR >= ${u}(uniforms.Dy_shape[${p}]) || 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 = (${u}(dyCCorner) + ${u}(wC)) / ${u}(uniforms.strides.y);\n              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n              if (dyC < 0.0 || dyC >= ${u}(uniforms.Dy_shape[${m}]) ||\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 = ${a?I.get("batch","idyR","idyC","inputChannel"):I.get("batch","inputChannel","idyR","idyC")};\n                let wValue = ${$.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${o?"bias[d1]":`${u}(0.0)`};\n          ${v.setByOffset("global_idx","value")};\n        `;return`\n  ${e.registerUniforms(l).declareVariables(...C,v)}\n  ${_}\n\n    ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};\n  ${s?A:T}}`},eo=(e,t,r)=>{let o=e.length>2,n=t.outputShape,s=z.size(n),u=[Math.ceil(s/64),1,1];De("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${u}`);let 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l=M("x",t,[1],4),a=M("bias",t,[1],4),p=F("y",t,[1],4),m=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],f=_=>`\n      let bias${_}_offset: u32 = (global_idx * 4 + ${_}) % uniforms.bias_size;\n      let bias${_} = ${a.getByOffset(`bias${_}_offset / 4`)}[bias${_}_offset % 4];`,b=n?`\n      let bias = ${a.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${f(0)}${f(1)}${f(2)}${f(3)}\n      let bias = ${l.type.value}(bias0, bias1, bias2, bias3);`;return`${u.registerUniforms(m).declareVariables(l,a,p)}\n\n    ${Ln(et(t))}\n\n    ${u.mainStart(Kr)}\n      ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}\n\n      let x = ${l.getByOffset("global_idx")};\n      ${b}\n      let x_in = x + bias;\n      ${p.setByOffset("global_idx",Fn("x_in"))}\n    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y=M("x",e[0].dataType,p.length,l),$=M("scale",e[1].dataType,e[1].dims),I=M("bias",e[2].dataType,e[2].dims),C=F("output",e[0].dataType,p.length,l),v=[y,$,I,C],A=y.type.value,T=l===1?"f32":`vec${l}<f32>`,D=64,U=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n  var<workgroup> meanShared : f32;\n  var<workgroup> squaredNormShared : f32;\n  var<workgroup> workgroupShared : array<${T}, ${D}>;\n  const workgroupSize = ${D}u;\n  ${_.registerUniforms(U).declareVariables(...v)}\n  ${_.mainStart(D)}\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 = ${T}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      initial = initial + ${T}(${y.get("batch","channel","h")});\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate 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>= 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 = ${Ye("f32",a)};\n    var squaredSum = ${Ye("f32",a)};\n    for (var i: u32 = wgOffset; i < wgMax; i++) {\n        let value = ${f}(input[offset + i * uniforms.C]);\n        sum += value;\n        squaredSum += value * value;\n    }\n    output[global_idx] = ${b("sum","squaredSum")};\n  }`},v=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${a}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:[n,u,p,2],dataType:1}],dispatchGroup:{x:n*u/a},programUniforms:I}),getShaderSource:C},{inputs:[t],outputs:[-1]})[0],A=[{type:12,data:_},{type:12,data:s},{type:12,data:Math.floor(u/a)},{type:12,data:Math.floor(p*u/a)}],T=["type","type","type"],D=U=>{let V=M("scale",r.dataType,r.dims,a),H=M("bias",o.dataType,o.dims,a);return`\n  @group(0) @binding(0) var<storage, read> input : array<${m}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${V.type.storage}>;\n  @group(0) @binding(2) var<storage, read> bias : array<${H.type.storage}>;\n  @group(0) @binding(3) var<storage, read_write> output : array<${m}>;\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  ${U.mainStart()}\n    ${U.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 = ${Ye("f32",a)};\n    var squaredSum = ${Ye("f32",a)};\n    for (var i: u32 = 0; i < min(${p}, uniforms.H); i++) {\n        let value = input[offset + i + currentChannelNumber * ${p}];\n        sum += value[0];\n        squaredSum += value[1];\n    }\n    sum = sum / f32(uniforms.H);\n    squaredSum = squaredSum / f32(uniforms.H);\n    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${l}));\n    let channelScale = invStdDev * ${f}(scale[currentChannelNumber]);\n    let channelShift = ${f}(bias[currentChannelNumber]) - sum * channelScale;\n\n    output[global_idx] = ${b("channelScale","channelShift")};\n  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${a};${l}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:[n,u,2],dataType:1}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:A}),getShaderSource:D},{inputs:[v,r,o],outputs:[-1]})[0]},nc=(e,t,r)=>{let o=t[0].dims,n=o,s=o[0],u=o[o.length-1],l=z.sizeFromDimension(o,1)/u,a=Me(u),p=z.size(n)/a,m=[{type:12,data:l},{type:12,data:Math.floor(u/a)}],f=["type","type"],b=rc(e,t[0],t[1],t[2],s,l,u,r.epsilon),_=y=>{let $=Pe(t[0].dataType),I=a===1?"vec2f":`mat2x${a}f`,C=a===1?$:`vec${a}<${$}>`,v=M("input",t[0].dataType,t[0].dims,a),A=F("output",t[0].dataType,n,a);return`\n  @group(0) @binding(0) var<storage, read> input : array<${v.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${I}>;\n  @group(0) @binding(2) var<storage, read_write> output : array<${A.type.storage}>;\n  struct Uniforms {H: u32, C : u32};\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\n\n  ${y.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], ${C}(scale[0]), ${C}(scale[1]));\n  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${a}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:m}),getShaderSource:_},{inputs:[t[0],b]})},zs=(e,t)=>{t.format==="NHWC"?nc(e,e.inputs,t):e.compute(tc(e.inputs,t))}});var oc,ac,Vs,Ns=q(()=>{"use strict";ie();_e();be();oc=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},ac=(e,t,r)=>{let o=e[0].dims,n=e[1],s=e[2],u=o,l=z.normalizeAxis(t.axis,o.length),a=z.sizeToDimension(o,l),p=z.sizeFromDimension(o,l),m=z.size(n.dims),f=s?z.size(s.dims):0;if(m!==p||s&&f!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.\n       Size of scale and bias (if provided) must match this.\n       Got scale size of ${m} and bias size of ${f}`);let b=[];for(let T=0;T<o.length;++T)T<l?b.push(o[T]):b.push(1);let _=Me(p),y=["type","type"],$=[{type:12,data:a},{type:1,data:p},{type:12,data:Math.floor(p/_)},{type:1,data:t.epsilon}];s&&y.push("type");let I=r>1,C=r>2,v=T=>{let D=Pe(e[0].dataType),U=[M("x",e[0].dataType,e[0].dims,_),M("scale",n.dataType,n.dims,_)];s&&U.push(M("bias",s.dataType,s.dims,_)),U.push(F("output",e[0].dataType,u,_)),I&&U.push(F("mean_data_output",1,b)),C&&U.push(F("inv_std_output",1,b));let V=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n  ${T.registerUniforms(V).declareVariables(...U)}\n  ${T.mainStart()}\n    ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var mean_vector = ${Ye("f32",_)};\n    var mean_square_vector = ${Ye("f32",_)};\n\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n      let value = ${st(D,_,"x[h + offset]")};\n      mean_vector += value;\n      mean_square_vector += value * value;\n    }\n    let mean = ${tt("mean_vector",_)} / uniforms.norm_size;\n    let inv_std_dev = inverseSqrt(${tt("mean_square_vector",_)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\n\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n      let f32input = ${st(D,_,"x[j + offset]")};\n      let f32scale = ${st(D,_,"scale[j]")};\n      output[j + offset] = ${U[0].type.value}((f32input - mean) * inv_std_dev * f32scale\n        ${s?`+ ${st(D,_,"bias[j]")}`:""}\n      );\n    }\n\n    ${I?"mean_data_output[global_idx] = mean":""};\n    ${C?"inv_std_output[global_idx] = inv_std_dev":""};\n  }`},A=[{dims:u,dataType:e[0].dataType}];return I&&A.push({dims:b,dataType:1}),C&&A.push({dims:b,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${_};${r}`,inputDependencies:y},getRunData:()=>({outputs:A,dispatchGroup:{x:Math.ceil(a/64)},programUniforms:$}),getShaderSource:v}},Vs=(e,t)=>{oc(e.inputs),e.compute(ac(e.inputs,t,e.outputCount))}});var ic,sc,Ws,Gs,Hs=q(()=>{"use strict";ie();_e();je();be();ic=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],o=r.dims.length;if(r.dims[o-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let n=Math.floor((t.k+t.blockSize-1)/t.blockSize),s=t.blockSize/8*t.bits,u=e[1];if(!z.areEqual(u.dims,[t.n,n,s]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let a=e[2].dims;if(z.size(a)!==t.n*n)throw new Error("scales input size error.");if(e.length===4){let m=e[3].dims,f=t.bits>4?t.n*n:t.n*Math.floor((n+1)/2);if(z.size(m)!==f)throw new Error("zeroPoints input size error.")}},sc=(e,t)=>{let r=e[0].dims,o=r.length,n=r.slice(0,o-1).concat(t.n),s=r[o-2],l=t.blockSize/8*t.bits/4,a=Me(s),p=Me(t.n),m=Me(t.k),f=Me(l),b=z.size(n)/p/a,_=[{type:12,data:b},{type:12,data:t.k},{type:12,data:t.n},{type:12,data:t.accuracyLevel},{type:12,data:t.bits},{type:12,data:t.blockSize}],y=r.slice();y.splice(-1,1,t.k/m);let $=z.convertShape(e[1].dims).slice();$.splice(-1,1,l/f),_.push(...j(y)),_.push(...j($)),_.push(...j(e[2].dims)),e.length===4&&_.push(...j(z.convertShape(e[3].dims)));let I=n.slice();I.splice(-1,1,t.n/p),_.push(...j(I));let C=v=>{let A=M("a",e[0].dataType,y.length,m),T=M("b",12,$.length,f),D=M("scales",e[2].dataType,e[2].dims.length),U=[A,T,D],V=e.length===4?M("zero_points",12,e[3].dims.length):void 0;V&&U.push(V);let H=F("output",e[0].dataType,n.length,p),R=[{name:"output_size",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"accuracy_level",type:"u32"},{name:"bits",type:"u32"},{name:"block_size",type:"u32"}],L=Math.floor((t.k+t.blockSize-1)/t.blockSize),pe=Pe(e[0].dataType),Ie=(()=>{switch(m){case 1:return`array<${pe}, 8>`;case 2:return`mat4x2<${pe}>`;case 4:return`mat2x4<${pe}>`;default:throw new Error(`${m}-component is not supported.`)}})(),we=`\n        fn dequantize(quantized: ${Ie}, zero_point: ${pe}, scale: ${pe}) -> ${Ie} {\n          ${(()=>m===1?`var dequantized = ${Ie}(${Array.from({length:8},(Q,xe)=>`(quantized[${xe}] - zero_point) * scale`).join(", ")});\n              return dequantized;`:`var zero_points: ${Ie} = ${Ie}(${Array(8).fill("zero_point").join(",")});\n              return (quantized - zero_points) * scale;`)()}\n        }`,ne=`\n        fn ortUnpack8x4snorm(value: u32) -> ${Ie} {\n          var quantized: ${Ie};\n          var offset: u32 = 0;\n          let count: u32 = 4;\n          for (var i: u32 = 0; i < 8u; i++) {\n            var result = ${pe}(extractBits(value, offset, count));\n            ${(()=>{switch(m){case 1:return"quantized[i] = result;";case 2:return"quantized[i / 2][i % 2] = result;";case 4:return"quantized[i / 4][i % 4] = result;";default:throw new Error(`${m}-component is not supported.`)}})()}\n            offset += count;\n          }\n          return quantized;\n        }`,ze=V?`\n          zero_point_offset += 4;\n          if (zero_point_offset == 32) {\n            zero_point_offset = 0;\n            zero_point_index++;\n            zero_point_word = ${V.getByOffset("zero_point_index")};\n          }`:"";return`\n        ${we};\n        ${ne};\n        ${v.registerUniforms(R).declareVariables(...U,H)}\n        ${v.mainStart()}\n          ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n          var output_values: array<${H.type.value}, ${a}>;\n          var output_indices = ${H.offsetToIndices("global_idx")};\n          var n = ${H.indicesGet("output_indices",o-1)};\n          var m = ${H.indicesGet("output_indices",o-2)};\n          var a_indices: ${A.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          ${V?`\n          var zero_point_index: u32 = n * ${p} * ((${L} + 1) / 2) / 4;\n          var zero_point_word: u32 = ${V.getByOffset("zero_point_index")};\n          var zero_point_offset: u32 = 0;`:""}\n          var scale_index = n * ${L*p};\n          var b_indices: ${T.type.indices};\n          for (var c: u32 = 0; c < ${p}; c++) {\n            ${T.indicesSet("b_indices","0",`n * ${p} + c`)};\n            var block_offset: u32 = 0;\n            for (var block: u32 = 0; block < ${L}; block++) {\n              // The scale and zero points are computed per block.\n              let scale = ${D.getByOffset("scale_index")};\n              // The default zero point is 8 for unsigned 4-bit quantization.\n              let zero_point = ${pe}(${V?"extractBits(zero_point_word, zero_point_offset, 4)":8});\n              ${T.indicesSet("b_indices","1","block")};\n              var word_offset: u32 = block_offset;\n              for (var word: u32 = 0; word < ${l}; word += ${f}) {\n                ${T.indicesSet("b_indices","2","word")};\n                let b_data = ${T.getByIndices("b_indices")};\n                for (var i: u32 = 0; i < ${f}; i++) {\n                  let b_value = ${f===1?"b_data":"b_data[word + i]"};\n                  let b_quantized_values: ${Ie} = ortUnpack8x4snorm(b_value);\n                  let b_dequantized_values = dequantize(b_quantized_values, zero_point, scale);\n                  // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\n                  var offset: u32 = word_offset;\n                  for (var j: u32 = 0; j < 8/${m}; j++) {\n                    ${A.indicesSet("a_indices",o-1,`offset/${m}`)};\n                    for (var k: u32 = 0; k < ${a}u; k++) {\n                      ${A.indicesSet("a_indices",o-2,`m * ${a} + k`)};\n                      let a_data = ${A.getByIndices("a_indices")};\n                      output_values[k]${p>1?"[c]":""} += ${m===1?"a_data * b_dequantized_values[j]":"dot(a_data, b_dequantized_values[j])"};\n                    }\n                    offset += ${m};\n                  }\n                  word_offset += 8;\n                }\n              }\n              scale_index++;\n              ${ze}\n              block_offset += uniforms.block_size;\n            }\n            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.\n            ${V?`if (zero_point_offset % 8 > 0) {\n                ${ze}\n              }`:""}\n            }\n            for (var k: u32 = 0u; k < ${a}u; k++) {\n              ${H.indicesSet("output_indices",o-2,`${a+" * m + k"}`)};\n              ${H.setByIndices("output_indices","output_values[k]")}\n            }\n        }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:_}),getShaderSource:C}},Ws=(e,t)=>{ic(e.inputs,t),e.compute(sc(e.inputs,t))},Gs=e=>$e(e)});var uc,Fs,Ls,dc,oo,qs,Ks=q(()=>{"use strict";ie();_e();je();Gr();Hn();be();Yt();uc=(e,t)=>{let r=e[0],o=e[1],n=e[2],s=e[3],u=e[4],l=e[5],a=e[6],p=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 m=!1,f=r.dims[0],b=r.dims[1],_=r.dims.length===3?m?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],y=b,$=0,I=0,C=Math.floor(_/t.numHeads);if(a&&p){if(a.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');$=a.dims[2],I=a.dims[2]}else if(a||p)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let v;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)\');v=2,y=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==C)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(n)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');v=5,y=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==C)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');v=0,y=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\');v=3}if(s){if(s.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(n&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let A=0;if(u){A=8;let H=u.dims;throw H.length===1?H[0]===f?A=1:H[0]===3*f+2&&(A=3):H.length===2&&H[0]===f&&H[1]===y&&(A=5),A===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let T=!1,D=_;if(n){if(n.dims.length!==3&&n.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(r.dims[0]!==n.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(n.dims.length===3){if(y!==n.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');D=n.dims[2]}else{if(y!==n.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');D=n.dims[1]*n.dims[3],T=!0}}let U=$+y,V=!1;if(u)throw new Error("Key padding mask is not supported");if(l)throw new Error("extraAddQk is not supported");if(a)throw new Error("pastKey is not supported");if(p)throw new Error("pastValue is not supported");return{batchSize:f,sequenceLength:b,pastSequenceLength:$,kvSequenceLength:y,totalSequenceLength:U,maxSequenceLength:I,inputHiddenSize:0,hiddenSize:_,vHiddenSize:D,headSize:C,vHeadSize:Math.floor(D/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:A,scale:t.scale,broadcastResPosBias:V,passPastInKv:T,qkvFormat:v}},Fs=e=>$e({...e}),Ls=$e({perm:[0,2,1,3]}),dc=(e,t,r,o,n,s,u)=>{let l=[o,n,s],a=z.size(l),p=[{type:12,data:a},{type:12,data:u},{type:12,data:s}],m=f=>{let b=F("qkv_with_bias",t.dataType,l),_=M("qkv",t.dataType,l),y=M("bias",r.dataType,l),$=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n  ${f.registerUniforms($).declareVariables(_,y,b)}\n  ${f.mainStart()}\n    ${f.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:l,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:m},{inputs:[t,r],outputs:[-1]})[0]},oo=(e,t,r,o,n,s,u,l)=>{let a=s;if(u){if(o===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return a=dc(e,s,u,t,o,r*n,l),a=a.reshape([t,o,r,n]),e.compute(ot(a,Ls.perm),{inputs:[a],outputs:[-1]})[0]}else return s.dims.length===3&&(a=s.reshape([t,o,r,n])),e.compute(ot(a,Ls.perm),{inputs:[a],outputs:[-1]})[0]},qs=(e,t)=>{let r=uc(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let o=e.inputs[1]&&e.inputs[2]&&e.inputs[1].dims.length===4&&e.inputs[2].dims.length===4,n=oo(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,e.inputs[0],e.inputs[3],0);if(o)return Qr(e,n,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t);let s=oo(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,e.inputs[1],e.inputs[3],r.hiddenSize),u=oo(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,e.inputs[2],e.inputs[3],2*r.hiddenSize);Qr(e,n,s,u,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],r,t)}});var lc,cc,pc,mc,fc,hc,gc,yc,js,Ys=q(()=>{"use strict";ie();_e();be();lc=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].")}},cc=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n            k = i32(${e.indicesGet("indices",n)}) - ${re("uniforms.pads",n,r)};\n            if (k < 0) {\n              break;\n            }\n            if (k >= i32(${re("uniforms.x_shape",n,t)})) {\n              break;\n            }\n            offset += k * i32(${re("uniforms.x_strides",n,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            ${o}\n            value = x[offset];\n          }\n      `},pc=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${re("uniforms.pads",n,r)};\n                if (k < 0) {\n                  k = -k;\n                }\n                {\n                  let _2n_1 = 2 * (i32(${re("uniforms.x_shape",n,t)}) - 1);\n                  k = k % _2n_1;\n                  if(k >= i32(${re("uniforms.x_shape",n,t)})) {\n                    k = _2n_1 - k;\n                  }\n                }\n                offset += k * i32(${re("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},mc=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${re("uniforms.pads",n,r)};\n                if (k < 0) {\n                  k = 0;\n                }\n                if (k >= i32(${re("uniforms.x_shape",n,t)})) {\n                  k = i32(${re("uniforms.x_shape",n,t)}) - 1;\n                }\n                offset += k * i32(${re("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},fc=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${re("uniforms.pads",n,r)};\n                if (k < 0)  {\n                  k += i32(${re("uniforms.x_shape",n,t)}]);\n                }\n                if (k >= i32(${re("uniforms.x_shape",n,t)})) {\n                  k -= i32(${re("uniforms.x_shape",n,t)});\n                }\n                offset += k * i32(${re("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},hc=(e,t,r)=>{switch(r.mode){case 0:return cc(e,t,r.pads.length);case 1:return pc(e,t,r.pads.length);case 2:return mc(e,t,r.pads.length);case 3:return fc(e,t,r.pads.length);default:throw new Error("Invalid mode")}},gc=(e,t)=>{let r=z.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,n=z.size(r),s=[{type:12,data:n},{type:12,data:t.pads}];t.mode===0&&s.push({type:e[0].dataType,data:t.value}),s.push(...j(e[0].dims,r));let u=["rank"],l=a=>{let p=F("output",e[0].dataType,r.length),m=M("x",e[0].dataType,o.length),f=m.type.value,b=hc(p,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:f}),`\n            ${a.registerUniforms(_).declareVariables(m,p)}\n            ${a.mainStart()}\n            ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n            let indices = ${p.offsetToIndices("global_idx")};\n\n            var value = ${f}(0);\n            ${b}\n            output[global_idx] = value;\n        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(z.size(r)/64)},programUniforms:s}),getShaderSource:l}},yc=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,n=e[0].dims.length,s=new Int32Array(2*n).fill(0);if(e.length>=4){let l=e[3].getBigInt64Array();for(let a=0;a<l.length;a++)s[Number(l[a])]=Number(r[a]),s[Number(l[a])+n]=Number(r[a+l.length])}else r.forEach((l,a)=>s[Number(a)]=Number(l));let u=[];return s.forEach(l=>u.push(l)),{mode:t.mode,value:o,pads:u}}else return t},js=(e,t)=>{lc(e.inputs);let r=yc(e.inputs,t);e.compute(gc(e.inputs,r),{inputs:[0]})}});var dn,Zs,Qs,Xs,Js,bc,wc,eu,tu,ru,nu,ou,au,iu,su,uu,du,lu,cu,pu=q(()=>{"use strict";Kt();ie();_e();be();dn=e=>{if(qt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Zs=(e,t,r)=>{let o=t.format==="NHWC",n=e.dims.slice();o&&n.splice(1,0,n.pop());let s=Object.hasOwnProperty.call(t,"dilations"),u=t.kernelShape.slice(),l=t.strides.slice(),a=s?t.dilations.slice():[],p=t.pads.slice();zt.adjustPoolAttributes(r,n,u,l,a,p);let m=zt.computePoolOutputShape(r,n,l,a,u,p,t.autoPad),f=Object.assign({},t);s?Object.assign(f,{kernelShape:u,strides:l,pads:p,dilations:a,cacheKey:t.cacheKey}):Object.assign(f,{kernelShape:u,strides:l,pads:p,cacheKey:t.cacheKey});let b=m.slice();return b.push(b.splice(1,1)[0]),[f,o?b:m]},Qs=(e,t)=>{let r=t.format==="NHWC",o=z.size(e),n=z.size(t.kernelShape),s=[{type:12,data:o},{type:12,data:n}],u=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let l=t.kernelShape[t.kernelShape.length-1],a=t.strides[t.strides.length-1],p=t.pads[t.pads.length/2-1],m=t.pads[t.pads.length-1],f=!!(p+m);s.push({type:12,data:l},{type:12,data:a},{type:12,data:p},{type:12,data:m}),u.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let b=!1;if(t.kernelShape.length===2){let _=t.kernelShape[t.kernelShape.length-2],y=t.strides[t.strides.length-2],$=t.pads[t.pads.length/2-2],I=t.pads[t.pads.length-2];b=!!($+I),s.push({type:12,data:_},{type:12,data:y},{type:12,data:$},{type:12,data:I}),u.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,u,!0,f,b]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let l=z.computeStrides(t.kernelShape);s.push({type:12,data:l},{type:12,data:t.pads},{type:12,data:t.strides}),u.push({name:"kernelStrides",type:"u32",length:l.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let a=t.pads.reduce((p,m)=>p+m);return[s,u,!!a,!1,!1]}},Xs=(e,t,r,o,n,s,u,l,a,p,m,f)=>{let b=n.format==="NHWC",_=t.type.value,y=F("output",t.type.tensor,o);if(n.kernelShape.length<=2){let $="",I="",C="",v=r-(b?2:1);if(m?$=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${v}] = indices[${v}] * uniforms.sw - uniforms.pwStart + i;\n                  if (xIndices[${v}] < 0 || xIndices[${v}]\n                      >= uniforms.x_shape[${v}]) {\n                    pad++;\n                    continue;\n                  }\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${s}\n                }`:$=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${v}] = indices[${v}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${s}\n                }`,n.kernelShape.length===2){let T=r-(b?3:2);f?I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${T}] = indices[${T}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${T}] < 0 || xIndices[${T}] >= uniforms.x_shape[${T}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${T}] = indices[${T}] * uniforms.sh - uniforms.phStart + j;\n                `,C=`\n              }\n            `}return`\n            ${e.registerUniforms(a).declareVariables(t,y)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n              let indices = ${y.offsetToIndices("global_idx")};\n              var xIndices = ${y.offsetToIndices("global_idx")};\n\n              var value = ${_}(${l});\n              var pad = 0;\n              ${I}\n              ${$}\n              ${C}\n              ${u}\n\n              output[global_idx] = value;\n            }`}else{if(b)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let $=n.kernelShape.length,I=n.pads.length,C="";return p?C=`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset("xIndices")}];\n                ${s}\n              }`:C=`\n              }\n              let x_val = x[${t.indicesToOffset("xIndices")}];\n              ${s}\n            `,`\n            ${e.registerUniforms(a).declareVariables(t,y)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n              let indices = ${y.offsetToIndices("global_idx")};\n              var xIndices = ${y.offsetToIndices("global_idx")};\n\n              var offsets: array<u32, ${$}>;\n\n              var value = ${_}(${l});\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 < ${$-1}u; j++) {\n                  offsets[j] = offset / ${re("uniforms.kernelStrides","j",$)};\n                  offset -= offsets[j] * ${re("uniforms.kernelStrides","j",$)};\n                }\n                offsets[${$-1}] = offset;\n\n                isPad = false;\n                for (var j = ${r-$}u; j < ${r}u; j++) {\n                  xIndices[j] = indices[j] * ${re("uniforms.strides",`j - ${r-$}u`,$)}\n                    + offsets[j - ${r-$}u] - ${re("uniforms.pads","j - 2u",I)};\n                  ${C}\n              }\n              ${u}\n\n              output[global_idx] = value;\n            }`}},Js=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,bc=e=>`${Js(e)};${e.countIncludePad}`,wc=e=>`${Js(e)};${e.storageOrder};${e.dilations}`,eu=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}),tu=(e,t,r,o)=>{let[n,s]=Zs(t,o,r),u=M("x",t.dataType,t.dims.length),l=u.type.value,a="value += x_val;",p="";n.countIncludePad?p+=`value /= ${l}(uniforms.kernelSize);`:p+=`value /= ${l}(i32(uniforms.kernelSize) - pad);`;let[m,f,b,_,y]=Qs(s,n);m.push(...j(t.dims,s));let $=["rank"];return{name:e,shaderCache:{hint:`${o.cacheKey};${b};${_};${y}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(z.size(s)/64)},programUniforms:m}),getShaderSource:I=>Xs(I,u,t.dims.length,s.length,n,a,p,0,f,b,_,y)}},ru=e=>{let t=e.count_include_pad!==0,r=eu(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:bc(o)}},nu=(e,t)=>{dn(e.inputs),e.compute(tu("AveragePool",e.inputs[0],!1,t))},ou={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},au=e=>{let t=e.format;return{format:t,...ou,cacheKey:t}},iu=(e,t)=>{dn(e.inputs),e.compute(tu("GlobalAveragePool",e.inputs[0],!0,t))},su=(e,t,r,o)=>{let[n,s]=Zs(t,o,r),u=`\n      value = max(x_val, value);\n    `,l="",a=M("x",t.dataType,t.dims.length),p=["rank"],[m,f,b,_,y]=Qs(s,n);return m.push(...j(t.dims,s)),{name:e,shaderCache:{hint:`${o.cacheKey};${b};${_};${y}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(z.size(s)/64)},programUniforms:m}),getShaderSource:$=>Xs($,a,t.dims.length,s.length,n,u,l,t.dataType===10?-65504:-1e5,f,b,_,y)}},uu=(e,t)=>{dn(e.inputs),e.compute(su("MaxPool",e.inputs[0],!1,t))},du=e=>{let t=e.storage_order,r=e.dilations,o=eu(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 n={storageOrder:t,dilations:r,...o,cacheKey:""};return{...n,cacheKey:wc(n)}},lu=e=>{let t=e.format;return{format:t,...ou,cacheKey:t}},cu=(e,t)=>{dn(e.inputs),e.compute(su("GlobalMaxPool",e.inputs[0],!0,t))}});var $c,_c,mu,fu=q(()=>{"use strict";Kt();ie();be();$c=(e,t,r)=>{let o=e===t,n=e<t&&r<0,s=e>t&&r>0;if(o||n||s)throw new Error("Range these inputs\' contents are invalid.")},_c=(e,t,r,o)=>{let n=Math.abs(Math.ceil((t-e)/r)),s=[n],u=n,l=[{type:12,data:u},{type:o,data:e},{type:o,data:r},...j(s)],a=p=>{let m=F("output",o,s.length),f=m.type.value,b=[{name:"outputSize",type:"u32"},{name:"start",type:f},{name:"delta",type:f}];return`\n        ${p.registerUniforms(b).declareVariables(m)}\n        ${p.mainStart()}\n        ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        output[global_idx] = uniforms.start + ${f}(global_idx) * uniforms.delta;\n      }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:a,getRunData:()=>({outputs:[{dims:s,dataType:o}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l})}},mu=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]),qt.webgpu.validateInputContent&&$c(t,r,o),e.compute(_c(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var xc,Sc,Cc,Ic,Ac,Tc,Ec,Pc,Oc,kc,Rc,hu,Bc,Dc,Mc,zc,Uc,gu,yu,bu=q(()=>{"use strict";ie();_e();je();be();xc=(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")}},Sc=(e,t,r)=>{t.every(n=>n>=0&&n<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((n,s)=>o[n]=e[s]),o},Cc=(e,t,r,o,n,s)=>{let[u,l,a]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],p=e[0].dims.length;if(u>0&&e.length>u&&e[u].dims.length>0)e[u].getFloat32Array().forEach(m=>s.push(m));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(l>0&&e.length>l&&e[l].dims.length>0){if(e[l].getFloat32Array().forEach(m=>o.push(m)),o.length!==0&&o.length!==p&&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");xc(o,t),t.axes.length>0&&Sc(o,t.axes,p).forEach((m,f)=>o[f]=m)}if(a>0&&e.length>a&&(e[a].getBigInt64Array().forEach(m=>n.push(Number(m))),n.length!==p||r>=18&&n.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(n.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 n<"u"&&o.length>0&&n.length>p)throw new Error("Resize requires only of scales or sizes to be specified")},Ic=(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`)}})()+"}",Ac=(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`)}})()+"}",Tc=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),n=e.length===0?o:e.slice();return t.length>0?(t.forEach((s,u)=>{o[s]=n[u],o[u+r]=n[t.length+u]}),o):n},Ec=(e,t,r,o)=>{let n=[];if(r.length>0)if(o.length>0){if(e.forEach(s=>n.push(s)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((s,u)=>n[s]=r[u])}else r.forEach(s=>n.push(s));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");n=e.map((s,u)=>Math.round(s*t[u]))}return n},Pc=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>t[s]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>t[s]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let n=e.slice();return r.axes.length>0?(r.axes.forEach(s=>t[s]=o),r.axes.forEach(s=>n[s]=Math.round(e[s]*t[s]))):(t.fill(o,0,t.length),n.forEach((s,u)=>n[u]=Math.round(s*t[u]))),n},Oc=(e,t,r,o,n)=>`\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 = ${re("uniforms.scales","i",o)};\n        var roi_low = ${re("uniforms.roi","i",n)};\n        var roi_hi = ${re("uniforms.roi",`i + ${t.length}`,n)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${re("uniforms.input_shape","i",t.length)};\n          var output_shape_i = ${re("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    }`,kc=(e,t,r,o,n,s,u)=>`\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 = ${re("uniforms.scales","i",n)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${re("uniforms.roi","i",s)};\n          var roi_hi = ${re("uniforms.roi",`i + ${r.length}`,s)};\n          var input_shape_i = ${re("uniforms.input_shape","i",r.length)};\n          var output_shape_i = ${re("uniforms.output_shape","i",o.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${u} || (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    }`,Rc=(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 >= ${re("uniforms.input_shape","i",t.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,hu=(e,t,r,o)=>e.rank>o?`\n    ${e.indicesSet("input_indices",t,"channel")};\n    ${e.indicesSet("input_indices",r,"batch")};\n`:"",Bc=(e,t,r,o,n)=>{let[u,l,a,p]=r.length===2?[-1,0,1,-1]:[0,2,3,1],m=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${m} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",l,`max(0, min(row, ${r[l]} - 1))`)};\n      ${e.indicesSet("input_indices",a,`max(0, min(col, ${r[a]} - 1))`)};\n      ${hu(e,p,u,2)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${m} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${m} = originalIndices[${l}];\n      var col:${m} = originalIndices[${a}];\n      ${o?`if (row < 0 || row > (${r[l]} - 1) || col < 0 || col > (${r[a]} - 1)) {\n        return ${n};\n      }`:""};\n      row = max(0, min(row, ${r[l]} - 1));\n      col = max(0, min(col, ${r[a]} - 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 = ${r.length>2?`u32(originalIndices[${p}])`:"0"};\n      var batch: u32 =  ${r.length>2?`u32(originalIndices[${u}])`:"0"};\n      var x11: ${m} = getInputValue(batch, channel, row1, col1);\n      var x12: ${m} = getInputValue(batch, channel, row1, col2);\n      var x21: ${m} = getInputValue(batch, channel, row2, col1);\n      var x22: ${m} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${m} = abs(row - ${m}(row1));\n      var dx2: ${m} = abs(${m}(row2) - row);\n      var dy1: ${m} = abs(col - ${m}(col1));\n      var dy2: ${m} = abs(${m}(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    }`},Dc=(e,t,r,o,n,s,u,l,a,p)=>{let m=r.length===2,f=!0,[b,_]=m?[0,1]:f?[2,3]:[1,2],y=e.type.value,$=I=>{let C=I===b?"row":"col";return`\n      fn ${C}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${y} {\n        var output_index = ${t.indicesGet("output_indices",I)};\n        var originalIdx: ${y} = getOriginalCoordinateFromResizedCoordinate(output_index, ${n[I]},\n        ${o[I]}, ${r[I]}, ${s[I]}, ${s[I]} + ${r.length});\n        var fractOriginalIdx: ${y} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${l} && (originalIdx < 0 || originalIdx > (${r[I]} - 1))) {\n          return ${a};\n        }\n        var data: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${C}: ${y} = originalIdx + ${y}(i);\n          if (${C} < 0 || ${C} >= ${r[I]}) {\n            ${(()=>p?`coefs[i + 1] = 0.0;\n                        continue;`:l?`return ${a};`:`${C} = max(0, min(${C}, ${r[I]} - 1));`)()};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",I,`u32(${C})`)};\n          data[i + 1] = ${I===b?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${$(b)};\n    ${$(_)};\n  fn getCubicInterpolationCoefs(s: ${y}) -> array<${y}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${y} = 1.0 - absS;\n    var twoMinusAbsS: ${y} = 2.0 - absS;\n    var onePlusAbsS: ${y} = 1.0 + absS;\n    coeffs[0] = ((${u} * onePlusAbsS - 5 * ${u}) * onePlusAbsS + 8 * ${u}) * onePlusAbsS - 4 * ${u};\n    coeffs[1] = ((${u} + 2) * absS - (${u} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${u} + 2) * oneMinusAbsS - (${u} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${u} * twoMinusAbsS - 5 * ${u}) * twoMinusAbsS + 8 * ${u}) * twoMinusAbsS - 4 * ${u};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${y}, 4>, coefs: array<${y}, 4>) -> ${y} {\n    var coefsSum: ${y} = 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}) -> ${y} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},Mc=(e,t,r,o,n)=>{let[u,l,a,p,m]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],f=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${f} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",l,`max(0, min(depth, ${r[l]} - 1))`)};\n      ${e.indicesSet("input_indices",a,`max(0, min(height, ${r[a]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(width, ${r[p]} - 1))`)};\n      ${hu(e,m,u,3)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${f} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${f} = originalIndices[${l}];\n      var height:${f} = originalIndices[${a}];\n      var width:${f} = originalIndices[${p}];\n      ${o?`if (depth < 0 || depth > (${r[l]} - 1) || height < 0 || height > (${r[a]} - 1) || width < 0 || (width > ${r[p]} - 1)) {\n      return ${n};\n        }`:""};\n\n    depth = max(0, min(depth, ${r[l]} - 1));\n      height = max(0, min(height, ${r[a]} - 1));\n      width = max(0, min(width, ${r[p]} - 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 = ${r.length>3?`u32(originalIndices[${m}])`:"0"};\n      var batch: u32 =  ${r.length>3?`u32(originalIndices[${u}])`:"0"};\n\n      var x111: ${f} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${f} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${f} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${f} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${f} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${f} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${f} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${f} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${f} = abs(depth - ${f}(depth1));\n      var dx2: ${f} = abs(${f}(depth2) - depth);\n      var dy1: ${f} = abs(height - ${f}(height1));\n      var dy2: ${f} = abs(${f}(height2) - height);\n      var dz1: ${f} = abs(width - ${f}(width1));\n      var dz2: ${f} = abs(${f}(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    }`},zc=(e,t,r,o,n,s)=>{let u=e.dims,l=Tc(s,t.axes,u.length),a=Ec(u,o,n,t.axes),p=o.slice();o.length===0&&(p=u.map((v,A)=>v===0?1:a[A]/v),t.keepAspectRatioPolicy!=="stretch"&&(a=Pc(u,p,t)));let m=F("output",e.dataType,a.length),f=M("input",e.dataType,u.length),b=z.size(a),_=u.length===a.length&&u.every((v,A)=>v===a[A]),y=t.coordinateTransformMode==="tf_crop_and_resize",$=t.extrapolationValue,I=f.type.value,C=v=>`\n      ${_?"":`\n      ${Ic(t.coordinateTransformMode,I)};\n      ${(()=>{switch(t.mode){case"nearest":return`\n              ${Rc(f,u)};\n              ${Ac(t.nearestMode,r,I)};\n              ${kc(f,m,u,a,p.length,l.length,y)};\n              `;case"linear":return`\n              ${Oc(m,u,a,p.length,l.length)};\n              ${(()=>{if(u.length===2||u.length===4)return`${Bc(f,m,u,y,$)}`;if(u.length===3||u.length===5)return`${Mc(f,m,u,y,$)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n            `;case"cubic":return`\n            ${(()=>{if(u.length===2||u.length===4)return`${Dc(f,m,u,a,p,l,t.cubicCoeffA,y,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      ${v.registerUniform("output_size","u32").registerUniform("scales","f32",p.length).registerUniform("roi","f32",l.length).declareVariables(f,m)}\n      ${v.mainStart()}\n        ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n        ${_?"output[global_idx] = input[global_idx];":`\n        let output_indices = ${m.offsetToIndices("global_idx")};\n        var input_indices: ${f.type.indices};\n        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n                if (checkInputIndices(input_indices)) {\n                  output[global_idx] = ${f.getByIndices("input_indices")};\n                } else {\n                  output[global_idx] = ${t.extrapolationValue};\n                }`;case"linear":return`output[global_idx] = ${u.length===2||u.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}|${r}|${p.length>0?p:""}|${n.length>0?n:""}|${l.length>0?l:""}|${_}|${u}`,inputDependencies:["rank"]},getShaderSource:C,getRunData:()=>({outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:[{type:12,data:b},{type:1,data:p},{type:1,data:l},...j(u,a)]})}},Uc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},gu=(e,t)=>{let r=[],o=[],n=[],s=Uc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Cc(e.inputs,t,s,r,o,n),e.compute(zc(e.inputs[0],t,s,r,o,n),{inputs:[0]})},yu=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,n=e.cubicCoeffA,s=e.excludeOutside!==0,u=e.extrapolationValue,l=e.keepAspectRatioPolicy,a=e.mode,p=e.nearestMode===""?"simple":e.nearestMode;return $e({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:n,excludeOutside:s,extrapolationValue:u,keepAspectRatioPolicy:l,mode:a,nearestMode:p})}});var Vc,Nc,wu,vu=q(()=>{"use strict";ie();_e();be();Vc=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],o=e[2];if(t.dataType!==r.dataType||t.dataType!==o.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let n=t.dims[t.dims.length-1],s=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==n)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(o.dims.length!==1)throw new Error("Gamma must be 1D");if(o.dims[o.dims.length-1]!==n)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let u=e[3];if(u.dims.length!==1)throw new Error("Beta must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let u=e[4];if(u.dims.length!==1)throw new Error("Bias must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Bias must have the same hidden size as input")}},Nc=(e,t,r,o)=>{let n=e[0].dims,s=z.size(n),u=n,l=s,a=n.slice(-1)[0],p=o?n.slice(0,-1).concat(1):[],m=e.length>3,f=e.length>4,b=o&&r>1,_=o&&r>2,y=r>3,$=Me(a),I=[{type:12,data:l},{type:12,data:$},{type:12,data:a},{type:1,data:t.epsilon}],C=A=>{let T=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],D=[M("x",e[0].dataType,e[0].dims,$),M("skip",e[1].dataType,e[1].dims,$),M("gamma",e[2].dataType,e[2].dims,$)];m&&D.push(M("beta",e[3].dataType,e[3].dims,$)),f&&D.push(M("bias",e[4].dataType,e[4].dims,$)),D.push(F("output",e[0].dataType,u,$)),b&&D.push(F("mean_output",1,p)),_&&D.push(F("inv_std_output",1,p)),y&&D.push(F("input_skip_bias_sum",e[0].dataType,u,$));let U=Pe(e[0].dataType);return`\n\n      ${A.registerUniforms(T).declareVariables(...D)}\n\n      ${A.mainStart()}\n        ${A.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 = ${Ye("f32",$)};\n        var squareSum = ${Ye("f32",$)};\n        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n          let skip_value = skip[offset + i];\n          let bias_value = ${f?"bias[i]":"0.0"};\n          let input_value = x[offset + i];\n          let value = input_value + skip_value + bias_value;\n          ${y?"input_skip_bias_sum[offset + i] = value;":""}\n          output[offset + i] = value;\n          let f32_value = ${st(U,$,"value")};\n          sum += f32_value;\n          squareSum += f32_value * f32_value;\n        }\n        let mean = ${tt("sum",$)} / f32(uniforms.hidden_size);\n        let inv_std_dev = inverseSqrt(${tt("squareSum",$)} / f32(uniforms.hidden_size) - mean * mean + uniforms.epsilon);\n        ${b?"mean_output[global_idx] = mean;":""}\n        ${_?"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] - ${U}(mean)) * ${U}(inv_std_dev) * gamma[i] + ${m?"beta[i]":"0.0"};\n        }\n      }`},v=[{dims:u,dataType:e[0].dataType}];return r>1&&v.push({dims:p,dataType:1}),r>2&&v.push({dims:p,dataType:1}),r>3&&v.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${$};${b};${_};${y}`,inputDependencies:e.map((A,T)=>"type")},getShaderSource:C,getRunData:()=>({outputs:v,dispatchGroup:{x:Math.ceil(l/a/64)},programUniforms:I})}},wu=(e,t)=>{Vc(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(Nc(e.inputs,t,e.outputCount,!1),{outputs:o})}});var Wc,ln,Gc,$u,Hc,Lc,_u,xu,Su=q(()=>{"use strict";ie();_e();je();be();Wc=(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`)})},ln=(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 ${t} must be an array of int32 or int64`);return r},Gc=(e,t)=>{if(e.length>1){let r=ln(e,1),o=ln(e,2),n=ln(e,3);return n.length===0&&(n=[...Array(e[0].dims.length).keys()]),$e({starts:r,ends:o,axes:n})}else return t},$u=(e,t,r,o,n)=>{let s=e;return e<0&&(s+=r[o[t]]),n[t]<0?Math.max(0,Math.min(s,r[o[t]]-1)):Math.max(0,Math.min(s,r[o[t]]))},Hc=(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 = ${re("uniforms.input_shape","i",r.length)};\n            let steps_i = ${re("uniforms.steps","i",r.length)};\n            let signs_i = ${re("uniforms.signs","i",r.length)};\n            let starts_i = ${re("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            ${e.indicesSet("input_indices","i","input_index")};\n          }\n          return input_indices;\n      }`,Lc=(e,t)=>{let r=e[0].dims,o=z.size(r),n=t.axes.length>0?z.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],s=ln(e,4);s.forEach(C=>C!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(n.length).fill(1));let u=t.starts.map((C,v)=>$u(C,v,r,n,s)),l=t.ends.map((C,v)=>$u(C,v,r,n,s));if(n.length!==u.length||n.length!==l.length)throw new Error("start, ends and axes should have the same number of elements");if(n.length!==r.length)for(let C=0;C<r.length;++C)n.includes(C)||(u.splice(C,0,0),l.splice(C,0,r[C]),s.splice(C,0,1));let a=s.map(C=>Math.sign(C));s.forEach((C,v,A)=>{if(C<0){let T=(l[v]-u[v])/C,D=u[v],U=D+T*s[v];u[v]=U,l[v]=D,A[v]=-C}});let p=r.slice(0);n.forEach((C,v)=>{p[C]=Math.ceil((l[C]-u[C])/s[C])});let m={dims:p,dataType:e[0].dataType},f=F("output",e[0].dataType,p.length),b=M("input",e[0].dataType,e[0].dims.length),_=z.size(p),y=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:u.length},{name:"signs",type:"i32",length:a.length},{name:"steps",type:"u32",length:s.length}],$=[{type:12,data:_},{type:12,data:u},{type:6,data:a},{type:12,data:s},...j(e[0].dims,p)],I=C=>`\n      ${C.registerUniforms(y).declareVariables(b,f)}\n        ${Hc(b,f,r)}\n        ${C.mainStart()}\n          ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n          let output_indices = ${f.offsetToIndices("global_idx")};\n          let input_indices = calculateInputIndices(output_indices);\n          ${f.setByOffset("global_idx",b.getByIndices("input_indices"))}\n      }`;return{name:"Slice",shaderCache:{hint:`${a.length}_${u.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[m],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:$})}},_u=(e,t)=>{Wc(e.inputs,t);let r=Gc(e.inputs,t);e.compute(Lc(e.inputs,r),{inputs:[0]})},xu=e=>{let t=e.starts,r=e.ends,o=e.axes;return $e({starts:t,ends:r,axes:o})}});var Fc,qc,Cu,Iu,Au=q(()=>{"use strict";ie();_e();je();be();Fc=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},qc=(e,t)=>{let r=e.dims,o=z.size(r),n=64,s=t.axis;if(s<0&&(s=r.length+s),s<r.length-1)throw new Error("softmax only supports last axis for now.");let u=r[s],l=o/u,a=Me(u),p=u/a,m=(I,C)=>C===4?`max(max(${I}.x, ${I}.y), max(${I}.z, ${I}.w))`:C===2?`max(${I}.x, ${I}.y)`:C===3?`max(max(${I}.x, ${I}.y), ${I}.z)`:I,f=M("x",e.dataType,e.dims,a),b=F("result",e.dataType,e.dims,a),_=f.type.value,y=Pe(e.dataType)==="f32"?`var threadMax = ${_}(-3.402823e+38f);`:`var threadMax = ${_}(-65504.0h);`,$=I=>`\n      var<workgroup> rowMaxShared : ${_};\n      var<workgroup> rowSumShared : ${_};\n      var<workgroup> threadShared : array<${_}, ${n}>;\n\n      fn getValue(row: i32, col: i32, row_stride: i32) -> ${_} {\n        let index = row * row_stride + col;\n        return x[index];\n      }\n\n      fn setValue(row: i32, col: i32, row_stride: i32, value: ${_}) {\n        let index = row * row_stride + col;\n        result[index] = value;\n      }\n      ${I.registerUniform("packedCols","i32").declareVariables(f,b)}\n      ${I.mainStart()}\n        let gindex = i32(global_idx);\n        let lindex = i32(local_idx);\n        const wg = ${n};\n        let row = gindex / wg;\n        let cols = uniforms.packedCols;\n        let row_stride : i32 = uniforms.packedCols;\n\n        // find the rows max\n        ${y}\n        for (var col = lindex; col < cols; col += wg) {\n          let value = getValue(row, col, row_stride);\n          threadMax = max(threadMax, value);\n        }\n        if (lindex < cols) {\n          threadShared[lindex] = threadMax;\n        }\n        workgroupBarrier();\n\n        var reduceSize = min(cols, wg);\n        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {\n          reduceSize = currSize + (reduceSize & 1);\n          if (lindex < currSize) {\n            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);\n          }\n          workgroupBarrier();\n        }\n        if (lindex == 0) {\n          rowMaxShared = ${_}(${m("threadShared[0]",a)});\n        }\n        workgroupBarrier();\n\n        // find the rows sum\n        var threadSum = ${_}(0.0);\n        for (var col = lindex; col < cols; col += wg) {\n          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);\n          threadSum += subExp;\n        }\n        threadShared[lindex] = threadSum;\n        workgroupBarrier();\n\n        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {\n          if (lindex < currSize) {\n            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];\n          }\n          workgroupBarrier();\n        }\n        if (lindex == 0) {\n          rowSumShared = ${_}(${tt("threadShared[0]",a)});\n        }\n        workgroupBarrier();\n\n        // calculate final value for each element in the row\n        for (var col = lindex; col < cols; col += wg) {\n          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;\n          setValue(row, col, row_stride, value);\n        }\n      }`;return{name:"Softmax",shaderCache:{hint:`${a}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:l},programUniforms:[{type:12,data:p}]}),getShaderSource:$}},Cu=(e,t)=>{Fc(e.inputs),e.compute(qc(e.inputs[0],t))},Iu=e=>$e({axis:e.axis})});var Kc,jc,Yc,Zc,Qc,Tu,Eu,Pu=q(()=>{"use strict";ie();_e();je();be();Kc=e=>{if(!e||e.length<1)throw new Error("too few inputs")},jc=(e,t)=>{let r=[],o=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),o=r.length),$e({numOutputs:o,axis:t.axis,splitSizes:r})},Yc=e=>`\nfn calculateOutputIndex(index: u32) -> u32 {\n    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {\n    if (index < ${re("uniforms.size_in_split_axis","i",e)}) {\n        return i;\n    }\n    }\n    return ${e}u;\n}`,Zc=e=>{let t=e.length,r=[];for(let o=0;o<t;++o){let n=e[o].setByIndices("indices","input[global_idx]");t===1?r.push(n):o===0?r.push(`if (output_number == ${o}u) { ${n} }`):o===t-1?r.push(`else { ${n} }`):r.push(`else if (output_number == ${o}) { ${n} }`)}return`\n      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {\n        ${r.join(`\n`)}\n      }`},Qc=(e,t)=>{let r=e[0].dims,o=z.size(r),n=e[0].dataType,s=z.normalizeAxis(t.axis,r.length),u=new Array(t.numOutputs),l=M("input",n,r.length),a=new Array(t.numOutputs),p=[],m=[],f=0,b=[{type:12,data:o}];for(let y=0;y<t.numOutputs;y++){f+=t.splitSizes[y],a[y]=f;let $=r.slice();$[t.axis]=t.splitSizes[y],m.push($),u[y]=F(`output${y}`,n,$.length),p.push({dims:m[y],dataType:e[0].dataType})}b.push({type:12,data:a},...j(r,...m));let _=y=>`\n  ${y.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",a.length).declareVariables(l,...u)}\n  ${Yc(a.length)}\n  ${Zc(u)}\n\n  ${y.mainStart()}\n    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}\n\n    var indices = ${l.offsetToIndices("global_idx")};\n    var index = ${l.indicesGet("indices",s)};\n    let output_number = calculateOutputIndex(index);\n    if (output_number != 0) {\n      index -= ${re("uniforms.size_in_split_axis","output_number - 1u",a.length)};\n      ${l.indicesSet("indices",s,"index")};\n    }\n    writeBufferData(output_number, indices, global_idx);\n  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:_,getRunData:()=>({outputs:p,dispatchGroup:{x:Math.ceil(o/64)},programUniforms:b})}},Tu=(e,t)=>{Kc(e.inputs);let r=e.inputs.length===1?t:jc(e.inputs,t);e.compute(Qc(e.inputs,r),{inputs:[0]})},Eu=e=>{let t=e.axis,r=e.splitSizes,o=e.numOutputs<0?r.length:e.numOutputs;if(o!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return $e({axis:t,numOutputs:o,splitSizes:r})}});var Ou,Xc,Jc,ep,ku,Ru=q(()=>{"use strict";ie();_e();be();Ou=e=>Array.from(e.getBigInt64Array(),Number),Xc=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 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= 0; i < ${t.length}; i++) {\n        let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")};\n        let input_dim_value = ${l.indicesGet("output_indices","i")}  % input_dim_i;\n\n        ${u.indicesSet("input_indices","i","input_dim_value")}\n      }\n      ${l.setByOffset("global_idx",u.getByIndices("input_indices"))}\n    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:12,data:n},...j(e[0].dims,o)]}),getShaderSource:a}},ku=e=>{Xc(e.inputs),e.compute(ep(e.inputs),{inputs:[0]})}});var tp,rp,Bu,Du=q(()=>{"use strict";ie();_e();be();tp=(e,t,r,o,n)=>{let s=F("output_data",n,r.length,4),u=M("a_data",t[1].dataType,t[1].dims.length,4),l=M("b_data",t[2].dataType,t[2].dims.length,4),a=M("c_data",t[0].dataType,t[0].dims.length,4),p,m=(f,b,_)=>`select(${b}, ${f}, ${_})`;if(!o)p=s.setByOffset("global_idx",m(u.getByOffset("global_idx"),l.getByOffset("global_idx"),a.getByOffset("global_idx")));else{let f=(b,_,y="")=>{let $=`a_data[index_a${_}][component_a${_}]`,I=`b_data[index_b${_}][component_b${_}]`,C=`bool(c_data[index_c${_}] & (0xffu << (component_c${_} * 8)))`;return`\n            let output_indices${_} = ${s.offsetToIndices(`global_idx * 4u + ${_}u`)};\n            let offset_a${_} = ${u.broadcastedIndicesToOffset(`output_indices${_}`,s)};\n            let offset_b${_} = ${l.broadcastedIndicesToOffset(`output_indices${_}`,s)};\n            let offset_c${_} = ${a.broadcastedIndicesToOffset(`output_indices${_}`,s)};\n            let index_a${_} = offset_a${_} / 4u;\n            let index_b${_} = offset_b${_} / 4u;\n            let index_c${_} = offset_c${_} / 4u;\n            let component_a${_} = offset_a${_} % 4u;\n            let component_b${_} = offset_b${_} % 4u;\n            let component_c${_} = offset_c${_} % 4u;\n            ${b}[${_}] = ${y}(${m($,I,C)});\n          `};n===9?p=`\n            var data = vec4<u32>(0);\n            ${f("data",0,"u32")}\n            ${f("data",1,"u32")}\n            ${f("data",2,"u32")}\n            ${f("data",3,"u32")}\n            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:p=`\n            ${f("output_data[global_idx]",0)}\n            ${f("output_data[global_idx]",1)}\n            ${f("output_data[global_idx]",2)}\n            ${f("output_data[global_idx]",3)}\n          `}return`\n        ${e.registerUniform("vec_size","u32").declareVariables(a,u,l,s)}\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n        ${p}\n      }`},rp=e=>{let t=e[1].dims,r=e[2].dims,o=e[0].dims,n=e[1].dataType,s=!(z.areEqual(t,r)&&z.areEqual(r,o)),u=t,l=z.size(t);if(s){let p=mt.calcShape(mt.calcShape(t,r,!1),o,!1);if(!p)throw new Error("Can\'t perform where op on the given 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cn,Uu=q(()=>{"use strict";Kt();Pt();be();cn=class{constructor(t){this.backend=t;this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,r){this.repo.set(t,r)}run(t,r,o,n,s){Mt(t.programInfo.name);let u=this.backend.device,l=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let a=[];for(let m of r)a.push({binding:a.length,resource:{buffer:m.buffer}});for(let m of o)a.push({binding:a.length,resource:{buffer:m.buffer}});s&&a.push({binding:a.length,resource:s});let p=u.createBindGroup({layout:t.computePipeline.getBindGroupLayout(0),entries:a,label:t.programInfo.name});if(this.backend.sessionStatus==="capturing"){let m={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:p,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(m)}l.setPipeline(t.computePipeline),l.setBindGroup(0,p),l.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Et(t.programInfo.name)}dispose(){}build(t,r){Mt(t.name);let o=this.backend.device,n=[];o.features.has("shader-f16")&&n.push("enable f16;");let s=xa(r),u=t.getShaderSource(s),l=`${n.join(`\n`)}\n${s.additionalImplementations}\n${u}`,a=o.createShaderModule({code:l,label:t.name});De("verbose",()=>`[WebGPU] ${t.name} shader code: ${l}`);let p=o.createComputePipeline({compute:{module:a,entryPoint:"main"},layout:"auto",label:t.name});return Et(t.name),{programInfo:t,computePipeline:p}}normalizeDispatchGroupSize(t){let r=typeof t=="number"?t:t.x,o=typeof t=="number"?1:t.y||1,n=typeof t=="number"?1:t.z||1,s=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(r<=s&&o<=s&&n<=s)return[r,o,n];let u=r*o*n,l=Math.ceil(Math.sqrt(u));if(l>s){if(l=Math.ceil(Math.cbrt(u)),l>s)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[l,l,l]}else return[l,l,1]}}});var np,op,ao,pn,Vu=q(()=>{"use strict";Kt();ie();Pt();ba();_a();zu();Uu();np=(e,t)=>{if(t.length!==e.length)throw new Error(`inputDependencies length ${t.length} is not equal to inputTensors length ${e.length}.`);let r=[];for(let o=0;o<e.length;++o){let n=e[o].dataType;switch(t[o]){case"none":{r.push("");break}case"type":{r.push(`${n}`);break}case"rank":{let s=e[o].dims.length;r.push(`${n};${s}`);break}case"dims":{let s=e[o].dims.join(",");r.push(`${n};${s}`);break}default:throw new Error(`unsupported input dependency: ${t[o]}`)}}return r.join("|")},op=(e,t,r)=>{let o=e.name;return e.shaderCache?.hint&&(o+="["+e.shaderCache.hint+"]"),o+=":"+r+`:${np(t,e.shaderCache?.inputDependencies??new Array(t.length).fill("dims"))}`,o},ao=class{constructor(t){t&&(this.architecture=t.architecture,this.vendor=t.vendor)}isArchitecture(t){return this.architecture===t}isVendor(t){return this.vendor===t}},pn=class{constructor(){this.currentSessionId=null;this.currentKernelId=null;this.commandEncoder=null;this.computePassEncoder=null;this.maxDispatchNumber=16;this.pendingDispatchNumber=0;this.pendingKernels=[];this.pendingQueries=new Map;this.sessionStatus="default";this.capturedCommandList=new Map;this.capturedPendingKernels=new Map;this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. (should not happen)");let t=this.kernelCustomData.get(this.currentKernelId);return t||(t={},this.kernelCustomData.set(this.currentKernelId,t)),t}async initialize(t,r){this.env=t;let o=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:r.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.limits.maxStorageBufferBindingSize,maxBufferSize:r.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:r.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:r.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:r.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:r.limits.maxComputeWorkgroupSizeZ},requiredFeatures:o};r.features.has("chromium-experimental-timestamp-query-inside-passes")?o.push("chromium-experimental-timestamp-query-inside-passes"):r.features.has("timestamp-query")&&o.push("timestamp-query"),r.features.has("shader-f16")&&o.push("shader-f16"),this.device=await r.requestDevice(n),this.adapterInfo=new ao(await r.requestAdapterInfo()),this.gpuDataManager=$a(this),this.programManager=new cn(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,ga(t.logLevel,!!t.debug),this.device.onuncapturederror=s=>{s.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${s.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:r,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(),r={};this.queryType==="at-passes"&&(r.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=t.beginComputePass(r)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Mt(),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 r=new BigUint64Array(t.getMappedRange()),o=this.pendingQueries.get(t);for(let n=0;n<r.length/2;n++){let s=o[n],u=s.kernelId,l=this.kernels.get(u),a=l.kernelType,p=l.kernelName,m=s.programName,f=s.inputTensorViews,b=s.outputTensorViews,_=r[n*2],y=r[n*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=_);let $=Number(_-this.queryTimeBase),I=Number(y-this.queryTimeBase);if(!Number.isSafeInteger($)||!Number.isSafeInteger(I))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:f.map(C=>({dims:C.dims,dataType:Tt(C.dataType)})),outputsMetadata:b.map(C=>({dims:C.dims,dataType:Tt(C.dataType)})),kernelId:u,kernelType:a,kernelName:p,programName:m,startTime:$,endTime:I});else{let C="";f.forEach((A,T)=>{C+=`input[${T}]: [${A.dims}] | ${Tt(A.dataType)}, `});let v="";b.forEach((A,T)=>{v+=`output[${T}]: [${A.dims}] | ${Tt(A.dataType)}, `}),console.log(`[profiling] kernel "${u}|${a}|${p}|${m}" ${C}${v}execution time: ${I-$} ns`)}kn("GPU",`${m}::${_}::${y}`)}t.unmap(),this.pendingQueries.delete(t)}),Et()}run(t,r,o,n,s){Mt(t.name);let u=[];for(let v=0;v<r.length;++v){let A=r[v].data;if(A===0)continue;let T=this.gpuDataManager.get(A);if(!T)throw new Error(`no GPU data for input: ${A}`);u.push(T)}let{outputs:l,dispatchGroup:a,programUniforms:p}=t.getRunData(r),m=o.length===0?l.map((v,A)=>A):o;if(m.length!==l.length)throw new Error(`Output size ${m.length} must be equal to ${l.length}.`);let f=[],b=[];for(let v=0;v<l.length;++v){if(!Number.isInteger(m[v])||m[v]<-3||m[v]>=l.length)throw new Error(`Invalid output index: ${m[v]}`);if(m[v]===-3)continue;let A=m[v]===-1,T=m[v]===-2,D=A||T?s(l[v].dataType,l[v].dims):n(m[v],l[v].dataType,l[v].dims);if(f.push(D),D.data===0)continue;let U=this.gpuDataManager.get(D.data);if(!U)throw new Error(`no GPU data for output: ${D.data}`);if(A&&this.temporaryData.push(U),T){let V=this.kernelPersistentData.get(this.currentKernelId);V||(V=[],this.kernelPersistentData.set(this.currentKernelId,V)),V.push(U)}b.push(U)}if(u.length!==r.length||b.length!==f.length){if(b.length===0)return Et(t.name),f;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let _;if(p){let v=0,A=[];p.forEach(V=>{let H=typeof V.data=="number"?[V.data]:V.data;if(H.length===0)return;let R=V.type===10?2:4,L,pe;V.type===10?(pe=H.length>4?16:H.length>2?8:H.length*R,L=H.length>4?16:R*H.length):(pe=H.length<=2?H.length*R:16,L=16),v=Math.ceil(v/pe)*pe,A.push(v);let Ie=V.type===10?8:4;v+=H.length>4?Math.ceil(H.length/Ie)*L:H.length*R});let T=16;v=Math.ceil(v/T)*T;let D=new ArrayBuffer(v);p.forEach((V,H)=>{let R=A[H],L=typeof V.data=="number"?[V.data]:V.data;if(V.type===6)new Int32Array(D,R,L.length).set(L);else if(V.type===12)new Uint32Array(D,R,L.length).set(L);else if(V.type===10)new Uint16Array(D,R,L.length).set(L);else if(V.type===1)new Float32Array(D,R,L.length).set(L);else throw new Error(`Unsupported uniform type: ${Tt(V.type)}`)});let U=this.gpuDataManager.create(v,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(U.buffer,0,D,0,v),this.gpuDataManager.release(U.id),_={offset:0,size:v,buffer:U.buffer}}let y=this.programManager.normalizeDispatchGroupSize(a),$=y[1]===1&&y[2]===1,I=op(t,r,$),C=this.programManager.getArtifact(I);if(C||(C=this.programManager.build(t,y),this.programManager.setArtifact(I,C),De("info",()=>`[artifact] key: ${I}, programName: ${t.name}`)),De("info",()=>`[ProgramManager] run "${t.name}" (key=${I}) with ${y[0]}x${y[1]}x${y[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let v={kernelId:this.currentKernelId,programName:C.programInfo.name,inputTensorViews:r,outputTensorViews:f};this.pendingKernels.push(v),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(v)}return this.programManager.run(C,u,b,y,_),Et(t.name),f}upload(t,r){this.gpuDataManager.upload(t,r)}memcpy(t,r){this.gpuDataManager.memcpy(t,r)}async download(t,r){await this.gpuDataManager.download(t,r)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,r,o,n){let s=Mu.get(t);if(!s)throw new Error(`kernel not implemented: ${t}`);let u={kernelType:t,kernelName:n,kernelEntry:s[0],attributes:[s[1],o]};this.kernels.set(r,u)}releaseKernel(t){let r=this.kernelPersistentData.get(t);if(r){for(let o of r)this.gpuDataManager.release(o.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,r,o){let n=this.kernels.get(t);if(!n)throw new Error(`kernel not created: ${t}`);let s=n.kernelType,u=n.kernelName,l=n.kernelEntry,a=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${s}] ${u}" is not allowed to be called recursively`);this.currentKernelId=t,a[0]&&(a[1]=a[0](a[1]),a[0]=void 0),De("info",()=>`[WebGPU] Start to run kernel "[${s}] ${u}"...`);let p=this.env.debug;this.temporaryData=[];try{return p&&this.device.pushErrorScope("validation"),l(r,a[1]),0}catch(m){return o.push(Promise.resolve(`[WebGPU] Kernel "[${s}] ${u}" failed. ${m}`)),1}finally{p&&o.push(this.device.popErrorScope().then(m=>m?`GPU validation error for kernel "[${s}] ${u}": ${m.message}`:null));for(let m of this.temporaryData)this.gpuDataManager.release(m.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,r,o,n){let s=this.sessionExternalDataMapping.get(t);s||(s=new Map,this.sessionExternalDataMapping.set(t,s));let u=s.get(r),l=this.gpuDataManager.registerExternalBuffer(o,n,u?.[1]);return s.set(r,[l,o]),l}unregisterBuffers(t){let r=this.sessionExternalDataMapping.get(t);r&&(r.forEach(o=>this.gpuDataManager.unregisterExternalBuffer(o[1])),this.sessionExternalDataMapping.delete(t))}getBuffer(t){let r=this.gpuDataManager.get(t);if(!r)throw new Error(`no GPU data for buffer: ${t}`);return r.buffer}createDownloader(t,r,o){return async()=>{let n=await Dn(this,t,r);return ya(n.buffer,o)}}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(){De("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(){De("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){De("info","replay"),this.sessionStatus="replaying";let t=this.capturedCommandList.get(this.currentSessionId),r=this.capturedPendingKernels.get(this.currentSessionId),o=t.length;this.pendingKernels=[];for(let n=0;n<o;n++){let s=this.getComputePassEncoder(),u=t[n];this.writeTimestamp(this.pendingDispatchNumber*2),s.setPipeline(u.computePipeline),s.setBindGroup(0,u.bindGroup),s.dispatchWorkgroups(...u.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(r[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()}}});var Nu={};Mr(Nu,{init:()=>ap});var wr,io,ap,Wu=q(()=>{"use strict";ie();Vu();Pt();_e();wr=class e{constructor(t,r,o,n){this.module=t;this.dataType=r;this.data=o;this.dims=n}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let t=z.size(this.dims);return t===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,t)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let t=z.size(this.dims);return t===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let t=z.size(this.dims);return t===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(z.size(t)!==z.size(this.dims))throw new Error("Invalid new shape");return new e(this.module,this.dataType,this.data,t)}},io=class{constructor(t,r,o){this.module=t;this.backend=r;this.customDataOffset=0;this.customDataSize=0;this.adapterInfo=r.adapterInfo;let n=t.HEAPU32,s=o>>>2;this.opKernelContext=n[s++];let u=n[s++];this.outputCount=n[s++],this.customDataOffset=n[s++],this.customDataSize=n[s++];let l=[];for(let a=0;a<u;a++){let p=n[s++],m=n[s++],f=n[s++],b=[];for(let _=0;_<f;_++)b.push(n[s++]);l.push(new wr(t,p,m,b))}this.inputs=l}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(t,r){let o=r?.inputs?.map(l=>typeof l=="number"?this.inputs[l]:l)??this.inputs,n=r?.outputs??[],s=(l,a,p)=>new wr(this.module,a,this.output(l,p),p),u=(l,a)=>{let p=fr(l);if(!p)throw new Error(`Unsupported data type: ${l}`);let m=p*z.size(a),f=m>0?this.backend.gpuDataManager.create(m).id:0;return new wr(this.module,l,f,a)};return this.backend.run(t,o,n,s,u)}output(t,r){let o=this.module.stackSave();try{let n=this.module.stackAlloc((1+r.length)*4),s=n>>2;this.module.HEAPU32[s++]=r.length;for(let u=0;u<r.length;u++)this.module.HEAPU32[s++]=r[u];return this.module._JsepOutput(this.opKernelContext,t,n)}catch(n){throw new Error(`Failed to generate kernel\'s output[${t}] with dims [${r}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(o)}}},ap=async(e,t,r,o)=>{let n=t.jsepInit;if(!n)throw new Error("Failed to initialize JSEP. 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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'}),ur,St,jr,kn,Cn,ds,Ba,vr,$r,Kd,Tn,Nf,Lf,Uf,Ff,Wf,Gf,Vf,Hf=X(()=>{Ft(),v0(),qn(),ur=()=>!!De.wasm.proxy&&typeof document<"u",jr=!1,kn=!1,Cn=!1,Ba=new Map,vr=(t,e)=>{let r=Ba.get(t);r?r.push(e):Ba.set(t,[e])},$r=()=>{if(jr||!kn||Cn||!St)throw new Error("worker not ready")},Kd=t=>{switch(t.data.type){case"init-wasm":jr=!1,t.data.err?(Cn=!0,ds[1](t.data.err)):(kn=!0,ds[0]());break;case"init-ep":case"copy-from":case"create":case"release":case"run":case"end-profiling":{let e=Ba.get(t.data.type);t.data.err?e.shift()[1](t.data.err):e.shift()[0](t.data.out);break}}},Tn=typeof document<"u"?document?.currentScript?.src:void 0,Nf=async()=>{if(!kn){if(jr)throw new Error("multiple calls to 'initWasm()' detected.");if(Cn)throw new Error("previous call to 'initWasm()' failed.");if(jr=!0,ur())return De.wasm.wasmPaths===void 0&&Tn&&Tn.indexOf("blob:")!==0&&(De.wasm.wasmPaths=Tn.substr(0,+Tn.lastIndexOf("/")+1)),new Promise((t,e)=>{St?.terminate();let r=URL.createObjectURL(new Blob([$0()],{type:"text/javascript"}));St=new Worker(r,{name:"ort-wasm-proxy-worker"}),St.onerror=a=>e(a),St.onmessage=Kd,URL.revokeObjectURL(r),ds=[t,e];let n={type:"init-wasm",in:De};St.postMessage(n)});try{await ep(De.wasm),await Mf(De),kn=!0}catch(t){throw Cn=!0,t}finally{jr=!1}}},Lf=async t=>{if(ur())return $r(),new Promise((e,r)=>{vr("init-ep",[e,r]);let n={type:"init-ep",in:{epName:t,env:De}};St.postMessage(n)});await Of(De,t)},Uf=async t=>ur()?($r(),new Promise((e,r)=>{vr("copy-from",[e,r]);let n={type:"copy-from",in:{buffer:t}};St.postMessage(n,[t.buffer])})):Ps(t),Ff=async(t,e)=>{if(ur()){if(e?.preferredOutputLocation)throw new Error('session option "preferredOutputLocation" is not supported for proxy.');return $r(),new Promise((r,n)=>{vr("create",[r,n]);let a={type:"create",in:{model:t,options:{...e}}},i=[];t instanceof Uint8Array&&i.push(t.buffer),St.postMessage(a,i)})}else return zf(t,e)},Wf=async t=>{if(ur())return $r(),new Promise((e,r)=>{vr("release",[e,r]);let n={type:"release",in:t};St.postMessage(n)});Pf(t)},Gf=async(t,e,r,n,a,i)=>{if(ur()){if(r.some(s=>s[3]!=="cpu"))throw new Error("input tensor on GPU is not supported for proxy.");if(a.some(s=>s))throw new Error("pre-allocated output tensor is not supported for 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fetchModelAndCopyToWasmMemory(t){return Uf(await ti(t))}async loadModel(t,e){Ut();let r;typeof t=="string"?typeof process<"u"&&process.versions&&process.versions.node?r=await ti(t):r=await this.fetchModelAndCopyToWasmMemory(t):r=t,[this.sessionId,this.inputNames,this.outputNames]=await Ff(r,e),Tt()}async dispose(){return Wf(this.sessionId)}async run(t,e,r){Ut();let n=[],a=[];Object.entries(t).forEach(u=>{let h=u[0],m=u[1],g=this.inputNames.indexOf(h);if(g===-1)throw new Error(`invalid input '${h}'`);n.push(m),a.push(g)});let i=[],s=[];Object.entries(e).forEach(u=>{let h=u[0],m=u[1],g=this.outputNames.indexOf(h);if(g===-1)throw new Error(`invalid output '${h}'`);i.push(m),s.push(g)});let o=n.map((u,h)=>cs(u,()=>`input "${this.inputNames[a[h]]}"`)),l=i.map((u,h)=>u?cs(u,()=>`output "${this.outputNames[s[h]]}"`):null),d=await Gf(this.sessionId,a,o,s,l,r),c={};for(let u=0;u<d.length;u++)c[this.outputNames[s[u]]]=i[u]??Yd(d[u]);return Tt(),c}startProfiling(){}endProfiling(){Vf(this.sessionId)}}}),Xd,jf,S0=X(()=>{Ft(),Hf(),x0(),Xd=()=>{if((typeof De.wasm.initTimeout!="number"||De.wasm.initTimeout<0)&&(De.wasm.initTimeout=0),typeof De.wasm.simd!="boolean"&&(De.wasm.simd=!0),typeof De.wasm.proxy!="boolean"&&(De.wasm.proxy=!1),typeof De.wasm.trace!="boolean"&&(De.wasm.trace=!1),typeof De.wasm.numThreads!="number"||!Number.isInteger(De.wasm.numThreads)||De.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(De.wasm.numThreads=1);let t=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;De.wasm.numThreads=Math.min(4,Math.ceil((t||1)/2))}},jf=class{async init(t){Xd(),await Nf(),await Lf(t)}async createInferenceSessionHandler(t,e){let r=new qf;return await r.loadModel(t,e),Promise.resolve(r)}}}),Kf={};Zr(Kf,{wasmBackend:()=>Yf});var Yf,E0=X(()=>{S0(),Yf=new jf});Ft();Ft();Ft();var k0="1.17.3",C0=Hc;{let t=(E0(),Mr(Kf)).wasmBackend;Ar("webgpu",t,5),Ar("webnn",t,5),Ar("cpu",t,10),Ar("wasm",t,10)}Object.defineProperty(De.versions,"web",{value:k0,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 T0=Object.freeze(Object.defineProperty({__proto__:null,get InferenceSession(){return qs},get TRACE(){return Nn},get TRACE_FUNC_BEGIN(){return Ut},get TRACE_FUNC_END(){return Tt},get Tensor(){return ft},get TrainingSession(){return js},default:C0,get env(){return De},get registerBackend(){return Ar}},Symbol.toStringTag,{value:"Module"})),I0=(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 l,d;o===void 0||o.mean===void 0?l=[255,255,255,255]:typeof o.mean=="number"?l=[o.mean,o.mean,o.mean,o.mean]:(l=[o.mean[0],o.mean[1],o.mean[2],0],o.mean[3]!==void 0&&(l[3]=o.mean[3])),o===void 0||o.bias===void 0?d=[0,0,0,0]:typeof 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defined");if(e.height===void 0||e.width===void 0)throw new Error("Image height and width must be defined");if(e.tensorLayout==="NHWC")throw new Error("NHWC Tensor layout is not supported yet");const{height:r,width:n}=e,a=e.norm??{mean:255,bias:0};let i,s;typeof a.mean=="number"?i=[a.mean,a.mean,a.mean,a.mean]:i=[a.mean[0],a.mean[1],a.mean[2],a.mean[3]??255],typeof a.bias=="number"?s=[a.bias,a.bias,a.bias,a.bias]:s=[a.bias[0],a.bias[1],a.bias[2],a.bias[3]??0];const o=e.format!==void 0?e.format:"RGBA",l=e.tensorFormat!==void 0&&e.tensorFormat!==void 0?e.tensorFormat:"RGB",d=r*n,c=l==="RGBA"?new Float32Array(d*4):new Float32Array(d*3);let u=4,h=0,m=1,g=2,_=3,b=0,$=d,y=d*2,E=-1;o==="RGB"&&(u=3,h=0,m=1,g=2,_=-1),l==="RGBA"?E=d*3:l==="RBG"?(b=0,y=d,$=d*2):l==="BGR"&&(y=0,$=d,b=d*2);for(let T=0;T<d;T++,h+=u,g+=u,m+=u,_+=u)c[b++]=(t[h]+s[0])/i[0],c[$++]=(t[m]+s[1])/i[1],c[y++]=(t[g]+s[2])/i[2],E!==-1&&_!==-1&&(c[E++]=(t[_]+s[3])/i[3]);return l==="RGBA"?new qt("float32",c,[1,4,r,n]):new qt("float32",c,[1,3,r,n])},M0=async(t,e)=>{const r=typeof HTMLImageElement<"u"&&t instanceof HTMLImageElement,n=typeof ImageData<"u"&&t instanceof ImageData,a=typeof ImageBitmap<"u"&&t instanceof ImageBitmap,i=typeof t=="string";let s,o=e??{};const l=()=>{if(typeof document<"u")return document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},d=c=>c instanceof HTMLCanvasElement||c instanceof OffscreenCanvas?c.getContext("2d"):null;if(r){const c=l();c.width=t.width,c.height=t.height;const u=d(c);if(u!=null){let h=t.height,m=t.width;if(e!==void 0&&e.resizedHeight!==void 0&&e.resizedWidth!==void 0&&(h=e.resizedHeight,m=e.resizedWidth),e!==void 0){if(o=e,e.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");o.tensorFormat="RGBA",o.height=h,o.width=m}else o.tensorFormat="RGBA",o.height=h,o.width=m;u.drawImage(t,0,0),s=u.getImageData(0,0,m,h).data}else throw new Error("Can not access image data")}else if(n){let c,u;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(c=e.resizedHeight,u=e.resizedWidth):(c=t.height,u=t.width),e!==void 0&&(o=e),o.format="RGBA",o.height=c,o.width=u,e!==void 0){const h=l();h.width=u,h.height=c;const m=d(h);if(m!=null)m.putImageData(t,0,0),s=m.getImageData(0,0,u,c).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 c=l();c.width=t.width,c.height=t.height;const u=d(c);if(u!=null){const h=t.height,m=t.width;return u.drawImage(t,0,0,m,h),s=u.getImageData(0,0,m,h).data,o.height=h,o.width=m,ps(s,o)}else throw new Error("Can not access image data")}else{if(i)return new Promise((c,u)=>{const h=l(),m=d(h);if(!t||!m)return u();const g=new Image;g.crossOrigin="Anonymous",g.src=t,g.onload=()=>{h.width=g.width,h.height=g.height,m.drawImage(g,0,0,h.width,h.height);const _=m.getImageData(0,0,h.width,h.height);o.height=h.height,o.width=h.width,c(ps(_.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return ps(s,o);throw new Error("Input data provided is not supported - aborted tensor creation")},O0=(t,e)=>{const{width:r,height:n,download:a,dispose:i}=e,s=[1,n,r,4];return new qt({location:"texture",type:"float32",texture:t,dims:s,download:a,dispose:i})},z0=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:i}=e;return new qt({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:i})},P0=(t,e,r)=>new qt({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),li=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["float16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),Rs=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let Qd=!1;const R0=()=>{if(!Qd){Qd=!0;const t=typeof BigInt64Array<"u"&&typeof BigInt64Array.from=="function",e=typeof BigUint64Array<"u"&&typeof BigUint64Array.from=="function";t&&(li.set("int64",BigInt64Array),Rs.set(BigInt64Array,"int64")),e&&(li.set("uint64",BigUint64Array),Rs.set(BigUint64Array,"uint64"))}},B0=t=>{let e=1;for(let r=0;r<t.length;r++){const n=t[r];if(typeof n!="number"||!Number.isSafeInteger(n))throw new TypeError(`dims[${r}] must be an integer, got: ${n}`);if(n<0)throw new RangeError(`dims[${r}] must be a non-negative integer, got: ${n}`);e*=n}return e},D0=(t,e)=>{switch(t.location){case"cpu":return new qt(t.type,t.data,e);case"cpu-pinned":return new qt({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new qt({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new qt({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let qt=class{constructor(e,r,n){R0();let a,i;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,a=e.type,i=e.dims,e.location){case"cpu-pinned":{const o=li.get(a);if(!o)throw new TypeError(`unsupported type "${a}" to create tensor from pinned buffer`);if(!(e.data instanceof o))throw new TypeError(`buffer should be of type ${o.name}`);this.cpuData=e.data;break}case"texture":{if(a!=="float32")throw new TypeError(`unsupported type "${a}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(a!=="float32"&&a!=="float16"&&a!=="int32"&&a!=="int64"&&a!=="uint32"&&a!=="bool")throw new TypeError(`unsupported type "${a}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let o,l;if(typeof e=="string")if(a=e,l=n,e==="string"){if(!Array.isArray(r))throw new TypeError("A string tensor's data must be a string array.");o=r}else{const d=li.get(e);if(d===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(r)){if(e==="float16")throw new TypeError("Creating a float16 tensor from number array is not supported. 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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}}const ec=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 ae{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}ort_tensor;constructor(...e){return 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We(r.value.size);default:throw new Error(`Unknown ObjectValue filter: ${n.value}`)}throw new Error(`Cannot apply filter "${n.value}" to type: ${r.type}`)}else if(t.filter.type==="CallExpression"){const n=t.filter;if(n.callee.type!=="Identifier")throw new Error(`Unknown filter: ${n.callee.type}`);const a=n.callee.value;if(r instanceof Ye){switch(a){case"selectattr":{if(r.value.some(c=>!(c instanceof Dt)))throw new Error("`selectattr` can only be applied to array of objects");if(n.args.some(c=>c.type!=="StringLiteral"))throw new Error("arguments of `selectattr` must be strings");const[i,s,o]=n.args.map(c=>this.evaluate(c,e));let l;if(s){const c=e.tests.get(s.value);if(!c)throw new Error(`Unknown test: ${s.value}`);l=c}else l=(...c)=>c[0].__bool__().value;const d=r.value.filter(c=>{const u=c.value.get(i.value);return u?l(u,o):!1});return new Ye(d)}}throw new Error(`Unknown ArrayValue filter: ${a}`)}else throw new Error(`Cannot apply filter "${a}" to type: ${r.type}`)}throw new Error(`Unknown filter: ${t.filter.type}`)}evaluateTestExpression(t,e){const r=this.evaluate(t.operand,e),n=e.tests.get(t.test.value);if(!n)throw new Error(`Unknown test: ${t.test.value}`);const a=n(r);return new Ze(t.negate?!a:a)}evaluateUnaryExpression(t,e){const r=this.evaluate(t.argument,e);switch(t.operator.value){case"not":return new Ze(!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 Ae(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 Dt(n));const a=this.evaluate(t.callee,e);if(a.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${a.type}`);return a.value(r,e)}evaluateSliceExpression(t,e,r){if(!(t instanceof Ye||t instanceof Ae))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 We||n instanceof Bt))throw new Error("Slice start must be numeric or undefined");if(!(a instanceof We||a instanceof Bt))throw new Error("Slice stop must be numeric or undefined");if(!(i instanceof We||i instanceof Bt))throw new Error("Slice step must be numeric or undefined");return t instanceof Ye?new Ye(dc(t.value,n.value,a.value,i.value)):new Ae(dc(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 Ae(t.property.value);let a;if(r instanceof Dt){if(!(n instanceof Ae))throw new Error(`Cannot access property with non-string: got ${n.type}`);a=r.value.get(n.value)??r.builtins.get(n.value)}else if(r instanceof Ye||r instanceof Ae)if(n instanceof We)a=r.value.at(n.value),r instanceof Ae&&(a=new Ae(r.value.at(n.value)));else if(n instanceof Ae)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 Ae))throw new Error(`Cannot access property with non-string: got ${n.type}`);a=r.builtins.get(n.value)}return a instanceof tr?a:new Bt}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 Dt))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 Rn}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 Ds(e),n=this.evaluate(t.iterable,r);if(!(n instanceof Ye))throw new Error(`Expected iterable type in for loop: got ${n.type}`);let a="";for(let i=0;i<n.value.length;++i){const s=new Map([["index",new We(i+1)],["index0",new We(i)],["revindex",new We(n.value.length-i)],["revindex0",new We(n.value.length-i-1)],["first",new Ze(i===0)],["last",new Ze(i===n.value.length-1)],["length",new We(n.value.length)],["previtem",i>0?n.value[i-1]:new Bt],["nextitem",i<n.value.length-1?n.value[i+1]:new Bt]]);r.setVariable("loop",new Dt(s));const o=n.value[i];if(t.loopvar.type==="Identifier")r.setVariable(t.loopvar.value,o);else if(t.loopvar.type==="TupleLiteral"){const d=t.loopvar;if(o.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${o.type}`);const c=o;if(d.value.length!==c.value.length)throw new Error(`Too ${d.value.length>c.value.length?"few":"many"} items to unpack`);for(let u=0;u<d.value.length;++u){if(d.value[u].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${d.value[u].type}`);r.setVariable(d.value[u].value,c.value[u])}}const l=this.evaluateBlock(t.body,r);a+=l.value}return new Ae(a)}evaluate(t,e){if(t===void 0)return new Bt;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 We(Number(t.value));case"StringLiteral":return new Ae(t.value);case"BooleanLiteral":return new Ze(t.value);case"ArrayLiteral":return new Ye(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 Ae))throw new Error(`Object keys must be strings: got ${i.type}`);r.set(i.value,this.evaluate(a,e))}return new Dt(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 Ka(t){switch(typeof t){case"number":return new We(t);case"string":return new Ae(t);case"boolean":return new Ze(t);case"object":return t===null?new Rn:Array.isArray(t)?new Ye(t.map(Ka)):new Dt(new Map(Object.entries(t).map(([e,r])=>[e,Ka(r)])));case"function":return new Jt((e,r)=>{const n=t(...e.map(a=>a.value))??null;return Ka(n)});default:throw new Error(`Cannot convert to runtime value: ${t}`)}}var $w=class{parsed;constructor(t){const e=aw(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=ww(e)}render(t){const e=new Ds;e.set("false",!1),e.set("true",!0),e.set("raise_exception",a=>{throw new Error(a)}),e.set("range",yw);for(const[a,i]of Object.entries(t))e.set(a,i);return new vw(e).run(this.parsed).value}};async function em(t,e){const r=await Promise.all([pr(t,"tokenizer.json",!0,e),pr(t,"tokenizer_config.json",!0,e)]);return e.legacy!==null&&(r[1].legacy=e.legacy),r}function xw(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 mi(t,e=!0){if(t.Regex!==void 0){let r=t.Regex.replace(/\\([#&~])/g,"$1");for(const[n,a]of Cw)r=r.replaceAll(n,a);return new RegExp(r,"gu")}else if(t.String!==void 0){const r=Sc(t.String);return new RegExp(e?r:`(${r})`,"gu")}else return console.warn("Unknown pattern type:",t),null}function ho(t){return new Map(Object.entries(t))}function tm(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 fo(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 rm(t){return t.replace(/[\u0300-\u036f]/g,"")}function Sw(t){return rm(t.toLowerCase())}function Ew(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 kw(t){return t.match(/\S+/g)||[]}const Wn="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",Cw=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class Tw{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 Yn extends mt{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 Iw(e);case"Unigram":return new Aw(e,...r);case"BPE":return new Ow(e);default:if(e.vocab)return new zw(e,...r);throw new Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){let r=this.encode(e);return this.fuse_unk&&(r=Ew(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 Iw extends Yn{constructor(e){super(e),this.tokens_to_ids=ho(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 l=a.length,d=null;for(;s<l;){let c=a.slice(s,l).join("");if(s>0&&(c=this.config.continuing_subword_prefix+c),this.tokens_to_ids.has(c)){d=c;break}--l}if(d===null){i=!0;break}o.push(d),s=l}i?r.push(this.unk_token):r.push(...o)}return r}}class Aw extends Yn{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=kc(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new J0,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 l=this.tokens_to_ids.get(o),d=this.scores[l],c=o.length;e.insert(a,c,d,l),!s&&c===1&&(s=!0)}s||e.insert(a,1,this.unkScore,this.unk_token_id),a+=1}}tokenize(e){const r=new ew(e,this.bosTokenId,this.eosTokenId);return this.populateNodes(r),r.tokens()}encode(e){const r=[];for(const n of e){const a=this.tokenize(n);r.push(...a)}return r}}const nm=(()=>{const t=[...Array.from({length:94},(a,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]]))})(),Mw=Xg(nm);class Ow extends Yn{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=ho(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r;this.bpe_ranks=new Map(e.merges.map((r,n)=>[r,n])),this.merges=e.merges.map(r=>r.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(e){if(e.length===0)return[];const r=this.cache.get(e);if(r!==void 0)return r;const n=Array.from(e);this.end_of_word_suffix&&(n[n.length-1]+=this.end_of_word_suffix);let a=[];if(n.length>1){const i=new Z0((l,d)=>l.score<d.score);let s={token:n[0],bias:0,prev:null,next:null},o=s;for(let l=1;l<n.length;++l){const d={bias:l/n.length,token:n[l],prev:o,next:null};o.next=d,this._add_node(i,o),o=d}for(;!i.isEmpty();){const l=i.pop();if(l.deleted||!l.next||l.next.deleted)continue;if(l.deleted=!0,l.next.deleted=!0,l.prev){const c={...l.prev};l.prev.deleted=!0,l.prev=c,c.prev?c.prev.next=c:s=c}const d={token:l.token+l.next.token,bias:l.bias,prev:l.prev,next:l.next.next};d.prev?(d.prev.next=d,this._add_node(i,d.prev)):s=d,d.next&&(d.next.prev=d,this._add_node(i,d))}for(let l=s;l!==null;l=l.next)a.push(l.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){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 zw extends Yn{constructor(e,r){super(e),this.tokens_to_ids=ho(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 vt extends mt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new Gw(e);case"Precompiled":return new ly(e);case"Sequence":return new Ww(e);case"Replace":return new Pw(e);case"NFC":return new Rw(e);case"NFKC":return new Bw(e);case"NFKD":return new Dw(e);case"Strip":return new Nw(e);case"StripAccents":return new Lw(e);case"Lowercase":return new Uw(e);case"Prepend":return new Fw(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class Pw extends vt{normalize(e){const r=mi(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class Rw extends vt{normalize(e){return e=e.normalize("NFC"),e}}class Bw extends vt{normalize(e){return e=e.normalize("NFKC"),e}}class Dw extends vt{normalize(e){return e=e.normalize("NFKD"),e}}class Nw extends vt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class Lw extends vt{normalize(e){return e=rm(e),e}}class Uw extends vt{normalize(e){return e=e.toLowerCase(),e}}class Fw extends vt{normalize(e){return e=this.config.prepend+e,e}}class Ww extends vt{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>vt.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class Gw extends vt{_tokenize_chinese_chars(e){const r=[];for(let n=0;n<e.length;++n){const a=e[n],i=a.charCodeAt(0);this._is_chinese_char(i)?(r.push(" "),r.push(a),r.push(" ")):r.push(a)}return r.join("")}_is_chinese_char(e){return e>=19968&&e<=40959||e>=13312&&e<=19903||e>=131072&&e<=173791||e>=173824&&e<=177983||e>=177984&&e<=178207||e>=178208&&e<=183983||e>=63744&&e<=64255||e>=194560&&e<=195103}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(e){switch(e){case"	":case`
`:case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/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 It extends mt{static fromConfig(e){if(e===null)return null;switch(e.type){case"BertPreTokenizer":return new Vw(e);case"Sequence":return new uy(e);case"Whitespace":return new dy(e);case"WhitespaceSplit":return new cy(e);case"Metaspace":return new sm(e);case"ByteLevel":return new Hw(e);case"Split":return new qw(e);case"Punctuation":return new jw(e);case"Digits":return new Kw(e);case"Replace":return new py(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,r){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,r){return(Array.isArray(e)?e.map(n=>this.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class Vw extends It{constructor(e){super(),this.pattern=new RegExp(`[^\\s${Wn}]+|[${Wn}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class Hw extends It{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=nm,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),i=>this.byte_encoder[i]).join(""))}}class qw extends It{constructor(e){super(),this.config=e,this.pattern=mi(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:xw(e,this.pattern)}}class jw extends It{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${Wn}]+|[${Wn}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Kw extends It{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class gi extends mt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new Yw(e);case"ByteLevel":return new Xw(e);case"RobertaProcessing":return new im(e);case"BertProcessing":return new am(e);default:throw new Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...r){throw Error("post_process should be implemented in subclass.")}_call(e,...r){return this.post_process(e,...r)}}class am extends gi{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=st([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const i=n&&this instanceof im?[this.sep]:[],s=n?[this.sep]:[];e=st(e,i,r,s),a=st(a,new Array(r.length+i.length+s.length).fill(1))}return{tokens:e,token_type_ids:a}}}class im extends am{}class Yw extends gi{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=st(i,e),s=st(s,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(i=st(i,r),s=st(s,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:i,token_type_ids:s}}}class Xw extends gi{post_process(e,r=null){return r&&(e=st(e,r)),{tokens:e}}}class $t extends mt{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 ty(e);case"Metaspace":return new oy(e);case"ByteLevel":return new ry(e);case"Replace":return new Qw(e);case"ByteFallback":return new Zw(e);case"Fuse":return new Jw(e);case"Strip":return new ey(e);case"Sequence":return new ay(e);case"CTC":return new ny(e);case"BPEDecoder":return new iy(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 Qw extends $t{decode_chain(e){const r=mi(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class Zw extends $t{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 Jw extends $t{decode_chain(e){return[e.join("")]}}class ey extends $t{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 ty extends $t{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=fo(r)),r))}}class ry extends $t{constructor(e){super(e),this.byte_decoder=Mw,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 ny extends $t{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=fo(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class ay extends $t{constructor(e){super(e),this.decoders=e.decoders.map(r=>$t.fromConfig(r))}decode_chain(e){return this.decoders.reduce((r,n)=>n.decode_chain(r),e)}}class iy extends $t{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 sy extends $t{decode_chain(e){let r="";for(let n=1;n<e.length;n+=2)r+=e[n];return[r]}}class sm extends It{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 oy extends $t{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 ly extends vt{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 uy extends It{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>It.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class dy extends It{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class cy extends It{constructor(e){super()}pre_tokenize_text(e,r){return kw(e)}}class py extends It{constructor(e){super(),this.config=e,this.pattern=mi(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 hy=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function fy(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"?st(t[a],o):st(o,t[a])}}function my(t,e){for(const r of Object.keys(t))t[r].length=e}class xe extends mt{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=vt.fromConfig(e.normalizer),this.pre_tokenizer=It.fromConfig(e.pre_tokenizer),this.model=Yn.fromConfig(e.model,r),this.post_processor=gi.fromConfig(e.post_processor),this.decoder=$t.fromConfig(e.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const n of e.added_tokens){const a=new Tw(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=>`${n.lstrip?"\\s*":""}(${Sc(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 l=await em(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,legacy:o});return new this(...l)}_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}={}){const l=Array.isArray(e);let d;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");d=e.map((u,h)=>this._encode_plus(u,{text_pair:r[h],add_special_tokens:n}))}else d=e.map(u=>this._encode_plus(u,{add_special_tokens:n}))}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).");d=[this._encode_plus(e,{text_pair:r,add_special_tokens:n})]}if(s===null?a==="max_length"?s=this.model_max_length:s=mr(d.map(u=>u.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 u=0;u<d.length;++u)d[u].input_ids.length!==s&&(d[u].input_ids.length>s?i&&my(d[u],s):a&&fy(d[u],s,h=>h==="input_ids"?this.pad_token_id:0,this.padding_side));const c={};if(o){if(!(a&&i)&&d.some(h=>{for(const m of Object.keys(h))if(h[m].length!==d[0][m]?.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 u=[d.length,d[0].input_ids.length];for(const h of Object.keys(d[0]))c[h]=new ae("int64",BigInt64Array.from(d.flatMap(m=>m[h]).map(BigInt)),u)}else{for(const u of Object.keys(d[0]))c[u]=d.map(h=>h[u]);if(!l)for(const u of Object.keys(c))c[u]=c[u][0]}return c}_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=Sw(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}={}){const{tokens:a,token_type_ids:i}=this._tokenize_helper(e,{pair:r,add_special_tokens:n}),s=this.model.convert_tokens_to_ids(a),o={input_ids:s,attention_mask:new Array(s.length).fill(1)};return this.return_token_type_ids&&i&&(o.token_type_ids=i),o}_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:st(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 this._encode_plus(e,{text_pair:r,add_special_tokens:n}).input_ids}batch_decode(e,r={}){return e instanceof ae&&(e=e.tolist()),e.map(n=>this.decode(n,r))}decode(e,r={}){if(e instanceof ae&&(e=tm(e)),!Array.isArray(e)||e.length===0||!Qg(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=fo(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:l=!0,tokenizer_kwargs:d={},...c}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const g=this.chat_template??this.default_chat_template;if(r!==null&&Object.hasOwn(g,r))r=g[r];else if(r===null&&"default"in g)r=g.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(g).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 u=this._compiled_template_cache.get(r);u===void 0&&(u=new $w(r),this._compiled_template_cache.set(r,u));const h=Object.create(null);for(const g of hy){const _=this.getToken(g);_&&(h[g]=_)}const m=u.render({messages:e,add_generation_prompt:n,...h,...c});return a?this._call(m,{add_special_tokens:!1,padding:i,truncation:s,max_length:o,return_tensor:l,...d}).input_ids:m}}class gy extends xe{return_token_type_ids=!0}class _y extends xe{return_token_type_ids=!0}class wy extends xe{return_token_type_ids=!0}class yy extends xe{return_token_type_ids=!0}class by extends xe{return_token_type_ids=!0}class vy extends xe{return_token_type_ids=!0}class $y extends xe{return_token_type_ids=!0}class xy extends xe{return_token_type_ids=!0}class Sy extends xe{return_token_type_ids=!0}class Ey extends xe{}class ky extends xe{}class Cy extends xe{return_token_type_ids=!0;constructor(e,r){super(e,r),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Ty extends xe{return_token_type_ids=!0}class Iy extends xe{}class om extends xe{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}'}class Ay extends xe{}class lm extends xe{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,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 mo(this,e,r,n)}}class My extends lm{}class Oy extends xe{}class zy extends om{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 Da="▁";class um extends xe{_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 sm({replacement:Da,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(Da+e.replaceAll(Da," "));return r.length>1&&r[0]===Da&&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 Py extends um{}class Ry extends xe{}class By extends xe{}class Dy extends xe{}class Ny extends xe{}class Ly extends xe{}class Uy extends xe{}class Fy extends xe{_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 Wy extends xe{}function mo(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 Gy extends xe{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return mo(this,e,r,n)}}class Vy extends xe{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,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 mo(this,e,r,n)}}const dm=[["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"]],Na=new Map(dm),Hy=new Map([...dm.map(([t,e])=>[e,t]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class qy extends xe{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}';_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 l(){return{language:s,timestamp:[null,null],text:""}}const d=[];let c=l(),u=0;const h=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let m=[],g=[],_=!1,b=null;const $=new Set(this.all_special_ids);for(const k of e){const T=k.tokens,A=o?k.token_timestamps:null;let R=null,W=h;if("stride"in k){const[ee,P,J]=k.stride;if(u-=P,b=ee-J,P&&(W=P/a+h),J)for(let te=T.length-1;te>=0;--te){const F=T[te];if(F>=h){if(R!==null&&(F-h)*a<b)break;R=F}}}let K=[],q=[];for(let ee=0;ee<T.length;++ee){const P=T[ee];if($.has(P)){const J=this.decode([P]),te=Na.get(J.slice(2,-2));if(te!==void 0){if(s!==null&&te!==s&&!r){m.push(K);const F=this.findLongestCommonSequence(m)[0],O=this.decode(F);c.text=O,d.push(c),m=[],K=[],c=l()}s=c.language=te}}else if(P>=h){const J=(P-h)*a+u,te=Ca(J,2);if(R!==null&&P>=R)_=!0;else if(_||m.length>0&&P<W)_=!1;else if(c.timestamp[0]===null)c.timestamp[0]=te;else if(te!==c.timestamp[0]){c.timestamp[1]=te,m.push(K),o&&g.push(q);const[F,O]=this.findLongestCommonSequence(m,g),G=this.decode(F);c.text=G,o&&(c.words=this.collateWordTimestamps(F,O,s)),d.push(c),m=[],K=[],g=[],q=[],c=l()}}else if(K.push(P),o){let J=Ca(A[ee]+u,2),te;ee+1<A.length?te=Ca(A[ee+1]+u,2):te=null,q.push([J,te])}}if("stride"in k){const[ee,P,J]=k.stride;u+=ee-J}K.length>0?(m.push(K),o&&g.push(q)):m.every(ee=>ee.length===0)&&(c=l(),m=[],K=[],g=[],q=[])}if(m.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[k,T]=this.findLongestCommonSequence(m,g),A=this.decode(k);c.text=A,o&&(c.words=this.collateWordTimestamps(k,T,s)),d.push(c)}let y=Object.create(null);const E=d.map(k=>k.text).join("");if(r||n){for(let k=0;k<d.length;++k){const T=d[k];r||delete T.timestamp,n||delete T.language}if(o){const k=[];for(const T of d)for(const A of T.words)k.push(A);y={chunks:k}}else y={chunks:d}}return[E,y]}findLongestCommonSequence(e,r=null){let n=e[0],a=n.length,i=[];const s=Array.isArray(r)&&r.length>0;let o=s?[]:null,l=s?r[0]:null;for(let d=1;d<e.length;++d){const c=e[d];let u=0,h=[a,a,0,0];const m=c.length;for(let k=1;k<a+m;++k){const T=k/1e4,A=Math.max(0,a-k),R=Math.min(a,a+m-k),W=n.slice(A,R),K=Math.max(0,k-a),q=Math.min(m,k),ee=c.slice(K,q);if(W.length!==ee.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const P=W.filter((te,F)=>te===ee[F]).length,J=P/k+T;P>1&&J>u&&(u=J,h=[A,R,K,q])}const[g,_,b,$]=h,y=Math.floor((_+g)/2),E=Math.floor(($+b)/2);i.push(...n.slice(0,y)),n=c.slice(E),a=n.length,s&&(o.push(...l.slice(0,y)),l=r[d].slice(E))}return i.push(...n),s?(o.push(...l),[i,o]):[i,[]]}collateWordTimestamps(e,r,n){const[a,i,s]=this.combineTokensIntoWords(e,n),o=[];for(let l=0;l<a.length;++l){const d=s[l];o.push({text:a[l],timestamp:[r[d.at(0)][0],r[d.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&&r.decode_with_timestamps?(e instanceof ae&&(e=tm(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(const s of e)if(s>=a){const o=Ca((s-a)*n,2);i.push(`<|${o}|>`),i.push([])}else i[i.length-1].push(s);return i=i.map(s=>typeof s=="string"?s:super.decode(s,r)),i.join("")}splitTokensOnUnicode(e){const r=this.decode(e,{decode_with_timestamps:!0}),n="�",a=[],i=[],s=[];let o=[],l=[],d=0;for(let c=0;c<e.length;++c){const u=e[c];o.push(u),l.push(c);const h=this.decode(o,{decode_with_timestamps:!0});(!h.includes(n)||r[d+h.indexOf(n)]===n)&&(a.push(h),i.push(o),s.push(l),o=[],l=[],d+=h.length)}return[a,i,s]}splitTokensOnSpaces(e){const[r,n,a]=this.splitTokensOnUnicode(e),i=[],s=[],o=[],l=new RegExp(`^[${Wn}]$`,"gu");for(let d=0;d<r.length;++d){const c=r[d],u=n[d],h=a[d],m=u[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),g=c.startsWith(" "),_=c.trim(),b=l.test(_);if(m||g||b||i.length===0)i.push(c),s.push(u),o.push(h);else{const $=i.length-1;i[$]+=c,s[$].push(...u),o[$].push(...h)}}return[i,s,o]}mergePunctuations(e,r,n,a,i){const s=structuredClone(e),o=structuredClone(r),l=structuredClone(n);let d=s.length-2,c=s.length-1;for(;d>=0;)s[d].startsWith(" ")&&a.includes(s[d].trim())?(s[c]=s[d]+s[c],o[c]=st(o[d],o[c]),l[c]=st(l[d],l[c]),s[d]="",o[d]=[],l[d]=[]):c=d,--d;for(d=0,c=1;c<s.length;)!s[d].endsWith(" ")&&i.includes(s[c])?(s[d]+=s[c],o[d]=st(o[d],o[c]),l[d]=st(l[d],l[c]),s[c]="",o[c]=[],l[c]=[]):d=c,++c;return[s.filter(u=>u),o.filter(u=>u.length>0),l.filter(u=>u.length>0)]}get_decoder_prompt_ids({language:e=null,task:r=null,no_timestamps:n=!0}={}){const a=[];if(e){e=e.toLowerCase();let i=Hy.get(e);if(i===void 0)if(Na.has(e))i=e;else{const l=e.length===2?Na.keys():Na.values();throw new Error(`Language "${e}" is not supported. Must be one of: ${JSON.stringify(l)}`)}const s=this.model.tokens_to_ids.get(`<|${i}|>`);if(s===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);a.push(s)}else a.push(null);if(r){if(r=r.toLowerCase(),r!=="transcribe"&&r!=="translate")throw new Error(`Task "${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 https://github.com/xenova/transformers.js/issues/new/choose.`);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 https://github.com/xenova/transformers.js/issues/new/choose.');a.push(i)}return a.map((i,s)=>[s+1,i]).filter(i=>i[1]!==null)}}class jy extends xe{}class Ky extends xe{}class Yy extends xe{}class Xy extends xe{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: ${JSON.stringify(this.supported_language_codes)}`),st([a],super._encode_text(i))}}}class Qy extends xe{}class cm extends xe{_default_chat_template="{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}"}class Zy extends cm{}class Jy extends xe{}class eb extends xe{}class tb extends xe{constructor(e,r){super(e,r),this.decoder=new sy({})}}class rb extends xe{}class nb{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:Iy,DistilBertTokenizer:Ey,CamembertTokenizer:ky,DebertaTokenizer:by,DebertaV2Tokenizer:vy,BertTokenizer:gy,HerbertTokenizer:$y,ConvBertTokenizer:xy,RoFormerTokenizer:Sy,XLMTokenizer:Cy,ElectraTokenizer:Ty,MobileBertTokenizer:wy,SqueezeBertTokenizer:yy,AlbertTokenizer:_y,GPT2Tokenizer:om,BartTokenizer:Ay,MBartTokenizer:lm,MBart50Tokenizer:My,RobertaTokenizer:Oy,WhisperTokenizer:qy,CodeGenTokenizer:jy,CLIPTokenizer:Ky,SiglipTokenizer:Yy,MarianTokenizer:Xy,BloomTokenizer:zy,NllbTokenizer:Gy,M2M100Tokenizer:Vy,LlamaTokenizer:um,CodeLlamaTokenizer:Py,XLMRobertaTokenizer:Ry,MPNetTokenizer:By,FalconTokenizer:Dy,GPTNeoXTokenizer:Ny,EsmTokenizer:Ly,Wav2Vec2CTCTokenizer:Qy,BlenderbotTokenizer:cm,BlenderbotSmallTokenizer:Zy,SpeechT5Tokenizer:Jy,NougatTokenizer:eb,VitsTokenizer:tb,Qwen2Tokenizer:Uy,GemmaTokenizer:Fy,Grok1Tokenizer:Wy,CohereTokenizer:rb,PreTrainedTokenizer:xe};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[l,d]=await em(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,legacy:o}),c=d.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let u=this.TOKENIZER_CLASS_MAPPING[c];return u||(console.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),u=xe),new u(l,d)}}async function ab(t,e){return await pr(t,"config.json",!0,e)}class ib{constructor(e){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,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"}={}){let o=n??await ab(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s});return new this(o)}}class dr{static async from_pretrained(...e){return ib.from_pretrained(...e)}}const La=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),sb=function(){let t;return async function(){if(t===void 0)if(Dn.IS_NODE_ENV)t=!0;else if(!Dn.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}}(),yt=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4"}),ob=Object.freeze({[La.cpu]:yt.q8,[La.gpu]:yt.fp32,[La.wasm]:yt.q8,[La.webgpu]:yt.fp32}),cc=Object.freeze({[yt.fp32]:"",[yt.fp16]:"_fp16",[yt.int8]:"_int8",[yt.uint8]:"_uint8",[yt.q8]:"_quantized",[yt.q4]:"_q4",[yt.bnb4]:"_bnb4"});class rr extends mt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class go extends mt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class pc extends mt{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 lb extends rr{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 ub extends rr{constructor(e,r){super(),this.max_length=e,this.forced_eos_token_id=r}_call(e,r){}}class db extends rr{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 cb extends rr{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),l=a.get(o)??[];l.push(i[i.length-1]),a.set(o,l)}return a}getGeneratedNgrams(e,r){const n=r.slice(r.length+1-this.no_repeat_ngram_size,r.length);return e.get(JSON.stringify(n))??[]}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 pb extends rr{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 hb extends rr{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 fb extends rr{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 mb extends rr{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 gb extends rr{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 _b extends go{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 wb extends go{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 yb extends go{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 bb{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,Yr(e,Object.getOwnPropertyNames(this)))}}class pm extends mt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class _o extends mt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof _o&&(e=e.criteria),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 vb extends pm{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 $b extends pm{constructor(e){super(),Array.isArray(e)||(e=[e]),this.eos_token_id=e}_call(e,r){return e.map(n=>this.eos_token_id.includes(n.at(-1)))}}class _i extends mt{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 Sb(e);if(e.num_beams>1)return new Eb(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 xb(e)}}class xb extends _i{sample(e,r=-1){let n=this.getLogits(e,r);return[[mr(n)[1],0]]}}class Sb extends _i{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=Ec(a,n),s=Hn(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 Eb extends _i{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=Ec(a,n),s=Hn(i.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},(o,l)=>[i[l][0],Math.log(s[l])])}}const fe={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},ci=new Map,hm=new Map,Bn=new Map;async function kb(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=U0(n);let i=r.dtype;if(typeof i!="string"&&(i&&i.hasOwnProperty(e)?i=i[e]:(i=ob[a[0]],console.warn(`Dtype not specified for ${e}. Using the default dtype: ${i}.`))),cc.hasOwnProperty(i)){if(i===yt.fp16&&!await sb())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${i}. Should be one of: ${Object.keys(yt).join(", ")}`);const s=cc[i],o=`${r.subfolder??""}/${e}${s}.onnx`,l=await vs(t,o,!0,r),d={...r.session_options};if(d.executionProviders??=a,d.externalData!==void 0)for(let c=0;c<d.externalData.length;++c){const u=d.externalData[c];if(typeof u.data=="string"){const h=await vs(t,u.data,!0,r);u.data=h}}return{buffer:l,session_options:d}}async function Sr(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async s=>kb(t,e[s],r))),i={};for(let s=0;s<n.length;++s){const{buffer:o,session_options:l}=a[s],d=await Xf(o,l);i[n[s]]=d}return i}function Cb(t,e){const r=Object.create(null),n=[];for(const s of t.inputNames){const o=e[s];if(!(o instanceof ae)){n.push(s);continue}r[s]=F0()?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 fr(t,e){const r=Cb(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([i,s])=>[i,s.ort_tensor]));let a=await t.run(n);a=fm(a);for(const[i,s]of Object.entries(r))i.startsWith("past_key_values")&&s.dispose();return a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function fm(t){for(let e in t)Qf(t[e])?t[e]=new ae(t[e]):typeof t[e]=="object"&&fm(t[e]);return t}function Tb(t){if(t instanceof ae)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 ae("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new ae("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function mm(t){return new ae("bool",[t],[1])}async function hc(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const l=Yr(e,t.sessions.model.inputNames);r=(await Gn(t,l)).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 wo(t,s,!0)}async function Gn(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 ae("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await fr(r,n)}async function wo(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=mm(!!a)),t.addPastKeyValues(i,a);const s=Yr(i,n.inputNames);return await fr(n,s)}function Ib(t,e,r,n){if(t.sessions.model.inputNames.includes("position_ids")&&r.attention_mask&&!r.position_ids){const[i,s]=r.attention_mask.dims,o=new BigInt64Array(r.attention_mask.data.length);for(let l=0;l<i;++l){const d=l*s;let c=BigInt(0);for(let u=0;u<s;++u){const h=d+u;r.attention_mask.data[h]===0n?o[h]=BigInt(1):(o[h]=c,c+=r.attention_mask.data[h])}}r.position_ids=new ae("int64",o,r.attention_mask.dims),r.past_key_values&&(r.position_ids=r.position_ids.slice(null,-1).unsqueeze_(-1))}return r}function Ab(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(s=>[s.at(-1)])),a.decoder_input_ids=Tb(e),a}class Z extends mt{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(e,r){super(),this.config=e,this.sessions=r;const n=Bn.get(this.constructor),a=ci.get(n);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,a===fe.DecoderOnly?(this.can_generate=!0,this._forward=wo,this._prepare_inputs_for_generation=Ib):a===fe.Seq2Seq||a===fe.Vision2Seq||a===fe.Musicgen?(this.can_generate=!0,this._forward=hc,this._prepare_inputs_for_generation=Ab):a===fe.EncoderDecoder?this._forward=hc:a===fe.ImageTextToText?(this.can_generate=!0,console.warn("TODO: Implement visionDecoderForward")):this._forward=Gn}async dispose(){const e=[];for(let r of Object.keys(this)){let n=this[r];n?.handler?.dispose!==void 0&&e.push(n.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:l="onnx",device:d=null,dtype:c=null,session_options:u={}}={}){let h={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,model_file_name:o,subfolder:l,device:d,dtype:c,session_options:u};const m=Bn.get(this),g=ci.get(m);let _;return g===fe.DecoderOnly?_=await Promise.all([dr.from_pretrained(e,h),Sr(e,{model:h.model_file_name??"model"},h),pr(e,"generation_config.json",!1,h)]):g===fe.Seq2Seq||g===fe.Vision2Seq?_=await Promise.all([dr.from_pretrained(e,h),Sr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},h),pr(e,"generation_config.json",!1,h)]):g===fe.MaskGeneration?_=await Promise.all([dr.from_pretrained(e,h),Sr(e,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},h)]):g===fe.EncoderDecoder?_=await Promise.all([dr.from_pretrained(e,h),Sr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},h)]):g===fe.ImageTextToText?_=await Promise.all([dr.from_pretrained(e,h),Sr(e,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},h),pr(e,"generation_config.json",!1,h)]):g===fe.Musicgen?_=await Promise.all([dr.from_pretrained(e,h),Sr(e,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},h),pr(e,"generation_config.json",!1,h)]):(g!==fe.EncoderOnly&&console.warn(`Model type for '${m??n?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),_=await Promise.all([dr.from_pretrained(e,h),Sr(e,{model:h.model_file_name??"model"},h)])),new this(..._)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_warper(e){const r=new pc;return e.temperature!==null&&e.temperature!==1&&r.push(new _b(e.temperature)),e.top_k!==null&&e.top_k!==0&&r.push(new yb(e.top_k)),e.top_p!==null&&e.top_p<1&&r.push(new wb(e.top_p)),r}_get_logits_processor(e,r,n=null){const a=new pc;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&a.push(new pb(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&a.push(new cb(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&a.push(new mb(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&a.push(new hb(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 fb(r,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&a.push(new lb(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&a.push(new ub(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){let i=r>1||e.forced_bos_token_id===null?r:r+1;e.forced_decoder_ids!==null&&(i+=e.forced_decoder_ids[e.forced_decoder_ids.length-1][0]),a.push(new db(e.begin_suppress_tokens,i))}return e.guidance_scale!==null&&e.guidance_scale>1&&a.push(new gb(e.guidance_scale)),n!==null&&a.extend(n),a}_prepare_generation_config(e,r){const n=new bb(this.config);return"generation_config"in this&&Object.assign(n,this.generation_config),e&&Object.assign(n,e),r&&Object.assign(n,Yr(r,Object.getOwnPropertyNames(n))),n}_get_stopping_criteria(e,r=null){const n=new _o;return e.max_length!==null&&n.push(new vb(e.max_length,this.config.max_position_embeddings??null)),e.eos_token_id!==null&&n.push(new $b(e.eos_token_id)),r&&n.extend(r),n}_validate_model_class(){if(!this.can_generate){const e=[So,gg,mg,fg],r=Bn.get(this.constructor),n=new Set,a=this.config.model_type;for(const s of e){const o=s.get(a);o&&n.add(o[0])}let i=`The current model class (${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 ae("int64",e,[e.length,1]),a||(n.attention_mask=jt([n.attention_mask,ui([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=Yr(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=Yr(r,this.sessions.model.inputNames);let{last_hidden_state:s}=await Gn(this,i);return a.guidance_scale!==null&&a.guidance_scale>1&&(s=jt([s,K0(s,0)],0),"attention_mask"in r&&(r.attention_mask=jt([r.attention_mask,Q0(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}){a=a??i;let o;if(this.config.model_type==="musicgen")o=new Array(e*this.config.decoder.num_codebooks).fill(a);else if(Array.isArray(a)){if(a.length!==e)throw new Error(`\`decoder_start_token_id\` expcted to have length ${e} but got ${a.length}`);o=a}else o=new Array(e).fill(a);const d=new ae("int64",o,[o.length,1]);return n.decoder_attention_mask=Y0(d),{input_ids:d,model_inputs:n}}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,streamer:i=null,...s}){this._validate_model_class(),r=this._prepare_generation_config(r,s);let{inputs_tensor:o,model_inputs:l,model_input_name:d}=this._prepare_model_inputs({inputs:e,model_kwargs:s});const c=this.config.is_encoder_decoder;c&&("encoder_outputs"in l||(l=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:o,model_inputs:l,model_input_name:d,generation_config:r})));let u;c?{input_ids:u,model_inputs:l}=this._prepare_decoder_input_ids_for_generation({batch_size:l[d].dims.at(0),model_input_name:d,model_kwargs:l,decoder_start_token_id:r.decoder_start_token_id,bos_token_id:r.bos_token_id,generation_config:r}):u=l[d];let h=u.dims.at(-1);r.max_new_tokens!==null&&(r.max_length=h+r.max_new_tokens);const m=this._get_logits_processor(r,h,n),g=this._get_stopping_criteria(r,a),_=l[d].dims.at(0),b=_i.getSampler(r),$=new Array(_).fill(0),y=u.tolist();for(i&&i.put(y);;){l=this.prepare_inputs_for_generation(y,l,r);const E=await this.forward(l),k=E.logits.slice(null,-1,null),T=m(y,k),A=[];for(let W=0;W<T.dims.at(0);++W){const K=T[W],q=b(K);for(const[ee,P]of q){const J=BigInt(ee);$[W]+=P,y[W].push(J),A.push(J)}}if(i&&i.put(y),g(y).every(W=>W))break;l=this._update_model_kwargs_for_generation({generated_input_ids:A,outputs:E,model_inputs:l,is_encoder_decoder:c})}return i&&i.end(),new ae("int64",y.flat(),[y.length,y[0].length])}addAttentionsToBeam(e,r){if(this.config.is_encoder_decoder){if(!r.cross_attentions||r.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(r.cross_attentions)}if(!r.decoder_attentions||r.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(r.decoder_attentions)}groupBeams(e){const r=Object.create(null);for(const n of e)r[n.id]===void 0?r[n.id]=[n]:r[n.id].push(n);return Object.values(r)}getPastKeyValues(e,r){const n=Object.create(null);for(const a in e)if(a.startsWith("present")){let i=a.replace("present","past_key_values");r&&a.includes("encoder")?n[i]=r[i]:n[i]=e[a]}return n}getAttentions(e){const r=Object.create(null);for(const n of["cross_attentions","decoder_attentions"]){const a=[];for(const i in e)if(i.startsWith(n)){const s=i.split(".").pop();a[s]=e[i]}r[n]=a}return r}addPastKeyValues(e,r){if(r)Object.assign(e,r);else{const a="float32",i=[];if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??!0)){let s=[1,this.num_encoder_heads,0,this.encoder_dim_kv],o=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let l=0;l<this.num_decoder_layers;++l)e[`past_key_values.${l}.encoder.key`]=new ae(a,i,s),e[`past_key_values.${l}.encoder.value`]=new ae(a,i,s),e[`past_key_values.${l}.decoder.key`]=new ae(a,i,o),e[`past_key_values.${l}.decoder.value`]=new ae(a,i,o)}else if(this.config.model_type==="falcon"){let s=[1*this.num_heads,0,this.dim_kv];for(let o=0;o<this.num_layers;++o)e[`past_key_values.${o}.key`]=new ae(a,i,s),e[`past_key_values.${o}.value`]=new ae(a,i,s)}else if(this.config.multi_query){let s=[1*this.num_heads,0,2*this.dim_kv];for(let o=0;o<this.num_layers;++o)e[`past_key_values.${o}.key_value`]=new ae(a,i,s)}else if(this.config.model_type==="bloom"){let s=[1*this.num_heads,this.dim_kv,0],o=[1*this.num_heads,0,this.dim_kv];for(let l=0;l<this.num_layers;++l)e[`past_key_values.${l}.key`]=new ae(a,i,s),e[`past_key_values.${l}.value`]=new ae(a,i,o)}else{let s=[1,this.num_heads,0,this.dim_kv];for(let o=0;o<this.num_layers;++o)e[`past_key_values.${o}.key`]=new ae(a,i,s),e[`past_key_values.${o}.value`]=new ae(a,i,s)}}}}class At{}class Xn extends Z{}class Mb extends Xn{}class Ob extends Xn{async _call(e){return new ct(await super._call(e))}}class zb extends Xn{async _call(e){return new Me(await super._call(e))}}class Pb extends Xn{async _call(e){return new dt(await super._call(e))}}class Rb extends Xn{async _call(e){return new gt(await super._call(e))}}class Bb extends Z{}class Db extends Bb{}class Qn extends Z{}class Nb extends Qn{}class Lb extends Qn{async _call(e){return new ct(await super._call(e))}}class Ub extends Qn{async _call(e){return new Me(await super._call(e))}}class Fb extends Qn{async _call(e){return new dt(await super._call(e))}}class Wb extends Qn{async _call(e){return new gt(await super._call(e))}}class Zn extends Z{}class Gb extends Zn{}class Vb extends Zn{async _call(e){return new ct(await super._call(e))}}class Hb extends Zn{async _call(e){return new Me(await super._call(e))}}class qb extends Zn{async _call(e){return new dt(await super._call(e))}}class jb extends Zn{async _call(e){return new gt(await super._call(e))}}class Jn extends Z{}class Kb extends Jn{}class Yb extends Jn{async _call(e){return new ct(await super._call(e))}}class Xb extends Jn{async _call(e){return new Me(await super._call(e))}}class Qb extends Jn{async _call(e){return new dt(await super._call(e))}}class Zb extends Jn{async _call(e){return new gt(await super._call(e))}}class ea extends Z{}class Jb extends ea{}class ev extends ea{async _call(e){return new ct(await super._call(e))}}class tv extends ea{async _call(e){return new Me(await super._call(e))}}class rv extends ea{async _call(e){return new dt(await super._call(e))}}class nv extends ea{async _call(e){return new gt(await super._call(e))}}class ta extends Z{}class av extends ta{}class iv extends ta{async _call(e){return new ct(await super._call(e))}}class sv extends ta{async _call(e){return new Me(await super._call(e))}}class ov extends ta{async _call(e){return new dt(await super._call(e))}}class lv extends ta{async _call(e){return new gt(await super._call(e))}}class ra extends Z{}class uv extends ra{}class dv extends ra{async _call(e){return new ct(await super._call(e))}}class cv extends ra{async _call(e){return new Me(await super._call(e))}}class pv extends ra{async _call(e){return new dt(await super._call(e))}}class hv extends ra{async _call(e){return new gt(await super._call(e))}}class na extends Z{}class fv extends na{}class mv extends na{async _call(e){return new Me(await super._call(e))}}class gv extends na{async _call(e){return new dt(await super._call(e))}}class _v extends na{async _call(e){return new gt(await super._call(e))}}class wv extends na{async _call(e){return new ct(await super._call(e))}}class wi extends Z{}class yv extends wi{}class bv extends wi{async _call(e){return new ct(await super._call(e))}}class vv extends wi{async _call(e){return new Me(await super._call(e))}}class $v extends wi{async _call(e){return new dt(await super._call(e))}}class yi extends Z{}class xv extends yi{}class Sv extends yi{async _call(e){return new ct(await super._call(e))}}class Ev extends yi{async _call(e){return new Me(await super._call(e))}}class kv extends yi{async _call(e){return new gt(await super._call(e))}}class aa extends Z{}class Cv extends aa{}class Tv extends aa{async _call(e){return new ct(await super._call(e))}}class Iv extends aa{async _call(e){return new Me(await super._call(e))}}class Av extends aa{async _call(e){return new dt(await super._call(e))}}class Mv extends aa{async _call(e){return new gt(await super._call(e))}}class bi extends Z{}class Ov extends bi{}class zv extends bi{async _call(e){return new ct(await super._call(e))}}class Pv extends bi{async _call(e){return new Me(await super._call(e))}}class Rv extends bi{async _call(e){return new gt(await super._call(e))}}class vi extends Z{}class Bv extends vi{}class Dv extends vi{async _call(e){return new Me(await super._call(e))}}class Nv extends vi{async _call(e){return new gt(await super._call(e))}}class Lv extends vi{async _call(e){return new ct(await super._call(e))}}class gm extends Z{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,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class Uv extends gm{}class Fv extends gm{}class _m extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class Wv extends _m{}class Gv extends _m{}class wm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class Vv extends wm{}class Hv extends wm{}class yo extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class qv extends yo{}class jv extends yo{}class Kv extends yo{async _call(e){return new Me(await super._call(e))}}class $i extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class Yv extends $i{}class Xv extends $i{}class Qv extends $i{async _call(e){return new Me(await super._call(e))}}class Zv extends $i{constructor(e,r,n){super(e,r,n),this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class ym extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class Jv extends ym{}class e2 extends ym{}class bm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class t2 extends bm{}class r2 extends bm{}class ia extends Z{}class n2 extends ia{}class a2 extends ia{async _call(e){return new ct(await super._call(e))}}class i2 extends ia{async _call(e){return new Me(await super._call(e))}}class s2 extends ia{async _call(e){return new dt(await super._call(e))}}class o2 extends ia{async _call(e){return new gt(await super._call(e))}}class sa extends Z{}class l2 extends sa{}class u2 extends sa{async _call(e){return new ct(await super._call(e))}}class d2 extends sa{async _call(e){return new Me(await super._call(e))}}class c2 extends sa{async _call(e){return new dt(await super._call(e))}}class p2 extends sa{async _call(e){return new gt(await super._call(e))}}class oa extends Z{}class h2 extends oa{}class f2 extends oa{async _call(e){return new ct(await super._call(e))}}class m2 extends oa{async _call(e){return new Me(await super._call(e))}}class g2 extends oa{async _call(e){return new dt(await super._call(e))}}class _2 extends oa{async _call(e){return new gt(await super._call(e))}}class vm extends Z{}class w2 extends vm{}class y2 extends vm{}class $m extends Z{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,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class b2 extends $m{}class v2 extends $m{_retrieve_init_tokens(e){throw e.decoder_start_token_id,new Error("Not implemented yet")}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,language:i=null,task:s=null,...o}){throw new Error("WhisperForConditionalGeneration.generate is not yet in Transformers.js v3.")}_extract_token_timestamps(e,r,n=null,a=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let i=this.config.median_filter_width;i===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),i=7);const s=e.cross_attentions.map(d=>{let c=Array.from({length:this.config.decoder_layers},(b,$)=>jt(d.map(y=>y[$]),2)),u=Fn(r.map(([b,$])=>n?c[b].slice(null,$,null,[0,n]):c[b].slice(null,$)));u=u.transpose(1,0,2,3);let[h,m]=V0(u,-2,0,!0),g=u.clone();for(let b=0;b<g.dims[0];++b){let $=g[b];for(let y=0;y<$.dims[0];++y){let E=$[y];const k=h[b][y][0],T=m[b][y][0];for(let A=0;A<E.dims[0];++A){let R=E[A];for(let W=0;W<R.data.length;++W)R.data[W]=(R.data[W]-T.data[W])/k.data[W];R.data.set(o_(R.data,i))}}}return co(g,1)}),o=[e.sequences.length,e.sequences[0].length],l=new ae("float32",new Float32Array(o[0]*o[1]),o);for(let d=0;d<o[0];++d){const c=s[d].neg().squeeze_(0);let[u,h]=H0(c),m=Array.from({length:u.length-1},(b,$)=>u[$+1]-u[$]),g=st([1],m).map(b=>!!b),_=[];for(let b=0;b<g.length;++b)g[b]&&_.push(h[b]*a);l[d].data.set(_,1)}return l}}class $2 extends Z{main_input_name="pixel_values";constructor(e,r,n){super(e,r),this.generation_config=n;const a=this.config.encoder,i=this.config.decoder,s=a.model_type;(pg.get(s)??hg.get(s))||console.warn(`Model type for encoder '${s}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);const l=So.get(i.model_type);if(!l)throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);const d=l[1],c=new d(i,{},n);this.add_encoder_pkv="num_decoder_layers"in c,this.add_encoder_pkv?(this.num_decoder_layers=c.num_decoder_layers,this.num_decoder_heads=c.num_decoder_heads,this.decoder_dim_kv=c.decoder_dim_kv,this.num_encoder_layers=c.num_encoder_layers,this.num_encoder_heads=c.num_encoder_heads,this.encoder_dim_kv=c.encoder_dim_kv):(this.num_layers=c.num_layers,this.num_heads=c.num_heads,this.dim_kv=c.dim_kv)}}class x2 extends Z{forward_params=["input_ids","past_key_values","pixel_values","attention_mask"];constructor(e,r,n){super(e,r),this.generation_config=n;const a=this.config.text_config;this.config.pad_token_id=a.eos_token_id,this.num_heads=a.num_attention_heads,this.num_layers=a.num_hidden_layers,this.dim_kv=a.hidden_size/this.num_heads}}class S2 extends x2{async encode_image({pixel_values:e}){return(await fr(this.sessions.vision_encoder,{pixel_values:e})).image_features}async encode_text({input_ids:e}){return(await fr(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(h=>h.findIndex(m=>m==i)),l=o.every(h=>h===-1),d=o.every(h=>h!==-1);if(!l&&!d)throw new Error("Every input should contain either 0 or 1 image token.");if(l)return{inputs_embeds:e,attention_mask:a,position_ids:null};let c=[],u=[];for(let h=0;h<o.length;++h){const m=o[h],g=e[h],_=r[h],b=a[h];c.push(jt([g.slice([0,m]),_,g.slice([m+1,g.dims[0]])],0)),u.push(jt([b.slice([0,m]),ui([_.dims[0]]),b.slice([m+1,b.dims[0]])],0))}return{inputs_embeds:Fn(c,0),attention_mask:Fn(u,0),position_ids:null}}prepare_inputs_for_generation(e,r,n){return r}async forward({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:l=null,...d}){if(!i){if(i=await this.encode_text({input_ids:e}),n&&e.dims[1]!==1){const u=await this.encode_image({pixel_values:n});({inputs_embeds:i,inputs_embeds:i,attention_mask:r,position_ids:a}=this._merge_input_ids_with_image_features({image_features:u,inputs_embeds:i,input_ids:e,attention_mask:r}))}else if(s&&n&&e.dims[1]===1){const u=e.dims[1],h=Object.values(s)[0].dims.at(-2);r=jt([ui([e.dims[0],h]),r.slice(null,[r.dims[1]-u,r.dims[1]])],1)}}return await wo(this,{inputs_embeds:i,past_key_values:s,attention_mask:r,generation_config:o,logits_processor:l},!0)}}class xi extends Z{}class E2 extends xi{}class xm extends xi{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class Sm extends xi{static async from_pretrained(e,r={}){return r.model_file_name??="vision_model",super.from_pretrained(e,r)}}class Em extends Z{}class k2 extends Em{}class C2 extends Em{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class T2 extends xi{static async from_pretrained(e,r={}){return r.model_file_name??="vision_model",super.from_pretrained(e,r)}}class I2 extends Z{}class A2 extends I2{}class km extends Z{}class M2 extends km{}class O2 extends km{}class Cm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class z2 extends Cm{}class P2 extends Cm{}class Tm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_heads,this.num_layers=this.config.num_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class R2 extends Tm{}class B2 extends Tm{}class Im extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class D2 extends Im{}class N2 extends Im{}class Am extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class L2 extends Am{}class U2 extends Am{}class Mm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class F2 extends Mm{}class W2 extends Mm{}class Om extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class G2 extends Om{}class V2 extends Om{}class zm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_key_value_heads??this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class H2 extends zm{}class q2 extends zm{}class Pm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_key_value_heads??this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class j2 extends Pm{}class K2 extends Pm{}class Rm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class Y2 extends Rm{}class X2 extends Rm{}class Bm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.hidden_size/this.num_heads}}class Q2 extends Bm{}class Z2 extends Bm{}class Dm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.n_heads,this.num_layers=this.config.n_layers,this.dim_kv=this.config.d_model/this.num_heads}}class J2 extends Dm{}class e1 extends Dm{}class Nm extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class t1 extends Nm{}class r1 extends Nm{}class Lm extends Z{}class n1 extends Lm{}class a1 extends Lm{async _call(e){return new Me(await super._call(e))}}class i1 extends Z{}class s1 extends i1{async _call(e){return new fx(await super._call(e))}}class Um extends Z{}class o1 extends Um{}class l1 extends Um{async _call(e){return new Me(await super._call(e))}}class Fm extends Z{}class u1 extends Fm{}class d1 extends Fm{}class Wm extends Z{}class c1 extends Wm{}class p1 extends Wm{}class Gm extends Z{}class h1 extends Gm{}class f1 extends Gm{async _call(e){return new Me(await super._call(e))}}class bo extends Z{}class m1 extends bo{}class g1 extends bo{async _call(e){return new Vm(await super._call(e))}}class _1 extends bo{async _call(e){return new w1(await super._call(e))}}class Vm extends At{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class w1 extends At{constructor({logits:e,pred_boxes:r,pred_masks:n}){super(),this.logits=e,this.pred_boxes=r,this.pred_masks=n}}class Hm extends Z{}class y1 extends Hm{}class b1 extends Hm{async _call(e){return new v1(await super._call(e))}}class v1 extends Vm{}class qm extends Z{}class $1 extends qm{}class x1 extends qm{async _call(e){return new Me(await super._call(e))}}class jm extends Z{}class S1 extends jm{}class E1 extends jm{async _call(e){return new Me(await super._call(e))}}class Km extends Z{}class k1 extends Km{}class C1 extends Km{async _call(e){return new Me(await super._call(e))}}class Ym extends Z{}class T1 extends Ym{}class I1 extends Ym{}class Xm extends Z{}class A1 extends Xm{}class M1 extends Xm{}class O1 extends Z{}class z1 extends O1{}class Qm extends Z{}class P1 extends Qm{}class R1 extends Qm{}class B1 extends Z{}class D1 extends B1{}class Zm extends Z{}class N1 extends Zm{}class L1 extends Zm{async _call(e){return new Me(await super._call(e))}}class Jm extends Z{}class U1 extends Jm{}class F1 extends Jm{async _call(e){return new Me(await super._call(e))}}class eg extends Z{}class W1 extends eg{}class G1 extends eg{async _call(e){return new Me(await super._call(e))}}class tg extends Z{}class V1 extends tg{}class H1 extends tg{async _call(e){return new q1(await super._call(e))}}class q1 extends At{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class j1 extends Z{}class K1 extends j1{async get_image_embeddings({pixel_values:e}){return await Gn(this,{pixel_values:e})}async forward(e){if((!e.image_embeddings||!e.image_positional_embeddings)&&(e={...e,...await this.get_image_embeddings(e)}),!e.input_labels&&e.input_points){const n=e.input_points.dims.slice(0,-1),a=n.reduce((i,s)=>i*s,1);e.input_labels=new ae("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 fr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new Y1(await super._call(e))}}class Y1 extends At{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class rg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class X1 extends rg{}class Q1 extends rg{}class ng extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class Z1 extends ng{}class J1 extends ng{}class Dr extends Z{}class e$ extends Dr{}class t$ extends Dr{async _call(e){return new en(await super._call(e))}}class r$ extends Dr{async _call(e){return new Me(await super._call(e))}}class n$ extends Dr{async _call(e){return new dt(await super._call(e))}}class vo extends Z{}class a$ extends vo{}class i$ extends vo{async _call(e){return new en(await super._call(e))}}class s$ extends vo{async _call(e){return new Me(await super._call(e))}}class Si extends Z{}class o$ extends Si{}class l$ extends Si{async _call(e){return new en(await super._call(e))}}class u$ extends Si{async _call(e){return new Me(await super._call(e))}}class d$ extends Si{async _call(e){return new dt(await super._call(e))}}class $o extends Z{}class c$ extends $o{}class p$ extends $o{async _call(e){return new en(await super._call(e))}}class h$ extends $o{async _call(e){return new Me(await super._call(e))}}class f$ extends Dr{}class m$ extends Dr{async _call(e){return new en(await super._call(e))}}class g$ extends Dr{async _call(e){return new Me(await super._call(e))}}class la extends Z{}class _$ extends la{}class w$ extends la{async _call(e){return new en(await super._call(e))}}class y$ extends la{async _call(e){return new Me(await super._call(e))}}class b$ extends la{async _call(e){return new hx(await super._call(e))}}class v$ extends la{async _call(e){return new dt(await super._call(e))}}class ag extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.hidden_size/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.hidden_size/this.num_encoder_heads}}class $$ extends ag{}class x$ extends ag{async generate_speech(e,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:i=20,vocoder:s=null}={}){const o={input_ids:e},{encoder_outputs:l,encoder_attention_mask:d}=await Gn(this,o),c=l.dims[1]/this.config.reduction_factor,u=Math.floor(c*i),h=Math.floor(c*a),m=this.config.num_mel_bins;let g=[],_=null,b=null,$=0;for(;;){++$;const k=mm(!!b);let T;b?T=b.output_sequence_out:T=new ae("float32",new Float32Array(m),[1,1,m]);let A={use_cache_branch:k,output_sequence:T,encoder_attention_mask:d,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(A,_),b=await fr(this.sessions.decoder_model_merged,A),_=this.getPastKeyValues(b,_);const{prob:R,spectrum:W}=b;if(g.push(W),$>=h&&(Array.from(R.data).filter(K=>K>=n).length>0||$>=u))break}const y=jt(g),{waveform:E}=await fr(s.sessions.model,{spectrogram:y});return{spectrogram:y,waveform:E}}}class S$ extends Z{main_input_name="spectrogram"}class E$ extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_encoder_layers=this.num_decoder_layers=this.config.decoder_layers,this.num_encoder_heads=this.num_decoder_heads=this.config.decoder_attention_heads,this.encoder_dim_kv=this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads}}class k$ extends E${}class ig extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class C$ extends ig{}class T$ extends ig{}class sg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class I$ extends sg{}class A$ extends sg{}class og extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class M$ extends og{}class O$ extends og{}class xo extends Z{}class z$ extends xo{}class P$ extends xo{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class R$ extends xo{static async from_pretrained(e,r={}){return r.model_file_name??="audio_model",super.from_pretrained(e,r)}}class B$ extends Z{}class lg extends B${async _call(e){return new mx(await super._call(e))}}class ug extends Z{}class D$ extends ug{}class N$ extends ug{}class L$ extends Z{constructor(e,r,n){super(e,r),this.generation_config=n,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class U$ extends L${}class dg extends Z{}class F$ extends dg{}class W$ extends dg{async _call(e){return new Me(await super._call(e))}}class cg extends Z{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;const a=e.decoder;this.num_encoder_layers=this.num_decoder_layers=a.num_hidden_layers,this.num_encoder_heads=this.num_decoder_heads=a.num_attention_heads,this.encoder_dim_kv=this.decoder_dim_kv=a.hidden_size/this.num_decoder_heads}_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 d=0;d<e.size;++d){if(e.data[d]===this.config.decoder.pad_token_id)continue;const c=d%n,u=Math.floor(d/n)%a,h=c-u;h>0&&h<=i&&(e.data[s++]=e.data[d])}const o=Math.floor(r/a),l=s/(o*a);return new ae(e.type,e.data.slice(0,s),[o,a,l])}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 fr(this.sessions.encodec_decode,{audio_codes:n});return a}}class G${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:l="onnx",device:d=null,dtype:c=null,session_options:u={}}={}){let h={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,model_file_name:o,subfolder:l,device:d,dtype:c,session_options:u};if(n=await dr.from_pretrained(e,h),h.config||(h.config=n),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let m of this.MODEL_CLASS_MAPPINGS){const g=m.get(n.model_type);if(g)return await g[1].from_pretrained(e,h)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${n.model_type}", attempting to construct from base class.`),await Z.from_pretrained(e,h);throw Error(`Unsupported model type: ${n.model_type}`)}}const pg=new Map([["bert",["BertModel",Mb]],["nomic_bert",["NomicBertModel",Db]],["roformer",["RoFormerModel",Nb]],["electra",["ElectraModel",Kb]],["esm",["EsmModel",yv]],["convbert",["ConvBertModel",Gb]],["camembert",["CamembertModel",Jb]],["deberta",["DebertaModel",av]],["deberta-v2",["DebertaV2Model",uv]],["mpnet",["MPNetModel",Cv]],["albert",["AlbertModel",Bv]],["distilbert",["DistilBertModel",fv]],["roberta",["RobertaModel",n2]],["xlm",["XLMModel",l2]],["xlm-roberta",["XLMRobertaModel",h2]],["clap",["ClapModel",z$]],["clip",["CLIPModel",E2]],["clipseg",["CLIPSegModel",M2]],["chinese_clip",["ChineseCLIPModel",A2]],["siglip",["SiglipModel",k2]],["mobilebert",["MobileBertModel",xv]],["squeezebert",["SqueezeBertModel",Ov]],["wav2vec2",["Wav2Vec2Model",e$]],["wav2vec2-bert",["Wav2Vec2BertModel",c$]],["unispeech",["UniSpeechModel",a$]],["unispeech-sat",["UniSpeechSatModel",o$]],["hubert",["HubertModel",f$]],["wavlm",["WavLMModel",_$]],["audio-spectrogram-transformer",["ASTModel",w2]],["vits",["VitsModel",lg]],["detr",["DetrModel",m1]],["table-transformer",["TableTransformerModel",y1]],["vit",["ViTModel",n1]],["mobilevit",["MobileViTModel",o1]],["owlvit",["OwlViTModel",u1]],["owlv2",["Owlv2Model",c1]],["beit",["BeitModel",h1]],["deit",["DeiTModel",$1]],["convnext",["ConvNextModel",N1]],["convnextv2",["ConvNextV2Model",U1]],["dinov2",["Dinov2Model",W1]],["resnet",["ResNetModel",S1]],["swin",["SwinModel",k1]],["swin2sr",["Swin2SRModel",T1]],["donut-swin",["DonutSwinModel",D1]],["yolos",["YolosModel",V1]],["dpt",["DPTModel",A1]],["glpn",["GLPNModel",P1]],["hifigan",["SpeechT5HifiGan",S$]],["efficientnet",["EfficientNetModel",F$]]]),hg=new Map([["t5",["T5Model",Uv]],["longt5",["LongT5Model",Wv]],["mt5",["MT5Model",Vv]],["bart",["BartModel",qv]],["mbart",["MBartModel",Yv]],["marian",["MarianModel",X1]],["whisper",["WhisperModel",b2]],["m2m_100",["M2M100Model",Z1]],["blenderbot",["BlenderbotModel",Jv]],["blenderbot-small",["BlenderbotSmallModel",t2]]]),V$=new Map([["bloom",["BloomModel",Q2]],["gpt2",["GPT2Model",z2]],["gptj",["GPTJModel",L2]],["gpt_bigcode",["GPTBigCodeModel",F2]],["gpt_neo",["GPTNeoModel",R2]],["gpt_neox",["GPTNeoXModel",D2]],["codegen",["CodeGenModel",G2]],["llama",["LlamaModel",H2]],["qwen2",["Qwen2Model",j2]],["phi",["PhiModel",Y2]],["mpt",["MptModel",J2]],["opt",["OPTModel",t1]],["mistral",["MistralModel",C$]],["starcoder2",["Starcoder2Model",I$]],["falcon",["FalconModel",M$]]]),fg=new Map([["speecht5",["SpeechT5ForSpeechToText",$$]],["whisper",["WhisperForConditionalGeneration",v2]]]),H$=new Map([["speecht5",["SpeechT5ForTextToSpeech",x$]]]),q$=new Map([["vits",["VitsModel",lg]],["musicgen",["MusicgenForConditionalGeneration",cg]]]),j$=new Map([["bert",["BertForSequenceClassification",zb]],["roformer",["RoFormerForSequenceClassification",Ub]],["electra",["ElectraForSequenceClassification",Xb]],["esm",["EsmForSequenceClassification",vv]],["convbert",["ConvBertForSequenceClassification",Hb]],["camembert",["CamembertForSequenceClassification",tv]],["deberta",["DebertaForSequenceClassification",sv]],["deberta-v2",["DebertaV2ForSequenceClassification",cv]],["mpnet",["MPNetForSequenceClassification",Iv]],["albert",["AlbertForSequenceClassification",Dv]],["distilbert",["DistilBertForSequenceClassification",mv]],["roberta",["RobertaForSequenceClassification",i2]],["xlm",["XLMForSequenceClassification",d2]],["xlm-roberta",["XLMRobertaForSequenceClassification",m2]],["bart",["BartForSequenceClassification",Kv]],["mbart",["MBartForSequenceClassification",Qv]],["mobilebert",["MobileBertForSequenceClassification",Ev]],["squeezebert",["SqueezeBertForSequenceClassification",Pv]]]),K$=new Map([["bert",["BertForTokenClassification",Pb]],["roformer",["RoFormerForTokenClassification",Fb]],["electra",["ElectraForTokenClassification",Qb]],["esm",["EsmForTokenClassification",$v]],["convbert",["ConvBertForTokenClassification",qb]],["camembert",["CamembertForTokenClassification",rv]],["deberta",["DebertaForTokenClassification",ov]],["deberta-v2",["DebertaV2ForTokenClassification",pv]],["mpnet",["MPNetForTokenClassification",Av]],["distilbert",["DistilBertForTokenClassification",gv]],["roberta",["RobertaForTokenClassification",s2]],["xlm",["XLMForTokenClassification",c2]],["xlm-roberta",["XLMRobertaForTokenClassification",g2]]]),mg=new Map([["t5",["T5ForConditionalGeneration",Fv]],["longt5",["LongT5ForConditionalGeneration",Gv]],["mt5",["MT5ForConditionalGeneration",Hv]],["bart",["BartForConditionalGeneration",jv]],["mbart",["MBartForConditionalGeneration",Xv]],["marian",["MarianMTModel",Q1]],["m2m_100",["M2M100ForConditionalGeneration",J1]],["blenderbot",["BlenderbotForConditionalGeneration",e2]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",r2]]]),So=new Map([["bloom",["BloomForCausalLM",Z2]],["gpt2",["GPT2LMHeadModel",P2]],["gptj",["GPTJForCausalLM",U2]],["gpt_bigcode",["GPTBigCodeForCausalLM",W2]],["gpt_neo",["GPTNeoForCausalLM",B2]],["gpt_neox",["GPTNeoXForCausalLM",N2]],["codegen",["CodeGenForCausalLM",V2]],["llama",["LlamaForCausalLM",q2]],["qwen2",["Qwen2ForCausalLM",K2]],["phi",["PhiForCausalLM",X2]],["mpt",["MptForCausalLM",e1]],["opt",["OPTForCausalLM",r1]],["mbart",["MBartForCausalLM",Zv]],["mistral",["MistralForCausalLM",T$]],["starcoder2",["Starcoder2ForCausalLM",A$]],["falcon",["FalconForCausalLM",O$]],["trocr",["TrOCRForCausalLM",k$]],["stablelm",["StableLmForCausalLM",U$]]]),Y$=new Map([["bert",["BertForMaskedLM",Ob]],["roformer",["RoFormerForMaskedLM",Lb]],["electra",["ElectraForMaskedLM",Yb]],["esm",["EsmForMaskedLM",bv]],["convbert",["ConvBertForMaskedLM",Vb]],["camembert",["CamembertForMaskedLM",ev]],["deberta",["DebertaForMaskedLM",iv]],["deberta-v2",["DebertaV2ForMaskedLM",dv]],["mpnet",["MPNetForMaskedLM",Tv]],["albert",["AlbertForMaskedLM",Lv]],["distilbert",["DistilBertForMaskedLM",wv]],["roberta",["RobertaForMaskedLM",a2]],["xlm",["XLMWithLMHeadModel",u2]],["xlm-roberta",["XLMRobertaForMaskedLM",f2]],["mobilebert",["MobileBertForMaskedLM",Sv]],["squeezebert",["SqueezeBertForMaskedLM",zv]]]),X$=new Map([["bert",["BertForQuestionAnswering",Rb]],["roformer",["RoFormerForQuestionAnswering",Wb]],["electra",["ElectraForQuestionAnswering",Zb]],["convbert",["ConvBertForQuestionAnswering",jb]],["camembert",["CamembertForQuestionAnswering",nv]],["deberta",["DebertaForQuestionAnswering",lv]],["deberta-v2",["DebertaV2ForQuestionAnswering",hv]],["mpnet",["MPNetForQuestionAnswering",Mv]],["albert",["AlbertForQuestionAnswering",Nv]],["distilbert",["DistilBertForQuestionAnswering",_v]],["roberta",["RobertaForQuestionAnswering",o2]],["xlm",["XLMForQuestionAnswering",p2]],["xlm-roberta",["XLMRobertaForQuestionAnswering",_2]],["mobilebert",["MobileBertForQuestionAnswering",kv]],["squeezebert",["SqueezeBertForQuestionAnswering",Rv]]]),gg=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",$2]]]),Q$=new Map([["llava",["LlavaForConditionalGeneration",S2]]]),Z$=new Map([["vit",["ViTForImageClassification",a1]],["mobilevit",["MobileViTForImageClassification",l1]],["beit",["BeitForImageClassification",f1]],["deit",["DeiTForImageClassification",x1]],["convnext",["ConvNextForImageClassification",L1]],["convnextv2",["ConvNextV2ForImageClassification",F1]],["dinov2",["Dinov2ForImageClassification",G1]],["resnet",["ResNetForImageClassification",E1]],["swin",["SwinForImageClassification",C1]],["segformer",["SegformerForImageClassification",D$]],["efficientnet",["EfficientNetForImageClassification",W$]]]),J$=new Map([["detr",["DetrForObjectDetection",g1]],["table-transformer",["TableTransformerForObjectDetection",b1]],["yolos",["YolosForObjectDetection",H1]]]),ex=new Map([["owlvit",["OwlViTForObjectDetection",d1]],["owlv2",["Owlv2ForObjectDetection",p1]]]),tx=new Map([["detr",["DetrForSegmentation",_1]],["clipseg",["CLIPSegForImageSegmentation",O2]]]),rx=new Map([["segformer",["SegformerForSemanticSegmentation",N$]]]),nx=new Map([["sam",["SamModel",K1]]]),ax=new Map([["wav2vec2",["Wav2Vec2ForCTC",t$]],["wav2vec2-bert",["Wav2Vec2BertForCTC",p$]],["unispeech",["UniSpeechForCTC",i$]],["unispeech-sat",["UniSpeechSatForCTC",l$]],["wavlm",["WavLMForCTC",w$]],["hubert",["HubertForCTC",m$]]]),ix=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",r$]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",h$]],["unispeech",["UniSpeechForSequenceClassification",s$]],["unispeech-sat",["UniSpeechSatForSequenceClassification",u$]],["wavlm",["WavLMForSequenceClassification",y$]],["hubert",["HubertForSequenceClassification",g$]],["audio-spectrogram-transformer",["ASTForAudioClassification",y2]]]),sx=new Map([["wavlm",["WavLMForXVector",b$]]]),ox=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",d$]],["wavlm",["WavLMForAudioFrameClassification",v$]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",n$]]]),lx=new Map([["vitmatte",["VitMatteForImageMatting",s1]]]),ux=new Map([["swin2sr",["Swin2SRForImageSuperResolution",I1]]]),dx=new Map([["dpt",["DPTForDepthEstimation",M1]],["depth_anything",["DepthAnythingForDepthEstimation",z1]],["glpn",["GLPNForDepthEstimation",R1]]]),cx=new Map([["clip",["CLIPVisionModelWithProjection",Sm]],["siglip",["SiglipVisionModel",T2]]]),_g=[[pg,fe.EncoderOnly],[hg,fe.EncoderDecoder],[V$,fe.DecoderOnly],[j$,fe.EncoderOnly],[K$,fe.EncoderOnly],[mg,fe.Seq2Seq],[fg,fe.Seq2Seq],[So,fe.DecoderOnly],[Y$,fe.EncoderOnly],[X$,fe.EncoderOnly],[gg,fe.Vision2Seq],[Q$,fe.ImageTextToText],[Z$,fe.EncoderOnly],[tx,fe.EncoderOnly],[rx,fe.EncoderOnly],[lx,fe.EncoderOnly],[ux,fe.EncoderOnly],[dx,fe.EncoderOnly],[J$,fe.EncoderOnly],[ex,fe.EncoderOnly],[nx,fe.MaskGeneration],[ax,fe.EncoderOnly],[ix,fe.EncoderOnly],[H$,fe.Seq2Seq],[q$,fe.EncoderOnly],[sx,fe.EncoderOnly],[ox,fe.EncoderOnly],[cx,fe.EncoderOnly]];for(const[t,e]of _g)for(const[r,n]of t.values())ci.set(r,e),Bn.set(n,r),hm.set(r,n);const px=[["MusicgenForConditionalGeneration",cg,fe.Musicgen],["CLIPTextModelWithProjection",xm,fe.EncoderOnly],["SiglipTextModel",C2,fe.EncoderOnly],["ClapTextModelWithProjection",P$,fe.EncoderOnly],["ClapAudioModelWithProjection",R$,fe.EncoderOnly]];for(const[t,e,r]of px)ci.set(t,r),Bn.set(e,t),hm.set(t,e);class oS extends G${static MODEL_CLASS_MAPPINGS=_g.map(e=>e[0]);static BASE_IF_FAIL=!0}class Me extends At{constructor({logits:e}){super(),this.logits=e}}class hx extends At{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class dt extends At{constructor({logits:e}){super(),this.logits=e}}class ct extends At{constructor({logits:e}){super(),this.logits=e}}class gt extends At{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class en extends At{constructor({logits:e}){super(),this.logits=e}}class fx extends At{constructor({alphas:e}){super(),this.alphas=e}}class mx extends At{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const Et=typeof self<"u",gx=Et&&self.constructor.name==="DedicatedWorkerGlobalScope";let Er,wg,cr;if(Et)Er=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},cr=self.createImageBitmap,wg=self.ImageData;else if(Xe)cr=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.raw().toBuffer({resolveWithObject:!0}),i=new Nt(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 _x={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},wx=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class Nt{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 Nt)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!Et)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new Nt(n,e.width,e.height,4)}static async fromURL(e){const r=await bs(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(Et){const r=await cr(e),n=Er(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=Xe(await e.arrayBuffer());return await cr(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 Nt(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=_x[n]??n;if(Et){const i=this.channels,s=this.toCanvas(),o=Er(e,r).getContext("2d");return o.drawImage(s,0,0,e,r),new Nt(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 cr(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(Et){const i=this.channels,s=this.toCanvas(),o=this.width+e+r,l=this.height+n+a,d=Er(o,l).getContext("2d");return d.drawImage(s,0,0,this.width,this.height,e,n,o,l),new Nt(d.getImageData(0,0,o,l).data,o,l,4).convert(i)}else{const i=this.toSharp().extend({left:e,right:r,top:n,bottom:a});return await cr(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(Et){const o=this.channels,l=this.toCanvas(),d=Er(i,s).getContext("2d");return d.drawImage(l,e,r,i,s,0,0,i,s),new Nt(d.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 cr(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(Et){const i=this.channels,s=this.toCanvas(),o=Er(e,r).getContext("2d");let l=0,d=0,c=0,u=0;return n>=0?l=n:c=-n,a>=0?d=a:u=-a,o.drawImage(s,l,d,e,r,c,u,e,r),new Nt(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 l=[0,0],d=0;n<0?(l[0]=Math.floor(-n),l[1]=e-this.width-l[0]):d=Math.floor(n),i=i.extend({top:s[0],bottom:s[1],left:l[0],right:l[1]}).extract({left:d,top:o,width:e,height:r})}return await cr(i)}}async toBlob(e="image/png",r=1){if(!Et)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 ae("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(!Et)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=Er(e.width,e.height),n=new wg(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 Nt(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(Et){if(gx)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=wx.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(_t.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Et)throw new Error("toSharp() is only supported in server-side environments.");return Xe(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}function fc(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 yx={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 fs(t,e="htk"){const r=yx[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 bx={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 vx(t,e="htk"){const r=bx[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 $x(t,e){const r=Float64Array.from({length:e.length-1},(s,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let s=0;s<t.length;++s){const o=n[s];for(let l=0;l<e.length;++l)o[l]=e[l]-t[s]}const a=e.length-2,i=Array.from({length:a},()=>new Array(t.length));for(let s=0;s<t.length;++s){const o=n[s];for(let l=0;l<a;++l){const d=-o[l]/r[l],c=o[l+2]/r[l+1];i[l][s]=Math.max(0,Math.min(d,c))}}return i}function mc(t,e,r){const n=(e-t)/(r-1);return Float64Array.from({length:r},(a,i)=>t+n*i)}function Vn(t,e,r,n,a,i=null,s="htk",o=!1){if(i!==null&&i!=="slaney")throw new Error('norm must be one of null or "slaney"');const l=fs(r,s),d=fs(n,s),c=mc(l,d,e+2);let u=vx(c,s),h;if(o){const g=a/(t*2);h=fs(Float64Array.from({length:t},(_,b)=>b*g),s),u=c}else h=mc(0,Math.floor(a/2),t);const m=$x(h,u);if(i!==null&&i==="slaney")for(let g=0;g<e;++g){const _=m[g],b=2/(u[g+2]-u[g]);for(let $=0;$<t;++$)_[$]*=b}return m}function xx(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[Qa(i,a)];for(let i=1;i<=r;++i)n[a+e+i]=t[Qa(a-i,a)];return n}function yg(t,e,r,n,a){if(r<=0)throw new Error("reference must be greater than zero");if(n<=0)throw new Error("min_value must be greater than zero");r=Math.max(n,r);const 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=mr(t)[0]-a;for(let o=0;o<t.length;++o)t[o]=Math.max(t[o],s)}return t}function Sx(t,e=1,r=1e-5,n=null){return yg(t,20,e,r,n)}function Ex(t,e=1,r=1e-10,n=null){return yg(t,10,e,r,n)}function Ei(t,e,r,n,{fft_length:a=null,power:i=1,center:s=!0,pad_mode:o="reflect",onesided:l=!0,preemphasis:d=null,mel_filters:c=null,mel_floor:u=1e-10,log_mel:h=null,reference:m=1,min_value:g=1e-10,db_range:_=null,remove_dc_offset:b=null,max_num_frames:$=null,do_pad:y=!0,transpose:E=!1}={}){const k=e.length;if(a===null&&(a=r),r>a)throw Error(`frame_length (${r}) may not be larger than fft_length (${a})`);if(k!==r)throw new Error(`Length of the window (${k}) must equal frame_length (${r})`);if(n<=0)throw new Error("hop_length must be greater than zero");if(s){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const O=Math.floor((a-1)/2)+1;t=xx(t,O,O)}const T=Math.floor(1+Math.floor((t.length-r)/n)),A=l?Math.floor(a/2)+1:a;let R=T,W=T;$!==null&&($>T?y&&(W=$):W=R=$);const K=new s_(a),q=new Float64Array(a),ee=new Float64Array(K.outputBufferSize),P=new Array(R);for(let O=0;O<R;++O){const G=O*n;for(let j=0;j<r;++j)q[j]=t[G+j];if(b){let j=0;for(let L=0;L<r;++L)j+=q[L];const se=j/r;for(let L=0;L<r;++L)q[L]-=se}if(d!==null){for(let j=r-1;j>=1;--j)q[j]-=d*q[j-1];q[0]*=1-d}for(let j=0;j<e.length;++j)q[j]*=e[j];K.realTransform(ee,q);const ie=new Array(A);for(let j=0;j<ie.length;++j){const se=j<<1;ie[j]=ee[se]**2+ee[se+1]**2}P[O]=ie}if(i!==null&&i!==2){const O=2/i;for(let G=0;G<P.length;++G){const ie=P[G];for(let j=0;j<ie.length;++j)ie[j]**=O}}const J=c.length,te=new Float32Array(J*W),F=E?[W,J]:[J,W];for(let O=0;O<J;++O){const G=c[O];for(let ie=0;ie<R;++ie){const j=P[ie];let se=0;for(let L=0;L<A;++L)se+=G[L]*j[L];te[E?ie*J+O:O*R+ie]=Math.max(u,se)}}if(i!==null&&h!==null){const O=Math.min(te.length,R*J);switch(h){case"log":for(let G=0;G<O;++G)te[G]=Math.log(te[G]);break;case"log10":for(let G=0;G<O;++G)te[G]=Math.log10(te[G]);break;case"dB":if(i===1)Sx(te,m,g,_);else if(i===2)Ex(te,m,g,_);else throw new Error(`Cannot use log_mel option '${h}' with power ${i}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${h}'`)}}return{data:te,dims:F}}function ki(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=fc(i);break;case"povey":s=fc(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 (${n})`);return s}function kx([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function Eo(t,e=.5,r=null,n=!1){const a=t.logits,i=t.pred_boxes,[s,o,l]=a.dims;if(r!==null&&r.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let d=[];for(let c=0;c<s;++c){let u=r!==null?r[c]:null,h={boxes:[],classes:[],scores:[]},m=a[c],g=i[c];for(let _=0;_<o;++_){let b=m[_],$=[],y;if(n){y=b.sigmoid().data;for(let E=0;E<y.length;++E)y[E]>e&&$.push(E)}else{let E=mr(b.data)[1];if(E===l-1||(y=Hn(b.data),y[E]<e))continue;$.push(E)}for(const E of $){let k=g[_].data;k=kx(k),u!==null&&(k=k.map((T,A)=>T*u[(A+1)%2])),h.boxes.push(k),h.classes.push(E),h.scores.push(y[E])}}d.push(h)}return d}function ua(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${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 gc(t,e,r=0,n=null){const a=t/e;let i=l_(a)*e;return n!==null&&i>n&&(i=Math.floor(a)*e),i<r&&(i=Math.ceil(a)*e),i}function ms([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}class Nr extends mt{constructor(e){super(),this.config=e}}class je extends Nr{constructor(e){super(e),this.image_mean=this.config.image_mean??this.config.mean,this.image_std=this.config.image_std??this.config.std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.size_divisibility=this.config.size_divisibility??this.config.size_divisor,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size)}async thumbnail(e,r,n=2){const a=e.height,i=e.width,s=r.height,o=r.width;let l=Math.min(a,s),d=Math.min(i,o);return l===a&&d===i?e:(a>i?d=Math.floor(i*l/a):i>a&&(l=Math.floor(a*d/i)),await e.resize(d,l,{resample:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),a=kc(n.data)[0],s=mr(n.data)[0]-a;if(s===0)return e;const o=r/255;let l=n.width,d=n.height,c=0,u=0;const h=n.data;for(let m=0;m<n.height;++m){const g=m*n.width;for(let _=0;_<n.width;++_)(h[g+_]-a)/s<o&&(l=Math.min(l,_),d=Math.min(d,m),c=Math.max(c,_),u=Math.max(u,m))}return e=await e.crop([l,d,c,u]),e}pad_image(e,r,n,{mode:a="constant",center:i=!1,constant_values:s=0}={}){const[o,l,d]=r;let c,u;if(typeof n=="number"?(c=n,u=n):(c=n.width,u=n.height),c!==l||u!==o){const h=new Float32Array(c*u*d);if(Array.isArray(s))for(let _=0;_<h.length;++_)h[_]=s[_%d];else s!==0&&h.fill(s);const[m,g]=i?[Math.floor((c-l)/2),Math.floor((u-o)/2)]:[0,0];for(let _=0;_<o;++_){const b=(_+g)*c,$=_*l;for(let y=0;y<l;++y){const E=(b+y+m)*d,k=($+y)*d;for(let T=0;T<d;++T)h[E+T]=e[k+T]}}if(a==="symmetric"){if(i)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const _=o-1,b=l-1;for(let $=0;$<u;++$){const y=$*c,E=Qa($,_)*l;for(let k=0;k<c;++k){if($<o&&k<l)continue;const T=(y+k)*d,A=(E+Qa(k,b))*d;for(let R=0;R<d;++R)h[T+R]=e[A+R]}}}e=h,r=[u,c,d]}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:l}=r;i=Math.min(o,l)}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),l=n*o,d=a*o,c=s===void 0?1:Math.min(s/l,s/d);let u=Math.floor(Number((l*c).toFixed(2))),h=Math.floor(Number((d*c).toFixed(2)));return this.size_divisibility!==void 0&&([u,h]=ms([u,h],this.size_divisibility)),[u,h]}else if(r!==void 0&&r.width!==void 0&&r.height!==void 0){let o=r.width,l=r.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let d=l/a,c=o/n;Math.abs(1-c)<Math.abs(1-d)?d=c:c=d,l=gc(d*a,this.config.ensure_multiple_of),o=gc(c*n,this.config.ensure_multiple_of)}return[o,l]}else{if(this.size_divisibility!==void 0)return ms([n,a],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(r)}`)}}async resize(e){const[r,n]=this.get_resize_output_image_size(e,this.size);return await e.resize(r,n,{resample:this.resample})}async preprocess(e,{do_normalize:r=null,do_pad:n=null,do_convert_rgb:a=null,do_convert_grayscale:i=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[s,o]=e.size;if(a??this.do_convert_rgb?e=e.rgb():i&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let h,m;Number.isInteger(this.crop_size)?(h=this.crop_size,m=this.crop_size):(h=this.crop_size.width,m=this.crop_size.height),e=await e.center_crop(h,m)}const l=[e.height,e.width];let d=Float32Array.from(e.data),c=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(d),r??this.do_normalize){let h=this.image_mean;Array.isArray(this.image_mean)||(h=new Array(e.channels).fill(h));let m=this.image_std;if(Array.isArray(this.image_std)||(m=new Array(e.channels).fill(h)),h.length!==e.channels||m.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${h.length}) and \`image_std\` (${m.length}) must match the number of channels in the image (${e.channels}).`);for(let g=0;g<d.length;g+=e.channels)for(let _=0;_<e.channels;++_)d[g+_]=(d[g+_]-h[_])/m[_]}if(n??this.do_pad){if(this.pad_size)[d,c]=this.pad_image(d,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){const[h,m]=ms([c[1],c[0]],this.size_divisibility);[d,c]=this.pad_image(d,c,{width:h,height:m})}}const u=new ae("float32",d,c).permute(2,0,1);return{original_size:[o,s],reshaped_input_size:l,pixel_values:u}}async _call(e,...r){Array.isArray(e)||(e=[e]);const n=await Promise.all(e.map(i=>this.preprocess(i)));return{pixel_values:Fn(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 Cx extends je{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 l=n[s];o!==null&&(l=Zf(l,o,"bilinear",!1));const[d,c]=o??l.dims.slice(-2),u=new ae("int32",new Int32Array(d*c),[d,c]),h=l[0].data,m=u.data;for(let $=1;$<l.dims[0];++$){const y=l[$].data;for(let E=0;E<y.length;++E)y[E]>h[E]&&(h[E]=y[E],m[E]=$)}const g=new Array(l.dims[0]),_=u.data;for(let $=0;$<_.length;++$){const y=_[$];g[y]=y}const b=g.filter($=>$!==void 0);i.push({segmentation:u,labels:b})}return i}}class bg extends je{}class Tx extends bg{}class Ix extends je{}class Ax extends je{}class vg extends je{}class Mx extends vg{}class Ox extends je{}class zx extends je{}class $g extends je{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 Px extends $g{}class Rx extends je{}class Bx extends je{}class Dx extends je{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 Nx extends je{}class xg extends je{post_process_object_detection(...e){return Eo(...e)}}class Lx extends xg{}class Ux extends je{}class Fx extends je{}class Sg extends je{pad_image(e,r,n,a={}){const[i,s,o]=r;let l=this.image_mean;Array.isArray(this.image_mean)||(l=new Array(o).fill(l));let d=this.image_std;Array.isArray(d)||(d=new Array(o).fill(l));const c=l.map((u,h)=>-u/d[h]);return super.pad_image(e,r,n,{center:!0,constant_values:c,...a})}}class Wx extends Sg{}class Gx extends je{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new ae("int64",new BigInt64Array(n.reduce((i,s)=>i*s)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return Eo(...e)}remove_low_and_no_objects(e,r,n,a){let i=[],s=[],o=[];for(let l=0;l<e.dims[0];++l){let d=e[l],c=r[l],u=mr(d.data)[1];if(u===a)continue;let m=Hn(d.data)[u];m>n&&(i.push(c),s.push(m),o.push(u))}return[i,s,o]}check_segment_validity(e,r,n,a=.5,i=.8){let s=[],o=0,l=0;const d=r[n].data;for(let u=0;u<e.length;++u)e[u]===n&&(s.push(u),++o),d[u]>=a&&++l;let c=o>0&&l>0;return c&&(c=o/l>i),[c,s]}compute_segments(e,r,n,a,i,s=null,o=null){let[l,d]=o??e[0].dims,c=new ae("int32",new Int32Array(l*d),[l,d]),u=[];if(o!==null)for(let b=0;b<e.length;++b)e[b]=Zf(e[b],o,"bilinear",!1);let h=new Int32Array(e[0].data.length),m=new Float32Array(e[0].data.length);for(let b=0;b<e.length;++b){let $=r[b];const y=e[b].data;for(let E=0;E<y.length;++E)y[E]*=$,y[E]>m[E]&&(h[E]=b,m[E]=y[E])}let g=0;const _=c.data;for(let b=0;b<n.length;++b){let $=n[b],[y,E]=this.check_segment_validity(h,e,b,a,i);if(y){++g;for(let k of E)_[k]=g;u.push({id:g,label_id:$,score:r[b]})}}return[c,u]}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,d=e.pred_masks.sigmoid();let[c,u,h]=o.dims;if(h-=1,s!==null&&s.length!==c)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let m=[];for(let g=0;g<c;++g){let _=s!==null?s[g]:null,b=o[g],$=d[g],[y,E,k]=this.remove_low_and_no_objects(b,$,r,h);if(k.length===0){let[R,W]=_??$.dims.slice(-2),K=new ae("int32",new Int32Array(R*W).fill(-1),[R,W]);m.push({segmentation:K,segments_info:[]});continue}let[T,A]=this.compute_segments(y,E,k,n,a,i,_);m.push({segmentation:T,segments_info:A})}return m}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class Vx extends je{post_process_object_detection(...e){return Eo(...e)}}class Hx extends je{reshape_input_points(e,r,n,a=!1){e=structuredClone(e);let i=Ko(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],l=n[s],d=[l[0]/o[0],l[1]/o[1]];for(let c=0;c<e[s].length;++c)for(let u=0;u<e[s][c].length;++u)for(let h=0;h<e[s][c][u].length;++h)e[s][c][u][h]*=d[h%2]}return new ae("float32",Float32Array.from(e.flat(1/0)),i)}add_input_labels(e,r){let n=Ko(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 ae("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 l=[s.height,s.width];for(let d=0;d<r.length;++d){const c=r[d],u=n[d];let h=await tc(e[d],{mode:"bilinear",size:l});if(h=h.slice(null,null,[0,u[0]],[0,u[1]]),h=await tc(h,{mode:"bilinear",size:c}),i){const m=h.data,g=new Uint8Array(m.length);for(let _=0;_<m.length;++_)m[_]>a&&(g[_]=1);h=new ae("bool",g,h.dims)}o.push(h)}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 qx extends je{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 jx extends je{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:Fn(n.map((s,o)=>jt([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 Kx extends Nr{constructor(e){super(e),this.config.mel_filters??=Vn(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=ki(this.config.n_fft,"hann")}_extract_fbank_features(e){const{data:r,dims:n}=Ei(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=mr(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){ua(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 ae("float32",n,[1,...a])}}}class Yx extends Nr{_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){ua(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 ae("float32",r,n),attention_mask:new ae("int64",new BigInt64Array(r.length).fill(1n),n)}}}class Xx extends Nr{constructor(e){super(e);const r=this.config.sampling_rate,n=Vn(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=ki(400,"povey",{periodic:!1})}_extract_fbank_features(e,r){return e=e.map(n=>n*32768),Ei(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}={}){ua(e,"SeamlessM4TFeatureExtractor");let s=this._extract_fbank_features(e,this.config.max_length);const o=s.data;if(a){const[_,b]=s.dims;for(let $=0;$<b;++$){let y=0;for(let A=0;A<_;++A)y+=o[A*b+$];const E=y/_;let k=0;for(let A=0;A<_;++A)k+=(o[A*b+$]-E)**2;k/=_-1;const T=Math.sqrt(k+1e-7);for(let A=0;A<_;++A){const R=A*b+$;o[R]=(o[R]-E)/T}}}let l;if(r){const[_,b]=s.dims,$=_%n;if($>0){const y=new Float32Array(b*(_+$));y.set(o),y.fill(this.config.padding_value,o.length);const E=_+$;s={data:y,dims:[E,b]},i&&(l=new ae("int64",new BigInt64Array(E),[1,E]),l.data.fill(1n,0,_))}}const[d,c]=s.dims,u=this.config.stride;if(d%u!==0)throw new Error(`The number of frames (${d}) must be a multiple of the stride (${u}).`);const m=new ae("float32",o,s.dims).view(1,Math.floor(d/u),c*u),g={input_features:m};if(i){const _=m.dims[1],b=new BigInt64Array(_);if(l){const $=l.data;for(let y=1,E=0;y<d;y+=u,++E)b[E]=$[y]}else b.fill(1n);g.attention_mask=new ae("int64",b,[1,_])}return g}}class Qx extends Nr{constructor(e){super(e);const r=this.config.sampling_rate,n=Vn(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=ki(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(e,r){return Ei(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){ua(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 ae("float32",r.data,[1,...r.dims])}}}class Zx extends Nr{constructor(e){super(e),this.mel_filters=Vn(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=Vn(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=ki(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 l=Math.floor(Math.random()*(o+1));e=e.subarray(l,l+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 "${n}" not implemented`);else{if(o<0){let l=new Float64Array(r);if(l.set(e),a==="repeat")for(let d=e.length;d<r;d+=e.length)l.set(e.subarray(0,Math.min(e.length,r-d)),d);else if(a==="repeatpad")for(let d=e.length;d<-o;d+=e.length)l.set(e,d);e=l}if(n==="fusion")throw new Error(`Truncation strategy "${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 Ei(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}={}){ua(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 ae("float32",n.data,[1,...n.dims])}}}class Jx extends Nr{}class tn extends mt{constructor(e){super(),this.feature_extractor=e}async _call(e,...r){return await this.feature_extractor(e,...r)}}class eS extends tn{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 tS extends tn{async _call(e){return await this.feature_extractor(e)}}class rS extends tn{async _call(e){return await this.feature_extractor(e)}}class nS extends tn{async _call(e){return await this.feature_extractor(e)}}class aS extends tn{}class iS{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor:je,WhisperFeatureExtractor:Kx,ViTFeatureExtractor:Rx,MobileViTFeatureExtractor:Nx,OwlViTFeatureExtractor:xg,Owlv2ImageProcessor:Lx,CLIPFeatureExtractor:vg,CLIPImageProcessor:Mx,ChineseCLIPFeatureExtractor:Ox,SiglipImageProcessor:zx,ConvNextFeatureExtractor:$g,ConvNextImageProcessor:Px,SegformerFeatureExtractor:Cx,BitImageProcessor:Ix,DPTImageProcessor:Tx,DPTFeatureExtractor:bg,GLPNFeatureExtractor:Ax,BeitFeatureExtractor:Fx,DeiTFeatureExtractor:Ux,DetrFeatureExtractor:Gx,YolosFeatureExtractor:Vx,DonutFeatureExtractor:Sg,NougatImageProcessor:Wx,EfficientNetImageProcessor:Dx,ViTImageProcessor:Bx,VitMatteImageProcessor:jx,SamImageProcessor:Hx,Swin2SRImageProcessor:qx,Wav2Vec2FeatureExtractor:Yx,SeamlessM4TFeatureExtractor:Xx,SpeechT5FeatureExtractor:Jx,ASTFeatureExtractor:Qx,ClapFeatureExtractor:Zx};static PROCESSOR_CLASS_MAPPING={WhisperProcessor:tS,Wav2Vec2ProcessorWithLM:rS,SamProcessor:eS,SpeechT5Processor:nS,OwlViTProcessor:aS};static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main"}={}){let o=n??await pr(e,"preprocessor_config.json",!0,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s}),l=o.feature_extractor_type??o.image_processor_type,d=this.FEATURE_EXTRACTOR_CLASS_MAPPING[l];if(!d)if(o.size!==void 0)console.warn(`Feature extractor type "${l}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),d=je;else throw new Error(`Unknown Feature Extractor type: ${l}`);let c=this.PROCESSOR_CLASS_MAPPING[o.processor_class]??tn,u=new d(o);return new c(u)}}const 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