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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 p=j.size(i);return{outputs:[{dims:i,dataType:d[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...ce(d[0].dims,i)]}},getShaderSource:l}},qo=(t,e)=>{Go(t.inputs),t.compute(Lt(t.inputs[0],e.perm))},jo=t=>We({perm:t.perm})}),Ko,Yo,Xo,Qo,Zo,Jo,el,tl,rl,nl,St,al,il,sl,ol,ll,ul,dl,cl,pl,hl,ng=V(()=>{me(),Ce(),Se(),xi(),Kr(),Ko={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"},Yo={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"},Xo={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},Qo={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},Zo=(t,e)=>{let r=[];for(let n=e-t;n<e;++n)r.push(n);return r},Jo=(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]},el=(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},tl=(t,e)=>{for(let r=0;r<t.length;++r)if(t[t.length-r-1]!==e-1-r)return!1;return!0},rl=(t,e)=>{let r=[];if(!tl(t,e)){for(let n=0;n<e;++n)t.indexOf(n)===-1&&r.push(n);t.forEach(n=>r.push(n))}return r},nl=(t,e,r,n,a,i,s)=>{let o=r[0].dims,l=j.size(i),d=j.size(s),p=G("_A",r[0].dataType,o),u=de("output",a,i),h=32,m=`
          var<workgroup> aBestValues : array<f32, ${h}>;
       `;return{name:t,shaderCache:e,getShaderSource:_=>`
        ${_.registerUniform("reduceSize","u32").declareVariables(p,u)}
        ${m}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${_.mainStart(h)}

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

          var bestValue = f32(${Xo[n]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${h}) {
           let candidate = f32(${p.getByOffset("offset + k")});
           bestValue = ${Ko[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 = ${Yo[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}(${Qo[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:l},programUniforms:[{type:12,data:d}]})}},St=(t,e,r,n)=>{let a=t.inputs.length===1?r:$i(t.inputs,r),i=a.axes;i.length===0&&!a.noopWithEmptyAxes&&(i=t.inputs[0].dims.map((m,_)=>_));let s=j.normalizeAxes(i,t.inputs[0].dims.length),o=s,l=t.inputs[0],d=rl(o,t.inputs[0].dims.length);d.length>0&&(l=t.compute(Lt(t.inputs[0],d),{inputs:[0],outputs:[-1]})[0],o=Zo(o.length,l.dims.length));let[p,u]=Jo(l.dims,o),h=p;a.keepDims&&(h=el(p,s)),t.compute(nl(e,{hint:a.cacheKey,inputDependencies:["type"]},[l],n,t.inputs[0].dataType,h,u),{inputs:[l]})},al=(t,e)=>{St(t,"ReduceMeanShared",e,"mean")},il=(t,e)=>{St(t,"ReduceL1Shared",e,"l1")},sl=(t,e)=>{St(t,"ReduceL2Shared",e,"l2")},ol=(t,e)=>{St(t,"ReduceLogSumExpShared",e,"logSumExp")},ll=(t,e)=>{St(t,"ReduceMaxShared",e,"max")},ul=(t,e)=>{St(t,"ReduceMinShared",e,"min")},dl=(t,e)=>{St(t,"ReduceProdShared",e,"prod")},cl=(t,e)=>{St(t,"ReduceSumShared",e,"sum")},pl=(t,e)=>{St(t,"ReduceSumSquareShared",e,"sumSquare")},hl=(t,e)=>{St(t,"ReduceLogSumShared",e,"logSum")}}),Et,fl,ta,$i,Ct,ml,gl,_l,yl,wl,bl,vl,$l,xl,Sl,It,El,Cl,Il,kl,Tl,Al,Ml,Ol,zl,Rl,xi=V(()=>{me(),Ce(),tt(),Se(),ng(),Et=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.")},fl=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],ta=(t,e,r,n,a,i,s=!1,o=!1)=>{let l=[],d=r[0].dims,p=d.length,u=j.normalizeAxes(a,p),h=!o&&u.length===0;d.forEach((y,v)=>{h||u.indexOf(v)>=0?s&&l.push(1):l.push(y)});let m=l.length,_=j.size(l);return{name:t,shaderCache:e,getShaderSource:y=>{let v=[],x=G("_A",r[0].dataType,p),w=de("output",i,m),E=n(x,w,u),I=E[2];for(let k=0,A=0;k<p;k++)h||u.indexOf(k)>=0?(s&&A++,I=`for(var j${k}: u32 = 0; j${k} < ${d[k]}; j${k}++) {
                  ${E[2].includes("last_index")?`let last_index = j${k};`:""}
                  ${x.indicesSet("input_indices",k,`j${k}`)}
                  ${I}
                }`):(v.push(`${x.indicesSet("input_indices",k,w.indicesGet("output_indices",A))};`),A++);return`

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

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

          ${v.join(`
`)}
          ${E[0]}       // init ops for reduce max/min
          ${E[1]}
          ${I}
          ${E[3]}
          ${E.length===4?w.setByOffset("global_idx","value"):E.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:l,dataType:i}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...ce(d,l)]})}},$i=(t,e)=>{let r=[];return t[1].dims[0]>0&&t[1].getBigInt64Array().forEach(n=>r.push(Number(n))),We({axes:r,keepDims:e.keepDims,noopWithEmptyAxes:e.noopWithEmptyAxes})},Ct=(t,e,r,n)=>{let a=t.inputs,i=a.length===1?r:$i(a,r);t.compute(ta(e,{hint:i.cacheKey,inputDependencies:["rank"]},[a[0]],i.noopWithEmptyAxes&&i.axes.length===0?fl:n,i.axes,a[0].dataType,i.keepDims,i.noopWithEmptyAxes),{inputs:[0]})},ml=(t,e)=>{Et(t.inputs),Ct(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},gl=(t,e)=>{Et(t.inputs),Ct(t,"ReduceL1",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},_l=(t,e)=>{Et(t.inputs),Ct(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);"])},yl=(t,e)=>{Et(t.inputs),Ct(t,"ReduceLogSumExp",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},wl=(t,e)=>{Et(t.inputs),Ct(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, ${r.getByIndices("input_indices")});`,""]})},bl=(t,e)=>{Et(t.inputs),Ct(t,"ReduceMean",e,(r,n,a)=>{let i=1;for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&(i*=t.inputs[0].dims[s]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${i});`]})},vl=(t,e)=>{Et(t.inputs),Ct(t,"ReduceMin",e,(r,n,a)=>{let i=[];for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&i.push(`input_indices[${s}] = 0;`);return[`${i.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},$l=(t,e)=>{Et(t.inputs),Ct(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},xl=(t,e)=>{Et(t.inputs),Ct(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},Sl=(t,e)=>{Et(t.inputs),Ct(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},It=(t,e,r)=>{if(e.length===0)return r;let n=1,a=1;for(let i=0;i<e.length;i++)e.indexOf(i)===-1?n*=t[i]:a*=t[i];return a<32&&n>1024},El=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?bl(t,e):al(t,e)},Cl=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?gl(t,e):il(t,e)},Il=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?_l(t,e):sl(t,e)},kl=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?yl(t,e):ol(t,e)},Tl=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?wl(t,e):ll(t,e)},Al=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?vl(t,e):ul(t,e)},Ml=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?$l(t,e):dl(t,e)},Ol=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?xl(t,e):cl(t,e)},zl=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Sl(t,e):pl(t,e)},Rl=(t,e)=>{It(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?ml(t,e):hl(t,e)}}),Si,Pl,Bl,Ei,ag=V(()=>{me(),tt(),xi(),Si=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.")},Pl=(t,e)=>{Si(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 = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(ta("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Bl=(t,e)=>{Si(t.inputs);let r=(n,a,i)=>{let s=[];for(let o=0;o<n.rank;o++)(i.indexOf(o)>=0||i.length===0)&&s.push(`input_indices[${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(ta("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Ei=t=>We(t)}),Dl,Nl,Ll,Ul,ra,Fl,Wl,Gl=V(()=>{me(),mi(),Se(),Dl=(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],p=r.dims[2];if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(a.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let 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 _=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 y=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||(y=s.dims[3])}let v=_+y,x=-1,w=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:y,kvSequenceLength:_,totalSequenceLength:v,maxSequenceLength:x,inputHiddenSize:p,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:w,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Nl=(t,e,r,n)=>{let a=Qe(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=Ze(e.dataType,a),p=u=>{let h=de("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 _=mt(e.dataType),y=[{name:"d_inv",type:_},{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(y).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 = ${ct("f32",a,"-3.402823e+38f")};
    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
      thread_max_vector = max(${Nt(_,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 = ${ct("f32",a,"0")};
    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
      sumVector += exp(${Nt(_,a,"x[offset + i]")} - maxValue);
    }
    wgSum[local_idx] = ${At("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] = ${ct(_,a,"uniforms.d_inv")};
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
        let f32input = ${Nt(_,a,"x[offset + i]")};
        x[offset + i] = ${h.type.value}(exp(f32input - maxValue) / sum);
      }
    }
  }`};t.compute({name:"AttentionProbsSoftmax",shaderCache:{hint:`${i};${d};${a}`},getShaderSource:p,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:l})},{inputs:[e],outputs:[]})},Ll=(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=Qe(a.headSize),d=a.headSize/l,p=12,u={x:Math.ceil(a.totalSequenceLength/p),y:Math.ceil(a.sequenceLength/p),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],_=v=>{let x=G("q",e.dataType,e.dims,l),w=G("key",r.dataType,r.dims,l),E=de("output",e.dataType,s),I=Ze(e.dataType),k=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"alpha",type:I}];return`
  const beta: ${I} = 1.0;
  const TILE_SIZE = ${p}u;

  var<workgroup> tileQ: array<${x.type.storage}, ${p*p}>;
  var<workgroup> tileK: array<${x.type.storage}, ${p*p}>;
  ${v.registerUniforms(k).declareVariables(x,w,E)}
  ${v.mainStart([p,p,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 = ${ct(I,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] = ${At("value",l)} * uniforms.alpha;
    }
  }`},y=t.compute({name:"AttentionProbs",shaderCache:{hint:`${l}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:h}),getShaderSource:_},{inputs:m,outputs:[-1]})[0];return Nl(t,y,a.batchSize*a.numHeads*a.sequenceLength,a.totalSequenceLength),y},Ul=(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 p=G("probs",e.dataType,e.dims),u=G("v",r.dataType,r.dims),h=de("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 = ${i}u;
  var<workgroup> tileQ: array<${p.type.value}, ${i*i}>;
  var<workgroup> tileK: array<${p.type.value}, ${i*i}>;
  ${d.registerUniforms(m).declareVariables(p,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 = ${p.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]},ra=(t,e,r,n,a,i,s,o,l,d,p)=>{let u=Ll(t,e,r,l,d,p);Ul(t,u,n,d)},Fl=(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}],p=u=>{let h=de("output_q",l[0].dataType,r),m=de("output_k",l[0].dataType,r),_=de("output_v",l[0].dataType,r),y=G("input",l[0].dataType,l[0].dims),v=G("weight",l[1].dataType,l[1].dims),x=G("bias",l[2].dataType,l[2].dims),w=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 = ${s}u;
  var<workgroup> tileInput: array<${w}, ${s*s}>;
  var<workgroup> tileWeightQ: array<${w}, ${s*s}>;
  var<workgroup> tileWeightK: array<${w}, ${s*s}>;
  var<workgroup> tileWeightV: array<${w}, ${s*s}>;
  ${u.registerUniforms(E).declareVariables(y,v,x,h,m,_)}
  ${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 = ${w}(0);
    var valueK = ${w}(0);
    var valueV = ${w}(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:p},{inputs:l,outputs:[-1,-1,-1]})},Wl=(t,e)=>{let r=Dl(t.inputs,e),[n,a,i]=Fl(t,r);return ra(t,n,a,i,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),Vl,Hl,ql,jl,ig=V(()=>{xt(),me(),Ce(),tt(),Se(),Vl=(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")},Hl=(t,e)=>{let{epsilon:r,spatial:n,format:a}=e,i=t[0].dims,s=n?Qe(i[i.length-1]):1,o=a==="NHWC"&&i.length>1?s:1,l=j.size(i)/s,d=n,p=d?i.length:i,u=G("x",t[0].dataType,t[0].dims,s),h=G("scale",t[1].dataType,t[1].dims,o),m=G("bias",t[2].dataType,t[2].dims,o),_=G("inputMean",t[3].dataType,t[3].dims,o),y=G("inputVar",t[4].dataType,t[4].dims,o),v=de("y",t[0].dataType,p,s),x=()=>{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=`
            ${v.indicesSet("outputIndices","0","0")}
            let cOffset = ${v.indicesToOffset("outputIndices")};`;else{E=`var cIndices = ${h.type.indices}(0);
                       cIndices[0] = outputIndices[${i.length-1}];`;for(let I=1;I<h.rank;I++)E+=`cIndices[${I}] = outputIndices[${I}];`;E+=`let cOffset = ${h.indicesToOffset("cIndices")};`}return E},w=E=>`
  const epsilon = ${r};
  ${E.registerUniform("outputSize","u32").declareVariables(u,h,m,_,y,v)}
  ${E.mainStart()}
  ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${v.offsetToIndices(`global_idx * ${s}`)};
    ${x()}
    let scale = ${h.getByOffset("cOffset")};
    let bias = ${m.getByOffset("cOffset")};
    let inputMean = ${_.getByOffset("cOffset")};
    let inputVar = ${y.getByOffset("cOffset")};
    let x = ${u.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${v.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${e.epsilon}_${e.format}_${n}_${s}`,inputDependencies:d?["rank","type","type","type","type"]:void 0},getShaderSource:w,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d?[{type:12,data:l},...ce(i)]:[{type:12,data:l}]})}},ql=t=>We(t),jl=(t,e)=>{let{inputs:r,outputCount:n}=t,a=ql({...e,outputCount:n});if(Oe.webgpu.validateInputContent&&Vl(r,a),e.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");t.compute(Hl(r,a))}}),Kl,Yl,Xl,sg=V(()=>{Ce(),Se(),Kl=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")},Yl=t=>{let e=t[0].dims,r=t[0].dims[2],n=j.size(e)/4,a=t[0].dataType,i=G("input",a,e,4),s=G("bias",a,[r],4),o=G("residual",a,e,4),l=de("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")};
    ${l.setByOffset("global_idx","value")}
  }`}},Xl=t=>{Kl(t.inputs),t.compute(Yl(t.inputs))}}),Ql,Te,Zl,Jl,eu,tu,ru,nu,au,iu,su,ou,lu,uu,du,cu,na,pu,aa,hu,fu,mu,gu,_u,yu,wu,bu,vu,$u,xu,Su,Eu,Cu,Iu,ku,Ci,Tu,Ii,ki,Au,Mu,Ou,Ti=V(()=>{me(),Ce(),tt(),Se(),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=G("inputData",r,[s],4),d=de("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)}
  }`},Te=(t,e,r,n,a,i=t.dataType)=>({name:e,shaderCache:{hint:a,inputDependencies:["type"]},getShaderSource:s=>Ql(s,j.size(t.dims),t.dataType,i,r,n),getRunData:s=>({outputs:[{dims:t.dims,dataType:i}],dispatchGroup:{x:Math.ceil(j.size(s[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(j.size(t.dims)/4)}]})}),Zl=t=>{t.compute(Te(t.inputs[0],"Abs","abs"))},Jl=t=>{t.compute(Te(t.inputs[0],"Acos","acos"))},eu=t=>{t.compute(Te(t.inputs[0],"Acosh","acosh"))},tu=t=>{t.compute(Te(t.inputs[0],"Asin","asin"))},ru=t=>{t.compute(Te(t.inputs[0],"Asinh","asinh"))},nu=t=>{t.compute(Te(t.inputs[0],"Atan","atan"))},au=t=>{t.compute(Te(t.inputs[0],"Atanh","atanh"))},iu=t=>We(t),su=(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): ${e.to}`)}t.compute(Te(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},ou=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:yi,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:wi;return We({min:e,max:r})},lu=(t,e)=>{let r=t.inputs.length===1?e:ou(t.inputs),n=mt(t.inputs[0].dataType);t.compute(Te(t.inputs[0],"Clip",a=>`clamp(${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]})},uu=t=>{t.compute(Te(t.inputs[0],"Ceil","ceil"))},du=t=>{t.compute(Te(t.inputs[0],"Cos","cos"))},cu=t=>{t.compute(Te(t.inputs[0],"Cosh","cosh"))},na=t=>We(t),pu=(t,e)=>{let r=mt(t.inputs[0].dataType);t.compute(Te(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))},aa=(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));
}`,hu=t=>{let e=mt(t.inputs[0].dataType);t.compute(Te(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,aa(e)))},fu=t=>{t.compute(Te(t.inputs[0],"Exp","exp"))},mu=t=>{t.compute(Te(t.inputs[0],"Floor","floor"))},gu=t=>{let e=mt(t.inputs[0].dataType);t.compute(Te(t.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,aa(e)))},_u=(t,e)=>{let r=mt(t.inputs[0].dataType);t.compute(Te(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))},yu=t=>{t.compute(Te(t.inputs[0],"Not",e=>`!${e}`))},wu=t=>{t.compute(Te(t.inputs[0],"Neg",e=>`-${e}`))},bu=t=>{t.compute(Te(t.inputs[0],"Reciprocal",e=>`1.0/${e}`))},vu=t=>{let e=mt(t.inputs[0].dataType);t.compute(Te(t.inputs[0],"Relu",r=>`select(vec4<${e}>(0.0), ${r}, ${r} > vec4<${e}>(0.0))`))},$u=t=>{t.compute(Te(t.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},xu=t=>We(t),Su=(t,e)=>{let r=mt(t.inputs[0].dataType);t.compute(Te(t.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${e.alpha} * ${n} + vec4<${r}>(${e.beta})))`,void 0,e.cacheKey))},Eu=t=>{t.compute(Te(t.inputs[0],"Sin","sin"))},Cu=t=>{t.compute(Te(t.inputs[0],"Sinh","sinh"))},Iu=t=>{t.compute(Te(t.inputs[0],"Sqrt","sqrt"))},ku=t=>{t.compute(Te(t.inputs[0],"Tan","tan"))},Ci=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,Tu=t=>{t.compute(Te(t.inputs[0],"Tanh",Ci))},Ii=(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 ${Ci("v")};
}
`,ki=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,Au=t=>{let e=mt(t.inputs[0].dataType);t.compute(Te(t.inputs[0],"FastGelu",ki,Ii(e),void 0,t.inputs[0].dataType))},Mu=(t,e)=>{let r=mt(t.inputs[0].dataType);return t.compute(Te(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},Ou=t=>{t.compute(Te(t.inputs[0],"Log","log"))}}),zu,Ru,Pu,og=V(()=>{Ce(),Se(),Ti(),zu=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")},Ru=t=>{let e=t[0].dims.slice();e[2]=e[2]/2;let r=G("input",t[0].dataType,t[0].dims,4),n=G("bias",t[0].dataType,[t[0].dims[2]],4),a=de("output",t[0].dataType,e,4),i=j.size(e)/4,s=Ze(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)}

  ${aa(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);

    ${a.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Pu=t=>{zu(t.inputs),t.compute(Ru(t.inputs))}}),Bu,Du,kt,Nu,Lu,Uu,Fu,Wu,Gu,Vu,Hu,qu,ju,lg=V(()=>{me(),Ce(),Se(),Bu=(t,e,r,n,a,i,s,o,l,d,p,u)=>{let h,m;typeof o=="string"?h=m=(w,E)=>`${o}((${w}),(${E}))`:typeof o=="function"?h=m=o:(h=o.scalar,m=o.vector);let _=de("outputData",p,n.length,4),y=G("aData",l,e.length,4),v=G("bData",d,r.length,4),x;if(a)if(i){let w=j.size(e)===1,E=j.size(r)===1,I=e.length>0&&e[e.length-1]%4===0,k=r.length>0&&r[r.length-1]%4===0;w||E?x=_.setByOffset("global_idx",m(w?`${y.type.value}(${y.getByOffset("0")}.x)`:y.getByOffset("global_idx"),E?`${v.type.value}(${v.getByOffset("0")}.x)`:v.getByOffset("global_idx"))):x=`
            let outputIndices = ${_.offsetToIndices("global_idx * 4u")};
            let offsetA = ${y.broadcastedIndicesToOffset("outputIndices",_)};
            let offsetB = ${v.broadcastedIndicesToOffset("outputIndices",_)};
            ${_.setByOffset("global_idx",m(s||I?y.getByOffset("offsetA / 4u"):`${y.type.value}(${y.getByOffset("offsetA / 4u")}[offsetA % 4u])`,s||k?v.getByOffset("offsetB / 4u"):`${v.type.value}(${v.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else x=_.setByOffset("global_idx",m(y.getByOffset("global_idx"),v.getByOffset("global_idx")));else{if(!i)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let w=(E,I,k="")=>{let A=`aData[indexA${I}][componentA${I}]`,D=`bData[indexB${I}][componentB${I}]`;return`
            let outputIndices${I} = ${_.offsetToIndices(`global_idx * 4u + ${I}u`)};
            let offsetA${I} = ${y.broadcastedIndicesToOffset(`outputIndices${I}`,_)};
            let offsetB${I} = ${v.broadcastedIndicesToOffset(`outputIndices${I}`,_)};
            let indexA${I} = offsetA${I} / 4u;
            let indexB${I} = offsetB${I} / 4u;
            let componentA${I} = offsetA${I} % 4u;
            let componentB${I} = offsetB${I} % 4u;
            ${E}[${I}] = ${k}(${h(A,D)});
          `};p===9?x=`
            var data = vec4<u32>(0);
            ${w("data",0,"u32")}
            ${w("data",1,"u32")}
            ${w("data",2,"u32")}
            ${w("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:x=`
            ${w("outputData[global_idx]",0)}
            ${w("outputData[global_idx]",1)}
            ${w("outputData[global_idx]",2)}
            ${w("outputData[global_idx]",3)}
          `}return`
        ${t.registerUniform("vec_size","u32").declareVariables(y,v,_)}

        ${u??""}

        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${x}
      }`},Du=(t,e,r,n,a,i,s=r.dataType)=>{let o=!j.areEqual(r.dims,n.dims),l=r.dims,d=j.size(r.dims),p=!1,u=!1,h=[o];if(o){let m=Cr.calcShape(r.dims,n.dims,!1);if(!m)throw new Error("Can't perform binary op on the given tensors");l=m,d=j.size(l);let _=j.size(r.dims)===1,y=j.size(n.dims)===1,v=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,x=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;h.push(_),h.push(y),h.push(v),h.push(x);let w=1;for(let E=1;E<l.length;E++){let I=r.dims[r.dims.length-E]??1,k=n.dims[n.dims.length-E]??1;if(I===k)w*=I;else break}w%4===0?(u=!0,p=!0):(_||y||v||x)&&(p=!0)}else p=!0;return h.push(p),{name:t,shaderCache:{hint:e+h.map(m=>m.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:m=>Bu(m,r.dims,n.dims,l,p,o,u,a,r.dataType,n.dataType,s,i),getRunData:()=>({outputs:[{dims:l,dataType:s}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:Math.ceil(j.size(l)/4)},...ce(r.dims,n.dims,l)]})}},kt=(t,e,r,n,a,i)=>{t.compute(Du(e,a??"",t.inputs[0],t.inputs[1],r,n,i))},Nu=t=>{kt(t,"Add",(e,r)=>`${e}+${r}`)},Lu=t=>{kt(t,"Div",(e,r)=>`${e}/${r}`)},Uu=t=>{kt(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4<u32>(${e}==${r})`},void 0,void 0,9)},Fu=t=>{kt(t,"Mul",(e,r)=>`${e}*${r}`)},Wu=t=>{let e=G("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;kt(t,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},`
    fn pow_custom(a : ${e}, b : ${e}) -> ${e} {
      if (b == ${e}(0.0)) {
        return ${e}(1.0);
      } else if (a < ${e}(0.0) && f32(b) != floor(f32(b))) {
        return ${e}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${e}(1.0), round(f32(abs(b) % ${e}(2.0))) != 1.0) * ${e}(${e==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${e}>, b : vec4<${e}>) -> vec4<${e}> {
      // TODO: implement vectorized pow
      return vec4<${e}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},Gu=t=>{kt(t,"Sub",(e,r)=>`${e}-${r}`)},Vu=t=>{kt(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4<u32>(${e}>${r})`},void 0,void 0,9)},Hu=t=>{kt(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4<u32>(${e}<${r})`},void 0,void 0,9)},qu=t=>{kt(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4<u32>(${e}>=${r})`},void 0,void 0,9)},ju=t=>{kt(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4<u32>(${e}<=${r})`},void 0,void 0,9)}}),Ku,Yu,Xu,Qu,Zu,Ju,ug=V(()=>{me(),Ce(),tt(),Se(),Ku=(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")})}})},Yu=(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;
  }`,Xu=(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}) { ${i} }`)}return n.join(`
`)},Qu=(t,e,r,n)=>{let a=j.size(r),i=new Array(t.length),s=new Array(t.length),o=0,l=[],d=[],p=[{type:12,data:a}];for(let y=0;y<t.length;++y)o+=t[y].dims[e],i[y]=o,d.push(t[y].dims.length),s[y]=G(`input${y}`,n,d[y]),l.push("rank"),p.push({type:12,data:i[y]});for(let y=0;y<t.length;++y)p.push(...ce(t[y].dims));p.push(...ce(r));let u=de("output",n,r.length),h=u.indicesGet("indices",e),m=Array.from(Array(i.length).keys()).map(y=>`uniforms.sizeInConcatAxis${y}`).join(","),_=y=>`

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

  ${Yu(i.length,m)}

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

    ${Xu(s,u)}
  }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:_}},Zu=(t,e)=>{let r=t.inputs,n=r[0].dims,a=j.normalizeAxis(e.axis,n.length);Ku(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=>j.size(o.dims)>0);t.compute(Qu(s,a,i,r[0].dataType),{inputs:s})},Ju=t=>We({axis:t.axis})}),sr,or,lr,Ai,ur=V(()=>{me(),Ce(),sr=(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 ${t.activation}`)}},or=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},lr=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},Ai=t=>{let e=t?.activation||"";if(e==="HardSigmoid"){let[r,n]=t?.activation_params||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=t?.activation_params||[yi,wi];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=t?.activation_params||[.01];return{activation:e,alpha:r}}return{activation:e}}}),ot,Mi,Oi=V(()=>{ot=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},Mi=t=>`
      ${t?"value = value + getBiasByOutputCoords(coords);":""}
      `}),zi,ed=V(()=>{zi=t=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${t}.x), i32(${t}.y), i32(${t}.z), 1));
}
`}),td,rd,ia,Ri,nd,sa,ad,Pi,oa=V(()=>{me(),Ce(),Se(),ur(),Oi(),td=(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":""});
        `,rd=(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];"}
        }`,ia=(t,e,r="f32",n,a=!1,i=32,s=!1,o=32)=>{let l=e[1]*t[1],d=e[0]*t[0],p=a?l:i,u=a?i:l,h=p/e[0],m=i/e[1];if(!((a&&h===4&&t[1]===4||!a&&(h===3||h===4))&&p%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 ${p} must be divisible by workgroupSize[0]${e[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${h}<${r}>, ${p/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;
          ${td(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];"}

          ${rd(a,h)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},Ri=(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":""});
            `,nd=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",sa=(t,e,r="f32",n,a=!1,i=32,s=!1,o=32,l=!1)=>{let d=t[1]*e[1],p=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],_=u/e[0],y=i/e[1],v=l?`
    let localRow = i32(localId.y);
    let localCol = i32(localId.x);
    let globalRowStart = i32(workgroupId.y) * ${d};
    let globalColStart = i32(workgroupId.x) * ${p};

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

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

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

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

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

  // Compute acc values for a single thread.
  var BCached : array<${r}, colPerThread>;
  for (var k = 0; k < tileInner; k = k + 1) {
    for (var inner = 0; inner < colPerThread; inner = inner + 1) {
      BCached[inner] = mm_Bsub[k][tileCol + inner];
    }

    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      ${nd(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}, ${p}>, ${i}>;
  const rowPerThread = ${t[1]};
  const colPerThread = ${t[0]};
  const tileInner = ${i};

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

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

    // Without this initialization strange values show up in acc.
    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = 0.0;
      }
    }
    ${v}
  }
`},ad=(t,e,r,n,a,i=!1)=>{let[s,o,l]=a,[d,p,u,h]=n,m=jr(s,l),_=jr(o,l),y=Ze(n[0].type.tensor),v=()=>{let w=p.rank,E=d.rank,I=`var aIndices: ${p.type.indices};`;for(let k=w-2-1,A=E-1;k>=0;k--,A--)I+=`
aIndices[${k}] = ${E>1?`batchIndices[${A}]`:"batchIndices"};`;return m.forEach(k=>{I+=`
aIndices[${k}] = 0;`}),I+=`
aIndices[${w-2}] = u32(row);
                   aIndices[${w-1}] = u32(colIn);`,I},x=()=>{let w=u.rank,E=d.rank,I=`var bIndices: ${u.type.indices};`;for(let k=w-2-1,A=E-1;k>=0;k--,A--)I+=`
bIndices[${k}] = ${E>1?`batchIndices[${A}]`:"batchIndices"};`;return _.forEach(k=>{I+=`
bIndices[${k}] = 0;`}),I+=`
bIndices[${w-2}] = u32(row);
                   bIndices[${w-1}] = u32(colIn);`,I};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${ot(t,y)} {
      var value = ${ot(t,y)}(0.0);
      let col = colIn * ${t};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${v()}
        value = ${p.getByIndices("aIndices")};
      }
      return value;
    }

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${ot(t,y)}) {
      let col = colIn * ${t};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${e?`value = value + ${i?"bias[colIn]":`${ot(t,y)}(bias[row])`};`:""}
        ${r}
        ${h.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},Pi=(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),p=j.size(d),u=i[i.length-2],h=i[i.length-1],m=s[s.length-1],_=h%4===0&&m%4===0,y=u<=8?[4,1,1]:[4,4,1],v=[8,8,1],x=[Math.ceil(m/v[0]/y[0]),Math.ceil(u/v[1]/y[1]),Math.ceil(p/v[2]/y[2])],w=_?4:1,E=[...o,u,h/w],I=E.length,k=[...l,h,m/w],A=k.length,D=[p,u,m/w],L=[{type:6,data:u},{type:6,data:m},{type:6,data:h}];or(e,L),L.push(...ce(d,E,k));let Y=["rank","rank"],H=t.length>2;H&&(L.push(...ce(t[2].dims)),Y.push("rank")),L.push(...ce(D));let re=N=>{let ee=d.length,oe=bi("batchDims",t[0].dataType,ee,1),Ae=Ze(t[0].dataType),q=G("a",t[0].dataType,I,w),J=G("b",t[1].dataType,A,w),ae=de("result",t[0].dataType,D.length,w),W=[q,J];if(H){let ze=a?w:1;W.push(G("bias",t[2].dataType,t[2].dims.length,ze))}let te=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];lr(e,te);let B=Ze(ae.type.tensor),Q=sr(e,ae.type.value,B),he=ad(w,H,Q,[oe,q,J,ae],[o,l,d],a);return`
  ${N.registerUniforms(te).registerInternalVariables(oe).declareVariables(...W,ae)}
  ${he}
  ${_?ia(y,v,Ae,oe):sa(y,v,Ae,oe)}
                   `};return{name:"MatMul",shaderCache:{hint:`${y};${e.activation};${_};${a}`,inputDependencies:Y},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:x[0],y:x[1],z:x[2]},programUniforms:L}),getShaderSource:re}}}),id,sd,dg=V(()=>{me(),ir(),Se(),ur(),Oi(),ed(),oa(),id=(t,e,r,n,a=!1,i,s=4,o=4,l=4,d="f32")=>{let p=Y=>{switch(Y){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${d}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Y} is not supported.`)}},u=Y=>{switch(Y){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 ${Y} 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);
    `,_=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",y=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",v=t?"row":"col",x=t?"col":"row",w=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${v} / outWidth;
    let outCol = ${v} % outWidth;

    let WRow = ${x} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${x} / 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 = ${x} % inChannels;
    var resData = ${ot(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 < ${_} && xCol >= 0 && xCol < ${y}) {
      ${h}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${p(s)}
    }
    return resData;`,E=t?e&&n?`
    let col = colIn * ${s};
    ${w}`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${w}
    }
    return ${ot(s,d)}(0.0);`:n&&r?`
    let col = colIn * ${s};
    ${w}`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${w}
    }
    return ${ot(s,d)}(0.0);`,I=`${u(o)}`,k=ot(l,d),A=ot(t?s:o,d),D=ot(t?o:s,d),L=sr(i,k,d);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${A} {
      ${t?E:I}
    }

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {
      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}
      ${Mi(a)}
      ${L}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},sd=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[0].dims[1],p=r[0],u=l?r[2]:r[3],h=l?r[1]:r[2],m=l?r[3]:r[1],_=l&&(d%4===0||d%3===0)&&m%4===0,y=l?m:u*h,v=l?u*h:m,x=[8,8,1],w=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(y/x[0]/w[0]),Math.ceil(v/x[1]/w[1]),Math.ceil(p/x[2]/w[2])];Ke("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let I=_?l&&d%4!==0?3:4:1,k=x[1]*w[1],A=x[0]*w[0],D=Math.max(x[0]*I,x[1]),L=n%k===0,Y=a%A===0,H=i%D===0,re=_?[I,4,4]:[1,1,1],N=[{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}];or(e,N),N.push(...ce(t[0].dims,t[1].dims));let ee=["rank","rank"];s&&(N.push(...ce(t[2].dims)),ee.push("rank")),N.push(...ce(r));let oe=Ae=>{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}];lr(e,q);let J=_?4:1,ae=Ze(t[0].dataType),W=`
      fn setOutputAtIndex(flatIndex : i32, value : ${_?`vec4<${ae}>`:ae}) {
        result[flatIndex] = ${_?`vec4<${ae}>`:ae}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${_?`vec4<${ae}>`:ae}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${_?"/ 4":""}, value);
      }`,te=G("x",t[0].dataType,t[0].dims.length,I===3?1:I),B=G("w",t[1].dataType,t[1].dims.length,J),Q=[te,B],he=de("result",t[0].dataType,r.length,J);if(s){let ze=G("bias",t[2].dataType,t[2].dims.length,J);Q.push(ze),W+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${_?`vec4<${ae}>`:ae} {
          return bias[coords.${l?"w":"y"}${_?"/ 4":""}];
        }`}return`
        ${zi("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 };
        ${Ae.registerUniforms(q).declareVariables(...Q,he)}
        ${W}
        ${id(l,L,Y,H,s,e,re[0],re[1],re[2],ae)}
        ${_?ia(w,x,ae,void 0,!l,D):sa(w,x,ae,void 0,!l,D,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${I};${_};${L};${Y};${H};${k};${A};${D}`,inputDependencies:ee},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:N}),getShaderSource:oe}}}),Bi,od,cg=V(()=>{me(),Ce(),Se(),fd(),ur(),Bi=(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=la(i,s,e.dilations,e.pads,e.strides,l),p=j.size(d),u=[{type:12,data:p},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];or(e,u),u.push(...ce(i,s,d));let h=["rank","rank"];n&&(u.push(...ce(t[2].dims)),h.push("rank")),u.push(...ce(d));let m=_=>{let y=de("output",t[0].dataType,d.length),v=Ze(y.type.tensor),x=sr(e,y.type.value,v),w=G("x",t[0].dataType,i.length),E=G("w",t[1].dataType,s.length),I=[w,E];n&&I.push(G("b",t[2].dataType,t[2].dims));let k=[{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 lr(e,k),`
  ${_.registerUniforms(k).declareVariables(...I,y)}

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

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

        if (xHeight < 0u || xHeight >= uniforms.x_shape[${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?w.get("batch","xHeight","xWidth","input_channel"):w.get("batch","input_channel","xHeight","xWidth")};
          let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${a}
    ${x}
    ${y.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(p/64)},programUniforms:u}),getShaderSource:m}},od=(t,e,r)=>{let n=t.length>2,a=Qe(r[3]),i=Qe(r[2]),s=j.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],p=[{type:12,data:s},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];or(e,p),p.push(...ce(o,l,d));let u=(i-1)*e.strides[1]+l[1],h=m=>{let _=de("output",t[0].dataType,d.length,a),y=Ze(_.type.tensor),v=sr(e,_.type.value,y),x=G("x",t[0].dataType,o.length,a),w=G("w",t[1].dataType,l.length,a),E=[x,w];n&&E.push(G("b",t[2].dataType,t[2].dims,a));let I=n?"value += b[output_channel];":"",k=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return lr(e,k),`
  ${m.registerUniforms(k).declareVariables(...E,_)}
  ${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<${x.type.value}, ${u}>;
    var values: array<${_.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] = ${x.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${x.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${l[1]}; w_width++) {
          let w_val = ${w.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];
      ${I}
      ${v}
      ${_.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${i};${u};${l[0]};${l[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:p}),getShaderSource:h}}}),Di,ld,ud,dd=V(()=>{me(),Ce(),oa(),Se(),ur(),Di=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i[i.length-2],l=s[s.length-1],d=i[i.length-1],p=Qe(l),u=Qe(d),h=Qe(o),m=j.size(r)/p/h,_=t.length>2,y=n?n.slice(0,-2):r.slice(0,-2),v=[j.size(y),o,l],x=[{type:12,data:m},{type:12,data:o},{type:12,data:l},{type:12,data:d}];or(e,x),x.push(...ce(y,i,s)),_&&x.push(...ce(t[2].dims)),x.push(...ce(v));let w=E=>{let I=bi("batch_dims",t[0].dataType,y.length),k=G("a",t[0].dataType,i.length,u),A=G("b",t[1].dataType,s.length,p),D=de("output",t[0].dataType,v.length,p),L=Ze(D.type.tensor),Y=sr(e,D.type.value,L),H=[k,A],re="";if(_){let W=a?p:1;H.push(G("bias",t[2].dataType,t[2].dims.length,W)),re=`${a?`value += bias[col / ${W}];`:`value += ${D.type.value}(bias[row + i]);`}`}let N=i.slice(0,-2),ee=s.slice(0,-2),oe=jr(N,y),Ae=jr(ee,y),q=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];lr(e,q);let J=(W,te)=>{let B=W.rank,Q=W.name;if(B===2)return`var ${Q}_indices = ${W.type.indices}(0u, 0u);`;let he=I.rank,ze=`var ${Q}_indices: ${W.type.indices};`;for(let Re=B-2-1,Je=he-1;Re>=0;Re--,Je--)ze+=`
${Q}_indices[${Re}] = ${he>1?`batch_indices[${Je}]`:"batch_indices"};`;return te.forEach(Re=>{ze+=`
${Q}_indices[${Re}] = 0;`}),ze+=`${Q}_indices[${B-2}] = 0u;
                     ${Q}_indices[${B-1}] = 0u;`,ze},ae=()=>{let W=`var a_data: ${k.type.value};`;for(let te=0;te<u;te++)W+=`
              let b_data${te} = b[(b_offset + (k + ${te}) * uniforms.N + col) / ${p}];`;for(let te=0;te<h;te++){W+=`a_data = a[(a_offset + (row + ${te}) * uniforms.K + k) / ${u}];`;for(let B=0;B<u;B++)W+=`
            values[${te}] = fma(${A.type.value}(a_data${u===1?"":`[${B}]`}), b_data${B}, values[${te}]);
`}return W};return`
  ${E.registerUniforms(q).registerInternalVariables(I).declareVariables(...H,D)}
  ${E.mainStart()}
    ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${p})) * ${p};
    var index1 = global_idx / (uniforms.N / ${p});
    let stride1 = uniforms.M / ${h};
    let row = (index1 % stride1) * ${h};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${I.offsetToIndices("batch")};`}
    ${J(k,oe)}
    let a_offset = ${k.indicesToOffset("a_indices")};
    ${J(A,Ae)}
    let b_offset = ${A.indicesToOffset("b_indices")};
    var values: array<${D.type.value}, ${h}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${u}) {
      ${ae()}
    }
    for (var i = 0u; i < ${h}u; i++) {
      var value = values[i];
      ${re}
      ${Y}
      let cur_indices = ${D.type.indices}(batch, row + i, col);
      let offset = ${D.indicesToOffset("cur_indices")};
      ${D.setByOffset(`offset / ${p}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${p};${u};${h};${a}`,inputDependencies:_?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:x}),getShaderSource:w}},ld=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.")},ud=t=>{ld(t.inputs);let e=Cr.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(Di(t.inputs,{activation:""},e)):t.compute(Pi(t.inputs,{activation:""},e))}}),la,ua,cd,Ni,Li,pd,hd,Ui,fd=V(()=>{Ce(),dg(),oa(),cg(),ur(),dd(),Kr(),la=(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],p=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-p[m]+a[m])/a[m]));return u.splice(0,0,s),u.splice(i?3:1,0,d),u},ua=[2,3,1,0],cd=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[1]*e.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let a=t[0].dims.length-2;if(e.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(e.strides.length!==a)throw new Error(`strides should be ${a}D`);if(e.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},Ni=(t,e)=>{let r=t.kernelShape.slice();for(let i=2;i<e[1].dims.length;++i)r[i-2]===0&&(r[i-2]=e[1].dims[i]);let n=t.pads.slice();Qn.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,r,n,t.format==="NHWC",t.autoPad);let a=Object.assign({},t);return Object.assign(a,{kernelShape:r,pads:n}),a},Li=t=>{let e=Ai(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,i=t.group,s=t.kernel_shape,o=t.pads,l=t.strides,d=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:i,kernelShape:s,pads:o,strides:l,wIsConst:d,...e,cacheKey:`${t.format};${e.activation};`}},pd=(t,e,r)=>{let n=Ni(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=la(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),D=t.kernelCustomData.wT??t.compute(Lt(e[1],ua),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=D);let L=[e[0],D];e.length===3&&L.push(e[2]),t.compute(od(L,n,A),{inputs:L})}else t.compute(Bi(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],p=e[1].dims[3],u=la(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),h=u[a?1:2],m=u[a?2:3],_=u[a?3:1],y=a&&d===s&&p===o&&r.pads[0]===0&&r.pads[1]===0;if(y||d===1&&p===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let A=u[0],D,L,Y,H=[];if(a){let ee=t.kernelCustomData.wT??t.compute(Lt(e[1],ua),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=ee),y){let oe=s*o*l;D=e[0].reshape([1,A,oe]),L=ee.reshape([1,oe,_]),Y=[1,A,_]}else D=e[0].reshape([A,s*o,l]),L=ee.reshape([1,l,_]),Y=[A,h*m,_];H.push(D),H.push(L)}else D=e[0].reshape([A,l,s*o]),L=e[1].reshape([1,_,l]),Y=[A,_,h*m],H.push(L),H.push(D);i&&H.push(e[2]);let re=Y[2],N=H[0].dims[H[0].dims.length-1];re<8&&N<8?t.compute(Di(H,n,u,Y,a),{inputs:H}):t.compute(Pi(H,n,u,Y,a),{inputs:H});return}let v=!0,x=t.kernelCustomData.wT??t.compute(Lt(e[1],ua),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=x);let w=[e[0],x];i&&w.push(e[2]);let E=a?h*m:_,I=a?_:h*m,k=d*p*l;t.compute(sd(w,n,u,E,I,k,i,v),{inputs:w})},hd=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&n.push(t.inputs[2]);let a=[0,e.pads[0],0,e.pads[1]],i=[1].concat(e.strides),s=[1].concat(e.dilations),o=[1].concat(e.kernelShape),l=Ni({...e,pads:a,strides:i,dilations:s,kernelShape:o},n);t.compute(Bi(n,l,d=>r?[d[0],d[2],d[3]]:[]))},Ui=(t,e)=>{cd(t.inputs,e),t.inputs[0].dims.length===3?hd(t,e):pd(t,t.inputs,e)}}),md,gd,pg=V(()=>{me(),ir(),Se(),ur(),Oi(),ed(),oa(),md=(t,e=!1,r,n,a=4)=>{let i=v=>{switch(v){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`
            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];
            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];
            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];
            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];
            return ${n}(v0, v1, v2, v3);
            `;default:throw new Error(`innerElementSize ${v} is not supported.`)}},s=t?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,o=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,l=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",d=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",p=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 = ${p} / outWidth;
      let outCol = ${p} % 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);`,_=`
      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);
      `,y=sr(r,n);return`
  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} {
    ${t?m:_}
  }

  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} {
    ${t?_: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}
      ${Mi(e)}
      ${y}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${a}] = value;
    }
  }`},gd=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[0].dims[1],p=r[0],u=l?r[2]:r[3],h=l?r[1]:r[2],m=l?r[3]:r[1],_=l?d%4===0&&m%4===0:u%4===0&&m%4===0,y=l?m:u*h,v=l?u*h:m,x=_?[8,8,1]:[y<=4||v<=4?4:16,y>4&&v<=4?4:16,1],w=_?[4,4,1]:[y<=4?1:4,y>4&&v<=4?1:4,1],E=[Math.ceil(y/x[0]/w[0]),Math.ceil(v/x[1]/w[1]),Math.ceil(p/x[2]/w[2])];Ke("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let I=_?4:1,k=Math.max(x[0]*I,x[1]),A=_?4:1,D=[e.kernelShape[l?1:2],e.kernelShape[l?2:3]],L=[D[0]+(e.dilations[0]<=1?0:(D[0]-1)*(e.dilations[0]-1)),D[1]+(e.dilations[1]<=1?0:(D[1]-1)*(e.dilations[1]-1))],Y=[L[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),L[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],H=[{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:D},{type:6,data:Y}];or(e,H),H.push(...ce(t[0].dims,t[1].dims));let re=["rank","rank"];s&&(H.push(...ce(t[2].dims)),re.push("rank")),H.push(...ce(r));let N=ee=>{let oe=G("x",t[0].dataType,t[0].dims.length,A),Ae=G("w",t[1].dataType,t[1].dims.length,1),q=de("result",t[0].dataType,r.length,A),J=[oe,Ae],ae="";if(s){let B=G("bias",t[2].dataType,t[2].dims.length,A);J.push(B),ae+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${B.type.value} {
            return bias[coords.${l?"w":"y"}${_?"/ 4":""}];
          }`}let W=[{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:D.length},{name:"pads",type:"i32",length:Y.length}];lr(e,W);let te=Ze(t[0].dataType,1);if(te!=="f16"&&te!=="f32")throw new Error(`elemType ${te} is not supported.`);return`
        ${zi("uniforms.result_strides")}
        ${ee.registerUniforms(W).declareVariables(...J,q)};
        ${ae}
        ${md(l,s,e,oe.type.value,I)}
        ${_?ia(w,x,te,void 0,!l,k):sa(w,x,te,void 0,!l,k,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${w};${x};${_}`,inputDependencies:re},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:H}),getShaderSource:N}}}),_d,Fi,hg=V(()=>{me(),ir(),Ce(),Se(),_d=(t,e,r,n,a,i=!1,s,o,l=!1)=>{let d=l?1:2,p=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 _=i?4:1,y=G("W",e[1].dataType,e[1].dims.length,_),v=G("Dy",e[0].dataType,e[0].dims.length,_),x=[v,y];n&&x.push(G("bias",e[2].dataType,[r[u]].length,_));let w=de("result",e[0].dataType,r.length,_),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 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

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

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

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

                var xValue = ${v.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${s}>(dot(xValue, wValue0),
                                      dot(xValue, wValue1),
                                      dot(xValue, wValue2),
                                      dot(xValue, wValue3));
                dotProd[0] = dotProd[0] + tmpval;
              }
            } else if (bDyCVal2) {
              let d2Length = uniforms.Dy_shape[3];
              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {
                let wValue0 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

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

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

    ${t.mainStart()}
    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
  ${i?E:I}}`},Fi=(t,e,r)=>{let n=t.length>2,a=e.outputShape,i=j.size(a),s=[Math.ceil(i/64),1,1];Ke("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${s}`);let o=e.format==="NHWC",l=["rank","rank"],d=[e.strides[0],e.strides[1]],p=[e.kernelShape[o?1:2],e.kernelShape[o?2:3]],u=[e.dilations[0],e.dilations[1]],h=[p[0]+(e.dilations[0]<=1?0:(e.kernelShape[o?1:2]-1)*(e.dilations[0]-1)),p[1]+(e.dilations[1]<=1?0:(e.kernelShape[o?2:3]-1)*(e.dilations[1]-1))],m=[h[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),h[1]-1-Math.floor(e.pads[1]+e.pads[3])/2],_=!1,y=e.group,v=t[1].dims,x=v[0]/y,w=v[1],E=[{type:6,data:i},{type:12,data:d},{type:12,data:p},{type:12,data:u},{type:12,data:h},{type:6,data:m},{type:12,data:x},{type:12,data:w},...ce(t[0].dims,t[1].dims)];n&&(E.push(...ce(t[2].dims)),l.push("rank")),E.push(...ce(a));let I=s[1]===1&&s[2]===1,k=A=>{let 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t.autoPad>"u"?0:t.autoPad],a=t.dilations,i=t.group,s=t.kernelShape,o=t.pads,l=t.strides,d=t.wIsConst(),p=t.outputPadding,u=t.outputShape;return{autoPad:n,format:r,dilations:a,group:i,kernelShape:s,outputPadding:p,outputShape:u,pads:o,strides:l,wIsConst:d,...e,cacheKey:`${t.format};${e.activation};`}},$d=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[0];if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let a=t[1].dims[1]*e.group;if(t.length===3&&(t[2].dims.length!==1||t[2].dims[0]!==a))throw new Error("invalid bias");let i=t[0].dims.length-2;if(e.dilations.reduce((s,o)=>s+o,0)>0&&e.dilations.length!==i)throw new Error(`dilations should be 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x=[e[0],v],w=e.length===3;w&&(!a&&e[2].dims.length===1?x.push(e[2].reshape([e[2].dims[0],1,1])):x.push(e[2])),t.compute(gd(x,n,i,h,m,_,w,y),{inputs:x})},Ed=(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]])];n.length===3&&n.push(t.inputs[2]);let a=e.kernelShape;(a.length===0||a[0]===0)&&(a=[t.inputs[1].dims[2]]);let i=e.dilations;(i.length===0||i[0]===0)&&(i=[1]);let s=e.strides;(s.length===0||s[0]===0)&&(s=[1]);let o=e.pads;o.length===0&&(o=[0,0]),o=[0,o[0],0,o[1]],s=[1].concat(s),i=[1].concat(i),a=[1].concat(a);let l=Wi({...e,pads:o,strides:s,dilations:i,kernelShape:a},n);t.compute(Fi(n,l,d=>r?[d[0],d[2],d[3]]:[d[0],d[1],d[3]]))},Cd=(t,e)=>{$d(t.inputs,e),t.inputs[0].dims.length===3?Ed(t,e):Sd(t,t.inputs,e)}}),Id,kd,Td,mg=V(()=>{me(),Ce(),tt(),Se(),Id=(t,e,r,n)=>{let a=j.size(e),i=e.length,s=G("input",t,i),o=de("output",t,i),l=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),d=j.normalizeAxis(l,i),p=u=>{let h=` i32(${s.indicesGet("inputIndices","uniforms.axis")}) `,m=ge("uniforms.input_shape","uniforms.axis",i),_=n.reverse?h+(n.exclusive?" + 1":""):"0",y=n.reverse?m:h+(n.exclusive?"":" + 1");return`
                ${u.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(s,o)}
                ${u.mainStart()}
                  ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
                  var inputIndices = ${o.offsetToIndices("global_idx")};
                  var sum = ${o.type.value}(0);
                  let first : i32 = ${_};
                  let last : i32 = ${y};
                  for (var i : i32 = first; i < last; i++) {
                    ${s.indicesSet("inputIndices","uniforms.axis","u32(i)")};
                    sum = sum + ${s.getByIndices("inputIndices")};
                  }
                  ${o.setByOffset("global_idx","sum")};
                }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:e,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},{type:6,data:d},...ce(e,e)]}),getShaderSource:p}},kd=(t,e)=>{let r=t.inputs[0].dims,n=t.inputs[0].dataType,a=t.inputs[1];t.compute(Id(n,r,a,e),{inputs:[0]})},Td=t=>{let e=t.exclusive===1,r=t.reverse===1;return We({exclusive:e,reverse:r})}}),da,Yr,Gi,Ad,Md,Od,zd,Vi,Rd,Pd,Bd,gg=V(()=>{me(),Ce(),tt(),Se(),da="[a-zA-Z]|\\.\\.\\.",Yr="("+da+")+",Gi="^"+Yr+"$",Ad="("+Yr+",)*"+Yr,Md="^"+Ad+"$",Od=class{constructor(t=-1){this.symbolToIndices=new Map,this.inputIndex=t}addSymbol(t,e){let r=this.symbolToIndices.get(t);r===void 0?r=[e]:r.push(e),this.symbolToIndices.set(t,r)}},zd=class{constructor(t,e){this.equation=e,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[r,n]=e.includes("->")?e.split("->",2):[e,""];if(!r.match(RegExp(Md)))throw new Error("Invalid LHS term");if(r.split(",").forEach((a,i)=>{let s=t[i].dims.slice();if(!a.match(RegExp(Gi)))throw new Error("Invalid LHS term");let o=this.processTerm(a,!0,s,i);this.lhs.push(o)}),n==="")n+=[...this.symbolToInfo.entries()].filter(([a,i])=>i.count===1||a==="...").map(([a])=>a).join("");else if(!n.match(RegExp(Yr)))throw new Error("Invalid RHS");n.match(RegExp(da,"g"))?.forEach(a=>{if(a==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let i=this.symbolToInfo.get(a);if(i===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(i.dimValue)}}),this.rhs=this.processTerm(n,!1,this.outputDims)}addSymbol(t,e,r){let n=this.symbolToInfo.get(t);if(n!==void 0){if(n.dimValue!==e&&n.count!==1)throw new Error("Dimension mismatch");n.count++,n.inputIndices.push(r)}else n={count:1,dimValue:e,inputIndices:[r]};this.symbolToInfo.set(t,n)}processTerm(t,e,r,n=-1){let a=r.length,i=!1,s=[],o=0;if(!t.match(RegExp(Gi))&&!e&&t!=="")throw new Error("Invalid LHS term");let l=t.match(RegExp(da,"g")),d=new Od(n);return l?.forEach((p,u)=>{if(p==="..."){if(i)throw new Error("Only one ellipsis is allowed per input term");i=!0;let h=a-l.length+1;if(h<0)throw new Error("Ellipsis out of bounds");if(s=r.slice(o,o+h),this.hasEllipsis){if(this.ellipsisDims.length!==s.length||this.ellipsisDims.toString()!==s.toString())throw new Error("Ellipsis dimensions mismatch")}else if(e)this.hasEllipsis=!0,this.ellipsisDims=s;else throw new Error("Ellipsis must be specified in the LHS");for(let m=0;m<s.length;m++){let _=String.fromCharCode(48+m);d.addSymbol(_,u+m),this.addSymbol(_,r[o++],n)}}else d.addSymbol(p,u+(this.hasEllipsis?this.ellipsisDims.length-1:0)),this.addSymbol(p,r[o++],n)}),d}},Vi=t=>t+"_max",Rd=(t,e,r,n)=>{let a=t.map(d=>d.length).map((d,p)=>G(`input${p}`,e,d)),i=j.size(n),s=de("output",e,n.length),o=[...r.symbolToInfo.keys()].filter(d=>!r.rhs.symbolToIndices.has(d)),l=d=>{let p=[],u="var prod = 1.0;",h="var sum = 0.0;",m="sum += prod;",_=[],y=[],v=[],x=[],w=r.symbolToInfo.size===r.rhs.symbolToIndices.size;r.symbolToInfo.forEach((I,k)=>{if(r.rhs.symbolToIndices.has(k)){let A=r.rhs.symbolToIndices.get(k)?.[0];A!==void 0&&r.lhs.forEach((D,L)=>{if(I.inputIndices.includes(L)){let Y=D.symbolToIndices.get(k);if(Y===void 0)throw new Error("Invalid symbol error");Y.forEach(H=>{p.push(`${a[L].indicesSet(`input${L}Indices`,H,s.indicesGet("outputIndices",A))}`)})}})}else r.lhs.forEach((A,D)=>{if(I.inputIndices.includes(D)){let L=A.symbolToIndices.get(k);if(L===void 0)throw new Error("Invalid symbol error");L.forEach(Y=>{_.push(`${a[D].indicesSet(`input${D}Indices`,Y,`${k}`)}`)}),x.push(`prod *= ${a[D].getByIndices(`input${D}Indices`)};`)}}),y.push(`for(var ${k}: u32 = 0; ${k} < uniforms.${Vi(k)}; ${k}++) {`),v.push("}")});let E=w?[...p,`let sum = ${a.map((I,k)=>I.getByIndices(`input${k}Indices`)).join(" * ")};`]:[...p,h,...y,..._,u,...x,m,...v];return`
            ${d.registerUniforms(o.map(I=>({name:`${Vi(I)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...a,s)}

            ${d.mainStart()}
            ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
            var outputIndices = ${s.offsetToIndices("global_idx")};
            ${a.map((I,k)=>`var input${k}Indices: ${a[k].type.indices};`).join(`
`)}
            ${E.join(`
`)};
            ${s.setByOffset("global_idx","sum")};
          }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:t.map(()=>"rank")},getRunData:()=>{let d=o.filter(u=>r.symbolToInfo.has(u)).map(u=>({type:12,data:r.symbolToInfo.get(u)?.dimValue||0}));d.push({type:12,data:i});let p=t.map((u,h)=>[...ce(u)]).reduce((u,h)=>u.concat(h),d);return p.push(...ce(n)),{outputs:[{dims:n,dataType:e}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:p}},getShaderSource:l}},Pd=(t,e)=>{let r=new zd(t.inputs,e.equation),n=r.outputDims,a=t.inputs.map((i,s)=>i.dims);t.compute(Rd(a,t.inputs[0].dataType,r,n))},Bd=t=>{let e=t.equation.replace(/\s+/g,"");return We({equation:e})}}),Dd,Hi,Nd,Ld,Ud,_g=V(()=>{me(),Ce(),Se(),Dd=t=>{if(!t||t.length!==2)throw new Error("Expand requires 2 input.");let e=t[0].dims,r=Array.from(t[1].getBigInt64Array(),Number),n=r.length<e.length?0:r.length-e.length,a=e.length<r.length?0:e.length-r.length;for(;n<r.length&&a<e.length;++n,++a)if(r[n]!==e[a]&&r[n]!==1&&e[a]!==1)throw new Error("Expand requires shape to be broadcastable to input")},Hi=(t,e)=>{let r=t.length-e.length,n=[];for(let a=0;a<r;++a)n.push(t[a]);for(let a=0;a<e.length;++a)n.push(e[a]===1?t[a+r]:e[a]);return n},Nd=(t,e)=>t.length>e.length?Hi(t,e):Hi(e,t),Ld=t=>{let e=t[0].dims,r=Array.from(t[1].getBigInt64Array(),Number),n=Nd(e,r),a=t[0].dataType,i=a===9?4:1,s=Math.ceil(j.size(n)/i),o=d=>{let p=G("input",a,e.length,i),u=de("output",a,n.length,i),h;if(a===9){let m=(_,y,v="")=>`
          let outputIndices${y} = ${u.offsetToIndices(`outputOffset + ${y}u`)};
          let offset${y} = ${p.broadcastedIndicesToOffset(`outputIndices${y}`,u)};
          let index${y} = offset${y} / 4u;
          let component${y} = offset${y} % 4u;
          ${_}[${y}] = ${v}(${p.getByOffset(`index${y}`)}[component${y}]);
        `;h=`
        let outputOffset = global_idx * ${i};
        var data = vec4<u32>(0);
        ${m("data",0,"u32")}
        ${m("data",1,"u32")}
        ${m("data",2,"u32")}
        ${m("data",3,"u32")}
        ${u.setByOffset("global_idx","data")}
      }`}else h=`
        let outputIndices = ${u.offsetToIndices("global_idx")};
        let inputOffset = ${p.broadcastedIndicesToOffset("outputIndices",u)};
        ${u.setByOffset("global_idx",p.getByOffset("inputOffset"))}
      }`;return`
    ${d.registerUniform("vec_size","u32").declareVariables(p,u)}
    ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
    ${h}`},l=[{type:12,data:s},...ce(e,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:o,getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:l})}},Ud=t=>{Dd(t.inputs),t.compute(Ld(t.inputs),{inputs:[0]})}}),Fd,Wd,yg=V(()=>{me(),Ce(),Se(),Ti(),Fd=t=>{let e=t[0].dataType,r=j.size(t[0].dims),n=j.size(t[1].dims),a=n%4===0,i=s=>{let o=G("x",e,[1],4),l=G("bias",e,[1],4),d=de("y",e,[1],4),p=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],u=m=>`
      let bias${m}_offset: u32 = (global_idx * 4 + ${m}) % uniforms.bias_size;
      let bias${m} = ${l.getByOffset(`bias${m}_offset / 4`)}[bias${m}_offset % 4];`,h=a?`
      let bias = ${l.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${u(0)}${u(1)}${u(2)}${u(3)}
      let bias = ${o.type.value}(bias0, bias1, bias2, bias3);`;return`${s.registerUniforms(p).declareVariables(o,l,d)}

    ${Ii(mt(e))}

    ${s.mainStart(Zn)}
      ${s.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}

      let x = ${o.getByOffset("global_idx")};
      ${h}
      let x_in = x + bias;
      ${d.setByOffset("global_idx",ki("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getShaderSource:i,getRunData:s=>({outputs:[{dims:s[0].dims,dataType:s[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/Zn/4)}})}},Wd=t=>{t.inputs.length<2||j.size(t.inputs[1].dims)===0?Au(t):t.compute(Fd(t.inputs))}}),Gd,Vd,Hd,qd,wg=V(()=>{me(),Ce(),tt(),Se(),Gd=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},Vd=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,i=j.normalizeAxis(e.axis,a),s=r.slice(0);s.splice(i,1,...n);let o=r[i],l=t[0].dataType===9?4:1,d=Math.ceil(j.size(s)/l),p=[{type:12,data:d},{type:6,data:o},{type:12,data:i},...ce(t[0].dims,t[1].dims,s)],u=h=>{let m=G("data",t[0].dataType,t[0].dims.length,l),_=G("inputIndices",t[1].dataType,t[1].dims.length),y=de("output",t[0].dataType,s.length,l),v=w=>{let E=n.length,I=`var indicesIndices${w}  = ${_.type.indices}(0);`;for(let k=0;k<E;k++)I+=`${E>1?`indicesIndices${w}[${k}]`:`indicesIndices${w}`} = ${s.length>1?`outputIndices${w}[uniforms.axis + ${k}]`:`outputIndices${w}`};`;I+=`
          var idx${w} = ${_.getByIndices(`indicesIndices${w}`)};
          if (idx${w} < 0) {
            idx${w} = idx${w} + uniforms.axisDimLimit;
          }
          var dataIndices${w} : ${m.type.indices};
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          let outputIndices${I} = ${y.offsetToIndices(`outputOffset + ${I}u`)};
          ${v(I)};
          let offset${I} = ${m.indicesToOffset(`dataIndices${I}`)};
          let index${I} = offset${I} / 4u;
          let component${I} = offset${I} % 4u;
          ${E}[${I}] = ${k}(${m.getByOffset(`index${I}`)}[component${I}]);
        `;x=`
        let outputOffset = global_idx * ${l};
        var value = vec4<u32>(0);
        ${w("value",0,"u32")}
        ${w("value",1,"u32")}
        ${w("value",2,"u32")}
        ${w("value",3,"u32")}
        ${y.setByOffset("global_idx","value")}
      `}else x=`
      let outputIndices = ${y.offsetToIndices("global_idx")};
      ${v("")};
      let value = ${m.getByIndices("dataIndices")};
      ${y.setByOffset("global_idx","value")};
      `;return`
      ${h.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(m,_,y)}
      ${h.mainStart()}
        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${x}
      }`};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:p}),getShaderSource:u}},Hd=t=>We({axis:t.axis}),qd=(t,e)=>{let r=t.inputs;Gd(r),t.compute(Vd(t.inputs,e))}}),jd,Kd,Yd,Xd,bg=V(()=>{me(),Ce(),tt(),Se(),jd=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.`)},Kd=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,i=t[1].dims,s=t[1].dataType,o=j.normalizeAxis(e.axis,a),l=r[o],d=i.slice(0),p=j.size(d),u=G("input",n,a),h=G("indicesInput",s,i.length),m=de("output",n,d.length),_=[{type:12,data:p},{type:6,data:l},{type:12,data:o}];return _.push(...ce(r,i,d)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:_}),getShaderSource:y=>`
      ${y.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(u,h,m)}
      ${y.mainStart()}
      ${y.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")};
  }`}},Yd=t=>We({axis:t.axis}),Xd=(t,e)=>{let r=t.inputs;jd(r),t.compute(Kd(t.inputs,e))}}),Qd,Zd,Jd,ec,vg=V(()=>{me(),Ce(),Se(),Qd=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")},Zd=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,i,s]=Uo.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=j.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}],p=["type","type"];t.length===3&&(d.push(...ce(t[2].dims)),p.push("rank")),d.push(...ce(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 _=e.alpha===1?"":"value *= uniforms.alpha;",y=G("a",t[0].dataType,t[0].dims),v=G("b",t[1].dataType,t[1].dims),x=y.type.value,w=null,E=[y,v];t.length===3&&(w=G("c",t[2].dataType,t[2].dims.length),E.push(w));let I=de("output",t[0].dataType,o.length);E.push(I);let k=[{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(k).declareVariables(...E)}

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

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

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

    ${_}
    ${w!=null?`let cOffset = ${w.broadcastedIndicesToOffset("vec2(m, n)",I)}; value += ${x}(uniforms.beta) * ${w.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:u}},Jd=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};${t.alpha===1}`}},ec=(t,e)=>{Qd(t.inputs),t.compute(Zd(t.inputs,e))}}),tc,rc,nc,ac,$g=V(()=>{me(),Ce(),Se(),tc=(t,e)=>{let r=t[0].dims,n=r,a=2,i=j.sizeToDimension(r,a),s=j.sizeFromDimension(r,a),o=Qe(s),l=s/o,d=[r[0],r[1],l],p=["rank","type","type"],u=[{type:12,data:s},{type:12,data:l}];u.push(...ce(d,d));let h=m=>{let _=G("x",t[0].dataType,d.length,o),y=G("scale",t[1].dataType,t[1].dims),v=G("bias",t[2].dataType,t[2].dims),x=de("output",t[0].dataType,d.length,o),w=[_,y,v,x],E=_.type.value,I=o===1?"f32":`vec${o}<f32>`,k=64,A=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`
  var<workgroup> meanShared : f32;
  var<workgroup> squaredNormShared : f32;
  var<workgroup> workgroupShared : array<${I}, ${k}>;
  const workgroupSize = ${k}u;
  ${m.registerUniforms(A).declareVariables(...w)}
  ${m.mainStart(k)}
    let norm = global_idx / workgroupSize;
    let batch = norm / uniforms.x_shape[1];
    let channel = norm % uniforms.x_shape[1];
    let localIndex = local_id.x;

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

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

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

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

  ${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 = ${ct("f32",l)};
    var squaredSum = ${ct("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] = ${h("sum","squaredSum")};
  }`},w=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:[a,s,d,2],dataType:1}],dispatchGroup:{x:a*s/l},programUniforms:v}),getShaderSource:x},{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)}],I=["type","type","type"],k=A=>{let D=G("scale",r.dataType,r.dims,l),L=G("bias",n.dataType,n.dims,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${p}>;
  @group(0) @binding(1) var<storage, read> scale : array<${D.type.storage}>;
  @group(0) @binding(2) var<storage, read> bias : array<${L.type.storage}>;
  @group(0) @binding(3) var<storage, read_write> output : array<${p}>;
  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};
  @group(0) @binding(4) var<uniform> uniforms: Uniforms;

  ${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 = ${ct("f32",l)};
    var squaredSum = ${ct("f32",l)};
    for (var i: u32 = 0; i < min(${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};${o}`,inputDependencies:I},getRunData:()=>({outputs:[{dims:[a,s,2],dataType:1}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:E}),getShaderSource:k},{inputs:[w,r,n],outputs:[-1]})[0]},nc=(t,e,r)=>{let n=e[0].dims,a=n,i=n[0],s=n[n.length-1],o=j.sizeFromDimension(n,1)/s,l=Qe(s),d=j.size(a)/l,p=[{type:12,data:o},{type:12,data:Math.floor(s/l)}],u=["type","type"],h=rc(t,e[0],e[1],e[2],i,o,s,r.epsilon),m=_=>{let y=Ze(e[0].dataType),v=l===1?"vec2f":`mat2x${l}f`,x=l===1?y:`vec${l}<${y}>`,w=G("input",e[0].dataType,e[0].dims,l),E=de("output",e[0].dataType,a,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${w.type.storage}>;
  @group(0) @binding(1) var<storage, read> scaleInput : array<${v}>;
  @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;

  ${_.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], ${x}(scale[0]), ${x}(scale[1]));
  }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:m},{inputs:[e[0],h]})},ac=(t,e)=>{e.format==="NHWC"?nc(t,t.inputs,e):t.compute(tc(t.inputs,e))}}),ic,sc,oc,xg=V(()=>{me(),Ce(),Se(),ic=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},sc=(t,e,r)=>{let n=t[0].dims,a=t[1],i=t[2],s=n,o=j.normalizeAxis(e.axis,n.length),l=j.sizeToDimension(n,o),d=j.sizeFromDimension(n,o),p=j.size(a.dims),u=i?j.size(i.dims):0;if(p!==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 ${p} and bias size of ${u}`);let h=[];for(let I=0;I<n.length;++I)I<o?h.push(n[I]):h.push(1);let m=Qe(d),_=["type","type"],y=[{type:12,data:l},{type:1,data:d},{type:12,data:Math.floor(d/m)},{type:1,data:e.epsilon}];i&&_.push("type");let v=r>1,x=r>2,w=I=>{let k=Ze(t[0].dataType),A=[G("x",t[0].dataType,t[0].dims,m),G("scale",a.dataType,a.dims,m)];i&&A.push(G("bias",i.dataType,i.dims,m)),A.push(de("output",t[0].dataType,s,m)),v&&A.push(de("mean_data_output",1,h)),x&&A.push(de("inv_std_output",1,h));let D=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${I.registerUniforms(D).declareVariables(...A)}
  ${I.mainStart()}
    ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${ct("f32",m)};
    var mean_square_vector = ${ct("f32",m)};

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

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

    ${v?"mean_data_output[global_idx] = mean":""};
    ${x?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},E=[{dims:s,dataType:t[0].dataType}];return v&&E.push({dims:h,dataType:1}),x&&E.push({dims:h,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${m};${r}`,inputDependencies:_},getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:y}),getShaderSource:w}},oc=(t,e)=>{ic(t.inputs),t.compute(sc(t.inputs,e,t.outputCount))}}),lc,uc,dc,cc,Sg=V(()=>{me(),Ce(),tt(),Se(),lc=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((e.k+e.blockSize-1)/e.blockSize),i=e.blockSize/8*e.bits,s=t[1];if(!j.areEqual(s.dims,[e.n,a,i]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(j.size(o)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let l=t[3].dims,d=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(j.size(l)!==d)throw new Error("zeroPoints input size error.")}},uc=(t,e)=>{let r=t[0].dims,n=r.length,a=r.slice(0,n-1).concat(e.n),i=r[n-2],s=e.blockSize/8*e.bits/4,o=Qe(i),l=Qe(e.n),d=Qe(e.k),p=Qe(s),u=j.size(a)/l/o,h=[{type:12,data:u},{type:12,data:e.k},{type:12,data:e.n},{type:12,data:e.accuracyLevel},{type:12,data:e.bits},{type:12,data:e.blockSize}],m=r.slice();m.splice(-1,1,e.k/d);let _=j.convertShape(t[1].dims).slice();_.splice(-1,1,s/p),h.push(...ce(m)),h.push(...ce(_)),h.push(...ce(t[2].dims)),t.length===4&&h.push(...ce(j.convertShape(t[3].dims)));let y=a.slice();y.splice(-1,1,e.n/l),h.push(...ce(y));let v=x=>{let w=G("a",t[0].dataType,m.length,d),E=G("b",12,_.length,p),I=G("scales",t[2].dataType,t[2].dims.length),k=[w,E,I],A=t.length===4?G("zero_points",12,t[3].dims.length):void 0;A&&k.push(A);let D=de("output",t[0].dataType,a.length,l),L=[{name:"output_size",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"accuracy_level",type:"u32"},{name:"bits",type:"u32"},{name:"block_size",type:"u32"}],Y=Math.floor((e.k+e.blockSize-1)/e.blockSize),H=Ze(t[0].dataType),re=(()=>{switch(d){case 1:return`array<${H}, 8>`;case 2:return`mat4x2<${H}>`;case 4:return`mat2x4<${H}>`;default:throw new Error(`${d}-component is not supported.`)}})(),N=`
        fn dequantize(quantized: ${re}, zero_point: ${H}, scale: ${H}) -> ${re} {
          ${d===1?`var dequantized = ${re}(${Array.from({length:8},(Ae,q)=>`(quantized[${q}] - zero_point) * scale`).join(", ")});
              return dequantized;`:`var zero_points: ${re} = ${re}(${Array(8).fill("zero_point").join(",")});
              return (quantized - zero_points) * scale;`}
        }`,ee=`
        fn ortUnpack8x4snorm(value: u32) -> ${re} {
          var quantized: ${re};
          var offset: u32 = 0;
          let count: u32 = 4;
          for (var i: u32 = 0; i < 8u; i++) {
            var result = ${H}(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;
        }`,oe=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`
        ${N};
        ${ee};
        ${x.registerUniforms(L).declareVariables(...k,D)}
        ${x.mainStart()}
          ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var output_values: array<${D.type.value}, ${o}>;
          var output_indices = ${D.offsetToIndices("global_idx")};
          var n = ${D.indicesGet("output_indices",n-1)};
          var m = ${D.indicesGet("output_indices",n-2)};
          var a_indices: ${w.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 * ${l} * ((${Y} + 1) / 2) / 4;
          var zero_point_word: u32 = ${A.getByOffset("zero_point_index")};
          var zero_point_offset: u32 = 0;`:""}
          var scale_index = n * ${Y*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 < ${Y}; block++) {
              // The scale and zero points are computed per block.
              let scale = ${I.getByOffset("scale_index")};
              // The default zero point is 8 for unsigned 4-bit quantization.
              let zero_point = ${H}(${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 += ${p}) {
                ${E.indicesSet("b_indices","2","word")};
                let b_data = ${E.getByIndices("b_indices")};
                for (var i: u32 = 0; i < ${p}; i++) {
                  let b_value = ${p===1?"b_data":"b_data[word + i]"};
                  let b_quantized_values: ${re} = ortUnpack8x4snorm(b_value);
                  let b_dequantized_values = dequantize(b_quantized_values, zero_point, scale);
                  // Number of B elements per 32-bit word is 32/bits = 32/4 = 8
                  var offset: u32 = word_offset;
                  for (var j: u32 = 0; j < 8/${d}; j++) {
                    ${w.indicesSet("a_indices",n-1,`offset/${d}`)};
                    for (var k: u32 = 0; k < ${o}u; k++) {
                      ${w.indicesSet("a_indices",n-2,`m * ${o} + k`)};
                      let a_data = ${w.getByIndices("a_indices")};
                      output_values[k]${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++;
              ${oe}
              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) {
                ${oe}
              }`:""}
            }
            for (var k: u32 = 0u; k < ${o}u; k++) {
              ${D.indicesSet("output_indices",n-2,`${o+" * m + k"}`)};
              ${D.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${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:v}},dc=(t,e)=>{lc(t.inputs,e),t.compute(uc(t.inputs,e))},cc=t=>We(t)}),pc,hc,qi,fc,ca,mc,Eg=V(()=>{me(),Ce(),tt(),mi(),Gl(),Se(),Kr(),pc=(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 p=!1,u=r.dims[0],h=r.dims[1],m=r.dims.length===3?p?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],_=h,y=0,v=0,x=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');y=l.dims[2],v=l.dims[2]}else if(l||d)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let w;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)');w=2,_=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==x)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.');w=5,_=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==x)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');w=0,_=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');w=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 L=s.dims;throw L.length===1?L[0]===u?E=1:L[0]===3*u+2&&(E=3):L.length===2&&L[0]===u&&L[1]===_&&(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 I=!1,k=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(_!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');k=a.dims[2]}else{if(_!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');k=a.dims[1]*a.dims[3],I=!0}}let A=y+_,D=!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:y,kvSequenceLength:_,totalSequenceLength:A,maxSequenceLength:v,inputHiddenSize:0,hiddenSize:m,vHiddenSize:k,headSize:x,vHeadSize:Math.floor(k/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:E,scale:e.scale,broadcastResPosBias:D,passPastInKv:I,qkvFormat:w}},hc=t=>We({...t}),qi=We({perm:[0,2,1,3]}),fc=(t,e,r,n,a,i,s)=>{let o=[n,a,i],l=j.size(o),d=[{type:12,data:l},{type:12,data:s},{type:12,data:i}],p=u=>{let h=de("qkv_with_bias",e.dataType,o),m=G("qkv",e.dataType,o),_=G("bias",r.dataType,o),y=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${u.registerUniforms(y).declareVariables(m,_,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:p},{inputs:[e,r],outputs:[-1]})[0]},ca=(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=fc(t,i,s,e,n,r*a,o),l=l.reshape([e,n,r,a]),t.compute(Lt(l,qi.perm),{inputs:[l],outputs:[-1]})[0]}else return i.dims.length===3&&(l=i.reshape([e,n,r,a])),t.compute(Lt(l,qi.perm),{inputs:[l],outputs:[-1]})[0]},mc=(t,e)=>{let r=pc(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=ca(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,t.inputs[0],t.inputs[3],0);if(n)return ra(t,a,t.inputs[1],t.inputs[2],t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e);let i=ca(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,t.inputs[1],t.inputs[3],r.hiddenSize),s=ca(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,t.inputs[2],t.inputs[3],2*r.hiddenSize);ra(t,a,i,s,t.inputs[4],void 0,t.inputs[6],t.inputs[7],t.inputs[5],r,e)}}),gc,_c,yc,wc,bc,vc,$c,xc,Sc,Cg=V(()=>{me(),Ce(),Se(),gc=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1&&t[0].dataType!==10)throw new Error("Input type must be float or float16.");if(t.length>=2){let e=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(e=t[3].dims[0]*2===t[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},_c=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
            k = i32(${t.indicesGet("indices",a)}) - ${ge("uniforms.pads",a,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${ge("uniforms.x_shape",a,e)})) {
              break;
            }
            offset += k * i32(${ge("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;
            ${n}
            value = x[offset];
          }
      `},yc=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ge("uniforms.pads",a,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${ge("uniforms.x_shape",a,e)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${ge("uniforms.x_shape",a,e)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${ge("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},wc=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ge("uniforms.pads",a,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${ge("uniforms.x_shape",a,e)})) {
                  k = i32(${ge("uniforms.x_shape",a,e)}) - 1;
                }
                offset += k * i32(${ge("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},bc=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ge("uniforms.pads",a,r)};
                if (k < 0)  {
                  k += i32(${ge("uniforms.x_shape",a,e)}]);
                }
                if (k >= i32(${ge("uniforms.x_shape",a,e)})) {
                  k -= i32(${ge("uniforms.x_shape",a,e)});
                }
                offset += k * i32(${ge("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},vc=(t,e,r)=>{switch(r.mode){case 0:return _c(t,e,r.pads.length);case 1:return yc(t,e,r.pads.length);case 2:return wc(t,e,r.pads.length);case 3:return bc(t,e,r.pads.length);default:throw new Error("Invalid mode")}},$c=(t,e)=>{let r=j.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=j.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(...ce(t[0].dims,r));let s=["rank"],o=l=>{let d=de("output",t[0].dataType,r.length),p=G("x",t[0].dataType,n.length),u=p.type.value,h=vc(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(p,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(j.size(r)/64)},programUniforms:i}),getShaderSource:o}},xc=(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},Sc=(t,e)=>{gc(t.inputs);let r=xc(t.inputs,e);t.compute($c(t.inputs,r),{inputs:[0]})}}),Xr,ji,Ki,Yi,Xi,Ec,Cc,Qi,Zi,Ic,kc,Ji,Tc,Ac,es,Mc,Oc,zc,Rc,Ig=V(()=>{xt(),me(),Ce(),Se(),Xr=t=>{if(Oe.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},ji=(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();Qn.adjustPoolAttributes(r,a,s,o,l,d);let p=Qn.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=p.slice();return h.push(h.splice(1,1)[0]),[u,n?h:p]},Ki=(t,e)=>{let r=e.format==="NHWC",n=j.size(t),a=j.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],p=e.pads[e.pads.length-1],u=!!(d+p);i.push({type:12,data:o},{type:12,data:l},{type:12,data:d},{type:12,data:p}),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],_=e.strides[e.strides.length-2],y=e.pads[e.pads.length/2-2],v=e.pads[e.pads.length-2];h=!!(y+v),i.push({type:12,data:m},{type:12,data:_},{type:12,data:y},{type:12,data:v}),s.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,s,!0,u,h]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=j.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,p)=>d+p);return[i,s,!!l,!1,!1]}},Yi=(t,e,r,n,a,i,s,o,l,d,p,u)=>{let h=a.format==="NHWC",m=e.type.value,_=de("output",e.type.tensor,n);if(a.kernelShape.length<=2){let y="",v="",x="",w=r-(h?2:1);if(p?y=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${w}] = indices[${w}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${w}] < 0 || xIndices[${w}]
                      >= uniforms.x_shape[${w}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`:y=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${w}] = indices[${w}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`,a.kernelShape.length===2){let E=r-(h?3:2);u?v=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${E}] = indices[${E}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${E}] < 0 || xIndices[${E}] >= uniforms.x_shape[${E}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:v=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${E}] = indices[${E}] * uniforms.sh - uniforms.phStart + j;
                `,x=`
              }
            `}return`
            ${t.registerUniforms(l).declareVariables(e,_)}

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

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

              var value = ${m}(${o});
              var pad = 0;
              ${v}
              ${y}
              ${x}
              ${s}

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

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

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

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

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

              output[global_idx] = value;
            }`}},Xi=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,Ec=t=>`${Xi(t)};${t.countIncludePad}`,Cc=t=>`${Xi(t)};${t.storageOrder};${t.dilations}`,Qi=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}),Zi=(t,e,r,n)=>{let[a,i]=ji(e,n,r),s=G("x",e.dataType,e.dims.length),o=s.type.value,l="value += x_val;",d="";a.countIncludePad?d+=`value /= ${o}(uniforms.kernelSize);`:d+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[p,u,h,m,_]=Ki(i,a);p.push(...ce(e.dims,i));let y=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${h};${m};${_}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(j.size(i)/64)},programUniforms:p}),getShaderSource:v=>Yi(v,s,e.dims.length,i.length,a,l,d,0,u,h,m,_)}},Ic=t=>{let e=t.count_include_pad!==0,r=Qi(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:Ec(n)}},kc=(t,e)=>{Xr(t.inputs),t.compute(Zi("AveragePool",t.inputs[0],!1,e))},Ji={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Tc=t=>{let e=t.format;return{format:e,...Ji,cacheKey:e}},Ac=(t,e)=>{Xr(t.inputs),t.compute(Zi("GlobalAveragePool",t.inputs[0],!0,e))},es=(t,e,r,n)=>{let[a,i]=ji(e,n,r),s=`
      value = max(x_val, value);
    `,o="",l=G("x",e.dataType,e.dims.length),d=["rank"],[p,u,h,m,_]=Ki(i,a);return p.push(...ce(e.dims,i)),{name:t,shaderCache:{hint:`${n.cacheKey};${h};${m};${_}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(j.size(i)/64)},programUniforms:p}),getShaderSource:y=>Yi(y,l,e.dims.length,i.length,a,s,o,e.dataType===10?-65504:-1e5,u,h,m,_)}},Mc=(t,e)=>{Xr(t.inputs),t.compute(es("MaxPool",t.inputs[0],!1,e))},Oc=t=>{let e=t.storage_order,r=t.dilations,n=Qi(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:Cc(a)}},zc=t=>{let e=t.format;return{format:e,...Ji,cacheKey:e}},Rc=(t,e)=>{Xr(t.inputs),t.compute(es("GlobalMaxPool",t.inputs[0],!0,e))}}),Pc,Bc,Dc,kg=V(()=>{xt(),me(),Se(),Pc=(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.")},Bc=(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},...ce(i)],l=d=>{let p=de("output",n,i.length),u=p.type.value,h=[{name:"outputSize",type:"u32"},{name:"start",type:u},{name:"delta",type:u}];return`
        ${d.registerUniforms(h).declareVariables(p)}
        ${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})}},Dc=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]),Oe.webgpu.validateInputContent&&Pc(e,r,n),t.compute(Bc(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),Nc,Lc,Uc,Fc,Wc,Gc,Vc,Hc,qc,jc,Kc,ts,Yc,Xc,Qc,Zc,Jc,ep,tp,Tg=V(()=>{me(),Ce(),tt(),Se(),Nc=(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")}},Lc=(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},Uc=(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(p=>i.push(p));else if(e.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&t.length>o&&t[o].dims.length>0){if(t[o].getFloat32Array().forEach(p=>n.push(p)),n.length!==0&&n.length!==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");Nc(n,e),e.axes.length>0&&Lc(n,e.axes,d).forEach((p,u)=>n[u]=p)}if(l>0&&t.length>l&&(t[l].getBigInt64Array().forEach(p=>a.push(Number(p))),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")},Fc=(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`)}})()+"}",Wc=(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`)}})()+"}",Gc=(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},Vc=(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},Hc=(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},qc=(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 = ${ge("uniforms.scales","i",n)};
        var roi_low = ${ge("uniforms.roi","i",a)};
        var roi_hi = ${ge("uniforms.roi",`i + ${e.length}`,a)};
        if (scale == 1.0) {
          original_indices[i] = ${t.type.value}(output_index);
        } else {
          var input_shape_i = ${ge("uniforms.input_shape","i",e.length)};
          var output_shape_i = ${ge("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;
    }`,jc=(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 = ${ge("uniforms.scales","i",a)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${ge("uniforms.roi","i",i)};
          var roi_hi = ${ge("uniforms.roi",`i + ${r.length}`,i)};
          var input_shape_i = ${ge("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${ge("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${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;
    }`,Kc=(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 >= ${ge("uniforms.input_shape","i",e.length)}) {
          return false;
        }
      }
      return true;
    }`,ts=(t,e,r,n)=>t.rank>n?`
    ${t.indicesSet("input_indices",e,"channel")};
    ${t.indicesSet("input_indices",r,"batch")};
`:"",Yc=(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))`)};
      ${ts(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);
    }`},Xc=(t,e,r,n,a,i,s,o,l,d)=>{let p=r.length===2,[u,h]=p?[0,1]:[2,3],m=t.type.value,_=y=>{let v=y===u?"row":"col";return`
      fn ${v}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${m} {
        var output_index = ${e.indicesGet("output_indices",y)};
        var originalIdx: ${m} = getOriginalCoordinateFromResizedCoordinate(output_index, ${a[y]},
        ${n[y]}, ${r[y]}, ${i[y]}, ${i[y]} + ${r.length});
        var fractOriginalIdx: ${m} = originalIdx - floor(originalIdx);
        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);

        if (${o} && (originalIdx < 0 || originalIdx > (${r[y]} - 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 ${v}: ${m} = originalIdx + ${m}(i);
          if (${v} < 0 || ${v} >= ${r[y]}) {
            ${d?`coefs[i + 1] = 0.0;
                        continue;`:o?`return ${l};`:`${v} = max(0, min(${v}, ${r[y]} - 1));`};
          }
        var input_indices_copy: ${t.type.indices} = input_indices;
          ${t.indicesSet("input_indices_copy",y,`u32(${v})`)};
          data[i + 1] = ${y===u?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
        }
        return cubicInterpolation1D(data, coefs);
      }`};return`
    ${_(u)};
    ${_(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);
  }
    `},Qc=(t,e,r,n,a)=>{let[i,s,o,l,d]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],p=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${p} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",s,`max(0, min(depth, ${r[s]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(height, ${r[o]} - 1))`)};
      ${t.indicesSet("input_indices",l,`max(0, min(width, ${r[l]} - 1))`)};
      ${ts(t,d,i,3)}
      return ${t.getByIndices("input_indices")};
    }

    fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${p} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var depth:${p} = originalIndices[${s}];
      var height:${p} = originalIndices[${o}];
      var width:${p} = originalIndices[${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: ${p} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${p} = abs(depth - ${p}(depth1));
      var dx2: ${p} = abs(${p}(depth2) - depth);
      var dy1: ${p} = abs(height - ${p}(height1));
      var dy2: ${p} = abs(${p}(height2) - height);
      var dz1: ${p} = abs(width - ${p}(width1));
      var dz2: ${p} = abs(${p}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},Zc=(t,e,r,n,a,i)=>{let s=t.dims,o=Gc(i,e.axes,s.length),l=Vc(s,n,a,e.axes),d=n.slice();n.length===0&&(d=s.map((w,E)=>w===0?1:l[E]/w),e.keepAspectRatioPolicy!=="stretch"&&(l=Hc(s,d,e)));let p=de("output",t.dataType,l.length),u=G("input",t.dataType,s.length),h=j.size(l),m=s.length===l.length&&s.every((w,E)=>w===l[E]),_=e.coordinateTransformMode==="tf_crop_and_resize",y=e.extrapolationValue,v=u.type.value,x=w=>`
      ${m?"":`
      ${Fc(e.coordinateTransformMode,v)};
      ${(()=>{switch(e.mode){case"nearest":return`
              ${Kc(u,s)};
              ${Wc(e.nearestMode,r,v)};
              ${jc(u,p,s,l,d.length,o.length,_)};
              `;case"linear":return`
              ${qc(p,s,l,d.length,o.length)};
              ${(()=>{if(s.length===2||s.length===4)return`${Yc(u,p,s,_,y)}`;if(s.length===3||s.length===5)return`${Qc(u,p,s,_,y)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(s.length===2||s.length===4)return`${Xc(u,p,s,l,d,o,e.cubicCoeffA,_,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")}})()};
      `}
      ${w.registerUniform("output_size","u32").registerUniform("scales","f32",d.length).registerUniform("roi","f32",o.length).declareVariables(u,p)}
      ${w.mainStart()}
        ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${m?"output[global_idx] = input[global_idx];":`
        let output_indices = ${p.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:x,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},...ce(s,l)]})}},Jc=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},ep=(t,e)=>{let r=[],n=[],a=[],i=Jc(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Uc(t.inputs,e,i,r,n,a),t.compute(Zc(t.inputs[0],e,i,r,n,a),{inputs:[0]})},tp=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 We({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:i,extrapolationValue:s,keepAspectRatioPolicy:o,mode:l,nearestMode:d})}}),rp,np,ap,Ag=V(()=>{me(),Ce(),Se(),rp=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")}},np=(t,e,r,n)=>{let a=t[0].dims,i=j.size(a),s=a,o=i,l=a.slice(-1)[0],d=n?a.slice(0,-1).concat(1):[],p=t.length>3,u=t.length>4,h=n&&r>1,m=n&&r>2,_=r>3,y=Qe(l),v=[{type:12,data:o},{type:12,data:y},{type:12,data:l},{type:1,data:e.epsilon}],x=E=>{let I=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],k=[G("x",t[0].dataType,t[0].dims,y),G("skip",t[1].dataType,t[1].dims,y),G("gamma",t[2].dataType,t[2].dims,y)];p&&k.push(G("beta",t[3].dataType,t[3].dims,y)),u&&k.push(G("bias",t[4].dataType,t[4].dims,y)),k.push(de("output",t[0].dataType,s,y)),h&&k.push(de("mean_output",1,d)),m&&k.push(de("inv_std_output",1,d)),_&&k.push(de("input_skip_bias_sum",t[0].dataType,s,y));let A=Ze(t[0].dataType);return`

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

      ${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 = ${ct("f32",y)};
        var squareSum = ${ct("f32",y)};
        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;
          ${_?"input_skip_bias_sum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32_value = ${Nt(A,y,"value")};
          sum += f32_value;
          squareSum += f32_value * f32_value;
        }
        let mean = ${At("sum",y)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${At("squareSum",y)} / 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] + ${p?"beta[i]":"0.0"};
        }
      }`},w=[{dims:s,dataType:t[0].dataType}];return r>1&&w.push({dims:d,dataType:1}),r>2&&w.push({dims:d,dataType:1}),r>3&&w.push({dims:a,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${y};${h};${m};${_}`,inputDependencies:t.map((E,I)=>"type")},getShaderSource:x,getRunData:()=>({outputs:w,dispatchGroup:{x:Math.ceil(o/l/64)},programUniforms:v})}},ap=(t,e)=>{rp(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(np(t.inputs,e,t.outputCount,!1),{outputs:r})}}),ip,Qr,sp,rs,op,lp,up,dp,Mg=V(()=>{me(),Ce(),tt(),Se(),ip=(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`)})},Qr=(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},sp=(t,e)=>{if(t.length>1){let r=Qr(t,1),n=Qr(t,2),a=Qr(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),We({starts:r,ends:n,axes:a})}else return e},rs=(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]]))},op=(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 = ${ge("uniforms.input_shape","i",r.length)};
            let steps_i = ${ge("uniforms.steps","i",r.length)};
            let signs_i = ${ge("uniforms.signs","i",r.length)};
            let starts_i = ${ge("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;
      }`,lp=(t,e)=>{let r=t[0].dims,n=j.size(r),a=e.axes.length>0?j.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],i=Qr(t,4);i.forEach(x=>x!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(a.length).fill(1));let s=e.starts.map((x,w)=>rs(x,w,r,a,i)),o=e.ends.map((x,w)=>rs(x,w,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 x=0;x<r.length;++x)a.includes(x)||(s.splice(x,0,0),o.splice(x,0,r[x]),i.splice(x,0,1));let l=i.map(x=>Math.sign(x));i.forEach((x,w,E)=>{if(x<0){let I=(o[w]-s[w])/x,k=s[w],A=k+I*i[w];s[w]=A,o[w]=k,E[w]=-x}});let d=r.slice(0);a.forEach((x,w)=>{d[x]=Math.ceil((o[x]-s[x])/i[x])});let p={dims:d,dataType:t[0].dataType},u=de("output",t[0].dataType,d.length),h=G("input",t[0].dataType,t[0].dims.length),m=j.size(d),_=[{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}],y=[{type:12,data:m},{type:12,data:s},{type:6,data:l},{type:12,data:i},...ce(t[0].dims,d)],v=x=>`
      ${x.registerUniforms(_).declareVariables(h,u)}
        ${op(h,u,r)}
        ${x.mainStart()}
          ${x.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:v,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:y})}},up=(t,e)=>{ip(t.inputs,e);let r=sp(t.inputs,e);t.compute(lp(t.inputs,r),{inputs:[0]})},dp=t=>{let e=t.starts,r=t.ends,n=t.axes;return We({starts:e,ends:r,axes:n})}}),cp,pp,hp,fp,Og=V(()=>{me(),Ce(),tt(),Se(),cp=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},pp=(t,e)=>{let r=t.dims,n=j.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=Qe(s),d=s/l,p=(v,x)=>x===4?`max(max(${v}.x, ${v}.y), max(${v}.z, ${v}.w))`:x===2?`max(${v}.x, ${v}.y)`:x===3?`max(max(${v}.x, ${v}.y), ${v}.z)`:v,u=G("x",t.dataType,t.dims,l),h=de("result",t.dataType,t.dims,l),m=u.type.value,_=Ze(t.dataType)==="f32"?`var threadMax = ${m}(-3.402823e+38f);`:`var threadMax = ${m}(-65504.0h);`,y=v=>`
      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;
      }
      ${v.registerUniform("packedCols","i32").declareVariables(u,h)}
      ${v.mainStart()}
        let gindex = i32(global_idx);
        let lindex = i32(local_idx);
        const wg = ${a};
        let row = gindex / wg;
        let cols = uniforms.packedCols;
        let row_stride : i32 = uniforms.packedCols;

        // find the rows max
        ${_}
        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 = ${m}(${p("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 = ${m}(${At("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:y}},hp=(t,e)=>{cp(t.inputs),t.compute(pp(t.inputs[0],e))},fp=t=>We({axis:t.axis})}),mp,gp,_p,yp,wp,bp,vp,zg=V(()=>{me(),Ce(),tt(),Se(),mp=t=>{if(!t||t.length<1)throw new Error("too few inputs")},gp=(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),We({numOutputs:n,axis:e.axis,splitSizes:r})},_p=t=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${t}u; i += 1u ) {
    if (index < ${ge("uniforms.size_in_split_axis","i",t)}) {
        return i;
    }
    }
    return ${t}u;
}`,yp=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) {
        ${r.join(`
`)}
      }`},wp=(t,e)=>{let r=t[0].dims,n=j.size(r),a=t[0].dataType,i=j.normalizeAxis(e.axis,r.length),s=new Array(e.numOutputs),o=G("input",a,r.length),l=new Array(e.numOutputs),d=[],p=[],u=0,h=[{type:12,data:n}];for(let _=0;_<e.numOutputs;_++){u+=e.splitSizes[_],l[_]=u;let y=r.slice();y[e.axis]=e.splitSizes[_],p.push(y),s[_]=de(`output${_}`,a,y.length),d.push({dims:p[_],dataType:t[0].dataType})}h.push({type:12,data:l},...ce(r,...p));let m=_=>`
  ${_.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...s)}
  ${_p(l.length)}
  ${yp(s)}

  ${_.mainStart()}
    ${_.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 -= ${ge("uniforms.size_in_split_axis","output_number - 1u",l.length)};
      ${o.indicesSet("indices",i,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:m,getRunData:()=>({outputs:d,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:h})}},bp=(t,e)=>{mp(t.inputs);let r=t.inputs.length===1?e:gp(t.inputs,e);t.compute(wp(t.inputs,r),{inputs:[0]})},vp=t=>{let e=t.axis,r=t.splitSizes,n=t.numOutputs<0?r.length:t.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return We({axis:e,numOutputs:n,splitSizes:r})}}),ns,$p,xp,Sp,Ep,Rg=V(()=>{me(),Ce(),Se(),ns=t=>Array.from(t.getBigInt64Array(),Number),$p=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(ns(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")},xp=(t,e)=>{let r=[];for(let n=0;n<t.length;++n)r.push(t[n]*e[n]);return r},Sp=t=>{let e=t[0].dims,r=ns(t[1]),n=xp(e,r),a=j.size(n),i=t[0].dataType,s=G("input",i,e.length),o=de("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},...ce(t[0].dims,n)]}),getShaderSource:l}},Ep=t=>{$p(t.inputs),t.compute(Sp(t.inputs),{inputs:[0]})}}),Cp,Ip,kp,Pg=V(()=>{me(),Ce(),Se(),Cp=(t,e,r,n,a)=>{let i=de("output_data",a,r.length,4),s=G("a_data",e[1].dataType,e[1].dims.length,4),o=G("b_data",e[2].dataType,e[2].dims.length,4),l=G("c_data",e[0].dataType,e[0].dims.length,4),d,p=(u,h,m)=>`select(${h}, ${u}, ${m})`;if(!n)d=i.setByOffset("global_idx",p(s.getByOffset("global_idx"),o.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let u=(h,m,_="")=>{let y=`a_data[index_a${m}][component_a${m}]`,v=`b_data[index_b${m}][component_b${m}]`,x=`bool(c_data[index_c${m}] & (0xffu << (component_c${m} * 8)))`;return`
            let output_indices${m} = ${i.offsetToIndices(`global_idx * 4u + ${m}u`)};
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e.requestDevice(n),this.adapterInfo=new zp(await e.requestAdapterInfo()),this.gpuDataManager=Do(this),this.programManager=new Ap(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Oo(t.logLevel,!!t.debug),this.device.onuncapturederror=a=>{a.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${a.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:e,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let 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t;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&t.mapAsync(GPUMapMode.READ).then(()=>{let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let n=0;n<e.length/2;n++){let a=r[n],i=a.kernelId,s=this.kernels.get(i),o=s.kernelType,l=s.kernelName,d=a.programName,p=a.inputTensorViews,u=a.outputTensorViews,h=e[n*2],m=e[n*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=h);let 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m=this.programManager.normalizeDispatchGroupSize(o),_=m[1]===1&&m[2]===1,y=Op(t,e,_),v=this.programManager.getArtifact(y);if(v||(v=this.programManager.build(t,m),this.programManager.setArtifact(y,v),Ke("info",()=>`[artifact] key: ${y}, programName: ${t.name}`)),Ke("info",()=>`[ProgramManager] run "${t.name}" (key=${y}) with ${m[0]}x${m[1]}x${m[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let x={kernelId:this.currentKernelId,programName:v.programInfo.name,inputTensorViews:e,outputTensorViews:p};this.pendingKernels.push(x),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(x)}return this.programManager.run(v,i,u,m,h),ft(t.name),p}upload(t,e){this.gpuDataManager.upload(t,e)}memcpy(t,e){this.gpuDataManager.memcpy(t,e)}async download(t,e){await this.gpuDataManager.download(t,e)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,e,r,n){let a=Tp.get(t);if(!a)throw new Error(`kernel not implemented: ${t}`);let i={kernelType:t,kernelName:n,kernelEntry:a[0],attributes:[a[1],r]};this.kernels.set(e,i)}releaseKernel(t){let e=this.kernelPersistentData.get(t);if(e){for(let r of e)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,e,r){let n=this.kernels.get(t);if(!n)throw new Error(`kernel not created: ${t}`);let a=n.kernelType,i=n.kernelName,s=n.kernelEntry,o=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${a}] ${i}" is not allowed to be called recursively`);this.currentKernelId=t,o[0]&&(o[1]=o[0](o[1]),o[0]=void 0),Ke("info",()=>`[WebGPU] Start to run kernel "[${a}] ${i}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),s(e,o[1]),0}catch(d){return r.push(Promise.resolve(`[WebGPU] Kernel "[${a}] ${i}" failed. ${d}`)),1}finally{l&&r.push(this.device.popErrorScope().then(d=>d?`GPU validation 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this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){Ke("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(){Ke("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){Ke("info","replay"),this.sessionStatus="replaying";let t=this.capturedCommandList.get(this.currentSessionId),e=this.capturedPendingKernels.get(this.currentSessionId),r=t.length;this.pendingKernels=[];for(let n=0;n<r;n++){let 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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'}),Ht,gt,Ir,Zr,Jr,ss,ha,dr,cr,jp,en,Kp,Yp,Xp,Qp,Zp,Jp,eh,th=V(()=>{xt(),Ug(),Hr(),Ht=()=>!!Oe.wasm.proxy&&typeof document<"u",Ir=!1,Zr=!1,Jr=!1,ha=new Map,dr=(t,e)=>{let r=ha.get(t);r?r.push(e):ha.set(t,[e])},cr=()=>{if(Ir||!Zr||Jr||!gt)throw new Error("worker not 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Jr=!0,t}finally{Ir=!1}}},Yp=async t=>{if(Ht())return cr(),new Promise((e,r)=>{dr("init-ep",[e,r]);let n={type:"init-ep",in:{epName:t,env:Oe}};gt.postMessage(n)});await Up(Oe,t)},Xp=async t=>Ht()?(cr(),new Promise((e,r)=>{dr("copy-from",[e,r]);let n={type:"copy-from",in:{buffer:t}};gt.postMessage(n,[t.buffer])})):as(t),Qp=async(t,e)=>{if(Ht()){if(e?.preferredOutputLocation)throw new Error('session option "preferredOutputLocation" is not supported for proxy.');return cr(),new Promise((r,n)=>{dr("create",[r,n]);let a={type:"create",in:{model:t,options:{...e}}},i=[];t instanceof Uint8Array&&i.push(t.buffer),gt.postMessage(a,i)})}else return Wp(t,e)},Zp=async t=>{if(Ht())return cr(),new Promise((e,r)=>{dr("release",[e,r]);let n={type:"release",in:t};gt.postMessage(n)});Gp(t)},Jp=async(t,e,r,n,a,i)=>{if(Ht()){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 proxy.");return cr(),new Promise((s,o)=>{dr("run",[s,o]);let l=r,d={type:"run",in:{sessionId:t,inputIndices:e,inputs:l,outputIndices:n,options:i}};gt.postMessage(d,qp(l))})}else return Vp(t,e,r,n,a,i)},eh=async t=>{if(Ht())return cr(),new Promise((e,r)=>{dr("end-profiling",[e,r]);let n={type:"end-profiling",in:t};gt.postMessage(n)});Hp(t)}}),os,rh,nh,Wg=V(()=>{xt(),th(),me(),Io(),os=(t,e)=>{switch(t.location){case"cpu":return[t.type,t.dims,t.data,"cpu"];case"gpu-buffer":return[t.type,t.dims,{gpuBuffer:t.gpuBuffer},"gpu-buffer"];default:throw new Error(`invalid data location: ${t.location} for ${e()}`)}},rh=t=>{switch(t[3]){case"cpu":return new st(t[0],t[2],t[1]);case"gpu-buffer":{let e=t[0];if(!hi(e))throw new Error(`not supported data type: ${e} for deserializing GPU tensor`);let{gpuBuffer:r,download:n,dispose:a}=t[2];return st.fromGpuBuffer(r,{dataType:e,dims:t[1],download:n,dispose:a})}default:throw new Error(`invalid data location: ${t[3]}`)}},nh=class{async fetchModelAndCopyToWasmMemory(t){return Xp(await Xn(t))}async loadModel(t,e){$t();let r;typeof t=="string"?typeof process<"u"&&process.versions&&process.versions.node?r=await Xn(t):r=await this.fetchModelAndCopyToWasmMemory(t):r=t,[this.sessionId,this.inputNames,this.outputNames]=await Qp(r,e),ft()}async dispose(){return Zp(this.sessionId)}async run(t,e,r){$t();let n=[],a=[];Object.entries(t).forEach(u=>{let h=u[0],m=u[1],_=this.inputNames.indexOf(h);if(_===-1)throw new Error(`invalid input '${h}'`);n.push(m),a.push(_)});let i=[],s=[];Object.entries(e).forEach(u=>{let h=u[0],m=u[1],_=this.outputNames.indexOf(h);if(_===-1)throw new Error(`invalid output '${h}'`);i.push(m),s.push(_)});let o=n.map((u,h)=>os(u,()=>`input "${this.inputNames[a[h]]}"`)),l=i.map((u,h)=>u?os(u,()=>`output "${this.outputNames[s[h]]}"`):null),d=await Jp(this.sessionId,a,o,s,l,r),p={};for(let u=0;u<d.length;u++)p[this.outputNames[s[u]]]=i[u]??rh(d[u]);return ft(),p}startProfiling(){}endProfiling(){eh(this.sessionId)}}}),ah,ih,Gg=V(()=>{xt(),th(),Wg(),ah=()=>{if((typeof Oe.wasm.initTimeout!="number"||Oe.wasm.initTimeout<0)&&(Oe.wasm.initTimeout=0),typeof Oe.wasm.simd!="boolean"&&(Oe.wasm.simd=!0),typeof Oe.wasm.proxy!="boolean"&&(Oe.wasm.proxy=!1),typeof Oe.wasm.trace!="boolean"&&(Oe.wasm.trace=!1),typeof Oe.wasm.numThreads!="number"||!Number.isInteger(Oe.wasm.numThreads)||Oe.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(Oe.wasm.numThreads=1);let t=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;Oe.wasm.numThreads=Math.min(4,Math.ceil((t||1)/2))}},ih=class{async init(t){ah(),await Kp(),await Yp(t)}async createInferenceSessionHandler(t,e){let r=new nh;return await r.loadModel(t,e),Promise.resolve(r)}}}),sh={};Er(sh,{wasmBackend:()=>oh});var oh,Vg=V(()=>{Gg(),oh=new ih});xt(),xt(),xt();var Hg="1.17.3",qg=io;{let t=(Vg(),tr(sh)).wasmBackend;rr("webgpu",t,5),rr("webnn",t,5),rr("cpu",t,10),rr("wasm",t,10)}Object.defineProperty(Oe.versions,"web",{value:Hg,enumerable:!0});/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*//**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */var jg=Object.freeze({__proto__:null,get InferenceSession(){return ai},get TRACE(){return Gr},get TRACE_FUNC_BEGIN(){return $t},get TRACE_FUNC_END(){return ft},get Tensor(){return st},get TrainingSession(){return ii},default:qg,get env(){return Oe},get registerBackend(){return rr}});const Kg=(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 o.bias=="number"?d=[o.bias,o.bias,o.bias,o.bias]:(d=[o.bias[0],o.bias[1],o.bias[2],0],o.bias[3]!==void 0&&(d[3]=o.bias[3]));const 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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,p=l==="RGBA"?new Float32Array(d*4):new Float32Array(d*3);let u=4,h=0,m=1,_=2,y=3,v=0,x=d,w=d*2,E=-1;o==="RGB"&&(u=3,h=0,m=1,_=2,y=-1),l==="RGBA"?E=d*3:l==="RBG"?(v=0,w=d,x=d*2):l==="BGR"&&(w=0,x=d,v=d*2);for(let k=0;k<d;k++,h+=u,_+=u,m+=u,y+=u)p[v++]=(t[h]+s[0])/i[0],p[x++]=(t[m]+s[1])/i[1],p[w++]=(t[_]+s[2])/i[2],E!==-1&&y!==-1&&(p[E++]=(t[y]+s[3])/i[3]);return l==="RGBA"?new Mt("float32",p,[1,4,r,n]):new Mt("float32",p,[1,3,r,n])},Xg=async(t,e)=>{const r=typeof HTMLImageElement<"u"&&t instanceof HTMLImageElement,n=typeof ImageData<"u"&&t instanceof 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0?(p=e.resizedHeight,u=e.resizedWidth):(p=t.height,u=t.width),e!==void 0&&(o=e),o.format="RGBA",o.height=p,o.width=u,e!==void 0){const h=l();h.width=u,h.height=p;const m=d(h);if(m!=null)m.putImageData(t,0,0),s=m.getImageData(0,0,u,p).data;else throw new Error("Can not access image data")}else s=t.data}else if(a){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const p=l();p.width=t.width,p.height=t.height;const u=d(p);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,ls(s,o)}else throw new Error("Can not access image data")}else{if(i)return new Promise((p,u)=>{const h=l(),m=d(h);if(!t||!m)return u();const _=new Image;_.crossOrigin="Anonymous",_.src=t,_.onload=()=>{h.width=_.width,h.height=_.height,m.drawImage(_,0,0,h.width,h.height);const y=m.getImageData(0,0,h.width,h.height);o.height=h.height,o.width=h.width,p(ls(y.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return ls(s,o);throw new Error("Input data provided is not supported - aborted tensor creation")},Qg=(t,e)=>{const{width:r,height:n,download:a,dispose:i}=e,s=[1,n,r,4];return new Mt({location:"texture",type:"float32",texture:t,dims:s,download:a,dispose:i})},Zg=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:i}=e;return new Mt({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:i})},Jg=(t,e,r)=>new Mt({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),fa=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["float16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),us=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let lh=!1;const e0=()=>{if(!lh){lh=!0;const t=typeof BigInt64Array<"u"&&typeof BigInt64Array.from=="function",e=typeof BigUint64Array<"u"&&typeof BigUint64Array.from=="function";t&&(fa.set("int64",BigInt64Array),us.set(BigInt64Array,"int64")),e&&(fa.set("uint64",BigUint64Array),us.set(BigUint64Array,"uint64"))}},t0=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},r0=(t,e)=>{switch(t.location){case"cpu":return new Mt(t.type,t.data,e);case"cpu-pinned":return new Mt({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new Mt({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new Mt({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let Mt=class{constructor(e,r,n){e0();let a,i;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,a=e.type,i=e.dims,e.location){case"cpu-pinned":{const o=fa.get(a);if(!o)throw new TypeError(`unsupported type "${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=fa.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 hh=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 Z{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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ve=Object.freeze({Text:"Text",NumericLiteral:"NumericLiteral",BooleanLiteral:"BooleanLiteral",StringLiteral:"StringLiteral",Identifier:"Identifier",Equals:"Equals",OpenParen:"OpenParen",CloseParen:"CloseParen",OpenStatement:"OpenStatement",CloseStatement:"CloseStatement",OpenExpression:"OpenExpression",CloseExpression:"CloseExpression",OpenSquareBracket:"OpenSquareBracket",CloseSquareBracket:"CloseSquareBracket",OpenCurlyBracket:"OpenCurlyBracket",CloseCurlyBracket:"CloseCurlyBracket",Comma:"Comma",Dot:"Dot",Colon:"Colon",Pipe:"Pipe",CallOperator:"CallOperator",AdditiveBinaryOperator:"AdditiveBinaryOperator",MultiplicativeBinaryOperator:"MultiplicativeBinaryOperator",ComparisonBinaryOperator:"ComparisonBinaryOperator",UnaryOperator:"UnaryOperator",Set:"Set",If:"If",For:"For",In:"In",Is:"Is",NotIn:"NotIn",Else:"Else",EndIf:"EndIf",ElseIf:"ElseIf",EndFor:"EndFor",And:"And",Or:"Or",Not:"UnaryOperator"});Object.freeze({set:ve.Set,for:ve.For,in:ve.In,is:ve.Is,if:ve.If,else:ve.Else,endif:ve.EndIf,elif:ve.ElseIf,endfor:ve.EndFor,and:ve.And,or:ve.Or,not:ve.Not,"not 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y0=[["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"]];new Map(y0);async function w0(t,e){return await xr(t,"config.json",!0,e)}class b0{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 w0(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s});return new this(o)}}class jt{static async from_pretrained(...e){return b0.from_pretrained(...e)}}const ga=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),v0=function(){let t;return async function(){if(t===void 0)if(Lr.IS_NODE_ENV)t=!0;else if(!Lr.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}}(),Tt=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8"}),$0=Object.freeze({[ga.cpu]:Tt.q8,[ga.gpu]:Tt.fp32,[ga.wasm]:Tt.q8,[ga.webgpu]:Tt.fp32}),yh=Object.freeze({[Tt.fp32]:"",[Tt.fp16]:"_fp16",[Tt.int8]:"_int8",[Tt.uint8]:"_uint8",[Tt.q8]:"_quantized"});class Ut extends Dt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class hs extends Dt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class wh extends Dt{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 x0 extends Ut{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 S0 extends Ut{constructor(e,r){super(),this.max_length=e,this.forced_eos_token_id=r}_call(e,r){}}class E0 extends Ut{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 C0 extends Ut{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 I0 extends Ut{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 k0 extends Ut{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 T0 extends Ut{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 A0 extends Ut{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 M0 extends Ut{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 O0 extends hs{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 z0 extends hs{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 R0 extends hs{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 ${e}`);this.top_k=Math.max(e,n),this.filter_value=r}}class P0{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(e){Object.assign(this,$r(e,Object.getOwnPropertyNames(this)))}}class bh extends Dt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class fs extends Dt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof fs&&(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 B0 extends bh{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 D0 extends bh{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 _a extends Dt{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 L0(e);if(e.num_beams>1)return new U0(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 N0(e)}}class N0 extends _a{sample(e,r=-1){let n=this.getLogits(e,r);return[[er(n)[1],0]]}}class L0 extends _a{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=Us(a,n),s=Vn(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 U0 extends _a{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=Us(a,n),s=Vn(i.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},(o,l)=>[i[l][0],Math.log(s[l])])}}const pe={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},ya=new Map,vh=new Map,an=new Map;async function F0(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=i0(n);let i=r.dtype;if(typeof i!="string"&&(i&&i.hasOwnProperty(e)?i=i[e]:(i=$0[a[0]],console.warn(`Dtype not specified for ${e}. Using the default dtype: ${i}.`))),yh.hasOwnProperty(i)){if(i===Tt.fp16&&!await v0())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${i}. 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The following inputs will be ignored: "${s.join(", ")}".`)}return r}async function Kt(t,e){const r=W0(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([i,s])=>[i,s.ort_tensor]));let a=await t.run(n);a=$h(a);for(const[i,s]of Object.entries(r))i.startsWith("past_key_values")&&s.dispose();return a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function $h(t){for(let e in t)dh(t[e])?t[e]=new Z(t[e]):typeof t[e]=="object"&&$h(t[e]);return t}function G0(t){if(t instanceof Z)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 Z("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new Z("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function xh(t){return new Z("bool",[t],[1])}async function Sh(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const l=$r(e,t.sessions.model.inputNames);r=(await sn(t,l)).last_hidden_state}const{input_ids:a,decoder_input_ids:i,...s}=e;return s.input_ids=i,s.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(s.encoder_attention_mask=e.attention_mask),await ms(t,s,!0)}async function sn(t,e){const r=t.sessions.model,n=Object.create(null);for(const a of r.inputNames)n[a]=e[a];return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new Z("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await Kt(r,n)}async function ms(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...i}=e;n.inputNames.includes("use_cache_branch")&&(i.use_cache_branch=xh(!!a)),t.addPastKeyValues(i,a);const s=$r(i,n.inputNames);return await Kt(n,s)}function V0(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 p=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]=p,p+=r.attention_mask.data[h])}}r.position_ids=new Z("int64",o,r.attention_mask.dims),r.past_key_values&&(r.position_ids=r.position_ids.slice(null,-1).unsqueeze_(-1))}return r}function H0(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(s=>[s.at(-1)])),a.decoder_input_ids=G0(e),a}class K extends Dt{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(e,r){super(),this.config=e,this.sessions=r;const n=an.get(this.constructor),a=ya.get(n);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,a===pe.DecoderOnly?(this.can_generate=!0,this._forward=ms,this._prepare_inputs_for_generation=V0):a===pe.Seq2Seq||a===pe.Vision2Seq||a===pe.Musicgen?(this.can_generate=!0,this._forward=Sh,this._prepare_inputs_for_generation=H0):a===pe.EncoderDecoder?this._forward=Sh:a===pe.ImageTextToText?(this.can_generate=!0,console.warn("TODO: Implement visionDecoderForward")):this._forward=sn}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:p=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:p,session_options:u};const m=an.get(this),_=ya.get(m);let y;return _===pe.DecoderOnly?y=await Promise.all([jt.from_pretrained(e,h),hr(e,{model:h.model_file_name??"model"},h),xr(e,"generation_config.json",!1,h)]):_===pe.Seq2Seq||_===pe.Vision2Seq?y=await Promise.all([jt.from_pretrained(e,h),hr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},h),xr(e,"generation_config.json",!1,h)]):_===pe.MaskGeneration?y=await Promise.all([jt.from_pretrained(e,h),hr(e,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},h)]):_===pe.EncoderDecoder?y=await Promise.all([jt.from_pretrained(e,h),hr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},h)]):_===pe.ImageTextToText?y=await Promise.all([jt.from_pretrained(e,h),hr(e,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},h),xr(e,"generation_config.json",!1,h)]):_===pe.Musicgen?y=await Promise.all([jt.from_pretrained(e,h),hr(e,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},h),xr(e,"generation_config.json",!1,h)]):(_!==pe.EncoderOnly&&console.warn(`Model type for '${m??n?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),y=await Promise.all([jt.from_pretrained(e,h),hr(e,{model:h.model_file_name??"model"},h)])),new this(...y)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_warper(e){const r=new wh;return e.temperature!==null&&e.temperature!==1&&r.push(new O0(e.temperature)),e.top_k!==null&&e.top_k!==0&&r.push(new R0(e.top_k)),e.top_p!==null&&e.top_p<1&&r.push(new z0(e.top_p)),r}_get_logits_processor(e,r,n=null){const a=new wh;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&a.push(new I0(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&a.push(new C0(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&a.push(new A0(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&a.push(new k0(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&a.push(new T0(r,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&a.push(new x0(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&a.push(new S0(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){let i=r>1||e.forced_bos_token_id===null?r:r+1;e.forced_decoder_ids!==null&&(i+=e.forced_decoder_ids[e.forced_decoder_ids.length-1][0]),a.push(new E0(e.begin_suppress_tokens,i))}return e.guidance_scale!==null&&e.guidance_scale>1&&a.push(new M0(e.guidance_scale)),n!==null&&a.extend(n),a}_prepare_generation_config(e,r){const n=new P0(this.config);return"generation_config"in this&&Object.assign(n,this.generation_config),e&&Object.assign(n,e),r&&Object.assign(n,$r(r,Object.getOwnPropertyNames(n))),n}_get_stopping_criteria(e,r=null){const n=new fs;return e.max_length!==null&&n.push(new B0(e.max_length,this.config.max_position_embeddings??null)),e.eos_token_id!==null&&n.push(new D0(e.eos_token_id)),r&&n.extend(r),n}_validate_model_class(){if(!this.can_generate){const e=[vs,Ef,Sf,xf],r=an.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: ${[...n].join(", ")}`),Error(i)}}prepare_inputs_for_generation(...e){return this._prepare_inputs_for_generation(this,...e)}_update_model_kwargs_for_generation({generated_input_ids:e,outputs:r,model_inputs:n,is_encoder_decoder:a}){return n.past_key_values=this.getPastKeyValues(r,n.past_key_values),n.input_ids=new Z("int64",e,[e.length,1]),a||(n.attention_mask=Ot([n.attention_mask,ma([n.attention_mask.dims[0],1])],1)),n.position_ids=null,n}_prepare_model_inputs({inputs:e,bos_token_id:r,model_kwargs:n}){const a=$r(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=$r(r,this.sessions.model.inputNames);let{last_hidden_state:s}=await sn(this,i);return a.guidance_scale!==null&&a.guidance_scale>1&&(s=Ot([s,h0(s,0)],0),"attention_mask"in r&&(r.attention_mask=Ot([r.attention_mask,g0(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 Z("int64",o,[o.length,1]);return n.decoder_attention_mask=f0(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 p=this.config.is_encoder_decoder;p&&("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;p?{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),_=this._get_stopping_criteria(r,a),y=l[d].dims.at(0),v=_a.getSampler(r),x=new Array(y).fill(0),w=u.tolist();for(i&&i.put(w);;){l=this.prepare_inputs_for_generation(w,l,r);const E=await this.forward(l),I=E.logits.slice(null,-1,null),k=m(w,I),A=[];for(let L=0;L<k.dims.at(0);++L){const Y=k[L],H=v(Y);for(const[re,N]of H){const ee=BigInt(re);x[L]+=N,w[L].push(ee),A.push(ee)}}if(i&&i.put(w),_(w).every(L=>L))break;l=this._update_model_kwargs_for_generation({generated_input_ids:A,outputs:E,model_inputs:l,is_encoder_decoder:p})}return i&&i.end(),new Z("int64",w.flat(),[w.length,w[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 Z(a,i,s),e[`past_key_values.${l}.encoder.value`]=new Z(a,i,s),e[`past_key_values.${l}.decoder.key`]=new Z(a,i,o),e[`past_key_values.${l}.decoder.value`]=new Z(a,i,o)}else if(this.config.model_type==="falcon"){let s=[1*this.num_heads,0,this.dim_kv];for(let o=0;o<this.num_layers;++o)e[`past_key_values.${o}.key`]=new Z(a,i,s),e[`past_key_values.${o}.value`]=new Z(a,i,s)}else if(this.config.multi_query){let s=[1*this.num_heads,0,2*this.dim_kv];for(let o=0;o<this.num_layers;++o)e[`past_key_values.${o}.key_value`]=new Z(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.${l}.key`]=new Z(a,i,s),e[`past_key_values.${l}.value`]=new Z(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.${o}.key`]=new Z(a,i,s),e[`past_key_values.${o}.value`]=new Z(a,i,s)}}}}class _t{}class on extends K{}class q0 extends on{}class j0 extends on{async _call(e){return new nt(await super._call(e))}}class K0 extends on{async _call(e){return new ke(await super._call(e))}}class Y0 extends on{async _call(e){return new rt(await super._call(e))}}class X0 extends on{async _call(e){return new lt(await super._call(e))}}class Q0 extends K{}class Z0 extends Q0{}class ln extends K{}class J0 extends ln{}class e_ extends ln{async _call(e){return new nt(await super._call(e))}}class t_ extends ln{async _call(e){return new ke(await super._call(e))}}class r_ extends ln{async _call(e){return new rt(await super._call(e))}}class n_ extends ln{async _call(e){return new lt(await super._call(e))}}class un extends K{}class a_ extends un{}class i_ extends un{async _call(e){return new nt(await super._call(e))}}class s_ extends un{async _call(e){return new ke(await super._call(e))}}class o_ extends un{async _call(e){return new rt(await super._call(e))}}class l_ extends un{async _call(e){return new lt(await super._call(e))}}class dn extends K{}class u_ extends dn{}class d_ extends dn{async _call(e){return new nt(await super._call(e))}}class c_ extends dn{async _call(e){return new ke(await super._call(e))}}class p_ extends dn{async _call(e){return new rt(await super._call(e))}}class h_ extends dn{async _call(e){return new lt(await super._call(e))}}class cn extends K{}class f_ extends cn{}class m_ extends cn{async _call(e){return new nt(await super._call(e))}}class g_ extends cn{async _call(e){return new ke(await super._call(e))}}class __ extends cn{async _call(e){return new rt(await super._call(e))}}class y_ extends cn{async _call(e){return new lt(await super._call(e))}}class pn extends K{}class w_ extends pn{}class b_ extends pn{async _call(e){return new nt(await super._call(e))}}class v_ extends pn{async _call(e){return new ke(await super._call(e))}}class $_ extends pn{async _call(e){return new rt(await super._call(e))}}class x_ extends pn{async _call(e){return new lt(await super._call(e))}}class hn extends K{}class S_ extends hn{}class E_ extends hn{async _call(e){return new nt(await super._call(e))}}class C_ extends hn{async _call(e){return new ke(await super._call(e))}}class I_ extends hn{async _call(e){return new rt(await super._call(e))}}class k_ extends hn{async _call(e){return new lt(await super._call(e))}}class fn extends K{}class T_ extends fn{}class A_ extends fn{async _call(e){return new ke(await super._call(e))}}class M_ extends fn{async _call(e){return new rt(await super._call(e))}}class O_ extends fn{async _call(e){return new lt(await super._call(e))}}class z_ extends fn{async _call(e){return new nt(await super._call(e))}}class wa extends K{}class R_ extends wa{}class P_ extends wa{async _call(e){return new nt(await super._call(e))}}class B_ extends wa{async _call(e){return new ke(await super._call(e))}}class D_ extends wa{async _call(e){return new rt(await super._call(e))}}class ba extends K{}class N_ extends ba{}class L_ extends ba{async _call(e){return new nt(await super._call(e))}}class U_ extends ba{async _call(e){return new ke(await super._call(e))}}class F_ extends ba{async _call(e){return new lt(await super._call(e))}}class mn extends K{}class W_ extends mn{}class G_ extends mn{async _call(e){return new nt(await super._call(e))}}class V_ extends mn{async _call(e){return new ke(await super._call(e))}}class H_ extends mn{async _call(e){return new rt(await super._call(e))}}class q_ extends mn{async _call(e){return new lt(await super._call(e))}}class va extends K{}class j_ extends va{}class K_ extends va{async _call(e){return new nt(await super._call(e))}}class Y_ extends va{async _call(e){return new ke(await super._call(e))}}class X_ extends va{async _call(e){return new lt(await super._call(e))}}class $a extends K{}class Q_ extends $a{}class Z_ extends $a{async _call(e){return new ke(await super._call(e))}}class J_ extends $a{async _call(e){return new lt(await super._call(e))}}class ey extends $a{async _call(e){return new nt(await super._call(e))}}class Eh extends K{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 ty extends Eh{}class ry extends Eh{}class Ch extends K{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 ny extends Ch{}class ay extends Ch{}class Ih extends K{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 iy extends Ih{}class sy extends Ih{}class gs extends K{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 oy extends gs{}class ly extends gs{}class uy extends gs{async _call(e){return new ke(await super._call(e))}}class xa extends K{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 dy extends xa{}class cy extends xa{}class py extends xa{async _call(e){return new ke(await super._call(e))}}class hy extends xa{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 kh extends K{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 fy extends kh{}class my extends kh{}class Th extends K{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 gy extends Th{}class _y extends Th{}class gn extends K{}class yy extends gn{}class wy extends gn{async _call(e){return new nt(await super._call(e))}}class by extends gn{async _call(e){return new ke(await super._call(e))}}class vy extends gn{async _call(e){return new rt(await super._call(e))}}class $y extends gn{async _call(e){return new lt(await super._call(e))}}class _n extends K{}class xy extends _n{}class Sy extends _n{async _call(e){return new nt(await super._call(e))}}class Ey extends _n{async _call(e){return new ke(await super._call(e))}}class Cy extends _n{async _call(e){return new rt(await super._call(e))}}class Iy extends _n{async _call(e){return new lt(await super._call(e))}}class yn extends K{}class ky extends yn{}class Ty extends yn{async _call(e){return new nt(await super._call(e))}}class Ay extends yn{async _call(e){return new ke(await super._call(e))}}class My extends yn{async _call(e){return new rt(await super._call(e))}}class Oy extends yn{async _call(e){return new lt(await super._call(e))}}class Ah extends K{}class zy extends Ah{}class Ry extends Ah{}class Mh extends K{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 Py extends Mh{}class By extends Mh{_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 p=Array.from({length:this.config.decoder_layers},(v,x)=>Ot(d.map(w=>w[x]),2)),u=nn(r.map(([v,x])=>n?p[v].slice(null,x,null,[0,n]):p[v].slice(null,x)));u=u.transpose(1,0,2,3);let[h,m]=u0(u,-2,0,!0),_=u.clone();for(let v=0;v<_.dims[0];++v){let x=_[v];for(let w=0;w<x.dims[0];++w){let E=x[w];const I=h[v][w][0],k=m[v][w][0];for(let A=0;A<E.dims[0];++A){let D=E[A];for(let L=0;L<D.data.length;++L)D.data[L]=(D.data[L]-k.data[L])/I.data[L];D.data.set(Cm(D.data,i))}}}return cs(_,1)}),o=[e.sequences.length,e.sequences[0].length],l=new Z("float32",new Float32Array(o[0]*o[1]),o);for(let d=0;d<o[0];++d){const p=s[d].neg().squeeze_(0);let[u,h]=d0(p),m=Array.from({length:u.length-1},(v,x)=>u[x+1]-u[x]),_=gm([1],m).map(v=>!!v),y=[];for(let v=0;v<_.length;++v)_[v]&&y.push(h[v]*a);l[d].data.set(y,1)}return l}}class Dy extends K{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;(vf.get(s)??$f.get(s))||console.warn(`Model type for encoder '${s}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);const l=vs.get(i.model_type);if(!l)throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);const d=l[1],p=new d(i,{},n);this.add_encoder_pkv="num_decoder_layers"in p,this.add_encoder_pkv?(this.num_decoder_layers=p.num_decoder_layers,this.num_decoder_heads=p.num_decoder_heads,this.decoder_dim_kv=p.decoder_dim_kv,this.num_encoder_layers=p.num_encoder_layers,this.num_encoder_heads=p.num_encoder_heads,this.encoder_dim_kv=p.encoder_dim_kv):(this.num_layers=p.num_layers,this.num_heads=p.num_heads,this.dim_kv=p.dim_kv)}}class Ny extends K{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 Ly extends Ny{async encode_image({pixel_values:e}){return(await Kt(this.sessions.vision_encoder,{pixel_values:e})).image_features}async encode_text({input_ids:e}){return(await Kt(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 p=[],u=[];for(let h=0;h<o.length;++h){const m=o[h],_=e[h],y=r[h],v=a[h];p.push(Ot([_.slice([0,m]),y,_.slice([m+1,_.dims[0]])],0)),u.push(Ot([v.slice([0,m]),ma([y.dims[0]]),v.slice([m+1,v.dims[0]])],0))}return{inputs_embeds:nn(p,0),attention_mask:nn(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=Ot([ma([e.dims[0],h]),r.slice(null,[r.dims[1]-u,r.dims[1]])],1)}}return await ms(this,{inputs_embeds:i,past_key_values:s,attention_mask:r,generation_config:o,logits_processor:l},!0)}}class Sa extends K{}class Uy extends Sa{}class Fy extends Sa{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class Wy extends Sa{static async from_pretrained(e,r={}){return r.model_file_name??="vision_model",super.from_pretrained(e,r)}}class Oh extends K{}class Gy extends Oh{}class Vy extends Oh{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class Hy extends Sa{static async from_pretrained(e,r={}){return r.model_file_name??="vision_model",super.from_pretrained(e,r)}}class qy extends K{}class jy extends qy{}class zh extends K{}class Ky extends zh{}class Yy extends zh{}class Rh extends K{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 Xy extends Rh{}class Qy extends Rh{}class Ph extends K{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 Zy extends Ph{}class Jy extends Ph{}class Bh extends K{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 ew extends Bh{}class tw extends Bh{}class Dh extends K{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 rw extends Dh{}class nw extends Dh{}class Nh extends K{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 aw extends Nh{}class iw extends Nh{}class Lh extends K{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 sw extends Lh{}class ow extends Lh{}class Uh extends K{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 lw extends Uh{}class uw extends Uh{}class Fh extends K{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 dw extends Fh{}class cw extends Fh{}class Wh extends K{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 pw extends Wh{}class hw extends Wh{}class Gh extends K{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 fw extends Gh{}class mw extends Gh{}class Vh extends K{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 gw extends Vh{}class _w extends Vh{}class Hh extends K{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 yw extends Hh{}class ww extends Hh{}class qh extends K{}class bw extends qh{}class vw extends qh{async _call(e){return new ke(await super._call(e))}}class $w extends K{}class xw extends $w{async _call(e){return new Av(await super._call(e))}}class jh extends K{}class Sw extends jh{}class Ew extends jh{async _call(e){return new ke(await super._call(e))}}class Kh extends K{}class Cw extends Kh{}class Iw extends Kh{}class Yh extends K{}class kw extends Yh{}class Tw extends Yh{}class Xh extends K{}class Aw extends Xh{}class Mw extends Xh{async _call(e){return new ke(await super._call(e))}}class _s extends K{}class Ow extends _s{}class zw extends _s{async _call(e){return new Qh(await super._call(e))}}class Rw extends _s{async _call(e){return new Pw(await super._call(e))}}class Qh extends _t{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class Pw extends _t{constructor({logits:e,pred_boxes:r,pred_masks:n}){super(),this.logits=e,this.pred_boxes=r,this.pred_masks=n}}class Zh extends K{}class Bw extends Zh{}class Dw extends Zh{async _call(e){return new Nw(await super._call(e))}}class Nw extends Qh{}class Jh extends K{}class Lw extends Jh{}class Uw extends Jh{async _call(e){return new ke(await super._call(e))}}class ef extends K{}class Fw extends ef{}class Ww extends ef{async _call(e){return new ke(await super._call(e))}}class tf extends K{}class Gw extends tf{}class Vw extends tf{async _call(e){return new ke(await super._call(e))}}class rf extends K{}class Hw extends rf{}class qw extends rf{}class nf extends K{}class jw extends nf{}class Kw extends nf{}class Yw extends K{}class Xw extends Yw{}class af extends K{}class Qw extends af{}class Zw extends af{}class Jw extends K{}class eb extends Jw{}class sf extends K{}class tb extends sf{}class rb extends sf{async _call(e){return new ke(await super._call(e))}}class of extends K{}class nb extends of{}class ab extends of{async _call(e){return new ke(await super._call(e))}}class lf extends K{}class ib extends lf{}class sb extends lf{async _call(e){return new ke(await super._call(e))}}class uf extends K{}class ob extends uf{}class lb extends uf{async _call(e){return new ub(await super._call(e))}}class ub extends _t{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class db extends K{}class df extends db{async get_image_embeddings({pixel_values:e}){return await sn(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){const r=e.input_points.dims.slice(0,-1),n=r.reduce((a,i)=>a*i,1);e.input_labels=new Z("int64",new BigInt64Array(n).fill(1n),r)}return await Kt(this.sessions.prompt_encoder_mask_decoder,{input_points:e.input_points,input_labels:e.input_labels,image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings})}async _call(e){return new cb(await super._call(e))}}class cb extends _t{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class cf extends K{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 pb extends cf{}class hb extends cf{}class pf extends K{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 fb extends pf{}class mb extends pf{}class fr extends K{}class gb extends fr{}class _b extends fr{async _call(e){return new kr(await super._call(e))}}class yb extends fr{async _call(e){return new ke(await super._call(e))}}class wb extends fr{async _call(e){return new rt(await super._call(e))}}class ys extends K{}class bb extends ys{}class vb extends ys{async _call(e){return new kr(await super._call(e))}}class $b extends ys{async _call(e){return new ke(await super._call(e))}}class Ea extends K{}class xb extends Ea{}class Sb extends Ea{async _call(e){return new kr(await super._call(e))}}class Eb extends Ea{async _call(e){return new ke(await super._call(e))}}class Cb extends Ea{async _call(e){return new rt(await super._call(e))}}class ws extends K{}class Ib extends ws{}class kb extends ws{async _call(e){return new kr(await super._call(e))}}class Tb extends ws{async _call(e){return new ke(await super._call(e))}}class Ab extends fr{}class Mb extends fr{async _call(e){return new kr(await super._call(e))}}class Ob extends fr{async _call(e){return new ke(await super._call(e))}}class wn extends K{}class zb extends wn{}class Rb extends wn{async _call(e){return new kr(await super._call(e))}}class Pb extends wn{async _call(e){return new ke(await super._call(e))}}class Bb extends wn{async _call(e){return new Tv(await super._call(e))}}class Db extends wn{async _call(e){return new rt(await super._call(e))}}class hf extends K{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 Nb extends hf{}class Lb extends hf{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 sn(this,o),p=l.dims[1]/this.config.reduction_factor,u=Math.floor(p*i),h=Math.floor(p*a),m=this.config.num_mel_bins;let _=[],y=null,v=null,x=0;for(;;){++x;const I=xh(!!v);let k;v?k=v.output_sequence_out:k=new Z("float32",new Float32Array(m),[1,1,m]);let A={use_cache_branch:I,output_sequence:k,encoder_attention_mask:d,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(A,y),v=await Kt(this.sessions.decoder_model_merged,A),y=this.getPastKeyValues(v,y);const{prob:D,spectrum:L}=v;if(_.push(L),x>=h&&(Array.from(D.data).filter(Y=>Y>=n).length>0||x>=u))break}const w=Ot(_),{waveform:E}=await Kt(s.sessions.model,{spectrogram:w});return{spectrogram:w,waveform:E}}}class Ub extends K{main_input_name="spectrogram"}class Fb extends K{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 Wb extends Fb{}class ff extends K{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 Gb extends ff{}class Vb extends ff{}class mf extends K{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 Hb extends mf{}class qb extends mf{}class gf extends K{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 jb extends gf{}class Kb extends gf{}class bs extends K{}class Yb extends bs{}class Xb extends bs{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class Qb extends bs{static async from_pretrained(e,r={}){return r.model_file_name??="audio_model",super.from_pretrained(e,r)}}class Zb extends K{}class _f extends Zb{async _call(e){return new Mv(await super._call(e))}}class yf extends K{}class Jb extends yf{}class ev extends yf{}class tv extends K{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 rv extends tv{}class wf extends K{}class nv extends wf{}class av extends wf{async _call(e){return new ke(await super._call(e))}}class bf extends K{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 p=d%n,u=Math.floor(d/n)%a,h=p-u;h>0&&h<=i&&(e.data[s++]=e.data[d])}const o=Math.floor(r/a),l=s/(o*a);return new Z(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 Kt(this.sessions.encodec_decode,{audio_codes:n});return a}}class iv{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:p=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:p,session_options:u};if(n=await jt.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 _=m.get(n.model_type);if(_)return await _[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 K.from_pretrained(e,h);throw Error(`Unsupported model type: ${n.model_type}`)}}const vf=new Map([["bert",["BertModel",q0]],["nomic_bert",["NomicBertModel",Z0]],["roformer",["RoFormerModel",J0]],["electra",["ElectraModel",u_]],["esm",["EsmModel",R_]],["convbert",["ConvBertModel",a_]],["camembert",["CamembertModel",f_]],["deberta",["DebertaModel",w_]],["deberta-v2",["DebertaV2Model",S_]],["mpnet",["MPNetModel",W_]],["albert",["AlbertModel",Q_]],["distilbert",["DistilBertModel",T_]],["roberta",["RobertaModel",yy]],["xlm",["XLMModel",xy]],["xlm-roberta",["XLMRobertaModel",ky]],["clap",["ClapModel",Yb]],["clip",["CLIPModel",Uy]],["clipseg",["CLIPSegModel",Ky]],["chinese_clip",["ChineseCLIPModel",jy]],["siglip",["SiglipModel",Gy]],["mobilebert",["MobileBertModel",N_]],["squeezebert",["SqueezeBertModel",j_]],["wav2vec2",["Wav2Vec2Model",gb]],["wav2vec2-bert",["Wav2Vec2BertModel",Ib]],["unispeech",["UniSpeechModel",bb]],["unispeech-sat",["UniSpeechSatModel",xb]],["hubert",["HubertModel",Ab]],["wavlm",["WavLMModel",zb]],["audio-spectrogram-transformer",["ASTModel",zy]],["vits",["VitsModel",_f]],["detr",["DetrModel",Ow]],["table-transformer",["TableTransformerModel",Bw]],["vit",["ViTModel",bw]],["mobilevit",["MobileViTModel",Sw]],["owlvit",["OwlViTModel",Cw]],["owlv2",["Owlv2Model",kw]],["beit",["BeitModel",Aw]],["deit",["DeiTModel",Lw]],["convnext",["ConvNextModel",tb]],["convnextv2",["ConvNextV2Model",nb]],["dinov2",["Dinov2Model",ib]],["resnet",["ResNetModel",Fw]],["swin",["SwinModel",Gw]],["swin2sr",["Swin2SRModel",Hw]],["donut-swin",["DonutSwinModel",eb]],["yolos",["YolosModel",ob]],["dpt",["DPTModel",jw]],["glpn",["GLPNModel",Qw]],["hifigan",["SpeechT5HifiGan",Ub]],["efficientnet",["EfficientNetModel",nv]]]),$f=new Map([["t5",["T5Model",ty]],["longt5",["LongT5Model",ny]],["mt5",["MT5Model",iy]],["bart",["BartModel",oy]],["mbart",["MBartModel",dy]],["marian",["MarianModel",pb]],["whisper",["WhisperModel",Py]],["m2m_100",["M2M100Model",fb]],["blenderbot",["BlenderbotModel",fy]],["blenderbot-small",["BlenderbotSmallModel",gy]]]),sv=new Map([["bloom",["BloomModel",fw]],["gpt2",["GPT2Model",Xy]],["gptj",["GPTJModel",rw]],["gpt_bigcode",["GPTBigCodeModel",aw]],["gpt_neo",["GPTNeoModel",Zy]],["gpt_neox",["GPTNeoXModel",ew]],["codegen",["CodeGenModel",sw]],["llama",["LlamaModel",lw]],["qwen2",["Qwen2Model",dw]],["phi",["PhiModel",pw]],["mpt",["MptModel",gw]],["opt",["OPTModel",yw]],["mistral",["MistralModel",Gb]],["starcoder2",["Starcoder2Model",Hb]],["falcon",["FalconModel",jb]]]),xf=new Map([["speecht5",["SpeechT5ForSpeechToText",Nb]],["whisper",["WhisperForConditionalGeneration",By]]]),ov=new Map([["speecht5",["SpeechT5ForTextToSpeech",Lb]]]),lv=new Map([["vits",["VitsModel",_f]],["musicgen",["MusicgenForConditionalGeneration",bf]]]),uv=new Map([["bert",["BertForSequenceClassification",K0]],["roformer",["RoFormerForSequenceClassification",t_]],["electra",["ElectraForSequenceClassification",c_]],["esm",["EsmForSequenceClassification",B_]],["convbert",["ConvBertForSequenceClassification",s_]],["camembert",["CamembertForSequenceClassification",g_]],["deberta",["DebertaForSequenceClassification",v_]],["deberta-v2",["DebertaV2ForSequenceClassification",C_]],["mpnet",["MPNetForSequenceClassification",V_]],["albert",["AlbertForSequenceClassification",Z_]],["distilbert",["DistilBertForSequenceClassification",A_]],["roberta",["RobertaForSequenceClassification",by]],["xlm",["XLMForSequenceClassification",Ey]],["xlm-roberta",["XLMRobertaForSequenceClassification",Ay]],["bart",["BartForSequenceClassification",uy]],["mbart",["MBartForSequenceClassification",py]],["mobilebert",["MobileBertForSequenceClassification",U_]],["squeezebert",["SqueezeBertForSequenceClassification",Y_]]]),dv=new Map([["bert",["BertForTokenClassification",Y0]],["roformer",["RoFormerForTokenClassification",r_]],["electra",["ElectraForTokenClassification",p_]],["esm",["EsmForTokenClassification",D_]],["convbert",["ConvBertForTokenClassification",o_]],["camembert",["CamembertForTokenClassification",__]],["deberta",["DebertaForTokenClassification",$_]],["deberta-v2",["DebertaV2ForTokenClassification",I_]],["mpnet",["MPNetForTokenClassification",H_]],["distilbert",["DistilBertForTokenClassification",M_]],["roberta",["RobertaForTokenClassification",vy]],["xlm",["XLMForTokenClassification",Cy]],["xlm-roberta",["XLMRobertaForTokenClassification",My]]]),Sf=new Map([["t5",["T5ForConditionalGeneration",ry]],["longt5",["LongT5ForConditionalGeneration",ay]],["mt5",["MT5ForConditionalGeneration",sy]],["bart",["BartForConditionalGeneration",ly]],["mbart",["MBartForConditionalGeneration",cy]],["marian",["MarianMTModel",hb]],["m2m_100",["M2M100ForConditionalGeneration",mb]],["blenderbot",["BlenderbotForConditionalGeneration",my]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",_y]]]),vs=new Map([["bloom",["BloomForCausalLM",mw]],["gpt2",["GPT2LMHeadModel",Qy]],["gptj",["GPTJForCausalLM",nw]],["gpt_bigcode",["GPTBigCodeForCausalLM",iw]],["gpt_neo",["GPTNeoForCausalLM",Jy]],["gpt_neox",["GPTNeoXForCausalLM",tw]],["codegen",["CodeGenForCausalLM",ow]],["llama",["LlamaForCausalLM",uw]],["qwen2",["Qwen2ForCausalLM",cw]],["phi",["PhiForCausalLM",hw]],["mpt",["MptForCausalLM",_w]],["opt",["OPTForCausalLM",ww]],["mbart",["MBartForCausalLM",hy]],["mistral",["MistralForCausalLM",Vb]],["starcoder2",["Starcoder2ForCausalLM",qb]],["falcon",["FalconForCausalLM",Kb]],["trocr",["TrOCRForCausalLM",Wb]],["stablelm",["StableLmForCausalLM",rv]]]),cv=new Map([["bert",["BertForMaskedLM",j0]],["roformer",["RoFormerForMaskedLM",e_]],["electra",["ElectraForMaskedLM",d_]],["esm",["EsmForMaskedLM",P_]],["convbert",["ConvBertForMaskedLM",i_]],["camembert",["CamembertForMaskedLM",m_]],["deberta",["DebertaForMaskedLM",b_]],["deberta-v2",["DebertaV2ForMaskedLM",E_]],["mpnet",["MPNetForMaskedLM",G_]],["albert",["AlbertForMaskedLM",ey]],["distilbert",["DistilBertForMaskedLM",z_]],["roberta",["RobertaForMaskedLM",wy]],["xlm",["XLMWithLMHeadModel",Sy]],["xlm-roberta",["XLMRobertaForMaskedLM",Ty]],["mobilebert",["MobileBertForMaskedLM",L_]],["squeezebert",["SqueezeBertForMaskedLM",K_]]]),pv=new Map([["bert",["BertForQuestionAnswering",X0]],["roformer",["RoFormerForQuestionAnswering",n_]],["electra",["ElectraForQuestionAnswering",h_]],["convbert",["ConvBertForQuestionAnswering",l_]],["camembert",["CamembertForQuestionAnswering",y_]],["deberta",["DebertaForQuestionAnswering",x_]],["deberta-v2",["DebertaV2ForQuestionAnswering",k_]],["mpnet",["MPNetForQuestionAnswering",q_]],["albert",["AlbertForQuestionAnswering",J_]],["distilbert",["DistilBertForQuestionAnswering",O_]],["roberta",["RobertaForQuestionAnswering",$y]],["xlm",["XLMForQuestionAnswering",Iy]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Oy]],["mobilebert",["MobileBertForQuestionAnswering",F_]],["squeezebert",["SqueezeBertForQuestionAnswering",X_]]]),Ef=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Dy]]]),hv=new Map([["llava",["LlavaForConditionalGeneration",Ly]]]),fv=new Map([["vit",["ViTForImageClassification",vw]],["mobilevit",["MobileViTForImageClassification",Ew]],["beit",["BeitForImageClassification",Mw]],["deit",["DeiTForImageClassification",Uw]],["convnext",["ConvNextForImageClassification",rb]],["convnextv2",["ConvNextV2ForImageClassification",ab]],["dinov2",["Dinov2ForImageClassification",sb]],["resnet",["ResNetForImageClassification",Ww]],["swin",["SwinForImageClassification",Vw]],["segformer",["SegformerForImageClassification",Jb]],["efficientnet",["EfficientNetForImageClassification",av]]]),mv=new Map([["detr",["DetrForObjectDetection",zw]],["table-transformer",["TableTransformerForObjectDetection",Dw]],["yolos",["YolosForObjectDetection",lb]]]),gv=new Map([["owlvit",["OwlViTForObjectDetection",Iw]],["owlv2",["Owlv2ForObjectDetection",Tw]]]),_v=new Map([["detr",["DetrForSegmentation",Rw]],["clipseg",["CLIPSegForImageSegmentation",Yy]]]),yv=new Map([["segformer",["SegformerForSemanticSegmentation",ev]]]),wv=new Map([["sam",["SamModel",df]]]),bv=new Map([["wav2vec2",["Wav2Vec2ForCTC",_b]],["wav2vec2-bert",["Wav2Vec2BertForCTC",kb]],["unispeech",["UniSpeechForCTC",vb]],["unispeech-sat",["UniSpeechSatForCTC",Sb]],["wavlm",["WavLMForCTC",Rb]],["hubert",["HubertForCTC",Mb]]]),vv=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",yb]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Tb]],["unispeech",["UniSpeechForSequenceClassification",$b]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Eb]],["wavlm",["WavLMForSequenceClassification",Pb]],["hubert",["HubertForSequenceClassification",Ob]],["audio-spectrogram-transformer",["ASTForAudioClassification",Ry]]]),$v=new Map([["wavlm",["WavLMForXVector",Bb]]]),xv=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Cb]],["wavlm",["WavLMForAudioFrameClassification",Db]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",wb]]]),Sv=new Map([["vitmatte",["VitMatteForImageMatting",xw]]]),Ev=new Map([["swin2sr",["Swin2SRForImageSuperResolution",qw]]]),Cv=new Map([["dpt",["DPTForDepthEstimation",Kw]],["depth_anything",["DepthAnythingForDepthEstimation",Xw]],["glpn",["GLPNForDepthEstimation",Zw]]]),Iv=new Map([["clip",["CLIPVisionModelWithProjection",Wy]],["siglip",["SiglipVisionModel",Hy]]]),Cf=[[vf,pe.EncoderOnly],[$f,pe.EncoderDecoder],[sv,pe.DecoderOnly],[uv,pe.EncoderOnly],[dv,pe.EncoderOnly],[Sf,pe.Seq2Seq],[xf,pe.Seq2Seq],[vs,pe.DecoderOnly],[cv,pe.EncoderOnly],[pv,pe.EncoderOnly],[Ef,pe.Vision2Seq],[hv,pe.ImageTextToText],[fv,pe.EncoderOnly],[_v,pe.EncoderOnly],[yv,pe.EncoderOnly],[Sv,pe.EncoderOnly],[Ev,pe.EncoderOnly],[Cv,pe.EncoderOnly],[mv,pe.EncoderOnly],[gv,pe.EncoderOnly],[wv,pe.MaskGeneration],[bv,pe.EncoderOnly],[vv,pe.EncoderOnly],[ov,pe.Seq2Seq],[lv,pe.EncoderOnly],[$v,pe.EncoderOnly],[xv,pe.EncoderOnly],[Iv,pe.EncoderOnly]];for(const[t,e]of Cf)for(const[r,n]of t.values())ya.set(r,e),an.set(n,r),vh.set(r,n);const kv=[["MusicgenForConditionalGeneration",bf,pe.Musicgen],["CLIPTextModelWithProjection",Fy,pe.EncoderOnly],["SiglipTextModel",Vy,pe.EncoderOnly],["ClapTextModelWithProjection",Xb,pe.EncoderOnly],["ClapAudioModelWithProjection",Qb,pe.EncoderOnly]];for(const[t,e,r]of kv)ya.set(t,r),an.set(e,t),vh.set(t,e);class B2 extends iv{static MODEL_CLASS_MAPPINGS=Cf.map(e=>e[0]);static BASE_IF_FAIL=!0}class ke extends _t{constructor({logits:e}){super(),this.logits=e}}class Tv extends _t{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class rt extends _t{constructor({logits:e}){super(),this.logits=e}}class nt extends _t{constructor({logits:e}){super(),this.logits=e}}class lt extends _t{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class kr extends _t{constructor({logits:e}){super(),this.logits=e}}class Av extends _t{constructor({alphas:e}){super(),this.alphas=e}}class Mv extends _t{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const yt=typeof self<"u",Ov=yt&&self.constructor.name==="DedicatedWorkerGlobalScope";let mr,If,Yt;if(yt)mr=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},Yt=self.createImageBitmap,If=self.ImageData;else if(He)Yt=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.raw().toBuffer({resolveWithObject:!0}),i=new wt(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 zv={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},Rv=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class wt{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 wt)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!yt)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new wt(n,e.width,e.height,4)}static async fromURL(e){const r=await Ka(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(yt){const r=await Yt(e),n=mr(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=He(await e.arrayBuffer());return await Yt(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 wt(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=zv[n]??n;if(yt){const i=this.channels,s=this.toCanvas(),o=mr(e,r).getContext("2d");return o.drawImage(s,0,0,e,r),new wt(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 Yt(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(yt){const i=this.channels,s=this.toCanvas(),o=this.width+e+r,l=this.height+n+a,d=mr(o,l).getContext("2d");return d.drawImage(s,0,0,this.width,this.height,e,n,o,l),new wt(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 Yt(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(yt){const o=this.channels,l=this.toCanvas(),d=mr(i,s).getContext("2d");return d.drawImage(l,e,r,i,s,0,0,i,s),new wt(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 Yt(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(yt){const i=this.channels,s=this.toCanvas(),o=mr(e,r).getContext("2d");let l=0,d=0,p=0,u=0;return n>=0?l=n:p=-n,a>=0?d=a:u=-a,o.drawImage(s,l,d,e,r,p,u,e,r),new wt(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 Yt(i)}}async toBlob(e="image/png",r=1){if(!yt)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 Z("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(!yt)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=mr(e.width,e.height),n=new If(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 wt(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(yt){if(Ov)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=Rv.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(dt.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(yt)throw new Error("toSharp() is only supported in server-side environments.");return He(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}function kf(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 Pv={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 $s(t,e="htk"){const r=Pv[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}const Bv={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,r=15,n=Math.log(6.4)/27)=>t>=r?e*Math.exp(n*(t-r)):200*t/3};function Dv(t,e="htk"){const r=Bv[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}function Nv(t,e){const r=Float64Array.from({length:e.length-1},(s,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let s=0;s<t.length;++s){const o=n[s];for(let l=0;l<e.length;++l)o[l]=e[l]-t[s]}const a=e.length-2,i=Array.from({length:a},()=>new Array(t.length));for(let s=0;s<t.length;++s){const o=n[s];for(let l=0;l<a;++l){const d=-o[l]/r[l],p=o[l+2]/r[l+1];i[l][s]=Math.max(0,Math.min(d,p))}}return i}function Tf(t,e,r){const n=(e-t)/(r-1);return Float64Array.from({length:r},(a,i)=>t+n*i)}function bn(t,e,r,n,a,i=null,s="htk",o=!1){if(i!==null&&i!=="slaney")throw new Error('norm must be one of null or "slaney"');const l=$s(r,s),d=$s(n,s),p=Tf(l,d,e+2);let u=Dv(p,s),h;if(o){const _=a/(t*2);h=$s(Float64Array.from({length:t},(y,v)=>v*_),s),u=p}else h=Tf(0,Math.floor(a/2),t);const m=Nv(h,u);if(i!==null&&i==="slaney")for(let _=0;_<e;++_){const y=m[_],v=2/(u[_+2]-u[_]);for(let x=0;x<t;++x)y[x]*=v}return m}function Lv(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[Fn(i,a)];for(let i=1;i<=r;++i)n[a+e+i]=t[Fn(a-i,a)];return n}function Af(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=er(t)[0]-a;for(let o=0;o<t.length;++o)t[o]=Math.max(t[o],s)}return t}function Uv(t,e=1,r=1e-5,n=null){return Af(t,20,e,r,n)}function Fv(t,e=1,r=1e-10,n=null){return Af(t,10,e,r,n)}function Ca(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:p=null,mel_floor:u=1e-10,log_mel:h=null,reference:m=1,min_value:_=1e-10,db_range:y=null,remove_dc_offset:v=null,max_num_frames:x=null,do_pad:w=!0,transpose:E=!1}={}){const I=e.length;if(a===null&&(a=r),r>a)throw Error(`frame_length (${r}) may not be larger than fft_length (${a})`);if(I!==r)throw new Error(`Length of the window (${I}) must equal frame_length (${r})`);if(n<=0)throw new Error("hop_length must be greater than zero");if(s){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const q=Math.floor((a-1)/2)+1;t=Lv(t,q,q)}const k=Math.floor(1+Math.floor((t.length-r)/n)),A=l?Math.floor(a/2)+1:a;let D=k,L=k;x!==null&&(x>k?w&&(L=x):L=D=x);const Y=new Em(a),H=new Float64Array(a),re=new Float64Array(Y.outputBufferSize),N=new Array(D);for(let q=0;q<D;++q){const J=q*n;for(let W=0;W<r;++W)H[W]=t[J+W];if(v){let W=0;for(let B=0;B<r;++B)W+=H[B];const te=W/r;for(let B=0;B<r;++B)H[B]-=te}if(d!==null){for(let W=r-1;W>=1;--W)H[W]-=d*H[W-1];H[0]*=1-d}for(let W=0;W<e.length;++W)H[W]*=e[W];Y.realTransform(re,H);const ae=new Array(A);for(let W=0;W<ae.length;++W){const te=W<<1;ae[W]=re[te]**2+re[te+1]**2}N[q]=ae}if(i!==null&&i!==2){const q=2/i;for(let J=0;J<N.length;++J){const ae=N[J];for(let W=0;W<ae.length;++W)ae[W]**=q}}const ee=p.length,oe=new Float32Array(ee*L),Ae=E?[L,ee]:[ee,L];for(let q=0;q<ee;++q){const J=p[q];for(let ae=0;ae<D;++ae){const W=N[ae];let te=0;for(let B=0;B<A;++B)te+=J[B]*W[B];oe[E?ae*ee+q:q*D+ae]=Math.max(u,te)}}if(i!==null&&h!==null){const q=Math.min(oe.length,D*ee);switch(h){case"log":for(let J=0;J<q;++J)oe[J]=Math.log(oe[J]);break;case"log10":for(let J=0;J<q;++J)oe[J]=Math.log10(oe[J]);break;case"dB":if(i===1)Uv(oe,m,_,y);else if(i===2)Fv(oe,m,_,y);else throw new Error(`Cannot use log_mel option '${h}' with power ${i}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${h}'`)}}return{data:oe,dims:Ae}}function Ia(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=kf(i);break;case"povey":s=kf(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 (${n})`);return s}function Wv([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function xs(t,e=.5,r=null,n=!1){const a=t.logits,i=t.pred_boxes,[s,o,l]=a.dims;if(r!==null&&r.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let d=[];for(let p=0;p<s;++p){let u=r!==null?r[p]:null,h={boxes:[],classes:[],scores:[]},m=a[p],_=i[p];for(let y=0;y<o;++y){let v=m[y],x=[],w;if(n){w=v.sigmoid().data;for(let E=0;E<w.length;++E)w[E]>e&&x.push(E)}else{let E=er(v.data)[1];if(E===l-1||(w=Vn(v.data),w[E]<e))continue;x.push(E)}for(const E of x){let I=_[y].data;I=Wv(I),u!==null&&(I=I.map((k,A)=>k*u[(A+1)%2])),h.boxes.push(I),h.classes.push(E),h.scores.push(w[E])}}d.push(h)}return d}function vn(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${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 Mf(t,e,r=0,n=null){const a=t/e;let i=Im(a)*e;return n!==null&&i>n&&(i=Math.floor(a)*e),i<r&&(i=Math.ceil(a)*e),i}function Ss([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}class gr extends Dt{constructor(e){super(),this.config=e}}class Ge extends gr{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=xm(n.data)[0],s=er(n.data)[0]-a;if(s===0)return e;const o=r/255;let l=n.width,d=n.height,p=0,u=0;const h=n.data;for(let m=0;m<n.height;++m){const _=m*n.width;for(let y=0;y<n.width;++y)(h[_+y]-a)/s<o&&(l=Math.min(l,y),d=Math.min(d,m),p=Math.max(p,y),u=Math.max(u,m))}return e=await e.crop([l,d,p,u]),e}pad_image(e,r,n,{mode:a="constant",center:i=!1,constant_values:s=0}={}){const[o,l,d]=r;let p,u;if(typeof n=="number"?(p=n,u=n):(p=n.width,u=n.height),p!==l||u!==o){const h=new Float32Array(p*u*d);if(Array.isArray(s))for(let y=0;y<h.length;++y)h[y]=s[y%d];else s!==0&&h.fill(s);const[m,_]=i?[Math.floor((p-l)/2),Math.floor((u-o)/2)]:[0,0];for(let y=0;y<o;++y){const v=(y+_)*p,x=y*l;for(let w=0;w<l;++w){const E=(v+w+m)*d,I=(x+w)*d;for(let k=0;k<d;++k)h[E+k]=e[I+k]}}if(a==="symmetric"){if(i)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const y=o-1,v=l-1;for(let x=0;x<u;++x){const w=x*p,E=Fn(x,y)*l;for(let I=0;I<p;++I){if(x<o&&I<l)continue;const k=(w+I)*d,A=(E+Fn(I,v))*d;for(let D=0;D<d;++D)h[k+D]=e[A+D]}}}e=h,r=[u,p,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,p=s===void 0?1:Math.min(s/l,s/d);let u=Math.floor(Number((l*p).toFixed(2))),h=Math.floor(Number((d*p).toFixed(2)));return this.size_divisibility!==void 0&&([u,h]=Ss([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,p=o/n;Math.abs(1-p)<Math.abs(1-d)?d=p:p=d,l=Mf(d*a,this.config.ensure_multiple_of),o=Mf(p*n,this.config.ensure_multiple_of)}return[o,l]}else{if(this.size_divisibility!==void 0)return Ss([n,a],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(r)}`)}}async resize(e){const[r,n]=this.get_resize_output_image_size(e,this.size);return await e.resize(r,n,{resample:this.resample})}async preprocess(e,{do_normalize:r=null,do_pad:n=null,do_convert_rgb:a=null,do_convert_grayscale:i=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[s,o]=e.size;if(a??this.do_convert_rgb?e=e.rgb():i&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let h,m;Number.isInteger(this.crop_size)?(h=this.crop_size,m=this.crop_size):(h=this.crop_size.width,m=this.crop_size.height),e=await e.center_crop(h,m)}const l=[e.height,e.width];let d=Float32Array.from(e.data),p=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(d),r??this.do_normalize){let h=this.image_mean;Array.isArray(this.image_mean)||(h=new Array(e.channels).fill(h));let m=this.image_std;if(Array.isArray(this.image_std)||(m=new Array(e.channels).fill(h)),h.length!==e.channels||m.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${h.length}) and \`image_std\` (${m.length}) must match the number of channels in the image (${e.channels}).`);for(let _=0;_<d.length;_+=e.channels)for(let y=0;y<e.channels;++y)d[_+y]=(d[_+y]-h[y])/m[y]}if(n??this.do_pad){if(this.pad_size)[d,p]=this.pad_image(d,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){const[h,m]=Ss([p[1],p[0]],this.size_divisibility);[d,p]=this.pad_image(d,p,{width:h,height:m})}}const u=new Z("float32",d,p).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:nn(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 Gv extends Ge{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=fh(l,o,"bilinear",!1));const[d,p]=o??l.dims.slice(-2),u=new Z("int32",new Int32Array(d*p),[d,p]),h=l[0].data,m=u.data;for(let x=1;x<l.dims[0];++x){const w=l[x].data;for(let E=0;E<w.length;++E)w[E]>h[E]&&(h[E]=w[E],m[E]=x)}const _=new Array(l.dims[0]),y=u.data;for(let x=0;x<y.length;++x){const w=y[x];_[w]=w}const v=_.filter(x=>x!==void 0);i.push({segmentation:u,labels:v})}return i}}class Of extends Ge{}class Vv extends Of{}class Hv extends Ge{}class qv extends Ge{}class zf extends Ge{}class jv extends zf{}class Kv extends Ge{}class Yv extends Ge{}class Rf extends Ge{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 Xv extends Rf{}class Qv extends Ge{}class Zv extends Ge{}class Jv extends Ge{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 e2 extends Ge{}class Pf extends Ge{post_process_object_detection(...e){return xs(...e)}}class t2 extends Pf{}class r2 extends Ge{}class n2 extends Ge{}class Bf extends Ge{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 p=l.map((u,h)=>-u/d[h]);return super.pad_image(e,r,n,{center:!0,constant_values:p,...a})}}class a2 extends Bf{}class i2 extends Ge{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new Z("int64",new BigInt64Array(n.reduce((i,s)=>i*s)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return xs(...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],p=r[l],u=er(d.data)[1];if(u===a)continue;let m=Vn(d.data)[u];m>n&&(i.push(p),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 p=o>0&&l>0;return p&&(p=o/l>i),[p,s]}compute_segments(e,r,n,a,i,s=null,o=null){let[l,d]=o??e[0].dims,p=new Z("int32",new Int32Array(l*d),[l,d]),u=[];if(o!==null)for(let v=0;v<e.length;++v)e[v]=fh(e[v],o,"bilinear",!1);let h=new Int32Array(e[0].data.length),m=new Float32Array(e[0].data.length);for(let v=0;v<e.length;++v){let x=r[v];const w=e[v].data;for(let E=0;E<w.length;++E)w[E]*=x,w[E]>m[E]&&(h[E]=v,m[E]=w[E])}let _=0;const y=p.data;for(let v=0;v<n.length;++v){let x=n[v],[w,E]=this.check_segment_validity(h,e,v,a,i);if(w){++_;for(let I of E)y[I]=_;u.push({id:_,label_id:x,score:r[v]})}}return[p,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[p,u,h]=o.dims;if(h-=1,s!==null&&s.length!==p)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let m=[];for(let _=0;_<p;++_){let y=s!==null?s[_]:null,v=o[_],x=d[_],[w,E,I]=this.remove_low_and_no_objects(v,x,r,h);if(I.length===0){let[D,L]=y??x.dims.slice(-2),Y=new Z("int32",new Int32Array(D*L).fill(-1),[D,L]);m.push({segmentation:Y,segments_info:[]});continue}let[k,A]=this.compute_segments(w,E,I,n,a,i,y);m.push({segmentation:k,segments_info:A})}return m}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class s2 extends Ge{post_process_object_detection(...e){return xs(...e)}}class o2 extends Ge{reshape_input_points(e,r,n){e=structuredClone(e);let a=Ns(e);if(a.length===3)a=[1,...a],e=[e];else if(a.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let i=0;i<e.length;++i){let s=r[i],o=n[i],l=[o[0]/s[0],o[1]/s[1]];for(let d=0;d<e[i].length;++d)for(let p=0;p<e[i][d].length;++p)for(let u=0;u<e[i][d][p].length;++u)e[i][d][p][u]*=l[u]}return new Z("float32",Float32Array.from(e.flat(1/0)),a)}add_input_labels(e,r){let n=Ns(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 Z("int64",e.flat(1/0).map(BigInt),n)}async _call(e,r=null,n=null){const a=await super._call(e);if(r&&(a.input_points=this.reshape_input_points(r,a.original_sizes,a.reshaped_input_sizes)),n){if(!a.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");a.input_labels=this.add_input_labels(n,a.input_points)}return a}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 p=r[d],u=n[d];let h=await mh(e[d],{mode:"bilinear",size:l});if(h=h.slice(null,null,[0,u[0]],[0,u[1]]),h=await mh(h,{mode:"bilinear",size:p}),i){const m=h.data,_=new Uint8Array(m.length);for(let y=0;y<m.length;++y)m[y]>a&&(_[y]=1);h=new Z("bool",_,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 l2 extends Ge{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 u2 extends Ge{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:nn(n.map((s,o)=>Ot([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 d2 extends gr{constructor(e){super(e),this.config.mel_filters??=bn(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=Ia(this.config.n_fft,"hann")}_extract_fbank_features(e){const{data:r,dims:n}=Ca(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=er(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){vn(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 Z("float32",n,[1,...a])}}}class c2 extends gr{_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){vn(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 Z("float32",r,n),attention_mask:new Z("int64",new BigInt64Array(r.length).fill(1n),n)}}}class p2 extends gr{constructor(e){super(e);const r=this.config.sampling_rate,n=bn(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=Ia(400,"povey",{periodic:!1})}_extract_fbank_features(e,r){return e=e.map(n=>n*32768),Ca(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}={}){vn(e,"SeamlessM4TFeatureExtractor");let s=this._extract_fbank_features(e,this.config.max_length);const o=s.data;if(a){const[y,v]=s.dims;for(let x=0;x<v;++x){let w=0;for(let A=0;A<y;++A)w+=o[A*v+x];const E=w/y;let I=0;for(let A=0;A<y;++A)I+=(o[A*v+x]-E)**2;I/=y-1;const k=Math.sqrt(I+1e-7);for(let A=0;A<y;++A){const D=A*v+x;o[D]=(o[D]-E)/k}}}let l;if(r){const[y,v]=s.dims,x=y%n;if(x>0){const w=new Float32Array(v*(y+x));w.set(o),w.fill(this.config.padding_value,o.length);const E=y+x;s={data:w,dims:[E,v]},i&&(l=new Z("int64",new BigInt64Array(E),[1,E]),l.data.fill(1n,0,y))}}const[d,p]=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 (${u}).`);const m=new Z("float32",o,s.dims).view(1,Math.floor(d/u),p*u),_={input_features:m};if(i){const y=m.dims[1],v=new BigInt64Array(y);if(l){const x=l.data;for(let w=1,E=0;w<d;w+=u,++E)v[E]=x[w]}else v.fill(1n);_.attention_mask=new Z("int64",v,[1,y])}return _}}class h2 extends gr{constructor(e){super(e);const r=this.config.sampling_rate,n=bn(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;a<n.length;++a)n[a].push(0);this.mel_filters=n,this.window=Ia(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(e,r){return Ca(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e){vn(e,"ASTFeatureExtractor");const r=this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){const n=this.std*2,a=r.data;for(let i=0;i<a.length;++i)a[i]=(a[i]-this.mean)/n}return{input_values:new Z("float32",r.data,[1,...r.dims])}}}class f2 extends gr{constructor(e){super(e),this.mel_filters=bn(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=bn(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=Ia(this.config.fft_window_size,"hann")}_get_input_mel(e,r,n,a){let i,s=!1;const o=e.length-r;if(o>0)if(n==="rand_trunc"){s=!0;const l=Math.floor(Math.random()*(o+1));e=e.subarray(l,l+r),i=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),i.dims=[1,...i.dims]}else throw new Error(`Truncation strategy "${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 Ca(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}={}){vn(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 Z("float32",n.data,[1,...n.dims])}}}class m2 extends gr{}class Tr extends Dt{constructor(e){super(),this.feature_extractor=e}async _call(e,...r){return await this.feature_extractor(e,...r)}}class g2 extends Tr{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 _2 extends Tr{async _call(e){return await this.feature_extractor(e)}}class y2 extends Tr{async _call(e){return await this.feature_extractor(e)}}class w2 extends Tr{async _call(e){return await this.feature_extractor(e)}}class b2 extends Tr{}class v2{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor:Ge,WhisperFeatureExtractor:d2,ViTFeatureExtractor:Qv,MobileViTFeatureExtractor:e2,OwlViTFeatureExtractor:Pf,Owlv2ImageProcessor:t2,CLIPFeatureExtractor:zf,CLIPImageProcessor:jv,ChineseCLIPFeatureExtractor:Kv,SiglipImageProcessor:Yv,ConvNextFeatureExtractor:Rf,ConvNextImageProcessor:Xv,SegformerFeatureExtractor:Gv,BitImageProcessor:Hv,DPTImageProcessor:Vv,DPTFeatureExtractor:Of,GLPNFeatureExtractor:qv,BeitFeatureExtractor:n2,DeiTFeatureExtractor:r2,DetrFeatureExtractor:i2,YolosFeatureExtractor:s2,DonutFeatureExtractor:Bf,NougatImageProcessor:a2,EfficientNetImageProcessor:Jv,ViTImageProcessor:Zv,VitMatteImageProcessor:u2,SamImageProcessor:o2,Swin2SRImageProcessor:l2,Wav2Vec2FeatureExtractor:c2,SeamlessM4TFeatureExtractor:p2,SpeechT5FeatureExtractor:m2,ASTFeatureExtractor:h2,ClapFeatureExtractor:f2};static PROCESSOR_CLASS_MAPPING={WhisperProcessor:_2,Wav2Vec2ProcessorWithLM:y2,SamProcessor:g2,SpeechT5Processor:w2,OwlViTProcessor:b2};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 xr(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=Ge;else throw new Error(`Unknown Feature Extractor type: ${l}`);let p=this.PROCESSOR_CLASS_MAPPING[o.processor_class]??Tr,u=new d(o);return new p(u)}}class $2{static model_id="Xenova/slimsam-77-uniform";static model;static processor;static getInstance(){return this.model||(this.model=df.from_pretrained(this.model_id,{dtype:{vision_encoder:"fp16",prompt_encoder_mask_decoder:"q8"},device:{vision_encoder:"webgpu",prompt_encoder_mask_decoder:"wasm"}})),this.processor||(this.processor=v2.from_pretrained(this.model_id)),Promise.all([this.model,this.processor])}}let Es=null,Ar=null,Df=!1;self.onmessage=async t=>{const[e,r]=await $2.getInstance();Df||(Df=!0,self.postMessage({type:"ready"}));const{type:n,data:a}=t.data;if(n==="reset")Ar=null,Es=null;else if(n==="segment"){self.postMessage({type:"segment_result",data:"start"});const i=await wt.read(t.data.data);Ar=await r(i),Es=await e.get_image_embeddings(Ar),self.postMessage({type:"segment_result",data:"done"})}else if(n==="decode"){const i=Ar.reshaped_input_sizes[0],s=a.map(m=>[m.point[0]*i[1],m.point[1]*i[0]]),o=a.map(m=>BigInt(m.label)),l=new Z("float32",s.flat(1/0),[1,1,s.length,2]),d=new Z("int64",o.flat(1/0),[1,1,o.length]),{pred_masks:p,iou_scores:u}=await e({...Es,input_points:l,input_labels:d}),h=await r.post_process_masks(p,Ar.original_sizes,Ar.reshaped_input_sizes);self.postMessage({type:"decode_result",data:{mask:wt.fromTensor(h[0][0]),scores:u.data}})}else throw new Error(`Unknown message type: ${n}`)}})();