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Ur({location:"ml-tensor",mlTensor:e.mlTensor,type:e.type,dims:r});default:throw new Error(`tensorReshape: tensor location ${e.location} is not supported`)}};let Ur=class{constructor(r,t,s){X0();let o,n;if(typeof r=="object"&&"location"in r)switch(this.dataLocation=r.location,o=r.type,n=r.dims,r.location){case"cpu-pinned":{const a=zn.get(o);if(!a)throw new TypeError(`unsupported type "${o}" to create tensor from pinned buffer`);if(!(r.data instanceof a))throw new TypeError(`buffer should be of type ${a.name}`);this.cpuData=r.data;break}case"texture":{if(o!=="float32")throw new TypeError(`unsupported type "${o}" to create tensor from texture`);this.gpuTextureData=r.texture,this.downloader=r.download,this.disposer=r.dispose;break}case"gpu-buffer":{if(o!=="float32"&&o!=="float16"&&o!=="int32"&&o!=="int64"&&o!=="uint32"&&o!=="uint8"&&o!=="bool"&&o!=="uint4"&&o!=="int4")throw new TypeError(`unsupported type "${o}" to create tensor from gpu 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 * ONNX Runtime Web v1.22.0-dev.20250409-89f8206ba4
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
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`)+this.internalVariables.map(e=>e.impl()).join(`
`)}get variablesInfo(){if(this.uniforms.length===0)return;let e=r=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(r)];return this.uniforms.map(r=>[e(r.type),r.length??1])}},zc=(e,r)=>new Lc(e,r)}),Bc,il,Rc,jc,Nc,Vc,Lr,Uc,Wc,zs=Be(()=>{ut(),mt(),Xt(),ft(),Bc=(e,r)=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(r.length!==0&&r.length!==e[0].dims.length)throw new Error(`perm size ${r.length} does not match input rank ${e[0].dims.length}`)},il=(e,r)=>r.length!==0?r:[...new Array(e).keys()].reverse(),Rc=(e,r)=>Me.sortBasedOnPerm(e,il(e.length,r)),jc=(e,r,t,s)=>{let o=`fn perm(i: ${s.type.indices}) -> ${t.type.indices} {
    var a: ${t.type.indices};`;for(let n=0;n<r;++n)o+=`a[${e[n]}]=i[${n}];`;return o+="return a;}"},Nc=(e,r)=>{let t=[],s=[];for(let o=0;o<e.length;++o)e[o]!==1&&t.push(e[o]),e[r[o]]!==1&&s.push(r[o]);return{newShape:t,newPerm:s}},Vc=(e,r)=>{let t=0;for(let s=0;s<e.length;++s)if(r[e[s]]!==1){if(e[s]<t)return!1;t=e[s]}return!0},Lr=(e,r)=>{let t=e.dataType,s=e.dims.length,o=il(s,r),n=Rc(e.dims,o),i=e.dims,a=n,l=s<2||Vc(o,e.dims),u;if(l)return u=_=>{let T=Pe("input",t,i,4),$=Ye("output",t,a,4);return`
  ${_.registerUniform("output_size","u32").declareVariables(T,$)}
  ${_.mainStart()}
    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    output[global_idx] = input[global_idx];
  }`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let _=Me.size(n);return{outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(_/64/4)},programUniforms:[{type:12,data:Math.ceil(_/4)}]}},getShaderSource:u};let{newShape:p,newPerm:d}=Nc(e.dims,o),c=Me.areEqual(d,[2,3,1]),f=Me.areEqual(d,[3,1,2]);if(p.length===2||c||f){i=c?[p[0],p[1]*p[2]]:f?[p[0]*p[1],p[2]]:p,a=[i[1],i[0]];let _=16;return u=T=>{let $=Pe("a",t,i.length),w=Ye("output",t,a.length);return`
  ${T.registerUniform("output_size","u32").declareVariables($,w)}
  var<workgroup> tile : array<array<${w.type.value}, ${_+1}>, ${_}>;
  ${T.mainStart([_,_,1])}
    let stride = (uniforms.output_shape[1] - 1) / ${_} + 1;
    let workgroup_id_x = workgroup_index % stride;
    let workgroup_id_y = workgroup_index / stride;
    let input_col = workgroup_id_y * ${_}u + local_id.x;
    let input_row = workgroup_id_x * ${_}u + local_id.y;
    if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
      tile[local_id.y][local_id.x] = ${$.getByIndices(`${$.type.indices}(input_row, input_col)`)};
    }
    workgroupBarrier();

    let output_col = workgroup_id_x * ${_}u + local_id.x;
    let output_row = workgroup_id_y * ${_}u + local_id.y;
    if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) {
      ${w.setByIndices(`${w.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
    }
  }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let T=Me.size(n);return{outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(a[1]/_),y:Math.ceil(a[0]/_)},programUniforms:[{type:12,data:T},...st(i,a)]}},getShaderSource:u}}return u=_=>{let T=Pe("a",t,i.length),$=Ye("output",t,a.length);return`
  ${_.registerUniform("output_size","u32").declareVariables(T,$)}

  ${jc(o,s,T,$)}

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

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

    ${$.setByOffset("global_idx",T.getByIndices("aIndices"))}
  }`},{name:"Transpose",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>{let _=Me.size(n);return{outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...st(i,a)]}},getShaderSource:u}},Uc=(e,r)=>{Bc(e.inputs,r.perm),e.compute(Lr(e.inputs[0],r.perm))},Wc=e=>St({perm:e.perm})}),Gc,Kc,Hc,qc,Xc,Qc,Jc,Yc,Zc,ep,ss,tp,rp,sp,np,op,ip,ap,lp,up,dp,Tv=Be(()=>{ut(),mt(),ft(),ll(),zs(),Gc={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"},Kc={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"},Hc={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},qc={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},Xc=(e,r)=>{let t=[];for(let s=r-e;s<r;++s)t.push(s);return t},Qc=(e,r)=>{let t=[],s=e.length;for(let n=0;n<s;n++)r.indexOf(n)===-1&&t.push(e[n]);let o=r.map(n=>e[n]);return[t,o]},Jc=(e,r)=>{let t=e.length+r.length,s=[],o=0;for(let n=0;n<t;n++)r.indexOf(n)===-1?s.push(e[o++]):s.push(1);return s},Yc=(e,r)=>{for(let t=0;t<e.length;++t)if(e[e.length-t-1]!==r-1-t)return!1;return!0},Zc=(e,r)=>{let t=[];if(!Yc(e,r)){for(let s=0;s<r;++s)e.indexOf(s)===-1&&t.push(s);e.forEach(s=>t.push(s))}return t},ep=(e,r,t,s,o,n,i)=>{let a=t[0].dims,l=Me.size(n),u=Me.size(i),p=Pe("_A",t[0].dataType,a),d=Ye("output",o,n),c=64;l===1&&(c=256);let f=`
          var<workgroup> aBestValues : array<f32, ${c}>;
       `,_=T=>`
        ${T.registerUniform("reduceSize","u32").declareVariables(p,d)}
        ${f}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${T.mainStart(c)}

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

          var bestValue = f32(${Hc[s]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${c}) {
           let candidate = f32(${p.getByOffset("offset + k")});
           bestValue = ${Gc[s]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

         var reduceSize = min(Length, ${c}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 = ${Kc[s]};
            aBestValues[local_idx] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (local_idx == 0u) {
          ${d.setByOffset("outputIndex",`${s==="mean"?`${d.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${d.type.storage}(${qc[s]})`}`)};
         }
        }`;return{name:e,shaderCache:{hint:`${r};${c}`,inputDependencies:["type"]},getShaderSource:_,getRunData:()=>({outputs:[{dims:n,dataType:o}],dispatchGroup:{x:l},programUniforms:[{type:12,data:u}]})}},ss=(e,r,t,s)=>{let o=e.inputs.length===1?t:al(e.inputs,t),n=o.axes;n.length===0&&!o.noopWithEmptyAxes&&(n=e.inputs[0].dims.map((f,_)=>_));let i=Me.normalizeAxes(n,e.inputs[0].dims.length),a=i,l=e.inputs[0],u=Zc(a,e.inputs[0].dims.length);u.length>0&&(l=e.compute(Lr(e.inputs[0],u),{inputs:[0],outputs:[-1]})[0],a=Xc(a.length,l.dims.length));let[p,d]=Qc(l.dims,a),c=p;o.keepDims&&(c=Jc(p,i)),e.compute(ep(r,o.cacheKey,[l],s,e.inputs[0].dataType,c,d),{inputs:[l]})},tp=(e,r)=>{ss(e,"ReduceMeanShared",r,"mean")},rp=(e,r)=>{ss(e,"ReduceL1Shared",r,"l1")},sp=(e,r)=>{ss(e,"ReduceL2Shared",r,"l2")},np=(e,r)=>{ss(e,"ReduceLogSumExpShared",r,"logSumExp")},op=(e,r)=>{ss(e,"ReduceMaxShared",r,"max")},ip=(e,r)=>{ss(e,"ReduceMinShared",r,"min")},ap=(e,r)=>{ss(e,"ReduceProdShared",r,"prod")},lp=(e,r)=>{ss(e,"ReduceSumShared",r,"sum")},up=(e,r)=>{ss(e,"ReduceSumSquareShared",r,"sumSquare")},dp=(e,r)=>{ss(e,"ReduceLogSumShared",r,"logSum")}}),ns,cp,ui,al,os,pp,hp,mp,_p,fp,gp,wp,Mp,bp,yp,is,vp,xp,Tp,Ep,Pp,Cp,Sp,$p,kp,Ip,ll=Be(()=>{ut(),mt(),Xt(),ft(),Tv(),ns=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},cp=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],ui=(e,r,t,s,o,n,i=!1,a=!1)=>{let l=[],u=t[0].dims,p=u.length,d=Me.normalizeAxes(o,p),c=!a&&d.length===0;u.forEach((T,$)=>{c||d.indexOf($)>=0?i&&l.push(1):l.push(T)});let f=l.length,_=Me.size(l);return{name:e,shaderCache:r,getShaderSource:T=>{let $=[],w=Pe("_A",t[0].dataType,p),g=Ye("output",n,f),S=s(w,g,d),E=S[2];for(let y=0,M=0;y<p;y++)c||d.indexOf(y)>=0?(i&&M++,E=`for(var j${y}: u32 = 0; j${y} < ${u[y]}; j${y}++) {
                  ${S[2].includes("last_index")?`let last_index = j${y};`:""}
                  ${w.indicesSet("input_indices",y,`j${y}`)}
                  ${E}
                }`):($.push(`${w.indicesSet("input_indices",y,g.indicesGet("output_indices",M))};`),M++);return`

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

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

          ${$.join(`
`)}
          ${S[0]}       // init ops for reduce max/min
          ${S[1]}
          ${E}
          ${S[3]}
          ${S.length===4?g.setByOffset("global_idx","value"):S.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:l,dataType:n}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...st(u,l)]})}},al=(e,r)=>{let t=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(s=>t.push(Number(s))),St({axes:t,keepDims:r.keepDims,noopWithEmptyAxes:r.noopWithEmptyAxes})},os=(e,r,t,s)=>{let o=e.inputs,n=o.length===1?t:al(o,t);e.compute(ui(r,{hint:n.cacheKey,inputDependencies:["rank"]},[o[0]],n.noopWithEmptyAxes&&n.axes.length===0?cp:s,n.axes,o[0].dataType,n.keepDims,n.noopWithEmptyAxes),{inputs:[0]})},pp=(e,r)=>{ns(e.inputs),os(e,"ReduceLogSum",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,"value = log(value);"])},hp=(e,r)=>{ns(e.inputs),os(e,"ReduceL1",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += abs(${t.getByIndices("input_indices")});`,""])},mp=(e,r)=>{ns(e.inputs),os(e,"ReduceL2",r,(t,s)=>[`var t = ${s.type.value}(0); var value = ${s.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},_p=(e,r)=>{ns(e.inputs),os(e,"ReduceLogSumExp",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += exp(${t.getByIndices("input_indices")});`,"value = log(value);"])},fp=(e,r)=>{ns(e.inputs),os(e,"ReduceMax",r,(t,s,o)=>{let n=[];for(let i=0;i<t.rank;i++)(o.indexOf(i)>=0||o.length===0)&&n.push(t.indicesSet("input_indices",i,0));return[`${n.join(`
`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = max(value, ${t.getByIndices("input_indices")});`,""]})},gp=(e,r)=>{ns(e.inputs),os(e,"ReduceMean",r,(t,s,o)=>{let n=1;for(let i=0;i<t.rank;i++)(o.indexOf(i)>=0||o.length===0)&&(n*=e.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${t.getByIndices("input_indices")});`,`let value = ${s.type.value}(sum / ${n});`]})},wp=(e,r)=>{ns(e.inputs),os(e,"ReduceMin",r,(t,s,o)=>{let n=[];for(let i=0;i<t.rank;i++)(o.indexOf(i)>=0||o.length===0)&&n.push(`input_indices[${i}] = 0;`);return[`${n.join(`
`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = min(value, ${t.getByIndices("input_indices")});`,""]})},Mp=(e,r)=>{ns(e.inputs),os(e,"ReduceProd",r,(t,s)=>[`var value = ${s.type.storage}(1);`,"",`value *= ${t.getByIndices("input_indices")};`,""])},bp=(e,r)=>{ns(e.inputs),os(e,"ReduceSum",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,""])},yp=(e,r)=>{ns(e.inputs),os(e,"ReduceSumSquare",r,(t,s)=>[`var t = ${s.type.value}(0); var value = ${s.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += t * t;`,""])},is=(e,r,t)=>{if(r.length===0)return t;let s=1,o=1;for(let n=0;n<r.length;n++)r.indexOf(n)===-1?s*=e[n]:o*=e[n];return o<32&&s>1024},vp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?gp(e,r):tp(e,r)},xp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?hp(e,r):rp(e,r)},Tp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?mp(e,r):sp(e,r)},Ep=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?_p(e,r):np(e,r)},Pp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?fp(e,r):op(e,r)},Cp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?wp(e,r):ip(e,r)},Sp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Mp(e,r):ap(e,r)},$p=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?bp(e,r):lp(e,r)},kp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?yp(e,r):up(e,r)},Ip=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?pp(e,r):dp(e,r)}}),ul,Ap,Fp,dl,Ev=Be(()=>{ut(),Xt(),ll(),ul=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Ap=(e,r)=>{ul(e.inputs);let t=(s,o,n)=>{let i=[];for(let a=0;a<s.rank;a++)(n.indexOf(a)>=0||n.length===0)&&i.push(`input_indices[${a}] = 0;`);return[`${i.join(`
`)}`,`var value = ${s.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?"<=":"<"} value) {
         value = ${s.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",o.setByOffset("global_idx","best_index")]};e.compute(ui("ArgMin",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},Fp=(e,r)=>{ul(e.inputs);let t=(s,o,n)=>{let i=[];for(let a=0;a<s.rank;a++)(n.indexOf(a)>=0||n.length===0)&&i.push(`input_indices[${a}] = 0;`);return[`${i.join(`
`)}`,`var value = ${s.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?">=":">"} value) {
         value = ${s.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",o.setByOffset("global_idx","best_index")]};e.compute(ui("argMax",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},dl=e=>St(e)}),Op,di,Dp,Lp,zp,mo,Bp,Rp,cl=Be(()=>{ut(),mt(),el(),ft(),Op=(e,r)=>{let t=e[0],s=e[1],o=e[2],n=e[3],i=e[4],a=e[5];if(i&&a)throw new Error("Attention cannot have both past and attention_bias");if(t.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let l=t.dims[0],u=t.dims[1],p=t.dims[2];if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(s.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(s.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(o.dims[0]!==s.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let d=o.dims[0]/3,c=d,f=c;if(r.qkvHiddenSizes.length>0){if(r.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let S of r.qkvHiddenSizes)if(S%r.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");d=r.qkvHiddenSizes[0],c=r.qkvHiddenSizes[1],f=r.qkvHiddenSizes[2]}let _=u;if(d!==c)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==d+c+f)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let T=0;if(i){if(c!==f)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==l)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==r.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==c/r.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');r.pastPresentShareBuffer||(T=i.dims[3])}let $=_+T,w=-1,g=0;if(n)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");if(a){if(a.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(a.dims[0]!==l||a.dims[1]!==r.numHeads||a.dims[2]!==u||a.dims[3]!==$)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:l,sequenceLength:u,pastSequenceLength:T,kvSequenceLength:_,totalSequenceLength:$,maxSequenceLength:w,inputHiddenSize:p,hiddenSize:d,vHiddenSize:f,headSize:Math.floor(d/r.numHeads),vHeadSize:Math.floor(f/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:g,scale:r.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},di=(e,r,t)=>r&&e?`
      let total_sequence_length_input = u32(${r.getByOffset("0")});
      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
      total_sequence_length = u32(${e==null?void 0:e.getByOffset("batchIdx")}) + 1;
      var past_sequence_length: u32 = 0;
      if (is_first_prompt == false) {
        past_sequence_length = total_sequence_length - sequence_length;
      }
       `:`
    ${t?"let past_sequence_length = uniforms.past_sequence_length":""};
    let present_sequence_length = total_sequence_length;
    `,Dp=(e,r,t,s,o,n,i,a)=>{let l=Kt(i?1:n),u=64,p=n/l;p<u&&(u=32);let d=Math.ceil(n/l/u),c=[{type:12,data:r},{type:12,data:t},{type:12,data:s},{type:12,data:o},{type:12,data:p},{type:12,data:d}],f=lr(e.dataType,l),_=yr(1,l),T=["type"];i&&T.push("type"),a&&T.push("type");let $=w=>{let g=Ye("x",e.dataType,e.dims,l),S=[g],E=i?Pe("seq_lens",i.dataType,i.dims):void 0;E&&S.push(E);let y=a?Pe("total_sequence_length_input",a.dataType,a.dims):void 0;y&&S.push(y);let M=yr(e.dataType),v=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${u}>;
  var<workgroup> thread_sum: array<f32, ${u}>;
  ${w.registerUniforms(v).declareVariables(...S)}
  ${w.mainStart([u,1,1])}
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let sequence_length = uniforms.sequence_length;
    var total_sequence_length = uniforms.total_sequence_length;
    ${di(E,y,!1)}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${u}) * uniforms.total_sequence_length + local_offset;
    let seq_causal_length = ${i?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
    var thread_max_vector = ${_}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${_}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(l){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${l}`)}})()};
    workgroupBarrier();

    var max_value =  f32(-3.402823e+38f);
    for (var i = 0u; i < ${u}; i++) {
      max_value = max(thread_max[i], max_value);
    }

    var sum_vector = ${_}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      sum_vector += exp(${_}(x[offset + i]) - max_value);
    }
    thread_sum[local_idx] = ${(()=>{switch(l){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${l}`)}})()};
    workgroupBarrier();

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

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        x[offset + i] = ${g.type.value}(${M}(1.0) / ${M}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${_}(x[offset + i]);
        x[offset + i] = ${g.type.value}(exp(f32input - max_value) / sum);
      }
    }
      ${i?`
        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
          x[offset + total_seq_id] = ${g.type.value}(${M}(0));
        }`:""};
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${u};${f};${l}`,inputDependencies:T},getShaderSource:$,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:o,z:r*t},programUniforms:c})}},Lp=(e,r,t,s,o,n,i,a,l)=>{let u=i+n.kvSequenceLength,p=[n.batchSize,n.numHeads,n.sequenceLength,u],d=e>1&&s,c=n.kvNumHeads?n.kvNumHeads:n.numHeads,f=d?[n.batchSize,c,u,n.headSize]:void 0,_=n.nReps?n.nReps:1,T=n.scale===0?1/Math.sqrt(n.headSize):n.scale,$=Kt(n.headSize),w=n.headSize/$,g=12,S={x:Math.ceil(u/g),y:Math.ceil(n.sequenceLength/g),z:n.batchSize*n.numHeads},E=[{type:12,data:n.sequenceLength},{type:12,data:w},{type:12,data:u},{type:12,data:n.numHeads},{type:12,data:n.headSize},{type:1,data:T},{type:12,data:i},{type:12,data:n.kvSequenceLength},{type:12,data:_}],y=d&&s&&Me.size(s.dims)>0,M=["type","type"];y&&M.push("type"),o&&M.push("type"),a&&M.push("type"),l&&M.push("type");let v=[{dims:p,dataType:r.dataType,gpuDataType:0}];d&&v.push({dims:f,dataType:r.dataType,gpuDataType:0});let C=A=>{let B=Pe("q",r.dataType,r.dims,$),K=Pe("key",t.dataType,t.dims,$),G=[B,K];if(y){let me=Pe("past_key",s.dataType,s.dims,$);G.push(me)}o&&G.push(Pe("attention_bias",o.dataType,o.dims));let j=a?Pe("seq_lens",a.dataType,a.dims):void 0;j&&G.push(j);let ee=l?Pe("total_sequence_length_input",l.dataType,l.dims):void 0;ee&&G.push(ee);let H=Ye("output",r.dataType,p),Z=[H];d&&Z.push(Ye("present_key",r.dataType,f,$));let X=yr(1,$),oe=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${g}u;

  var<workgroup> tileQ: array<${B.type.storage}, ${g*g}>;
  var<workgroup> tileK: array<${B.type.storage}, ${g*g}>;
  ${A.registerUniforms(oe).declareVariables(...G,...Z)}
  ${A.mainStart([g,g,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${_===1?"headIdx":"headIdx / uniforms.n_reps"};
    let kv_num_heads = ${_===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let sequence_length = uniforms.M;
    var total_sequence_length = uniforms.N;
    ${di(j,ee,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${y&&d?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${d?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
    var value = ${X}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
      ${y&&d?`
              if (n + local_id.y < past_sequence_length) {
                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
              }`:`
          if (n + local_id.y < uniforms.kv_sequence_length) {
            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
          }`}
      ${d?`if (n + local_id.y < present_sequence_length) {
        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
      }`:""}
      }
      workgroupBarrier();

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

      workgroupBarrier();
    }

    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch($){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${$}`)}})()};
        output[outputIdx] = ${H.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${$};${o!==void 0};${s!==void 0};${e}`,inputDependencies:M},getRunData:()=>({outputs:v,dispatchGroup:S,programUniforms:E}),getShaderSource:C}},zp=(e,r,t,s,o,n,i=void 0,a=void 0)=>{let l=n+o.kvSequenceLength,u=o.nReps?o.nReps:1,p=o.vHiddenSize*u,d=e>1&&s,c=o.kvNumHeads?o.kvNumHeads:o.numHeads,f=d?[o.batchSize,c,l,o.headSize]:void 0,_=[o.batchSize,o.sequenceLength,p],T=12,$={x:Math.ceil(o.vHeadSize/T),y:Math.ceil(o.sequenceLength/T),z:o.batchSize*o.numHeads},w=[{type:12,data:o.sequenceLength},{type:12,data:l},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:p},{type:12,data:n},{type:12,data:o.kvSequenceLength},{type:12,data:u}],g=d&&s&&Me.size(s.dims)>0,S=["type","type"];g&&S.push("type"),i&&S.push("type"),a&&S.push("type");let E=[{dims:_,dataType:r.dataType,gpuDataType:0}];d&&E.push({dims:f,dataType:r.dataType,gpuDataType:0});let y=M=>{let v=Pe("probs",r.dataType,r.dims),C=Pe("v",t.dataType,t.dims),A=[v,C];g&&A.push(Pe("past_value",s.dataType,s.dims));let B=i?Pe("seq_lens",i.dataType,i.dims):void 0;i&&A.push(B);let K=a?Pe("total_sequence_length_input",a.dataType,a.dims):void 0;a&&A.push(K);let G=[Ye("output",r.dataType,_)];d&&G.push(Ye("present_value",r.dataType,f));let j=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${T}u;
  var<workgroup> tileQ: array<${v.type.value}, ${T*T}>;
  var<workgroup> tileV: array<${v.type.value}, ${T*T}>;
  ${M.registerUniforms(j).declareVariables(...A,...G)}
  ${M.mainStart([T,T,1])}
   let headIdx = workgroup_id.z % uniforms.num_heads;
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let kvHeadIdx = ${u===1?"headIdx":"headIdx / uniforms.n_reps"};
   let kv_num_heads = ${u===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
   let m = global_id.y;
   let n = global_id.x;
   let sequence_length = uniforms.M;
   var total_sequence_length = uniforms.K;
   ${di(B,K,!0)}
   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
   ${g&&d?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${d?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${v.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) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
        ${g&&d?`
        if (w + local_id.y < past_sequence_length) {
          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
        }
      `:`
            if (w + local_id.y < uniforms.kv_sequence_length) {
              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
            }`}
        ${d?`
            if (w + local_id.y < present_sequence_length) {
          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
        }`:""}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + headIdx * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`};return{name:"AttentionScore",shaderCache:{hint:`${s!==void 0};${e}`,inputDependencies:S},getRunData:()=>({outputs:E,dispatchGroup:$,programUniforms:w}),getShaderSource:y}},mo=(e,r,t,s,o,n,i,a,l,u,p=void 0,d=void 0)=>{let c=Math.min(e.outputCount,1+(i?1:0)+(a?1:0)),f=c>1?u.pastSequenceLength:0,_=f+u.kvSequenceLength,T=l&&Me.size(l.dims)>0?l:void 0,$=[r,t];c>1&&i&&Me.size(i.dims)>0&&$.push(i),T&&$.push(T),p&&$.push(p),d&&$.push(d);let w=e.compute(Lp(c,r,t,i,T,u,f,p,d),{inputs:$,outputs:c>1?[-1,1]:[-1]})[0];e.compute(Dp(w,u.batchSize,u.numHeads,f,u.sequenceLength,_,p,d),{inputs:p&&d?[w,p,d]:[w],outputs:[]});let g=[w,s];c>1&&a&&Me.size(a.dims)>0&&g.push(a),p&&g.push(p),d&&g.push(d),e.compute(zp(c,w,s,a,u,f,p,d),{inputs:g,outputs:c>1?[0,2]:[0]})},Bp=(e,r)=>{let t=[r.batchSize,r.numHeads,r.sequenceLength,r.headSize],s=r.sequenceLength,o=r.inputHiddenSize,n=r.headSize,i=12,a={x:Math.ceil(r.headSize/i),y:Math.ceil(r.sequenceLength/i),z:r.batchSize*r.numHeads},l=[e.inputs[0],e.inputs[1],e.inputs[2]],u=[{type:12,data:s},{type:12,data:o},{type:12,data:n},{type:12,data:r.numHeads},{type:12,data:r.headSize},{type:12,data:r.hiddenSize},{type:12,data:r.hiddenSize+r.hiddenSize+r.vHiddenSize}],p=d=>{let c=Ye("output_q",l[0].dataType,t),f=Ye("output_k",l[0].dataType,t),_=Ye("output_v",l[0].dataType,t),T=Pe("input",l[0].dataType,l[0].dims),$=Pe("weight",l[1].dataType,l[1].dims),w=Pe("bias",l[2].dataType,l[2].dims),g=T.type.storage,S=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
  const TILE_SIZE = ${i}u;
  var<workgroup> tileInput: array<${g}, ${i*i}>;
  var<workgroup> tileWeightQ: array<${g}, ${i*i}>;
  var<workgroup> tileWeightK: array<${g}, ${i*i}>;
  var<workgroup> tileWeightV: array<${g}, ${i*i}>;
  ${d.registerUniforms(S).declareVariables(T,$,w,c,f,_)}
  ${d.mainStart([i,i,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_id.x;

    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;
    let biasOffsetQ = headNumber * uniforms.head_size;
    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;
    let biasOffsetV = uniforms.hidden_size + biasOffsetK;

    var valueQ = ${g}(0);
    var valueK = ${g}(0);
    var valueV = ${g}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:a,programUniforms:u}),getShaderSource:p},{inputs:l,outputs:[-1,-1,-1]})},Rp=(e,r)=>{let t=Op(e.inputs,r),[s,o,n]=Bp(e,t);return mo(e,s,o,n,e.inputs[4],void 0,void 0,void 0,e.inputs[5],t)}}),jp,Np,Vp,Up,Pv=Be(()=>{ts(),ut(),mt(),Xt(),ft(),jp=(e,r)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let t=(s,o,n)=>{let i=o.length;if(i!==s.length)throw new Error(`${n}: num dimensions != ${i}`);o.forEach((a,l)=>{if(a!==s[l])throw new Error(`${n}: dim[${l}] do not match`)})};if(e[0].dims.length>1){let s=r.format==="NHWC"?r.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,r.spatial?2:void 0);t(e[1].dims,s,"Invalid input scale"),t(e[2].dims,s,"Invalid input B"),t(e[3].dims,s,"Invalid input mean"),t(e[4].dims,s,"Invalid input var")}else t(e[1].dims,[1],"Invalid input scale"),t(e[2].dims,[1],"Invalid input B"),t(e[3].dims,[1],"Invalid input mean"),t(e[4].dims,[1],"Invalid input var")},Np=(e,r)=>{let{epsilon:t,spatial:s,format:o}=r,n=e[0].dims,i=s?Kt(n[n.length-1]):1,a=o==="NHWC"&&n.length>1?i:1,l=Me.size(n)/i,u=s,p=u?n.length:n,d=Pe("x",e[0].dataType,e[0].dims,i),c=Pe("scale",e[1].dataType,e[1].dims,a),f=Pe("bias",e[2].dataType,e[2].dims,a),_=Pe("inputMean",e[3].dataType,e[3].dims,a),T=Pe("inputVar",e[4].dataType,e[4].dims,a),$=Ye("y",e[0].dataType,p,i),w=()=>{let S="";if(s)S=`let cOffset = ${n.length===1?"0u":o==="NHWC"?`outputIndices[${n.length-1}] / ${i}`:"outputIndices[1]"};`;else if(o==="NCHW")S=`
            ${$.indicesSet("outputIndices","0","0")}
            let cOffset = ${$.indicesToOffset("outputIndices")};`;else{S=`var cIndices = ${c.type.indices}(0);
                       cIndices[0] = outputIndices[${n.length-1}];`;for(let E=1;E<c.rank;E++)S+=`cIndices[${E}] = outputIndices[${E}];`;S+=`let cOffset = ${c.indicesToOffset("cIndices")};`}return S},g=S=>`
  const epsilon = ${t};
  ${S.registerUniform("outputSize","u32").declareVariables(d,c,f,_,T,$)}
  ${S.mainStart()}
  ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${$.offsetToIndices(`global_idx * ${i}`)};
    ${w()}
    let scale = ${c.getByOffset("cOffset")};
    let bias = ${f.getByOffset("cOffset")};
    let inputMean = ${_.getByOffset("cOffset")};
    let inputVar = ${T.getByOffset("cOffset")};
    let x = ${d.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${$.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${r.epsilon}_${r.format}_${s}_${i}`,inputDependencies:u?["rank","type","type","type","type"]:void 0},getShaderSource:g,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u?[{type:12,data:l},...st(n)]:[{type:12,data:l}]})}},Vp=e=>St(e),Up=(e,r)=>{let{inputs:t,outputCount:s}=e,o=Vp({...r,outputCount:s});if(Bt.webgpu.validateInputContent&&jp(t,o),r.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Np(t,o))}}),Wp,Gp,Kp,Cv=Be(()=>{mt(),ft(),Wp=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")},Gp=e=>{let r=e[0].dims,t=e[0].dims[2],s=Me.size(r)/4,o=e[0].dataType,n=Pe("input",o,r,4),i=Pe("bias",o,[t],4),a=Pe("residual",o,r,4),l=Ye("output",o,r,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:u=>`
  const channels = ${t}u / 4;
  ${u.declareVariables(n,i,a,l)}

  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes(s)}
    let value = ${n.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${a.getByOffset("global_idx")};
    ${l.setByOffset("global_idx","value")}
  }`}},Kp=e=>{Wp(e.inputs),e.compute(Gp(e.inputs))}}),Hp,Et,qp,Xp,Qp,Jp,Yp,Zp,eh,th,rh,sh,nh,oh,ih,ah,_o,lh,ci,uh,dh,ch,ph,hh,mh,_h,fh,gh,wh,Mh,bh,yh,vh,xh,Th,pl,Eh,hl,ml,Ph,Ch,Sh,$h,kh,Ih,_l=Be(()=>{ut(),mt(),Xt(),ft(),Hp=(e,r,t,s,o,n,i)=>{let a=Math.ceil(r/4),l="";typeof o=="string"?l=`${o}(a)`:l=o("a");let u=Pe("inputData",t,[a],4),p=Ye("outputData",s,[a],4),d=[{name:"vec_size",type:"u32"}];return i&&d.push(...i),`
      ${e.registerUniforms(d).declareVariables(u,p)}

  ${n??""}

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

    let a = ${u.getByOffset("global_idx")};
    ${p.setByOffset("global_idx",l)}
  }`},Et=(e,r,t,s,o,n=e.dataType,i,a)=>{let l=[{type:12,data:Math.ceil(Me.size(e.dims)/4)}];return i&&l.push(...i),{name:r,shaderCache:{hint:o,inputDependencies:["type"]},getShaderSource:u=>Hp(u,Me.size(e.dims),e.dataType,n,t,s,a),getRunData:u=>({outputs:[{dims:e.dims,dataType:n}],dispatchGroup:{x:Math.ceil(Me.size(u[0].dims)/64/4)},programUniforms:l})}},qp=e=>{e.compute(Et(e.inputs[0],"Abs","abs"))},Xp=e=>{e.compute(Et(e.inputs[0],"Acos","acos"))},Qp=e=>{e.compute(Et(e.inputs[0],"Acosh","acosh"))},Jp=e=>{e.compute(Et(e.inputs[0],"Asin","asin"))},Yp=e=>{e.compute(Et(e.inputs[0],"Asinh","asinh"))},Zp=e=>{e.compute(Et(e.inputs[0],"Atan","atan"))},eh=e=>{e.compute(Et(e.inputs[0],"Atanh","atanh"))},th=e=>St(e),rh=(e,r)=>{let t;switch(r.to){case 10:t="vec4<f16>";break;case 1:t="vec4<f32>";break;case 12:t="vec4<u32>";break;case 6:t="vec4<i32>";break;case 9:t="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${r.to}`)}e.compute(Et(e.inputs[0],"Cast",t,void 0,r.cacheKey,r.to))},sh=e=>{let r,t,s=e.length>=2&&e[1].data!==0,o=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:r=s?e[1].getFloat32Array()[0]:-34028234663852886e22,t=o?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:r=s?e[1].getUint16Array()[0]:64511,t=o?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return St({min:r,max:t})},nh=(e,r)=>{let t=r||sh(e.inputs),s=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"Clip",o=>`clamp(${o}, vec4<${s}>(uniforms.min), vec4<${s}>(uniforms.max))`,void 0,t.cacheKey,void 0,[{type:e.inputs[0].dataType,data:t.min},{type:e.inputs[0].dataType,data:t.max}],[{name:"min",type:s},{name:"max",type:s}]),{inputs:[0]})},oh=e=>{e.compute(Et(e.inputs[0],"Ceil","ceil"))},ih=e=>{e.compute(Et(e.inputs[0],"Cos","cos"))},ah=e=>{e.compute(Et(e.inputs[0],"Cosh","cosh"))},_o=e=>St(e),lh=(e,r)=>{let t=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"Elu",s=>`elu_vf32(${s})`,`
  const elu_alpha_ = ${t}(${r.alpha});

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

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

fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {
  let absv = abs(v);
  let x = 1.0 / (1.0 + r0 * absv);
  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
}`,uh=e=>{let r=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"Erf",t=>`erf_vf32(${t})`,ci(r)))},dh=e=>{e.compute(Et(e.inputs[0],"Exp","exp"))},ch=e=>{e.compute(Et(e.inputs[0],"Floor","floor"))},ph=e=>{let r=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"Gelu",t=>`0.5 * ${t} * (1.0 + erf_vf32(${t} * 0.7071067811865475))`,ci(r)))},hh=(e,r)=>{let t=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"LeakyRelu",s=>`select(leaky_relu_alpha_ * ${s}, ${s}, ${s} >= vec4<${t}>(0.0))`,`const leaky_relu_alpha_ = ${t}(${r.alpha});`,r.cacheKey))},mh=e=>{e.compute(Et(e.inputs[0],"Not",r=>`!${r}`))},_h=e=>{e.compute(Et(e.inputs[0],"Neg",r=>`-${r}`))},fh=e=>{e.compute(Et(e.inputs[0],"Reciprocal",r=>`1.0/${r}`))},gh=e=>{let r=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"Relu",t=>`select(vec4<${r}>(0.0), ${t}, ${t} > vec4<${r}>(0.0))`))},wh=e=>{e.compute(Et(e.inputs[0],"Sigmoid",r=>`(1.0 / (1.0 + exp(-${r})))`))},Mh=e=>St(e),bh=(e,r)=>{let t=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"HardSigmoid",s=>`max(vec4<${t}>(0.0), min(vec4<${t}>(1.0), ${r.alpha} * ${s} + vec4<${t}>(${r.beta})))`,void 0,r.cacheKey))},yh=e=>{e.compute(Et(e.inputs[0],"Sin","sin"))},vh=e=>{e.compute(Et(e.inputs[0],"Sinh","sinh"))},xh=e=>{e.compute(Et(e.inputs[0],"Sqrt","sqrt"))},Th=e=>{e.compute(Et(e.inputs[0],"Tan","tan"))},pl=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Eh=e=>{e.compute(Et(e.inputs[0],"Tanh",pl))},hl=(e="f32")=>`
const fast_gelu_a: ${e} = 0.5;
const fast_gelu_b: ${e} = 0.7978845608028654;
const fast_gelu_c: ${e} = 0.035677408136300125;

fn tanh_v(v: vec4<${e}>) -> vec4<${e}> {
  return ${pl("v")};
}
`,ml=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Ph=e=>{let r=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"FastGelu",ml,hl(r),void 0,e.inputs[0].dataType))},Ch=(e,r)=>{let t=yr(e.inputs[0].dataType);return e.compute(Et(e.inputs[0],"ThresholdedRelu",s=>`select(vec4<${t}>(0.0), ${s}, ${s} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${t}>(${r.alpha});`,r.cacheKey)),0},Sh=e=>{e.compute(Et(e.inputs[0],"Log","log"))},$h=(e,r)=>`
const alpha = vec4<${e}>(${r});
const one = ${e}(1.0);
const zero = ${e}(0.0);

fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> {
  let v = x *alpha;
  var x1 : vec4<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,kh=e=>`quick_gelu_impl(${e})`,Ih=(e,r)=>{let t=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"QuickGelu",kh,$h(t,r.alpha),r.cacheKey,e.inputs[0].dataType))}}),Ah,Fh,Oh,Sv=Be(()=>{mt(),ft(),_l(),Ah=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")},Fh=e=>{let r=e[0].dims.slice();r[2]=r[2]/2;let t=Pe("input",e[0].dataType,e[0].dims,4),s=Pe("bias",e[0].dataType,[e[0].dims[2]],4),o=Ye("output",e[0].dataType,r,4),n=Me.size(r)/4,i=lr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:a=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

  ${a.declareVariables(t,s,o)}

  ${ci(i)}

  ${a.mainStart()}
    ${a.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    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);

    ${o.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Oh=e=>{Ah(e.inputs),e.compute(Fh(e.inputs))}}),Dh,Lh,as,zh,Bh,Rh,jh,Nh,Vh,Uh,Wh,Gh,Kh,$v=Be(()=>{ut(),mt(),ft(),Dh=(e,r,t,s,o,n,i,a,l,u,p,d)=>{let c,f;typeof a=="string"?c=f=(g,S)=>`${a}((${g}),(${S}))`:typeof a=="function"?c=f=a:(c=a.scalar,f=a.vector);let _=Ye("outputData",p,s.length,4),T=Pe("aData",l,r.length,4),$=Pe("bData",u,t.length,4),w;if(o)if(n){let g=Me.size(r)===1,S=Me.size(t)===1,E=r.length>0&&r[r.length-1]%4===0,y=t.length>0&&t[t.length-1]%4===0;g||S?w=_.setByOffset("global_idx",f(g?`${T.type.value}(${T.getByOffset("0")}.x)`:T.getByOffset("global_idx"),S?`${$.type.value}(${$.getByOffset("0")}.x)`:$.getByOffset("global_idx"))):w=`
            let outputIndices = ${_.offsetToIndices("global_idx * 4u")};
            let offsetA = ${T.broadcastedIndicesToOffset("outputIndices",_)};
            let offsetB = ${$.broadcastedIndicesToOffset("outputIndices",_)};
            ${_.setByOffset("global_idx",f(i||E?T.getByOffset("offsetA / 4u"):`${T.type.value}(${T.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||y?$.getByOffset("offsetB / 4u"):`${$.type.value}(${$.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else w=_.setByOffset("global_idx",f(T.getByOffset("global_idx"),$.getByOffset("global_idx")));else{if(!n)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let g=(S,E,y="")=>{let M=`aData[indexA${E}][componentA${E}]`,v=`bData[indexB${E}][componentB${E}]`;return`
            let outputIndices${E} = ${_.offsetToIndices(`global_idx * 4u + ${E}u`)};
            let offsetA${E} = ${T.broadcastedIndicesToOffset(`outputIndices${E}`,_)};
            let offsetB${E} = ${$.broadcastedIndicesToOffset(`outputIndices${E}`,_)};
            let indexA${E} = offsetA${E} / 4u;
            let indexB${E} = offsetB${E} / 4u;
            let componentA${E} = offsetA${E} % 4u;
            let componentB${E} = offsetB${E} % 4u;
            ${S}[${E}] = ${y}(${c(M,v)});
          `};p===9?w=`
            var data = vec4<u32>(0);
            ${g("data",0,"u32")}
            ${g("data",1,"u32")}
            ${g("data",2,"u32")}
            ${g("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:w=`
            ${g("outputData[global_idx]",0)}
            ${g("outputData[global_idx]",1)}
            ${g("outputData[global_idx]",2)}
            ${g("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(T,$,_)}

        ${d??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${w}
      }`},Lh=(e,r,t,s,o,n,i=t.dataType)=>{let a=t.dims.map(T=>Number(T)??1),l=s.dims.map(T=>Number(T)??1),u=!Me.areEqual(a,l),p=a,d=Me.size(a),c=!1,f=!1,_=[u];if(u){let T=Nn.calcShape(a,l,!1);if(!T)throw new Error("Can't perform binary op on the given tensors");p=T.slice(),d=Me.size(p);let $=Me.size(a)===1,w=Me.size(l)===1,g=a.length>0&&a[a.length-1]%4===0,S=l.length>0&&l[l.length-1]%4===0;_.push($),_.push(w),_.push(g),_.push(S);let E=1;for(let y=1;y<p.length;y++){let M=a[a.length-y],v=l[l.length-y];if(M===v)E*=M;else break}E%4===0?(f=!0,c=!0):($||w||g||S)&&(c=!0)}else c=!0;return _.push(c),{name:e,shaderCache:{hint:r+_.map(T=>T.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:T=>Dh(T,a,l,p,c,u,f,o,t.dataType,s.dataType,i,n),getRunData:()=>({outputs:[{dims:p,dataType:i}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:Math.ceil(Me.size(p)/4)},...st(a,l,p)]})}},as=(e,r,t,s,o,n)=>{e.compute(Lh(r,o??"",e.inputs[0],e.inputs[1],t,s,n))},zh=e=>{as(e,"Add",(r,t)=>`${r}+${t}`)},Bh=e=>{as(e,"Div",(r,t)=>`${r}/${t}`)},Rh=e=>{as(e,"Equal",{scalar:(r,t)=>`u32(${r}==${t})`,vector:(r,t)=>`vec4<u32>(${r}==${t})`},void 0,void 0,9)},jh=e=>{as(e,"Mul",(r,t)=>`${r}*${t}`)},Nh=e=>{let r=Pe("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;as(e,"Pow",{scalar:(t,s)=>`pow_custom(${t},${s})`,vector:(t,s)=>`pow_vector_custom(${t},${s})`},`
    fn pow_custom(a : ${r}, b : ${r}) -> ${r} {
      if (b == ${r}(0.0)) {
        return ${r}(1.0);
      } else if (a < ${r}(0.0) && f32(b) != floor(f32(b))) {
        return ${r}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${r}(1.0), round(f32(abs(b) % ${r}(2.0))) != 1.0) * ${r}(${r==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${r}>, b : vec4<${r}>) -> vec4<${r}> {
      // TODO: implement vectorized pow
      return vec4<${r}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},Vh=e=>{as(e,"Sub",(r,t)=>`${r}-${t}`)},Uh=e=>{as(e,"Greater",{scalar:(r,t)=>`u32(${r}>${t})`,vector:(r,t)=>`vec4<u32>(${r}>${t})`},void 0,void 0,9)},Wh=e=>{as(e,"Less",{scalar:(r,t)=>`u32(${r}<${t})`,vector:(r,t)=>`vec4<u32>(${r}<${t})`},void 0,void 0,9)},Gh=e=>{as(e,"GreaterOrEqual",{scalar:(r,t)=>`u32(${r}>=${t})`,vector:(r,t)=>`vec4<u32>(${r}>=${t})`},void 0,void 0,9)},Kh=e=>{as(e,"LessOrEqual",{scalar:(r,t)=>`u32(${r}<=${t})`,vector:(r,t)=>`vec4<u32>(${r}<=${t})`},void 0,void 0,9)}}),Hh,qh,Xh,Qh,Jh,Yh,kv=Be(()=>{ut(),mt(),Xt(),ft(),Hh=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");let t=0,s=e[t],o=s.dataType,n=s.dims.length;e.forEach((i,a)=>{if(a!==t){if(i.dataType!==o)throw new Error("input tensors should be one type");if(i.dims.length!==n)throw new Error("input tensors should have the same shape");i.dims.forEach((l,u)=>{if(u!==r&&l!==s.dims[u])throw new Error("non concat dimensions must match")})}})},qh=(e,r)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${e}u>(${r});
    for (var i: u32 = 0u; i < ${e}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${e}u;
  }`,Xh=(e,r)=>{let t=e.length,s=[];for(let o=0;o<t;++o){let n=r.setByOffset("global_idx",e[o].getByIndices("indices"));t===1?s.push(n):o===0?s.push(`if (inputIndex == ${o}u) { ${n} }`):o===t-1?s.push(`else { ${n} }`):s.push(`else if (inputIndex == ${o}) { ${n} }`)}return s.join(`
`)},Qh=(e,r,t,s)=>{let o=Me.size(t),n=new Array(e.length),i=new Array(e.length),a=0,l=[],u=[],p=[{type:12,data:o}];for(let T=0;T<e.length;++T)a+=e[T].dims[r],n[T]=a,u.push(e[T].dims.length),i[T]=Pe(`input${T}`,s,u[T]),l.push("rank"),p.push({type:12,data:n[T]});for(let T=0;T<e.length;++T)p.push(...st(e[T].dims));p.push(...st(t));let d=Ye("output",s,t.length),c=d.indicesGet("indices",r),f=Array.from(Array(n.length).keys()).map(T=>`uniforms.sizeInConcatAxis${T}`).join(","),_=T=>`

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

  ${qh(n.length,f)}

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

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

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

    ${Xh(i,d)}
  }`;return{name:"Concat",shaderCache:{hint:`${r}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:t,dataType:s}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:p}),getShaderSource:_}},Jh=(e,r)=>{let t=e.inputs,s=t[0].dims,o=Me.normalizeAxis(r.axis,s.length);Hh(t,o);let n=s.slice();n[o]=t.reduce((a,l)=>a+(l.dims.length>o?l.dims[o]:0),0);let i=t.filter(a=>Me.size(a.dims)>0);e.compute(Qh(i,o,n,t[0].dataType),{inputs:i})},Yh=e=>St({axis:e.axis})}),rn,sn,nn,fl,on=Be(()=>{ut(),mt(),rn=(e,r,t="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${r}(0.0));`;case"Sigmoid":return`value = (${r}(1.0) / (${r}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${r}(${t}(uniforms.clip_min)), ${r}(${t}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${r}(0.0), min(${r}(1.0), ${t}(uniforms.alpha) * value + ${t}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${t}(uniforms.alpha) * value, value, value >= ${r}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
        `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},sn=(e,r)=>{e.activation==="Clip"?r.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?r.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&r.push({type:1,data:e.alpha})},nn=(e,r)=>{e.activation==="Clip"?r.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?r.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&r.push({name:"alpha",type:"f32"})},fl=e=>{let r=(e==null?void 0:e.activation)||"";if(r==="HardSigmoid"){let[t,s]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:r,alpha:t,beta:s}}else if(r==="Clip"){let[t,s]=(e==null?void 0:e.activation_params)||[yc,vc];return{activation:r,clipMax:s,clipMin:t}}else if(r==="LeakyRelu"){let[t]=(e==null?void 0:e.activation_params)||[.01];return{activation:r,alpha:t}}return{activation:r}}}),_r,Zh,gl=Be(()=>{_r=(e,r)=>{switch(e){case 1:return r;case 2:return`vec2<${r}>`;case 3:return`vec3<${r}>`;case 4:return`vec4<${r}>`;default:throw new Error(`${e}-component is not supported.`)}},Zh=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),em,Iv=Be(()=>{em=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),fo,wl,Ml=Be(()=>{ut(),mt(),ft(),on(),fo=(e,r,t,s,o)=>{let n=s-t;return`
      ${Array.from({length:t}).map((i,a)=>`
      if (${et(r.shape,a,r.rank)} != 1) {
        ${r.indicesSet(e,a,et(o,a+n,s))}
      } else {
        ${r.indicesSet(e,a,0)}
      }`).join("")}
`},wl=(e,r,t,s,o=!1,n)=>{let i=e[0].dims,a=e[1].dims,l=i[i.length-2],u=a[a.length-1],p=i[i.length-1],d=Kt(u),c=Kt(p),f=Kt(l),_=Me.size(t)/d/f,T=e.length>2,$=s?s.slice(0,-2):t.slice(0,-2),w=[Me.size($),l,u],g=[{type:12,data:_},{type:12,data:l},{type:12,data:u},{type:12,data:p}];sn(r,g),g.push(...st($,i,a)),T&&g.push(...st(e[2].dims)),g.push(...st(w));let S=E=>{let y=ol("batch_dims",e[0].dataType,$.length),M=Pe("a",e[0].dataType,i.length,c),v=Pe("b",e[1].dataType,a.length,d),C=Ye("output",e[0].dataType,w.length,d),A=lr(C.type.tensor),B=rn(r,C.type.value,A),K=[M,v],G="";if(T){let H=o?d:1;K.push(Pe("bias",e[2].dataType,e[2].dims.length,H)),G=`${o?`value += bias[col / ${H}];`:`value += ${C.type.value}(bias[row + i]);`}`}let j=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];nn(r,j);let ee=()=>{let H=`var a_data: ${M.type.value};`;for(let Z=0;Z<c;Z++)H+=`
              let b_data${Z} = b[(b_offset + (k + ${Z}) * uniforms.N + col) / ${d}];`;for(let Z=0;Z<f;Z++){H+=`a_data = a[(a_offset + (row + ${Z}) * uniforms.K + k) / ${c}];`;for(let X=0;X<c;X++)H+=`
            values[${Z}] = fma(${v.type.value}(a_data${c===1?"":`[${X}]`}), b_data${X}, values[${Z}]);
`}return H};return`
  ${E.registerUniforms(j).registerInternalVariables(y).declareVariables(...K,C)}
  ${E.mainStart()}
    ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${d})) * ${d};
    var index1 = global_idx / (uniforms.N / ${d});
    let stride1 = uniforms.M / ${f};
    let row = (index1 % stride1) * ${f};
    let batch = index1 / stride1;

    ${t.length===2?"":`let batch_indices = ${y.offsetToIndices("batch")};`}

    var a_indices: ${M.type.indices};
    ${fo("a_indices",M,M.rank-2,y.rank,"batch_indices")}
    ${M.indicesSet("a_indices",M.rank-2,0)}
    ${M.indicesSet("a_indices",M.rank-1,0)}
    let a_offset = ${M.indicesToOffset("a_indices")};

    var b_indices: ${v.type.indices};
    ${fo("b_indices",v,v.rank-2,y.rank,"batch_indices")}
    ${v.indicesSet("b_indices",v.rank-2,0)}
    ${v.indicesSet("b_indices",v.rank-1,0)}
    let b_offset = ${v.indicesToOffset("b_indices")};
    var values: array<${C.type.value}, ${f}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${c}) {
      ${ee()}
    }
    for (var i = 0u; i < ${f}u; i++) {
      var value = values[i];
      ${G}
      ${B}
      let cur_indices = ${C.type.indices}(batch, row + i, col);
      let offset = ${C.indicesToOffset("cur_indices")};
      ${C.setByOffset(`offset / ${d}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${r.activation};${d};${c};${f};${o}`,inputDependencies:T?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:g}),getShaderSource:S}}}),tm,rm,bl,yl,sm,vl,nm,pi,xl=Be(()=>{ut(),mt(),ft(),on(),Ml(),gl(),tm=(e,r)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${r?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${r?", batchIndices":""});
        `,rm=(e,r)=>e?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${r===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];
          ${r===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];
          ${r===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,bl=(e,r,t="f32",s,o=!1,n=32,i=!1,a=32)=>{let l=r[1]*e[1],u=r[0]*e[0],p=o?l:n,d=o?n:l,c=p/r[0],f=n/r[1];if(!((o&&c===4&&e[1]===4||!o&&(c===3||c===4))&&p%r[0]===0&&n%r[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${c} must be 3 or 4.
  tileAWidth ${p} must be divisible by workgroupSize[0]${r[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${c}<${t}>, ${p/c}>, ${d}>;
var<workgroup> mm_Bsub: array<array<vec4<${t}>, ${u/e[0]}>, ${n}>;

const rowPerThread = ${e[1]};
const colPerThread = ${e[0]};
const innerElementSize = ${c};
const tileInner = ${n};

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

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

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

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

  // Loop over shared dimension.
  let tileRowB = localRow * ${f};
  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;
          ${tm(o,s)}
      }

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

          ${rm(o,c)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},yl=(e,r)=>e?`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              kStart + inputRow,
              globalRowStart + inputCol${r?", batchIndices":""});
            `:`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              globalRowStart + inputRow,
              kStart + inputCol${r?", batchIndices":""});
            `,sm=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",vl=(e,r,t="f32",s,o=!1,n=32,i=!1,a=32,l=!1)=>{let u=e[1]*r[1],p=e[0]*r[0],d=o?u:n,c=o?n:u;if(!(c%r[1]===0&&d%r[0]===0&&n%r[1]===0))throw new Error(`tileAHight ${c} must be divisible by workgroupSize[1]${r[1]}, tileAWidth ${d} must be divisible by workgroupSize[0]${r[0]}, tileInner ${n} must be divisible by workgroupSize[1]${r[1]}`);let f=c/r[1],_=d/r[0],T=n/r[1],$=l?`
    let localRow = i32(localId.y);
    let localCol = i32(localId.x);
    let globalRowStart = i32(workgroupId.y) * ${u};
    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 < ${c}; inputRow = inputRow + ${r[1]}) {
        for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${r[0]}) {
          ${yl(o,s)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${n}; inputRow = inputRow + ${r[1]}) {
            for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${r[0]}) {
          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
            kStart + inputRow,
            globalColStart + inputCol${s?", batchIndices":""});
        }
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

      // Compute acc values for a single thread.
      var BCached : array<${t}, 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 * ${r[0]}];
        }
        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let ACached = ${o?`mm_Asub[k][localRow + innerRow * ${r[1]}];`:`mm_Asub[localRow + innerRow * ${r[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 * ${r[1]};
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        let gCol = globalColStart + localCol + innerCol * ${r[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) * ${u};

let tileRowA = i32(localId.y) * ${f};
let tileColA = i32(localId.x) * ${_};
let tileRowB = i32(localId.y) * ${T};
// 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 < ${f}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${_}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${yl(o,s)}
    }
  }

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

  // Compute acc values for a single thread.
  var BCached : array<${t}, 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) {
      ${sm(o)}
      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<${t}, ${d}>, ${c}>;
  var<workgroup> mm_Bsub : array<array<${t}, ${p}>, ${n}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${n};

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

    var acc : array<array<${t}, colPerThread>, rowPerThread>;
    ${$}
  }
`},nm=(e,r,t,s,o=!1)=>{let[n,i,a,l]=s,u=lr(s[0].type.tensor);return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${n.type.indices}) -> ${_r(e,u)} {
      var value = ${_r(e,u)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        var aIndices: ${i.type.indices};
        ${fo("aIndices",i,i.rank-2,n.rank,"batchIndices")}
        ${i.indicesSet("aIndices",i.rank-2,"u32(row)")}
        ${i.indicesSet("aIndices",i.rank-1,"u32(colIn)")}
        value = ${i.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${n.type.indices}) -> ${_r(e,u)} {
      var value = ${_r(e,u)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
      {
        var bIndices: ${a.type.indices};
        ${fo("bIndices",a,a.rank-2,n.rank,"batchIndices")}
        ${a.indicesSet("bIndices",a.rank-2,"u32(row)")}
        ${a.indicesSet("bIndices",a.rank-1,"u32(colIn)")}
        value = ${a.getByIndices("bIndices")};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${_r(e,u)}) {
      let col = colIn * ${e};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${r?`value = value + ${o?"bias[colIn]":`${_r(e,u)}(bias[row])`};`:""}
        ${t}
        ${l.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},pi=(e,r,t,s,o=!1,n)=>{let i=e[0].dims,a=e[1].dims,l=i.slice(0,-2),u=a.slice(0,-2),p=s?s.slice(0,-2):t.slice(0,-2),d=Me.size(p),c=i[i.length-2],f=i[i.length-1],_=a[a.length-1],T=f%4===0&&_%4===0,$=c<=8?[4,1,1]:[4,4,1],w=[8,8,1],g=[Math.ceil(_/w[0]/$[0]),Math.ceil(c/w[1]/$[1]),Math.ceil(d/w[2]/$[2])],S=T?4:1,E=[...l,c,f/S],y=E.length,M=[...u,f,_/S],v=M.length,C=[d,c,_/S],A=[{type:6,data:c},{type:6,data:_},{type:6,data:f}];sn(r,A),A.push(...st(p,E,M));let B=["rank","rank"],K=e.length>2;K&&(A.push(...st(e[2].dims)),B.push("rank")),A.push(...st(C));let G=j=>{let ee=p.length,H=ol("batchDims",e[0].dataType,ee,1),Z=lr(e[0].dataType),X=Pe("a",e[0].dataType,y,S),oe=Pe("b",e[1].dataType,v,S),me=Ye("result",e[0].dataType,C.length,S),ae=[X,oe];if(K){let _e=o?S:1;ae.push(Pe("bias",e[2].dataType,e[2].dims.length,_e))}let V=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];nn(r,V);let F=lr(me.type.tensor),W=rn(r,me.type.value,F),re=nm(S,K,W,[H,X,oe,me],o);return`
  ${j.registerUniforms(V).registerInternalVariables(H).declareVariables(...ae,me)}
  ${re}
  ${T?bl($,w,Z,H):vl($,w,Z,H)}
                   `};return{name:"MatMul",shaderCache:{hint:`${$};${r.activation};${T};${o}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:g[0],y:g[1],z:g[2]},programUniforms:A}),getShaderSource:G}}}),om,im,Av=Be(()=>{ut(),Es(),ft(),on(),gl(),Iv(),xl(),om=(e,r,t,s,o=!1,n,i=4,a=4,l=4,u="f32")=>{let p=A=>{switch(A){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${u}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${A} is not supported.`)}},d=A=>{switch(A){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 ${A} is not supported.`)}},c=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,f=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,_=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",T=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",$=e?"row":"col",w=e?"col":"row",g=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${$} / outWidth;
    let outCol = ${$} % outWidth;

    let WRow = ${w} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${w} / 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 = ${w} % inChannels;
    var resData = ${_r(i,u)}(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 < ${T}) {
      ${c}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${p(i)}
    }
    return resData;`,S=e?r&&s?`
    let col = colIn * ${i};
    ${g}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${g}
    }
    return ${_r(i,u)}(0.0);`:s&&t?`
    let col = colIn * ${i};
    ${g}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${g}
    }
    return ${_r(i,u)}(0.0);`,E=e?s&&t?d(a):`
    let col = colIn * ${a};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${d(a)}
    }
    return ${_r(a,u)}(0.0);`:`
    let col = colIn * ${a};
    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
      ${d(a)}
    }
    return ${_r(a,u)}(0.0);`,y=_r(l,u),M=_r(e?i:a,u),v=_r(e?a:i,u),C=rn(n,y,u);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${M} {
      ${e?S:E}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${v} {
      ${e?E:S}
    }

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${y}) {
      let col = colIn * ${l};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${f}
      ${Zh(o)}
      ${C}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},im=(e,r,t,s,o,n,i,a,l)=>{let u=r.format==="NHWC",p=u?e[0].dims[3]:e[0].dims[1],d=t[0],c=u?t[2]:t[3],f=u?t[1]:t[2],_=u?t[3]:t[1],T=u&&(p%4===0||p%3===0)&&_%4===0,$=u?_:c*f,w=u?c*f:_,g=[8,8,1],S=s<=8?[4,1,1]:[4,4,1],E=[Math.ceil($/g[0]/S[0]),Math.ceil(w/g[1]/S[1]),Math.ceil(d/g[2]/S[2])];vt("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let y=T?u&&p%4!==0?3:4:1,M=g[1]*S[1],v=g[0]*S[0],C=Math.max(g[0]*y,g[1]),A=s%M===0,B=o%v===0,K=n%C===0,G=T?[y,4,4]:[1,1,1],j=[{type:6,data:s},{type:6,data:o},{type:6,data:n},{type:6,data:[r.pads[0],r.pads[1]]},{type:6,data:r.strides},{type:6,data:r.dilations}];sn(r,j),j.push(...st(e[0].dims,e[1].dims));let ee=["rank","rank"];i&&(j.push(...st(e[2].dims)),ee.push("rank")),j.push(...st(t));let H=Z=>{let X=[{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}];nn(r,X);let oe=T?4:1,me=lr(e[0].dataType),ae=`
      fn setOutputAtIndex(flatIndex : i32, value : ${T?`vec4<${me}>`:me}) {
        result[flatIndex] = ${T?`vec4<${me}>`:me}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${T?`vec4<${me}>`:me}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${T?"/ 4":""}, value);
      }`,V=Pe("x",e[0].dataType,e[0].dims.length,y===3?1:y),F=Pe("w",e[1].dataType,e[1].dims.length,oe),W=[V,F],re=Ye("result",e[0].dataType,t.length,oe);if(i){let _e=Pe("bias",e[2].dataType,e[2].dims.length,oe);W.push(_e),ae+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${T?`vec4<${me}>`:me} {
          return bias[coords.${u?"w":"y"}${T?"/ 4":""}];
        }`}return`
        ${em("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 };
        ${Z.registerUniforms(X).declareVariables(...W,re)}
        ${ae}
        ${om(u,A,B,K,i,r,G[0],G[1],G[2],me)}
        ${T?bl(S,g,me,void 0,!u,C):vl(S,g,me,void 0,!u,C,!1,void 0,a)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${r.cacheKey};${y};${T};${A};${B};${K};${M};${v};${C}`,inputDependencies:ee},getRunData:()=>({outputs:[{dims:l?l(t):t,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:j}),getShaderSource:H}}}),am,Tl,go,lm,El,um,dm,cm,Fv=Be(()=>{ut(),Es(),mt(),ft(),on(),gl(),am=e=>{let r=1;for(let t=0;t<e.length;t++)r*=e[t];return r},Tl=e=>typeof e=="number"?[e,e,e]:e,go=(e,r)=>r<=1?e:e+(e-1)*(r-1),lm=(e,r,t,s=1)=>{let o=go(r,s);return Math.floor((e[0]*(t-1)-t+o)/2)},El=(e,r,t,s,o)=>{o==null&&(o=lm(e,r[0],s[0]));let n=[0,0,0,t];for(let i=0;i<3;i++)e[i]+2*o>=r[i]&&(n[i]=Math.trunc((e[i]-r[i]+2*o)/s[i]+1));return n},um=(e,r,t,s,o,n,i,a,l,u)=>{let p,d,c,f;if(e==="VALID"&&(e=0),typeof e=="number"){p={top:e,bottom:e,left:e,right:e,front:e,back:e};let _=El([r,t,s,1],[a,l,u],1,[o,n,i],e);d=_[0],c=_[1],f=_[2]}else if(Array.isArray(e)){if(!e.every((T,$,w)=>T===w[0]))throw Error(`Unsupported padding parameter: ${e}`);p={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let _=El([r,t,s,1],[a,l,u],1,[o,n,i],e[0]);d=_[0],c=_[1],f=_[2]}else if(e==="SAME_UPPER"){d=Math.ceil(r/o),c=Math.ceil(t/n),f=Math.ceil(s/i);let _=(d-1)*o+a-r,T=(c-1)*n+l-t,$=(f-1)*i+u-s,w=Math.floor(_/2),g=_-w,S=Math.floor(T/2),E=T-S,y=Math.floor($/2),M=$-y;p={top:S,bottom:E,left:y,right:M,front:w,back:g}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:d,outHeight:c,outWidth:f}},dm=(e,r,t,s,o,n=!1,i="channelsLast")=>{let a,l,u,p,d;if(i==="channelsLast")[a,l,u,p,d]=e;else if(i==="channelsFirst")[a,d,l,u,p]=e;else throw new Error(`Unknown dataFormat ${i}`);let[c,,f,_,T]=r,[$,w,g]=Tl(t),[S,E,y]=Tl(s),M=go(f,S),v=go(_,E),C=go(T,y),{padInfo:A,outDepth:B,outHeight:K,outWidth:G}=um(o,l,u,p,$,w,g,M,v,C),j=n?c*d:c,ee=[0,0,0,0,0];return i==="channelsFirst"?ee=[a,j,B,K,G]:i==="channelsLast"&&(ee=[a,B,K,G,j]),{batchSize:a,dataFormat:i,inDepth:l,inHeight:u,inWidth:p,inChannels:d,outDepth:B,outHeight:K,outWidth:G,outChannels:j,padInfo:A,strideDepth:$,strideHeight:w,strideWidth:g,filterDepth:f,filterHeight:_,filterWidth:T,effectiveFilterDepth:M,effectiveFilterHeight:v,effectiveFilterWidth:C,dilationDepth:S,dilationHeight:E,dilationWidth:y,inShape:e,outShape:ee,filterShape:r}},cm=(e,r,t,s,o,n)=>{let i=n==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let a=[64,1,1],l={x:t.map(($,w)=>w)},u=[Math.ceil(am(l.x.map($=>t[$]))/a[0]),1,1];vt("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${u}`);let p=1,d=Me.size(t),c=[{type:12,data:d},{type:12,data:s},{type:12,data:o},{type:12,data:r.strides},{type:12,data:r.dilations}];sn(r,c),c.push(...st(e[0].dims,e[1].dims));let f=["rank","rank"],_=e.length===3;_&&(c.push(...st(e[2].dims)),f.push("rank")),c.push(...st(t));let T=$=>{let w=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:s.length},{name:"pads",type:"u32",length:o.length},{name:"strides",type:"u32",length:r.strides.length},{name:"dilations",type:"u32",length:r.dilations.length}];nn(r,w);let g=1,S=lr(e[0].dataType),E=Pe("x",e[0].dataType,e[0].dims.length,p),y=Pe("W",e[1].dataType,e[1].dims.length,g),M=[E,y],v=Ye("result",e[0].dataType,t.length,g),C="";if(_){let K=Pe("bias",e[2].dataType,e[2].dims.length,g);M.push(K),C+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${S} {
          return bias[${i?et("coords",4,5):et("coords",1,5)}];
        }`}let A=_r(p,S),B=rn(r,A,S);return`
            ${C}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${E.getByIndices("aIndices")};
            }
            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${y.getByIndices("aIndices")};
            }
          ${$.registerUniforms(w).declareVariables(...M,v)}
          ${$.mainStart()}
          ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${v.offsetToIndices("global_idx")};
              let batch = ${et("coords",0,E.rank)};
              let d2 = ${i?et("coords",E.rank-1,E.rank):et("coords",1,E.rank)};
              let xFRCCorner = vec3<u32>(${i?et("coords",1,E.rank):et("coords",2,E.rank)},
              ${i?et("coords",2,E.rank):et("coords",3,E.rank)},
              ${i?et("coords",3,E.rank):et("coords",4,E.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${i?et("uniforms.x_shape",1,E.rank):et("uniforms.x_shape",2,E.rank)};
              let xShapeZ = ${i?et("uniforms.x_shape",2,E.rank):et("uniforms.x_shape",3,E.rank)};
              let xShapeW = ${i?et("uniforms.x_shape",3,E.rank):et("uniforms.x_shape",4,E.rank)};
              let xShapeU = ${i?et("uniforms.x_shape",4,E.rank):et("uniforms.x_shape",1,E.rank)};
              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
              let inputDepthVec4Remainder = xShapeU % 4;

              var value = 0.0;
              for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) {
                let xF = xFCorner + wF * uniforms.dilations[0];
                if (xF < 0 || xF >= xShapeY) {
                  continue;
                }

                for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) {
                  let xR = xRCorner + wR * uniforms.dilations[1];
                  if (xR < 0 || xR >= xShapeZ) {
                    continue;
                  }

                  for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) {
                    let xC = xCCorner + wC * uniforms.dilations[2];
                    if (xC < 0 || xC >= xShapeW) {
                      continue;
                    }

                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
                      ${i?`let xValues = vec4<f32>(
                               getX(batch, xF, xR, xC, d1),
                               getX(batch, xF, xR, xC, d1 + 1),
                               getX(batch, xF, xR, xC, d1 + 2),
                               getX(batch, xF, xR, xC, d1 + 3));
                            `:`let xValues = vec4<f32>(
                               getX(batch, d1, xF, xR, xC),
                               getX(batch, d1 + 1, xF, xR, xC),
                               getX(batch, d1 + 2, xF, xR, xC),
                               getX(batch, d1 + 3, xF, xR, xC));
                            `}
                            let wValues = vec4<f32>(
                              getW(d2, d1, wF, wR, wC),
                              getW(d2, d1 + 1, wF, wR, wC),
                              getW(d2, d1 + 2, wF, wR, wC),
                              getW(d2, d1 + 3, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                    if (inputDepthVec4Remainder == 1) {
                        ${i?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
                    } else if (inputDepthVec4Remainder == 2) {
                      ${i?`let xValues = vec2<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
                      `:`let xValues = vec2<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
                    `}
                    let wValues = vec2<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
                      value += dot(xValues, wValues);
                    } else if (inputDepthVec4Remainder == 3) {
                      ${i?`let xValues = vec3<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
                      `:`let xValues = vec3<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
                    `}
                    let wValues = vec3<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                  }
                }
              }
              ${_?"value = value + getBiasByOutputCoords(coords)":""};
              ${B}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${r.cacheKey};${i};${p};${_}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:u[0],y:u[1],z:u[2]},programUniforms:c}),getShaderSource:T}}}),pm,hm,Ov=Be(()=>{ut(),mt(),ft(),on(),pm=(e,r,t,s)=>{let o=e.length>2,n=o?"value += b[output_channel];":"",i=e[0].dims,a=e[1].dims,l=r.format==="NHWC",u=l?t[3]:t[1],p=u/r.group,d=l&&p>=4?Kt(u):1,c=Me.size(t)/d,f=[{type:12,data:c},{type:12,data:r.dilations},{type:12,data:[r.strides[0],r.strides[1]]},{type:12,data:[r.pads[0],r.pads[1]]},{type:12,data:p}];sn(r,f),f.push(...st(i,[a[0],a[1],a[2],a[3]/d]));let _=o?["rank","rank","rank"]:["rank","rank"];f.push(...st([t[0],t[1],t[2],t[3]/d]));let T=$=>{let w=Ye("output",e[0].dataType,t.length,d),g=lr(w.type.tensor),S=rn(r,w.type.value,g),E=Pe("x",e[0].dataType,i.length),y=Pe("w",e[1].dataType,a.length,d),M=[E,y];o&&M.push(Pe("b",e[2].dataType,e[2].dims,d));let v=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:r.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];nn(r,v);let C=l?`
      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

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

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

          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
            let input_channel = in_channel_offset + wInChannel;
            let xVal = ${E.get("batch","xHeight","xWidth","input_channel")};
            let wVal = ${y.get("wHeight","wWidth","wInChannel","output_channel")};
            value += xVal * wVal;
          }
        }
      }
      `:`
      for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {
        let input_channel = in_channel_offset + 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[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[3]) {
              continue;
            }

            let xVal = ${E.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${y.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${$.registerUniforms(v).declareVariables(...M,w)}

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

    let outputIndices = ${w.offsetToIndices("global_idx")};
    let batch: u32 = outputIndices[0];
    let output_channel: u32 = outputIndices[${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 * ${d} / uniforms.output_channels_per_group;
    var in_channel_offset = group_id * uniforms.w_shape[${l?2:1}];

    var value: ${w.type.value} = ${w.type.value}(0);
    ${C}
    ${n}
    ${S}
    ${w.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:`${r.cacheKey}_${d}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:f}),getShaderSource:T}},hm=(e,r,t,s)=>{let o=e.length>2,n=Kt(t[3]),i=Kt(t[2]),a=Me.size(t)/n/i,l=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/n],u=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/n],p=[t[0],t[1],t[2],t[3]/n],d=[{type:12,data:a},{type:6,data:[r.strides[0],r.strides[1]]},{type:6,data:[r.pads[0],r.pads[1]]}];sn(r,d),d.push(...st(l,u,p));let c=(i-1)*r.strides[1]+u[1],f=_=>{let T=Ye("output",e[0].dataType,p.length,n),$=lr(T.type.tensor),w=rn(r,T.type.value,$),g=Pe("x",e[0].dataType,l.length,n),S=Pe("w",e[1].dataType,u.length,n),E=[g,S];o&&E.push(Pe("b",e[2].dataType,e[2].dims,n));let y=o?"value += b[output_channel];":"",M=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return nn(r,M),`
  ${_.registerUniforms(M).declareVariables(...E,T)}
  ${_.mainStart()}
    ${_.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<${g.type.value}, ${c}>;
    var values: array<${T.type.value}, ${i}>;
    let input_channel = output_channel;
    // Use constant instead of uniform can give better performance for w's height/width.
    for (var w_height: u32 = 0u; w_height < ${u[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${c}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${g.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${g.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${u[1]}; w_width++) {
          let w_val = ${S.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];
      ${y}
      ${w}
      ${T.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${r.cacheKey};${n};${i};${c};${u[0]};${u[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:f}}}),mm,hi,_m,mi,Pl,Cl,fm,gm,Sl,Dv=Be(()=>{mt(),Av(),Fv(),xl(),Ov(),on(),Ml(),zs(),mm=(e,r,t,s,o,n)=>{let i=e[0],a=e.slice(n?1:2,n?3:4),l=a.length,u=r[0],p=r.slice(2).map((c,f)=>c+(c-1)*(t[f]-1)),d=a.map((c,f)=>c+s[f]+s[f+l]).map((c,f)=>Math.floor((c-p[f]+o[f])/o[f]));return d.splice(0,0,i),d.splice(n?3:1,0,u),d},hi=[2,3,1,0],_m=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],s=e[1].dims[1]*r.group;if(t!==s)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 o=e[0].dims.length-2;if(r.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(r.strides.length!==o)throw new Error(`strides should be ${o}D`);if(r.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},mi=(e,r)=>{let t=e.kernelShape.slice();t.length<r[1].dims.length-2&&t.push(...Array(r[1].dims.length-2-t.length).fill(0));for(let n=2;n<r[1].dims.length;++n)t[n-2]===0&&(t[n-2]=r[1].dims[n]);let s=e.pads.slice();ni.adjustPadsBasedOnAutoPad(r[0].dims,e.strides,e.dilations,t,s,e.format==="NHWC",e.autoPad);let o=Object.assign({},e);return Object.assign(o,{kernelShape:t,pads:s}),o},Pl=e=>{let r=fl(e),t=e.format,s=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],o=e.dilations,n=e.group,i=e.kernel_shape,a=e.pads,l=e.strides,u=e.w_is_const();return{autoPad:s,format:t,dilations:o,group:n,kernelShape:i,pads:a,strides:l,wIsConst:u,...r,cacheKey:`${e.format};${r.activation};`}},Cl=(e,r,t,s)=>{let o=t.format==="NHWC",n=mm(r[0].dims,r[1].dims,t.dilations,t.pads,t.strides,o);if(t.group!==1){let M=[r[0]];if(o){let v=e.kernelCustomData.wT??e.compute(Lr(r[1],hi),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=v),M.push(v)}else M.push(r[1]);r.length===3&&M.push(r[2]),!e.adapterInfo.isArchitecture("ampere")&&o&&r[1].dims[0]===t.group&&r[1].dims[1]===1&&t.dilations[0]===1&&t.dilations[1]===1?e.compute(hm(M,t,n,s),{inputs:M}):e.compute(pm(M,t,n,s),{inputs:M});return}let i=r.length===3,a=r[0].dims[o?1:2],l=r[0].dims[o?2:3],u=r[0].dims[o?3:1],p=r[1].dims[2],d=r[1].dims[3],c=n[o?1:2],f=n[o?2:3],_=n[o?3:1],T=o&&p===a&&d===l&&t.pads[0]===0&&t.pads[1]===0;if(T||p===1&&d===1&&t.dilations[0]===1&&t.dilations[1]===1&&t.strides[0]===1&&t.strides[1]===1&&t.pads[0]===0&&t.pads[1]===0){let M=n[0],v,C,A,B=[];if(o){let j=e.kernelCustomData.wT??e.compute(Lr(r[1],hi),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=j),T){let ee=a*l*u;v=r[0].reshape([1,M,ee]),C=j.reshape([1,ee,_]),A=[1,M,_]}else v=r[0].reshape([M,a*l,u]),C=j.reshape([1,u,_]),A=[M,c*f,_];B.push(v),B.push(C)}else v=r[0].reshape([M,u,a*l]),C=r[1].reshape([1,_,u]),A=[M,_,c*f],B.push(C),B.push(v);i&&B.push(r[2]);let K=A[2],G=B[0].dims[B[0].dims.length-1];K<8&&G<8?e.compute(wl(B,t,n,A,o,s),{inputs:B}):e.compute(pi(B,t,n,A,o,s),{inputs:B});return}let $=!0,w=e.kernelCustomData.wT??e.compute(Lr(r[1],hi),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=w);let g=[r[0],w];i&&g.push(r[2]);let S=o?c*f:_,E=o?_:c*f,y=p*d*u;e.compute(im(g,t,n,S,E,y,i,$,s),{inputs:g})},fm=(e,r)=>{let t=r.format==="NHWC",s=[e.inputs[0].reshape(t?[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&&s.push(e.inputs[2]);let o=[0,r.pads[0],0,r.pads[1]],n=[1].concat(r.strides),i=[1].concat(r.dilations),a=[1].concat(r.kernelShape),l=mi({...r,pads:o,strides:n,dilations:i,kernelShape:a},s);Cl(e,s,l,u=>t?[u[0],u[2],u[3]]:[u[0],u[1],u[3]])},gm=(e,r,t)=>{let s=t.format==="NHWC"?"channelsLast":"channelsFirst",o=mi(t,r),n=t.autoPad==="NOTSET"?t.pads:t.autoPad,i=dm(r[0].dims,r[1].dims,t.strides,t.dilations,n,!1,s);e.compute(cm(r,o,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],s))},Sl=(e,r)=>{if(_m(e.inputs,r),e.inputs[0].dims.length===3)fm(e,r);else if(e.inputs[0].dims.length===5)gm(e,e.inputs,r);else{let t=mi(r,e.inputs);Cl(e,e.inputs,t)}}}),wm,Lv=Be(()=>{ut(),Es(),mt(),ft(),wm=(e,r,t)=>{let s=e.length>2,o=r.outputShape,n=r.format==="NHWC",i=r.group,a=e[1].dims,l=a[2]/i,u=a[3],p=n?Kt(l):1,d=n&&u===1&&l>=4,c=d?Math.floor(l/4)*4:Math.floor(l/p)*p,f=l-c,_=n?Kt(u):1,T=n?u===1?p:_:1,$=Me.size(o)/_,w=[Math.ceil($/64),1,1];vt("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${w}`);let g=["rank","rank"],S=[r.strides[0],r.strides[1]],E=[r.kernelShape[n?1:2],r.kernelShape[n?2:3]],y=[r.dilations[0],r.dilations[1]],M=[E[0]+(r.dilations[0]<=1?0:(r.kernelShape[n?1:2]-1)*(r.dilations[0]-1)),E[1]+(r.dilations[1]<=1?0:(r.kernelShape[n?2:3]-1)*(r.dilations[1]-1))],v=[M[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),M[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],C=[{type:12,data:$},{type:12,data:S},{type:12,data:E},{type:12,data:y},{type:12,data:M},{type:6,data:v},{type:12,data:c},{type:12,data:l},{type:12,data:u},...st(e[0].dims,e[1].dims)];s&&(C.push(...st(e[2].dims)),g.push("rank")),C.push(...st(o));let A=B=>{let K=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:S.length},{name:"filter_dims",type:"u32",length:E.length},{name:"dilations",type:"u32",length:E.length},{name:"effective_filter_dims",type:"u32",length:M.length},{name:"pads",type:"i32",length:v.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],G=lr(e[0].dataType),j=n?1:2,ee=n?2:3,H=n?3:1,Z=Pe("W",e[1].dataType,e[1].dims.length,T),X=Pe("Dy",e[0].dataType,e[0].dims.length,p),oe=[X,Z];s&&oe.push(Pe("bias",e[2].dataType,[o[H]].length,_));let me=Ye("result",e[0].dataType,o.length,_),ae=()=>{let W="";if(d)p===4?W+=`
        let xValue = ${X.getByOffset("x_offset")};
        let wValue = ${Z.getByOffset("w_offset")};
        dotProd = dotProd + dot(xValue, wValue);
        x_offset += 1u;
        w_offset += 1u;`:p===2?W+=`
          dotProd = dotProd + dot(vec4<${G}>(${X.getByOffset("x_offset")}, ${X.getByOffset("x_offset + 1u")}), vec4<${G}>(${Z.getByOffset("w_offset")}, ${Z.getByOffset("w_offset + 1u")}));
          x_offset += 2u;
          w_offset += 2u;`:p===1&&(W+=`
          dotProd = dotProd + dot(vec4<${G}>(${X.getByOffset("x_offset")}, ${X.getByOffset("x_offset + 1u")}, ${X.getByOffset("x_offset + 2u")}, ${X.getByOffset("x_offset + 3u")}), vec4<${G}>(${Z.getByOffset("w_offset")}, ${Z.getByOffset("w_offset + 1u")}, ${Z.getByOffset("w_offset + 2u")}, ${Z.getByOffset("w_offset + 3u")}));
          x_offset += 4u;
          w_offset += 4u;`);else if(W+=`
                  let xValue = ${n?X.getByOffset(`${X.indicesToOffset(`${X.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}`):X.get("batch","inputChannel","idyR","idyC")};
        `,p===1)W+=`
          let w_offset = ${Z.indicesToOffset(`${Z.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
          let wValue = ${Z.getByOffset(`w_offset / ${T}`)};
          dotProd = dotProd + xValue * wValue;`;else for(let re=0;re<p;re++)W+=`
            let wValue${re} = ${Z.getByOffset(`${Z.indicesToOffset(`${Z.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${re}, wOutChannel)`)} / ${T}`)};
            dotProd = dotProd + xValue[${re}] * wValue${re};`;return W},V=()=>{if(f===0)return"";if(!d)throw new Error(`packInputAs4 ${d} is not true.`);let W="";if(p===1){W+="dotProd = dotProd";for(let re=0;re<f;re++)W+=`
            + ${X.getByOffset(`x_offset + ${re}`)} * ${Z.getByOffset(`w_offset + ${re}`)}`;W+=";"}else if(p===2){if(f!==2)throw new Error(`Invalid inputChannelsRemainder ${f}.`);W+=`
          let xValue = ${X.getByOffset("x_offset")};
          let wValue = ${Z.getByOffset("w_offset")};
          dotProd = dotProd + dot(xValue, wValue);`}return W},F=`
            let outputIndices = ${me.offsetToIndices(`global_idx * ${_}`)};
            let batch = ${me.indicesGet("outputIndices",0)};
            let d1 = ${me.indicesGet("outputIndices",H)};
            let r = ${me.indicesGet("outputIndices",j)};
            let c = ${me.indicesGet("outputIndices",ee)};
            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 = ${me.type.value}(0.0);
            var wR: u32 = 0;
            if (uniforms.dilations.x == 1) {
              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
            }
            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
              if (wR % uniforms.dilations.x != 0) {
                continue;
              }
              let dyR = (${G}(dyRCorner) + ${G}(wR)) / ${G}(uniforms.strides[0]);
              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
              if (dyR < 0.0 || dyR >= ${G}(uniforms.Dy_shape[${j}]) || fract(dyR) > 0.0 ||
                  wRPerm < 0) {
                continue;
              }
              let idyR: u32 = u32(dyR);
              var wC: u32 = 0;
              if (uniforms.dilations.y == 1) {
                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
              }
              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
                if (wC % uniforms.dilations.y != 0) {
                  continue;
                }
                let dyC = (${G}(dyCCorner) + ${G}(wC)) / ${G}(uniforms.strides.y);
                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
                if (dyC < 0.0 || dyC >= ${G}(uniforms.Dy_shape[${ee}]) ||
                    fract(dyC) > 0.0 || wCPerm < 0) {
                  continue;
                }
                let idyC: u32 = u32(dyC);
                var inputChannel = groupId * uniforms.input_channels_per_group;
                ${d?`
                var x_offset = ${X.indicesToOffset(`${X.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p};
                var w_offset = ${Z.indicesToOffset(`${Z.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${T};
                  `:""}
                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${d?4:p}) {
                  ${ae()}
                  inputChannel = inputChannel + ${d?4:p};
                }
                ${V()}
                wC = wC + uniforms.strides.y - 1;
              }
              wR = wR + uniforms.strides[0] - 1;
            }
            let value = dotProd${s?` + bias[d1 / ${_}]`:""};
            ${me.setByOffset("global_idx","value")};
          `;return`
    ${B.registerUniforms(K).declareVariables(...oe,me)}
      ${B.mainStart()}
      ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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        ${g("value",3,"u32")}
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      `}else w=`
      let outputIndices = ${T.offsetToIndices("global_idx")};
      ${$("")};
      let value = ${f.getByIndices("dataIndices")};
      ${T.setByOffset("global_idx","value")};
      `;return`
      ${c.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(f,_,T)}
      ${c.mainStart()}
        ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${w}
      }`};return{name:"Gather",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:p}),getShaderSource:d}},Xm=e=>St({axis:e.axis}),Qm=(e,r)=>{let t=e.inputs;Hm(t),e.compute(qm(e.inputs,r))}}),Jm,Ym,Zm,Wv=Be(()=>{ut(),mt(),ft(),Jm=(e,r,t,s,o,n,i,a,l)=>{let u=[{type:12,data:n},{type:12,data:s},{type:12,data:o},{type:12,data:t},{type:12,data:i},{type:12,data:a},{type:12,data:l}],p=[n];u.push(...st(r.dims,p));let d=c=>{let f=Pe("indices_data",r.dataType,r.dims.length),_=Ye("input_slice_offsets_data",12,1,1),T=[f,_],$=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:o.length},{name:"sizes_from_slice_dims_data",type:"u32",length:t.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return`
  ${c.registerUniforms($).declareVariables(...T)}
  ${c.mainStart()}
    ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let batch_idx = global_idx / uniforms.num_slices_per_batch;
    let base_offset = batch_idx * uniforms.input_batch_stride;

    let slice_indices_base_offset = global_idx * uniforms.num_slice_dims;
    var relative_slice_offset = 0;
    for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) {
      var index = i32(indices_data[dim_idx + slice_indices_base_offset].x);
      let input_dim_idx = uniforms.batch_dims + dim_idx;
      if (index < 0) {
        ${o.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"}
      }
      ${t.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"}
    }

    input_slice_offsets_data[global_idx] =  base_offset + u32(relative_slice_offset);
  }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${o.length}_${t.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:u}),getShaderSource:d},{inputs:[r],outputs:[-1]})[0]},Ym=(e,r)=>{let t=e.inputs,s=t[0].dims,o=t[0].dataType,n=t[1].dims,i=n[n.length-1],a=Me.sizeToDimension(n,n.length-1),l=Me.sizeFromDimension(s,r.batchDims+i),u=Me.sizeToDimension(s,r.batchDims),p=Me.sizeFromDimension(s,r.batchDims),d=a/u,c=new Array(i),f=l;for(let E=0;E<i;++E)c[i-1-E]=f,f*=s[r.batchDims+i-1-E];let _=Jm(e,t[1],c,r.batchDims,s,a,d,p,i),T=r.batchDims+i;if(T>s.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let $=n.slice(0,-1).concat(s.slice(T)),w=Me.size($),g=[{type:12,data:w},{type:12,data:l},...st(t[0].dims,_.dims,$)],S=E=>{let y=Pe("data",t[0].dataType,t[0].dims.length),M=Pe("slice_offsets",12,_.dims.length),v=Ye("output",t[0].dataType,$.length);return`
          ${E.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(y,M,v)}
            ${E.mainStart()}
            ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
        }`};e.compute({name:"GatherND",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:$,dataType:o}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:g}),getShaderSource:S},{inputs:[t[0],_]})},Zm=e=>({batchDims:e.batch_dims,cacheKey:""})}),e_,t_,r_,s_,Gv=Be(()=>{ut(),mt(),Xt(),ft(),e_=(e,r)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let t=Me.normalizeAxis(r.quantizeAxis,e[0].dims.length),s=r.blockSize,o=e[0],n=e[2],i=e.length===4?e[3]:void 0;if(n.dims.length!==o.dims.length||!o.dims.map((a,l)=>l===t?Math.ceil(a/s)===n.dims[l]:a===n.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(i){if(i.dataType!==o.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(i.dims.length!==n.dims.length||!i.dims.map((a,l)=>a===n.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},t_=(e,r)=>{let t=e[0].dims,s=e[1].dims,o=t.length,n=Me.normalizeAxis(r.gatherAxis,o),i=Me.normalizeAxis(r.quantizeAxis,o),a=t.slice(0);a.splice(n,1,...s);let l=Me.size(a),u=e[2].dataType,p=e[0].dataType===22,d=[{type:12,data:l},{type:12,data:i},{type:12,data:n},{type:12,data:r.blockSize},...st(...e.map((f,_)=>f.dims),a)],c=f=>{let _=Pe("data",e[0].dataType,e[0].dims.length),T=Pe("inputIndices",e[1].dataType,e[1].dims.length),$=Pe("scales",e[2].dataType,e[2].dims.length),w=e.length>3?Pe("zeroPoint",e[3].dataType,e[3].dims.length):void 0,g=Ye("output",u,a.length),S=[_,T,$];w&&S.push(w);let E=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${f.registerUniforms(E).declareVariables(...S,g)}
        ${f.mainStart()}
        let output_indices = ${g.offsetToIndices("global_idx")};
        var indices_indices = ${T.type.indices}(0);
        ${s.length>1?`
          for (var i: u32 = 0; i < ${s.length}; i++) {
            let index = ${g.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${T.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${g.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${_.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${g.indicesGet("output_indices","i")};
          ${_.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${T.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${t[n]};
        }
        ${_.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${a.length}; i++) {
          let index = ${g.indicesGet("output_indices",`i + ${s.length} - 1`)};
          ${_.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${_.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${_.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
        let quantized_data = quantized_data_vec[data_index / 2];
        var scale_indices = data_indices;
        let quantize_axis_index = ${$.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${$.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${$.getByIndices("scale_indices")};
        ${w?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${w.indicesToOffset("zero_point_indices")};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${w.getByOffset("zero_point_offset / 8")};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${yr(u)}(quantized_data - zero_point) * scale;
        ${g.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${r.cacheKey};${e.filter((f,_)=>_!==1).map(f=>f.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(f,_)=>"rank")},getRunData:()=>({outputs:[{dims:a,dataType:u}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:c}},r_=(e,r)=>{let t=e.inputs;e_(t,r),e.compute(t_(e.inputs,r))},s_=e=>St({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),n_,o_,i_,a_,Kv=Be(()=>{ut(),mt(),Xt(),ft(),n_=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},o_=(e,r)=>{let t=e[0].dims,s=e[0].dataType,o=t.length,n=e[1].dims,i=e[1].dataType,a=Me.normalizeAxis(r.axis,o),l=t[a],u=n.slice(0),p=Me.size(u),d=Pe("input",s,o),c=Pe("indicesInput",i,n.length),f=Ye("output",s,u.length),_=[{type:12,data:p},{type:6,data:l},{type:12,data:a}];return _.push(...st(t,n,u)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:_}),getShaderSource:T=>`
      ${T.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(d,c,f)}
      ${T.mainStart()}
      ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${f.setByOffset("global_idx","value")};
  }`}},i_=e=>St({axis:e.axis}),a_=(e,r)=>{let t=e.inputs;n_(t),e.compute(o_(e.inputs,r))}}),l_,u_,d_,c_,Hv=Be(()=>{ut(),mt(),ft(),l_=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},u_=(e,r)=>{let t=e[0].dims.slice(),s=e[1].dims.slice(),[o,n,i]=bc.getShapeOfGemmResult(t,r.transA,s,r.transB,e.length===3?e[2].dims:void 0),a=[o,n];if(!a)throw new Error("Can't use gemm on the given tensors");let l=16,u=Math.ceil(n/l),p=Math.ceil(o/l),d=!0,c=Me.size(a),f=[{type:12,data:d?u:c},{type:12,data:o},{type:12,data:n},{type:12,data:i},{type:1,data:r.alpha},{type:1,data:r.beta}],_=["type","type"];e.length===3&&(f.push(...st(e[2].dims)),_.push("rank")),f.push(...st(a));let T=w=>{let g="";r.transA&&r.transB?g="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":r.transA&&!r.transB?g="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!r.transA&&r.transB?g="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!r.transA&&!r.transB&&(g="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let S=r.alpha===1?"":"value *= uniforms.alpha;",E=Pe("a",e[0].dataType,e[0].dims),y=Pe("b",e[1].dataType,e[1].dims),M=E.type.value,v=null,C=[E,y];e.length===3&&(v=Pe("c",e[2].dataType,e[2].dims.length),C.push(v));let A=Ye("output",e[0].dataType,a.length);C.push(A);let B=[{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`
  ${w.registerUniforms(B).declareVariables(...C)}

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

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

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

    ${S}
    ${v!=null?`let cOffset = ${v.broadcastedIndicesToOffset("vec2(m, n)",A)}; value += ${M}(uniforms.beta) * ${v.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`},$=w=>{let g=Pe("a",e[0].dataType,e[0].dims),S=Pe("b",e[1].dataType,e[1].dims),E=null,y=[g,S];e.length===3&&(E=Pe("c",e[2].dataType,e[2].dims.length),y.push(E));let M=Ye("output",e[0].dataType,a.length);y.push(M);let v=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],C="",A="";r.transA&&r.transB?(A=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${g.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${S.type.value}(0);
      }
      `,C="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):r.transA&&!r.transB?(A=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${g.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${S.type.value}(0);
      }
      `,C="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!r.transA&&r.transB?(A=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${g.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${S.type.value}(0);
      }
      `,C="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!r.transA&&!r.transB&&(A=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${g.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${S.type.value}(0);
      }
      `,C="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let B=r.alpha===1?"":"value *= uniforms.alpha;";return`
  ${w.registerUniforms(v).declareVariables(...y)}
  var<workgroup> tile_a: array<array<${g.type.storage}, ${l}>, ${l}>;
  var<workgroup> tile_b: array<array<${S.type.storage}, ${l}>, ${l}>;
  ${w.mainStart([l,l,1])}
    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${l};
    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${l};
    let num_tiles = (uniforms.K - 1) / ${l} + 1;
    var k_start = 0u;
    var value = ${M.type.value}(0);
    for (var t: u32 = 0u; t < num_tiles; t++) {
      ${A}
      k_start = k_start + ${l};
      workgroupBarrier();

      for (var k: u32 = 0u; k < ${l}; k++) {
        ${C}
      }
      workgroupBarrier();
    }

    ${B}
    let m = tile_row_start + local_id.y;
    let n = tile_col_start + local_id.x;
    ${E!=null?`let cOffset = ${E.broadcastedIndicesToOffset("vec2(m, n)",M)}; value += ${M.type.value}(uniforms.beta) * ${E.getByOffset("cOffset")};`:""}
    if (m < uniforms.M && n < uniforms.N) {
      output[m * uniforms.N + n] = value;
    }
  }`};return d?{name:"GemmShared",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:u*p},programUniforms:f}),getShaderSource:$}:{name:"Gemm",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:f}),getShaderSource:T}},d_=e=>{let r=e.transA,t=e.transB,s=e.alpha,o=e.beta;return{transA:r,transB:t,alpha:s,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},c_=(e,r)=>{l_(e.inputs),e.compute(u_(e.inputs,r))}}),ws,Ps,an,ln,p_,h_,m_,__,f_,g_,w_,M_,b_,y_,qv=Be(()=>{ut(),mt(),Xt(),ft(),[ws,Ps,an,ln]=[0,1,2,3],p_=e=>{if(e[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(e[0].dims.length!==e[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},h_=`
  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
    let cubic_alpha = -0.75f;
    let x_abs = abs(x);
    var coeffs: vec4<f32>;
    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
    return coeffs;
  }
`,m_=e=>`
  fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} {
    var v: vec4<f32>;
    var coeffs = gs_get_cubic_coeffs(x);
    for (var i = 0; i < 4; i++) {
      v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
    }
    coeffs = gs_get_cubic_coeffs(y);
    let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
    return pixel;
  }
`,__=e=>`
  fn gs_denormalize(n: f32, length: i32) -> f32 {
    ${e.alignCorners===0?`
    // alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
    return ((n + 1.0) * f32(length) - 1.0) / 2.0;
    `:`
    // alignCorners: true => [-1, 1] to [0, length - 1]
    return (n + 1.0) / 2.0 * (f32(length - 1));
    `}
  }
`,f_=e=>`
  ${e.paddingMode==="reflection"?`
      fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
        var dx = 0.0;
        var fx = f32(x);
        let range = x_max - x_min;
        if (fx < x_min) {
          dx = x_min - fx;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_min + r;
          } else {
            fx = x_max - r;
          }
        } else if (fx > x_max) {
          dx = fx - x_max;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_max - r;
          } else {
            fx = x_min + r;
          }
        }
        return u32(fx);
      }`:""}
`,g_=(e,r,t)=>`
  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${r} {
     var pixel = ${r}(0);
     var indices = vec4<u32>(0);
     indices[${ws}] = batch;
     indices[${Ps}] = channel;`+(()=>{switch(t.paddingMode){case"zeros":return`
          if (r >= 0 && r < H && c >=0 && c < W) {
            indices[${an}] = u32(r);
            indices[${ln}] = u32(c);
          } else {
            return ${r}(0);
          }
        `;case"border":return`
          indices[${an}] = u32(clamp(r, 0, H - 1));
          indices[${ln}] = u32(clamp(c, 0, W - 1));
        `;case"reflection":return`
          indices[${an}] = gs_reflect(r, border[1], border[3]);
          indices[${ln}] = gs_reflect(c, border[0], border[2]);
        `;default:throw new Error(`padding mode ${t.paddingMode} is not supported`)}})()+`
    return ${e.getByIndices("indices")};
  }
`,w_=(e,r,t)=>(()=>{switch(t.mode){case"nearest":return`
          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${ws}], indices[${Ps}], border);
        `;case"bilinear":return`
          let x1 = i32(floor(x));
          let y1 = i32(floor(y));
          let x2 = x1 + 1;
          let y2 = y1 + 1;

          let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${ws}], indices[${Ps}], border);
          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${ws}], indices[${Ps}], border);
          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${ws}], indices[${Ps}], border);
          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${ws}], indices[${Ps}], border);

          let dx2 = ${r}(f32(x2) - x);
          let dx1 = ${r}(x - f32(x1));
          let dy2 = ${r}(f32(y2) - y);
          let dy1 = ${r}(y - f32(y1));
          let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22);
        `;case"bicubic":return`
          let x0 = i32(floor(x)) - 1;
          let y0 = i32(floor(y)) - 1;
          var p: mat4x4<${r}>;
          for (var h = 0; h < 4; h++) {
            for (var w = 0; w < 4; w++) {
              p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${ws}], indices[${Ps}], border);
            }
          }

          let dx = x - f32(x0 + 1);
          let dy = y - f32(y0 + 1);
          let result = gs_bicubic_interpolate(p, dx, dy);
        `;default:throw new Error(`mode ${t.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,M_=(e,r)=>{let t=Pe("x",e[0].dataType,e[0].dims.length),s=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],o=Pe("grid",e[1].dataType,s.length,2),n=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];r.format==="NHWC"&&(n=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[ws,Ps,an,ln]=[0,3,1,2]);let i=Ye("output",e[0].dataType,n.length),a=t.type.value,l=Me.size(n),u=[{type:12,data:l},...st(e[0].dims,s,n)],p=d=>`
  ${d.registerUniform("output_size","u32").declareVariables(t,o,i)}
  ${h_}
  ${m_(a)}
  ${__(r)}
  ${f_(r)}
  ${g_(t,a,r)}

  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let H_in = i32(uniforms.x_shape[${an}]);
      let W_in = i32(uniforms.x_shape[${ln}]);

      ${r.alignCorners===0?`
      let x_min = -0.5;
      let x_max = f32(W_in) - 0.5;
      let y_min = -0.5;
      let y_max = f32(H_in) - 0.5;
      `:`
      let x_min = 0.0;
      let x_max = f32(W_in) - 1.0;
      let y_min = 0.0;
      let y_max = f32(H_in) - 1.0;
      `};
      let border = vec4<f32>(x_min, y_min, x_max, y_max);

      let indices = ${i.offsetToIndices("global_idx")};
      var grid_indices = vec3<u32>(indices[${ws}], indices[${an}], indices[${ln}]);
      let nxy = ${o.getByIndices("grid_indices")};
      var x = gs_denormalize(f32(nxy[0]), W_in);
      var y = gs_denormalize(f32(nxy[1]), H_in);

      ${w_(i,a,r)}
  }`;return{name:"GridSample",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:["type","type"]},getRunData:d=>{let c=Me.size(n);return{outputs:[{dims:n,dataType:d[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:u}},getShaderSource:p}},b_=(e,r)=>{p_(e.inputs),e.compute(M_(e.inputs,r))},y_=e=>St({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),Cr,v_,x_,Ol,T_,Mo,E_,P_=Be(()=>{ut(),mt(),Xt(),el(),cl(),ft(),zs(),Cr=(e,r)=>e.length>r&&e[r].dims.length>0?e[r]:void 0,v_=(e,r)=>{let t=e[0],s=Cr(e,1),o=Cr(e,2),n=Cr(e,3),i=Cr(e,4),a=Cr(e,5),l=Cr(e,6),u=Cr(e,7);if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let p=t.dims[0],d=t.dims[1],c=t.dims.length===3?t.dims[2]:r.numHeads*t.dims[4],f=d,_=0,T=0,$=Math.floor(c/r.numHeads);if(l&&u&&Me.size(l.dims)&&Me.size(u.dims)){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==p||l.dims[1]!==r.numHeads||l.dims[3]!==$)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[0]!==p||u.dims[1]!==r.numHeads||u.dims[3]!==$)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==u.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(u.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');_=l.dims[2],T=l.dims[2]}else if(l&&Me.size(l.dims)||u&&Me.size(u.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let w;if(s&&Me.size(s.dims)>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(s.dims[2]!==t.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');w=2,f=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==$)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');w=5,f=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==$)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');w=0,f=s.dims[2]}}else{if(t.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(t.dims[2]!==r.numHeads||t.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');w=3}if(n&&Me.size(n.dims)>0){if(n.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(s&&s.dims.length===5&&s.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let g=_+f,S=0;if(i&&Me.size(i.dims)>0){S=8;let v=i.dims;throw v.length===1?v[0]===p?S=1:v[0]===3*p+2&&(S=3):v.length===2&&v[0]===p&&v[1]===g&&(S=5),S===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let E=!1,y=c;if(o&&Me.size(o.dims)>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(f!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');y=o.dims[2]}else{if(f!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');y=o.dims[1]*o.dims[3],E=!0}}let M=!1;if(i&&Me.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(a&&Me.size(a.dims)>0){if(a.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(a.dims[0]!==p||a.dims[1]!==r.numHeads||a.dims[2]!==d||a.dims[3]!==g)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:d,pastSequenceLength:_,kvSequenceLength:f,totalSequenceLength:g,maxSequenceLength:T,inputHiddenSize:0,hiddenSize:c,vHiddenSize:y,headSize:$,vHeadSize:Math.floor(y/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:S,scale:r.scale,broadcastResPosBias:M,passPastInKv:E,qkvFormat:w}},x_=e=>St({...e}),Ol=St({perm:[0,2,1,3]}),T_=(e,r,t,s,o,n,i)=>{let a=[s,o,n],l=Me.size(a),u=[{type:12,data:l},{type:12,data:i},{type:12,data:n}],p=d=>{let c=Ye("qkv_with_bias",r.dataType,a),f=Pe("qkv",r.dataType,a),_=Pe("bias",t.dataType,a),T=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${d.registerUniforms(T).declareVariables(f,_,c)}
  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:p},{inputs:[r,t],outputs:[-1]})[0]},Mo=(e,r,t,s,o,n,i,a)=>{let l=n;if(i&&Me.size(i.dims)>0){if(s===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=T_(e,n,i,r,s,t*o,a),l=l.reshape([r,s,t,o]),t===1||s===1?l:e.compute(Lr(l,Ol.perm),{inputs:[l],outputs:[-1]})[0]}else return n.dims.length===3&&(l=n.reshape([r,s,t,o])),t===1||s===1?l:e.compute(Lr(l,Ol.perm),{inputs:[l],outputs:[-1]})[0]},E_=(e,r)=>{let t=v_(e.inputs,r),s=e.inputs[0],o=Cr(e.inputs,1),n=Cr(e.inputs,2),i=Cr(e.inputs,3),a=Cr(e.inputs,4),l=Cr(e.inputs,5),u=Cr(e.inputs,6),p=Cr(e.inputs,7);if(s.dims.length===5)throw new Error("Packed QKV is not implemented");if((o==null?void 0:o.dims.length)===5)throw new Error("Packed KV is not implemented");let d=o&&n&&o.dims.length===4&&n.dims.length===4,c=Mo(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,s,i,0);if(d)return mo(e,c,o,n,a,void 0,u,p,l,t);if(!o||!n)throw new Error("key and value must be provided");let f=Mo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.headSize,o,i,t.hiddenSize),_=Mo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.vHeadSize,n,i,2*t.hiddenSize);mo(e,c,f,_,a,void 0,u,p,l,t)}}),C_,S_,$_,k_,Dl,I_,A_,F_=Be(()=>{ut(),mt(),Xt(),ft(),C_=e=>{if(!e||e.length<1)throw new Error("too few inputs")},S_=(e,r)=>{let t=[],s=r.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(o=>t.push(Number(o))),s=t.length),St({numOutputs:s,axis:r.axis,splitSizes:t})},$_=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${et("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,k_=e=>{let r=e.length,t=[];for(let s=0;s<r;++s){let o=e[s].setByIndices("indices","input[global_idx]");r===1?t.push(o):s===0?t.push(`if (output_number == ${s}u) { ${o} }`):s===r-1?t.push(`else { ${o} }`):t.push(`else if (output_number == ${s}) { ${o} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${t.join(`
`)}
      }`},Dl=(e,r)=>{let t=e[0].dims,s=Me.size(t),o=e[0].dataType,n=Me.normalizeAxis(r.axis,t.length),i=new Array(r.numOutputs),a=Pe("input",o,t.length),l=new Array(r.numOutputs),u=[],p=[],d=0,c=[{type:12,data:s}];for(let _=0;_<r.numOutputs;_++){d+=r.splitSizes[_],l[_]=d;let T=t.slice();T[n]=r.splitSizes[_],p.push(T),i[_]=Ye(`output${_}`,o,T.length),u.push({dims:p[_],dataType:e[0].dataType})}c.push({type:12,data:l},...st(t,...p));let f=_=>`
  ${_.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(a,...i)}
  ${$_(l.length)}
  ${k_(i)}

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

    var indices = ${a.offsetToIndices("global_idx")};
    var index = ${a.indicesGet("indices",n)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${et("uniforms.size_in_split_axis","output_number - 1u",l.length)};
      ${a.indicesSet("indices",n,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getShaderSource:f,getRunData:()=>({outputs:u,dispatchGroup:{x:Math.ceil(s/64)},programUniforms:c})}},I_=(e,r)=>{C_(e.inputs);let t=e.inputs.length===1?r:S_(e.inputs,r);e.compute(Dl(e.inputs,t),{inputs:[0]})},A_=e=>{let r=e.axis,t=e.splitSizes,s=e.numOutputs<0?t.length:e.numOutputs;if(s!==t.length)throw new Error("numOutputs and splitSizes lengh must be equal");return St({axis:r,numOutputs:s,splitSizes:t})}}),O_,fi,D_,L_=Be(()=>{ut(),mt(),Xt(),ft(),O_=(e,r)=>{let[t,s,o,n]=e,{numHeads:i,rotaryEmbeddingDim:a}=r;if(t.dims.length!==3&&t.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${t.dims.length}`);if(!Me.areEqual(s.dims,[])&&!Me.areEqual(s.dims,[1])&&s.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${s.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(n.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${n.dims.length}`);if(!Me.areEqual(o.dims,n.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(a>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let l=t.dims[0],u=t.dims[t.dims.length-2],p=o.dims[0],d=Me.sizeFromDimension(t.dims,1)/u,c=a===0?o.dims[1]*2:d/i;if(a>c)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(s.dims.length===2){if(l!==s.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${s.dims[0]}`);if(u!==s.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${s.dims[1]}`)}if(c/2!==o.dims[1]&&a/2!==o.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${o.dims[1]}`);if(u>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},fi=(e,r)=>{let{interleaved:t,numHeads:s,rotaryEmbeddingDim:o,scale:n}=r,i=e[0].dims[0],a=Me.sizeFromDimension(e[0].dims,1),l=e[0].dims[e[0].dims.length-2],u=a/l,p=e[2].dims[1],d=o===0?p*2:u/s,c=new Array(i,l,u/d,d-p),f=Me.computeStrides(c),_=[{type:1,data:n},{type:12,data:c},{type:12,data:f},...e[0].dims.length===3?new Array({type:12,data:[a,u,d,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[a,d,l*d,1]}):[],...st(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],T=$=>{let w=Pe("input",e[0].dataType,e[0].dims.length),g=Pe("position_ids",e[1].dataType,e[1].dims.length),S=Pe("cos_cache",e[2].dataType,e[2].dims.length),E=Pe("sin_cache",e[3].dataType,e[3].dims.length),y=Ye("output",e[0].dataType,e[0].dims.length);return $.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:c.length},{name:"global_strides",type:"u32",length:f.length},{name:"input_output_strides",type:"u32",length:f.length}]),`
        ${$.declareVariables(w,g,S,E,y)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${g.broadcastedIndicesToOffset("bsnh.xy",Ye("",g.type.tensor,2))};
            let position_id =
                u32(${g.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${t});
            let j = i + select(half_rotary_emb_dim, 1, ${t});
            let re = ${w.getByOffset("i")} * ${S.get("position_id","bsnh[3]")} -
                ${w.getByOffset("j")} * ${E.get("position_id","bsnh[3]")};
            ${y.setByOffset("i","re")}
            let im = ${w.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} +
                ${w.getByOffset("j")} * ${S.get("position_id","bsnh[3]")};
            ${y.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${y.setByOffset("k",w.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:St({interleaved:t}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:T,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Me.size(c)/Vn)},programUniforms:_})}},D_=(e,r)=>{O_(e.inputs,r),e.compute(fi(e.inputs,r))}}),z_,B_,Ll,R_,j_,Xv=Be(()=>{Xt(),ut(),cl(),P_(),F_(),zs(),L_(),ft(),z_=(e,r)=>{if(r.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let t=e[0],s=e[1],o=e[2],n=e[3],i=e[4];if(r.doRotary!==0&&e.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(r.localWindowSize!==-1)throw new Error("Local attention is not supported");if(r.softcap!==0)throw new Error("Softcap is not supported");if(r.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(r.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let a=!1,l=t.dims[0],u=t.dims[1],p=t.dims.length===3?a?t.dims[2]/3:t.dims[2]:r.numHeads*t.dims[4],d=u,c=0,f=!s||s.dims.length===0,_=Math.floor(f?p/(r.numHeads+2*r.kvNumHeads):p/r.numHeads);f&&(p=_*r.numHeads);let T=n&&n.dims.length!==0,$=i&&i.dims.length!==0;if(T&&n.dims.length===4&&n.dims[0]===l&&n.dims[1]!==r.kvNumHeads&&n.dims[2]===r.kvNumHeads&&n.dims[3]===_)throw new Error("BSNH pastKey/pastValue is not supported");if(T&&$){if(n.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(i.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');c=n.dims[2]}else if(T||$)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let w=1;if(s&&s.dims.length>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(t.dims[2]%s.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');d=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==_)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');d=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==_)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');d=s.dims[2]}}else{if(t.dims.length!==3&&t.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(t.dims.length===5&&(t.dims[2]!==r.numHeads||t.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');w=3}let g=0,S=!1,E=r.kvNumHeads?_*r.kvNumHeads:p;if(o&&o.dims.length>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(d!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');E=o.dims[2]}else{if(d!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');E=o.dims[1]*o.dims[3],S=!0}}let y=e.length>4?e[5]:void 0;if(y&&y.dims.length!==1&&y.dims[0]!==l)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:l,sequenceLength:u,pastSequenceLength:c,kvSequenceLength:d,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:p,vHiddenSize:E,headSize:_,vHeadSize:Math.floor(E/r.kvNumHeads),numHeads:r.numHeads,kvNumHeads:r.kvNumHeads,nReps:r.numHeads/r.kvNumHeads,pastPresentShareBuffer:!1,maskType:g,scale:r.scale,broadcastResPosBias:!1,passPastInKv:S,qkvFormat:w}},B_=St({perm:[0,2,1,3]}),Ll=(e,r,t)=>{let s=r,o=t.kvNumHeads;return r.dims.length===3&&t.kvSequenceLength!==0&&(s=r.reshape([t.batchSize,t.kvSequenceLength,o,t.headSize]),s=e.compute(Lr(s,B_.perm),{inputs:[s],outputs:[-1]})[0]),s},R_=(e,r,t,s)=>{let o=7,n=["type","type"],i=[e*r],a=e*r,l=[{type:12,data:a},{type:12,data:r},{type:12,data:e}],u=p=>{let d=Pe("seq_lens",t.dataType,t.dims),c=Pe("total_seq_lens",s.dataType,s.dims),f=Ye("pos_ids",o,i),_=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return`
  ${p.registerUniforms(_).declareVariables(d,c,f)}
  ${p.mainStart()}
    ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let total_sequence_length = u32(${c.getByOffset("0")});
    let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length;
    let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length;
    let batch_idx = global_idx / uniforms.sequence_length;
    let sequence_idx = i32(global_idx % uniforms.sequence_length);
    var pos_id: i32 = 0;
    let seqlen = ${d.getByOffset("batch_idx")};
    let total_seqlen = seqlen + 1;
    if (is_first_prompt) {
      if (sequence_idx < total_seqlen) {
        pos_id = sequence_idx;
      } else {
        pos_id = 1;
      }
      ${f.setByOffset("global_idx","pos_id")}
    } else if (is_subsequent_prompt) {
      let past_seqlen = total_seqlen - i32(uniforms.sequence_length);
      if (past_seqlen + sequence_idx < total_seqlen) {
        pos_id = past_seqlen + sequence_idx;
      } else {
        pos_id = 1;
      }
      ${f.setByOffset("global_idx","pos_id")}
    } else if (global_idx < uniforms.batch_size) {
      ${f.setByOffset("global_idx","seqlen")}
    };
  }
  `};return{name:"GeneratePositionIds",shaderCache:{hint:`${e};${r}`,inputDependencies:n},getRunData:()=>({outputs:[{dims:i,dataType:o}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:l}),getShaderSource:u}},j_=(e,r)=>{var E;let t=z_(e.inputs,r);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((E=e.inputs[1])==null?void 0:E.dims.length)===5)throw new Error("Packed KV is not implemented");let s=e.inputs[0],o=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,n=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,i=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,a=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,l=e.inputs.length>4?e.inputs[5]:void 0,u=e.inputs.length>5?e.inputs[6]:void 0,p=t.kvNumHeads?t.kvNumHeads:t.numHeads,d=St({axis:2,numOutputs:3,splitSizes:[t.numHeads*t.headSize,p*t.headSize,p*t.headSize]}),[c,f,_]=!o&&!n?e.compute(Dl([s],d),{inputs:[s],outputs:[-1,-1,-1]}):[s,o,n],T,$;if(r.doRotary){let y=e.compute(R_(t.batchSize,t.sequenceLength,l,u),{inputs:[l,u],outputs:[-1]})[0],M=e.inputs[7],v=e.inputs[8],C=St({interleaved:r.rotaryInterleaved!==0,numHeads:t.numHeads,rotaryEmbeddingDim:0,scale:r.scale}),A=[c,y,M,v],B=[-1];T=e.compute(fi(A,C),{inputs:A,outputs:B})[0],A.splice(0,1,f);let K=St({interleaved:r.rotaryInterleaved!==0,numHeads:t.kvNumHeads,rotaryEmbeddingDim:0,scale:r.scale});$=e.compute(fi(A,K),{inputs:A,outputs:B})[0]}let w=Mo(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,r.doRotary?T:c,void 0,0),g=Ll(e,r.doRotary?$:f,t),S=Ll(e,_,t);mo(e,w,g,S,void 0,void 0,i,a,void 0,t,l,u)}}),zl,N_,V_,U_,Qv=Be(()=>{ut(),mt(),zs(),ft(),zl=(e,r,t,s,o,n,i,a)=>{let l=Kt(n),u=l===1?"f32":`vec${l}f`,p=l===1?"vec2f":`mat2x${l}f`,d=o*i,c=64;d===1&&(c=256);let f=[o,i,n/l],_=[o,i,2],T=["rank","type","type"],$=[];$.push(...st(f,_));let w=g=>{let S=Pe("x",r.dataType,3,l),E=Pe("scale",t.dataType,t.dims),y=Pe("bias",s.dataType,s.dims),M=Ye("output",1,3,2),v=[S,E,y,M];return`
  var<workgroup> workgroup_shared : array<${p}, ${c}>;
  const workgroup_size = ${c}u;
  ${g.declareVariables(...v)}
  ${g.mainStart(c)}
    let batch = workgroup_index / uniforms.x_shape[1];
    let channel = workgroup_index % uniforms.x_shape[1];
    let hight = uniforms.x_shape[2];
    // initialize workgroup memory
    var sum = ${u}(0);
    var squared_sum = ${u}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${u}(${S.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${p}(sum, squared_sum);
    workgroupBarrier();

    for (var currSize = workgroup_size >> 1;  currSize > 0; currSize = currSize >> 1) {
      if (local_idx < currSize) {
        workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize];
      }
      workgroupBarrier();
    }
    if (local_idx == 0) {
      let sum_final = ${Ls("workgroup_shared[0][0]",l)} / f32(hight * ${l});
      let squared_sum_final = ${Ls("workgroup_shared[0][1]",l)} / f32(hight * ${l});

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${a}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${a};${c}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:_,dataType:1}],dispatchGroup:{x:d},programUniforms:$}),getShaderSource:w},{inputs:[r,t,s],outputs:[-1]})[0]},N_=(e,r,t)=>{let s=r[0].dims,o=s,n=2,i=s[0],a=s[1],l=Me.sizeFromDimension(s,n),u=Kt(l),p=Me.size(o)/u,d=zl(e,r[0],r[1],r[2],i,l,a,t.epsilon),c=[i,a,l/u],f=[i,a],_=["type","none"],T=$=>{let w=Pe("x",r[0].dataType,c.length,u),g=Pe("scale_shift",1,f.length,2),S=Ye("output",r[0].dataType,c.length,u),E=[w,g,S];return`
  ${$.registerUniform("output_size","u32").declareVariables(...E)}
  ${$.mainStart()}
  ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let outputIndices = ${S.offsetToIndices("global_idx")};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${g.getByIndices("vec2<u32>(batch, channel)")};
      let value = ${w.getByOffset("global_idx")} * ${S.type.value}(scale_shift.x) + ${S.type.value}(scale_shift.y);
      ${S.setByOffset("global_idx","value")};
  }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${u}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:o,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...st(c,f,c)]}),getShaderSource:T},{inputs:[r[0],d]})},V_=(e,r,t)=>{let s=r[0].dims,o=s,n=s[0],i=s[s.length-1],a=Me.sizeFromDimension(s,1)/i,l=Kt(i),u=Me.size(o)/l,p=[{type:12,data:a},{type:12,data:Math.floor(i/l)}],d=["type","type"],c=!1,f=[0,s.length-1];for(let w=0;w<s.length-2;w++)c=c||s[w+1]!==1,f.push(w+1);c=c&&s[s.length-1]!==1;let _=c?e.compute(Lr(e.inputs[0],f),{inputs:[e.inputs[0]],outputs:[-1]})[0]:e.inputs[0].reshape(Array.from({length:s.length},(w,g)=>s[f[g]])),T=zl(e,_,r[1],r[2],n,a,i,t.epsilon),$=w=>{let g=lr(r[0].dataType),S=l===1?"vec2f":`mat${l}x2f`,E=v=>{let C=v===0?"x":"y",A=l===1?"f32":`vec${l}f`;switch(l){case 1:return`${g}(${A}(scale.${C}))`;case 2:return`vec2<${g}>(${A}(scale[0].${C}, scale[1].${C}))`;case 4:return`vec4<${g}>(${A}(scale[0].${C}, scale[1].${C}, scale[2].${C}, scale[3].${C}))`;default:throw new Error(`Not supported compoents ${l}`)}},y=Pe("input",r[0].dataType,r[0].dims,l),M=Ye("output",r[0].dataType,o,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${y.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${S}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${M.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${w.mainStart()}
    let current_image_number = global_idx / (uniforms.C * uniforms.H);
    let current_channel_number = global_idx % uniforms.C;

    let scale_offset = current_image_number * uniforms.C + current_channel_number;
    let scale = scale_input[scale_offset];
    output[global_idx] = fma(input[global_idx], ${E(0)}, ${E(1)});
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:p}),getShaderSource:$},{inputs:[r[0],T]})},U_=(e,r)=>{r.format==="NHWC"?V_(e,e.inputs,r):N_(e,e.inputs,r)}}),W_,G_,K_,Jv=Be(()=>{ut(),mt(),ft(),W_=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},G_=(e,r,t)=>{let s=r.simplified,o=e[0].dims,n=e[1],i=!s&&e[2],a=o,l=Me.normalizeAxis(r.axis,o.length),u=Me.sizeToDimension(o,l),p=Me.sizeFromDimension(o,l),d=Me.size(n.dims),c=i?Me.size(i.dims):0;if(d!==p||i&&c!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${d} and bias size of ${c}`);let f=[];for(let y=0;y<o.length;++y)y<l?f.push(o[y]):f.push(1);let _=Kt(p),T=["type","type"],$=[{type:12,data:u},{type:1,data:p},{type:12,data:Math.floor(p/_)},{type:1,data:r.epsilon}];i&&T.push("type");let w=t>1,g=t>2,S=y=>{let M=lr(e[0].dataType),v=[Pe("x",e[0].dataType,e[0].dims,_),Pe("scale",n.dataType,n.dims,_)];i&&v.push(Pe("bias",i.dataType,i.dims,_)),v.push(Ye("output",e[0].dataType,a,_)),w&&v.push(Ye("mean_data_output",1,f)),g&&v.push(Ye("inv_std_output",1,f));let C=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${y.registerUniforms(C).declareVariables(...v)}
  ${y.mainStart()}
    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${nl("f32",_)};
    var mean_square_vector = ${nl("f32",_)};

    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
      let value = ${Un(M,_,"x[h + offset]")};
      mean_vector += value;
      mean_square_vector += value * value;
    }
    let mean = ${Ls("mean_vector",_)} / uniforms.norm_size;
    let inv_std_dev = inverseSqrt(${Ls("mean_square_vector",_)} / uniforms.norm_size ${s?"":"- mean * mean"} + uniforms.epsilon);

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

    ${w?"mean_data_output[global_idx] = mean":""};
    ${g?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},E=[{dims:a,dataType:e[0].dataType}];return w&&E.push({dims:f,dataType:1}),g&&E.push({dims:f,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${_};${t};${s}`,inputDependencies:T},getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:$}),getShaderSource:S}},K_=(e,r)=>{W_(e.inputs),e.compute(G_(e.inputs,r,e.outputCount))}}),H_,q_,Yv=Be(()=>{mt(),Ml(),xl(),H_=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},q_=e=>{H_(e.inputs);let r=Nn.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!r)throw new Error("Can't use matmul on the given tensors");let t=r[r.length-1],s=e.inputs[0].dims[e.inputs[0].dims.length-1];if(t<8&&s<8)e.compute(wl(e.inputs,{activation:""},r));else{let o=r[r.length-2],n=Me.size(e.inputs[0].dims.slice(0,-2)),i=Me.size(e.inputs[1].dims.slice(0,-2));if(n!==1&&o===1&&i===1){let a=e.inputs[0].reshape([1,n,s]),l=e.inputs[1].reshape([1,s,t]),u=[1,n,t],p=[a,l];e.compute(pi(p,{activation:""},r,u),{inputs:p})}else e.compute(pi(e.inputs,{activation:""},r))}}}),X_,Q_,J_,Y_,Z_,Zv=Be(()=>{ut(),mt(),Xt(),ft(),X_=(e,r)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let t=e[0],s=t.dims.length;if(t.dims[s-1]!==r.k)throw new Error("The last dim of input shape does not match the k value");let o=Math.floor((r.k+r.blockSize-1)/r.blockSize),n=r.blockSize/8*r.bits,i=e[1];if(!Me.areEqual(i.dims,[r.n,o,n]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let a=e[2].dims;if(Me.size(a)!==r.n*o)throw new Error("scales input size error.");if(e.length===4){let l=e[3].dims,u=r.bits>4?r.n*o:r.n*Math.floor((o+1)/2);if(Me.size(l)!==u)throw new Error("zeroPoints input size error.")}},Q_=(e,r)=>{let t=e[0].dims,s=t.length,o=t[s-2],n=r.k,i=r.n,a=t.slice(0,s-2),l=Me.size(a),u=e[1].dims[2]/4,p=e[0].dataType,d=Kt(r.k),c=Kt(u),f=Kt(i),_=a.concat([o,i]),T=o>1&&i/f%2===0?2:1,$=Me.size(_)/f/T,w=64,g=[],S=[l,o,n/d],E=Me.convertShape(e[1].dims).slice();E.splice(-1,1,u/c),g.push(...st(S)),g.push(...st(E)),g.push(...st(e[2].dims)),e.length===4&&g.push(...st(Me.convertShape(e[3].dims)));let y=[l,o,i/f];g.push(...st(y));let M=v=>{let C=S.length,A=Pe("a",e[0].dataType,C,d),B=Pe("b",12,E.length,c),K=Pe("scales",e[2].dataType,e[2].dims.length),G=[A,B,K],j=e.length===4?Pe("zero_points",12,e[3].dims.length):void 0;j&&G.push(j);let ee=y.length,H=Ye("output",e[0].dataType,ee,f),Z=lr(e[0].dataType),X=(()=>{switch(d){case 1:return`array<${Z}, 8>`;case 2:return`mat4x2<${Z}>`;case 4:return`mat2x4<${Z}>`;default:throw new Error(`${d}-component is not supported.`)}})(),oe=()=>{let V=`
          // reuse a data
            var input_offset = ${A.indicesToOffset(`${A.type.indices}(batch, row, word_offset)`)};
            var a_data: ${X};
            for (var j: u32 = 0; j < ${8/d}; j++) {
              a_data[j] = ${A.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let F=0;F<f*T;F++)V+=`
            b_value = ${c===1?`b${F}_data`:`b${F}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${X}(${Array.from({length:4},(W,re)=>`${Z}(b_value_lower[${re}]), ${Z}(b_value_upper[${re}])`).join(", ")});
            b_dequantized_values = ${d===1?`${X}(${Array.from({length:8},(W,re)=>`(b_quantized_values[${re}] - ${j?`zero_point${F}`:"zero_point"}) * scale${F}`).join(", ")});`:`(b_quantized_values - ${X}(${Array(8).fill(`${j?`zero_point${F}`:"zero_point"}`).join(",")})) * scale${F};`};
            workgroup_shared[local_id.x * ${T} + ${Math.floor(F/f)}]${f>1?`[${F%f}]`:""} += ${Array.from({length:8/d},(W,re)=>`${d===1?`a_data[${re}] * b_dequantized_values[${re}]`:`dot(a_data[${re}], b_dequantized_values[${re}])`}`).join(" + ")};
          `;return V},me=()=>{let V=`
            var col_index = col * ${f};
            ${j?`
            let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
            var zero_point_byte_count: u32;
            var zero_point_word_index: u32;
            var zero_point_byte_offset: u32;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32;
            var zero_point_word: u32;`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${Z}(8);`}
            `;for(let F=0;F<f*T;F++)V+=`
            let scale${F} = ${K.getByOffset("col_index * nBlocksPerCol + block")};
            ${j?`
            zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u);
            zero_point_word_index = zero_point_byte_count >> 0x2u;
            zero_point_byte_offset = zero_point_byte_count & 0x3u;
            zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            zero_point_word = ${j.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${F} = ${Z}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return V},ae=()=>{let V=`col_index = col * ${f};`;for(let F=0;F<f*T;F++)V+=`
            let b${F}_data = ${B.getByIndices(`${B.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return V+=`
            var b_value: u32;
            let b_mask: u32 = 0x0F0F0F0Fu;
            var b_value_lower: vec4<u32>;
            var b_value_upper: vec4<u32>;
            var b_quantized_values: ${X};
            var b_dequantized_values: ${X};`,V};return`
        var<workgroup> workgroup_shared: array<${H.type.value}, ${T*w}>;
        ${v.declareVariables(...G,H)}
        ${v.mainStart([w,1,1])}
          let output_indices = ${H.offsetToIndices(`(global_idx / ${w}) * ${T}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let nBlocksPerCol = uniforms.b_shape[1];

          for (var block = local_id.x; block < nBlocksPerCol; block += ${w}) {
            //process one block
            var word_offset: u32 = block * ${r.blockSize/d};
            ${me()}
            for (var word: u32 = 0; word < ${u}; word += ${c}) {
              ${ae()}
              for (var i: u32 = 0; i < ${c}; i++) {
                ${oe()}
                word_offset += ${8/d};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${T}) {
            var output_value: ${H.type.value} = ${H.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < ${w}u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${T};
            }
            ${H.setByIndices(`${H.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${r.blockSize};${r.bits};${d};${c};${f};${T};${w}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:_,dataType:p}],dispatchGroup:{x:$},programUniforms:g}),getShaderSource:M}},J_=(e,r)=>{let t=e[0].dims,s=t.length,o=t[s-2],n=r.k,i=r.n,a=t.slice(0,s-2),l=Me.size(a),u=e[1].dims[2]/4,p=e[0].dataType,d=Kt(r.k),c=Kt(u),f=a.concat([o,i]),_=128,T=i%8===0?8:i%4===0?4:1,$=_/T,w=$*c*8,g=w/d,S=w/r.blockSize,E=Me.size(f)/T,y=[],M=[l,o,n/d],v=Me.convertShape(e[1].dims).slice();v.splice(-1,1,u/c),y.push(...st(M)),y.push(...st(v)),y.push(...st(e[2].dims)),e.length===4&&y.push(...st(Me.convertShape(e[3].dims)));let C=[l,o,i];y.push(...st(C));let A=B=>{let K=M.length,G=Pe("a",e[0].dataType,K,d),j=Pe("b",12,v.length,c),ee=Pe("scales",e[2].dataType,e[2].dims.length),H=[G,j,ee],Z=e.length===4?Pe("zero_points",12,e[3].dims.length):void 0;Z&&H.push(Z);let X=C.length,oe=Ye("output",e[0].dataType,X),me=lr(e[0].dataType),ae=()=>{switch(d){case 1:return`
          let a_data0 = vec4<${me}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
          let a_data1 = vec4<${me}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
          let a_data0 = vec4<${me}>(sub_a[word_offset], sub_a[word_offset + 1]);
          let a_data1 = vec4<${me}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${d}-component is not supported.`)}};return`
        var<workgroup> sub_a: array<${G.type.value}, ${g}>;
        var<workgroup> inter_results: array<array<${oe.type.value}, ${$}>, ${T}>;
        ${B.declareVariables(...H,oe)}
        ${B.mainStart([$,T,1])}
          let output_indices = ${oe.offsetToIndices(`workgroup_index * ${T}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let n_blocks_per_col = uniforms.b_shape[1];
          let num_tiles =  (n_blocks_per_col - 1) / ${S} + 1;

          // Loop over shared dimension.
          for (var tile: u32 = 0; tile < num_tiles; tile += 1) {
            let a_col_start = tile * ${g};
            // load one tile A data into shared memory.
            for (var a_offset = local_idx; a_offset < ${g}; a_offset += ${_})
            {
              let a_col = a_col_start + a_offset;
              if (a_col < uniforms.a_shape[2])
              {
                sub_a[a_offset] = ${G.getByIndices(`${G.type.indices}(batch, row, a_col)`)};
              } else {
                sub_a[a_offset] = ${G.type.value}(0);
              }
            }
            workgroupBarrier();

            // each thread process one block
            let b_row = col + local_id.y;
            let block = tile * ${S} + local_id.x;
            ${Z?`
            let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2;
            let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u);
            let zero_point_word_index = zero_point_byte_count >> 0x2u;
            let zero_point_byte_offset = zero_point_byte_count & 0x3u;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            let zero_point_word = ${Z.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point = ${me}((zero_point_word) & 0xFu);`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${me}(8);`}
            let scale = ${ee.getByOffset("b_row * n_blocks_per_col + block")};
            let b_data = ${j.getByIndices(`${j.type.indices}(b_row, block, 0)`)};
            var word_offset = local_id.x * ${r.blockSize/d};
            for (var i: u32 = 0; i < ${c}; i++) {
              ${ae()}
              let b_value = ${c===1?"b_data":"b_data[i]"};
              let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
              let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
              let b_quantized_values = mat2x4<${me}>(${Array.from({length:4},(V,F)=>`${me}(b_value_lower[${F}]), ${me}(b_value_upper[${F}])`).join(", ")});
              let b_dequantized_values = (b_quantized_values - mat2x4<${me}>(${Array(8).fill("zero_point").join(",")})) * scale;
              inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(V,F)=>`${`dot(a_data${F}, b_dequantized_values[${F}])`}`).join(" + ")};
              word_offset += ${8/d};
            }
            workgroupBarrier();
          }

          if (local_idx < ${T}) {
            var output_value: ${oe.type.value} = ${oe.type.value}(0);
            for (var b = 0u; b < ${$}; b++) {
              output_value += inter_results[local_idx][b];
            }
            if (col + local_idx < uniforms.output_shape[2])
            {
              ${oe.setByIndices(`${oe.type.indices}(batch, row, col + local_idx)`,"output_value")}
            }
          }
        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${r.blockSize};${d};${c};${$};${T}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:f,dataType:p}],dispatchGroup:{x:E},programUniforms:y}),getShaderSource:A}},Y_=(e,r)=>{X_(e.inputs,r),r.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(J_(e.inputs,r)):e.compute(Q_(e.inputs,r))},Z_=e=>St(e)}),ef,tf,rf,sf,nf,of,af,lf,uf,ex=Be(()=>{ut(),mt(),ft(),ef=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 r=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(r=e[3].dims[0]*2===e[1].dims[0]),!r)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},tf=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=`
            k = i32(${e.indicesGet("indices",o)}) - ${et("uniforms.pads",o,t)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${et("uniforms.x_shape",o,r)})) {
              break;
            }
            offset += k * i32(${et("uniforms.x_strides",o,r)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${s}
            value = x[offset];
          }
      `},rf=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=`
                k = i32(${e.indicesGet("indices",o)}) - ${et("uniforms.pads",o,t)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${et("uniforms.x_shape",o,r)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${et("uniforms.x_shape",o,r)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${et("uniforms.x_strides",o,r)});
            `;return`
              var offset = 0;
              var k = 0;
              ${s}
              value = x[offset];
          `},sf=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=`
                k = i32(${e.indicesGet("indices",o)}) - ${et("uniforms.pads",o,t)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${et("uniforms.x_shape",o,r)})) {
                  k = i32(${et("uniforms.x_shape",o,r)}) - 1;
                }
                offset += k * i32(${et("uniforms.x_strides",o,r)});
            `;return`
              var offset = 0;
              var k = 0;
              ${s}
              value = x[offset];
          `},nf=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=`
                k = i32(${e.indicesGet("indices",o)}) - ${et("uniforms.pads",o,t)};
                if (k < 0)  {
                  k += i32(${et("uniforms.x_shape",o,r)}]);
                }
                if (k >= i32(${et("uniforms.x_shape",o,r)})) {
                  k -= i32(${et("uniforms.x_shape",o,r)});
                }
                offset += k * i32(${et("uniforms.x_strides",o,r)});
            `;return`
              var offset = 0;
              var k = 0;
              ${s}
              value = x[offset];
          `},of=(e,r,t)=>{switch(t.mode){case 0:return tf(e,r,t.pads.length);case 1:return rf(e,r,t.pads.length);case 2:return sf(e,r,t.pads.length);case 3:return nf(e,r,t.pads.length);default:throw new Error("Invalid mode")}},af=(e,r)=>{let t=Me.padShape(e[0].dims.slice(),r.pads),s=e[0].dims,o=Me.size(t),n=[{type:12,data:o},{type:6,data:r.pads}],i=e.length>=3&&e[2].data;r.mode===0&&n.push({type:i?e[2].dataType:1,data:r.value}),n.push(...st(e[0].dims,t));let a=["rank"],l=u=>{let p=Ye("output",e[0].dataType,t.length),d=Pe("x",e[0].dataType,s.length),c=d.type.value,f=of(p,s.length,r),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:r.pads.length}];return r.mode===0&&_.push({name:"constant_value",type:i?c:"f32"}),`
            ${u.registerUniforms(_).declareVariables(d,p)}
            ${u.mainStart()}
            ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${c}(0);
            ${f}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${r.mode}${i}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Me.size(t)/64)},programUniforms:n}),getShaderSource:l}},lf=(e,r)=>{if(e.length>1){let t=e[1].getBigInt64Array(),s=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,o=e[0].dims.length,n=new Int32Array(2*o).fill(0);if(e.length>=4){let a=e[3].getBigInt64Array();for(let l=0;l<a.length;l++)n[Number(a[l])]=Number(t[l]),n[Number(a[l])+o]=Number(t[l+a.length])}else t.forEach((a,l)=>n[Number(l)]=Number(a));let i=[];return n.forEach(a=>i.push(a)),{mode:r.mode,value:s,pads:i}}else return r},uf=(e,r)=>{ef(e.inputs);let t=lf(e.inputs,r);e.compute(af(e.inputs,t),{inputs:[0]})}}),bo,Bl,Rl,jl,Nl,df,cf,Vl,Ul,pf,hf,Wl,mf,_f,Gl,ff,gf,wf,Mf,tx=Be(()=>{ts(),ut(),mt(),ft(),bo=e=>{if(Bt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Bl=(e,r,t)=>{let s=r.format==="NHWC",o=e.dims.slice();s&&o.splice(1,0,o.pop());let n=Object.hasOwnProperty.call(r,"dilations"),i=r.kernelShape.slice(),a=r.strides.slice(),l=n?r.dilations.slice():[],u=r.pads.slice();ni.adjustPoolAttributes(t,o,i,a,l,u);let p=ni.computePoolOutputShape(t,o,a,l,i,u,r.autoPad),d=Object.assign({},r);n?Object.assign(d,{kernelShape:i,strides:a,pads:u,dilations:l,cacheKey:r.cacheKey}):Object.assign(d,{kernelShape:i,strides:a,pads:u,cacheKey:r.cacheKey});let c=p.slice();return c.push(c.splice(1,1)[0]),[d,s?c:p]},Rl=(e,r)=>{let t=r.format==="NHWC",s=Me.size(e),o=Me.size(r.kernelShape),n=[{type:12,data:s},{type:12,data:o}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(r.kernelShape.length<=2){let a=r.kernelShape[r.kernelShape.length-1],l=r.strides[r.strides.length-1],u=r.pads[r.pads.length/2-1],p=r.pads[r.pads.length-1],d=!!(u+p);n.push({type:12,data:a},{type:12,data:l},{type:12,data:u},{type:12,data:p}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let c=!1;if(r.kernelShape.length===2){let f=r.kernelShape[r.kernelShape.length-2],_=r.strides[r.strides.length-2],T=r.pads[r.pads.length/2-2],$=r.pads[r.pads.length-2];c=!!(T+$),n.push({type:12,data:f},{type:12,data:_},{type:12,data:T},{type:12,data:$}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[n,i,!0,d,c]}else{if(t)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let a=Me.computeStrides(r.kernelShape);n.push({type:12,data:a},{type:12,data:r.pads},{type:12,data:r.strides}),i.push({name:"kernelStrides",type:"u32",length:a.length},{name:"pads",type:"u32",length:r.pads.length},{name:"strides",type:"u32",length:r.strides.length});let l=r.pads.reduce((u,p)=>u+p);return[n,i,!!l,!1,!1]}},jl=(e,r,t,s,o,n,i,a,l,u,p,d)=>{let c=o.format==="NHWC",f=r.type.value,_=Ye("output",r.type.tensor,s);if(o.kernelShape.length<=2){let T="",$="",w="",g=t-(c?2:1);if(p?T=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${g}] < 0 || xIndices[${g}]
                      >= uniforms.x_shape[${g}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${r.indicesToOffset("xIndices")}];
                  ${n}
                }`:T=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${r.indicesToOffset("xIndices")}];
                  ${n}
                }`,o.kernelShape.length===2){let S=t-(c?3:2);d?$=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${S}] = indices[${S}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${S}] < 0 || xIndices[${S}] >= uniforms.x_shape[${S}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:$=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${S}] = indices[${S}] * uniforms.sh - uniforms.phStart + j;
                `,w=`
              }
            `}return`
            ${e.registerUniforms(l).declareVariables(r,_)}

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

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

              var value = ${f}(${a});
              var pad = 0;
              ${$}
              ${T}
              ${w}
              ${i}

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

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

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

              var value = ${f}(${a});
              var pad = 0;
              var isPad = false;

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

                isPad = false;
                for (var j = ${t-T}u; j < ${t}u; j++) {
                  xIndices[j] = indices[j] * ${et("uniforms.strides",`j - ${t-T}u`,T)}
                    + offsets[j - ${t-T}u] - ${et("uniforms.pads","j - 2u",$)};
                  ${w}
              }
              ${i}

              output[global_idx] = value;
            }`}},Nl=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,df=e=>`${Nl(e)};${e.countIncludePad}`,cf=e=>`${Nl(e)};${e.storageOrder};${e.dilations}`,Vl=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}),Ul=(e,r,t,s)=>{let[o,n]=Bl(r,s,t),i=Pe("x",r.dataType,r.dims.length),a=i.type.value,l="value += x_val;",u="";o.countIncludePad?u+=`value /= ${a}(uniforms.kernelSize);`:u+=`value /= ${a}(i32(uniforms.kernelSize) - pad);`;let[p,d,c,f,_]=Rl(n,o);p.push(...st(r.dims,n));let T=["rank"];return{name:e,shaderCache:{hint:`${s.cacheKey};${c};${f};${_}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(Me.size(n)/64)},programUniforms:p}),getShaderSource:$=>jl($,i,r.dims.length,n.length,o,l,u,0,d,c,f,_)}},pf=e=>{let r=e.count_include_pad!==0,t=Vl(e);if(t.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let s={countIncludePad:r,...t,cacheKey:""};return{...s,cacheKey:df(s)}},hf=(e,r)=>{bo(e.inputs),e.compute(Ul("AveragePool",e.inputs[0],!1,r))},Wl={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},mf=e=>{let r=e.format;return{format:r,...Wl,cacheKey:r}},_f=(e,r)=>{bo(e.inputs),e.compute(Ul("GlobalAveragePool",e.inputs[0],!0,r))},Gl=(e,r,t,s)=>{let[o,n]=Bl(r,s,t),i=`
      value = max(x_val, value);
    `,a="",l=Pe("x",r.dataType,r.dims.length),u=["rank"],[p,d,c,f,_]=Rl(n,o);return p.push(...st(r.dims,n)),{name:e,shaderCache:{hint:`${s.cacheKey};${c};${f};${_}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(Me.size(n)/64)},programUniforms:p}),getShaderSource:T=>jl(T,l,r.dims.length,n.length,o,i,a,r.dataType===10?-65504:-1e5,d,c,f,_)}},ff=(e,r)=>{bo(e.inputs),e.compute(Gl("MaxPool",e.inputs[0],!1,r))},gf=e=>{let r=e.storage_order,t=e.dilations,s=Vl(e);if(r!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let o={storageOrder:r,dilations:t,...s,cacheKey:""};return{...o,cacheKey:cf(o)}},wf=e=>{let r=e.format;return{format:r,...Wl,cacheKey:r}},Mf=(e,r)=>{bo(e.inputs),e.compute(Gl("GlobalMaxPool",e.inputs[0],!0,r))}}),bf,yf,vf,xf,rx=Be(()=>{ut(),mt(),Xt(),ft(),bf=(e,r)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((t,s)=>t===e[2].dims[s]).reduce((t,s)=>t&&s,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(r.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((o,n)=>n===r.axis||o===e[0].dims[n]).reduce((o,n)=>o&&n,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let t=e[0].dims[r.axis],s=e[1].dims[r.axis];if(r.blockSize<Math.ceil(t/s)||r.blockSize>Math.ceil(t/(s-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},yf=(e,r)=>{let t=Me.normalizeAxis(r.axis,e[0].dims.length),s=e[0].dataType,o=s===3,n=e[0].dims,i=e[1].dataType,a=Me.size(n),l=s===3||s===2,u=l?[Math.ceil(Me.size(e[0].dims)/4)]:e[0].dims,p=e[1].dims,d=e.length>2?e[2]:void 0,c=d?l?[Math.ceil(Me.size(d.dims)/4)]:d.dims:void 0,f=p.length===0||p.length===1&&p[0]===1,_=f===!1&&p.length===1,T=Kt(a),$=f&&(!l||T===4),w=$?T:1,g=$&&!l?T:1,S=Pe("input",l?12:s,u.length,g),E=Pe("scale",i,p.length),y=d?Pe("zero_point",l?12:s,c.length):void 0,M=Ye("output",i,n.length,w),v=[S,E];y&&v.push(y);let C=[u,p];d&&C.push(c);let A=[{type:12,data:a/w},{type:12,data:t},{type:12,data:r.blockSize},...st(...C,n)],B=K=>{let G=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${K.registerUniforms(G).declareVariables(...v,M)}
      ${K.mainStart()}
          ${K.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${M.offsetToIndices("global_idx")};

          // Set input x
          ${l?`
            let input = ${S.getByOffset("global_idx / 4")};
            let x_vec = ${o?"unpack4xI8(input)":"unpack4xU8(input)"};
            let x_value = ${w===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${S.getByOffset("global_idx")};`};

          // Set scale input
          ${f?`let scale_value= ${E.getByOffset("0")}`:_?`
            let scale_index = ${M.indicesGet("output_indices","uniforms.axis")};
            let scale_value= ${E.getByOffset("scale_index")};`:`
            var scale_indices: ${E.type.indices} = output_indices;
            let index = ${E.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size;
            ${E.indicesSet("scale_indices","uniforms.axis","index")};
            let scale_value= ${E.getByIndices("scale_indices")};`};

          // Set zero-point input
          ${y?f?l?`
                let zero_point_input = ${y.getByOffset("0")};
                let zero_point_vec =  ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${y.getByOffset("0")}`:_?l?`
                let zero_point_index = ${M.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${y.getByOffset("zero_point_index / 4")};
                let zero_point_vec =  ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${M.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${y.getByOffset("zero_point_index")};`:l?`
                let zero_point_offset = ${E.indicesToOffset("scale_indices")};
                let zero_point_input = ${y.getByOffset("zero_point_offset / 4")};
                let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${y.getByIndices("scale_indices")};`:`let zero_point_value = ${l?o?"i32":"u32":S.type.value}(0);`};
      // Compute and write output
      ${M.setByOffset("global_idx",`${M.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:r.cacheKey,inputDependencies:y?["rank","rank","rank"]:["rank","rank"]},getShaderSource:B,getRunData:()=>({outputs:[{dims:n,dataType:i}],dispatchGroup:{x:Math.ceil(a/w/64),y:1,z:1},programUniforms:A})}},vf=(e,r)=>{bf(e.inputs,r),e.compute(yf(e.inputs,r))},xf=e=>St({axis:e.axis,blockSize:e.blockSize})}),Tf,Ef,Pf,sx=Be(()=>{ts(),ut(),ft(),Tf=(e,r,t)=>{let s=e===r,o=e<r&&t<0,n=e>r&&t>0;if(s||o||n)throw new Error("Range these inputs' contents are invalid.")},Ef=(e,r,t,s)=>{let o=Math.abs(Math.ceil((r-e)/t)),n=[o],i=o,a=[{type:12,data:i},{type:s,data:e},{type:s,data:t},...st(n)],l=u=>{let p=Ye("output",s,n.length),d=p.type.value,c=[{name:"outputSize",type:"u32"},{name:"start",type:d},{name:"delta",type:d}];return`
        ${u.registerUniforms(c).declareVariables(p)}
        ${u.mainStart()}
        ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${d}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${s}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:n,dataType:s}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:a})}},Pf=e=>{let r=0,t=0,s=0;e.inputs[0].dataType===6?(r=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],s=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(r=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],s=e.inputs[2].getFloat32Array()[0]),Bt.webgpu.validateInputContent&&Tf(r,t,s),e.compute(Ef(r,t,s,e.inputs[0].dataType),{inputs:[]})}}),Cf,Kl,Hl,Sf,$f,kf,nx=Be(()=>{ut(),mt(),Xt(),ft(),Cf=(e,r,t,s)=>{if(e!=="none"&&s!=="i32"&&s!=="u32"&&s!=="f32")throw new Error(`Input ${s} is not supported with reduction ${e}.`);let o=`{
                var oldValue = 0;
                loop {
                  let newValueF32 =`,n=`;
                  let newValue = bitcast<i32>(newValueF32);
                  let res = atomicCompareExchangeWeak(&${r}, oldValue, newValue);
                  if res.exchanged {
                    break;
                  }
                  oldValue = res.old_value;
                }
              }`;switch(e){case"none":return`${r}=${t};`;case"add":return s==="i32"||s==="u32"?`atomicAdd(&${r}, bitcast<${s}>(${t}));`:`
              ${o}bitcast<${s}>(oldValue) + (${t})${n}`;case"max":return s==="i32"||s==="u32"?`atomicMax(&${r}, bitcast<${s}>(${t}));`:`
                ${o}max(bitcast<f32>(oldValue), (${t}))${n}`;case"min":return s==="i32"||s==="u32"?`atomicMin(&${r}, bitcast<${s}>(${t}));`:`${o}min(bitcast<${s}>(oldValue), (${t}))${n}`;case"mul":return`${o}(bitcast<${s}>(oldValue) * (${t}))${n}`;default:throw new Error(`Reduction ${e} is not supported.`)}},Kl=(e,r)=>`${e===1?`
    let element_count_dim = uniforms.output_strides;
    let dim_value = uniforms.output_shape;`:`
    let element_count_dim = uniforms.output_strides[${r?"i - indices_start":"i"}];
    let dim_value = uniforms.output_shape[${r?"i - indices_start":"i"} + uniforms.last_index_dimension];`}
    
    if (index >= 0) {
      if (index >= i32(dim_value)) {
        index = i32(dim_value - 1);
      }
    } else {
      if (index < -i32(dim_value)) {
        index = 0;
      } else {
        index += i32(dim_value);
      }
    }
    data_offset += u32((u32(index) * element_count_dim));`,Hl=(e,r,t)=>`for (var i = 0u; i < uniforms.num_updates_elements; i++) {
        let value = updates[uniforms.num_updates_elements * ${t?"global_idx":"idx"} + i];
        ${Cf(e.reduction,"output[data_offset + i]","value",r)}
      }`,Sf=(e,r)=>{let t=e[0].dims,s=e[1].dims,o=t,n=1,i=Math.ceil(Me.size(s)/n),a=s[s.length-1],l=Me.sizeFromDimension(t,a),u=Me.sizeFromDimension(s,0)/a,p=[{type:12,data:i},{type:12,data:a},{type:12,data:l},...st(e[1].dims,e[2].dims,o)],d=c=>{let f=Pe("indices",e[1].dataType,e[1].dims.length),_=Pe("updates",e[2].dataType,e[2].dims.length,n),T=r.reduction!=="none"&&r.reduction!==""?Dc("output",e[0].dataType,o.length):Ye("output",e[0].dataType,o.length,n);return`
      ${c.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(f,_,T)}
      ${c.mainStart()}
        ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
  var hasDuplicates = false;
  if (${r.reduction==="none"}) {
    for (var i = 0; i < ${u}; i = i + 1) {
      for (var j = i + 1; j < ${u}; j = j + 1) {
        var index_i = i32(indices[i].x);
        var index_j = i32(indices[j].x);
        if (index_i == index_j) {
          hasDuplicates = true;
          break;
        }
      }
      if (hasDuplicates) {
        break;
      }
    }
  }

  if (${r.reduction==="none"} && hasDuplicates) {
    if (global_idx != 0u) {
      return;
    }
    // Process each index-update pair individually when duplicates exist
    for (var idx = 0u; idx < ${u}u; idx++) {
      var data_offset = 0u;
      for (var i = 0u; i < uniforms.last_index_dimension; i++) {
        var index = i32(indices[idx * uniforms.last_index_dimension + i].x);
        ${Kl(t.length,!1)}
      }
      ${Hl(r,T.type.value,!1)}
    }
    return;
  }

  var data_offset = 0u;
  var indices_start = uniforms.last_index_dimension * global_idx;
  var indices_end = indices_start + uniforms.last_index_dimension;
  for (var i = indices_start; i < indices_end; i++) {
    var index = i32(indices[i].x);
    ${Kl(t.length,!0)}
  }
  ${Hl(r,T.type.value,!0)}
  }`};return{name:"ScatterND",shaderCache:{hint:`${r.cacheKey}_${r.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:p}),getShaderSource:d}},$f=e=>St({reduction:e.reduction}),kf=(e,r)=>{e.compute(Sf(e.inputs,r),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),If,Af,Ff,ql,Of,Df,Lf,zf,Bf,Rf,jf,Nf,Xl,Vf,Uf,Wf,Gf,Kf,Hf,qf,ox=Be(()=>{ut(),mt(),Xt(),ft(),If=(e,r)=>{if(e.every(t=>t>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(r.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(r.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")}},Af=(e,r,t)=>{r.every(o=>o>=0&&o<t||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let s=new Array(t).fill(1);return r.forEach((o,n)=>s[o]=e[n]),s},Ff=(e,r,t,s,o,n)=>{let[i,a,l]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],u=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(p=>n.push(p));else if(r.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(a>0&&e.length>a&&e[a].dims.length===1&&e[a].dims[0]>0){if(e[a].getFloat32Array().forEach(p=>s.push(p)),s.length!==0&&s.length!==u&&t>=18&&s.length!==r.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");If(s,r),r.axes.length>0&&Af(s,r.axes,u).forEach((p,d)=>s[d]=p)}if(l>0&&e.length>l&&e[l].dims.length===1&&e[l].dims[0]>0&&(e[l].getBigInt64Array().forEach(p=>o.push(Number(p))),o.length!==0&&o.length!==u&&t>=18&&o.length!==r.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(r.axes.length>0){if(s.length!==0&&s.length!==r.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(o.length!==0&&o.length!==r.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof s<"u"&&typeof o<"u"&&s.length>0&&o.length>u)throw new Error("Resize requires only of scales or sizes to be specified")},ql=(e,r,t,s)=>`
  // 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 big = (${e}) * (${r});
  let whole = ${s}(big / (${t}));
  let fract = ${s}(big % (${t})) / ${s}(${t});
  return whole + fract;
`,Of=(e,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(e){case"asymmetric":return`
          if (xScale < 1.0 || floor(xScale) != xScale) {
            return ${r}(xResized) / ${r}(xScale);
          } else {
            ${ql("xResized","lengthOriginal","lengthResized",r)}
          }
        `;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${r}(xResized) + 0.5) / ${r}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    ${ql("xResized","lengthOriginal - 1","lengthResized - 1",r)}
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${r}(roiStart) * ${r}(lengthOriginal - 1) +
                        (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) /
                        ${r}(lengthResized - 1);
                  } else {
                    return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${r}xScale * ${r}(lengthResized);
                  const adjustment = ${r}(lengthResized) / outputWidth;
                  const center = ${r}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;case"half_pixel":return`return ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",Df=(e,r,t)=>`fn getNearestPixelFromOriginal(xOriginal: ${t}, isDownSample: bool) -> ${t} {`+(()=>{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(r<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Lf=(e,r,t)=>{let s=new Array(t).fill(0).concat(new Array(t).fill(1)),o=e.length===0?s:e.slice();return r.length>0?(r.forEach((n,i)=>{s[n]=o[i],s[i+t]=o[r.length+i]}),s):o},zf=(e,r,t,s)=>{let o=[];if(t.length>0)if(s.length>0){if(e.forEach(n=>o.push(n)),Math.max(...s)>e.length)throw new Error("axes is out of bound");s.forEach((n,i)=>o[n]=t[i])}else t.forEach(n=>o.push(n));else{if(r.length===0)throw new Error("Resize requires either scales or sizes.");o=e.map((n,i)=>Math.round(n*r[i]))}return o},Bf=(e,r,t)=>{let s=(()=>{switch(t.keepAspectRatioPolicy){case"not_larger":return t.axes.length>0?Math.min(...t.axes.map(n=>r[n]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case"not_smaller":return t.axes.length>0?Math.max(...t.axes.map(n=>r[n]),Number.MIN_VALUE):Math.max(...r,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${t.keepAspectRatioPolicy} is not supported`)}})();r.fill(1,0,r.length);let o=e.slice();return t.axes.length>0?(t.axes.forEach(n=>r[n]=s),t.axes.forEach(n=>o[n]=Math.round(e[n]*r[n]))):(r.fill(s,0,r.length),o.forEach((n,i)=>o[i]=Math.round(n*r[i]))),o},Rf=(e,r,t,s,o)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${t.length}> {
      var original_indices: array<${e.type.value}, ${t.length}>;
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var scale = ${et("uniforms.scales","i",s)};
        var roi_low = ${et("uniforms.roi","i",o)};
        var roi_hi = ${et("uniforms.roi",`i + ${r.length}`,o)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${et("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${et("uniforms.output_shape","i",t.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,jf=(e,r,t,s,o,n,i)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {
      var input_indices: ${e.type.indices};
      for (var i:u32 = 0; i < ${s.length}; i++) {
        var output_index = ${r.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${et("uniforms.scales","i",o)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${et("uniforms.roi","i",n)};
          var roi_hi = ${et("uniforms.roi",`i + ${t.length}`,n)};
          var input_shape_i = ${et("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${et("uniforms.output_shape","i",s.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${i} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${r.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${e.indicesSet("input_indices","i","input_index")}
      }
      return input_indices;
    }`,Nf=(e,r)=>`
    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var input_index = ${e.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${et("uniforms.input_shape","i",r.length)}) {
          return false;
        }
      }
      return true;
    }`,Xl=(e,r,t,s)=>e.rank>s?`
    ${e.indicesSet("input_indices",r,"channel")};
    ${e.indicesSet("input_indices",t,"batch")};
`:"",Vf=(e,r,t,s,o)=>{let[n,i,a,l]=t.length===2?[-1,0,1,-1]:[0,2,3,1],u=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${u} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",i,`max(0, min(row, ${t[i]} - 1))`)};
      ${e.indicesSet("input_indices",a,`max(0, min(col, ${t[a]} - 1))`)};
      ${Xl(e,l,n,2)}
      return ${e.getByIndices("input_indices")};
    }

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

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

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

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

    depth = max(0, min(depth, ${t[i]} - 1));
      height = max(0, min(height, ${t[a]} - 1));
      width = max(0, min(width, ${t[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 = ${t.length>3?`u32(originalIndices[${u}])`:"0"};
      var batch: u32 =  ${t.length>3?`u32(originalIndices[${n}])`:"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);
    }`},Gf=(e,r,t,s,o,n)=>{let i=e.dims,a=Lf(n,r.axes,i.length),l=zf(i,s,o,r.axes),u=s.slice();s.length===0&&(u=i.map((g,S)=>g===0?1:l[S]/g),r.keepAspectRatioPolicy!=="stretch"&&(l=Bf(i,u,r)));let p=Ye("output",e.dataType,l.length),d=Pe("input",e.dataType,i.length),c=Me.size(l),f=i.length===l.length&&i.every((g,S)=>g===l[S]),_=r.coordinateTransformMode==="tf_crop_and_resize",T=r.extrapolationValue,$=d.type.value,w=g=>`
      ${f?"":`
      ${Of(r.coordinateTransformMode,$)};
      ${(()=>{switch(r.mode){case"nearest":return`
              ${Nf(d,i)};
              ${Df(r.nearestMode,t,$)};
              ${jf(d,p,i,l,u.length,a.length,_)};
              `;case"linear":return`
              ${Rf(p,i,l,u.length,a.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${Vf(d,p,i,_,T)}`;if(i.length===3||i.length===5)return`${Wf(d,p,i,_,T)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${Uf(d,p,i,l,u,a,r.cubicCoeffA,_,r.extrapolationValue,r.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${g.registerUniform("output_size","u32").registerUniform("scales","f32",u.length).registerUniform("roi","f32",a.length).declareVariables(d,p)}
      ${g.mainStart()}
        ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${f?"output[global_idx] = input[global_idx];":`
        let output_indices = ${p.offsetToIndices("global_idx")};
        var input_indices: ${d.type.indices};
        ${(()=>{switch(r.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${d.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${r.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${r.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${r.cacheKey}|${t}|${u.length>0?r.mode==="cubic"?u:u.length:""}|${o.length>0?o:""}|${a.length>0?a:""}|${f}|${r.mode==="nearest"?i.length:i}`,inputDependencies:["rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:l,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:[{type:12,data:c},{type:1,data:u},{type:1,data:a},...st(i,l)]})}},Kf=e=>{let r=e.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]},Hf=(e,r)=>{let t=[],s=[],o=[],n=Kf(e);if(r.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Ff(e.inputs,r,n,t,s,o),e.compute(Gf(e.inputs[0],r,n,t,s,o),{inputs:[0]})},qf=e=>{let r=e.antialias,t=e.axes,s=e.coordinateTransformMode,o=e.cubicCoeffA,n=e.excludeOutside!==0,i=e.extrapolationValue,a=e.keepAspectRatioPolicy,l=e.mode,u=e.nearestMode===""?"simple":e.nearestMode;return St({antialias:r,axes:t,coordinateTransformMode:s,cubicCoeffA:o,excludeOutside:n,extrapolationValue:i,keepAspectRatioPolicy:a,mode:l,nearestMode:u})}}),Xf,Qf,Jf,ix=Be(()=>{ut(),mt(),ft(),Xf=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let r=e[0],t=e[1],s=e[2];if(r.dataType!==t.dataType||r.dataType!==s.dataType)throw new Error("All inputs must have the same data type");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Input must be 2D or 3D");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Skip must be 2D or 3D");let o=r.dims[r.dims.length-1],n=r.dims[r.dims.length-2];if(t.dims[t.dims.length-1]!==o)throw new Error("Skip must have the same hidden size as input");if(t.dims[t.dims.length-2]!==n)throw new Error("Skip must have the same sequence length as input");if(s.dims.length!==1)throw new Error("Gamma must be 1D");if(s.dims[s.dims.length-1]!==o)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let i=e[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let i=e[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Bias must have the same hidden size as input")}},Qf=(e,r,t,s)=>{let o=r.simplified,n=e[0].dims,i=Me.size(n),a=n,l=i,u=n.slice(-1)[0],p=s?n.slice(0,-1).concat(1):[],d=!o&&e.length>3,c=e.length>4,f=s&&t>1,_=s&&t>2,T=t>3,$=64,w=Kt(u),g=[{type:12,data:l},{type:12,data:w},{type:12,data:u},{type:1,data:r.epsilon}],S=y=>{let M=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],v=[Pe("x",e[0].dataType,e[0].dims,w),Pe("skip",e[1].dataType,e[1].dims,w),Pe("gamma",e[2].dataType,e[2].dims,w)];d&&v.push(Pe("beta",e[3].dataType,e[3].dims,w)),c&&v.push(Pe("bias",e[4].dataType,e[4].dims,w)),v.push(Ye("output",e[0].dataType,a,w)),f&&v.push(Ye("mean_output",1,p)),_&&v.push(Ye("inv_std_output",1,p)),T&&v.push(Ye("input_skip_bias_sum",e[0].dataType,a,w));let C=lr(e[0].dataType),A=lr(1,w);return`

      ${y.registerUniforms(M).declareVariables(...v)}
      var<workgroup> sum_shared : array<${A}, ${$}>;
      var<workgroup> sum_squared_shared : array<${A}, ${$}>;

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

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

        var reduce_size : u32 = ${$};
        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {
          reduce_size = curr_size + (reduce_size & 1);
          if (ix < curr_size) {
            sum_shared[ix] += sum_shared[ix + reduce_size];
            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
          }
          workgroupBarrier();
        }

        let sum = sum_shared[0];
        let square_sum = sum_squared_shared[0];
        let mean = ${Ls("sum",w)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Ls("square_sum",w)} / f32(uniforms.hidden_size) ${o?"":"- mean * mean"} + uniforms.epsilon);
        ${f?"mean_output[global_idx] = mean;":""}
        ${_?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${o?"":`- ${C}(mean)`}) *
            ${C}(inv_std_dev) * gamma[offset1d + i]
            ${d?"+ beta[offset1d + i]":""};
        }
      }`},E=[{dims:a,dataType:e[0].dataType}];return t>1&&E.push({dims:p,dataType:1}),t>2&&E.push({dims:p,dataType:1}),t>3&&E.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${w};${f};${_};${T}`,inputDependencies:e.map((y,M)=>"type")},getShaderSource:S,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/u)},programUniforms:g})}},Jf=(e,r)=>{Xf(e.inputs);let t=[0];e.outputCount>1&&t.push(-3),e.outputCount>2&&t.push(-3),e.outputCount>3&&t.push(3),e.compute(Qf(e.inputs,r,e.outputCount,!1),{outputs:t})}}),Yf,yo,Zf,Ql,eg,tg,rg,sg,ax=Be(()=>{ut(),mt(),Xt(),ft(),Yf=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");if(r.axes.length!==0){if(r.axes.length!==r.starts.length||r.axes.length!==r.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(r.starts.length!==r.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((t,s)=>{if(e[s+1].dataType!==6&&e[s+1].dataType!==7)throw new Error(`Input ${s} must be an array of int32 or int64`)})},yo=(e,r)=>{let t=[];if(e.length>r)if(e[r].dataType===7)e[r].getBigInt64Array().forEach(s=>t.push(Number(s)));else if(e[r].dataType===6)e[r].getInt32Array().forEach(s=>t.push(Number(s)));else throw new Error(`Input ${r} must be an array of int32 or int64`);return t},Zf=(e,r)=>{if(e.length>1){let t=yo(e,1),s=yo(e,2),o=yo(e,3);return o.length===0&&(o=[...Array(e[0].dims.length).keys()]),St({starts:t,ends:s,axes:o})}else return r},Ql=(e,r,t,s,o)=>{let n=e;return e<0&&(n+=t[s[r]]),o[r]<0?Math.max(0,Math.min(n,t[s[r]]-1)):Math.max(0,Math.min(n,t[s[r]]))},eg=(e,r,t)=>`fn calculateInputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${t.length}; i >= 0; i--) {
            let input_shape_i = ${et("uniforms.input_shape","i",t.length)};
            let steps_i = ${et("uniforms.steps","i",t.length)};
            let signs_i = ${et("uniforms.signs","i",t.length)};
            let starts_i = ${et("uniforms.starts","i",t.length)};
            var output_index = ${r.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${e.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,tg=(e,r)=>{let t=e[0].dims,s=Me.size(t),o=r.axes.length>0?Me.normalizeAxes(r.axes,t.length):[...Array(t.length).keys()],n=yo(e,4);n.forEach(w=>w!==0||(()=>{throw new Error("step cannot be 0")})),n.length===0&&(n=Array(o.length).fill(1));let i=r.starts.map((w,g)=>Ql(w,g,t,o,n)),a=r.ends.map((w,g)=>Ql(w,g,t,o,n));if(o.length!==i.length||o.length!==a.length)throw new Error("start, ends and axes should have the same number of elements");if(o.length!==t.length)for(let w=0;w<t.length;++w)o.includes(w)||(i.splice(w,0,0),a.splice(w,0,t[w]),n.splice(w,0,1));let l=n.map(w=>Math.sign(w));n.forEach((w,g,S)=>{if(w<0){let E=(a[g]-i[g])/w,y=i[g],M=y+E*n[g];i[g]=M,a[g]=y,S[g]=-w}});let u=t.slice(0);o.forEach((w,g)=>{u[w]=Math.ceil((a[w]-i[w])/n[w])});let p={dims:u,dataType:e[0].dataType},d=Ye("output",e[0].dataType,u.length),c=Pe("input",e[0].dataType,e[0].dims.length),f=Me.size(u),_=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:n.length}],T=[{type:12,data:f},{type:12,data:i},{type:6,data:l},{type:12,data:n},...st(e[0].dims,u)],$=w=>`
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          let output_indices = ${d.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${d.setByOffset("global_idx",c.getByIndices("input_indices"))}
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      var<workgroup> rowSumShared : ${S};
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        let index = row * row_stride + col;
        return x[index];
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      fn setValue(row: i32, col: i32, row_stride: i32, value: ${S}) {
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        let lindex = i32(local_idx);
        const wg = ${T};
        let row = gindex / wg;
        let cols = uniforms.packedCols;
        let row_stride : i32 = uniforms.packedCols;

        // find the rows max
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          let value = getValue(row, col, row_stride);
          threadMax = max(threadMax, value);
        }
        if (lindex < cols) {
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        }
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        var reduceSize = min(cols, wg);
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          workgroupBarrier();
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        if (lindex == 0) {
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        }
        workgroupBarrier();

        // find the rows sum
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        for (var col = lindex; col < cols; col += wg) {
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          threadSum += subExp;
        }
        threadShared[lindex] = threadSum;
        workgroupBarrier();

        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
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            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
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        }
        workgroupBarrier();

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
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      let output_indices = ${l.offsetToIndices("global_idx")};
      var input_indices: ${a.type.indices};
      for (var i = 0; i < ${t.length}; i++) {
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        let input_dim_value = ${l.indicesGet("output_indices","i")}  % input_dim_i;

        ${a.indicesSet("input_indices","i","input_dim_value")}
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      ${l.setByOffset("global_idx",a.getByIndices("input_indices"))}
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            let offset_b${f} = ${a.broadcastedIndicesToOffset(`output_indices${f}`,n)};
            let offset_c${f} = ${l.broadcastedIndicesToOffset(`output_indices${f}`,n)};
            let index_a${f} = offset_a${f} / 4u;
            let index_b${f} = offset_b${f} / 4u;
            let index_c${f} = offset_c${f} / 4u;
            let component_a${f} = offset_a${f} % 4u;
            let component_b${f} = offset_b${f} % 4u;
            let component_c${f} = offset_c${f} % 4u;
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            var data = vec4<u32>(0);
            ${d("data",0,"u32")}
            ${d("data",1,"u32")}
            ${d("data",2,"u32")}
            ${d("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:u=`
            ${d("output_data[global_idx]",0)}
            ${d("output_data[global_idx]",1)}
            ${d("output_data[global_idx]",2)}
            ${d("output_data[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(l,i,a,n)}
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T=Number(f-this.queryTimeBase),$=Number(_-this.queryTimeBase);if(!Number.isSafeInteger(T)||!Number.isSafeInteger($))throw new RangeError("incorrect timestamp range");if((s=this.env.webgpu.profiling)!=null&&s.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:d.map(w=>({dims:w.dims,dataType:Ts(w.dataType)})),outputsMetadata:c.map(w=>({dims:w.dims,dataType:Ts(w.dataType)})),kernelId:i,kernelType:l,kernelName:u,programName:p,startTime:T,endTime:$});else{let w="";d.forEach((S,E)=>{w+=`input[${E}]: [${S.dims}] | ${Ts(S.dataType)}, `});let g="";c.forEach((S,E)=>{g+=`output[${E}]: [${S.dims}] | ${Ts(S.dataType)}, `}),console.log(`[profiling] kernel "${i}|${l}|${u}|${p}" ${w}${g}execution time: ${$-T} ns`)}uo("GPU",`${p}::${f}::${_}`)}e.unmap(),this.pendingQueries.delete(e)}),Gr()}run(e,r,t,s,o,n){es(e.name);let i=[];for(let g=0;g<r.length;++g){let S=r[g].data;if(S===0)continue;let E=this.gpuDataManager.get(S);if(!E)throw new Error(`no GPU data for input: ${S}`);i.push(E)}let{outputs:a,dispatchGroup:l,programUniforms:u}=e.getRunData(r),p=t.length===0?a.map((g,S)=>S):t;if(p.length!==a.length)throw new Error(`Output size ${p.length} must be equal to ${a.length}.`);let d=[],c=[];for(let g=0;g<a.length;++g){if(!Number.isInteger(p[g])||p[g]<-3||p[g]>=n)throw new Error(`Invalid output index: ${p[g]}`);if(p[g]===-3)continue;let S=p[g]===-1,E=p[g]===-2,y=S||E?o(a[g].dataType,a[g].dims):s(p[g],a[g].dataType,a[g].dims);if(d.push(y),y.data===0)continue;let M=this.gpuDataManager.get(y.data);if(!M)throw new Error(`no GPU data for output: ${y.data}`);if(S&&this.temporaryData.push(M),E){let v=this.kernelPersistentData.get(this.currentKernelId);v||(v=[],this.kernelPersistentData.set(this.currentKernelId,v)),v.push(M)}c.push(M)}if(i.length!==r.length||c.length!==d.length){if(c.length===0)return Gr(e.name),d;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let f;if(u){let g=0,S=[];u.forEach(v=>{let C=typeof v.data=="number"?[v.data]:v.data;if(C.length===0)return;let A=v.type===10?2:4,B,K;v.type===10?(K=C.length>4?16:C.length>2?8:C.length*A,B=C.length>4?16:A*C.length):(K=C.length<=2?C.length*A:16,B=16),g=Math.ceil(g/K)*K,S.push(g);let G=v.type===10?8:4;g+=C.length>4?Math.ceil(C.length/G)*B:C.length*A});let E=16;g=Math.ceil(g/E)*E;let y=new ArrayBuffer(g);u.forEach((v,C)=>{let A=S[C],B=typeof v.data=="number"?[v.data]:v.data;if(v.type===6)new Int32Array(y,A,B.length).set(B);else if(v.type===12)new Uint32Array(y,A,B.length).set(B);else if(v.type===10)new Uint16Array(y,A,B.length).set(B);else if(v.type===1)new Float32Array(y,A,B.length).set(B);else throw new Error(`Unsupported uniform type: ${Ts(v.type)}`)});let M=this.gpuDataManager.create(g,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(M.buffer,0,y,0,g),this.gpuDataManager.release(M.id),f={offset:0,size:g,buffer:M.buffer}}let _=this.programManager.normalizeDispatchGroupSize(l),T=_[1]===1&&_[2]===1,$=Mg(e,r,T),w=this.programManager.getArtifact($);if(w||(w=this.programManager.build(e,_),this.programManager.setArtifact($,w),vt("info",()=>`[artifact] key: ${$}, programName: ${e.name}`)),u&&w.uniformVariablesInfo){if(u.length!==w.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${w.uniformVariablesInfo.length}, got ${u.length} in program "${w.programInfo.name}".`);for(let g=0;g<u.length;g++){let S=u[g],E=S.type,y=typeof S.data=="number"?1:S.data.length,[M,v]=w.uniformVariablesInfo[g];if(E!==M||y!==v)throw new Error(`Uniform variable ${g} mismatch: expect type ${M} with size ${v}, got type ${E} with size ${y} in program "${w.programInfo.name}".`)}}if(vt("info",()=>`[ProgramManager] run "${e.name}" (key=${$}) with ${_[0]}x${_[1]}x${_[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let g={kernelId:this.currentKernelId,programName:w.programInfo.name,inputTensorViews:r,outputTensorViews:d};this.pendingKernels.push(g),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(g)}return this.programManager.run(w,i,c,_,f),Gr(e.name),d}upload(e,r){this.gpuDataManager.upload(e,r)}memcpy(e,r){this.gpuDataManager.memcpy(e,r)}async download(e,r){await this.gpuDataManager.download(e,r)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,r,t,s){let o=_g.get(e);if(!o)throw new Error(`kernel not implemented: ${e}`);let n={kernelType:e,kernelName:s,kernelEntry:o[0],attributes:[o[1],t]};this.kernels.set(r,n)}releaseKernel(e){let r=this.kernelPersistentData.get(e);if(r){for(let t of r)this.gpuDataManager.release(t.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,r,t){let s=this.kernels.get(e);if(!s)throw new Error(`kernel not created: ${e}`);let o=s.kernelType,n=s.kernelName,i=s.kernelEntry,a=s.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${o}] ${n}" is not allowed to be called recursively`);this.currentKernelId=e,a[0]&&(a[1]=a[0](a[1]),a[0]=void 0),vt("info",()=>`[WebGPU] Start to run kernel "[${o}] ${n}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),i(r,a[1]),0}catch(u){return t.push(Promise.resolve(`[WebGPU] Kernel "[${o}] ${n}" failed. ${u}`)),1}finally{l&&t.push(this.device.popErrorScope().then(u=>u?`GPU validation error for kernel "[${o}] ${n}": ${u.message}`:null));for(let u of this.temporaryData)this.gpuDataManager.release(u.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,r,t,s){let o=this.sessionExternalDataMapping.get(e);o||(o=new Map,this.sessionExternalDataMapping.set(e,o));let n=o.get(r),i=this.gpuDataManager.registerExternalBuffer(t,s,n);return o.set(r,[i,t]),i}unregisterBuffers(e){let r=this.sessionExternalDataMapping.get(e);r&&(r.forEach(t=>this.gpuDataManager.unregisterExternalBuffer(t[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let r=this.gpuDataManager.get(e);if(!r)throw new Error(`no GPU data for buffer: ${e}`);return r.buffer}createDownloader(e,r,t){return async()=>{let s=await sl(this,e,r);return Ha(s.buffer,t)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){var e;this.queryType="none",(((e=this.env.webgpu.profiling)==null?void 0:e.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(){vt("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(){vt("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){vt("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),r=this.capturedPendingKernels.get(this.currentSessionId),t=e.length;this.pendingKernels=[];for(let s=0;s<t;s++){let o=this.getComputePassEncoder(),n=e[s];this.writeTimestamp(this.pendingDispatchNumber*2),o.setPipeline(n.computePipeline),o.setBindGroup(0,n.bindGroup),o.dispatchWorkgroups(...n.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(r[s]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),vg={};Rn(vg,{init:()=>Tg});var gi,xg,Tg,mx=Be(()=>{ut(),Es(),mt(),vv(),gi=class z0{constructor(r,t,s,o){this.module=r,this.dataType=t,this.data=s,this.dims=o}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,r)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,r)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,r)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,r)}reshape(r){if(Me.size(r)!==Me.size(this.dims))throw new Error("Invalid new shape");return new z0(this.module,this.dataType,this.data,r)}},xg=class{constructor(e,r,t){this.module=e,this.backend=r,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=r.adapterInfo;let s=e.PTR_SIZE,o=t/e.PTR_SIZE,n=s===4?"i32":"i64";this.opKernelContext=Number(e.getValue(s*o++,n));let i=Number(e.getValue(s*o++,n));this.outputCount=Number(e.getValue(s*o++,n)),this.customDataOffset=Number(e.getValue(s*o++,"*")),this.customDataSize=Number(e.getValue(s*o++,n));let a=[];for(let l=0;l<i;l++){let u=Number(e.getValue(s*o++,n)),p=Number(e.getValue(s*o++,"*")),d=Number(e.getValue(s*o++,n)),c=[];for(let f=0;f<d;f++)c.push(Number(e.getValue(s*o++,n)));a.push(new gi(e,u,p,c))}this.inputs=a}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(e,r){var i;let t=((i=r==null?void 0:r.inputs)==null?void 0:i.map(a=>typeof a=="number"?this.inputs[a]:a))??this.inputs,s=(r==null?void 0:r.outputs)??[],o=(a,l,u)=>new gi(this.module,l,this.output(a,u),u),n=(a,l)=>{let u=tn(a,l);if(!u)throw new Error(`Unsupported data type: ${a}`);let p=u>0?this.backend.gpuDataManager.create(u).id:0;return new gi(this.module,a,p,l)};return this.backend.run(e,t,s,o,n,this.outputCount)}output(e,r){let t=this.module.stackSave();try{let s=this.module.PTR_SIZE,o=s===4?"i32":"i64",n=this.module.stackAlloc((1+r.length)*s);this.module.setValue(n,r.length,o);for(let i=0;i<r.length;i++)this.module.setValue(n+s*(i+1),r[i],o);return this.module._JsepOutput(this.opKernelContext,e,n)}catch(s){throw new Error(`Failed to generate kernel's output[${e}] with dims [${r}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${s}`)}finally{this.module.stackRestore(t)}}},Tg=async(e,r,t,s)=>{let o=r.jsepInit;if(!o)throw new Error("Failed to initialize JSEP. 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It must be a GPUAdapter object.")}else{let i=e.webgpu.powerPreference;if(i!==void 0&&i!=="low-power"&&i!=="high-performance")throw new Error(`Invalid powerPreference setting: "${i}"`);let a=e.webgpu.forceFallbackAdapter;if(a!==void 0&&typeof a!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${a}"`);if(n=await navigator.gpu.requestAdapter({powerPreference:i,forceFallbackAdapter:a}),!n)throw new Error('Failed to get GPU adapter. 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* @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.
 * =============================================================================
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M.resize(ee,j,{resample:C}))}async crop_margin(M,v=200){const C=M.clone().grayscale(),A=(0,n.min)(C.data)[0],K=(0,n.max)(C.data)[0]-A;if(K===0)return M;const G=v/255;let j=C.width,ee=C.height,H=0,Z=0;const X=C.data;for(let oe=0;oe<C.height;++oe){const me=oe*C.width;for(let ae=0;ae<C.width;++ae)(X[me+ae]-A)/K<G&&(j=Math.min(j,ae),ee=Math.min(ee,oe),H=Math.max(H,ae),Z=Math.max(Z,oe))}return M=await M.crop([j,ee,H,Z]),M}pad_image(M,v,C,{mode:A="constant",center:B=!1,constant_values:K=0}={}){const[G,j,ee]=v;let H,Z;if(typeof C=="number"?(H=C,Z=C):C==="square"?H=Z=Math.max(G,j):(H=C.width,Z=C.height),H!==j||Z!==G){const X=new Float32Array(H*Z*ee);if(Array.isArray(K))for(let ae=0;ae<X.length;++ae)X[ae]=K[ae%ee];else K!==0&&X.fill(K);const[oe,me]=B?[Math.floor((H-j)/2),Math.floor((Z-G)/2)]:[0,0];for(let ae=0;ae<G;++ae){const V=(ae+me)*H,F=ae*j;for(let W=0;W<j;++W){const re=(V+W+oe)*ee,_e=(F+W)*ee;for(let se=0;se<ee;++se)X[re+se]=M[_e+se]}}if(A==="symmetric"){if(B)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const ae=G-1,V=j-1;for(let F=0;F<Z;++F){const W=F*H,re=(0,i.calculateReflectOffset)(F,ae)*j;for(let _e=0;_e<H;++_e){if(F<G&&_e<j)continue;const se=(W+_e)*ee,ce=(re+(0,i.calculateReflectOffset)(_e,V))*ee;for(let $e=0;$e<ee;++$e)X[se+$e]=M[ce+$e]}}}M=X,v=[Z,H,ee]}return[M,v]}rescale(M){for(let v=0;v<M.length;++v)M[v]=this.rescale_factor*M[v]}get_resize_output_image_size(M,v){const[C,A]=M.size;let B,K;if(this.do_thumbnail){const{height:G,width:j}=v;B=Math.min(G,j)}else Number.isInteger(v)?(B=v,K=this.config.max_size??B):v!==void 0&&(B=v.shortest_edge,K=v.longest_edge);if(B!==void 0||K!==void 0){const G=B===void 0?1:Math.max(B/C,B/A),j=C*G,ee=A*G,H=K===void 0?1:Math.min(K/j,K/ee);let Z=Math.floor(Number((j*H).toFixed(2))),X=Math.floor(Number((ee*H).toFixed(2)));return this.size_divisibility!==void 0&&([Z,X]=p([Z,X],this.size_divisibility)),[Z,X]}else if(v!==void 0&&v.width!==void 0&&v.height!==void 0){let G=v.width,j=v.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let ee=j/A,H=G/C;Math.abs(1-H)<Math.abs(1-ee)?ee=H:H=ee,j=u(ee*A,this.config.ensure_multiple_of),G=u(H*C,this.config.ensure_multiple_of)}return[G,j]}else{if(this.size_divisibility!==void 0)return p([C,A],this.size_divisibility);if(v.min_pixels!==void 0&&v.max_pixels!==void 0){const{min_pixels:G,max_pixels:j}=v,ee=this.config.patch_size*this.config.merge_size;return w(A,C,ee,G,j)}else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(v)}`)}}async resize(M){const[v,C]=this.get_resize_output_image_size(M,this.size);return await M.resize(v,C,{resample:this.resample})}async preprocess(M,{do_normalize:v=null,do_pad:C=null,do_convert_rgb:A=null,do_convert_grayscale:B=null,do_flip_channel_order:K=null}={}){this.do_crop_margin&&(M=await this.crop_margin(M));const[G,j]=M.size;if(A??this.do_convert_rgb?M=M.rgb():B&&(M=M.grayscale()),this.do_resize&&(M=await this.resize(M)),this.do_thumbnail&&(M=await this.thumbnail(M,this.size,this.resample)),this.do_center_crop){let oe,me;Number.isInteger(this.crop_size)?(oe=this.crop_size,me=this.crop_size):(oe=this.crop_size.width,me=this.crop_size.height),M=await M.center_crop(oe,me)}const ee=[M.height,M.width];let H=Float32Array.from(M.data),Z=[M.height,M.width,M.channels];if(this.do_rescale&&this.rescale(H),v??this.do_normalize){let oe=this.image_mean;Array.isArray(this.image_mean)||(oe=new Array(M.channels).fill(oe));let me=this.image_std;if(Array.isArray(this.image_std)||(me=new Array(M.channels).fill(oe)),oe.length!==M.channels||me.length!==M.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${oe.length}) and \`image_std\` (${me.length}) must match the number of channels in the image (${M.channels}).`);for(let ae=0;ae<H.length;ae+=M.channels)for(let V=0;V<M.channels;++V)H[ae+V]=(H[ae+V]-oe[V])/me[V]}if(C??this.do_pad){if(this.pad_size)[H,Z]=this.pad_image(H,[M.height,M.width,M.channels],this.pad_size);else if(this.size_divisibility){const[oe,me]=p([Z[1],Z[0]],this.size_divisibility);[H,Z]=this.pad_image(H,Z,{width:oe,height:me})}}if(K??this.do_flip_channel_order){if(Z[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let oe=0;oe<H.length;oe+=3){const me=H[oe];H[oe]=H[oe+2],H[oe+2]=me}}const X=new o.Tensor("float32",H,Z).permute(2,0,1);return{original_size:[j,G],reshaped_input_size:ee,pixel_values:X}}async _call(M,...v){Array.isArray(M)||(M=[M]);const C=await Promise.all(M.map(B=>this.preprocess(B)));return{pixel_values:(0,o.stack)(C.map(B=>B.pixel_values),0),original_sizes:C.map(B=>B.original_size),reshaped_input_sizes:C.map(B=>B.reshaped_input_size)}}static async from_pretrained(M,v){const C=await(0,a.getModelJSON)(M,l.IMAGE_PROCESSOR_NAME,!0,v);return new this(C)}}},"./src/base/processing_utils.js":(e,r,t)=>{t.r(r),t.d(r,{Processor:()=>i});var s=t("./src/utils/constants.js"),o=t("./src/utils/generic.js"),n=t("./src/utils/hub.js");class i extends o.Callable{constructor(l,u){super(),this.config=l,this.components=u}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(l,u={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(l,{tokenize:!1,...u})}batch_decode(...l){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...l)}decode(...l){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...l)}async _call(l,...u){for(const p of[this.image_processor,this.feature_extractor,this.tokenizer])if(p)return p(l,...u);throw 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_=i(p.decoder),T="num_decoder_layers"in _,$=(0,s.pick)(p,["model_type","is_encoder_decoder"]);return T?($.num_decoder_layers=_.num_decoder_layers,$.num_decoder_heads=_.num_decoder_heads,$.decoder_hidden_size=_.decoder_hidden_size,$.num_encoder_layers=_.num_encoder_layers,$.num_encoder_heads=_.num_encoder_heads,$.encoder_hidden_size=_.encoder_hidden_size):($.num_layers=_.num_layers,$.num_heads=_.num_heads,$.hidden_size=_.hidden_size),$}const f={...c,...(0,s.pick)(p,["model_type","multi_query","is_encoder_decoder"])};for(const _ in d)f[_]=p[d[_]];return f}function a(p,{prefix:d="past_key_values",batch_size:c=1}={}){const f={},_=p.normalized_config;if(_.is_encoder_decoder&&"num_encoder_heads"in _&&"num_decoder_heads"in _){const T=_.encoder_dim_kv??_.encoder_hidden_size/_.num_encoder_heads,$=_.decoder_dim_kv??_.decoder_hidden_size/_.num_decoder_heads,w=[c,_.num_encoder_heads,0,T],g=[c,_.num_decoder_heads,0,$];for(let 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async from_pretrained(d,{progress_callback:c=null,config:f=null,cache_dir:_=null,local_files_only:T=!1,revision:$="main"}={}){f&&!(f instanceof l)&&(f=new l(f));const w=f??await n(d,{progress_callback:c,config:f,cache_dir:_,local_files_only:T,revision:$});return new this(w)}}class u{static async from_pretrained(...d){return l.from_pretrained(...d)}}},"./src/env.js":(e,r,t)=>{var C,A;t.r(r),t.d(r,{apis:()=>$,env:()=>M});var s=t("?569f"),o=t("?3f59"),n=t("?154a");const i="3.5.0",a=typeof window<"u"&&typeof window.document<"u",l=typeof self<"u"&&((C=self.constructor)==null?void 0:C.name)==="DedicatedWorkerGlobalScope",u=typeof self<"u"&&"caches"in self,p=typeof navigator<"u"&&"gpu"in navigator,d=typeof navigator<"u"&&"ml"in navigator,c=typeof process<"u",f=c&&((A=process==null?void 0:process.release)==null?void 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o{constructor(i){te(this,"max_length",20);te(this,"max_new_tokens",null);te(this,"min_length",0);te(this,"min_new_tokens",null);te(this,"early_stopping",!1);te(this,"max_time",null);te(this,"do_sample",!1);te(this,"num_beams",1);te(this,"num_beam_groups",1);te(this,"penalty_alpha",null);te(this,"use_cache",!0);te(this,"temperature",1);te(this,"top_k",50);te(this,"top_p",1);te(this,"typical_p",1);te(this,"epsilon_cutoff",0);te(this,"eta_cutoff",0);te(this,"diversity_penalty",0);te(this,"repetition_penalty",1);te(this,"encoder_repetition_penalty",1);te(this,"length_penalty",1);te(this,"no_repeat_ngram_size",0);te(this,"bad_words_ids",null);te(this,"force_words_ids",null);te(this,"renormalize_logits",!1);te(this,"constraints",null);te(this,"forced_bos_token_id",null);te(this,"forced_eos_token_id",null);te(this,"remove_invalid_values",!1);te(this,"exponential_decay_length_penalty",null);te(this,"suppress_tokens",null);te(this,"streamer",null);te(this,"begin_suppress_tokens",null);te(this,"forced_decoder_ids",null);te(this,"guidance_scale",null);te(this,"num_return_sequences",1);te(this,"output_attentions",!1);te(this,"output_hidden_states",!1);te(this,"output_scores",!1);te(this,"return_dict_in_generate",!1);te(this,"pad_token_id",null);te(this,"bos_token_id",null);te(this,"eos_token_id",null);te(this,"encoder_no_repeat_ngram_size",0);te(this,"decoder_start_token_id",null);te(this,"generation_kwargs",{});Object.assign(this,(0,s.pick)(i,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(e,r,t)=>{t.r(r),t.d(r,{ClassifierFreeGuidanceLogitsProcessor:()=>w,ForcedBOSTokenLogitsProcessor:()=>l,ForcedEOSTokenLogitsProcessor:()=>u,LogitsProcessor:()=>n,LogitsProcessorList:()=>a,LogitsWarper:()=>i,MinLengthLogitsProcessor:()=>_,MinNewTokensLengthLogitsProcessor:()=>T,NoBadWordsLogitsProcessor:()=>$,NoRepeatNGramLogitsProcessor:()=>c,RepetitionPenaltyLogitsProcessor:()=>f,SuppressTokensAtBeginLogitsProcessor:()=>p,TemperatureLogitsWarper:()=>g,TopKLogitsWarper:()=>E,TopPLogitsWarper:()=>S,WhisperTimeStampLogitsProcessor:()=>d});var s=t("./src/utils/generic.js");t("./src/utils/tensor.js");var o=t("./src/utils/maths.js");class n extends s.Callable{_call(M,v){throw Error("`_call` should be implemented in a subclass")}}class i extends s.Callable{_call(M,v){throw Error("`_call` should be implemented in a subclass")}}class a extends s.Callable{constructor(){super(),this.processors=[]}push(M){this.processors.push(M)}extend(M){this.processors.push(...M)}_call(M,v){let C=v;for(const A of this.processors)C=A(M,C);return C}[Symbol.iterator](){return this.processors.values()}}class l extends n{constructor(M){super(),this.bos_token_id=M}_call(M,v){for(let C=0;C<M.length;++C)if(M[C].length===1){const A=v[C].data;A.fill(-1/0),A[this.bos_token_id]=0}return v}}class u extends n{constructor(M,v){super(),this.max_length=M,this.eos_token_id=Array.isArray(v)?v:[v]}_call(M,v){for(let C=0;C<M.length;++C)if(M[C].length===this.max_length-1){const A=v[C].data;A.fill(-1/0);for(const B of this.eos_token_id)A[B]=0}return v}}class p extends n{constructor(M,v){super(),this.begin_suppress_tokens=M,this.begin_index=v}_call(M,v){for(let C=0;C<M.length;++C)if(M[C].length===this.begin_index){const A=v[C].data;for(const B of this.begin_suppress_tokens)A[B]=-1/0}return v}}class d extends n{constructor(M,v){super(),this.eos_token_id=Array.isArray(M.eos_token_id)?M.eos_token_id[0]:M.eos_token_id,this.no_timestamps_token_id=M.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=v.length,v.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=M.max_initial_timestamp_index}_call(M,v){for(let C=0;C<M.length;++C){const A=v[C].data;if(A[this.no_timestamps_token_id]=-1/0,M[C].length===this.begin_index-1){A.fill(-1/0),A[this.timestamp_begin]=0;continue}const B=M[C].slice(this.begin_index),K=B.length>=1&&B[B.length-1]>=this.timestamp_begin,G=B.length<2||B[B.length-2]>=this.timestamp_begin;if(K&&(G?A.subarray(this.timestamp_begin).fill(-1/0):A.subarray(0,this.eos_token_id).fill(-1/0)),M[C].length===this.begin_index&&this.max_initial_timestamp_index!==null){const Z=this.timestamp_begin+this.max_initial_timestamp_index;A.subarray(Z+1).fill(-1/0)}const j=(0,o.log_softmax)(A),ee=Math.log(j.subarray(this.timestamp_begin).map(Math.exp).reduce((Z,X)=>Z+X)),H=(0,o.max)(j.subarray(0,this.timestamp_begin))[0];ee>H&&A.subarray(0,this.timestamp_begin).fill(-1/0)}return v}}class c extends n{constructor(M){super(),this.no_repeat_ngram_size=M}getNgrams(M){const v=M.length,C=[];for(let B=0;B<v+1-this.no_repeat_ngram_size;++B){const K=[];for(let G=0;G<this.no_repeat_ngram_size;++G)K.push(M[B+G]);C.push(K.map(Number))}const A=new Map;for(const B of C){const K=B.slice(0,B.length-1),G=JSON.stringify(K),j=A.get(G)??[];j.push(B[B.length-1]),A.set(G,j)}return A}getGeneratedNgrams(M,v){const C=v.slice(v.length+1-this.no_repeat_ngram_size,v.length);return M.get(JSON.stringify(C.map(Number)))??[]}calcBannedNgramTokens(M){const v=[];if(M.length+1<this.no_repeat_ngram_size)return v;{const C=this.getNgrams(M);return this.getGeneratedNgrams(C,M)}}_call(M,v){for(let C=0;C<M.length;++C){const A=v[C].data,B=this.calcBannedNgramTokens(M[C]);for(const K of B)A[K]=-1/0}return v}}class f extends n{constructor(M){super(),this.penalty=M}_call(M,v){for(let C=0;C<M.length;++C){const A=v[C].data;for(const B of new Set(M[C])){const K=Number(B);A[K]<0?A[K]*=this.penalty:A[K]/=this.penalty}}return v}}class _ extends n{constructor(M,v){super(),this.min_length=M,this.eos_token_id=Array.isArray(v)?v:[v]}_call(M,v){for(let C=0;C<M.length;++C)if(M[C].length<this.min_length){const A=v[C].data;for(const B of this.eos_token_id)A[B]=-1/0}return v}}class T extends n{constructor(M,v,C){super(),this.prompt_length_to_skip=M,this.min_new_tokens=v,this.eos_token_id=Array.isArray(C)?C:[C]}_call(M,v){for(let C=0;C<M.length;++C)if(M[C].length-this.prompt_length_to_skip<this.min_new_tokens){const B=v[C].data;for(const K of this.eos_token_id)B[K]=-1/0}return v}}class $ extends n{constructor(M,v){super(),this.bad_words_ids=M,this.eos_token_id=Array.isArray(v)?v:[v]}_call(M,v){for(let C=0;C<M.length;++C){const A=v[C].data,B=M[C];for(const K of this.bad_words_ids){if(B.length<K.length-1)continue;let G=!0;for(let j=1;j<=K.length-1;++j)if(K.at(-j-1)!=B.at(-j)){G=!1;break}G&&(A[K.at(-1)]=-1/0)}}return v}}class w extends n{constructor(M){if(super(),M<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${M}.`);this.guidance_scale=M}_call(M,v){if(v.dims[0]!==2*M.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 ${v.dims[0]} for the logits and ${M.length} for the input ids.`);const C=M.length,A=v.slice([0,C],null),B=v.slice([C,v.dims[0]],null);for(let K=0;K<B.data.length;++K)B.data[K]+=(A.data[K]-B.data[K])*this.guidance_scale;return B}}class g extends i{constructor(M){super(),this.temperature=M}_call(M,v){const C=v.data;for(let A=0;A<C.length;++A)C[A]/=this.temperature;return v}}class S extends i{constructor(M,{filter_value:v=-1/0,min_tokens_to_keep:C=1}={}){if(super(),M<0||M>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${M}`);if(!Number.isInteger(C)||C<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${C}`);this.top_p=M,this.filter_value=v,this.min_tokens_to_keep=C}}class E extends i{constructor(M,{filter_value:v=-1/0,min_tokens_to_keep:C=1}={}){if(super(),!Number.isInteger(M)||M<0)throw new Error(`\`top_k\` must be a positive integer, but is ${M}`);this.top_k=Math.max(M,C),this.filter_value=v}}},"./src/generation/logits_sampler.js":(e,r,t)=>{t.r(r),t.d(r,{LogitsSampler:()=>i});var s=t("./src/utils/generic.js"),o=t("./src/utils/tensor.js"),n=t("./src/utils/maths.js");t("./src/generation/configuration_utils.js");class i extends s.Callable{constructor(d){super(),this.generation_config=d}async _call(d){return this.sample(d)}async sample(d){throw Error("sample should be implemented in subclasses.")}getLogits(d,c){let f=d.dims.at(-1),_=d.data;if(c===-1)_=_.slice(-f);else{let T=c*f;_=_.slice(T,T+f)}return _}randomSelect(d){let c=0;for(let _=0;_<d.length;++_)c+=d[_];let f=Math.random()*c;for(let _=0;_<d.length;++_)if(f-=d[_],f<=0)return _;return 0}static getSampler(d){if(d.do_sample)return new l(d);if(d.num_beams>1)return new u(d);if(d.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${d.num_return_sequences}.`);return new a(d)}}class a extends i{async sample(d){const c=(0,n.max)(d.data)[1];return[[BigInt(c),0]]}}class l extends i{async sample(d){let c=d.dims.at(-1);this.generation_config.top_k>0&&(c=Math.min(this.generation_config.top_k,c));const[f,_]=await(0,o.topk)(d,c),T=(0,n.softmax)(f.data);return Array.from({length:this.generation_config.num_beams},()=>{const $=this.randomSelect(T);return[_.data[$],Math.log(T[$])]})}}class u extends i{async sample(d){let c=d.dims.at(-1);this.generation_config.top_k>0&&(c=Math.min(this.generation_config.top_k,c));const[f,_]=await(0,o.topk)(d,c),T=(0,n.softmax)(f.data);return Array.from({length:this.generation_config.num_beams},($,w)=>[_.data[w],Math.log(T[w])])}}},"./src/generation/stopping_criteria.js":(e,r,t)=>{t.r(r),t.d(r,{EosTokenCriteria:()=>a,InterruptableStoppingCriteria:()=>l,MaxLengthCriteria:()=>i,StoppingCriteria:()=>o,StoppingCriteriaList:()=>n});var s=t("./src/utils/generic.js");class o extends s.Callable{_call(p,d){throw Error("StoppingCriteria needs to be subclassed")}}class n extends s.Callable{constructor(){super(),this.criteria=[]}push(p){this.criteria.push(p)}extend(p){p instanceof n?p=p.criteria:p instanceof o&&(p=[p]),this.criteria.push(...p)}_call(p,d){const c=new Array(p.length).fill(!1);for(const f of this.criteria){const _=f(p,d);for(let T=0;T<c.length;++T)c[T]||(c[T]=_[T])}return c}[Symbol.iterator](){return this.criteria.values()}}class i extends o{constructor(p,d=null){super(),this.max_length=p,this.max_position_embeddings=d}_call(p){return p.map(d=>d.length>=this.max_length)}}class a extends o{constructor(p){super(),Array.isArray(p)||(p=[p]),this.eos_token_id=p}_call(p,d){return p.map(c=>{const f=c.at(-1);return this.eos_token_id.some(_=>f==_)})}}class l extends o{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(p,d){return new Array(p.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(e,r,t)=>{t.r(r),t.d(r,{BaseStreamer:()=>i,TextStreamer:()=>l,WhisperTextStreamer:()=>u});var s=t("./src/utils/core.js"),o=t("./src/tokenizers.js"),n=t("./src/env.js");class i{put(d){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const a=n.apis.IS_PROCESS_AVAILABLE?p=>process.stdout.write(p):p=>console.log(p);class l extends i{constructor(d,{skip_prompt:c=!1,callback_function:f=null,token_callback_function:_=null,skip_special_tokens:T=!0,decode_kwargs:$={},...w}={}){super(),this.tokenizer=d,this.skip_prompt=c,this.callback_function=f??a,this.token_callback_function=_,this.decode_kwargs={skip_special_tokens:T,...$,...w},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(d){var $;if(d.length>1)throw Error("TextStreamer only supports batch size of 1");const c=this.next_tokens_are_prompt;if(c&&(this.next_tokens_are_prompt=!1,this.skip_prompt))return;const f=d[0];($=this.token_callback_function)==null||$.call(this,f),this.token_cache=(0,s.mergeArrays)(this.token_cache,f);const _=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let T;c||_.endsWith(`
`)?(T=_.slice(this.print_len),this.token_cache=[],this.print_len=0):_.length>0&&(0,o.is_chinese_char)(_.charCodeAt(_.length-1))?(T=_.slice(this.print_len),this.print_len+=T.length):(T=_.slice(this.print_len,_.lastIndexOf(" ")+1),this.print_len+=T.length),this.on_finalized_text(T,!1)}end(){let d;this.token_cache.length>0?(d=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):d="",this.next_tokens_are_prompt=!0,this.on_finalized_text(d,!0)}on_finalized_text(d,c){var f,_;d.length>0&&((f=this.callback_function)==null||f.call(this,d)),c&&this.callback_function===a&&n.apis.IS_PROCESS_AVAILABLE&&((_=this.callback_function)==null||_.call(this,`
`))}}class u extends l{constructor(d,{skip_prompt:c=!1,callback_function:f=null,token_callback_function:_=null,on_chunk_start:T=null,on_chunk_end:$=null,on_finalize:w=null,time_precision:g=.02,skip_special_tokens:S=!0,decode_kwargs:E={}}={}){super(d,{skip_prompt:c,skip_special_tokens:S,callback_function:f,token_callback_function:_,decode_kwargs:E}),this.timestamp_begin=d.timestamp_begin,this.on_chunk_start=T,this.on_chunk_end=$,this.on_finalize=w,this.time_precision=g,this.waiting_for_timestamp=!1}put(d){var f,_;if(d.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const c=d[0];if(c.length===1){const T=Number(c[0])-this.timestamp_begin;if(T>=0){const $=T*this.time_precision;this.waiting_for_timestamp?(f=this.on_chunk_end)==null||f.call(this,$):(_=this.on_chunk_start)==null||_.call(this,$),this.waiting_for_timestamp=!this.waiting_for_timestamp,d=[[]]}}return super.put(d)}end(){var d;super.end(),(d=this.on_finalize)==null||d.call(this)}}},"./src/models.js":(e,r,t)=>{t.r(r),t.d(r,{ASTForAudioClassification:()=>Ii,ASTModel:()=>ki,ASTPreTrainedModel:()=>To,AlbertForMaskedLM:()=>N,AlbertForQuestionAnswering:()=>k,AlbertForSequenceClassification:()=>he,AlbertModel:()=>vn,AlbertPreTrainedModel:()=>xs,AutoModel:()=>fd,AutoModelForAudioClassification:()=>M0,AutoModelForAudioFrameClassification:()=>y0,AutoModelForAudioTextToText:()=>k0,AutoModelForCTC:()=>w0,AutoModelForCausalLM:()=>a0,AutoModelForDepthEstimation:()=>E0,AutoModelForDocumentQuestionAnswering:()=>v0,AutoModelForImageClassification:()=>c0,AutoModelForImageFeatureExtraction:()=>S0,AutoModelForImageMatting:()=>x0,AutoModelForImageSegmentation:()=>p0,AutoModelForImageTextToText:()=>$0,AutoModelForImageToImage:()=>T0,AutoModelForMaskGeneration:()=>g0,AutoModelForMaskedLM:()=>l0,AutoModelForNormalEstimation:()=>P0,AutoModelForObjectDetection:()=>_0,AutoModelForPoseEstimation:()=>C0,AutoModelForQuestionAnswering:()=>u0,AutoModelForSemanticSegmentation:()=>h0,AutoModelForSeq2SeqLM:()=>s0,AutoModelForSequenceClassification:()=>t0,AutoModelForSpeechSeq2Seq:()=>n0,AutoModelForTextToSpectrogram:()=>o0,AutoModelForTextToWaveform:()=>i0,AutoModelForTokenClassification:()=>r0,AutoModelForUniversalSegmentation:()=>m0,AutoModelForVision2Seq:()=>d0,AutoModelForXVector:()=>b0,AutoModelForZeroShotObjectDetection:()=>f0,BartForConditionalGeneration:()=>jt,BartForSequenceClassification:()=>kt,BartModel:()=>Pt,BartPretrainedModel:()=>xt,BaseModelOutput:()=>xe,BeitForImageClassification:()=>Rw,BeitModel:()=>Bw,BeitPreTrainedModel:()=>Ou,BertForMaskedLM:()=>Ae,BertForQuestionAnswering:()=>Ve,BertForSequenceClassification:()=>Fe,BertForTokenClassification:()=>ze,BertModel:()=>Se,BertPreTrainedModel:()=>be,BlenderbotForConditionalGeneration:()=>rr,BlenderbotModel:()=>Dt,BlenderbotPreTrainedModel:()=>Xr,BlenderbotSmallForConditionalGeneration:()=>Rr,BlenderbotSmallModel:()=>Qr,BlenderbotSmallPreTrainedModel:()=>wr,BloomForCausalLM:()=>aw,BloomModel:()=>iw,BloomPreTrainedModel:()=>vu,CLIPModel:()=>Ni,CLIPPreTrainedModel:()=>Fs,CLIPSegForImageSegmentation:()=>zo,CLIPSegModel:()=>Lo,CLIPSegPreTrainedModel:()=>qn,CLIPTextModel:()=>pu,CLIPTextModelWithProjection:()=>Vi,CLIPVisionModel:()=>hu,CLIPVisionModelWithProjection:()=>Ui,CamembertForMaskedLM:()=>Ft,CamembertForQuestionAnswering:()=>ys,CamembertForSequenceClassification:()=>ds,CamembertForTokenClassification:()=>cs,CamembertModel:()=>bs,CamembertPreTrainedModel:()=>Ar,CausalLMOutput:()=>In,CausalLMOutputWithPast:()=>Vx,ChineseCLIPModel:()=>Gi,ChineseCLIPPreTrainedModel:()=>Wi,ClapAudioModelWithProjection:()=>zb,ClapModel:()=>Db,ClapPreTrainedModel:()=>ta,ClapTextModelWithProjection:()=>Lb,CodeGenForCausalLM:()=>$n,CodeGenModel:()=>Go,CodeGenPreTrainedModel:()=>Zn,CohereForCausalLM:()=>Ug,CohereModel:()=>Vg,CoherePreTrainedModel:()=>mu,ConvBertForMaskedLM:()=>Ot,ConvBertForQuestionAnswering:()=>Ms,ConvBertForSequenceClassification:()=>ur,ConvBertForTokenClassification:()=>ls,ConvBertModel:()=>Gt,ConvBertPreTrainedModel:()=>wt,ConvNextForImageClassification:()=>IM,ConvNextModel:()=>kM,ConvNextPreTrainedModel:()=>qu,ConvNextV2ForImageClassification:()=>FM,ConvNextV2Model:()=>AM,ConvNextV2PreTrainedModel:()=>Xu,DPTForDepthEstimation:()=>pM,DPTModel:()=>cM,DPTPreTrainedModel:()=>Gu,DacDecoderModel:()=>Ey,DacDecoderOutput:()=>vy,DacEncoderModel:()=>Ty,DacEncoderOutput:()=>yy,DacModel:()=>xy,DacPreTrainedModel:()=>la,DebertaForMaskedLM:()=>Qe,DebertaForQuestionAnswering:()=>zr,DebertaForSequenceClassification:()=>tt,DebertaForTokenClassification:()=>Rt,DebertaModel:()=>De,DebertaPreTrainedModel:()=>Qt,DebertaV2ForMaskedLM:()=>hs,DebertaV2ForQuestionAnswering:()=>_s,DebertaV2ForSequenceClassification:()=>Kr,DebertaV2ForTokenClassification:()=>ms,DebertaV2Model:()=>ps,DebertaV2PreTrainedModel:()=>Sr,DecisionTransformerModel:()=>ny,DecisionTransformerPreTrainedModel:()=>sy,DeiTForImageClassification:()=>tM,DeiTModel:()=>eM,DeiTPreTrainedModel:()=>Nu,DepthAnythingForDepthEstimation:()=>mM,DepthAnythingPreTrainedModel:()=>hM,DepthProForDepthEstimation:()=>MM,DepthProPreTrainedModel:()=>wM,DetrForObjectDetection:()=>Nw,DetrForSegmentation:()=>Du,DetrModel:()=>jw,DetrObjectDetectionOutput:()=>Lu,DetrPreTrainedModel:()=>qi,DetrSegmentationOutput:()=>Vw,Dinov2ForImageClassification:()=>DM,Dinov2Model:()=>OM,Dinov2PreTrainedModel:()=>Qu,Dinov2WithRegistersForImageClassification:()=>zM,Dinov2WithRegistersModel:()=>LM,Dinov2WithRegistersPreTrainedModel:()=>Ju,DistilBertForMaskedLM:()=>ks,DistilBertForQuestionAnswering:()=>$s,DistilBertForSequenceClassification:()=>cn,DistilBertForTokenClassification:()=>xr,DistilBertModel:()=>vr,DistilBertPreTrainedModel:()=>Hr,DonutSwinModel:()=>$M,DonutSwinPreTrainedModel:()=>SM,EfficientNetForImageClassification:()=>Wb,EfficientNetModel:()=>Ub,EfficientNetPreTrainedModel:()=>ad,ElectraForMaskedLM:()=>Ss,ElectraForQuestionAnswering:()=>us,ElectraForSequenceClassification:()=>Ns,ElectraFor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s=t("./src/configs.js"),o=t("./src/backends/onnx.js"),n=t("./src/utils/dtypes.js"),i=t("./src/utils/generic.js"),a=t("./src/utils/core.js"),l=t("./src/utils/hub.js"),u=t("./src/utils/constants.js"),p=t("./src/generation/logits_process.js"),d=t("./src/generation/configuration_utils.js"),c=t("./src/utils/tensor.js"),f=t("./src/utils/image.js"),_=t("./src/utils/maths.js"),T=t("./src/generation/stopping_criteria.js"),$=t("./src/generation/logits_sampler.js"),w=t("./src/env.js"),g=t("./src/models/whisper/generation_whisper.js"),S=t("./src/models/whisper/common_whisper.js");const E={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7,MultiModality:8,Phi3V:9,AudioTextToText:10,AutoEncoder:11},y=new Map,M=new Map,v=new Map;async function C(b,P,D){var mr;let ne=((mr=D.config)==null?void 0:mr["transformers.js_config"])??{},ge=D.device??ne.device;ge&&typeof ge!="string"&&(ge.hasOwnProperty(P)?ge=ge[P]:(console.warn(`device not 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Using the default device.`),ge=null));const fe=ge??(w.apis.IS_NODE_ENV?"cpu":"wasm"),Ce=(0,o.deviceToExecutionProviders)(fe),Le=ne.device_config??{};Le.hasOwnProperty(fe)&&(ne={...ne,...Le[fe]});let Ne=D.dtype??ne.dtype;if(typeof Ne!="string"&&(Ne&&Ne.hasOwnProperty(P)?Ne=Ne[P]:(Ne=n.DEFAULT_DEVICE_DTYPE_MAPPING[fe]??n.DATA_TYPES.fp32,console.warn(`dtype not specified for "${P}". Using the default dtype (${Ne}) for this device (${fe}).`))),Ne===n.DATA_TYPES.auto){let bt=ne.dtype;typeof bt!="string"&&(bt=bt==null?void 0:bt[P]),bt&&bt!==n.DATA_TYPES.auto&&n.DATA_TYPES.hasOwnProperty(bt)?Ne=bt:Ne=n.DEFAULT_DEVICE_DTYPE_MAPPING[fe]??n.DATA_TYPES.fp32}const qe=Ne;if(n.DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty(qe)){if(qe===n.DATA_TYPES.fp16&&fe==="webgpu"&&!await(0,n.isWebGpuFp16Supported)())throw new Error(`The device (${fe}) does not support fp16.`)}else throw new Error(`Invalid dtype: ${qe}. Should be one of: ${Object.keys(n.DATA_TYPES).join(", ")}`);const it=ne.kv_cache_dtype,pt=it?typeof it=="string"?it:it[qe]??"float32":void 0;if(pt&&!["float32","float16"].includes(pt))throw new Error(`Invalid kv_cache_dtype: ${pt}. Should be one of: float32, float16`);const ot={dtype:qe,kv_cache_dtype:pt},Mt=n.DEFAULT_DTYPE_SUFFIX_MAPPING[qe],ct=`${P}${Mt}.onnx`,gt=`${D.subfolder??""}/${ct}`,rt={...D.session_options};rt.executionProviders??(rt.executionProviders=Ce);const yt=ne.free_dimension_overrides;yt?rt.freeDimensionOverrides??(rt.freeDimensionOverrides=yt):fe.startsWith("webnn")&&!rt.freeDimensionOverrides&&console.warn(`WebNN does not currently support dynamic shapes and requires 'free_dimension_overrides' to be set in config.json, preferably as a field within config["transformers.js_config"]["device_config"]["${fe}"]. When 'free_dimension_overrides' is not set, you may experience significant performance degradation.`);const Lt=w.apis.IS_NODE_ENV&&w.env.useFSCache,Ut=(0,l.getModelFile)(b,gt,!0,D,Lt),Jt=D.use_external_data_format??ne.use_external_data_format;let qt=[];if(Jt){let bt;typeof Jt=="object"?Jt.hasOwnProperty(ct)?bt=Jt[ct]:Jt.hasOwnProperty(P)?bt=Jt[P]:bt=!1:bt=Jt;const ir=+bt;if(ir>l.MAX_EXTERNAL_DATA_CHUNKS)throw new Error(`The number of external data chunks (${ir}) exceeds the maximum allowed value (${l.MAX_EXTERNAL_DATA_CHUNKS}).`);for(let Vr=0;Vr<ir;++Vr){const An=`${ct}_data${Vr===0?"":"_"+Vr}`,$r=`${D.subfolder??""}/${An}`;qt.push(new Promise(async(Jr,da)=>{const Fn=await(0,l.getModelFile)(b,$r,!0,D,Lt);Jr(Fn instanceof Uint8Array?{path:An,data:Fn}:An)}))}}else rt.externalData!==void 0&&(qt=rt.externalData.map(async bt=>{if(typeof bt.data=="string"){const ir=await(0,l.getModelFile)(b,bt.data,!0,D);return{...bt,data:ir}}return bt}));if(qt.length>0){const bt=await Promise.all(qt);w.apis.IS_NODE_ENV||(rt.externalData=bt)}if(fe==="webgpu"){const bt=(0,s.getKeyValueShapes)(D.config,{prefix:"present"});if(Object.keys(bt).length>0&&!(0,o.isONNXProxy)()){const ir={};for(const Vr in bt)ir[Vr]="gpu-buffer";rt.preferredOutputLocation=ir}}return{buffer_or_path:await Ut,session_options:rt,session_config:ot}}async function A(b,P,D){return Object.fromEntries(await Promise.all(Object.keys(P).map(async ne=>{const{buffer_or_path:ge,session_options:fe,session_config:Ce}=await C(b,P[ne],D),Le=await(0,o.createInferenceSession)(ge,fe,Ce);return[ne,Le]})))}async function B(b,P,D){return Object.fromEntries(await Promise.all(Object.keys(P).map(async ne=>{const ge=await(0,l.getModelJSON)(b,P[ne],!1,D);return[ne,ge]})))}function K(b,P){const D=Object.create(null),ne=[];for(const Ce of b.inputNames){const Le=P[Ce];if(!(Le instanceof c.Tensor)){ne.push(Ce);continue}D[Ce]=(0,o.isONNXProxy)()?Le.clone():Le}if(ne.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${ne.join(", ")}.`);const ge=Object.keys(P).length,fe=b.inputNames.length;if(ge>fe){let Ce=Object.keys(P).filter(Le=>!b.inputNames.includes(Le));console.warn(`WARNING: Too many inputs were provided (${ge} > ${fe}). The following inputs will be ignored: "${Ce.join(", ")}".`)}return D}async function G(b,P){const D=K(b,P);try{const ne=Object.fromEntries(Object.entries(D).map(([fe,Ce])=>[fe,Ce.ort_tensor]));let ge=await b.run(ne);return ge=j(ge),ge}catch(ne){const ge=Object.fromEntries(Object.entries(D).map(([fe,{type:Ce,dims:Le,data:Ne}])=>[fe,{type:Ce,dims:Le,data:Ne}]));throw console.error(`An error occurred during model execution: "${ne}".`),console.error("Inputs given to model:",ge),ne}}function j(b){for(let P in b)(0,o.isONNXTensor)(b[P])?b[P]=new c.Tensor(b[P]):typeof b[P]=="object"&&j(b[P]);return b}function ee(b){if(b instanceof c.Tensor)return b;if(b.length===0)throw Error("items must be non-empty");if(Array.isArray(b[0])){if(b.some(P=>P.length!==b[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 c.Tensor("int64",BigInt64Array.from(b.flat().map(P=>BigInt(P))),[b.length,b[0].length])}else return new c.Tensor("int64",BigInt64Array.from(b.map(P=>BigInt(P))),[1,b.length])}function H(b){return new c.Tensor("bool",[b],[1])}async function Z(b,P){let{encoder_outputs:D,input_ids:ne,decoder_input_ids:ge,...fe}=P;if(!D){const Le=(0,a.pick)(P,b.sessions.model.inputNames);D=(await X(b,Le)).last_hidden_state}return fe.input_ids=ge,fe.encoder_hidden_states=D,b.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(fe.encoder_attention_mask=P.attention_mask),await me(b,fe,!0)}async function X(b,P){const D=b.sessions.model,ne=(0,a.pick)(P,D.inputNames);if(D.inputNames.includes("inputs_embeds")&&!ne.inputs_embeds){if(!P.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");ne.inputs_embeds=await b.encode_text({input_ids:P.input_ids})}if(D.inputNames.includes("token_type_ids")&&!ne.token_type_ids){if(!ne.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");ne.token_type_ids=(0,c.zeros_like)(ne.input_ids)}if(D.inputNames.includes("pixel_mask")&&!ne.pixel_mask){if(!ne.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const ge=ne.pixel_values.dims;ne.pixel_mask=(0,c.ones)([ge[0],ge[2],ge[3]])}return await G(D,ne)}async function oe(b,P){const D=await b.encode(P);return await b.decode(D)}async function me(b,P,D=!1){const ne=b.sessions[D?"decoder_model_merged":"model"],{past_key_values:ge,...fe}=P;if(ne.inputNames.includes("use_cache_branch")&&(fe.use_cache_branch=H(!!ge)),ne.inputNames.includes("position_ids")&&fe.attention_mask&&!fe.position_ids){const Le=["paligemma","gemma3_text","gemma3"].includes(b.config.model_type)?1:0;fe.position_ids=ce(fe,ge,Le)}b.addPastKeyValues(fe,ge);const Ce=(0,a.pick)(fe,ne.inputNames);return await G(ne,Ce)}function ae({modality_token_id:b,inputs_embeds:P,modality_features:D,input_ids:ne,attention_mask:ge}){const fe=ne.tolist().map(qe=>qe.reduce((it,pt,ot)=>(pt==b&&it.push(ot),it),[])),Ce=fe.reduce((qe,it)=>qe+it.length,0),Le=D.dims[0];if(Ce!==Le)throw new Error(`Number of tokens and features do not match: tokens: ${Ce}, features ${Le}`);let Ne=0;for(let qe=0;qe<fe.length;++qe){const it=fe[qe],pt=P[qe];for(let ot=0;ot<it.length;++ot)pt[it[ot]].data.set(D[Ne++].data)}return{inputs_embeds:P,attention_mask:ge}}function V({image_token_id:b,inputs_embeds:P,image_features:D,input_ids:ne,attention_mask:ge}){return ae({modality_token_id:b,inputs_embeds:P,modality_features:D,input_ids:ne,attention_mask:ge})}function F({audio_token_id:b,inputs_embeds:P,audio_features:D,input_ids:ne,attention_mask:ge}){return ae({modality_token_id:b,inputs_embeds:P,modality_features:D,input_ids:ne,attention_mask:ge})}async function W(b,{encode_function:P,merge_function:D,modality_input_name:ne,modality_output_name:ge,input_ids:fe=null,attention_mask:Ce=null,position_ids:Le=null,inputs_embeds:Ne=null,past_key_values:qe=null,generation_config:it=null,logits_processor:pt=null,...ot}){const Mt=ot[ne];if(!Ne){if(Ne=await b.encode_text({input_ids:fe,...ot}),Mt&&fe.dims[1]!==1){const gt=await P({[ne]:Mt,...ot});({inputs_embeds:Ne,attention_mask:Ce}=D({[ge]:gt,inputs_embeds:Ne,input_ids:fe,attention_mask:Ce}))}else if(qe&&Mt&&fe.dims[1]===1){const gt=fe.dims[1],rt=Object.values(qe)[0].dims.at(-2);Ce=(0,c.cat)([(0,c.ones)([fe.dims[0],rt]),Ce.slice(null,[Ce.dims[1]-gt,Ce.dims[1]])],1)}}if(!Le&&b.config.model_type==="qwen2_vl"){const{image_grid_thw:gt,video_grid_thw:rt}=ot;[Le]=b.get_rope_index(fe,gt,rt,Ce)}return await me(b,{inputs_embeds:Ne,past_key_values:qe,attention_mask:Ce,position_ids:Le,generation_config:it,logits_processor:pt},!0)}async function re(b,P){return await W(b,{...P,modality_input_name:"audio_values",modality_output_name:"audio_features",encode_function:b.encode_audio.bind(b),merge_function:b._merge_input_ids_with_audio_features.bind(b)})}async function _e(b,P){return await W(b,{...P,modality_input_name:"pixel_values",modality_output_name:"image_features",encode_function:b.encode_image.bind(b),merge_function:b._merge_input_ids_with_image_features.bind(b)})}function se(b,P=0){const[D,ne]=b.dims,ge=b.data,fe=new BigInt64Array(ge.length);for(let Ce=0;Ce<D;++Ce){const Le=Ce*ne;let Ne=BigInt(P);for(let qe=0;qe<ne;++qe){const it=Le+qe;ge[it]===0n?fe[it]=BigInt(1):(fe[it]=Ne,Ne+=ge[it])}}return{data:fe,dims:b.dims}}function ce(b,P=null,D=0){const{input_ids:ne,inputs_embeds:ge,attention_mask:fe}=b,{data:Ce,dims:Le}=se(fe,D);let Ne=new c.Tensor("int64",Ce,Le);if(P){const qe=-(ne??ge).dims.at(1);Ne=Ne.slice(null,[qe,null])}return Ne}function $e(b,P,D,ne){if(D.past_key_values){const ge=Object.values(D.past_key_values)[0].dims.at(-2),{input_ids:fe,attention_mask:Ce}=D;if(!(Ce&&Ce.dims[1]>fe.dims[1])){if(ge<fe.dims[1])D.input_ids=fe.slice(null,[ge,null]);else if(b.config.image_token_index!=null&&fe.data.some(Le=>Le==b.config.image_token_index)){const Le=b.config.num_image_tokens;if(!Le)throw new Error("`num_image_tokens` is missing in the model configuration.");const Ne=fe.dims[1]-(ge-Le);D.input_ids=fe.slice(null,[-Ne,null]),D.attention_mask=(0,c.ones)([1,ge+Ne])}}}return D}function Ue(b,P,D,ne){return D.past_key_values&&(P=P.map(ge=>[ge.at(-1)])),{...D,decoder_input_ids:ee(P)}}function we(b,...P){return b.config.is_encoder_decoder?Ue(b,...P):$e(b,...P)}function q(b,P,D,ne){const ge=!!D.past_key_values;return ne.guidance_scale!==null&&ne.guidance_scale>1&&(ge?D.input_ids=(0,c.cat)([D.input_ids,D.input_ids],0):(D.input_ids=(0,c.cat)([D.input_ids,(0,c.full_like)(D.input_ids,BigInt(ne.pad_token_id))],0),D.attention_mask=(0,c.cat)([D.attention_mask,(0,c.full_like)(D.attention_mask,0n)],0))),(ge||!D.pixel_values)&&(D.pixel_values=(0,c.full)([0,0,3,384,384],1)),ge&&(D.images_seq_mask=new c.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),D.images_emb_mask=new c.Tensor("bool",new Array(0).fill(!1),[1,1,0])),D}class R extends i.Callable{constructor(D,ne,ge){super();te(this,"main_input_name","input_ids");te(this,"forward_params",["input_ids","attention_mask"]);this.config=D,this.sessions=ne,this.configs=ge;const fe=v.get(this.constructor),Ce=y.get(fe);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Ce){case E.DecoderOnly:this.can_generate=!0,this._forward=me,this._prepare_inputs_for_generation=$e;break;case E.Seq2Seq:case E.Vision2Seq:case E.Musicgen:this.can_generate=!0,this._forward=Z,this._prepare_inputs_for_generation=Ue;break;case E.EncoderDecoder:this._forward=Z;break;case E.ImageTextToText:this.can_generate=!0,this._forward=_e,this._prepare_inputs_for_generation=we;break;case E.AudioTextToText:this.can_generate=!0,this._forward=re,this._prepare_inputs_for_generation=we;break;case E.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=we;break;case E.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=q;break;case E.AutoEncoder:this._forward=oe;break;default:this._forward=X;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var ne;const D=[];for(const ge of Object.values(this.sessions))(ne=ge==null?void 0:ge.handler)!=null&&ne.dispose&&D.push(ge.handler.dispose());return await Promise.all(D)}static async from_pretrained(D,{progress_callback:ne=null,config:ge=null,cache_dir:fe=null,local_files_only:Ce=!1,revision:Le="main",model_file_name:Ne=null,subfolder:qe="onnx",device:it=null,dtype:pt=null,use_external_data_format:ot=null,session_options:Mt={}}={}){let ct={progress_callback:ne,config:ge,cache_dir:fe,local_files_only:Ce,revision:Le,model_file_name:Ne,subfolder:qe,device:it,dtype:pt,use_external_data_format:ot,session_options:Mt};const gt=v.get(this),rt=y.get(gt);ge=ct.config=await s.AutoConfig.from_pretrained(D,ct);let yt;if(rt===E.DecoderOnly)yt=await Promise.all([A(D,{model:ct.model_file_name??"model"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.Seq2Seq||rt===E.Vision2Seq)yt=await Promise.all([A(D,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.MaskGeneration)yt=await Promise.all([A(D,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},ct)]);else if(rt===E.EncoderDecoder)yt=await Promise.all([A(D,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},ct)]);else if(rt===E.ImageTextToText){const Lt={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};ge.is_encoder_decoder&&(Lt.model="encoder_model"),yt=await Promise.all([A(D,Lt,ct),B(D,{generation_config:"generation_config.json"},ct)])}else if(rt===E.AudioTextToText){const Lt={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",decoder_model_merged:"decoder_model_merged"};yt=await Promise.all([A(D,Lt,ct),B(D,{generation_config:"generation_config.json"},ct)])}else if(rt===E.Musicgen)yt=await Promise.all([A(D,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.MultiModality)yt=await Promise.all([A(D,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.Phi3V)yt=await Promise.all([A(D,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.AutoEncoder)yt=await Promise.all([A(D,{encoder_model:"encoder_model",decoder_model:"decoder_model"},ct)]);else{if(rt!==E.EncoderOnly){const Lt=gt??(ge==null?void 0:ge.model_type);Lt!=="custom"&&console.warn(`Model type for '${Lt}' not found, assuming encoder-only architecture. Please report this at ${u.GITHUB_ISSUE_URL}.`)}yt=await Promise.all([A(D,{model:ct.model_file_name??"model"},ct)])}return new this(ge,...yt)}async _call(D){return await this.forward(D)}async forward(D){return await this._forward(this,D)}get generation_config(){var D;return((D=this.configs)==null?void 0:D.generation_config)??null}_get_logits_warper(D){const ne=new p.LogitsProcessorList;return D.temperature!==null&&D.temperature!==1&&ne.push(new p.TemperatureLogitsWarper(D.temperature)),D.top_k!==null&&D.top_k!==0&&ne.push(new p.TopKLogitsWarper(D.top_k)),D.top_p!==null&&D.top_p<1&&ne.push(new p.TopPLogitsWarper(D.top_p)),ne}_get_logits_processor(D,ne,ge=null){const fe=new p.LogitsProcessorList;if(D.repetition_penalty!==null&&D.repetition_penalty!==1&&fe.push(new p.RepetitionPenaltyLogitsProcessor(D.repetition_penalty)),D.no_repeat_ngram_size!==null&&D.no_repeat_ngram_size>0&&fe.push(new p.NoRepeatNGramLogitsProcessor(D.no_repeat_ngram_size)),D.bad_words_ids!==null&&fe.push(new p.NoBadWordsLogitsProcessor(D.bad_words_ids,D.eos_token_id)),D.min_length!==null&&D.eos_token_id!==null&&D.min_length>0&&fe.push(new p.MinLengthLogitsProcessor(D.min_length,D.eos_token_id)),D.min_new_tokens!==null&&D.eos_token_id!==null&&D.min_new_tokens>0&&fe.push(new p.MinNewTokensLengthLogitsProcessor(ne,D.min_new_tokens,D.eos_token_id)),D.forced_bos_token_id!==null&&fe.push(new p.ForcedBOSTokenLogitsProcessor(D.forced_bos_token_id)),D.forced_eos_token_id!==null&&fe.push(new p.ForcedEOSTokenLogitsProcessor(D.max_length,D.forced_eos_token_id)),D.begin_suppress_tokens!==null){const Ce=ne>1||D.forced_bos_token_id===null?ne:ne+1;fe.push(new p.SuppressTokensAtBeginLogitsProcessor(D.begin_suppress_tokens,Ce))}return D.guidance_scale!==null&&D.guidance_scale>1&&fe.push(new p.ClassifierFreeGuidanceLogitsProcessor(D.guidance_scale)),ge!==null&&fe.extend(ge),fe}_prepare_generation_config(D,ne,ge=d.GenerationConfig){const fe={...this.config};for(const Le of["decoder","generator","text_config"])Le in fe&&Object.assign(fe,fe[Le]);const Ce=new ge(fe);return Object.assign(Ce,this.generation_config??{}),D&&Object.assign(Ce,D),ne&&Object.assign(Ce,(0,a.pick)(ne,Object.getOwnPropertyNames(Ce))),Ce}_get_stopping_criteria(D,ne=null){const ge=new T.StoppingCriteriaList;return D.max_length!==null&&ge.push(new T.MaxLengthCriteria(D.max_length,this.config.max_position_embeddings??null)),D.eos_token_id!==null&&ge.push(new T.EosTokenCriteria(D.eos_token_id)),ne&&ge.extend(ne),ge}_validate_model_class(){if(!this.can_generate){const D=[md,_d,hd,pd],ne=v.get(this.constructor),ge=new Set,fe=this.config.model_type;for(const Le of D){const Ne=Le.get(fe);Ne&&ge.add(Ne[0])}let Ce=`The current model class (${ne}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw ge.size>0&&(Ce+=` Please use the following class instead: ${[...ge].join(", ")}`),Error(Ce)}}prepare_inputs_for_generation(...D){return this._prepare_inputs_for_generation(this,...D)}_update_model_kwargs_for_generation({generated_input_ids:D,outputs:ne,model_inputs:ge,is_encoder_decoder:fe}){return ge.past_key_values=this.getPastKeyValues(ne,ge.past_key_values),ge.input_ids=new c.Tensor("int64",D.flat(),[D.length,1]),fe||(ge.attention_mask=(0,c.cat)([ge.attention_mask,(0,c.ones)([ge.attention_mask.dims[0],1])],1)),ge.position_ids=null,ge}_prepare_model_inputs({inputs:D,bos_token_id:ne,model_kwargs:ge}){const fe=(0,a.pick)(ge,this.forward_params),Ce=this.main_input_name;if(Ce in fe){if(D)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else fe[Ce]=D;return{inputs_tensor:fe[Ce],model_inputs:fe,model_input_name:Ce}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:D,model_inputs:ne,model_input_name:ge,generation_config:fe}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!ne.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:Le,pixel_values:Ne,attention_mask:qe,...it}=ne,pt=await this._prepare_inputs_embeds(ne);ne={...it,...(0,a.pick)(pt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Ce}=await X(this,ne);if(fe.guidance_scale!==null&&fe.guidance_scale>1)Ce=(0,c.cat)([Ce,(0,c.full_like)(Ce,0)],0),"attention_mask"in ne&&(ne.attention_mask=(0,c.cat)([ne.attention_mask,(0,c.zeros_like)(ne.attention_mask)],0));else if(ne.decoder_input_ids){const Le=ee(ne.decoder_input_ids).dims[0];if(Le!==Ce.dims[0]){if(Ce.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Ce.dims[0]}) than the decoder inputs (${Le}).`);Ce=(0,c.cat)(Array.from({length:Le},()=>Ce),0)}}return ne.encoder_outputs=Ce,ne}_prepare_decoder_input_ids_for_generation({batch_size:D,model_input_name:ne,model_kwargs:ge,decoder_start_token_id:fe,bos_token_id:Ce,generation_config:Le}){let{decoder_input_ids:Ne,...qe}=ge;if(!(Ne instanceof c.Tensor)){if(Ne)Array.isArray(Ne[0])||(Ne=Array.from({length:D},()=>Ne));else if(fe??(fe=Ce),this.config.model_type==="musicgen")Ne=Array.from({length:D*this.config.decoder.num_codebooks},()=>[fe]);else if(Array.isArray(fe)){if(fe.length!==D)throw new Error(`\`decoder_start_token_id\` expcted to have length ${D} but got ${fe.length}`);Ne=fe}else Ne=Array.from({length:D},()=>[fe]);Ne=ee(Ne)}return ge.decoder_attention_mask=(0,c.ones_like)(Ne),{input_ids:Ne,model_inputs:qe}}async generate({inputs:D=null,generation_config:ne=null,logits_processor:ge=null,stopping_criteria:fe=null,streamer:Ce=null,...Le}){this._validate_model_class(),ne=this._prepare_generation_config(ne,Le);let{inputs_tensor:Ne,model_inputs:qe,model_input_name:it}=this._prepare_model_inputs({inputs:D,model_kwargs:Le});const pt=this.config.is_encoder_decoder;pt&&("encoder_outputs"in qe||(qe=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Ne,model_inputs:qe,model_input_name:it,generation_config:ne})));let ot;pt?{input_ids:ot,model_inputs:qe}=this._prepare_decoder_input_ids_for_generation({batch_size:qe[it].dims.at(0),model_input_name:it,model_kwargs:qe,decoder_start_token_id:ne.decoder_start_token_id,bos_token_id:ne.bos_token_id,generation_config:ne}):ot=qe[it];let Mt=ot.dims.at(-1);ne.max_new_tokens!==null&&(ne.max_length=Mt+ne.max_new_tokens);const ct=this._get_logits_processor(ne,Mt,ge),gt=this._get_stopping_criteria(ne,fe),rt=qe[it].dims.at(0),yt=$.LogitsSampler.getSampler(ne),Lt=new Array(rt).fill(0),Ut=ot.tolist();Ce&&Ce.put(Ut);let Jt,qt={};for(;;){if(qe=this.prepare_inputs_for_generation(Ut,qe,ne),Jt=await this.forward(qe),ne.output_attentions&&ne.return_dict_in_generate){const $r=this.getAttentions(Jt);for(const Jr in $r)Jr in qt||(qt[Jr]=[]),qt[Jr].push($r[Jr])}const bt=Jt.logits.slice(null,-1,null),ir=ct(Ut,bt),Vr=[];for(let $r=0;$r<ir.dims.at(0);++$r){const Jr=ir[$r],da=await yt(Jr);for(const[Fn,ca]of da){const pa=BigInt(Fn);Lt[$r]+=ca,Ut[$r].push(pa),Vr.push([pa]);break}}if(Ce&&Ce.put(Vr),gt(Ut).every($r=>$r))break;qe=this._update_model_kwargs_for_generation({generated_input_ids:Vr,outputs:Jt,model_inputs:qe,is_encoder_decoder:pt})}Ce&&Ce.end();const nr=this.getPastKeyValues(Jt,qe.past_key_values,!0),mr=new c.Tensor("int64",Ut.flat(),[Ut.length,Ut[0].length]);if(ne.return_dict_in_generate)return{sequences:mr,past_key_values:nr,...qt};for(const bt of Object.values(Jt))bt.location==="gpu-buffer"&&bt.dispose();return mr}getPastKeyValues(D,ne,ge=!1){const fe=Object.create(null);for(const Ce in D)if(Ce.startsWith("present")){const Le=Ce.replace("present","past_key_values"),Ne=Ce.includes("encoder");if(Ne&&ne?fe[Le]=ne[Le]:fe[Le]=D[Ce],ne&&(!Ne||ge)){const qe=ne[Le];qe.location==="gpu-buffer"&&qe.dispose()}}return fe}getAttentions(D){const ne={};for(const ge of["cross_attentions","encoder_attentions","decoder_attentions"])for(const fe in D)fe.startsWith(ge)&&(ge in ne||(ne[ge]=[]),ne[ge].push(D[fe]));return ne}addPastKeyValues(D,ne){var ge,fe,Ce;if(ne)Object.assign(D,ne);else{const Le=this.sessions.decoder_model_merged??this.sessions.model,Ne=((ge=Le==null?void 0:Le.config)==null?void 0:ge.kv_cache_dtype)??"float32",qe=Ne==="float16"?new c.DataTypeMap.float16:[],it=((Ce=(fe=D[this.main_input_name]??D.attention_mask)==null?void 0:fe.dims)==null?void 0:Ce[0])??1,pt=(0,s.getKeyValueShapes)(this.config,{batch_size:it});for(const ot in pt)D[ot]=new c.Tensor(Ne,qe,pt[ot])}}async encode_image({pixel_values:D}){const ne=(await G(this.sessions.vision_encoder,{pixel_values:D})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${ne.dims[1]}).`),this.config.num_image_tokens=ne.dims[1]),ne}async encode_text({input_ids:D}){return(await G(this.sessions.embed_tokens,{input_ids:D})).inputs_embeds}async encode_audio({audio_values:D}){return(await G(this.sessions.audio_encoder,{audio_values:D})).audio_features}}class pe{}class xe extends pe{constructor({last_hidden_state:P,hidden_states:D=null,attentions:ne=null}){super(),this.last_hidden_state=P,this.hidden_states=D,this.attentions=ne}}class be extends R{}class Se extends be{}class Ae extends be{async _call(P){return new Mr(await super._call(P))}}class Fe extends be{async _call(P){return new _t(await super._call(P))}}class ze extends be{async _call(P){return new hr(await super._call(P))}}class Ve extends be{async _call(P){return new Er(await super._call(P))}}class O extends R{}class Y extends O{}class z extends O{async _call(P){return new Mr(await super._call(P))}}class J extends O{async _call(P){return new _t(await super._call(P))}}class le extends O{async _call(P){return new hr(await super._call(P))}}class ye extends R{}class Ee extends ye{}class ke extends R{}class Ie extends ke{}class Re extends ke{async _call(P){return new Mr(await super._call(P))}}class Xe extends ke{async _call(P){return new _t(await super._call(P))}}class Ge extends ke{async _call(P){return new hr(await super._call(P))}}class lt extends ke{async _call(P){return new Er(await super._call(P))}}class wt extends R{}class Gt extends wt{}class Ot extends wt{async _call(P){return new Mr(await super._call(P))}}class ur extends wt{async _call(P){return new _t(await super._call(P))}}class ls extends wt{async _call(P){return new hr(await super._call(P))}}class Ms extends wt{async _call(P){return new Er(await super._call(P))}}class Ir extends R{}class js extends Ir{}class Ss extends Ir{async _call(P){return new Mr(await super._call(P))}}class Ns extends Ir{async _call(P){return new _t(await super._call(P))}}class at extends Ir{async _call(P){return new hr(await super._call(P))}}class us extends Ir{async _call(P){return new Er(await super._call(P))}}class Ar extends R{}class bs extends Ar{}class Ft extends Ar{async _call(P){return new Mr(await super._call(P))}}class ds extends Ar{async _call(P){return new _t(await super._call(P))}}class cs extends Ar{async _call(P){return new hr(await super._call(P))}}class ys extends Ar{async _call(P){return new Er(await super._call(P))}}class Qt extends R{}class De extends Qt{}class Qe extends Qt{async _call(P){return new Mr(await super._call(P))}}class tt extends Qt{async _call(P){return new _t(await super._call(P))}}class Rt extends Qt{async _call(P){return new hr(await super._call(P))}}class zr extends Qt{async _call(P){return new Er(await super._call(P))}}class Sr extends R{}class ps extends Sr{}class hs extends Sr{async _call(P){return new Mr(await super._call(P))}}class Kr extends Sr{async _call(P){return new _t(await super._call(P))}}class ms extends Sr{async _call(P){return new hr(await super._call(P))}}class _s extends Sr{async _call(P){return new Er(await super._call(P))}}class Hr extends R{}class vr extends Hr{}class cn extends Hr{async _call(P){return new _t(await super._call(P))}}class xr extends Hr{async _call(P){return new hr(await super._call(P))}}class $s extends Hr{async _call(P){return new Er(await super._call(P))}}class ks extends Hr{async _call(P){return new Mr(await super._call(P))}}class fr extends R{}class Vs extends fr{}class pn extends fr{async _call(P){return new Mr(await super._call(P))}}class Fr extends fr{async _call(P){return new _t(await super._call(P))}}class hn extends fr{async _call(P){return new hr(await super._call(P))}}class qr extends R{}class Tr extends qr{}class Is extends qr{async _call(P){return new Mr(await super._call(P))}}class dr extends qr{async _call(P){return new _t(await super._call(P))}}class gr extends qr{async _call(P){return new Er(await super._call(P))}}class Br extends R{}class mn extends Br{}class _n extends Br{async _call(P){return new Mr(await super._call(P))}}class fn extends Br{async _call(P){return new _t(await super._call(P))}}class gn extends Br{async _call(P){return new hr(await super._call(P))}}class wn extends Br{async _call(P){return new Er(await super._call(P))}}class vs extends R{}class Mn extends vs{}class Us extends vs{async _call(P){return new Mr(await super._call(P))}}class bn extends vs{async _call(P){return new _t(await super._call(P))}}class yn extends vs{async _call(P){return new Er(await super._call(P))}}class xs extends R{}class vn extends xs{}class he extends xs{async _call(P){return new _t(await super._call(P))}}class k extends xs{async _call(P){return new Er(await super._call(P))}}class N extends xs{async _call(P){return new Mr(await super._call(P))}}class Q extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class ie extends Q{}class de extends Q{}class ve extends R{}class je extends ve{}class He extends ve{}class We extends R{}class Je extends We{}class dt extends We{}class xt extends R{}class Pt extends xt{}class jt extends xt{}class kt extends xt{async _call(P){return new _t(await super._call(P))}}class Ht extends R{}class br extends Ht{}class cr extends Ht{}class pr extends Ht{async _call(P){return new _t(await super._call(P))}}class Nt extends Ht{}class Xr extends R{}class Dt extends Xr{}class rr extends Xr{}class wr extends R{}class Qr extends wr{}class Rr extends wr{}class Yt extends R{}class jr extends Yt{}class or extends Yt{async _call(P){return new Mr(await super._call(P))}}class Vt extends Yt{async _call(P){return new _t(await super._call(P))}}class Zt extends Yt{async _call(P){return new hr(await super._call(P))}}class er extends Yt{async _call(P){return new Er(await super._call(P))}}class tr extends R{}class Ws extends tr{}class xn extends tr{async _call(P){return new Mr(await super._call(P))}}class vi extends tr{async _call(P){return new _t(await super._call(P))}}class xi extends tr{async _call(P){return new hr(await super._call(P))}}class Ti extends tr{async _call(P){return new Er(await super._call(P))}}class As extends R{}class Ei extends As{}class Pi extends As{async _call(P){return new Mr(await super._call(P))}}class Ci extends As{async _call(P){return new _t(await super._call(P))}}class Si extends As{async _call(P){return new hr(await super._call(P))}}class $i extends As{async _call(P){return new Er(await super._call(P))}}class To extends R{}class ki extends To{}class Ii extends To{}class Eo extends R{constructor(){super(...arguments);te(this,"requires_attention_mask",!1);te(this,"main_input_name","input_features");te(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Ai extends Eo{}class Po extends Eo{_prepare_generation_config(P,D){return super._prepare_generation_config(P,D,g.WhisperGenerationConfig)}_retrieve_init_tokens(P){const D=[P.decoder_start_token_id];let ne=P.language;const ge=P.task;if(P.is_multilingual){ne||(console.warn("No language specified - defaulting to English (en)."),ne="en");const Ce=`<|${(0,S.whisper_language_to_code)(ne)}|>`;D.push(P.lang_to_id[Ce]),D.push(P.task_to_id[ge??"transcribe"])}else if(ne||ge)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!P.return_timestamps&&P.no_timestamps_token_id&&D.at(-1)!==P.no_timestamps_token_id?D.push(P.no_timestamps_token_id):P.return_timestamps&&D.at(-1)===P.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),D.pop()),D.filter(fe=>fe!=null)}async generate({inputs:P=null,generation_config:D=null,logits_processor:ne=null,stopping_criteria:ge=null,...fe}){D=this._prepare_generation_config(D,fe);const Ce=fe.decoder_input_ids??this._retrieve_init_tokens(D);if(D.return_timestamps&&(ne??(ne=new p.LogitsProcessorList),ne.push(new p.WhisperTimeStampLogitsProcessor(D,Ce))),D.begin_suppress_tokens&&(ne??(ne=new p.LogitsProcessorList),ne.push(new p.SuppressTokensAtBeginLogitsProcessor(D.begin_suppress_tokens,Ce.length))),D.return_token_timestamps){if(!D.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");D.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),D.output_attentions=!0,D.return_dict_in_generate=!0}const Le=await super.generate({inputs:P,generation_config:D,logits_processor:ne,decoder_input_ids:Ce,...fe});return D.return_token_timestamps&&(Le.token_timestamps=this._extract_token_timestamps(Le,D.alignment_heads,D.num_frames)),Le}_extract_token_timestamps(P,D,ne=null,ge=.02){if(!P.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`.");ne==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let fe=this.config.median_filter_width;fe===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),fe=7);const Ce=P.cross_attentions,Le=Array.from({length:this.config.decoder_layers},(gt,rt)=>(0,c.cat)(Ce.map(yt=>yt[rt]),2)),Ne=(0,c.stack)(D.map(([gt,rt])=>{if(gt>=Le.length)throw new Error(`Layer index ${gt} is out of bounds for cross attentions (length ${Le.length}).`);return ne?Le[gt].slice(null,rt,null,[0,ne]):Le[gt].slice(null,rt)})).transpose(1,0,2,3),[qe,it]=(0,c.std_mean)(Ne,-2,0,!0),pt=Ne.clone();for(let gt=0;gt<pt.dims[0];++gt){const rt=pt[gt];for(let yt=0;yt<rt.dims[0];++yt){const Lt=rt[yt],Ut=qe[gt][yt][0].data,Jt=it[gt][yt][0].data;for(let qt=0;qt<Lt.dims[0];++qt){let nr=Lt[qt].data;for(let mr=0;mr<nr.length;++mr)nr[mr]=(nr[mr]-Jt[mr])/Ut[mr];nr.set((0,_.medianFilter)(nr,fe))}}}const ot=[(0,c.mean)(pt,1)],Mt=P.sequences.dims,ct=new c.Tensor("float32",new Float32Array(Mt[0]*Mt[1]),Mt);for(let gt=0;gt<Mt[0];++gt){const rt=ot[gt].neg().squeeze_(0),[yt,Lt]=(0,_.dynamic_time_warping)(rt.tolist()),Ut=Array.from({length:yt.length-1},(nr,mr)=>yt[mr+1]-yt[mr]),Jt=(0,a.mergeArrays)([1],Ut).map(nr=>!!nr),qt=[];for(let nr=0;nr<Jt.length;++nr)Jt[nr]&&qt.push(Lt[nr]*ge);ct[gt].data.set(qt,1)}return ct}}class Fi extends Po{}class Gn extends R{constructor(){super(...arguments);te(this,"requires_attention_mask",!1);te(this,"main_input_name","input_values");te(this,"forward_params",["input_values","decoder_input_ids","past_key_values"])}}class Oi extends Gn{}class Co extends Gn{}class So extends R{constructor(){super(...arguments);te(this,"main_input_name","pixel_values");te(this,"forward_params",["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"])}}class Di extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class Tn extends Di{_merge_input_ids_with_image_features({inputs_embeds:P,image_features:D,input_ids:ne,attention_mask:ge}){const fe=this.config.image_token_index,Le=ne.tolist().map(ot=>ot.findIndex(Mt=>Mt==fe)),Ne=Le.every(ot=>ot===-1),qe=Le.every(ot=>ot!==-1);if(!Ne&&!qe)throw new Error("Every input should contain either 0 or 1 image token.");if(Ne)return{inputs_embeds:P,attention_mask:ge};const it=[],pt=[];for(let ot=0;ot<Le.length;++ot){const Mt=Le[ot],ct=P[ot],gt=D[ot],rt=ge[ot];it.push((0,c.cat)([ct.slice([0,Mt]),gt,ct.slice([Mt+1,ct.dims[0]])],0)),pt.push((0,c.cat)([rt.slice([0,Mt]),(0,c.ones)([gt.dims[0]]),rt.slice([Mt+1,rt.dims[0]])],0))}return{inputs_embeds:(0,c.stack)(it,0),attention_mask:(0,c.stack)(pt,0)}}}class $o extends Tn{}class Gs extends Tn{}class Li extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"]);te(this,"main_input_name","inputs_embeds")}}class ko extends Li{_merge_input_ids_with_image_features({inputs_embeds:P,image_features:D,input_ids:ne,attention_mask:ge}){return{inputs_embeds:(0,c.cat)([D,P],1),attention_mask:(0,c.cat)([(0,c.ones)(D.dims.slice(0,2)),ge],1)}}async _prepare_inputs_embeds({input_ids:P,pixel_values:D,inputs_embeds:ne,attention_mask:ge}){if(!P&&!D)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let fe,Ce;return P&&(fe=await this.encode_text({input_ids:P})),D&&(Ce=await this.encode_image({pixel_values:D})),fe&&Ce?{inputs_embeds:ne,attention_mask:ge}=this._merge_input_ids_with_image_features({inputs_embeds:fe,image_features:Ce,input_ids:P,attention_mask:ge}):ne=fe||Ce,{inputs_embeds:ne,attention_mask:ge}}async forward({input_ids:P,pixel_values:D,attention_mask:ne,decoder_input_ids:ge,decoder_attention_mask:fe,encoder_outputs:Ce,past_key_values:Le,inputs_embeds:Ne,decoder_inputs_embeds:qe}){if(Ne||({inputs_embeds:Ne,attention_mask:ne}=await this._prepare_inputs_embeds({input_ids:P,pixel_values:D,inputs_embeds:Ne,attention_mask:ne})),!Ce){let{last_hidden_state:ot}=await X(this,{inputs_embeds:Ne,attention_mask:ne});Ce=ot}if(!qe){if(!ge)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");qe=await this.encode_text({input_ids:ge})}return await me(this,{inputs_embeds:qe,attention_mask:fe,encoder_attention_mask:ne,encoder_hidden_states:Ce,past_key_values:Le},!0)}}class zi extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class Bi extends zi{_merge_input_ids_with_image_features(P){const D=P.image_features.dims.at(-1),ne=P.image_features.view(-1,D);return V({image_token_id:this.config.image_token_index,...P,image_features:ne})}}class Ri extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"])}}class Ks extends Ri{async encode_image({pixel_values:P,pixel_attention_mask:D}){return(await G(this.sessions.vision_encoder,{pixel_values:P,pixel_attention_mask:D})).image_features}_merge_input_ids_with_image_features(P){const D=P.image_features.dims.at(-1),ne=P.image_features.view(-1,D);return V({image_token_id:this.config.image_token_id,...P,image_features:ne})}}class Kn extends Ks{}class ji extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"])}}class Io extends ji{async forward({input_ids:P=null,attention_mask:D=null,pixel_values:ne=null,image_sizes:ge=null,position_ids:fe=null,inputs_embeds:Ce=null,past_key_values:Le=null,generation_config:Ne=null,logits_processor:qe=null,...it}){if(!Ce){let ot;if(ne&&P.dims[1]!==1){if(!ge)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:ot}=await G(this.sessions.vision_encoder,{pixel_values:ne,image_sizes:ge}))}else{const Mt=this.config.normalized_config.hidden_size;ot=new c.Tensor("float32",[],[0,Mt])}({inputs_embeds:Ce}=await G(this.sessions.prepare_inputs_embeds,{input_ids:P,image_features:ot}))}return await me(this,{inputs_embeds:Ce,past_key_values:Le,attention_mask:D,position_ids:fe,generation_config:Ne,logits_processor:qe},!1)}}class Fs extends R{}class Ni extends Fs{}class pu extends Fs{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"text_model"})}}class Vi extends Fs{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"text_model"})}}class hu extends Fs{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"vision_model"})}}class Ui extends Fs{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"vision_model"})}}class Ao extends R{}class En extends Ao{}class Hs extends Ao{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"text_model"})}}class Fo extends Fs{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"vision_model"})}}class Wi extends R{}class Gi extends Wi{}class qs extends R{}class Hn extends qs{async forward(P){const D=!P.input_ids,ne=!P.pixel_values;if(D&&ne)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(D&&(P.input_ids=(0,c.ones)([P.pixel_values.dims[0],1])),ne){const{image_size:qe}=this.config.vision_config;P.pixel_values=(0,c.full)([0,3,qe,qe],0)}const{text_embeddings:ge,image_embeddings:fe,l2norm_text_embeddings:Ce,l2norm_image_embeddings:Le}=await super.forward(P),Ne={};return D||(Ne.text_embeddings=ge,Ne.l2norm_text_embeddings=Ce),ne||(Ne.image_embeddings=fe,Ne.l2norm_image_embeddings=Le),Ne}}class Oo extends qs{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"text_model"})}}class Do extends qs{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"vision_model"})}}class qn extends R{}class Lo extends qn{}class zo extends qn{}class Xn extends R{}class Ki extends Xn{}class Bo extends Xn{}class Qn extends R{}class Pn extends Qn{}class nt extends Qn{}class Ro extends R{}class jo extends Ro{}class No extends Ro{}class Xs extends R{}class Nr extends Xs{}class Cn extends Xs{}class Sn extends R{}class Vo extends Sn{}class Uo extends Sn{}class Jn extends R{}class Yn extends Jn{}class Wo extends Jn{}class Zn extends R{}class Go extends Zn{}class $n extends Zn{}class kn extends R{}class eo extends kn{}class Ko extends kn{}class to extends R{}class Ho extends to{}class qo extends to{}class ro extends R{}class Xo extends ro{}class h extends ro{}class x extends R{}class I extends x{}class L extends x{}class U extends R{}class ue extends U{}class Te extends U{}class Oe extends R{}class Ke extends Oe{}class Ze extends Oe{}class ht extends R{}class Tt extends ht{}class It extends ht{}class sr extends R{}class Os extends sr{}class Hi extends sr{}class mu extends R{}class Vg extends mu{}class Ug extends mu{}class _u extends R{}class Wg extends _u{}class Gg extends _u{}class fu extends R{}class Kg extends fu{}class Hg extends fu{}class gu extends R{}class qg extends gu{}class Xg extends gu{}class wu extends R{}class Qg extends wu{}class Jg extends wu{}class Mu extends R{}class Yg extends Mu{}class Zg extends Mu{}class ew extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"])}}class tw extends ew{get_rope_index(P,D,ne,ge){const{vision_config:fe,image_token_id:Ce,video_token_id:Le,vision_start_token_id:Ne}=this.config,qe=fe.spatial_merge_size??2,it=[];if(D||ne){let pt=P.tolist();ge||(ge=(0,c.ones_like)(P));const ot=ge.tolist(),Mt=Array.from({length:3},Lt=>Array.from({length:P.dims[0]},Ut=>Array.from({length:P.dims[1]},Jt=>1))),ct=D?D.tolist():[],gt=ne?ne.tolist():[];let rt=0,yt=0;for(let Lt=0;Lt<pt.length;++Lt){const Ut=pt[Lt].filter((Wt,ar)=>ot[Lt][ar]==1),qt=Ut.reduce((Wt,ar,Js)=>(ar==Ne&&Wt.push(Js),Wt),[]).map(Wt=>Ut[Wt+1]),nr=qt.filter(Wt=>Wt==Ce).length,mr=qt.filter(Wt=>Wt==Le).length;let bt=[],ir=0,Vr=nr,An=mr;for(let Wt=0;Wt<qt.length;++Wt){const ar=Ut.findIndex((Dn,fs)=>fs>ir&&Dn==Ce),Js=Ut.findIndex((Dn,fs)=>fs>ir&&Dn==Le),On=Vr>0&&ar!==-1?ar:Ut.length+1,oo=An>0&&Js!==-1?Js:Ut.length+1;let ha,gd,wd,Md;On<oo?([gd,wd,Md]=ct[rt],++rt,--Vr,ha=On):([gd,wd,Md]=gt[yt],++yt,--An,ha=oo);const[Ux,bd,ma]=[Number(gd),Math.floor(Number(wd)/qe),Math.floor(Number(Md)/qe)],yd=ha-ir,O0=bt.length>0?(0,_.max)(bt.at(-1))[0]+1:0;bt.push(Array.from({length:3*yd},(Dn,fs)=>O0+fs%yd));const vd=yd+O0,_a=Ux*bd*ma,Wx=Array.from({length:_a},(Dn,fs)=>vd+Math.floor(fs/(bd*ma))),Gx=Array.from({length:_a},(Dn,fs)=>vd+Math.floor(fs/ma)%bd),Kx=Array.from({length:_a},(Dn,fs)=>vd+fs%ma);bt.push([Wx,Gx,Kx].flat()),ir=ha+_a}if(ir<Ut.length){const Wt=bt.length>0?(0,_.max)(bt.at(-1))[0]+1:0,ar=Ut.length-ir;bt.push(Array.from({length:3*ar},(Js,On)=>Wt+On%ar))}const $r=bt.reduce((Wt,ar)=>Wt+ar.length,0),Jr=new Array($r);let da=0;for(let Wt=0;Wt<3;++Wt)for(let ar=0;ar<bt.length;++ar){const Js=bt[ar],On=Js.length/3;for(let oo=Wt*On;oo<(Wt+1)*On;++oo)Jr[da++]=Js[oo]}let Fn=0;const ca=ot[Lt];for(let Wt=0;Wt<ca.length;++Wt)if(ca[Wt]==1){for(let ar=0;ar<3;++ar)Mt[ar][Lt][Wt]=Jr[ar*$r/3+Fn];++Fn}const pa=(0,_.max)(Jr)[0];it.push(pa+1-pt[Lt].length)}return[new c.Tensor("int64",Mt.flat(1/0),[3,P.dims[0],P.dims[1]]),new c.Tensor("int64",it,[it.length,1])]}else if(ge){const{data:pt,dims:ot}=se(ge),Mt=BigInt64Array.from({length:3*pt.length},(gt,rt)=>pt[rt%pt.length]),ct=Array.from({length:ot[0]},(gt,rt)=>(0,_.max)(pt.subarray(ot[1]*rt,ot[1]*(rt+1)))[0]+1n+BigInt(ot[1]));return[new c.Tensor("int64",Mt,[3,...ot]),new c.Tensor("int64",ct,[ct.length,1])]}else{const[pt,ot]=P.dims,Mt=BigInt64Array.from({length:3*pt*ot},(ct,gt)=>BigInt(Math.floor(gt%ot/pt)));return[new c.Tensor("int64",Mt,[3,...P.dims]),(0,c.zeros)([pt,1])]}}async encode_image({pixel_values:P,image_grid_thw:D}){return(await G(this.sessions.vision_encoder,{pixel_values:P,grid_thw:D})).image_features}_merge_input_ids_with_image_features(P){return V({image_token_id:this.config.image_token_id,...P})}prepare_inputs_for_generation(P,D,ne){if(D.attention_mask&&!D.position_ids)if(!D.past_key_values)[D.position_ids,D.rope_deltas]=this.get_rope_index(D.input_ids,D.image_grid_thw,D.video_grid_thw,D.attention_mask);else{D.pixel_values=null;const ge=BigInt(Object.values(D.past_key_values)[0].dims.at(-2)),fe=D.rope_deltas.map(Ce=>ge+Ce);D.position_ids=(0,c.stack)([fe,fe,fe],0)}return D}}class bu extends R{}class rw extends bu{}class sw extends bu{}class yu extends R{}class nw extends yu{}class ow extends yu{}class vu extends R{}class iw extends vu{}class aw extends vu{}class xu extends R{}class lw extends xu{}class uw extends xu{}class Tu extends R{}class dw extends Tu{}class cw extends Tu{}class Eu extends R{}class pw extends Eu{}class hw extends Eu{async _call(P){return new _t(await super._call(P))}}class Pu extends R{}class mw extends Pu{}class _w extends Pu{async _call(P){return new _t(await super._call(P))}}class fw extends R{}class gw extends fw{}class Cu extends R{}class ww extends Cu{}class Mw extends Cu{async _call(P){return new _t(await super._call(P))}}class bw extends R{}class yw extends bw{}class Su extends R{}class vw extends Su{}class xw extends Su{async _call(P){return new _t(await super._call(P))}}class Tw extends R{}class Ew extends Tw{}class $u extends R{}class Pw extends $u{}class Cw extends $u{async _call(P){return new _t(await super._call(P))}}class Sw extends R{}class $w extends Sw{async _call(P){return new A0(await super._call(P))}}class ku extends R{}class kw extends ku{}class Iw extends ku{async _call(P){return new _t(await super._call(P))}}class Iu extends R{}class Aw extends Iu{}class Fw extends Iu{async _call(P){return new _t(await super._call(P))}}class Au extends R{}class Ow extends Au{}class Dw extends Au{}class Fu extends R{}class Lw extends Fu{}class zw extends Fu{}class Ou extends R{}class Bw extends Ou{}class Rw extends Ou{async _call(P){return new _t(await super._call(P))}}class qi extends R{}class jw extends qi{}class Nw extends qi{async _call(P){return new Lu(await super._call(P))}}class Du extends qi{async _call(P){return new Vw(await super._call(P))}}class Lu extends pe{constructor({logits:P,pred_boxes:D}){super(),this.logits=P,this.pred_boxes=D}}class Vw extends pe{constructor({logits:P,pred_boxes:D,pred_masks:ne}){super(),this.logits=P,this.pred_boxes=D,this.pred_masks=ne}}class zu extends R{}class Uw extends zu{}class Ww extends zu{async _call(P){return new Xi(await super._call(P))}}class Xi extends pe{constructor({logits:P,pred_boxes:D}){super(),this.logits=P,this.pred_boxes=D}}class Bu extends R{}class Gw extends Bu{}class Kw extends Bu{async _call(P){return new Hw(await super._call(P))}}class Hw extends Xi{}class Ru extends R{}class qw extends Ru{}class Xw extends Ru{async _call(P){return new Qw(await super._call(P))}}class Qw extends Xi{}class ju extends R{}class Jw extends ju{}class Yw extends ju{async _call(P){return new Zw(await super._call(P))}}class Zw extends Lu{}class Nu extends R{}class eM extends Nu{}class tM extends Nu{async _call(P){return new _t(await super._call(P))}}class Vu extends R{}class rM extends Vu{}class sM extends Vu{async _call(P){return new _t(await super._call(P))}}class Uu extends R{}class nM extends Uu{}class oM extends Uu{async _call(P){return new _t(await super._call(P))}}class Qi extends R{}class iM extends Qi{}class aM extends Qi{async _call(P){return new _t(await super._call(P))}}class lM extends Qi{}class Wu extends R{}class uM extends Wu{}class dM extends Wu{}class Gu extends R{}class cM extends Gu{}class pM extends Gu{}class hM extends R{}class mM extends hM{}class Ji extends R{}class _M extends Ji{}class fM extends Ji{}class gM extends Ji{}class wM extends R{}class MM extends wM{}class bM extends R{}class yM extends bM{}class vM extends R{}class xM extends vM{}class Ku extends R{}class TM extends Ku{}class EM extends Ku{}class Hu extends R{}class PM extends Hu{}class CM extends Hu{}class SM extends R{}class $M extends SM{}class qu extends R{}class kM extends qu{}class IM extends qu{async _call(P){return new _t(await super._call(P))}}class Xu extends R{}class AM extends Xu{}class FM extends Xu{async _call(P){return new _t(await super._call(P))}}class Qu extends R{}class OM extends Qu{}class DM extends Qu{async _call(P){return new _t(await super._call(P))}}class Ju extends R{}class LM extends Ju{}class zM extends Ju{async _call(P){return new _t(await super._call(P))}}class BM extends R{}class RM extends BM{}class Yu extends R{}class jM extends Yu{}class NM extends Yu{async _call(P){return new VM(await super._call(P))}}class VM extends pe{constructor({logits:P,pred_boxes:D}){super(),this.logits=P,this.pred_boxes=D}}class UM extends R{}class WM extends UM{async get_image_embeddings({pixel_values:P}){return await X(this,{pixel_values:P})}async forward(P){if((!P.image_embeddings||!P.image_positional_embeddings)&&(P={...P,...await this.get_image_embeddings(P)}),!P.input_labels&&P.input_points){const ne=P.input_points.dims.slice(0,-1),ge=ne.reduce((fe,Ce)=>fe*Ce,1);P.input_labels=new c.Tensor("int64",new BigInt64Array(ge).fill(1n),ne)}const D={image_embeddings:P.image_embeddings,image_positional_embeddings:P.image_positional_embeddings};return P.input_points&&(D.input_points=P.input_points),P.input_labels&&(D.input_labels=P.input_labels),P.input_boxes&&(D.input_boxes=P.input_boxes),await G(this.sessions.prompt_encoder_mask_decoder,D)}async _call(P){return new GM(await super._call(P))}}class GM extends pe{constructor({iou_scores:P,pred_masks:D}){super(),this.iou_scores=P,this.pred_masks=D}}class Zu extends R{}class KM extends Zu{}class HM extends Zu{}class ed extends R{}class qM extends ed{}class XM extends ed{}class Qs extends R{}class QM extends Qs{}class JM extends Qs{async _call(P){return new In(await super._call(P))}}class YM extends Qs{async _call(P){return new _t(await super._call(P))}}class ZM extends Qs{async _call(P){return new hr(await super._call(P))}}class td extends R{}class eb extends td{}class tb extends td{async _call(P){return new hr(await super._call(P))}}class rb extends R{}class sb extends rb{}class Yi extends R{}class nb extends Yi{}class ob extends Yi{async _call(P){return new In(await super._call(P))}}class ib extends Yi{async _call(P){return new _t(await super._call(P))}}class Qo extends R{}class ab extends Qo{}class lb extends Qo{async _call(P){return new In(await super._call(P))}}class ub extends Qo{async _call(P){return new _t(await super._call(P))}}class db extends Qo{async _call(P){return new hr(await super._call(P))}}class Zi extends R{}class cb extends Zi{}class pb extends Zi{async _call(P){return new In(await super._call(P))}}class hb extends Zi{async _call(P){return new _t(await super._call(P))}}class Sx extends R{}class mb extends Qs{}class _b extends Qs{async _call(P){return new In(await super._call(P))}}class fb extends Qs{async _call(P){return new _t(await super._call(P))}}class so extends R{}class gb extends so{}class wb extends so{async _call(P){return new In(await super._call(P))}}class Mb extends so{async _call(P){return new _t(await super._call(P))}}class bb extends so{async _call(P){return new I0(await super._call(P))}}class yb extends so{async _call(P){return new hr(await super._call(P))}}class vb extends R{}class xb extends vb{}class ea extends R{}class $x extends ea{}class Tb extends ea{}class Eb extends ea{async generate_speech(P,D,{threshold:ne=.5,minlenratio:ge=0,maxlenratio:fe=20,vocoder:Ce=null}={}){const Le={input_ids:P},{encoder_outputs:Ne,encoder_attention_mask:qe}=await X(this,Le),it=Ne.dims[1]/this.config.reduction_factor,pt=Math.floor(it*fe),ot=Math.floor(it*ge),Mt=this.config.num_mel_bins;let ct=[],gt=null,rt=null,yt=0;for(;;){++yt;const Jt=H(!!rt);let qt;rt?qt=rt.output_sequence_out:qt=new c.Tensor("float32",new Float32Array(Mt),[1,1,Mt]);let nr={use_cache_branch:Jt,output_sequence:qt,encoder_attention_mask:qe,speaker_embeddings:D,encoder_hidden_states:Ne};this.addPastKeyValues(nr,gt),rt=await G(this.sessions.decoder_model_merged,nr),gt=this.getPastKeyValues(rt,gt);const{prob:mr,spectrum:bt}=rt;if(ct.push(bt),yt>=ot&&(Array.from(mr.data).filter(ir=>ir>=ne).length>0||yt>=pt))break}const Lt=(0,c.cat)(ct),{waveform:Ut}=await G(Ce.sessions.model,{spectrogram:Lt});return{spectrogram:Lt,waveform:Ut}}}class Pb extends R{constructor(){super(...arguments);te(this,"main_input_name","spectrogram")}}class Cb extends R{}class Sb extends Cb{}class rd extends R{}class $b extends rd{}class kb extends rd{}class sd extends R{}class Ib extends sd{}class Ab extends sd{}class nd extends R{}class Fb extends nd{}class Ob extends nd{}class ta extends R{}class Db extends ta{}class Lb extends ta{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"text_model"})}}class zb extends ta{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"audio_model"})}}class Bb extends R{}class od extends Bb{async _call(P){return new F0(await super._call(P))}}class ra extends R{}class kx extends ra{}class Rb extends ra{}class jb extends ra{}class id extends R{}class Nb extends id{}class Vb extends id{}class ad extends R{}class Ub extends ad{}class Wb extends ad{async _call(P){return new _t(await super._call(P))}}class ld extends R{}class Ix extends ld{}class Ax extends ld{}class ud extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}_apply_and_filter_by_delay_pattern_mask(D){const[ne,ge]=D.dims,fe=this.config.decoder.num_codebooks,Ce=ge-fe;let Le=0;for(let it=0;it<D.size;++it){if(D.data[it]===this.config.decoder.pad_token_id)continue;const pt=it%ge,ot=Math.floor(it/ge)%fe,Mt=pt-ot;Mt>0&&Mt<=Ce&&(D.data[Le++]=D.data[it])}const Ne=Math.floor(ne/fe),qe=Le/(Ne*fe);return new c.Tensor(D.type,D.data.slice(0,Le),[Ne,fe,qe])}prepare_inputs_for_generation(D,ne,ge){let fe=structuredClone(D);for(let Le=0;Le<fe.length;++Le)for(let Ne=0;Ne<fe[Le].length;++Ne)Le%this.config.decoder.num_codebooks>=Ne&&(fe[Le][Ne]=BigInt(this.config.decoder.pad_token_id));return ge.guidance_scale!==null&&ge.guidance_scale>1&&(fe=fe.concat(fe)),super.prepare_inputs_for_generation(fe,ne,ge)}async generate(D){const ne=await super.generate(D),ge=this._apply_and_filter_by_delay_pattern_mask(ne).unsqueeze_(0),{audio_values:fe}=await G(this.sessions.encodec_decode,{audio_codes:ge});return fe}}class sa extends R{}class Gb extends sa{}class Kb extends sa{async _call(P){return new _t(await super._call(P))}}class Hb extends sa{}class na extends R{}class qb extends na{}class Xb extends na{async _call(P){return new _t(await super._call(P))}}class Qb extends na{}class oa extends R{}class Jb extends oa{}class Yb extends oa{async _call(P){return new _t(await super._call(P))}}class Zb extends oa{}class ia extends R{}class ey extends ia{}class ty extends ia{async _call(P){return new _t(await super._call(P))}}class ry extends ia{}class sy extends R{}class ny extends sy{}class oy extends R{}class iy extends oy{constructor(...D){super(...D);te(this,"forward_params",["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"]);this._generation_mode="text"}async forward(D){const ne=this._generation_mode??"text";let ge;if(ne==="text"||!D.past_key_values){const qe=this.sessions.prepare_inputs_embeds,it=(0,a.pick)(D,qe.inputNames);ge=await G(qe,it)}else{const qe=this.sessions.gen_img_embeds,it=(0,a.pick)({image_ids:D.input_ids},qe.inputNames);ge=await G(qe,it)}const fe={...D,...ge},Ce=await me(this,fe),Le=this.sessions[ne==="text"?"lm_head":"gen_head"];if(!Le)throw new Error(`Unable to find "${Le}" generation head`);const Ne=await G(Le,(0,a.pick)(Ce,Le.inputNames));return{...ge,...Ce,...Ne}}async generate(D){return this._generation_mode="text",super.generate(D)}async generate_images(D){this._generation_mode="image";const ne=(D.inputs??D[this.main_input_name]).dims[1],fe=(await super.generate(D)).slice(null,[ne,null]),Ce=this.sessions.image_decode,{decoded_image:Le}=await G(Ce,{generated_tokens:fe}),Ne=Le.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),qe=[];for(const it of Ne){const pt=f.RawImage.fromTensor(it);qe.push(pt)}return qe}}class ay extends pe{constructor({char_logits:P,bpe_logits:D,wp_logits:ne}){super(),this.char_logits=P,this.bpe_logits=D,this.wp_logits=ne}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class ly extends R{}class uy extends ly{async _call(P){return new ay(await super._call(P))}}class dd extends R{}class dy extends dd{}class cy extends dd{}class cd extends R{}class py extends cd{}class hy extends cd{}class my extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","position_ids","audio_values","past_key_values"])}}class _y extends my{_merge_input_ids_with_audio_features(P){const D=P.audio_features.dims.at(-1),ne=P.audio_features.view(-1,D);return F({audio_token_id:this.config.ignore_index,...P,audio_features:ne})}}class aa extends R{constructor(){super(...arguments);te(this,"main_input_name","input_values");te(this,"forward_params",["input_values"])}}class fy extends pe{constructor({audio_codes:P}){super(),this.audio_codes=P}}class gy extends pe{constructor({audio_values:P}){super(),this.audio_values=P}}class wy extends aa{async encode(P){return new fy(await G(this.sessions.encoder_model,P))}async decode(P){return new gy(await G(this.sessions.decoder_model,P))}}class My extends aa{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"encoder_model"})}}class by extends aa{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"decoder_model"})}}class la extends R{constructor(){super(...arguments);te(this,"main_input_name","input_values");te(this,"forward_params",["input_values"])}}class yy extends pe{constructor({audio_codes:P}){super(),this.audio_codes=P}}class vy extends pe{constructor({audio_values:P}){super(),this.audio_values=P}}class xy extends la{async encode(P){return new yy(await G(this.sessions.encoder_model,P))}async decode(P){return new vy(await G(this.sessions.decoder_model,P))}}class Ty extends la{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"encoder_model"})}}class Ey extends la{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"decoder_model"})}}class ua extends R{constructor(){super(...arguments);te(this,"main_input_name","input_values");te(this,"forward_params",["input_values"])}}class Py extends ua{async encode(P){return await G(this.sessions.encoder_model,P)}async decode(P){return await G(this.sessions.decoder_model,P)}}class Cy extends ua{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"encoder_model"})}}class Sy extends ua{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"decoder_model"})}}class Ct{static async from_pretrained(P,{progress_callback:D=null,config:ne=null,cache_dir:ge=null,local_files_only:fe=!1,revision:Ce="main",model_file_name:Le=null,subfolder:Ne="onnx",device:qe=null,dtype:it=null,use_external_data_format:pt=null,session_options:ot={}}={}){const Mt={progress_callback:D,config:ne,cache_dir:ge,local_files_only:fe,revision:Ce,model_file_name:Le,subfolder:Ne,device:qe,dtype:it,use_external_data_format:pt,session_options:ot};if(Mt.config=await s.AutoConfig.from_pretrained(P,Mt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);const ct=Mt.config.model_type;for(const gt of this.MODEL_CLASS_MAPPINGS){let rt=gt.get(ct);if(!rt){for(const yt of gt.values())if(yt[0]===ct){rt=yt;break}if(!rt)continue}return await rt[1].from_pretrained(P,Mt)}if(this.BASE_IF_FAIL)return e0.has(ct)||console.warn(`Unknown model class "${ct}", attempting to construct from base class.`),await R.from_pretrained(P,Mt);throw Error(`Unsupported model type: ${ct}`)}}te(Ct,"MODEL_CLASS_MAPPINGS",null),te(Ct,"BASE_IF_FAIL",!1);const Fx=new Map([["bert",["BertModel",Se]],["modernbert",["ModernBertModel",Y]],["nomic_bert",["NomicBertModel",Ee]],["roformer",["RoFormerModel",Ie]],["electra",["ElectraModel",js]],["esm",["EsmModel",Vs]],["convbert",["ConvBertModel",Gt]],["camembert",["CamembertModel",bs]],["deberta",["DebertaModel",De]],["deberta-v2",["DebertaV2Model",ps]],["mpnet",["MPNetModel",mn]],["albert",["AlbertModel",vn]],["distilbert",["DistilBertModel",vr]],["roberta",["RobertaModel",jr]],["xlm",["XLMModel",Ws]],["xlm-roberta",["XLMRobertaModel",Ei]],["clap",["ClapModel",Db]],["clip",["CLIPModel",Ni]],["clipseg",["CLIPSegModel",Lo]],["chinese_clip",["ChineseCLIPModel",Gi]],["siglip",["SiglipModel",En]],["jina_clip",["JinaCLIPModel",Hn]],["mobilebert",["MobileBertModel",Tr]],["squeezebert",["SqueezeBertModel",Mn]],["wav2vec2",["Wav2Vec2Model",QM]],["wav2vec2-bert",["Wav2Vec2BertModel",cb]],["unispeech",["UniSpeechModel",nb]],["unispeech-sat",["UniSpeechSatModel",ab]],["hubert",["HubertModel",mb]],["wavlm",["WavLMModel",gb]],["audio-spectrogram-transformer",["ASTModel",ki]],["vits",["VitsModel",od]],["pyannote",["PyAnnoteModel",eb]],["wespeaker-resnet",["WeSpeakerResNetModel",sb]],["detr",["DetrModel",jw]],["rt_detr",["RTDetrModel",Uw]],["rt_detr_v2",["RTDetrV2Model",Gw]],["rf_detr",["RFDetrModel",qw]],["table-transformer",["TableTransformerModel",Jw]],["vit",["ViTModel",pw]],["ijepa",["IJepaModel",mw]],["pvt",["PvtModel",ww]],["vit_msn",["ViTMSNModel",vw]],["vit_mae",["ViTMAEModel",yw]],["groupvit",["GroupViTModel",Ew]],["fastvit",["FastViTModel",Pw]],["mobilevit",["MobileViTModel",kw]],["mobilevitv2",["MobileViTV2Model",Aw]],["owlvit",["OwlViTModel",Ow]],["owlv2",["Owlv2Model",Lw]],["beit",["BeitModel",Bw]],["deit",["DeiTModel",eM]],["hiera",["HieraModel",rM]],["convnext",["ConvNextModel",kM]],["convnextv2",["ConvNextV2Model",AM]],["dinov2",["Dinov2Model",OM]],["dinov2_with_registers",["Dinov2WithRegistersModel",LM]],["resnet",["ResNetModel",nM]],["swin",["SwinModel",iM]],["swin2sr",["Swin2SRModel",uM]],["donut-swin",["DonutSwinModel",$M]],["yolos",["YolosModel",jM]],["dpt",["DPTModel",cM]],["glpn",["GLPNModel",PM]],["hifigan",["SpeechT5HifiGan",Pb]],["efficientnet",["EfficientNetModel",Ub]],["decision_transformer",["DecisionTransformerModel",ny]],["patchtst",["PatchTSTForPrediction",dy]],["patchtsmixer",["PatchTSMixerForPrediction",py]],["mobilenet_v1",["MobileNetV1Model",Gb]],["mobilenet_v2",["MobileNetV2Model",qb]],["mobilenet_v3",["MobileNetV3Model",Jb]],["mobilenet_v4",["MobileNetV4Model",ey]],["maskformer",["MaskFormerModel",TM]],["mgp-str",["MgpstrForSceneTextRecognition",uy]],["style_text_to_speech_2",["StyleTextToSpeech2Model",xb]]]),Ox=new Map([["t5",["T5Model",ie]],["longt5",["LongT5Model",je]],["mt5",["MT5Model",Je]],["bart",["BartModel",Pt]],["mbart",["MBartModel",br]],["marian",["MarianModel",KM]],["whisper",["WhisperModel",Ai]],["m2m_100",["M2M100Model",qM]],["blenderbot",["BlenderbotModel",Dt]],["blenderbot-small",["BlenderbotSmallModel",Qr]]]),Dx=new Map([["mimi",["MimiModel",wy]],["dac",["DacModel",xy]],["snac",["SnacModel",Py]]]),Lx=new Map([["bloom",["BloomModel",iw]],["jais",["JAISModel",Pn]],["gpt2",["GPT2Model",Ki]],["gptj",["GPTJModel",Vo]],["gpt_bigcode",["GPTBigCodeModel",Yn]],["gpt_neo",["GPTNeoModel",jo]],["gpt_neox",["GPTNeoXModel",Nr]],["codegen",["CodeGenModel",Go]],["llama",["LlamaModel",eo]],["exaone",["ExaoneModel",I]],["olmo",["OlmoModel",Ke]],["olmo2",["Olmo2Model",Tt]],["mobilellm",["MobileLLMModel",ue]],["granite",["GraniteModel",Os]],["cohere",["CohereModel",Vg]],["gemma",["GemmaModel",Wg]],["gemma2",["Gemma2Model",Kg]],["gemma3_text",["Gemma3Model",qg]],["helium",["HeliumModel",Ho]],["glm",["GlmModel",Xo]],["openelm",["OpenELMModel",Qg]],["qwen2",["Qwen2Model",Yg]],["phi",["PhiModel",rw]],["phi3",["Phi3Model",nw]],["mpt",["MptModel",lw]],["opt",["OPTModel",dw]],["mistral",["MistralModel",$b]],["starcoder2",["Starcoder2Model",Ib]],["falcon",["FalconModel",Fb]],["stablelm",["StableLmModel",Nb]]]),pd=new Map([["speecht5",["SpeechT5ForSpeechToText",Tb]],["whisper",["WhisperForConditionalGeneration",Po]],["lite-whisper",["LiteWhisperForConditionalGeneration",Fi]],["moonshine",["MoonshineForConditionalGeneration",Co]]]),$y=new Map([["speecht5",["SpeechT5ForTextToSpeech",Eb]]]),ky=new Map([["vits",["VitsModel",od]],["musicgen",["MusicgenForConditionalGeneration",ud]]]),Iy=new Map([["bert",["BertForSequenceClassification",Fe]],["modernbert",["ModernBertForSequenceClassification",J]],["roformer",["RoFormerForSequenceClassification",Xe]],["electra",["ElectraForSequenceClassification",Ns]],["esm",["EsmForSequenceClassification",Fr]],["convbert",["ConvBertForSequenceClassification",ur]],["camembert",["CamembertForSequenceClassification",ds]],["deberta",["DebertaForSequenceClassification",tt]],["deberta-v2",["DebertaV2ForSequenceClassification",Kr]],["mpnet",["MPNetForSequenceClassification",fn]],["albert",["AlbertForSequenceClassification",he]],["distilbert",["DistilBertForSequenceClassification",cn]],["roberta",["RobertaForSequenceClassification",Vt]],["xlm",["XLMForSequenceClassification",vi]],["xlm-roberta",["XLMRobertaForSequenceClassification",Ci]],["bart",["BartForSequenceClassification",kt]],["mbart",["MBartForSequenceClassification",pr]],["mobilebert",["MobileBertForSequenceClassification",dr]],["squeezebert",["SqueezeBertForSequenceClassification",bn]]]),Ay=new Map([["bert",["BertForTokenClassification",ze]],["modernbert",["ModernBertForTokenClassification",le]],["roformer",["RoFormerForTokenClassification",Ge]],["electra",["ElectraForTokenClassification",at]],["esm",["EsmForTokenClassification",hn]],["convbert",["ConvBertForTokenClassification",ls]],["camembert",["CamembertForTokenClassification",cs]],["deberta",["DebertaForTokenClassification",Rt]],["deberta-v2",["DebertaV2ForTokenClassification",ms]],["mpnet",["MPNetForTokenClassification",gn]],["distilbert",["DistilBertForTokenClassification",xr]],["roberta",["RobertaForTokenClassification",Zt]],["xlm",["XLMForTokenClassification",xi]],["xlm-roberta",["XLMRobertaForTokenClassification",Si]]]),hd=new Map([["t5",["T5ForConditionalGeneration",de]],["longt5",["LongT5ForConditionalGeneration",He]],["mt5",["MT5ForConditionalGeneration",dt]],["bart",["BartForConditionalGeneration",jt]],["mbart",["MBartForConditionalGeneration",cr]],["marian",["MarianMTModel",HM]],["m2m_100",["M2M100ForConditionalGeneration",XM]],["blenderbot",["BlenderbotForConditionalGeneration",rr]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Rr]]]),md=new Map([["bloom",["BloomForCausalLM",aw]],["gpt2",["GPT2LMHeadModel",Bo]],["jais",["JAISLMHeadModel",nt]],["gptj",["GPTJForCausalLM",Uo]],["gpt_bigcode",["GPTBigCodeForCausalLM",Wo]],["gpt_neo",["GPTNeoForCausalLM",No]],["gpt_neox",["GPTNeoXForCausalLM",Cn]],["codegen",["CodeGenForCausalLM",$n]],["llama",["LlamaForCausalLM",Ko]],["exaone",["ExaoneForCausalLM",L]],["olmo",["OlmoForCausalLM",Ze]],["olmo2",["Olmo2ForCausalLM",It]],["mobilellm",["MobileLLMForCausalLM",Te]],["granite",["GraniteForCausalLM",Hi]],["cohere",["CohereForCausalLM",Ug]],["gemma",["GemmaForCausalLM",Gg]],["gemma2",["Gemma2ForCausalLM",Hg]],["gemma3_text",["Gemma3ForCausalLM",Xg]],["helium",["HeliumForCausalLM",qo]],["glm",["GlmForCausalLM",h]],["openelm",["OpenELMForCausalLM",Jg]],["qwen2",["Qwen2ForCausalLM",Zg]],["phi",["PhiForCausalLM",sw]],["phi3",["Phi3ForCausalLM",ow]],["mpt",["MptForCausalLM",uw]],["opt",["OPTForCausalLM",cw]],["mbart",["MBartForCausalLM",Nt]],["mistral",["MistralForCausalLM",kb]],["starcoder2",["Starcoder2ForCausalLM",Ab]],["falcon",["FalconForCausalLM",Ob]],["trocr",["TrOCRForCausalLM",Sb]],["stablelm",["StableLmForCausalLM",Vb]],["phi3_v",["Phi3VForCausalLM",Io]]]),zx=new Map([["multi_modality",["MultiModalityCausalLM",iy]]]),Fy=new Map([["bert",["BertForMaskedLM",Ae]],["modernbert",["ModernBertForMaskedLM",z]],["roformer",["RoFormerForMaskedLM",Re]],["electra",["ElectraForMaskedLM",Ss]],["esm",["EsmForMaskedLM",pn]],["convbert",["ConvBertForMaskedLM",Ot]],["camembert",["CamembertForMaskedLM",Ft]],["deberta",["DebertaForMaskedLM",Qe]],["deberta-v2",["DebertaV2ForMaskedLM",hs]],["mpnet",["MPNetForMaskedLM",_n]],["albert",["AlbertForMaskedLM",N]],["distilbert",["DistilBertForMaskedLM",ks]],["roberta",["RobertaForMaskedLM",or]],["xlm",["XLMWithLMHeadModel",xn]],["xlm-roberta",["XLMRobertaForMaskedLM",Pi]],["mobilebert",["MobileBertForMaskedLM",Is]],["squeezebert",["SqueezeBertForMaskedLM",Us]]]),Oy=new Map([["bert",["BertForQuestionAnswering",Ve]],["roformer",["RoFormerForQuestionAnswering",lt]],["electra",["ElectraForQuestionAnswering",us]],["convbert",["ConvBertForQuestionAnswering",Ms]],["camembert",["CamembertForQuestionAnswering",ys]],["deberta",["DebertaForQuestionAnswering",zr]],["deberta-v2",["DebertaV2ForQuestionAnswering",_s]],["mpnet",["MPNetForQuestionAnswering",wn]],["albert",["AlbertForQuestionAnswering",k]],["distilbert",["DistilBertForQuestionAnswering",$s]],["roberta",["RobertaForQuestionAnswering",er]],["xlm",["XLMForQuestionAnswering",Ti]],["xlm-roberta",["XLMRobertaForQuestionAnswering",$i]],["mobilebert",["MobileBertForQuestionAnswering",gr]],["squeezebert",["SqueezeBertForQuestionAnswering",yn]]]),_d=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",So]],["idefics3",["Idefics3ForConditionalGeneration",Ks]],["smolvlm",["SmolVLMForConditionalGeneration",Kn]]]),Dy=new Map([["llava",["LlavaForConditionalGeneration",Tn]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",$o]],["moondream1",["Moondream1ForConditionalGeneration",Gs]],["florence2",["Florence2ForConditionalGeneration",ko]],["qwen2-vl",["Qwen2VLForConditionalGeneration",tw]],["idefics3",["Idefics3ForConditionalGeneration",Ks]],["smolvlm",["SmolVLMForConditionalGeneration",Kn]],["paligemma",["PaliGemmaForConditionalGeneration",Bi]]]),Ly=new Map([["ultravox",["UltravoxModel",_y]]]),Bx=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",So]]]),zy=new Map([["vit",["ViTForImageClassification",hw]],["ijepa",["IJepaForImageClassification",_w]],["pvt",["PvtForImageClassification",Mw]],["vit_msn",["ViTMSNForImageClassification",xw]],["fastvit",["FastViTForImageClassification",Cw]],["mobilevit",["MobileViTForImageClassification",Iw]],["mobilevitv2",["MobileViTV2ForImageClassification",Fw]],["beit",["BeitForImageClassification",Rw]],["deit",["DeiTForImageClassification",tM]],["hiera",["HieraForImageClassification",sM]],["convnext",["ConvNextForImageClassification",IM]],["convnextv2",["ConvNextV2ForImageClassification",FM]],["dinov2",["Dinov2ForImageClassification",DM]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",zM]],["resnet",["ResNetForImageClassification",oM]],["swin",["SwinForImageClassification",aM]],["segformer",["SegformerForImageClassification",Rb]],["efficientnet",["EfficientNetForImageClassification",Wb]],["mobilenet_v1",["MobileNetV1ForImageClassification",Kb]],["mobilenet_v2",["MobileNetV2ForImageClassification",Xb]],["mobilenet_v3",["MobileNetV3ForImageClassification",Yb]],["mobilenet_v4",["MobileNetV4ForImageClassification",ty]]]),By=new Map([["detr",["DetrForObjectDetection",Nw]],["rt_detr",["RTDetrForObjectDetection",Ww]],["rt_detr_v2",["RTDetrV2ForObjectDetection",Kw]],["rf_detr",["RFDetrForObjectDetection",Xw]],["table-transformer",["TableTransformerForObjectDetection",Yw]],["yolos",["YolosForObjectDetection",NM]]]),Ry=new Map([["owlvit",["OwlViTForObjectDetection",Dw]],["owlv2",["Owlv2ForObjectDetection",zw]],["grounding-dino",["GroundingDinoForObjectDetection",RM]]]),no=new Map([["detr",["DetrForSegmentation",Du]],["clipseg",["CLIPSegForImageSegmentation",zo]]]),jy=new Map([["segformer",["SegformerForSemanticSegmentation",jb]],["sapiens",["SapiensForSemanticSegmentation",_M]],["swin",["SwinForSemanticSegmentation",lM]],["mobilenet_v1",["MobileNetV1ForSemanticSegmentation",Hb]],["mobilenet_v2",["MobileNetV2ForSemanticSegmentation",Qb]],["mobilenet_v3",["MobileNetV3ForSemanticSegmentation",Zb]],["mobilenet_v4",["MobileNetV4ForSemanticSegmentation",ry]]]),Ny=new Map([["detr",["DetrForSegmentation",Du]],["maskformer",["MaskFormerForInstanceSegmentation",EM]]]),Vy=new Map([["sam",["SamModel",WM]]]),Uy=new Map([["wav2vec2",["Wav2Vec2ForCTC",JM]],["wav2vec2-bert",["Wav2Vec2BertForCTC",pb]],["unispeech",["UniSpeechForCTC",ob]],["unispeech-sat",["UniSpeechSatForCTC",lb]],["wavlm",["WavLMForCTC",wb]],["hubert",["HubertForCTC",_b]]]),Wy=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",YM]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",hb]],["unispeech",["UniSpeechForSequenceClassification",ib]],["unispeech-sat",["UniSpeechSatForSequenceClassification",ub]],["wavlm",["WavLMForSequenceClassification",Mb]],["hubert",["HubertForSequenceClassification",fb]],["audio-spectrogram-transformer",["ASTForAudioClassification",Ii]]]),Gy=new Map([["wavlm",["WavLMForXVector",bb]]]),Ky=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",db]],["wavlm",["WavLMForAudioFrameClassification",yb]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",ZM]],["pyannote",["PyAnnoteForAudioFrameClassification",tb]]]),Hy=new Map([["vitmatte",["VitMatteForImageMatting",$w]]]),Rx=new Map([["patchtst",["PatchTSTForPrediction",cy]],["patchtsmixer",["PatchTSMixerForPrediction",hy]]]),qy=new Map([["swin2sr",["Swin2SRForImageSuperResolution",dM]]]),Xy=new Map([["dpt",["DPTForDepthEstimation",pM]],["depth_anything",["DepthAnythingForDepthEstimation",mM]],["glpn",["GLPNForDepthEstimation",CM]],["sapiens",["SapiensForDepthEstimation",fM]],["depth_pro",["DepthProForDepthEstimation",MM]],["metric3d",["Metric3DForDepthEstimation",yM]],["metric3dv2",["Metric3Dv2ForDepthEstimation",xM]]]),Qy=new Map([["sapiens",["SapiensForNormalEstimation",gM]]]),Jy=new Map([["vitpose",["VitPoseForPoseEstimation",gw]]]),Yy=new Map([["clip",["CLIPVisionModelWithProjection",Ui]],["siglip",["SiglipVisionModel",Fo]],["jina_clip",["JinaCLIPVisionModel",Do]]]),Zy=[[Fx,E.EncoderOnly],[Ox,E.EncoderDecoder],[Lx,E.DecoderOnly],[Dx,E.AutoEncoder],[Iy,E.EncoderOnly],[Ay,E.EncoderOnly],[hd,E.Seq2Seq],[pd,E.Seq2Seq],[md,E.DecoderOnly],[zx,E.MultiModality],[Fy,E.EncoderOnly],[Oy,E.EncoderOnly],[_d,E.Vision2Seq],[Dy,E.ImageTextToText],[Ly,E.AudioTextToText],[zy,E.EncoderOnly],[no,E.EncoderOnly],[Ny,E.EncoderOnly],[jy,E.EncoderOnly],[Hy,E.EncoderOnly],[Rx,E.EncoderOnly],[qy,E.EncoderOnly],[Xy,E.EncoderOnly],[Qy,E.EncoderOnly],[Jy,E.EncoderOnly],[By,E.EncoderOnly],[Ry,E.EncoderOnly],[Vy,E.MaskGeneration],[Uy,E.EncoderOnly],[Wy,E.EncoderOnly],[$y,E.Seq2Seq],[ky,E.EncoderOnly],[Gy,E.EncoderOnly],[Ky,E.EncoderOnly],[Yy,E.EncoderOnly]];for(const[b,P]of Zy)for(const[D,ne]of b.values())y.set(D,P),v.set(ne,D),M.set(D,ne);const jx=[["MusicgenForConditionalGeneration",ud,E.Musicgen],["Phi3VForCausalLM",Io,E.Phi3V],["CLIPTextModelWithProjection",Vi,E.EncoderOnly],["SiglipTextModel",Hs,E.EncoderOnly],["JinaCLIPTextModel",Oo,E.EncoderOnly],["ClapTextModelWithProjection",Lb,E.EncoderOnly],["ClapAudioModelWithProjection",zb,E.EncoderOnly],["DacEncoderModel",Ty,E.EncoderOnly],["DacDecoderModel",Ey,E.EncoderOnly],["MimiEncoderModel",My,E.EncoderOnly],["MimiDecoderModel",by,E.EncoderOnly],["SnacEncoderModel",Cy,E.EncoderOnly],["SnacDecoderModel",Sy,E.EncoderOnly]];for(const[b,P,D]of jx)y.set(b,D),v.set(P,b),M.set(b,P);const e0=new Map([["modnet",no],["birefnet",no],["isnet",no],["ben",no]]);for(const[b,P]of e0.entries())P.set(b,["PreTrainedModel",R]),y.set(b,E.EncoderOnly),v.set(R,b),M.set(b,R);class fd extends Ct{}te(fd,"MODEL_CLASS_MAPPINGS",Zy.map(P=>P[0])),te(fd,"BASE_IF_FAIL",!0);class t0 extends Ct{}te(t0,"MODEL_CLASS_MAPPINGS",[Iy]);class r0 extends Ct{}te(r0,"MODEL_CLASS_MAPPINGS",[Ay]);class s0 extends Ct{}te(s0,"MODEL_CLASS_MAPPINGS",[hd]);class n0 extends Ct{}te(n0,"MODEL_CLASS_MAPPINGS",[pd]);class o0 extends Ct{}te(o0,"MODEL_CLASS_MAPPINGS",[$y]);class i0 extends Ct{}te(i0,"MODEL_CLASS_MAPPINGS",[ky]);class a0 extends Ct{}te(a0,"MODEL_CLASS_MAPPINGS",[md]);class l0 extends Ct{}te(l0,"MODEL_CLASS_MAPPINGS",[Fy]);class u0 extends Ct{}te(u0,"MODEL_CLASS_MAPPINGS",[Oy]);class d0 extends Ct{}te(d0,"MODEL_CLASS_MAPPINGS",[_d]);class c0 extends Ct{}te(c0,"MODEL_CLASS_MAPPINGS",[zy]);class p0 extends Ct{}te(p0,"MODEL_CLASS_MAPPINGS",[no]);class h0 extends Ct{}te(h0,"MODEL_CLASS_MAPPINGS",[jy]);class m0 extends Ct{}te(m0,"MODEL_CLASS_MAPPINGS",[Ny]);class _0 extends Ct{}te(_0,"MODEL_CLASS_MAPPINGS",[By]);class f0 extends Ct{}te(f0,"MODEL_CLASS_MAPPINGS",[Ry]);class g0 extends Ct{}te(g0,"MODEL_CLASS_MAPPINGS",[Vy]);class w0 extends Ct{}te(w0,"MODEL_CLASS_MAPPINGS",[Uy]);class M0 extends Ct{}te(M0,"MODEL_CLASS_MAPPINGS",[Wy]);class b0 extends Ct{}te(b0,"MODEL_CLASS_MAPPINGS",[Gy]);class y0 extends Ct{}te(y0,"MODEL_CLASS_MAPPINGS",[Ky]);class v0 extends Ct{}te(v0,"MODEL_CLASS_MAPPINGS",[Bx]);class x0 extends Ct{}te(x0,"MODEL_CLASS_MAPPINGS",[Hy]);class T0 extends Ct{}te(T0,"MODEL_CLASS_MAPPINGS",[qy]);class E0 extends Ct{}te(E0,"MODEL_CLASS_MAPPINGS",[Xy]);class P0 extends Ct{}te(P0,"MODEL_CLASS_MAPPINGS",[Qy]);class C0 extends Ct{}te(C0,"MODEL_CLASS_MAPPINGS",[Jy]);class S0 extends Ct{}te(S0,"MODEL_CLASS_MAPPINGS",[Yy]);class $0 extends Ct{}te($0,"MODEL_CLASS_MAPPINGS",[Dy]);class k0 extends Ct{}te(k0,"MODEL_CLASS_MAPPINGS",[Ly]);class Nx extends pe{constructor({logits:P,past_key_values:D,encoder_outputs:ne,decoder_attentions:ge=null,cross_attentions:fe=null}){super(),this.logits=P,this.past_key_values=D,this.encoder_outputs=ne,this.decoder_attentions=ge,this.cross_attentions=fe}}class _t extends pe{constructor({logits:P,...D}){super(),this.logits=P;const ne=Object.values(D);ne.length>0&&(this.attentions=ne)}}class I0 extends pe{constructor({logits:P,embeddings:D}){super(),this.logits=P,this.embeddings=D}}class hr extends pe{constructor({logits:P}){super(),this.logits=P}}class Mr extends pe{constructor({logits:P}){super(),this.logits=P}}class Er extends pe{constructor({start_logits:P,end_logits:D}){super(),this.start_logits=P,this.end_logits=D}}class In extends pe{constructor({logits:P}){super(),this.logits=P}}class Vx extends pe{constructor({logits:P,past_key_values:D}){super(),this.logits=P,this.past_key_values=D}}class A0 extends pe{constructor({alphas:P}){super(),this.alphas=P}}class F0 extends pe{constructor({waveform:P,spectrogram:D}){super(),this.waveform=P,this.spectrogram=D}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var o=t("./src/utils/audio.js");class n extends s.FeatureExtractor{constructor(a){super(a);const l=this.config.sampling_rate,u=(0,o.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(l/2),l,null,"kaldi",!0);this.mel_filters=u,this.window=(0,o.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(a,l){return(0,o.spectrogram)(a,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:l,transpose:!0})}async _call(a){(0,s.validate_audio_inputs)(a,"ASTFeatureExtractor");const l=await this._extract_fbank_features(a,this.config.max_length);if(this.config.do_normalize){const u=this.std*2,p=l.data;for(let d=0;d<p.length;++d)p[d]=(p[d]-this.mean)/u}return{input_values:l.unsqueeze_(0)}}}},"./src/models/auto/feature_extraction_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoFeatureExtractor:()=>i});var s=t("./src/utils/constants.js"),o=t("./src/utils/hub.js");t("./src/base/feature_extraction_utils.js");var n=t("./src/models/feature_extractors.js");class i{static async from_pretrained(l,u={}){const p=await(0,o.getModelJSON)(l,s.FEATURE_EXTRACTOR_NAME,!0,u),d=p.feature_extractor_type,c=n[d];if(!c)throw new Error(`Unknown feature_extractor_type: '${d}'. Please report this at ${s.GITHUB_ISSUE_URL}.`);return new c(p)}}},"./src/models/auto/image_processing_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoImageProcessor:()=>a});var s=t("./src/utils/constants.js"),o=t("./src/utils/hub.js"),n=t("./src/base/image_processors_utils.js"),i=t("./src/models/image_processors.js");class a{static async from_pretrained(u,p={}){const d=await(0,o.getModelJSON)(u,s.IMAGE_PROCESSOR_NAME,!0,p),c=d.image_processor_type??d.feature_extractor_type;let f=i[c];return f||(c!==void 0&&console.warn(`Image processor type '${c}' not found, assuming base ImageProcessor. Please report this at ${s.GITHUB_ISSUE_URL}.`),f=n.ImageProcessor),new f(d)}}},"./src/models/auto/processing_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoProcessor:()=>u});var s=t("./src/utils/constants.js"),o=t("./src/utils/hub.js"),n=t("./src/base/processing_utils.js"),i=t("./src/models/processors.js"),a=t("./src/models/image_processors.js"),l=t("./src/models/feature_extractors.js");class u{static async from_pretrained(d,c={}){const f=await(0,o.getModelJSON)(d,s.IMAGE_PROCESSOR_NAME,!0,c),{image_processor_type:_,feature_extractor_type:T,processor_class:$}=f;if($&&i[$])return i[$].from_pretrained(d,c);if(!_&&!T)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const w={};if(_){const S=a[_];if(!S)throw new Error(`Unknown image_processor_type: '${_}'.`);w.image_processor=new S(f)}if(T){const S=a[T];if(S)w.image_processor=new S(f);else{const E=l[T];if(!E)throw new Error(`Unknown feature_extractor_type: '${T}'.`);w.feature_extractor=new E(f)}}const g={};return new n.Processor(g,w)}}},"./src/models/beit/image_processing_beit.js":(e,r,t)=>{t.r(r),t.d(r,{BeitFeatureExtractor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/bit/image_processing_bit.js":(e,r,t)=>{t.r(r),t.d(r,{BitImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/chinese_clip/image_processing_chinese_clip.js":(e,r,t)=>{t.r(r),t.d(r,{ChineseCLIPFeatureExtractor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/clap/feature_extraction_clap.js":(e,r,t)=>{t.r(r),t.d(r,{ClapFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var o=t("./src/utils/audio.js");class n extends s.FeatureExtractor{constructor(a){super(a),this.mel_filters=(0,o.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,o.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,o.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(a,l,u,p){let d;const c=a.length-l;if(c>0)if(u==="rand_trunc"){const f=Math.floor(Math.random()*(c+1));a=a.subarray(f,f+l),d=await this._extract_fbank_features(a,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${u}" not implemented`);else{if(c<0){let f=new Float64Array(l);if(f.set(a),p==="repeat")for(let _=a.length;_<l;_+=a.length)f.set(a.subarray(0,Math.min(a.length,l-_)),_);else if(p==="repeatpad")for(let _=a.length;_<-c;_+=a.length)f.set(a,_);a=f}if(u==="fusion")throw new Error(`Truncation strategy "${u}" not implemented`);d=await this._extract_fbank_features(a,this.mel_filters_slaney,this.config.nb_max_samples)}return d.unsqueeze_(0)}async _extract_fbank_features(a,l,u=null){return(0,o.spectrogram)(a,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:l,log_mel:"dB",max_num_frames:u,do_pad:!1,transpose:!0})}async _call(a,{max_length:l=null}={}){return(0,s.validate_audio_inputs)(a,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(a,l??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}},"./src/models/clip/image_processing_clip.js":(e,r,t)=>{t.r(r),t.d(r,{CLIPFeatureExtractor:()=>n,CLIPImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/convnext/image_processing_convnext.js":(e,r,t)=>{t.r(r),t.d(r,{ConvNextFeatureExtractor:()=>n,ConvNextImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(a){super(a),this.crop_pct=this.config.crop_pct??224/256}async resize(a){var u;const l=(u=this.size)==null?void 0:u.shortest_edge;if(l===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(l<384){const p=Math.floor(l/this.crop_pct),[d,c]=this.get_resize_output_image_size(a,{shortest_edge:p});a=await a.resize(d,c,{resample:this.resample}),a=await a.center_crop(l,l)}else a=await a.resize(l,l,{resample:this.resample});return a}}class n extends o{}},"./src/models/dac/feature_extraction_dac.js":(e,r,t)=>{t.r(r),t.d(r,{DacFeatureExtractor:()=>o});var s=t("./src/models/encodec/feature_extraction_encodec.js");class o extends s.EncodecFeatureExtractor{}},"./src/models/deit/image_processing_deit.js":(e,r,t)=>{t.r(r),t.d(r,{DeiTFeatureExtractor:()=>n,DeiTImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/detr/image_processing_detr.js":(e,r,t)=>{t.r(r),t.d(r,{DetrFeatureExtractor:()=>i,DetrImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");class n extends s.ImageProcessor{async _call(l){const u=await super._call(l),p=[u.pixel_values.dims[0],64,64],d=(0,o.full)(p,1n);return{...u,pixel_mask:d}}post_process_object_detection(...l){return(0,s.post_process_object_detection)(...l)}post_process_panoptic_segmentation(...l){return(0,s.post_process_panoptic_segmentation)(...l)}post_process_instance_segmentation(...l){return(0,s.post_process_instance_segmentation)(...l)}}class i extends n{}},"./src/models/donut/image_processing_donut.js":(e,r,t)=>{t.r(r),t.d(r,{DonutFeatureExtractor:()=>n,DonutImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{pad_image(a,l,u,p={}){const[d,c,f]=l;let _=this.image_mean;Array.isArray(this.image_mean)||(_=new Array(f).fill(_));let T=this.image_std;Array.isArray(T)||(T=new Array(f).fill(_));const $=_.map((w,g)=>-w/T[g]);return super.pad_image(a,l,u,{center:!0,constant_values:$,...p})}}class n extends o{}},"./src/models/dpt/image_processing_dpt.js":(e,r,t)=>{t.r(r),t.d(r,{DPTFeatureExtractor:()=>n,DPTImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/efficientnet/image_processing_efficientnet.js":(e,r,t)=>{t.r(r),t.d(r,{EfficientNetImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(i){super(i),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(a=>a*a))}}},"./src/models/encodec/feature_extraction_encodec.js":(e,r,t)=>{t.r(r),t.d(r,{EncodecFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js"),o=t("./src/utils/tensor.js");class n extends s.FeatureExtractor{async _call(a){(0,s.validate_audio_inputs)(a,"EncodecFeatureExtractor"),a instanceof Float64Array&&(a=new Float32Array(a));const l=this.config.feature_size;if(a.length%l!==0)throw new Error(`The length of the audio data must be a multiple of the number of channels (${l}).`);const u=[1,l,a.length/l];return{input_values:new o.Tensor("float32",a,u)}}}},"./src/models/feature_extractors.js":(e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>s.ASTFeatureExtractor,ClapFeatureExtractor:()=>n.ClapFeatureExtractor,DacFeatureExtractor:()=>i.DacFeatureExtractor,EncodecFeatureExtractor:()=>o.EncodecFeatureExtractor,ImageFeatureExtractor:()=>T.ImageProcessor,MoonshineFeatureExtractor:()=>a.MoonshineFeatureExtractor,PyAnnoteFeatureExtractor:()=>l.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>u.SeamlessM4TFeatureExtractor,SnacFeatureExtractor:()=>p.SnacFeatureExtractor,SpeechT5FeatureExtractor:()=>d.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>c.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>f.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>_.WhisperFeatureExtractor});var s=t("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),o=t("./src/models/encodec/feature_extraction_encodec.js"),n=t("./src/models/clap/feature_extraction_clap.js"),i=t("./src/models/dac/feature_extraction_dac.js"),a=t("./src/models/moonshine/feature_extraction_moonshine.js"),l=t("./src/models/pyannote/feature_extraction_pyannote.js"),u=t("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),p=t("./src/models/snac/feature_extraction_snac.js"),d=t("./src/models/speecht5/feature_extraction_speecht5.js"),c=t("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),f=t("./src/models/wespeaker/feature_extraction_wespeaker.js"),_=t("./src/models/whisper/feature_extraction_whisper.js"),T=t("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(e,r,t)=>{t.r(r),t.d(r,{Florence2Processor:()=>i});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");class i extends s.Processor{constructor(l,u){super(l,u);const{tasks_answer_post_processing_type:p,task_prompts_without_inputs:d,task_prompts_with_input:c}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(p??{})),this.task_prompts_without_inputs=new Map(Object.entries(d??{})),this.task_prompts_with_input=new Map(Object.entries(c??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(l){typeof l=="string"&&(l=[l]);const u=[];for(const p of l)if(this.task_prompts_without_inputs.has(p))u.push(this.task_prompts_without_inputs.get(p));else{for(const[d,c]of this.task_prompts_with_input)if(p.includes(d)){u.push(c.replaceAll("{input}",p).replaceAll(d,""));break}u.length!==l.length&&u.push(p)}return u}post_process_generation(l,u,p){const d=this.tasks_answer_post_processing_type.get(u)??"pure_text";l=l.replaceAll("<s>","").replaceAll("</s>","");let c;switch(d){case"pure_text":c=l;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const f=d==="ocr"?"quad_boxes":"bboxes",_=l.matchAll(this.regexes[f]),T=[],$=[];for(const[w,g,...S]of _)T.push(g?g.trim():T.at(-1)??""),$.push(S.map((E,y)=>(Number(E)+.5)/this.size_per_bin*p[y%2]));c={labels:T,[f]:$};break;default:throw new Error(`Task "${u}" (of type "${d}") not yet implemented.`)}return{[u]:c}}async _call(l,u=null,p={}){if(!l&&!u)throw new Error("Either text or images must be provided");const d=await this.image_processor(l,p),c=u?this.tokenizer(u,p):{};return{...d,...c}}}te(i,"tokenizer_class",n.AutoTokenizer),te(i,"image_processor_class",o.AutoImageProcessor)},"./src/models/glpn/image_processing_glpn.js":(e,r,t)=>{t.r(r),t.d(r,{GLPNFeatureExtractor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/grounding_dino/image_processing_grounding_dino.js":(e,r,t)=>{t.r(r),t.d(r,{GroundingDinoImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");class n extends s.ImageProcessor{async _call(a){const l=await super._call(a),u=l.pixel_values.dims,p=(0,o.ones)([u[0],u[2],u[3]]);return{...l,pixel_mask:p}}}},"./src/models/grounding_dino/processing_grounding_dino.js":(e,r,t)=>{t.r(r),t.d(r,{GroundingDinoProcessor:()=>l});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js"),i=t("./src/base/image_processors_utils.js");function a(u,p){const c=u.dims.at(-1)-1,f=u.tolist();f.fill(!1,0,1),f.fill(!1,c);const _=p.tolist();return f.map((T,$)=>T?$:null).filter(T=>T!==null).map(T=>_[T])}class l extends s.Processor{async _call(p,d,c={}){const f=p?await this.image_processor(p,c):{};return{...d?this.tokenizer(d,c):{},...f}}post_process_grounded_object_detection(p,d,{box_threshold:c=.25,text_threshold:f=.25,target_sizes:_=null}={}){const{logits:T,pred_boxes:$}=p,w=T.dims[0];if(_!==null&&_.length!==w)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const g=T.dims.at(1),S=T.sigmoid(),E=S.max(-1).tolist(),y=$.tolist().map(v=>v.map(C=>(0,i.center_to_corners_format)(C))),M=[];for(let v=0;v<w;++v){const C=_!==null?_[v]:null;C!==null&&(y[v]=y[v].map(j=>j.map((ee,H)=>ee*C[(H+1)%2])));const A=E[v],B=[],K=[],G=[];for(let j=0;j<g;++j){const ee=A[j];if(ee<=c)continue;const H=y[v][j],Z=S[v][j];B.push(ee),G.push(H);const X=a(Z.gt(f),d[v]);K.push(X)}M.push({scores:B,boxes:G,labels:this.batch_decode(K)})}return M}}te(l,"tokenizer_class",n.AutoTokenizer),te(l,"image_processor_class",o.AutoImageProcessor)},"./src/models/idefics3/image_processing_idefics3.js":(e,r,t)=>{t.r(r),t.d(r,{Idefics3ImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");class n extends s.ImageProcessor{constructor(a){super(a),this.do_image_splitting=a.do_image_splitting??!0,this.max_image_size=a.max_image_size}get_resize_for_vision_encoder(a,l){let[u,p]=a.dims.slice(-2);const d=p/u;return p>=u?(p=Math.ceil(p/l)*l,u=Math.floor(p/d),u=Math.ceil(u/l)*l):(u=Math.ceil(u/l)*l,p=Math.floor(u*d),p=Math.ceil(p/l)*l),{height:u,width:p}}async _call(a,{do_image_splitting:l=null,return_row_col_info:u=!1}={}){let p;if(!Array.isArray(a))p=[[a]];else{if(a.length===0||!a[0])throw new Error("No images provided.");Array.isArray(a[0])?p=a:p=[a]}let d=[],c=[],f=[];const _=[],T=[];for(const v of p){let C=await Promise.all(v.map(K=>this.preprocess(K)));_.push(...C.map(K=>K.original_size)),T.push(...C.map(K=>K.reshaped_input_size)),C.forEach(K=>K.pixel_values.unsqueeze_(0));const{longest_edge:A}=this.max_image_size;let B;if(l??this.do_image_splitting){let K=new Array(C.length),G=new Array(C.length);B=await Promise.all(C.map(async(j,ee)=>{const H=this.get_resize_for_vision_encoder(j.pixel_values,A),Z=await(0,o.interpolate_4d)(j.pixel_values,{size:[H.height,H.width]}),{frames:X,num_splits_h:oe,num_splits_w:me}=await this.split_image(Z,this.max_image_size);return K[ee]=oe,G[ee]=me,(0,o.cat)(X,0)})),c.push(K),f.push(G)}else{const K=[A,A];B=await Promise.all(C.map(G=>(0,o.interpolate_4d)(G.pixel_values,{size:K}))),c.push(new Array(C.length).fill(0)),f.push(new Array(C.length).fill(0))}d.push((0,o.cat)(B,0))}const $=d.length,[w,g,S,E]=d[0].dims;let y,M;if($===1)y=d[0].unsqueeze_(0),M=(0,o.full)([$,w,S,E],!0);else{const v=Math.max(...d.map(B=>B.dims.at(0)));M=(0,o.full)([$,v,S,E],!0);const C=M.data,A=v*S*E;for(let B=0;B<$;++B){const K=d[B].dims[0];if(K<v){d[B]=(0,o.cat)([d[B],(0,o.full)([v-K,g,S,E],0)],0);const G=B*A+K*S*E,j=(B+1)*A;C.fill(!1,G,j)}}y=(0,o.stack)(d,0)}return{pixel_values:y,pixel_attention_mask:M,original_sizes:_,reshaped_input_sizes:T,...u?{rows:c,cols:f}:{}}}async split_image(a,{longest_edge:l}){const u=l,p=l,d=[],[c,f]=a.dims.slice(-2);let _=0,T=0;if(c>u||f>p){_=Math.ceil(c/u),T=Math.ceil(f/p);const $=Math.ceil(c/_),w=Math.ceil(f/T);for(let E=0;E<_;++E)for(let y=0;y<T;++y){let M,v,C,A;E===_-1?(v=c-$,A=c):(v=E*$,A=(E+1)*$),y===T-1?(M=f-w,C=f):(M=y*w,C=(y+1)*w);const B=[v,M],K=[A,C],G=await(0,o.slice)(a,B,K,[2,3]);d.push(G)}const g=u,S=p;(c!==g||f!==S)&&(a=await(0,o.interpolate_4d)(a,{size:[g,S]}))}return d.push(a),{frames:d,num_splits_h:_,num_splits_w:T}}}},"./src/models/idefics3/processing_idefics3.js":(e,r,t)=>{t.r(r),t.d(r,{Idefics3Processor:()=>p});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");t("./src/utils/image.js");var i=t("./src/utils/core.js");function a(d,c,f,_,T,$){let w="";for(let g=0;g<c;++g){for(let S=0;S<f;++S)w+=_+`<row_${g+1}_col_${S+1}>`+T.repeat(d);w+=`
`}return w+=`
${_}${$}`+T.repeat(d)+`${_}`,w}function l(d,c,f,_){return`${c}${_}`+f.repeat(d)+`${c}`}function u(d,c,f,_,T,$){return d===0&&c===0?l(f,_,T,$):a(f,d,c,_,T,$)}class p extends s.Processor{constructor(){super(...arguments);te(this,"fake_image_token","<fake_token_around_image>");te(this,"image_token","<image>");te(this,"global_img_token","<global-img>")}async _call(f,_=null,T={}){T.return_row_col_info??(T.return_row_col_info=!0);let $;_&&($=await this.image_processor(_,T)),Array.isArray(f)||(f=[f]);const w=$.rows??[new Array(f.length).fill(0)],g=$.cols??[new Array(f.length).fill(0)],S=this.config.image_seq_len,E=[],y=[];for(let v=0;v<f.length;++v){const C=f[v],A=w[v],B=g[v];E.push((0,i.count)(C,this.image_token));const K=A.map((ee,H)=>u(ee,B[H],S,this.fake_image_token,this.image_token,this.global_img_token)),G=C.split(this.image_token);if(G.length===0)throw new Error("The image token should be present in the text.");let j=G[0];for(let ee=0;ee<K.length;++ee)j+=K[ee]+G[ee+1];y.push(j)}return{...this.tokenizer(y),...$}}}te(p,"image_processor_class",o.AutoImageProcessor),te(p,"tokenizer_class",n.AutoTokenizer),te(p,"uses_processor_config",!0)},"./src/models/image_processors.js":(e,r,t)=>{t.r(r),t.d(r,{BeitFeatureExtractor:()=>s.BeitFeatureExtractor,BitImageProcessor:()=>o.BitImageProcessor,CLIPFeatureExtractor:()=>i.CLIPFeatureExtractor,CLIPImageProcessor:()=>i.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>n.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>a.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>a.ConvNextImageProcessor,DPTFeatureExtractor:()=>d.DPTFeatureExtractor,DPTImageProcessor:()=>d.DPTImageProcessor,DeiTFeatureExtractor:()=>l.DeiTFeatureExtractor,DeiTImageProcessor:()=>l.DeiTImageProcessor,DetrFeatureExtractor:()=>u.DetrFeatureExtractor,DetrImageProcessor:()=>u.DetrImageProcessor,DonutFeatureExtractor:()=>p.DonutFeatureExtractor,DonutImageProcessor:()=>p.DonutImageProcessor,EfficientNetImageProcessor:()=>c.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>f.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>_.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>T.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>w.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>g.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>S.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>E.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>E.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>y.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>y.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>M.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>M.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>v.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>v.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>C.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>C.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>A.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>A.MobileViTImageProcessor,NougatImageProcessor:()=>B.NougatImageProcessor,OwlViTFeatureExtractor:()=>G.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>G.OwlViTImageProcessor,Owlv2ImageProcessor:()=>K.Owlv2ImageProcessor,Phi3VImageProcessor:()=>j.Phi3VImageProcessor,PvtImageProcessor:()=>ee.PvtImageProcessor,Qwen2VLImageProcessor:()=>H.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>Z.RTDetrImageProcessor,SamImageProcessor:()=>X.SamImageProcessor,SegformerFeatureExtractor:()=>oe.SegformerFeatureExtractor,SegformerImageProcessor:()=>oe.SegformerImageProcessor,SiglipImageProcessor:()=>me.SiglipImageProcessor,SmolVLMImageProcessor:()=>ae.SmolVLMImageProcessor,Swin2SRImageProcessor:()=>V.Swin2SRImageProcessor,VLMImageProcessor:()=>$.VLMImageProcessor,ViTFeatureExtractor:()=>F.ViTFeatureExtractor,ViTImageProcessor:()=>F.ViTImageProcessor,VitMatteImageProcessor:()=>W.VitMatteImageProcessor,VitPoseImageProcessor:()=>re.VitPoseImageProcessor,YolosFeatureExtractor:()=>_e.YolosFeatureExtractor,YolosImageProcessor:()=>_e.YolosImageProcessor});var s=t("./src/models/beit/image_processing_beit.js"),o=t("./src/models/bit/image_processing_bit.js"),n=t("./src/models/chinese_clip/image_processing_chinese_clip.js"),i=t("./src/models/clip/image_processing_clip.js"),a=t("./src/models/convnext/image_processing_convnext.js"),l=t("./src/models/deit/image_processing_deit.js"),u=t("./src/models/detr/image_processing_detr.js"),p=t("./src/models/donut/image_processing_donut.js"),d=t("./src/models/dpt/image_processing_dpt.js"),c=t("./src/models/efficientnet/image_processing_efficientnet.js"),f=t("./src/models/glpn/image_processing_glpn.js"),_=t("./src/models/grounding_dino/image_processing_grounding_dino.js"),T=t("./src/models/idefics3/image_processing_idefics3.js"),$=t("./src/models/janus/image_processing_janus.js"),w=t("./src/models/jina_clip/image_processing_jina_clip.js"),g=t("./src/models/llava_onevision/image_processing_llava_onevision.js"),S=t("./src/models/mask2former/image_processing_mask2former.js"),E=t("./src/models/maskformer/image_processing_maskformer.js"),y=t("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),M=t("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),v=t("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),C=t("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),A=t("./src/models/mobilevit/image_processing_mobilevit.js"),B=t("./src/models/nougat/image_processing_nougat.js"),K=t("./src/models/owlv2/image_processing_owlv2.js"),G=t("./src/models/owlvit/image_processing_owlvit.js"),j=t("./src/models/phi3_v/image_processing_phi3_v.js"),ee=t("./src/models/pvt/image_processing_pvt.js"),H=t("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),Z=t("./src/models/rt_detr/image_processing_rt_detr.js"),X=t("./src/models/sam/image_processing_sam.js"),oe=t("./src/models/segformer/image_processing_segformer.js"),me=t("./src/models/siglip/image_processing_siglip.js"),ae=t("./src/models/smolvlm/image_processing_smolvlm.js"),V=t("./src/models/swin2sr/image_processing_swin2sr.js"),F=t("./src/models/vit/image_processing_vit.js"),W=t("./src/models/vitmatte/image_processing_vitmatte.js"),re=t("./src/models/vitpose/image_processing_vitpose.js"),_e=t("./src/models/yolos/image_processing_yolos.js")},"./src/models/janus/image_processing_janus.js":(e,r,t)=>{t.r(r),t.d(r,{VLMImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(i){super({do_pad:!0,pad_size:{width:i.image_size,height:i.image_size},...i}),this.constant_values=this.config.background_color.map(a=>a*this.rescale_factor)}pad_image(i,a,l,u){return super.pad_image(i,a,l,{constant_values:this.constant_values,center:!0,...u})}}},"./src/models/janus/processing_janus.js":(e,r,t)=>{t.r(r),t.d(r,{VLChatProcessor:()=>u});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js"),i=t("./src/utils/core.js"),a=t("./src/utils/tensor.js"),l=t("./src/utils/image.js");class u extends s.Processor{constructor(d,c){super(d,c),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(d,{images:c=null,chat_template:f="default"}={}){c?Array.isArray(c)||(c=[c]):c=await Promise.all(d.filter(B=>B.images).flatMap(B=>B.images).map(B=>l.RawImage.read(B)));const _=this.tokenizer,T=_.apply_chat_template(d,{tokenize:!1,add_generation_prompt:!0,chat_template:f}),$=B=>_.encode(B,{add_special_tokens:!1}),w=T.split(this.image_tag),g=w.length-1;if(c.length!==g)throw new Error(`Number of images provided (${c.length}) does not match number of "${this.image_tag}" image tags (${g})`);const[S,E,y]=_.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let M=$(w[0]),v=new Array(M.length).fill(!1);for(let B=1;B<w.length;++B){const K=new Array(this.num_image_tokens).fill(S),G=$(w[B]);M=(0,i.mergeArrays)(M,[E],K,[y],G);const j=new Array(this.num_image_tokens).fill(!0);v=(0,i.mergeArrays)(v,[!1],j,[!1],new Array(G.length).fill(!1))}const C=[1,M.length],A={input_ids:new a.Tensor("int64",M,C),attention_mask:new a.Tensor("int64",new Array(M.length).fill(1),C),images_seq_mask:new a.Tensor("bool",v,C),images_emb_mask:new a.Tensor("bool",new Array(g*this.num_image_tokens).fill(!0),[1,g,this.num_image_tokens])};if(c&&c.length>0){const B=await this.image_processor(c);return B.pixel_values.unsqueeze_(0),{...A,...B}}return A}}te(u,"image_processor_class",o.AutoImageProcessor),te(u,"tokenizer_class",n.AutoTokenizer),te(u,"uses_processor_config",!0)},"./src/models/jina_clip/image_processing_jina_clip.js":(e,r,t)=>{t.r(r),t.d(r,{JinaCLIPImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(i){const{resize_mode:a,fill_color:l,interpolation:u,size:p,...d}=i,c=a==="squash"?{width:p,height:p}:a==="shortest"?{shortest_edge:p}:{longest_edge:p},f=u==="bicubic"?3:2;super({...d,size:c,resample:f,do_center_crop:!0,crop_size:p,do_normalize:!0})}}},"./src/models/jina_clip/processing_jina_clip.js":(e,r,t)=>{t.r(r),t.d(r,{JinaCLIPProcessor:()=>i});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");class i extends s.Processor{async _call(l=null,u=null,p={}){if(!l&&!u)throw new Error("Either text or images must be provided");const d=l?this.tokenizer(l,p):{},c=u?await this.image_processor(u,p):{};return{...d,...c}}}te(i,"tokenizer_class",n.AutoTokenizer),te(i,"image_processor_class",o.AutoImageProcessor)},"./src/models/llava_onevision/image_processing_llava_onevision.js":(e,r,t)=>{t.r(r),t.d(r,{LlavaOnevisionImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/mask2former/image_processing_mask2former.js":(e,r,t)=>{t.r(r),t.d(r,{Mask2FormerImageProcessor:()=>o});var s=t("./src/models/maskformer/image_processing_maskformer.js");class o extends s.MaskFormerImageProcessor{}},"./src/models/maskformer/image_processing_maskformer.js":(e,r,t)=>{t.r(r),t.d(r,{MaskFormerFeatureExtractor:()=>n,MaskFormerImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{post_process_panoptic_segmentation(...a){return(0,s.post_process_panoptic_segmentation)(...a)}post_process_instance_segmentation(...a){return(0,s.post_process_instance_segmentation)(...a)}}class n extends o{}},"./src/models/mgp_str/processing_mgp_str.js":(e,r,t)=>{t.r(r),t.d(r,{MgpstrProcessor:()=>l});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js"),i=t("./src/utils/maths.js");const a={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class l extends s.Processor{get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(p,d){if(!a.hasOwnProperty(d))throw new Error(`Format ${d} is not supported.`);const[c,f]=a[d],_=this[c].bind(this),[T,$]=p.dims,w=[],g=[],S=p.tolist();for(let y=0;y<T;++y){const M=S[y],v=[],C=[];for(let B=1;B<$;++B){const[K,G]=(0,i.max)((0,i.softmax)(M[B]));if(C.push(K),G==f)break;v.push(G)}const A=C.length>0?C.reduce((B,K)=>B*K,1):0;g.push(v),w.push(A)}return[_(g),w]}char_decode(p){return this.char_tokenizer.batch_decode(p).map(d=>d.replaceAll(" ",""))}bpe_decode(p){return this.bpe_tokenizer.batch_decode(p)}wp_decode(p){return this.wp_tokenizer.batch_decode(p).map(d=>d.replaceAll(" ",""))}batch_decode([p,d,c]){const[f,_]=this._decode_helper(p,"char"),[T,$]=this._decode_helper(d,"bpe"),[w,g]=this._decode_helper(c,"wp"),S=[],E=[];for(let y=0;y<f.length;++y){const[M,v]=(0,i.max)([_[y],$[y],g[y]]);S.push([f[y],T[y],w[y]][v]),E.push(M)}return{generated_text:S,scores:E,char_preds:f,bpe_preds:T,wp_preds:w}}static async from_pretrained(...p){const d=await super.from_pretrained(...p),c=await n.AutoTokenizer.from_pretrained("Xenova/gpt2"),f=await n.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased");return d.components={image_processor:d.image_processor,char_tokenizer:d.tokenizer,bpe_tokenizer:c,wp_tokenizer:f},d}async _call(p,d=null){const c=await this.image_processor(p);return d&&(c.labels=this.tokenizer(d).input_ids),c}}te(l,"tokenizer_class",n.AutoTokenizer),te(l,"image_processor_class",o.AutoImageProcessor)},"./src/models/mobilenet_v1/image_processing_mobilenet_v1.js":(e,r,t)=>{t.r(r),t.d(r,{MobileNetV1FeatureExtractor:()=>n,MobileNetV1ImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/mobilenet_v2/image_processing_mobilenet_v2.js":(e,r,t)=>{t.r(r),t.d(r,{MobileNetV2FeatureExtractor:()=>n,MobileNetV2ImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/mobilenet_v3/image_processing_mobilenet_v3.js":(e,r,t)=>{t.r(r),t.d(r,{MobileNetV3FeatureExtractor:()=>n,MobileNetV3ImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/mobilenet_v4/image_processing_mobilenet_v4.js":(e,r,t)=>{t.r(r),t.d(r,{MobileNetV4FeatureExtractor:()=>n,MobileNetV4ImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/mobilevit/image_processing_mobilevit.js":(e,r,t)=>{t.r(r),t.d(r,{MobileViTFeatureExtractor:()=>n,MobileViTImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/moonshine/feature_extraction_moonshine.js":(e,r,t)=>{t.r(r),t.d(r,{MoonshineFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js"),o=t("./src/utils/tensor.js");class n extends s.FeatureExtractor{async _call(a){(0,s.validate_audio_inputs)(a,"MoonshineFeatureExtractor"),a instanceof Float64Array&&(a=new Float32Array(a));const l=[1,a.length];return{input_values:new 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For this call, we will infer how many images each text has and add special tokens."),T=d.map(g=>a(g,f,_,i,p.length)));const $=this.tokenizer(T,c);return{...await this.image_processor(p,c),...$}}}te(l,"tokenizer_class",n.AutoTokenizer),te(l,"image_processor_class",o.AutoImageProcessor),te(l,"uses_processor_config",!1)},"./src/models/phi3_v/image_processing_phi3_v.js":(e,r,t)=>{t.r(r),t.d(r,{Phi3VImageProcessor:()=>p});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");const n=336,i=[2,3],{ceil:a,floor:l,sqrt:u}=Math;class p extends s.ImageProcessor{constructor(c){super({...c,do_normalize:!0,do_pad:!0,pad_size:"custom",do_convert_rgb:!0,do_resize:!0}),this._num_crops=c.num_crops}calc_num_image_tokens_from_image_size(c,f){const{num_img_tokens:_}=this.config;return l((l(f/n)*l(c/n)+1)*_+1+(l(f/n)+1)*u(_))}get_resize_output_image_size(c,f){const _=this._num_crops,[T,$]=c.size;let w=T/$,g=1;for(;g*Math.ceil(g/w)<=_;)g+=1;g-=1;const S=Math.floor(g*336),E=Math.floor(S/w);return[S,E]}pad_image(c,f,_,T={}){const[$,w]=f,g=n*a($/n),S=n*a(w/n),E=[1,1,1].map((y,M)=>(y-this.image_mean[M])/this.image_std[M]);return super.pad_image(c,f,{width:S,height:g},{center:!0,constant_values:E,...T})}async _call(c,{num_crops:f=null}={}){if(this._num_crops=f??(f=this.config.num_crops),f<4||u(f)%1!==0)throw new Error("num_crops must be a square number >= 4");Array.isArray(c)||(c=[c]);const _=c.length,T=await Promise.all(c.map(v=>this.preprocess(v))),$=T.map(v=>v.original_size),w=T.map(v=>v.reshaped_input_size),g=[];for(const{pixel_values:v}of T){v.unsqueeze_(0);const[C,A]=v.dims.slice(-2),B=await(0,o.interpolate_4d)(v,{size:[n,n],mode:"bicubic"});if(f>0){const K=[],G=u(f),j=l(A/G),ee=l(C/G);for(let Z=0;Z<G;++Z)for(let X=0;X<G;++X){let oe,me,ae,V;Z===G-1?(me=C-ee,V=C):(me=Z*ee,V=(Z+1)*ee),X===G-1?(oe=A-j,ae=A):(oe=X*j,ae=(X+1)*j);const F=[me,oe],W=[V,ae],re=await(0,o.slice)(v,F,W,i);K.push(re)}const H=await(0,o.interpolate_4d)((0,o.cat)(K,0),{size:[n,n],mode:"bicubic"});g.push((0,o.cat)([B,H],0))}else g.push(B)}const S=(0,o.stack)(g,0),E=w.map(v=>v.map(C=>n*a(C/n))),y=new o.Tensor("int64",E.flat(),[_,2]),M=E.map(([v,C])=>this.calc_num_image_tokens_from_image_size(C,v));return{pixel_values:S,original_sizes:$,reshaped_input_sizes:w,image_sizes:y,num_img_tokens:M}}}},"./src/models/phi3_v/processing_phi3_v.js":(e,r,t)=>{t.r(r),t.d(r,{Phi3VProcessor:()=>l});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");t("./src/utils/image.js");const i="<|image|>",a=/<\|image_\d+\|>/g;class l extends s.Processor{async _call(p,d=null,{padding:c=!0,truncation:f=!0,num_crops:_=null}={}){Array.isArray(p)||(p=[p]);let T,$;if(d){$=await this.image_processor(d,{num_crops:_});const{num_img_tokens:w}=$,g=p.map((E,y)=>E.split(a).join(i.repeat(w[y])));T=this.tokenizer(g,{padding:c,truncation:f});const S=this.tokenizer.model.convert_tokens_to_ids([i])[0];T.input_ids.map_(E=>E==S?-E:E)}else T=this.tokenizer(p);return{...T,...$}}}te(l,"image_processor_class",o.AutoImageProcessor),te(l,"tokenizer_class",n.AutoTokenizer)},"./src/models/processors.js":(e,r,t)=>{t.r(r),t.d(r,{Florence2Processor:()=>s.Florence2Processor,GroundingDinoProcessor:()=>o.GroundingDinoProcessor,Idefics3Processor:()=>n.Idefics3Processor,JinaCLIPProcessor:()=>a.JinaCLIPProcessor,MgpstrProcessor:()=>l.MgpstrProcessor,MoonshineProcessor:()=>u.MoonshineProcessor,OwlViTProcessor:()=>p.OwlViTProcessor,PaliGemmaProcessor:()=>c.PaliGemmaProcessor,Phi3VProcessor:()=>d.Phi3VProcessor,PyAnnoteProcessor:()=>f.PyAnnoteProcessor,Qwen2VLProcessor:()=>_.Qwen2VLProcessor,SamProcessor:()=>T.SamProcessor,SmolVLMProcessor:()=>$.SmolVLMProcessor,SpeechT5Processor:()=>w.SpeechT5Processor,UltravoxProcessor:()=>g.UltravoxProcessor,VLChatProcessor:()=>i.VLChatProcessor,Wav2Vec2Processor:()=>S.Wav2Vec2Processor,Wav2Vec2ProcessorWithLM:()=>E.Wav2Vec2ProcessorWithLM,WhisperProcessor:()=>y.WhisperProcessor});var s=t("./src/models/florence2/processing_florence2.js"),o=t("./src/models/grounding_dino/processing_grounding_dino.js"),n=t("./src/models/idefics3/processing_idefics3.js"),i=t("./src/models/janus/processing_janus.js"),a=t("./src/models/jina_clip/processing_jina_clip.js"),l=t("./src/models/mgp_str/processing_mgp_str.js"),u=t("./src/models/moonshine/processing_moonshine.js"),p=t("./src/models/owlvit/processing_owlvit.js"),d=t("./src/models/phi3_v/processing_phi3_v.js"),c=t("./src/models/paligemma/processing_paligemma.js"),f=t("./src/models/pyannote/processing_pyannote.js"),_=t("./src/models/qwen2_vl/processing_qwen2_vl.js"),T=t("./src/models/sam/processing_sam.js"),$=t("./src/models/smolvlm/processing_smolvlm.js"),w=t("./src/models/speecht5/processing_speecht5.js"),g=t("./src/models/ultravox/processing_ultravox.js"),S=t("./src/models/wav2vec2/processing_wav2vec2.js"),E=t("./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js"),y=t("./src/models/whisper/processing_whisper.js")},"./src/models/pvt/image_processing_pvt.js":(e,r,t)=>{t.r(r),t.d(r,{PvtImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/pyannote/feature_extraction_pyannote.js":(e,r,t)=>{t.r(r),t.d(r,{PyAnnoteFeatureExtractor:()=>i});var s=t("./src/base/feature_extraction_utils.js"),o=t("./src/utils/tensor.js"),n=t("./src/utils/maths.js");class i extends s.FeatureExtractor{async _call(l){(0,s.validate_audio_inputs)(l,"PyAnnoteFeatureExtractor"),l instanceof Float64Array&&(l=new Float32Array(l));const u=[1,1,l.length];return{input_values:new o.Tensor("float32",l,u)}}samples_to_frames(l){return(l-this.config.offset)/this.config.step}post_process_speaker_diarization(l,u){const p=u/this.samples_to_frames(u)/this.config.sampling_rate,d=[];for(const c of l.tolist()){const f=[];let _=-1;for(let T=0;T<c.length;++T){const $=(0,n.softmax)(c[T]),[w,g]=(0,n.max)($),[S,E]=[T,T+1];g!==_?(_=g,f.push({id:g,start:S,end:E,score:w})):(f.at(-1).end=E,f.at(-1).score+=w)}d.push(f.map(({id:T,start:$,end:w,score:g})=>({id:T,start:$*p,end:w*p,confidence:g/(w-$)})))}return d}}},"./src/models/pyannote/processing_pyannote.js":(e,r,t)=>{t.r(r),t.d(r,{PyAnnoteProcessor:()=>n});var s=t("./src/base/processing_utils.js"),o=t("./src/models/pyannote/feature_extraction_pyannote.js");class n extends s.Processor{async _call(a){return await this.feature_extractor(a)}post_process_speaker_diarization(...a){return this.feature_extractor.post_process_speaker_diarization(...a)}get sampling_rate(){return this.feature_extractor.config.sampling_rate}}te(n,"feature_extractor_class",o.PyAnnoteFeatureExtractor)},"./src/models/qwen2_vl/image_processing_qwen2_vl.js":(e,r,t)=>{t.r(r),t.d(r,{Qwen2VLImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");class n extends s.ImageProcessor{async 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if(c.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let f=0;f<l.length;++f){let _=u[f],T=p[f],$=[T[0]/_[0],T[1]/_[1]];for(let w=0;w<l[f].length;++w)for(let g=0;g<l[f][w].length;++g)for(let S=0;S<l[f][w][g].length;++S)l[f][w][g][S]*=$[S%2]}return new n.Tensor("float32",Float32Array.from(l.flat(1/0)),c)}add_input_labels(l,u){let p=(0,o.calculateDimensions)(l);if(p.length===2)p=[1,...p],l=[l];else if(p.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(p.some((d,c)=>d!==u.dims[c]))throw Error(`The first ${p.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new n.Tensor("int64",l.flat(1/0).map(BigInt),p)}async _call(l,{input_points:u=null,input_labels:p=null,input_boxes:d=null}={}){const c=await super._call(l);if(u&&(c.input_points=this.reshape_input_points(u,c.original_sizes,c.reshaped_input_sizes)),p){if(!c.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");c.input_labels=this.add_input_labels(p,c.input_points)}return d&&(c.input_boxes=this.reshape_input_points(d,c.original_sizes,c.reshaped_input_sizes,!0)),c}async post_process_masks(l,u,p,{mask_threshold:d=0,binarize:c=!0,pad_size:f=null}={}){const _=[];f=f??this.pad_size;const T=[f.height,f.width];for(let $=0;$<u.length;++$){const w=u[$],g=p[$];let S=await(0,n.interpolate_4d)(l[$],{mode:"bilinear",size:T});if(S=S.slice(null,null,[0,g[0]],[0,g[1]]),S=await(0,n.interpolate_4d)(S,{mode:"bilinear",size:w}),c){const E=S.data,y=new Uint8Array(E.length);for(let M=0;M<E.length;++M)E[M]>d&&(y[M]=1);S=new n.Tensor("bool",y,S.dims)}_.push(S)}return 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this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=a([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=a([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=a([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=a([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=a([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=a([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=a([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}te(l,"session_options",{})},"./src/pipelines.js":(e,r,t)=>{t.r(r),t.d(r,{AudioClassificationPipeline:()=>G,AutomaticSpeechRecognitionPipeline:()=>ee,BackgroundRemovalPipeline:()=>oe,DepthEstimationPipeline:()=>_e,DocumentQuestionAnsweringPipeline:()=>F,FeatureExtractionPipeline:()=>B,FillMaskPipeline:()=>S,ImageClassificationPipeline:()=>Z,ImageFeatureExtractionPipeline:()=>K,ImageSegmentationPipeline:()=>X,ImageToImagePipeline:()=>re,ImageToTextPipeline:()=>H,ObjectDetectionPipeline:()=>ae,Pipeline:()=>T,QuestionAnsweringPipeline:()=>g,SummarizationPipeline:()=>y,Text2TextGenerationPipeline:()=>E,TextClassificationPipeline:()=>$,TextGenerationPipeline:()=>C,TextToAudioPipeline:()=>W,TokenClassificationPipeline:()=>w,TranslationPipeline:()=>M,ZeroShotAudioClassificationPipeline:()=>j,ZeroShotClassificationPipeline:()=>A,ZeroShotImageClassificationPipeline:()=>me,ZeroShotObjectDetectionPipeline:()=>V,pipeline:()=>$e});var s=t("./src/tokenizers.js"),o=t("./src/models.js"),n=t("./src/models/auto/processing_auto.js");t("./src/base/processing_utils.js");var i=t("./src/utils/generic.js"),a=t("./src/utils/core.js"),l=t("./src/utils/maths.js"),u=t("./src/utils/audio.js"),p=t("./src/utils/tensor.js"),d=t("./src/utils/image.js");async function c(we){return Array.isArray(we)||(we=[we]),await Promise.all(we.map(q=>d.RawImage.read(q)))}async function f(we,q){return Array.isArray(we)||(we=[we]),await Promise.all(we.map(R=>typeof R=="string"||R instanceof URL?(0,u.read_audio)(R,q):R instanceof Float64Array?new Float32Array(R):R))}function _(we,q){q&&(we=we.map(Se=>Se|0));const[R,pe,xe,be]=we;return{xmin:R,ymin:pe,xmax:xe,ymax:be}}class T extends i.Callable{constructor({task:q,model:R,tokenizer:pe=null,processor:xe=null}){super(),this.task=q,this.model=R,this.tokenizer=pe,this.processor=xe}async dispose(){await this.model.dispose()}}class $ extends T{constructor(q){super(q)}async _call(q,{top_k:R=1}={}){const pe=this.tokenizer(q,{padding:!0,truncation:!0}),xe=await this.model(pe),be=this.model.config.problem_type==="multi_label_classification"?Fe=>Fe.sigmoid():Fe=>new p.Tensor("float32",(0,l.softmax)(Fe.data),Fe.dims),Se=this.model.config.id2label,Ae=[];for(const Fe of xe.logits){const ze=be(Fe),Ve=await(0,p.topk)(ze,R),O=Ve[0].tolist(),z=Ve[1].tolist().map((J,le)=>({label:Se?Se[J]:`LABEL_${J}`,score:O[le]}));R===1?Ae.push(...z):Ae.push(z)}return Array.isArray(q)||R===1?Ae:Ae[0]}}class w extends T{constructor(q){super(q)}async _call(q,{ignore_labels:R=["O"]}={}){const pe=Array.isArray(q),xe=this.tokenizer(pe?q:[q],{padding:!0,truncation:!0}),Se=(await this.model(xe)).logits,Ae=this.model.config.id2label,Fe=[];for(let ze=0;ze<Se.dims[0];++ze){const Ve=xe.input_ids[ze],O=Se[ze],Y=[];for(let z=0;z<O.dims[0];++z){const J=O[z],le=(0,l.max)(J.data)[1],ye=Ae?Ae[le]:`LABEL_${le}`;if(R.includes(ye))continue;const Ee=this.tokenizer.decode([Ve[z].item()],{skip_special_tokens:!0});if(Ee==="")continue;const ke=(0,l.softmax)(J.data);Y.push({entity:ye,score:ke[le],index:z,word:Ee})}Fe.push(Y)}return pe?Fe:Fe[0]}}class g extends T{constructor(q){super(q)}async _call(q,R,{top_k:pe=1}={}){const xe=this.tokenizer(q,{text_pair:R,padding:!0,truncation:!0}),{start_logits:be,end_logits:Se}=await this.model(xe),Ae=xe.input_ids.tolist(),Fe=xe.attention_mask.tolist(),ze=this.tokenizer.all_special_ids,Ve=[];for(let O=0;O<be.dims[0];++O){const Y=Ae[O],z=Y.findIndex(Ie=>Ie==this.tokenizer.sep_token_id);Fe[O].map((Ie,Re)=>Ie==1&&(Re===0||Re>z&&ze.findIndex(Xe=>Xe==Y[Re])===-1));const J=be[O].tolist(),le=Se[O].tolist();for(let Ie=1;Ie<J.length;++Ie)(Fe[O]==0||Ie<=z||ze.findIndex(Re=>Re==Y[Ie])!==-1)&&(J[Ie]=-1/0,le[Ie]=-1/0);const ye=(0,l.softmax)(J).map((Ie,Re)=>[Ie,Re]),Ee=(0,l.softmax)(le).map((Ie,Re)=>[Ie,Re]);ye[0][0]=0,Ee[0][0]=0;const ke=(0,a.product)(ye,Ee).filter(Ie=>Ie[0][1]<=Ie[1][1]).map(Ie=>[Ie[0][1],Ie[1][1],Ie[0][0]*Ie[1][0]]).sort((Ie,Re)=>Re[2]-Ie[2]);for(let Ie=0;Ie<Math.min(ke.length,pe);++Ie){const[Re,Xe,Ge]=ke[Ie],lt=Y.slice(Re,Xe+1),wt=this.tokenizer.decode(lt,{skip_special_tokens:!0});Ve.push({answer:wt,score:Ge})}}return pe===1?Ve[0]:Ve}}class S extends T{constructor(q){super(q)}async _call(q,{top_k:R=5}={}){const pe=this.tokenizer(q,{padding:!0,truncation:!0}),{logits:xe}=await this.model(pe),be=[],Se=pe.input_ids.tolist();for(let Ae=0;Ae<Se.length;++Ae){const Fe=Se[Ae],ze=Fe.findIndex(J=>J==this.tokenizer.mask_token_id);if(ze===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const Ve=xe[Ae][ze],O=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Ve.data),Ve.dims),R),Y=O[0].tolist(),z=O[1].tolist();be.push(z.map((J,le)=>{const ye=Fe.slice();return ye[ze]=J,{score:Y[le],token:Number(J),token_str:this.tokenizer.decode([J]),sequence:this.tokenizer.decode(ye,{skip_special_tokens:!0})}}))}return Array.isArray(q)?be:be[0]}}class E extends T{constructor(R){super(R);te(this,"_key","generated_text")}async _call(R,pe={}){Array.isArray(R)||(R=[R]),this.model.config.prefix&&(R=R.map(ze=>this.model.config.prefix+ze));const xe=this.model.config.task_specific_params;xe&&xe[this.task]&&xe[this.task].prefix&&(R=R.map(ze=>xe[this.task].prefix+ze));const be=this.tokenizer,Se={padding:!0,truncation:!0};let Ae;this instanceof M&&"_build_translation_inputs"in be?Ae=be._build_translation_inputs(R,Se,pe):Ae=be(R,Se);const Fe=await this.model.generate({...Ae,...pe});return be.batch_decode(Fe,{skip_special_tokens:!0}).map(ze=>({[this._key]:ze}))}}class y extends E{constructor(R){super(R);te(this,"_key","summary_text")}}class M extends E{constructor(R){super(R);te(this,"_key","translation_text")}}function v(we){return Array.isArray(we)&&we.every(q=>"role"in q&&"content"in q)}class C extends T{constructor(q){super(q)}async _call(q,R={}){let pe=!1,xe=!1,be;if(typeof q=="string")be=q=[q];else if(Array.isArray(q)&&q.every(z=>typeof z=="string"))pe=!0,be=q;else{if(v(q))q=[q];else if(Array.isArray(q)&&q.every(v))pe=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");xe=!0,be=q.map(z=>this.tokenizer.apply_chat_template(z,{tokenize:!1,add_generation_prompt:!0}))}const Se=R.add_special_tokens??!1,Ae=xe?!1:R.return_full_text??!0;this.tokenizer.padding_side="left";const Fe=this.tokenizer(be,{add_special_tokens:Se,padding:!0,truncation:!0}),ze=await this.model.generate({...Fe,...R}),Ve=this.tokenizer.batch_decode(ze,{skip_special_tokens:!0});let O;!Ae&&Fe.input_ids.dims.at(-1)>0&&(O=this.tokenizer.batch_decode(Fe.input_ids,{skip_special_tokens:!0}).map(z=>z.length));const Y=Array.from({length:q.length},z=>[]);for(let z=0;z<Ve.length;++z){const J=Math.floor(z/ze.dims[0]*q.length);O&&(Ve[z]=Ve[z].slice(O[J])),Y[J].push({generated_text:xe?[...q[J],{role:"assistant",content:Ve[z]}]:Ve[z]})}return!pe&&Y.length===1?Y[0]:Y}}class A extends T{constructor(q){super(q),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([R,pe])=>[R.toLowerCase(),pe])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(q,R,{hypothesis_template:pe="This example is {}.",multi_label:xe=!1}={}){const be=Array.isArray(q);be||(q=[q]),Array.isArray(R)||(R=[R]);const Se=R.map(ze=>pe.replace("{}",ze)),Ae=xe||R.length===1,Fe=[];for(const ze of q){const Ve=[];for(const z of Se){const J=this.tokenizer(ze,{text_pair:z,padding:!0,truncation:!0}),le=await this.model(J);Ae?Ve.push([le.logits.data[this.contradiction_id],le.logits.data[this.entailment_id]]):Ve.push(le.logits.data[this.entailment_id])}const Y=(Ae?Ve.map(z=>(0,l.softmax)(z)[1]):(0,l.softmax)(Ve)).map((z,J)=>[z,J]).sort((z,J)=>J[0]-z[0]);Fe.push({sequence:ze,labels:Y.map(z=>R[z[1]]),scores:Y.map(z=>z[0])})}return be?Fe:Fe[0]}}class B extends T{constructor(q){super(q)}async _call(q,{pooling:R="none",normalize:pe=!1,quantize:xe=!1,precision:be="binary"}={}){const Se=this.tokenizer(q,{padding:!0,truncation:!0}),Ae=await this.model(Se);let Fe=Ae.last_hidden_state??Ae.logits??Ae.token_embeddings;if(R!=="none")if(R==="mean")Fe=(0,p.mean_pooling)(Fe,Se.attention_mask);else if(R==="cls")Fe=Fe.slice(null,0);else throw Error(`Pooling method '${R}' not supported.`);return pe&&(Fe=Fe.normalize(2,-1)),xe&&(Fe=(0,p.quantize_embeddings)(Fe,be)),Fe}}class K extends T{constructor(q){super(q)}async _call(q,{pool:R=null}={}){const pe=await c(q),{pixel_values:xe}=await this.processor(pe),be=await this.model({pixel_values:xe});let Se;if(R){if(!("pooler_output"in be))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Se=be.pooler_output}else Se=be.last_hidden_state??be.logits??be.image_embeds;return Se}}class G extends T{constructor(q){super(q)}async _call(q,{top_k:R=5}={}){const pe=this.processor.feature_extractor.config.sampling_rate,xe=await f(q,pe),be=this.model.config.id2label,Se=[];for(const Ae of xe){const Fe=await this.processor(Ae),Ve=(await this.model(Fe)).logits[0],O=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Ve.data),Ve.dims),R),Y=O[0].tolist(),J=O[1].tolist().map((le,ye)=>({label:be?be[le]:`LABEL_${le}`,score:Y[ye]}));Se.push(J)}return Array.isArray(q)?Se:Se[0]}}class j extends T{constructor(q){super(q)}async _call(q,R,{hypothesis_template:pe="This is a sound of {}."}={}){const xe=!Array.isArray(q);xe&&(q=[q]);const be=R.map(Ve=>pe.replace("{}",Ve)),Se=this.tokenizer(be,{padding:!0,truncation:!0}),Ae=this.processor.feature_extractor.config.sampling_rate,Fe=await f(q,Ae),ze=[];for(const Ve of Fe){const O=await this.processor(Ve),Y=await this.model({...Se,...O}),z=(0,l.softmax)(Y.logits_per_audio.data);ze.push([...z].map((J,le)=>({score:J,label:R[le]})))}return xe?ze[0]:ze}}class ee extends T{constructor(q){super(q)}async _call(q,R={}){switch(this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(q,R);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(q,R);case"moonshine":return this._call_moonshine(q,R);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(q,R){R.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),R.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const pe=!Array.isArray(q);pe&&(q=[q]);const xe=this.processor.feature_extractor.config.sampling_rate,be=await f(q,xe),Se=[];for(const Ae of be){const Fe=await this.processor(Ae),Ve=(await this.model(Fe)).logits[0],O=[];for(const z of Ve)O.push((0,l.max)(z.data)[1]);const Y=this.tokenizer.decode(O);Se.push({text:Y})}return pe?Se[0]:Se}async _call_whisper(q,R){const pe=R.return_timestamps??!1,xe=R.chunk_length_s??0,be=R.force_full_sequences??!1;let Se=R.stride_length_s??null;const Ae={...R};pe==="word"&&(Ae.return_token_timestamps=!0,Ae.return_timestamps=!1);const Fe=!Array.isArray(q);Fe&&(q=[q]);const ze=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,Ve=this.processor.feature_extractor.config.hop_length,O=this.processor.feature_extractor.config.sampling_rate,Y=await f(q,O),z=[];for(const J of Y){let le=[];if(xe>0){if(Se===null)Se=xe/6;else if(xe<=Se)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const ke=O*xe,Ie=O*Se,Re=ke-2*Ie;let Xe=0;for(;;){const Ge=Xe+ke,lt=J.subarray(Xe,Ge),wt=await this.processor(lt),Gt=Xe===0,Ot=Ge>=J.length;if(le.push({stride:[lt.length,Gt?0:Ie,Ot?0:Ie],input_features:wt.input_features,is_last:Ot}),Ot)break;Xe+=Re}}else le=[{stride:[J.length,0,0],input_features:(await this.processor(J)).input_features,is_last:!0}];for(const ke of le){Ae.num_frames=Math.floor(ke.stride[0]/Ve);const Ie=await this.model.generate({inputs:ke.input_features,...Ae});pe==="word"?(ke.tokens=Ie.sequences.tolist()[0],ke.token_timestamps=Ie.token_timestamps.tolist()[0].map(Re=>(0,l.round)(Re,2))):ke.tokens=Ie[0].tolist(),ke.stride=ke.stride.map(Re=>Re/O)}const[ye,Ee]=this.tokenizer._decode_asr(le,{time_precision:ze,return_timestamps:pe,force_full_sequences:be});z.push({text:ye,...Ee})}return Fe?z[0]:z}async _call_moonshine(q,R){const pe=!Array.isArray(q);pe&&(q=[q]);const xe=this.processor.feature_extractor.config.sampling_rate,be=await f(q,xe),Se=[];for(const Ae of be){const Fe=await this.processor(Ae),ze=Math.floor(Ae.length/xe)*6,Ve=await this.model.generate({max_new_tokens:ze,...R,...Fe}),O=this.processor.batch_decode(Ve,{skip_special_tokens:!0})[0];Se.push({text:O})}return pe?Se[0]:Se}}class H extends T{constructor(q){super(q)}async _call(q,R={}){const pe=Array.isArray(q),xe=await c(q),{pixel_values:be}=await this.processor(xe),Se=[];for(const Ae of be){Ae.dims=[1,...Ae.dims];const Fe=await this.model.generate({inputs:Ae,...R}),ze=this.tokenizer.batch_decode(Fe,{skip_special_tokens:!0}).map(Ve=>({generated_text:Ve.trim()}));Se.push(ze)}return pe?Se:Se[0]}}class Z extends T{constructor(q){super(q)}async _call(q,{top_k:R=5}={}){const pe=await c(q),{pixel_values:xe}=await this.processor(pe),be=await this.model({pixel_values:xe}),Se=this.model.config.id2label,Ae=[];for(const Fe of be.logits){const ze=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Fe.data),Fe.dims),R),Ve=ze[0].tolist(),Y=ze[1].tolist().map((z,J)=>({label:Se?Se[z]:`LABEL_${z}`,score:Ve[J]}));Ae.push(Y)}return Array.isArray(q)?Ae:Ae[0]}}class X extends T{constructor(q){super(q),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(q,{threshold:R=.5,mask_threshold:pe=.5,overlap_mask_area_threshold:xe=.8,label_ids_to_fuse:be=null,target_sizes:Se=null,subtask:Ae=null}={}){if(Array.isArray(q)&&q.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const ze=await c(q),Ve=ze.map(ke=>[ke.height,ke.width]),O=await this.processor(ze),{inputNames:Y,outputNames:z}=this.model.sessions.model;if(!Y.includes("pixel_values")){if(Y.length!==1)throw Error(`Expected a single input name, but got ${Y.length} inputs: ${Y}.`);const ke=Y[0];if(ke in O)throw Error(`Input name ${ke} already exists in the inputs.`);O[ke]=O.pixel_values}const J=await this.model(O);let le=null;if(Ae!==null)le=this.subtasks_mapping[Ae];else if(this.processor.image_processor){for(const[ke,Ie]of Object.entries(this.subtasks_mapping))if(Ie in this.processor.image_processor){le=this.processor.image_processor[Ie].bind(this.processor.image_processor),Ae=ke;break}}const ye=this.model.config.id2label,Ee=[];if(Ae)if(Ae==="panoptic"||Ae==="instance"){const ke=le(J,R,pe,xe,be,Se??Ve)[0],Ie=ke.segmentation;for(const Re of ke.segments_info){const Xe=new Uint8ClampedArray(Ie.data.length);for(let lt=0;lt<Ie.data.length;++lt)Ie.data[lt]===Re.id&&(Xe[lt]=255);const Ge=new d.RawImage(Xe,Ie.dims[1],Ie.dims[0],1);Ee.push({score:Re.score,label:ye[Re.label_id],mask:Ge})}}else if(Ae==="semantic"){const{segmentation:ke,labels:Ie}=le(J,Se??Ve)[0];for(const Re of Ie){const Xe=new Uint8ClampedArray(ke.data.length);for(let lt=0;lt<ke.data.length;++lt)ke.data[lt]===Re&&(Xe[lt]=255);const Ge=new d.RawImage(Xe,ke.dims[1],ke.dims[0],1);Ee.push({score:null,label:ye[Re],mask:Ge})}}else throw Error(`Subtask ${Ae} not supported.`);else{const Ie=J[z[0]];for(let Re=0;Re<Ve.length;++Re){const 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of ze.logits_per_image){const J=[...Ve(Y)].map((le,ye)=>({score:le,label:R[ye]}));J.sort((le,ye)=>ye.score-le.score),O.push(J)}return xe?O:O[0]}}class ae extends T{constructor(q){super(q)}async _call(q,{threshold:R=.9,percentage:pe=!1}={}){const xe=Array.isArray(q);if(xe&&q.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const be=await c(q),Se=pe?null:be.map(z=>[z.height,z.width]),{pixel_values:Ae,pixel_mask:Fe}=await this.processor(be),ze=await this.model({pixel_values:Ae,pixel_mask:Fe}),Ve=this.processor.image_processor.post_process_object_detection(ze,R,Se),O=this.model.config.id2label,Y=Ve.map(z=>z.boxes.map((J,le)=>({score:z.scores[le],label:O[z.classes[le]],box:_(J,!pe)})));return xe?Y:Y[0]}}class V extends T{constructor(q){super(q)}async _call(q,R,{threshold:pe=.1,top_k:xe=null,percentage:be=!1}={}){const Se=Array.isArray(q),Ae=await c(q),Fe=this.tokenizer(R,{padding:!0,truncation:!0}),ze=await this.processor(Ae),Ve=[];for(let O=0;O<Ae.length;++O){const Y=Ae[O],z=be?null:[[Y.height,Y.width]],J=ze.pixel_values[O].unsqueeze_(0),le=await this.model({...Fe,pixel_values:J});let ye;if("post_process_grounded_object_detection"in this.processor){const Ee=this.processor.post_process_grounded_object_detection(le,Fe.input_ids,{box_threshold:pe,text_threshold:pe,target_sizes:z})[0];ye=Ee.boxes.map((ke,Ie)=>({score:Ee.scores[Ie],label:Ee.labels[Ie],box:_(ke,!be)}))}else{const Ee=this.processor.image_processor.post_process_object_detection(le,pe,z,!0)[0];ye=Ee.boxes.map((ke,Ie)=>({score:Ee.scores[Ie],label:R[Ee.classes[Ie]],box:_(ke,!be)}))}ye.sort((Ee,ke)=>ke.score-Ee.score),xe!==null&&(ye=ye.slice(0,xe)),Ve.push(ye)}return Se?Ve:Ve[0]}}class F extends T{constructor(q){super(q)}async _call(q,R,pe={}){const xe=(await c(q))[0],{pixel_values:be}=await this.processor(xe),Se=`<s_docvqa><s_question>${R}</s_question><s_answer>`,Ae=this.tokenizer(Se,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,Fe=await this.model.generate({inputs:be,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Ae,...pe}),Ve=this.tokenizer.batch_decode(Fe)[0].match(/<s_answer>(.*?)<\/s_answer>/);let O=null;return Ve&&Ve.length>=2&&(O=Ve[1].trim()),[{answer:O}]}}class W extends T{constructor(R){super(R);te(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=R.vocoder??null}async _call(R,{speaker_embeddings:pe=null}={}){return this.processor?this._call_text_to_spectrogram(R,{speaker_embeddings:pe}):this._call_text_to_waveform(R)}async _call_text_to_waveform(R){const pe=this.tokenizer(R,{padding:!0,truncation:!0}),{waveform:xe}=await this.model(pe),be=this.model.config.sampling_rate;return new u.RawAudio(xe.data,be)}async _call_text_to_spectrogram(R,{speaker_embeddings:pe}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await o.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof pe=="string"||pe 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function $e(we,q=null,{progress_callback:R=null,config:pe=null,cache_dir:xe=null,local_files_only:be=!1,revision:Se="main",device:Ae=null,dtype:Fe=null,subfolder:ze="onnx",use_external_data_format:Ve=null,model_file_name:O=null,session_options:Y={}}={}){we=ce[we]??we;const z=se[we.split("_",1)[0]];if(!z)throw Error(`Unsupported pipeline: ${we}. 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M="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",v=new RegExp(`^[${M}]+$`,"gu"),C=".,!?…。，、।۔،",A=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],[` ?[^(\\s|[${C}])]+`,` ?[^\\s${C}]+`]]);class B{constructor(k){this.content=k.content,this.id=k.id,this.single_word=k.single_word??!1,this.lstrip=k.lstrip??!1,this.rstrip=k.rstrip??!1,this.special=k.special??!1,this.normalized=k.normalized??null}}class K extends s.Callable{constructor(k){super(),this.config=k,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(k,...N){switch(k.type){case"WordPiece":return new G(k);case"Unigram":return new j(k,...N);case"BPE":return new Z(k);default:if(k.vocab)return Array.isArray(k.vocab)?new j(k,...N):typeof k.vocab=="object"&&k.continuing_subword_prefix&&k.unk_token?new G(k):new X(k,...N);throw new Error(`Unknown TokenizerModel type: ${k.type}`)}}_call(k){return k=this.encode(k),this.fuse_unk&&(k=E(k,this.tokens_to_ids,this.unk_token_id)),k}encode(k){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(k){return k.map(N=>this.tokens_to_ids.get(N)??this.unk_token_id)}convert_ids_to_tokens(k){return k.map(N=>this.vocab[N]??this.unk_token)}}class G extends K{constructor(k){super(k),this.tokens_to_ids=_(k.vocab),this.unk_token_id=this.tokens_to_ids.get(k.unk_token),this.unk_token=k.unk_token,this.max_input_chars_per_word=k.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[N,Q]of this.tokens_to_ids)this.vocab[Q]=N}encode(k){const N=[];for(const Q of k){const ie=[...Q];if(ie.length>this.max_input_chars_per_word){N.push(this.unk_token);continue}let de=!1,ve=0;const je=[];for(;ve<ie.length;){let He=ie.length,We=null;for(;ve<He;){let Je=ie.slice(ve,He).join("");if(ve>0&&(Je=this.config.continuing_subword_prefix+Je),this.tokens_to_ids.has(Je)){We=Je;break}--He}if(We===null){de=!0;break}je.push(We),ve=He}de?N.push(this.unk_token):N.push(...je)}return N}}class j extends K{constructor(k,N){super(k);const Q=k.vocab.length;this.vocab=new Array(Q),this.scores=new Array(Q);for(let ie=0;ie<Q;++ie)[this.vocab[ie],this.scores[ie]]=k.vocab[ie];this.unk_token_id=k.unk_id,this.unk_token=this.vocab[k.unk_id],this.tokens_to_ids=new Map(this.vocab.map((ie,de)=>[ie,de])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=N.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,i.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new l.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(k){const N=k.chars,Q=1;let ie=0;for(;ie<N.length;){let de=!1;const ve=N.slice(ie).join(""),je=this.trie.commonPrefixSearch(ve);for(const He of je){const We=this.tokens_to_ids.get(He),Je=this.scores[We],dt=(0,o.len)(He);k.insert(ie,dt,Je,We),!de&&dt===Q&&(de=!0)}de||k.insert(ie,Q,this.unk_score,this.unk_token_id),ie+=Q}}tokenize(k){const N=new l.TokenLattice(k,this.bos_token_id,this.eos_token_id);return this.populateNodes(N),N.tokens()}encode(k){const N=[];for(const Q of k){const ie=this.tokenize(Q);N.push(...ie)}return N}}const ee=(()=>{const he=[...Array.from({length:94},(ie,de)=>de+33),...Array.from({length:12},(ie,de)=>de+161),...Array.from({length:82},(ie,de)=>de+174)],k=he.slice();let N=0;for(let ie=0;ie<256;++ie)he.includes(ie)||(he.push(ie),k.push(256+N),N+=1);const Q=k.map(ie=>String.fromCharCode(ie));return Object.fromEntries(he.map((ie,de)=>[ie,Q[de]]))})(),H=(0,o.reverseDictionary)(ee);class Z extends K{constructor(k){super(k),this.tokens_to_ids=_(k.vocab),this.unk_token_id=this.tokens_to_ids.get(k.unk_token),this.unk_token=k.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[Q,ie]of this.tokens_to_ids)this.vocab[ie]=Q;const N=Array.isArray(k.merges[0]);this.merges=N?k.merges:k.merges.map(Q=>Q.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((Q,ie)=>[JSON.stringify(Q),ie])),this.end_of_word_suffix=k.end_of_word_suffix,this.continuing_subword_suffix=k.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.max_length_to_cache=256,this.cache_capacity=1e4,this.cache=new l.LRUCache(this.cache_capacity)}clear_cache(){this.cache.clear()}bpe(k){if(k.length===0)return[];const N=this.cache.get(k);if(N!==void 0)return N;const Q=Array.from(k);this.end_of_word_suffix&&(Q[Q.length-1]+=this.end_of_word_suffix);let ie=[];if(Q.length>1){const de=new l.PriorityQueue((He,We)=>He.score<We.score);let ve={token:Q[0],bias:0,prev:null,next:null},je=ve;for(let He=1;He<Q.length;++He){const We={bias:He/Q.length,token:Q[He],prev:je,next:null};je.next=We,this._add_node(de,je),je=We}for(;!de.isEmpty();){const He=de.pop();if(He.deleted||!He.next||He.next.deleted)continue;if(He.deleted=!0,He.next.deleted=!0,He.prev){const Je={...He.prev};He.prev.deleted=!0,He.prev=Je,Je.prev?Je.prev.next=Je:ve=Je}const We={token:He.token+He.next.token,bias:He.bias,prev:He.prev,next:He.next.next};We.prev?(We.prev.next=We,this._add_node(de,We.prev)):ve=We,We.next&&(We.next.prev=We,this._add_node(de,We))}for(let He=ve;He!==null;He=He.next)ie.push(He.token)}else ie=Q;if(this.continuing_subword_suffix)for(let de=0;de<ie.length-1;++de)ie[de]+=this.continuing_subword_suffix;return k.length<this.max_length_to_cache&&this.cache.put(k,ie),ie}_add_node(k,N){const Q=this.bpe_ranks.get(JSON.stringify([N.token,N.next.token]));Q!==void 0&&(N.score=Q+N.bias,k.push(N))}encode(k){const N=[];for(const Q of k){if(this.ignore_merges&&this.tokens_to_ids.has(Q)){N.push(Q);continue}const ie=this.bpe(Q);for(const de of ie)if(this.tokens_to_ids.has(de))N.push(de);else if(this.byte_fallback){const ve=Array.from(this.text_encoder.encode(de)).map(je=>`<0x${je.toString(16).toUpperCase().padStart(2,"0")}>`);ve.every(je=>this.tokens_to_ids.has(je))?N.push(...ve):N.push(this.unk_token)}else N.push(this.unk_token)}return N}}class X extends K{constructor(k,N){super(k),this.tokens_to_ids=_(N.target_lang?k.vocab[N.target_lang]:k.vocab),this.bos_token=N.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=N.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=N.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=N.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[Q,ie]of this.tokens_to_ids)this.vocab[ie]=Q}encode(k){return k}}class oe extends s.Callable{constructor(k){super(),this.config=k}static fromConfig(k){if(k===null)return null;switch(k.type){case"BertNormalizer":return new we(k);case"Precompiled":return new Ot(k);case"Sequence":return new Ue(k);case"Replace":return new me(k);case"NFC":return new V(k);case"NFD":return new F(k);case"NFKC":return new W(k);case"NFKD":return new re(k);case"Strip":return new _e(k);case"StripAccents":return new se(k);case"Lowercase":return new ce(k);case"Prepend":return new $e(k);default:throw new Error(`Unknown Normalizer type: ${k.type}`)}}normalize(k){throw Error("normalize should be implemented in subclass.")}_call(k){return this.normalize(k)}}class me extends oe{normalize(k){const N=f(this.config.pattern);return N===null?k:k.replaceAll(N,this.config.content)}}class ae extends oe{constructor(){super(...arguments);te(this,"form")}normalize(N){return N=N.normalize(this.form),N}}class V extends ae{constructor(){super(...arguments);te(this,"form","NFC")}}class F extends ae{constructor(){super(...arguments);te(this,"form","NFD")}}class W extends ae{constructor(){super(...arguments);te(this,"form","NFKC")}}class re extends ae{constructor(){super(...arguments);te(this,"form","NFKD")}}class _e extends oe{normalize(k){return this.config.strip_left&&this.config.strip_right?k=k.trim():(this.config.strip_left&&(k=k.trimStart()),this.config.strip_right&&(k=k.trimEnd())),k}}class se extends oe{normalize(k){return k=w(k),k}}class ce extends oe{normalize(k){return k=k.toLowerCase(),k}}class $e extends oe{normalize(k){return k=this.config.prepend+k,k}}class Ue extends oe{constructor(k){super(k),this.normalizers=k.normalizers.map(N=>oe.fromConfig(N))}normalize(k){return this.normalizers.reduce((N,Q)=>Q.normalize(N),k)}}class we extends oe{_tokenize_chinese_chars(k){const N=[];for(let Q=0;Q<k.length;++Q){const ie=k[Q],de=ie.charCodeAt(0);S(de)?(N.push(" "),N.push(ie),N.push(" ")):N.push(ie)}return N.join("")}stripAccents(k){return k.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control(k){switch(k){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(k)}}_clean_text(k){const N=[];for(const Q of k){const ie=Q.charCodeAt(0);ie===0||ie===65533||this._is_control(Q)||(/^\s$/.test(Q)?N.push(" "):N.push(Q))}return N.join("")}normalize(k){return this.config.clean_text&&(k=this._clean_text(k)),this.config.handle_chinese_chars&&(k=this._tokenize_chinese_chars(k)),this.config.lowercase?(k=k.toLowerCase(),this.config.strip_accents!==!1&&(k=this.stripAccents(k))):this.config.strip_accents&&(k=this.stripAccents(k)),k}}class q extends s.Callable{static fromConfig(k){if(k===null)return null;switch(k.type){case"BertPreTokenizer":return new R(k);case"Sequence":return new ur(k);case"Whitespace":return new ls(k);case"WhitespaceSplit":return new Ms(k);case"Metaspace":return new wt(k);case"ByteLevel":return new pe(k);case"Split":return new xe(k);case"Punctuation":return new be(k);case"Digits":return new Se(k);case"Replace":return new Ir(k);default:throw new Error(`Unknown PreTokenizer type: ${k.type}`)}}pre_tokenize_text(k,N){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(k,N){return(Array.isArray(k)?k.map(Q=>this.pre_tokenize_text(Q,N)):this.pre_tokenize_text(k,N)).flat()}_call(k,N){return this.pre_tokenize(k,N)}}class R extends q{constructor(k){super(),this.pattern=new RegExp(`[^\\s${M}]+|[${M}]`,"gu")}pre_tokenize_text(k,N){return k.trim().match(this.pattern)||[]}}class pe extends q{constructor(k){super(),this.config=k,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=ee,this.text_encoder=new TextEncoder}pre_tokenize_text(k,N){return this.add_prefix_space&&!k.startsWith(" ")&&(k=" "+k),(this.use_regex?k.match(this.pattern)||[]:[k]).map(ie=>Array.from(this.text_encoder.encode(ie),de=>this.byte_encoder[de]).join(""))}}class xe extends q{constructor(k){super(),this.config=k,this.pattern=f(this.config.pattern,this.config.invert)}pre_tokenize_text(k,N){var Q;return this.pattern===null?[]:this.config.invert?k.match(this.pattern)||[]:((Q=this.config.behavior)==null?void 0:Q.toLowerCase())==="removed"?k.split(this.pattern).filter(ie=>ie):c(k,this.pattern)}}class be extends q{constructor(k){super(),this.config=k,this.pattern=new RegExp(`[^${M}]+|[${M}]+`,"gu")}pre_tokenize_text(k,N){return k.match(this.pattern)||[]}}class Se extends q{constructor(k){super(),this.config=k;const N=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(N,"gu")}pre_tokenize_text(k,N){return k.match(this.pattern)||[]}}class Ae extends s.Callable{constructor(k){super(),this.config=k}static fromConfig(k){if(k===null)return null;switch(k.type){case"TemplateProcessing":return new Ve(k);case"ByteLevel":return new O(k);case"RobertaProcessing":return new ze(k);case"BertProcessing":return new Fe(k);case"Sequence":return new Y(k);default:throw new Error(`Unknown PostProcessor type: ${k.type}`)}}post_process(k,...N){throw Error("post_process should be implemented in subclass.")}_call(k,...N){return this.post_process(k,...N)}}class Fe extends Ae{constructor(k){super(k),this.cls=k.cls[0],this.sep=k.sep[0]}post_process(k,N=null,{add_special_tokens:Q=!0}={}){Q&&(k=(0,o.mergeArrays)([this.cls],k,[this.sep]));let ie=new Array(k.length).fill(0);if(N!==null){const de=Q&&this instanceof ze?[this.sep]:[],ve=Q?[this.sep]:[];k=(0,o.mergeArrays)(k,de,N,ve),ie=(0,o.mergeArrays)(ie,new Array(N.length+de.length+ve.length).fill(1))}return{tokens:k,token_type_ids:ie}}}class ze extends Fe{}class Ve extends Ae{constructor(k){super(k),this.single=k.single,this.pair=k.pair}post_process(k,N=null,{add_special_tokens:Q=!0}={}){const ie=N===null?this.single:this.pair;let de=[],ve=[];for(const je of ie)"SpecialToken"in je?Q&&(de.push(je.SpecialToken.id),ve.push(je.SpecialToken.type_id)):"Sequence"in je&&(je.Sequence.id==="A"?(de=(0,o.mergeArrays)(de,k),ve=(0,o.mergeArrays)(ve,new Array(k.length).fill(je.Sequence.type_id))):je.Sequence.id==="B"&&(de=(0,o.mergeArrays)(de,N),ve=(0,o.mergeArrays)(ve,new Array(N.length).fill(je.Sequence.type_id))));return{tokens:de,token_type_ids:ve}}}class O extends Ae{post_process(k,N=null){return N&&(k=(0,o.mergeArrays)(k,N)),{tokens:k}}}class Y extends Ae{constructor(k){super(k),this.processors=k.processors.map(N=>Ae.fromConfig(N))}post_process(k,N=null,Q={}){let ie;for(const de of this.processors)if(de instanceof O)k=de.post_process(k).tokens,N&&(N=de.post_process(N).tokens);else{const ve=de.post_process(k,N,Q);k=ve.tokens,ie=ve.token_type_ids}return{tokens:k,token_type_ids:ie}}}class z extends s.Callable{constructor(k){super(),this.config=k,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=k.trim_offsets}static fromConfig(k){if(k===null)return null;switch(k.type){case"WordPiece":return new ke(k);case"Metaspace":return new Gt(k);case"ByteLevel":return new Ie(k);case"Replace":return new J(k);case"ByteFallback":return new le(k);case"Fuse":return new ye(k);case"Strip":return new Ee(k);case"Sequence":return new Xe(k);case"CTC":return new Re(k);case"BPEDecoder":return new Ge(k);default:throw new Error(`Unknown Decoder type: ${k.type}`)}}_call(k){return this.decode(k)}decode(k){return this.decode_chain(k).join("")}decode_chain(k){throw Error("`decode_chain` should be implemented in subclass.")}}class J extends z{decode_chain(k){const N=f(this.config.pattern);return N===null?k:k.map(Q=>Q.replaceAll(N,this.config.content))}}class le extends z{constructor(k){super(k),this.text_decoder=new TextDecoder}decode_chain(k){const N=[];let Q=[];for(const ie of k){let de=null;if(ie.length===6&&ie.startsWith("<0x")&&ie.endsWith(">")){const ve=parseInt(ie.slice(3,5),16);isNaN(ve)||(de=ve)}if(de!==null)Q.push(de);else{if(Q.length>0){const ve=this.text_decoder.decode(Uint8Array.from(Q));N.push(ve),Q=[]}N.push(ie)}}if(Q.length>0){const ie=this.text_decoder.decode(Uint8Array.from(Q));N.push(ie),Q=[]}return N}}class ye extends z{decode_chain(k){return[k.join("")]}}class Ee extends z{constructor(k){super(k),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(k){return k.map(N=>{let Q=0;for(let de=0;de<this.start&&N[de]===this.content;++de){Q=de+1;continue}let ie=N.length;for(let de=0;de<this.stop;++de){const ve=N.length-de-1;if(N[ve]===this.content){ie=ve;continue}else break}return N.slice(Q,ie)})}}class ke extends z{constructor(k){super(k),this.cleanup=k.cleanup}decode_chain(k){return k.map((N,Q)=>(Q!==0&&(N.startsWith(this.config.prefix)?N=N.replace(this.config.prefix,""):N=" "+N),this.cleanup&&(N=$(N)),N))}}class Ie extends z{constructor(k){super(k),this.byte_decoder=H,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(k){const N=k.join(""),Q=new Uint8Array([...N].map(de=>this.byte_decoder[de]));return this.text_decoder.decode(Q)}decode_chain(k){const N=[];let Q=[];for(const ie of k)this.added_tokens.find(de=>de.content===ie)!==void 0?(Q.length>0&&(N.push(this.convert_tokens_to_string(Q)),Q=[]),N.push(ie)):Q.push(ie);return Q.length>0&&N.push(this.convert_tokens_to_string(Q)),N}}class Re extends z{constructor(k){super(k),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(k){if(k.length===0)return"";const N=[k[0]];for(let de=1;de<k.length;++de)k[de]!==N.at(-1)&&N.push(k[de]);let ie=N.filter(de=>de!==this.pad_token).join("");return this.cleanup&&(ie=$(ie).replaceAll(this.word_delimiter_token," ").trim()),ie}decode_chain(k){return[this.convert_tokens_to_string(k)]}}class Xe extends z{constructor(k){super(k),this.decoders=k.decoders.map(N=>z.fromConfig(N))}decode_chain(k){return this.decoders.reduce((N,Q)=>Q.decode_chain(N),k)}}class Ge extends z{constructor(k){super(k),this.suffix=this.config.suffix}decode_chain(k){return k.map((N,Q)=>N.replaceAll(this.suffix,Q===k.length-1?"":" "))}}class lt extends z{decode_chain(k){let N="";for(let Q=1;Q<k.length;Q+=2)N+=k[Q];return[N]}}class wt extends q{constructor(k){super(),this.addPrefixSpace=k.add_prefix_space,this.replacement=k.replacement,this.strRep=k.str_rep||this.replacement,this.prepend_scheme=k.prepend_scheme??"always"}pre_tokenize_text(k,{section_index:N=void 0}={}){let Q=k.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!Q.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&N===0)&&(Q=this.strRep+Q),[Q]}}class Gt extends z{constructor(k){super(k),this.addPrefixSpace=k.add_prefix_space,this.replacement=k.replacement}decode_chain(k){const N=[];for(let Q=0;Q<k.length;++Q){let ie=k[Q].replaceAll(this.replacement," ");this.addPrefixSpace&&Q==0&&ie.startsWith(" ")&&(ie=ie.substring(1)),N.push(ie)}return N}}class Ot extends oe{constructor(k){super(k),this.charsmap=k.precompiled_charsmap}normalize(k){return k=k.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),k=k.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),k.includes("～")?k=k.split("～").map(Q=>Q.normalize("NFKC")).join("～"):k=k.normalize("NFKC"),k}}class ur extends q{constructor(k){super(),this.tokenizers=k.pretokenizers.map(N=>q.fromConfig(N))}pre_tokenize_text(k,N){return this.tokenizers.reduce((Q,ie)=>ie.pre_tokenize(Q,N),[k])}}class ls extends q{constructor(k){super()}pre_tokenize_text(k,N){return k.match(/\w+|[^\w\s]+/g)||[]}}class Ms extends q{constructor(k){super()}pre_tokenize_text(k,N){return y(k)}}class Ir extends q{constructor(k){super(),this.config=k,this.pattern=f(this.config.pattern),this.content=this.config.content}pre_tokenize_text(k,N){return this.pattern===null?[k]:[k.replaceAll(this.pattern,this.config.content)]}}const js=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ss(he,k,N,Q){for(const ie of Object.keys(he)){const de=k-he[ie].length,ve=N(ie),je=new Array(de).fill(ve);he[ie]=Q==="right"?(0,o.mergeArrays)(he[ie],je):(0,o.mergeArrays)(je,he[ie])}}function Ns(he,k){for(const N of Object.keys(he))he[N].length=k}class at extends s.Callable{constructor(N,Q){super();te(this,"return_token_type_ids",!1);te(this,"padding_side","right");this._tokenizer_config=Q,this.normalizer=oe.fromConfig(N.normalizer),this.pre_tokenizer=q.fromConfig(N.pre_tokenizer),this.model=K.fromConfig(N.model,Q),this.post_processor=Ae.fromConfig(N.post_processor),this.decoder=z.fromConfig(N.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ie of N.added_tokens){const de=new B(ie);this.added_tokens.push(de),this.model.tokens_to_ids.set(de.content,de.id),this.model.vocab[de.id]=de.content,de.special&&(this.special_tokens.push(de.content),this.all_special_ids.push(de.id))}if(this.additional_special_tokens=Q.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_splitter=new l.DictionarySplitter(this.added_tokens.map(ie=>ie.content)),this.added_tokens_map=new Map(this.added_tokens.map(ie=>[ie.content,ie])),this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=Q.model_max_length,this.remove_space=Q.remove_space,this.clean_up_tokenization_spaces=Q.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=Q.do_lowercase_and_remove_accent??!1,Q.padding_side&&(this.padding_side=Q.padding_side),this.legacy=!1,this.chat_template=Q.chat_template??null,Array.isArray(this.chat_template)){const ie=Object.create(null);for(const{name:de,template:ve}of this.chat_template){if(typeof de!="string"||typeof ve!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ie[de]=ve}this.chat_template=ie}this._compiled_template_cache=new Map}getToken(...N){for(const Q of N){const ie=this._tokenizer_config[Q];if(ie)if(typeof ie=="object"){if(ie.__type==="AddedToken")return ie.content;throw Error(`Unknown token: ${ie}`)}else return ie}return null}static async from_pretrained(N,{progress_callback:Q=null,config:ie=null,cache_dir:de=null,local_files_only:ve=!1,revision:je="main",legacy:He=null}={}){const We=await d(N,{progress_callback:Q,config:ie,cache_dir:de,local_files_only:ve,revision:je,legacy:He});return new this(...We)}_call(N,{text_pair:Q=null,add_special_tokens:ie=!0,padding:de=!1,truncation:ve=null,max_length:je=null,return_tensor:He=!0,return_token_type_ids:We=null}={}){const Je=Array.isArray(N);let dt;if(Je){if(N.length===0)throw Error("text array must be non-empty");if(Q!==null){if(Array.isArray(Q)){if(N.length!==Q.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");dt=N.map((Pt,jt)=>this._encode_plus(Pt,{text_pair:Q[jt],add_special_tokens:ie,return_token_type_ids:We}))}else dt=N.map(Pt=>this._encode_plus(Pt,{add_special_tokens:ie,return_token_type_ids:We}))}else{if(N==null)throw Error("text may not be null or undefined");if(Array.isArray(Q))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");dt=[this._encode_plus(N,{text_pair:Q,add_special_tokens:ie,return_token_type_ids:We})]}if(je===null?de==="max_length"?je=this.model_max_length:je=(0,i.max)(dt.map(Pt=>Pt.input_ids.length))[0]:ve||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),je=Math.min(je,this.model_max_length??1/0),de||ve)for(let Pt=0;Pt<dt.length;++Pt)dt[Pt].input_ids.length!==je&&(dt[Pt].input_ids.length>je?ve&&Ns(dt[Pt],je):de&&Ss(dt[Pt],je,jt=>jt==="input_ids"?this.pad_token_id:0,this.padding_side));const xt={};if(He){if(!(de&&ve)&&dt.some(jt=>{var kt;for(const Ht of Object.keys(jt))if(jt[Ht].length!==((kt=dt[0][Ht])==null?void 0:kt.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const Pt=[dt.length,dt[0].input_ids.length];for(const jt of Object.keys(dt[0]))xt[jt]=new a.Tensor("int64",BigInt64Array.from(dt.flatMap(kt=>kt[jt]).map(BigInt)),Pt)}else{for(const Pt of Object.keys(dt[0]))xt[Pt]=dt.map(jt=>jt[Pt]);if(!Je)for(const Pt of Object.keys(xt))xt[Pt]=xt[Pt][0]}return xt}_encode_text(N){if(N===null)return null;const Q=this.added_tokens_splitter.split(N);for(let de=0;de<Q.length;++de){const ve=this.added_tokens_map.get(Q[de]);ve&&(ve.lstrip&&de>0&&(Q[de-1]=Q[de-1].trimEnd()),ve.rstrip&&de<Q.length-1&&(Q[de+1]=Q[de+1].trimStart()))}return Q.flatMap((de,ve)=>{if(de.length===0)return[];if(this.added_tokens_map.has(de))return[de];if(this.remove_space===!0&&(de=de.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(de=g(de)),this.normalizer!==null&&(de=this.normalizer(de)),de.length===0)return[];const je=this.pre_tokenizer!==null?this.pre_tokenizer(de,{section_index:ve}):[de];return this.model(je)})}_encode_plus(N,{text_pair:Q=null,add_special_tokens:ie=!0,return_token_type_ids:de=null}={}){const{tokens:ve,token_type_ids:je}=this._tokenize_helper(N,{pair:Q,add_special_tokens:ie}),He=this.model.convert_tokens_to_ids(ve),We={input_ids:He,attention_mask:new Array(He.length).fill(1)};return(de??this.return_token_type_ids)&&je&&(We.token_type_ids=je),We}_tokenize_helper(N,{pair:Q=null,add_special_tokens:ie=!1}={}){const de=this._encode_text(N),ve=this._encode_text(Q);return this.post_processor?this.post_processor(de,ve,{add_special_tokens:ie}):{tokens:(0,o.mergeArrays)(de??[],ve??[])}}tokenize(N,{pair:Q=null,add_special_tokens:ie=!1}={}){return this._tokenize_helper(N,{pair:Q,add_special_tokens:ie}).tokens}encode(N,{text_pair:Q=null,add_special_tokens:ie=!0,return_token_type_ids:de=null}={}){return this._encode_plus(N,{text_pair:Q,add_special_tokens:ie,return_token_type_ids:de}).input_ids}batch_decode(N,Q={}){return N instanceof a.Tensor&&(N=N.tolist()),N.map(ie=>this.decode(ie,Q))}decode(N,Q={}){if(N instanceof a.Tensor&&(N=T(N)),!Array.isArray(N)||N.length===0||!(0,o.isIntegralNumber)(N[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(N,Q)}decode_single(N,{skip_special_tokens:Q=!1,clean_up_tokenization_spaces:ie=null}){let de=this.model.convert_ids_to_tokens(N);Q&&(de=de.filter(je=>!this.special_tokens.includes(je)));let ve=this.decoder?this.decoder(de):de.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(ve=ve.replaceAll(this.decoder.end_of_word_suffix," "),Q&&(ve=ve.trim())),(ie??this.clean_up_tokenization_spaces)&&(ve=$(ve)),ve}get_chat_template({chat_template:N=null,tools:Q=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const ie=this.chat_template;if(N!==null&&Object.hasOwn(ie,N))N=ie[N];else if(N===null)if(Q!==null&&"tool_use"in ie)N=ie.tool_use;else if("default"in ie)N=ie.default;else throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(ie).sort()}.`)}else if(N===null)if(this.chat_template)N=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return N}apply_chat_template(N,{tools:Q=null,documents:ie=null,chat_template:de=null,add_generation_prompt:ve=!1,tokenize:je=!0,padding:He=!1,truncation:We=!1,max_length:Je=null,return_tensor:dt=!0,return_dict:xt=!1,tokenizer_kwargs:Pt={},...jt}={}){if(de=this.get_chat_template({chat_template:de,tools:Q}),typeof de!="string")throw Error(`chat_template must be a string, but got ${typeof de}`);let kt=this._compiled_template_cache.get(de);kt===void 0&&(kt=new u.Template(de),this._compiled_template_cache.set(de,kt));const Ht=Object.create(null);for(const cr of js){const pr=this.getToken(cr);pr&&(Ht[cr]=pr)}const br=kt.render({messages:N,add_generation_prompt:ve,tools:Q,documents:ie,...Ht,...jt});if(je){const cr=this._call(br,{add_special_tokens:!1,padding:He,truncation:We,max_length:Je,return_tensor:dt,...Pt});return xt?cr:cr.input_ids}return br}}class us extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Ar extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class bs extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Ft extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class ds extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class cs extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class ys extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Qt extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class De extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Qe extends at{}class tt extends at{}class Rt extends at{constructor(N,Q){super(N,Q);te(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class zr extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Sr extends at{}class ps extends at{}class hs extends at{}class Kr extends at{constructor(k,N){super(k,N),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)),this.lang_to_token=Q=>Q}_build_translation_inputs(k,N,Q){return Tr(this,k,N,Q)}}class ms extends Kr{}class _s extends at{}class Hr extends at{}const vr="▁";class cn extends at{constructor(N,Q){super(N,Q);te(this,"padding_side","left");this.legacy=Q.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new wt({replacement:vr,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(N){if(N===null)return null;if(this.legacy||N.length===0)return super._encode_text(N);let Q=super._encode_text(vr+N.replaceAll(vr," "));return Q.length>1&&Q[0]===vr&&this.special_tokens.includes(Q[1])&&(Q=Q.slice(1)),Q}}class xr extends at{}class $s extends at{}class ks extends at{}class fr extends at{}class Vs extends at{}class pn extends at{}class Fr extends at{}class hn extends at{}class qr extends at{}function Tr(he,k,N,Q){if(!("language_codes"in he)||!Array.isArray(he.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in he)||!(he.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in he)||typeof he.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const ie=Q.src_lang,de=Q.tgt_lang;if(!he.language_codes.includes(de))throw new Error(`Target language code "${de}" is not valid. Must be one of: {${he.language_codes.join(", ")}}`);if(ie!==void 0){if(!he.language_codes.includes(ie))throw new Error(`Source language code "${ie}" is not valid. Must be one of: {${he.language_codes.join(", ")}}`);for(const ve of he.post_processor.config.single)if("SpecialToken"in ve&&he.languageRegex.test(ve.SpecialToken.id)){ve.SpecialToken.id=he.lang_to_token(ie);break}}return Q.forced_bos_token_id=he.model.convert_tokens_to_ids([he.lang_to_token(de)])[0],he._call(k,N)}class Is extends at{constructor(k,N){super(k,N),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)),this.lang_to_token=Q=>Q}_build_translation_inputs(k,N,Q){return Tr(this,k,N,Q)}}class dr extends at{constructor(k,N){super(k,N),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)).map(Q=>Q.slice(2,-2)),this.lang_to_token=Q=>`__${Q}__`}_build_translation_inputs(k,N,Q){return Tr(this,k,N,Q)}}class gr extends at{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(k,{return_timestamps:N=!1,return_language:Q=!1,time_precision:ie=null,force_full_sequences:de=!0}={}){if(ie===null)throw Error("Must specify time_precision");let ve=null;const je=N==="word";function He(){return{language:ve,timestamp:[null,null],text:""}}const We=[];let Je=He(),dt=0;const xt=this.timestamp_begin,jt=xt+1500;let kt=[],Ht=[],br=!1,cr=null;const pr=new Set(this.all_special_ids);for(const Dt of k){const rr=Dt.tokens,wr=je?Dt.token_timestamps:null;let Qr=null,Rr=xt;if("stride"in Dt){const[or,Vt,Zt]=Dt.stride;if(dt-=Vt,cr=or-Zt,Vt&&(Rr=Vt/ie+xt),Zt)for(let er=rr.length-1;er>=0;--er){const tr=Number(rr[er]);if(tr>=xt){if(Qr!==null&&(tr-xt)*ie<cr)break;Qr=tr}}}let Yt=[],jr=[];for(let or=0;or<rr.length;++or){const Vt=Number(rr[or]);if(pr.has(Vt)){const Zt=this.decode([Vt]),er=p.WHISPER_LANGUAGE_MAPPING.get(Zt.slice(2,-2));if(er!==void 0){if(ve!==null&&er!==ve&&!N){kt.push(Yt);const tr=this.findLongestCommonSequence(kt)[0],Ws=this.decode(tr);Je.text=Ws,We.push(Je),kt=[],Yt=[],Je=He()}ve=Je.language=er}}else if(Vt>=xt&&Vt<=jt){const Zt=(Vt-xt)*ie+dt,er=(0,i.round)(Zt,2);if(Qr!==null&&Vt>=Qr)br=!0;else if(br||kt.length>0&&Vt<Rr)br=!1;else if(Je.timestamp[0]===null)Je.timestamp[0]=er;else if(er!==Je.timestamp[0]){Je.timestamp[1]=er,kt.push(Yt),je&&Ht.push(jr);const[tr,Ws]=this.findLongestCommonSequence(kt,Ht),xn=this.decode(tr);Je.text=xn,je&&(Je.words=this.collateWordTimestamps(tr,Ws,ve)),We.push(Je),kt=[],Yt=[],Ht=[],jr=[],Je=He()}}else if(Yt.push(Vt),je){let Zt=(0,i.round)(wr[or]+dt,2),er;if(or+1<wr.length){er=(0,i.round)(wr[or+1]+dt,2);const tr=this.decode([Vt]);v.test(tr)&&(er=(0,i.round)(Math.min(Zt+ie,er),2))}else er=null;jr.push([Zt,er])}}if("stride"in Dt){const[or,Vt,Zt]=Dt.stride;dt+=or-Zt}Yt.length>0?(kt.push(Yt),je&&Ht.push(jr)):kt.every(or=>or.length===0)&&(Je=He(),kt=[],Yt=[],Ht=[],jr=[])}if(kt.length>0){if(de&&N)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[Dt,rr]=this.findLongestCommonSequence(kt,Ht),wr=this.decode(Dt);Je.text=wr,je&&(Je.words=this.collateWordTimestamps(Dt,rr,ve)),We.push(Je)}let Nt=Object.create(null);const Xr=We.map(Dt=>Dt.text).join("");if(N||Q){for(let Dt=0;Dt<We.length;++Dt){const rr=We[Dt];N||delete rr.timestamp,Q||delete rr.language}if(je){const Dt=[];for(const rr of We)for(const wr of rr.words)Dt.push(wr);Nt={chunks:Dt}}else Nt={chunks:We}}return[Xr,Nt]}findLongestCommonSequence(k,N=null){let Q=k[0],ie=Q.length,de=[];const ve=Array.isArray(N)&&N.length>0;let je=ve?[]:null,He=ve?N[0]:null;for(let We=1;We<k.length;++We){const Je=k[We];let dt=0,xt=[ie,ie,0,0];const Pt=Je.length;for(let Nt=1;Nt<ie+Pt;++Nt){const Xr=Math.max(0,ie-Nt),Dt=Math.min(ie,ie+Pt-Nt),rr=Q.slice(Xr,Dt),wr=Math.max(0,Nt-ie),Qr=Math.min(Pt,Nt),Rr=Je.slice(wr,Qr);if(rr.length!==Rr.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let Yt;ve?Yt=rr.filter((Vt,Zt)=>Vt===Rr[Zt]&&He[Xr+Zt]<=N[We][wr+Zt]).length:Yt=rr.filter((Vt,Zt)=>Vt===Rr[Zt]).length;const jr=Nt/1e4,or=Yt/Nt+jr;Yt>1&&or>dt&&(dt=or,xt=[Xr,Dt,wr,Qr])}const[jt,kt,Ht,br]=xt,cr=Math.floor((kt+jt)/2),pr=Math.floor((br+Ht)/2);de.push(...Q.slice(0,cr)),Q=Je.slice(pr),ie=Q.length,ve&&(je.push(...He.slice(0,cr)),He=N[We].slice(pr))}return de.push(...Q),ve?(je.push(...He),[de,je]):[de,[]]}collateWordTimestamps(k,N,Q){const[ie,de,ve]=this.combineTokensIntoWords(k,Q),je=[];for(let He=0;He<ie.length;++He){const We=ve[He];je.push({text:ie[He],timestamp:[N[We.at(0)][0],N[We.at(-1)][1]]})}return je}combineTokensIntoWords(k,N,Q=`"'“¡¿([{-`,ie=`"'.。,，!！?？:：”)]}、`){N=N??"english";let de,ve,je;return["chinese","japanese","thai","lao","myanmar"].includes(N)?[de,ve,je]=this.splitTokensOnUnicode(k):[de,ve,je]=this.splitTokensOnSpaces(k),this.mergePunctuations(de,ve,je,Q,ie)}decode(k,N){let Q;return N!=null&&N.decode_with_timestamps?(k instanceof a.Tensor&&(k=T(k)),Q=this.decodeWithTimestamps(k,N)):Q=super.decode(k,N),Q}decodeWithTimestamps(k,N){const Q=(N==null?void 0:N.time_precision)??.02,ie=Array.from(this.all_special_ids).at(-1)+1;let de=[[]];for(let ve of k)if(ve=Number(ve),ve>=ie){const je=((ve-ie)*Q).toFixed(2);de.push(`<|${je}|>`),de.push([])}else de[de.length-1].push(ve);return de=de.map(ve=>typeof ve=="string"?ve:super.decode(ve,N)),de.join("")}splitTokensOnUnicode(k){const N=this.decode(k,{decode_with_timestamps:!0}),Q="�",ie=[],de=[],ve=[];let je=[],He=[],We=0;for(let Je=0;Je<k.length;++Je){const dt=k[Je];je.push(dt),He.push(Je);const xt=this.decode(je,{decode_with_timestamps:!0});(!xt.includes(Q)||N[We+xt.indexOf(Q)]===Q)&&(ie.push(xt),de.push(je),ve.push(He),je=[],He=[],We+=xt.length)}return[ie,de,ve]}splitTokensOnSpaces(k){const[N,Q,ie]=this.splitTokensOnUnicode(k),de=[],ve=[],je=[],He=new RegExp(`^[${M}]$`,"gu");for(let We=0;We<N.length;++We){const Je=N[We],dt=Q[We],xt=ie[We],Pt=dt[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),jt=Je.startsWith(" "),kt=Je.trim(),Ht=He.test(kt);if(Pt||jt||Ht||de.length===0)de.push(Je),ve.push(dt),je.push(xt);else{const br=de.length-1;de[br]+=Je,ve[br].push(...dt),je[br].push(...xt)}}return[de,ve,je]}mergePunctuations(k,N,Q,ie,de){const ve=structuredClone(k),je=structuredClone(N),He=structuredClone(Q);let We=ve.length-2,Je=ve.length-1;for(;We>=0;)ve[We].startsWith(" ")&&ie.includes(ve[We].trim())?(ve[Je]=ve[We]+ve[Je],je[Je]=(0,o.mergeArrays)(je[We],je[Je]),He[Je]=(0,o.mergeArrays)(He[We],He[Je]),ve[We]="",je[We]=[],He[We]=[]):Je=We,--We;for(We=0,Je=1;Je<ve.length;)!ve[We].endsWith(" ")&&de.includes(ve[Je])?(ve[We]+=ve[Je],je[We]=(0,o.mergeArrays)(je[We],je[Je]),He[We]=(0,o.mergeArrays)(He[We],He[Je]),ve[Je]="",je[Je]=[],He[Je]=[]):We=Je,++Je;return[ve.filter(dt=>dt),je.filter(dt=>dt.length>0),He.filter(dt=>dt.length>0)]}}class Br extends at{}class mn extends at{}class _n extends at{}class fn extends at{constructor(k,N){super(k,N),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(Q=>this.languageRegex.test(Q)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(k){if(k===null)return null;const[N,...Q]=k.trim().split(this.languageRegex);if(Q.length===0)return super._encode_text(N);if(Q.length===2){const[ie,de]=Q;return this.supported_language_codes.includes(ie)||console.warn(`Unsupported language code "${ie}" detected, which may lead to unexpected behavior. 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Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(w,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const w=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let g=0,S=0;g<this.data.length;++g)w[S++]=this.data[g],w[S++]=this.data[g],w[S++]=this.data[g],w[S++]=255;break;case 3:for(let g=0,S=0;g<this.data.length;g+=3)w[S++]=this.data[g],w[S++]=this.data[g+1],w[S++]=this.data[g+2],w[S++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(w,this.width,this.height,4)}putAlpha(w){if(w.width!==this.width||w.height!==this.height)throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${w.width}x${w.height}`);if(w.channels!==1)throw new Error(`Expected mask to have 1 channel, but got ${w.channels}`);const g=this.data,S=w.data,E=this.width*this.height;if(this.channels===3){const y=new Uint8ClampedArray(E*4);for(let M=0,v=0,C=0;M<E;++M)y[C++]=g[v++],y[C++]=g[v++],y[C++]=g[v++],y[C++]=S[M];return this._update(y,this.width,this.height,4)}else if(this.channels===4){for(let y=0;y<E;++y)g[4*y+3]=S[y];return this}throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(w,g,{resample:S=2}={}){if(this.width===w&&this.height===g)return this;let E=c[S]??S;const y=(0,s.isNullishDimension)(w),M=(0,s.isNullishDimension)(g);if(y&&M)return this;if(y?w=g/this.height*this.width:M&&(g=w/this.width*this.height),d){const v=this.channels,C=this.toCanvas(),A=l(w,g).getContext("2d");return A.drawImage(C,0,0,w,g),new _(A.getImageData(0,0,w,g).data,w,g,4).convert(v)}else{let v=this.toSharp();switch(E){case"box":case"hamming":(E==="box"||E==="hamming")&&(console.warn(`Resampling method ${E} is not yet supported. Using bilinear instead.`),E="bilinear");case"nearest":case"bilinear":case"bicubic":v=v.affine([w/this.width,0,0,g/this.height],{interpolator:E});break;case"lanczos":v=v.resize({width:w,height:g,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${E} is not supported.`)}return await p(v)}}async pad([w,g,S,E]){if(w=Math.max(w,0),g=Math.max(g,0),S=Math.max(S,0),E=Math.max(E,0),w===0&&g===0&&S===0&&E===0)return this;if(d){const y=this.channels,M=this.toCanvas(),v=this.width+w+g,C=this.height+S+E,A=l(v,C).getContext("2d");return A.drawImage(M,0,0,this.width,this.height,w,S,this.width,this.height),new _(A.getImageData(0,0,v,C).data,v,C,4).convert(y)}else{const y=this.toSharp().extend({left:w,right:g,top:S,bottom:E});return await p(y)}}async crop([w,g,S,E]){if(w=Math.max(w,0),g=Math.max(g,0),S=Math.min(S,this.width-1),E=Math.min(E,this.height-1),w===0&&g===0&&S===this.width-1&&E===this.height-1)return this;const y=S-w+1,M=E-g+1;if(d){const v=this.channels,C=this.toCanvas(),A=l(y,M).getContext("2d");return A.drawImage(C,w,g,y,M,0,0,y,M),new _(A.getImageData(0,0,y,M).data,y,M,4).convert(v)}else{const v=this.toSharp().extract({left:w,top:g,width:y,height:M});return await p(v)}}async center_crop(w,g){if(this.width===w&&this.height===g)return this;const S=(this.width-w)/2,E=(this.height-g)/2;if(d){const y=this.channels,M=this.toCanvas(),v=l(w,g).getContext("2d");let C=0,A=0,B=0,K=0;return S>=0?C=S:B=-S,E>=0?A=E:K=-E,v.drawImage(M,C,A,w,g,B,K,w,g),new _(v.getImageData(0,0,w,g).data,w,g,4).convert(y)}else{let y=this.toSharp();if(S>=0&&E>=0)y=y.extract({left:Math.floor(S),top:Math.floor(E),width:w,height:g});else if(S<=0&&E<=0){const M=Math.floor(-E),v=Math.floor(-S);y=y.extend({top:M,left:v,right:w-this.width-v,bottom:g-this.height-M})}else{let M=[0,0],v=0;E<0?(M[0]=Math.floor(-E),M[1]=g-this.height-M[0]):v=Math.floor(E);let C=[0,0],A=0;S<0?(C[0]=Math.floor(-S),C[1]=w-this.width-C[0]):A=Math.floor(S),y=y.extend({top:M[0],bottom:M[1],left:C[0],right:C[1]}).extract({left:A,top:v,width:w,height:g})}return await p(y)}}async toBlob(w="image/png",g=1){if(!d)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:w,quality:g})}toTensor(w="CHW"){let g=new i.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(w!=="HWC")if(w==="CHW")g=g.permute(2,0,1);else throw new Error(`Unsupported channel format: ${w}`);return g}toCanvas(){if(!d)throw new Error("toCanvas() is only supported in browser environments.");const w=this.clone().rgba(),g=l(w.width,w.height),S=new u(w.data,w.width,w.height);return g.getContext("2d").putImageData(S,0,0),g}split(){const{data:w,width:g,height:S,channels:E}=this,y=w.constructor,M=w.length/E,v=Array.from({length:E},()=>new y(M));for(let C=0;C<M;++C){const A=E*C;for(let B=0;B<E;++B)v[B][C]=w[A+B]}return v.map(C=>new _(C,g,S,1))}_update(w,g,S,E=null){return this.data=w,this.width=g,this.height=S,E!==null&&(this.channels=E),this}clone(){return new _(this.data.slice(),this.width,this.height,this.channels)}convert(w){if(this.channels===w)return this;switch(w){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: ${this.channels}`)}return this}async save(w){if(d){if(n.apis.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const g=w.split(".").pop().toLowerCase(),S=f.get(g)??"image/png",E=await this.toBlob(S);(0,s.saveBlob)(w,E)}else{if(n.apis.IS_FS_AVAILABLE)return await this.toSharp().toFile(w);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(d)throw new Error("toSharp() is only supported in server-side environments.");return a(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}const T=_.read.bind(_)},"./src/utils/maths.js":(e,r,t)=>{t.r(r),t.d(r,{FFT:()=>T,bankers_round:()=>g,cos_sim:()=>l,dot:()=>a,dynamic_time_warping:()=>S,interpolate_data:()=>s,log_softmax:()=>i,magnitude:()=>u,max:()=>d,medianFilter:()=>$,min:()=>p,permute_data:()=>o,round:()=>w,softmax:()=>n});function s(E,[y,M,v],[C,A],B="bilinear",K=!1){const G=A/v,j=C/M,ee=new E.constructor(C*A*y),H=M*v,Z=C*A;for(let X=0;X<C;++X)for(let oe=0;oe<A;++oe){const me=X*A+oe,ae=(oe+.5)/G-.5,V=(X+.5)/j-.5;let F=Math.floor(ae),W=Math.floor(V);const re=Math.min(F+1,v-1),_e=Math.min(W+1,M-1);F=Math.max(F,0),W=Math.max(W,0);const se=ae-F,ce=V-W,$e=(1-se)*(1-ce),Ue=se*(1-ce),we=(1-se)*ce,q=se*ce,R=W*v,pe=_e*v,xe=R+F,be=R+re,Se=pe+F,Ae=pe+re;for(let Fe=0;Fe<y;++Fe){const ze=Fe*H;ee[Fe*Z+me]=$e*E[ze+xe]+Ue*E[ze+be]+we*E[ze+Se]+q*E[ze+Ae]}}return ee}function o(E,y,M){const v=new Array(M.length),C=new Array(M.length);for(let K=M.length-1,G=1;K>=0;--K)C[K]=G,v[K]=y[M[K]],G*=v[K];const A=M.map((K,G)=>C[M.indexOf(G)]),B=new E.constructor(E.length);for(let K=0;K<E.length;++K){let G=0;for(let j=y.length-1,ee=K;j>=0;--j)G+=ee%y[j]*A[j],ee=Math.floor(ee/y[j]);B[G]=E[K]}return[B,v]}function n(E){const y=d(E)[0],M=E.map(A=>Math.exp(A-y)),v=M.reduce((A,B)=>A+B,0);return M.map(A=>A/v)}function i(E){const y=d(E)[0];let M=0;for(let A=0;A<E.length;++A)M+=Math.exp(E[A]-y);const v=Math.log(M);return E.map(A=>A-y-v)}function a(E,y){let M=0;for(let v=0;v<E.length;++v)M+=E[v]*y[v];return M}function l(E,y){const M=a(E,y),v=u(E),C=u(y);return M/(v*C)}function u(E){return Math.sqrt(E.reduce((y,M)=>y+M*M,0))}function p(E){if(E.length===0)throw Error("Array must not be empty");let y=E[0],M=0;for(let v=1;v<E.length;++v)E[v]<y&&(y=E[v],M=v);return[y,M]}function d(E){if(E.length===0)throw Error("Array must not be empty");let y=E[0],M=0;for(let v=1;v<E.length;++v)E[v]>y&&(y=E[v],M=v);return[y,M]}function c(E){return 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ae=oe,V=ae+Z,F=V+Z,W=F+Z,re=y[ae],_e=y[ae+1],se=y[V],ce=y[V+1],$e=y[F],Ue=y[F+1],we=y[W],q=y[W+1],R=H[me],pe=v*H[me+1],xe=se*R-ce*pe,be=se*pe+ce*R,Se=H[2*me],Ae=v*H[2*me+1],Fe=$e*Se-Ue*Ae,ze=$e*Ae+Ue*Se,Ve=H[3*me],O=v*H[3*me+1],Y=we*Ve-q*O,z=we*O+q*Ve,J=re+Fe,le=_e+ze,ye=re-Fe,Ee=_e-ze,ke=xe+Y,Ie=be+z,Re=v*(xe-Y),Xe=v*(be-z);y[ae]=J+ke,y[ae+1]=le+Ie,y[V]=ye+Xe,y[V+1]=Ee-Re,y[F]=J-ke,y[F+1]=le-Ie,y[W]=ye-Xe,y[W+1]=Ee+Re}}}}_singleTransform2(y,M,v,C,A){const B=y[C],K=y[C+1],G=y[C+A],j=y[C+A+1];M[v]=B+G,M[v+1]=K+j,M[v+2]=B-G,M[v+3]=K-j}_singleTransform4(y,M,v,C,A,B){const K=A*2,G=A*3,j=y[C],ee=y[C+1],H=y[C+A],Z=y[C+A+1],X=y[C+K],oe=y[C+K+1],me=y[C+G],ae=y[C+G+1],V=j+X,F=ee+oe,W=j-X,re=ee-oe,_e=H+me,se=Z+ae,ce=B*(H-me),$e=B*(Z-ae);M[v]=V+_e,M[v+1]=F+se,M[v+2]=W+$e,M[v+3]=re-ce,M[v+4]=V-_e,M[v+5]=F-se,M[v+6]=W-$e,M[v+7]=re+ce}_realTransform4(y,M,v){const C=this._csize;let B=1<<this._width,K=C/B<<1,G,j;const ee=this._bitrev;if(K===4)for(G=0,j=0;G<C;G+=K,++j){const X=ee[j];this._singleRealTransform2(M,y,G,X>>>1,B>>>1)}else for(G=0,j=0;G<C;G+=K,++j){const X=ee[j];this._singleRealTransform4(M,y,G,X>>>1,B>>>1,v)}const H=this.table;for(B>>=2;B>=2;B>>=2){K=C/B<<1;const X=K>>>1,oe=X>>>1,me=oe>>>1;for(G=0;G<C;G+=K)for(let ae=0,V=0;ae<=me;ae+=2,V+=B){const F=G+ae,W=F+oe,re=W+oe,_e=re+oe,se=y[F],ce=y[F+1],$e=y[W],Ue=y[W+1],we=y[re],q=y[re+1],R=y[_e],pe=y[_e+1],xe=se,be=ce,Se=H[V],Ae=v*H[V+1],Fe=$e*Se-Ue*Ae,ze=$e*Ae+Ue*Se,Ve=H[2*V],O=v*H[2*V+1],Y=we*Ve-q*O,z=we*O+q*Ve,J=H[3*V],le=v*H[3*V+1],ye=R*J-pe*le,Ee=R*le+pe*J,ke=xe+Y,Ie=be+z,Re=xe-Y,Xe=be-z,Ge=Fe+ye,lt=ze+Ee,wt=v*(Fe-ye),Gt=v*(ze-Ee);if(y[F]=ke+Ge,y[F+1]=Ie+lt,y[W]=Re+Gt,y[W+1]=Xe-wt,ae===0){y[re]=ke-Ge,y[re+1]=Ie-lt;continue}if(ae===me)continue;const Ot=G+oe-ae,ur=G+X-ae;y[Ot]=Re-v*Gt,y[Ot+1]=-Xe-v*wt,y[ur]=ke-v*Ge,y[ur+1]=-Ie+v*lt}}const Z=C>>>1;for(let X=2;X<Z;X+=2)y[C-X]=y[X],y[C-X+1]=-y[X+1]}_singleRealTransform2(y,M,v,C,A){const 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2:--K;break;default:throw new Error(`Internal error in dynamic time warping. 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Please file a bug report.`)}return G.reverse(),j.reverse(),[G,j]}},"./src/utils/tensor.js":(e,r,t)=>{t.r(r),t.d(r,{DataTypeMap:()=>i,Tensor:()=>a,cat:()=>M,full:()=>j,full_like:()=>ee,interpolate:()=>p,interpolate_4d:()=>d,layer_norm:()=>g,matmul:()=>c,mean:()=>B,mean_pooling:()=>w,ones:()=>H,ones_like:()=>Z,permute:()=>u,quantize_embeddings:()=>ae,rand:()=>me,rfft:()=>f,slice:()=>$,stack:()=>v,std_mean:()=>A,topk:()=>_,zeros:()=>X,zeros_like:()=>oe});var s=t("./src/utils/maths.js"),o=t("./src/backends/onnx.js"),n=t("./src/ops/registry.js");const i=Object.freeze({float32:Float32Array,float16:typeof Float16Array<"u"?Float16Array:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array,uint4:Uint8Array,int4:Int8Array});class a{constructor(...F){te(this,"ort_tensor");return(0,o.isONNXTensor)(F[0])?this.ort_tensor=F[0]:this.ort_tensor=new 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vx=m.AutoTokenizer;m.AutomaticSpeechRecognitionPipeline,m.BackgroundRemovalPipeline,m.BartForConditionalGeneration,m.BartForSequenceClassification,m.BartModel,m.BartPretrainedModel,m.BartTokenizer,m.BaseModelOutput,m.BaseStreamer,m.BeitFeatureExtractor,m.BeitForImageClassification,m.BeitModel,m.BeitPreTrainedModel,m.BertForMaskedLM,m.BertForQuestionAnswering,m.BertForSequenceClassification,m.BertForTokenClassification,m.BertModel,m.BertPreTrainedModel,m.BertTokenizer,m.BitImageProcessor,m.BlenderbotForConditionalGeneration,m.BlenderbotModel,m.BlenderbotPreTrainedModel,m.BlenderbotSmallForConditionalGeneration,m.BlenderbotSmallModel,m.BlenderbotSmallPreTrainedModel,m.BlenderbotSmallTokenizer,m.BlenderbotTokenizer,m.BloomForCausalLM,m.BloomModel,m.BloomPreTrainedModel,m.BloomTokenizer,m.CLIPFeatureExtractor,m.CLIPImageProcessor,m.CLIPModel,m.CLIPPreTrainedModel,m.CLIPSegForImageSegmentation,m.CLIPSegModel,m.CLIPSegPreTrainedModel,m.CLIPTextModel,m.CLIPTextModelWithProjection,m.CLIPTokenizer,m.CLIPVisionModel,m.CLIPVisionModelWithProjection,m.CamembertForMaskedLM,m.CamembertForQuestionAnswering,m.CamembertForSequenceClassification,m.CamembertForTokenClassification,m.CamembertModel,m.CamembertPreTrainedModel,m.CamembertTokenizer,m.CausalLMOutput,m.CausalLMOutputWithPast,m.ChineseCLIPFeatureExtractor,m.ChineseCLIPModel,m.ChineseCLIPPreTrainedModel,m.ClapAudioModelWithProjection,m.ClapFeatureExtractor,m.ClapModel,m.ClapPreTrainedModel,m.ClapTextModelWithProjection,m.ClassifierFreeGuidanceLogitsProcessor,m.CodeGenForCausalLM,m.CodeGenModel,m.CodeGenPreTrainedModel,m.CodeGenTokenizer,m.CodeLlamaTokenizer,m.CohereForCausalLM,m.CohereModel,m.CoherePreTrainedModel,m.CohereTokenizer,m.ConvBertForMaskedLM,m.ConvBertForQuestionAnswering,m.ConvBertForSequenceClassification,m.ConvBertForTokenClassification,m.ConvBertModel,m.ConvBertPreTrainedModel,m.ConvBertTokenizer,m.ConvNextFeatureExtractor,m.ConvNextForImageClassification,m.ConvNextImageProcessor,m.ConvNextModel,m.ConvNextPreTrainedModel,m.ConvNextV2ForImageClassification,m.ConvNextV2Model,m.ConvNextV2PreTrainedModel,m.DPTFeatureExtractor,m.DPTForDepthEstimation,m.DPTImageProcessor,m.DPTModel,m.DPTPreTrainedModel,m.DacDecoderModel,m.DacDecoderOutput,m.DacEncoderModel,m.DacEncoderOutput,m.DacFeatureExtractor,m.DacModel,m.DacPreTrainedModel,m.DataTypeMap,m.DebertaForMaskedLM,m.DebertaForQuestionAnswering,m.DebertaForSequenceClassification,m.DebertaForTokenClassification,m.DebertaModel,m.DebertaPreTrainedModel,m.DebertaTokenizer,m.DebertaV2ForMaskedLM,m.DebertaV2ForQuestionAnswering,m.DebertaV2ForSequenceClassification,m.DebertaV2ForTokenClassification,m.DebertaV2Model,m.DebertaV2PreTrainedModel,m.DebertaV2Tokenizer,m.DecisionTransformerModel,m.DecisionTransformerPreTrainedModel,m.DeiTFeatureExtractor,m.DeiTForImageClassification,m.DeiTImageProcessor,m.DeiTModel,m.DeiTPreTrainedModel,m.DepthAnythingForDepthEstimation,m.DepthAnythingPreTrainedModel,m.DepthEstimationPipeline,m.DepthProForDepthEstimation,m.DepthProPreTrainedModel,m.DetrFeatureExtractor,m.DetrForObjectDetection,m.DetrForSegmentation,m.DetrImageProcessor,m.DetrModel,m.DetrObjectDetectionOutput,m.DetrPreTrainedModel,m.DetrSegmentationOutput,m.Dinov2ForImageClassification,m.Dinov2Model,m.Dinov2PreTrainedModel,m.Dinov2WithRegistersForImageClassification,m.Dinov2WithRegistersModel,m.Dinov2WithRegistersPreTrainedModel,m.DistilBertForMaskedLM,m.DistilBertForQuestionAnswering,m.DistilBertForSequenceClassification,m.DistilBertForTokenClassification,m.DistilBertModel,m.DistilBertPreTrainedModel,m.DistilBertTokenizer,m.DocumentQuestionAnsweringPipeline,m.DonutFeatureExtractor,m.DonutImageProcessor,m.DonutSwinModel,m.DonutSwinPreTrainedModel,m.EfficientNetForImageClassification,m.EfficientNetImageProcessor,m.EfficientNetModel,m.EfficientNetPreTrainedModel,m.ElectraForMaskedLM,m.ElectraForQuestionAnswering,m.ElectraForSequenceClassification,m.ElectraForTokenClassification,m.ElectraModel,m.ElectraPreTrainedModel,m.ElectraTokenizer,m.EncodecFeatureExtractor,m.EosTokenCriteria,m.EsmForMaskedLM,m.EsmForSequenceClassification,m.EsmForTokenClassification,m.EsmModel,m.EsmPreTrainedModel,m.EsmTokenizer,m.ExaoneForCausalLM,m.ExaoneModel,m.ExaonePreTrainedModel,m.FFT,m.FalconForCausalLM,m.FalconModel,m.FalconPreTrainedModel,m.FalconTokenizer,m.FastViTForImageClassification,m.FastViTModel,m.FastViTPreTrainedModel,m.FeatureExtractionPipeline,m.FeatureExtractor,m.FillMaskPipeline,m.Florence2ForConditionalGeneration,m.Florence2PreTrainedModel,m.Florence2Processor,m.ForcedBOSTokenLogitsProcessor,m.ForcedEOSTokenLogitsProcessor,m.GLPNFeatureExtractor,m.GLPNForDepthEstimation,m.GLPNModel,m.GLPNPreTrainedModel,m.GPT2LMHeadModel,m.GPT2Model,m.GPT2PreTrainedModel,m.GPT2Tokenizer,m.GPTBigCodeForCausalLM,m.GPTBigCodeModel,m.GPTBigCodePreTrainedModel,m.GPTJForCausalLM,m.GPTJModel,m.GPTJPreTrainedModel,m.GPTNeoForCausalLM,m.GPTNeoModel,m.GPTNeoPreTrainedModel,m.GPTNeoXForCausalLM,m.GPTNeoXModel,m.GPTNeoXPreTrainedModel,m.GPTNeoXTokenizer,m.Gemma2ForCausalLM,m.Gemma2Model,m.Gemma2PreTrainedModel,m.Gemma3ForCausalLM,m.Gemma3Model,m.Gemma3PreTrainedModel,m.GemmaForCausalLM,m.GemmaModel,m.GemmaPreTrainedModel,m.GemmaTokenizer,m.GlmForCausalLM,m.GlmModel,m.GlmPreTrainedModel,m.GraniteForCausalLM,m.GraniteModel,m.GranitePreTrainedModel,m.Grok1Tokenizer,m.GroundingDinoForObjectDetection,m.GroundingDinoImageProcessor,m.GroundingDinoPreTrainedModel,m.GroundingDinoProcessor,m.GroupViTModel,m.GroupViTPreTrainedModel,m.HeliumForCausalLM,m.HeliumModel,m.HeliumPreTrainedModel,m.HerbertTokenizer,m.HieraForImageClassification,m.HieraModel,m.HieraPreTrainedModel,m.HubertForCTC,m.HubertForSequenceClassification,m.HubertModel,m.HubertPreTrainedModel,m.IJepaForImageClassification,m.IJepaModel,m.IJepaPreTrainedModel,m.Idefics3ForConditionalGeneration,m.Idefics3ImageProcessor,m.Idefics3PreTrainedModel,m.Idefics3Processor,m.ImageClassificationPipeline,m.ImageFeatureExtractionPipeline,m.ImageFeatureExtractor,m.ImageMattingOutput,m.ImageProcessor,m.ImageSegmentationPipeline,m.ImageToImagePipeline,m.ImageToTextPipeline;var 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function Ex(){try{if(!await navigator.gpu.requestAdapter())throw new Error("WebGPU is not supported (no adapter found)")}catch(e){self.postMessage({status:"error",data:e.toString()})}}class cu{static async getInstance(r=null){return this.tokenizer??(this.tokenizer=vx.from_pretrained(this.model_id,{progress_callback:r})),this.model??(this.model=yx.from_pretrained(this.model_id,{dtype:"q4f16",device:"webgpu",progress_callback:r})),Promise.all([this.tokenizer,this.model])}}te(cu,"model_id","onnx-community/DeepSeek-R1-Distill-Qwen-1.5B-ONNX");const yi=new xx;async function Px(e){const[r,t]=await cu.getInstance(),s=r.apply_chat_template(e,{add_generation_prompt:!0,return_dict:!0}),[o,n]=r.encode("<think></think>",{add_special_tokens:!1});let i="thinking",a,l=0,u;const p=$=>{a??(a=performance.now()),l++>0&&(u=l/(performance.now()-a)*1e3),$[0]==n&&(i="answering")},d=$=>{self.postMessage({status:"update",output:$,tps:u,numTokens:l,state:i})},c=new Tx(r,{skip_prompt:!0,skip_special_tokens:!0,callback_function:d,token_callback_function:p});self.postMessage({status:"start"});const{past_key_values:f,sequences:_}=await t.generate({...s,do_sample:!1,max_new_tokens:2048,streamer:c,stopping_criteria:yi,return_dict_in_generate:!0}),T=r.batch_decode(_,{skip_special_tokens:!0});self.postMessage({status:"complete",output:T})}async function Cx(){self.postMessage({status:"loading",data:"Loading model..."});const[e,r]=await cu.getInstance(s=>{self.postMessage(s)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const t=e("a");await r.generate({...t,max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async e=>{const{type:r,data:t}=e.data;switch(r){case"check":Ex();break;case"load":Cx();break;case"generate":yi.reset(),Px(t);break;case"interrupt":yi.interrupt();break;case"reset":yi.reset();break}})})();