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 * ONNX Runtime Web v1.20.0-dev.20240827-1d059b8702
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
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  fn main(${i}) {
    ${a}
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`)}registerInternalVariable(e){if(e.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() instead.");this.internalVariables.push(e),this.appendVariableUniforms(e)}registerInternalVariables(...e){return e.forEach(t=>this.registerInternalVariable(t)),this}registerUniform(e,t,r=1){return this.uniforms.push({name:e,type:t,length:r}),this}registerUniforms(e){return this.uniforms=this.uniforms.concat(e),this}uniformDeclaration(){if(this.uniforms.length===0)return"";let e=[];for(let{name:t,type:r,length:n}of this.uniforms)if(n&&n>4)r==="f16"?e.push(`@align(16) ${t}:array<mat2x4<${r}>, ${Math.ceil(n/8)}>`):e.push(`${t}:array<vec4<${r}>, ${Math.ceil(n/4)}>`);else{let s=n==null||n===1?r:`vec${n}<${r}>`;e.push(`${t}:${s}`)}return`
      struct Uniforms { ${e.join(", ")} };
      @group(0) @binding(${this.variableIndex}) var<uniform> uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(e=>e.impl()).join(`
`)+this.internalVariables.map(e=>e.impl()).join(`
`)}get variablesInfo(){if(this.uniforms.length===0)return;let e=t=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(t)];return this.uniforms.map(t=>[e(t.type),t.length??1])}},Fa=(e,t)=>new yi(e,t),cs=(e,t)=>{let r=e.length,n=[];for(let s=0;s<r;s++){let i=r-1-s,a=e[i]||1;(t[t.length-1-s]||1)>1&&a===1&&n.unshift(i)}return n}}),Oa,bi,Es,za,kn,Da,Mi,ps=R(()=>{Kt(),Xt(),Cr(),sr(),Oa=e=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.")},bi=(e,t)=>t&&t.length!==e?[...new Array(e).keys()].reverse():t,Es=(e,t)=>Ie.sortBasedOnPerm(e,bi(e.length,t)),za=(e,t,r,n)=>{let s=[];s.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
    var a: ${r.type.indices};`);for(let i=0;i<t;++i)s.push(r.indicesSet("a",e[i],`i[${i}]`));return s.push("return a;}"),s.join(`
`)},kn=(e,t)=>{let r=e.dataType,n=e.dims.length,s=bi(n,t),i=Es(e.dims,s),a=Vt("output",r,i.length),u=Ze("a",r,n),d;if(s.length===2&&s[0]===1&&s[1]===0){let c=a.type.value,g=[16,16,1];d=l=>`
  ${l.registerUniform("output_size","u32").declareVariables(u,a)}
  var<workgroup> tile : array<array<${c}, ${g[0]+1}>, ${g[0]}>;
  ${l.mainStart(g)}
    var x = workgroup_id.x * ${g[0]}u + local_id.x;
    var y = workgroup_id.y * ${g[0]}u + local_id.y;
    let width = uniforms.output_shape[0];
    let height = uniforms.output_shape[1];
    if (x < width && y < height) {
      tile[local_id.y][local_id.x] = ${u.getByOffset("y * width + x")};
    }
    workgroupBarrier();
    x = workgroup_id.y * ${g[0]}u + local_id.x;
    y = workgroup_id.x * ${g[0]}u + local_id.y;
    if (x < height && y < width) {
      ${a.setByOffset("y * height + x","tile[local_id.x][local_id.y]")}
    }
  }`}else d=c=>`
  ${c.registerUniform("output_size","u32").declareVariables(u,a)}

  ${za(s,n,u,a)}

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

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

    ${a.setByOffset("global_idx",u.getByIndices("aIndices"))}
  }`;return{name:"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:()=>{let c=Ie.size(i);return{outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:[{type:12,data:c},...Pt(e.dims,i)]}},getShaderSource:d}},Da=(e,t)=>{Oa(e.inputs),e.compute(kn(e.inputs[0],t.perm))},Mi=e=>qt({perm:e.perm})}),Ba,La,Ra,Na,vi,ja,Va,xi,Ua,Wa,bn,Ga,qa,Ti,Ha,Ka,Si,Xa,Qa,$i,Ya,Cu=R(()=>{Kt(),Xt(),sr(),Fi(),ps(),Ba={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"},La={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"},Ra={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},Na={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},vi=(e,t)=>{let r=[];for(let n=t-e;n<t;++n)r.push(n);return r},ja=(e,t)=>{let r=[],n=e.length;for(let i=0;i<n;i++)t.indexOf(i)===-1&&r.push(e[i]);let s=t.map(i=>e[i]);return[r,s]},Va=(e,t)=>{let r=e.length+t.length,n=[],s=0;for(let i=0;i<r;i++)t.indexOf(i)===-1?n.push(e[s++]):n.push(1);return n},xi=(e,t)=>{for(let r=0;r<e.length;++r)if(e[e.length-r-1]!==t-1-r)return!1;return!0},Ua=(e,t)=>{let r=[];if(!xi(e,t)){for(let n=0;n<t;++n)e.indexOf(n)===-1&&r.push(n);e.forEach(n=>r.push(n))}return r},Wa=(e,t,r,n,s,i,a)=>{let u=r[0].dims,d=Ie.size(i),c=Ie.size(a),g=Ze("_A",r[0].dataType,u),l=Vt("output",s,i),y=32,v=`
          var<workgroup> aBestValues : array<f32, ${y}>;
       `;return{name:e,shaderCache:t,getShaderSource:S=>`
        ${S.registerUniform("reduceSize","u32").declareVariables(g,l)}
        ${v}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${S.mainStart(y)}

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

          var bestValue = f32(${Ra[n]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${y}) {
           let candidate = f32(${g.getByOffset("offset + k")});
           bestValue = ${Ba[n]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

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

         if (local_idx == 0u) {
          ${l.setByOffset("outputIndex",`${n==="mean"?`${l.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${l.type.storage}(${Na[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:i,dataType:s}],dispatchGroup:{x:d},programUniforms:[{type:12,data:c}]})}},bn=(e,t,r,n)=>{let s=e.inputs.length===1?r:Hs(e.inputs,r),i=s.axes;i.length===0&&!s.noopWithEmptyAxes&&(i=e.inputs[0].dims.map((v,S)=>S));let a=Ie.normalizeAxes(i,e.inputs[0].dims.length),u=a,d=e.inputs[0],c=Ua(u,e.inputs[0].dims.length);c.length>0&&(d=e.compute(kn(e.inputs[0],c),{inputs:[0],outputs:[-1]})[0],u=vi(u.length,d.dims.length));let[g,l]=ja(d.dims,u),y=g;s.keepDims&&(y=Va(g,a)),e.compute(Wa(t,{hint:s.cacheKey,inputDependencies:["type"]},[d],n,e.inputs[0].dataType,y,l),{inputs:[d]})},Ga=(e,t)=>{bn(e,"ReduceMeanShared",t,"mean")},qa=(e,t)=>{bn(e,"ReduceL1Shared",t,"l1")},Ti=(e,t)=>{bn(e,"ReduceL2Shared",t,"l2")},Ha=(e,t)=>{bn(e,"ReduceLogSumExpShared",t,"logSumExp")},Ka=(e,t)=>{bn(e,"ReduceMaxShared",t,"max")},Si=(e,t)=>{bn(e,"ReduceMinShared",t,"min")},Xa=(e,t)=>{bn(e,"ReduceProdShared",t,"prod")},Qa=(e,t)=>{bn(e,"ReduceSumShared",t,"sum")},$i=(e,t)=>{bn(e,"ReduceSumSquareShared",t,"sumSquare")},Ya=(e,t)=>{bn(e,"ReduceLogSumShared",t,"logSum")}}),Mn,Za,qs,Hs,xn,Ja,Ei,eo,to,Ci,ro,no,ki,so,io,vn,ao,oo,Pi,lo,uo,Ai,co,po,Ii,ho,Fi=R(()=>{Kt(),Xt(),Cr(),sr(),Cu(),Mn=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.")},Za=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],qs=(e,t,r,n,s,i,a=!1,u=!1)=>{let d=[],c=r[0].dims,g=c.length,l=Ie.normalizeAxes(s,g),y=!u&&l.length===0;c.forEach((x,G)=>{y||l.indexOf(G)>=0?a&&d.push(1):d.push(x)});let v=d.length,S=Ie.size(d);return{name:e,shaderCache:t,getShaderSource:x=>{let G=[],q=Ze("_A",r[0].dataType,g),k=Vt("output",i,v),Z=n(q,k,l),ee=Z[2];for(let fe=0,We=0;fe<g;fe++)y||l.indexOf(fe)>=0?(a&&We++,ee=`for(var j${fe}: u32 = 0; j${fe} < ${c[fe]}; j${fe}++) {
                  ${Z[2].includes("last_index")?`let last_index = j${fe};`:""}
                  ${q.indicesSet("input_indices",fe,`j${fe}`)}
                  ${ee}
                }`):(G.push(`${q.indicesSet("input_indices",fe,k.indicesGet("output_indices",We))};`),We++);return`

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

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

          ${G.join(`
`)}
          ${Z[0]}       // init ops for reduce max/min
          ${Z[1]}
          ${ee}
          ${Z[3]}
          ${Z.length===4?k.setByOffset("global_idx","value"):Z.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:d,dataType:i}],dispatchGroup:{x:Math.ceil(S/64)},programUniforms:[{type:12,data:S},...Pt(c,d)]})}},Hs=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),qt({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},xn=(e,t,r,n)=>{let s=e.inputs,i=s.length===1?r:Hs(s,r);e.compute(qs(t,{hint:i.cacheKey,inputDependencies:["rank"]},[s[0]],i.noopWithEmptyAxes&&i.axes.length===0?Za:n,i.axes,s[0].dataType,i.keepDims,i.noopWithEmptyAxes),{inputs:[0]})},Ja=(e,t)=>{Mn(e.inputs),xn(e,"ReduceLogSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},Ei=(e,t)=>{Mn(e.inputs),xn(e,"ReduceL1",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},eo=(e,t)=>{Mn(e.inputs),xn(e,"ReduceL2",t,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},to=(e,t)=>{Mn(e.inputs),xn(e,"ReduceLogSumExp",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},Ci=(e,t)=>{Mn(e.inputs),xn(e,"ReduceMax",t,(r,n,s)=>{let i=[];for(let a=0;a<r.rank;a++)(s.indexOf(a)>=0||s.length===0)&&i.push(r.indicesSet("input_indices",a,0));return[`${i.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = max(value, ${r.getByIndices("input_indices")});`,""]})},ro=(e,t)=>{Mn(e.inputs),xn(e,"ReduceMean",t,(r,n,s)=>{let i=1;for(let a=0;a<r.rank;a++)(s.indexOf(a)>=0||s.length===0)&&(i*=e.inputs[0].dims[a]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${i});`]})},no=(e,t)=>{Mn(e.inputs),xn(e,"ReduceMin",t,(r,n,s)=>{let i=[];for(let a=0;a<r.rank;a++)(s.indexOf(a)>=0||s.length===0)&&i.push(`input_indices[${a}] = 0;`);return[`${i.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},ki=(e,t)=>{Mn(e.inputs),xn(e,"ReduceProd",t,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},so=(e,t)=>{Mn(e.inputs),xn(e,"ReduceSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},io=(e,t)=>{Mn(e.inputs),xn(e,"ReduceSumSquare",t,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},vn=(e,t,r)=>{if(t.length===0)return r;let n=1,s=1;for(let i=0;i<t.length;i++)t.indexOf(i)===-1?n*=e[i]:s*=e[i];return s<32&&n>1024},ao=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?ro(e,t):Ga(e,t)},oo=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ei(e,t):qa(e,t)},Pi=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?eo(e,t):Ti(e,t)},lo=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?to(e,t):Ha(e,t)},uo=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ci(e,t):Ka(e,t)},Ai=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?no(e,t):Si(e,t)},co=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?ki(e,t):Xa(e,t)},po=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?so(e,t):Qa(e,t)},Ii=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?io(e,t):$i(e,t)},ho=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ja(e,t):Ya(e,t)}}),Ks,fo,mo,Xs,ku=R(()=>{Kt(),Cr(),Fi(),Ks=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.")},fo=(e,t)=>{Ks(e.inputs);let r=(n,s,i)=>{let a=[];for(let u=0;u<n.rank;u++)(i.indexOf(u)>=0||i.length===0)&&a.push(`input_indices[${u}] = 0;`);return[`${a.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",s.setByOffset("global_idx","best_index")]};e.compute(qs("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},mo=(e,t)=>{Ks(e.inputs);let r=(n,s,i)=>{let a=[];for(let u=0;u<n.rank;u++)(i.indexOf(u)>=0||i.length===0)&&a.push(`input_indices[${u}] = 0;`);return[`${a.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",s.setByOffset("global_idx","best_index")]};e.compute(qs("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Xs=e=>qt(e)}),_o,Oi,go,wo,hs,yo,bo,Qs=R(()=>{Kt(),Xt(),z(),sr(),_o=(e,t)=>{let r=e[0],n=e[1],s=e[2],i=e[3],a=e[4],u=e[5];if(a&&u)throw new Error("Attention cannot have both past and attention_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let d=r.dims[0],c=r.dims[1],g=r.dims[2];if(s.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==g)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(s.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let l=s.dims[0]/3,y=l,v=y;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let Z of t.qkvHiddenSizes)if(Z%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");l=t.qkvHiddenSizes[0],y=t.qkvHiddenSizes[1],v=t.qkvHiddenSizes[2]}let S=c;if(l!==y)throw new Error("qkv_hidden_sizes first element should be same as the second");if(s.dims[0]!==l+y+v)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let x=0;if(a){if(y!==v)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(a.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(a.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(a.dims[1]!==d)throw new Error('Input "past" second dimension must be batch_size');if(a.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(a.dims[4]!==y/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(x=a.dims[3])}let G=S+x,q=-1,k=0;if(i)throw new Error("Mask not supported");if(a)throw new Error("past is not supported");if(u){if(u.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(u.dims[0]!==d||u.dims[1]!==t.numHeads||u.dims[2]!==c||u.dims[3]!==G)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:d,sequenceLength:c,pastSequenceLength:x,kvSequenceLength:S,totalSequenceLength:G,maxSequenceLength:q,inputHiddenSize:g,hiddenSize:l,vHiddenSize:v,headSize:Math.floor(l/t.numHeads),vHeadSize:Math.floor(v/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:k,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Oi=(e,t,r)=>{let n=Sr(r),s=64,i=r/n;i<s&&(s=32);let a=Math.ceil(r/n/s),u=[{type:1,data:1/r},{type:12,data:i},{type:12,data:a}],d=Er(e.dataType,n),c=Tr(1,n),g=["type"],l=y=>{let v=Vt("x",e.dataType,e.dims,n),S=Tr(e.dataType),x=[{name:"d_inv",type:"f32"},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${s}>;
  var<workgroup> thread_sum: array<f32, ${s}>;
  ${y.registerUniforms(x).declareVariables(v)}
  ${y.mainStart([s,1,1])}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${s}) * uniforms.d_comp + local_offset;

    var thread_max_vector = ${c}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
      thread_max_vector = max(${c}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(n){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: ${n}`)}})()};
    workgroupBarrier();

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

    var sum_vector = ${c}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
      sum_vector += exp(${c}(x[offset + i]) - max_value);
    }
    thread_sum[local_idx] = ${(()=>{switch(n){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: ${n}`)}})()};
    workgroupBarrier();

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

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        x[offset + i] = ${v.type.value}(${S}(uniforms.d_inv));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        var f32input = ${c}(x[offset + i]);
        x[offset + i] = ${v.type.value}(exp(f32input - max_value) / sum);
      }
    }
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${d};${n}`,inputDependencies:g},getShaderSource:l,getRunData:()=>({outputs:[],dispatchGroup:{x:t},programUniforms:u})}},go=(e,t,r,n,s,i,a,u)=>{let d=u+i.kvSequenceLength,c=[i.batchSize,i.numHeads,i.sequenceLength,d],g=i.kvNumHeads===void 0&&e>1&&n,l=g?[i.batchSize,i.numHeads,d,i.headSize]:void 0,y=a.scale===0?1/Math.sqrt(i.headSize):a.scale,v=Sr(i.headSize),S=i.headSize/v,x=12,G={x:Math.ceil(d/x),y:Math.ceil(i.sequenceLength/x),z:i.batchSize*i.numHeads},q=[{type:12,data:i.sequenceLength},{type:12,data:S},{type:12,data:d},{type:12,data:i.numHeads},{type:1,data:y},{type:12,data:u},{type:12,data:i.kvSequenceLength}],k=g&&n&&Ie.size(n.dims)>0,Z=["type","type"];k&&Z.push("type"),s&&Z.push("type");let ee=[{dims:c,dataType:t.dataType,gpuDataType:0}];g&&ee.push({dims:l,dataType:t.dataType,gpuDataType:0});let fe=We=>{let Ne=Ze("q",t.dataType,t.dims,v),mt=Ze("key",r.dataType,r.dims,v),jt=[Ne,mt];if(k){let Pr=Ze("past_key",n.dataType,n.dims,v);jt.push(Pr)}s&&jt.push(Ze("attention_bias",s.dataType,s.dims));let Lt=Vt("output",t.dataType,c),dr=[Lt];g&&dr.push(Vt("present_key",t.dataType,l,v));let ar=Tr(1,v),_r=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"}];return`
  const TILE_SIZE = ${x}u;

  var<workgroup> tileQ: array<${Ne.type.storage}, ${x*x}>;
  var<workgroup> tileK: array<${Ne.type.storage}, ${x*x}>;
  ${We.registerUniforms(_r).declareVariables(...jt,...dr)}
  ${We.mainStart([x,x,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;
    ${k&&g?`
    let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx;
    let pastKeyOffset = uniforms.past_sequence_length * uniforms.K * headIdx;`:`
    let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;`}
    ${g?"let presentKeyOffset = headIdx * uniforms.N * uniforms.K;":""}
    var value = ${ar}(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;
      ${k&&g?`
              if (n + local_id.y < uniforms.past_sequence_length) {
                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
              } else {
                tileK[idx] =
                         key[kOffset + (n + local_id.y - uniforms.past_sequence_length) * uniforms.K + w + local_id.x];
              }`:"tileK[idx] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];"}
      ${g?"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 += ${ar}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);
      }

      workgroupBarrier();
    }

    let headOffset = headIdx * uniforms.M * uniforms.N;
    if (global_id.y < uniforms.M && global_id.x < uniforms.N) {
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(v){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: ${v}`)}})()};
        output[outputIdx] = ${Lt.type.value} (sum * uniforms.alpha) + ${s?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${v};${s!==void 0};${n!==void 0};${e}`,inputDependencies:Z},getRunData:()=>({outputs:ee,dispatchGroup:G,programUniforms:q}),getShaderSource:fe}},wo=(e,t,r,n,s,i)=>{let a=i+s.kvSequenceLength,u=s.nReps?s.nReps:1,d=s.vHiddenSize*u,c=s.kvNumHeads==null&&e>1&&n,g=c?[s.batchSize,s.numHeads,a,s.headSize]:void 0,l=[s.batchSize,s.sequenceLength,d],y=12,v={x:Math.ceil(s.vHeadSize/y),y:Math.ceil(s.sequenceLength/y),z:s.batchSize*s.numHeads},S=[{type:12,data:s.sequenceLength},{type:12,data:a},{type:12,data:s.vHeadSize},{type:12,data:s.numHeads},{type:12,data:d},{type:12,data:i},{type:12,data:s.kvSequenceLength}],x=c&&n&&Ie.size(n.dims)>0,G=["type","type"];x&&G.push("type");let q=[{dims:l,dataType:t.dataType,gpuDataType:0}];c&&q.push({dims:g,dataType:t.dataType,gpuDataType:0});let k=Z=>{let ee=Ze("probs",t.dataType,t.dims),fe=Ze("v",r.dataType,r.dims),We=[ee,fe];x&&We.push(Ze("past_value",n.dataType,n.dims));let Ne=[Vt("output",t.dataType,l)];c&&Ne.push(Vt("present_value",t.dataType,g));let mt=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"}];return`
  const TILE_SIZE = ${y}u;
  var<workgroup> tileQ: array<${ee.type.value}, ${y*y}>;
  var<workgroup> tileK: array<${ee.type.value}, ${y*y}>;
  ${Z.registerUniforms(mt).declareVariables(...We,...Ne)}
  ${Z.mainStart([y,y,1])}
   let headIdx = workgroup_id.z;
   let m = global_id.y;
   let n = global_id.x;

   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;
   ${x&&c?`
    let pastValueOffset = headIdx * uniforms.N * uniforms.past_sequence_length + n;
    let vOffset = headIdx * uniforms.N * uniforms.kv_sequence_length + n;
      `:`
   let offsetB = headIdx * uniforms.N * uniforms.K + n;
            `}
    ${c?"let presentValueOffset = headIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${ee.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;
        ${x&&c?`
        if (w + local_id.y < uniforms.past_sequence_length) {
          tileK[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
        } else {
          tileK[idx] = v[vOffset + (w + local_id.y - uniforms.past_sequence_length) * uniforms.N];
        }
      `:`
        tileK[idx] = v[offsetB + (w + local_id.y) * uniforms.N];
      `}
        ${c?"present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileK[idx];":""}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + currentBatchHeadNumber * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:G},getRunData:()=>({outputs:q,dispatchGroup:v,programUniforms:S}),getShaderSource:k}},hs=(e,t,r,n,s,i,a,u,d,c,g)=>{let l=Math.min(e.outputCount,1+(a?1:0)+(u?1:0)),y=c.kvNumHeads!==void 0||l>1?c.pastSequenceLength:0,v=y+c.kvSequenceLength,S=d&&Ie.size(d.dims)>0?d:void 0,x=[t,r];c.kvNumHeads===void 0&&l>1&&a&&Ie.size(a.dims)>0&&x.push(a),S&&x.push(S);let G=e.compute(go(l,t,r,a,S,c,g,y),{inputs:x,outputs:c.kvNumHeads===void 0&&l>1?[-1,1]:[-1]})[0];e.compute(Oi(G,c.batchSize*c.numHeads*c.sequenceLength,v),{inputs:[G],outputs:[]});let q=[G,n];c.kvNumHeads===void 0&&l>1&&u&&Ie.size(u.dims)>0&&q.push(u),e.compute(wo(l,G,n,u,c,y),{inputs:q,outputs:c.kvNumHeads===void 0&&l>1?[0,2]:[0]})},yo=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,s=t.inputHiddenSize,i=t.headSize,a=12,u={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),z:t.batchSize*t.numHeads},d=[e.inputs[0],e.inputs[1],e.inputs[2]],c=[{type:12,data:n},{type:12,data:s},{type:12,data:i},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],g=l=>{let y=Vt("output_q",d[0].dataType,r),v=Vt("output_k",d[0].dataType,r),S=Vt("output_v",d[0].dataType,r),x=Ze("input",d[0].dataType,d[0].dims),G=Ze("weight",d[1].dataType,d[1].dims),q=Ze("bias",d[2].dataType,d[2].dims),k=x.type.storage,Z=[{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 = ${a}u;
  var<workgroup> tileInput: array<${k}, ${a*a}>;
  var<workgroup> tileWeightQ: array<${k}, ${a*a}>;
  var<workgroup> tileWeightK: array<${k}, ${a*a}>;
  var<workgroup> tileWeightV: array<${k}, ${a*a}>;
  ${l.registerUniforms(Z).declareVariables(x,G,q,y,v,S)}
  ${l.mainStart([a,a,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_id.x;

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

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

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:c}),getShaderSource:g},{inputs:d,outputs:[-1,-1,-1]})},bo=(e,t)=>{let r=_o(e.inputs,t),[n,s,i]=yo(e,r);return hs(e,n,s,i,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}}),Mo,vo,zi,xo,Pu=R(()=>{Bt(),Kt(),Xt(),Cr(),sr(),Mo=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,s,i)=>{let a=s.length;if(a!==n.length)throw new Error(`${i}: num dimensions != ${a}`);s.forEach((u,d)=>{if(u!==n[d])throw new Error(`${i}: dim[${d}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,n,"Invalid input scale"),r(e[2].dims,n,"Invalid input B"),r(e[3].dims,n,"Invalid input mean"),r(e[4].dims,n,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input var")},vo=(e,t)=>{let{epsilon:r,spatial:n,format:s}=t,i=e[0].dims,a=n?Sr(i[i.length-1]):1,u=s==="NHWC"&&i.length>1?a:1,d=Ie.size(i)/a,c=n,g=c?i.length:i,l=Ze("x",e[0].dataType,e[0].dims,a),y=Ze("scale",e[1].dataType,e[1].dims,u),v=Ze("bias",e[2].dataType,e[2].dims,u),S=Ze("inputMean",e[3].dataType,e[3].dims,u),x=Ze("inputVar",e[4].dataType,e[4].dims,u),G=Vt("y",e[0].dataType,g,a),q=()=>{let Z="";if(n)Z=`let cOffset = ${i.length===1?"0u":s==="NHWC"?`outputIndices[${i.length-1}] / ${a}`:"outputIndices[1]"};`;else if(s==="NCHW")Z=`
            ${G.indicesSet("outputIndices","0","0")}
            let cOffset = ${G.indicesToOffset("outputIndices")};`;else{Z=`var cIndices = ${y.type.indices}(0);
                       cIndices[0] = outputIndices[${i.length-1}];`;for(let ee=1;ee<y.rank;ee++)Z+=`cIndices[${ee}] = outputIndices[${ee}];`;Z+=`let cOffset = ${y.indicesToOffset("cIndices")};`}return Z},k=Z=>`
  const epsilon = ${r};
  ${Z.registerUniform("outputSize","u32").declareVariables(l,y,v,S,x,G)}
  ${Z.mainStart()}
  ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${G.offsetToIndices(`global_idx * ${a}`)};
    ${q()}
    let scale = ${y.getByOffset("cOffset")};
    let bias = ${v.getByOffset("cOffset")};
    let inputMean = ${S.getByOffset("cOffset")};
    let inputVar = ${x.getByOffset("cOffset")};
    let x = ${l.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${G.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${a}`,inputDependencies:c?["rank","type","type","type","type"]:void 0},getShaderSource:k,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c?[{type:12,data:d},...Pt(i)]:[{type:12,data:d}]})}},zi=e=>qt(e),xo=(e,t)=>{let{inputs:r,outputCount:n}=e,s=zi({...t,outputCount:n});if(C.webgpu.validateInputContent&&Mo(r,s),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(vo(r,s))}}),To,So,Di,Au=R(()=>{Xt(),sr(),To=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")},So=e=>{let t=e[0].dims,r=e[0].dims[2],n=Ie.size(t)/4,s=e[0].dataType,i=Ze("input",s,t,4),a=Ze("bias",s,[r],4),u=Ze("residual",s,t,4),d=Vt("output",s,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:c=>`
  const channels = ${r}u / 4;
  ${c.declareVariables(i,a,u,d)}

  ${c.mainStart()}
    ${c.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${i.getByOffset("global_idx")}
      + ${a.getByOffset("global_idx % channels")} + ${u.getByOffset("global_idx")};
    ${d.setByOffset("global_idx","value")}
  }`}},Di=e=>{To(e.inputs),e.compute(So(e.inputs))}}),$o,br,Eo,Co,Bi,ko,Po,Li,Ao,Io,Ri,Fo,Oo,Ys,zo,Do,Cs,Ni,Zs,ji,Bo,Lo,Ro,Vi,No,jo,Vo,Ui,Uo,Wo,Wi,Go,qo,Gi,Ho,qi,Hi,Ki,Xi,Qi,Ko,Xo,Yi,Qo,Yo,Zi=R(()=>{Kt(),Xt(),Cr(),sr(),$o=(e,t,r,n,s,i)=>{let a=Math.ceil(t/4),u="";typeof s=="string"?u=`${s}(a)`:u=s("a");let d=Ze("inputData",r,[a],4),c=Vt("outputData",n,[a],4);return`
      ${e.registerUniform("vec_size","u32").declareVariables(d,c)}

  ${i??""}

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

    let a = ${d.getByOffset("global_idx")};
    ${c.setByOffset("global_idx",u)}
  }`},br=(e,t,r,n,s,i=e.dataType)=>({name:t,shaderCache:{hint:s,inputDependencies:["type"]},getShaderSource:a=>$o(a,Ie.size(e.dims),e.dataType,i,r,n),getRunData:a=>({outputs:[{dims:e.dims,dataType:i}],dispatchGroup:{x:Math.ceil(Ie.size(a[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(Ie.size(e.dims)/4)}]})}),Eo=e=>{e.compute(br(e.inputs[0],"Abs","abs"))},Co=e=>{e.compute(br(e.inputs[0],"Acos","acos"))},Bi=e=>{e.compute(br(e.inputs[0],"Acosh","acosh"))},ko=e=>{e.compute(br(e.inputs[0],"Asin","asin"))},Po=e=>{e.compute(br(e.inputs[0],"Asinh","asinh"))},Li=e=>{e.compute(br(e.inputs[0],"Atan","atan"))},Ao=e=>{e.compute(br(e.inputs[0],"Atanh","atanh"))},Io=e=>qt(e),Ri=(e,t)=>{let r;switch(t.to){case 10:r="vec4<f16>";break;case 1:r="vec4<f32>";break;case 12:r="vec4<u32>";break;case 6:r="vec4<i32>";break;case 9:r="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(br(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},Fo=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:Jr,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:dn;return qt({min:t,max:r})},Oo=(e,t)=>{let r=e.inputs.length===1?t:Fo(e.inputs),n=Tr(e.inputs[0].dataType);e.compute(br(e.inputs[0],"Clip",s=>`clamp(${s}, clip_min_, clip_max_)`,`
    const clip_min_: vec4<${n}> = vec4(${n}(${r.min}));
    const clip_max_: vec4<${n}> = vec4(${n}(${r.max}));
`,r.cacheKey),{inputs:[0]})},Ys=e=>{e.compute(br(e.inputs[0],"Ceil","ceil"))},zo=e=>{e.compute(br(e.inputs[0],"Cos","cos"))},Do=e=>{e.compute(br(e.inputs[0],"Cosh","cosh"))},Cs=e=>qt(e),Ni=(e,t)=>{let r=Tr(e.inputs[0].dataType);e.compute(br(e.inputs[0],"Elu",n=>`elu_vf32(${n})`,`
  const elu_alpha_ = ${r}(${t.alpha});

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

  fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,t.cacheKey))},Zs=(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));
}`,ji=e=>{let t=Tr(e.inputs[0].dataType);e.compute(br(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,Zs(t)))},Bo=e=>{e.compute(br(e.inputs[0],"Exp","exp"))},Lo=e=>{e.compute(br(e.inputs[0],"Floor","floor"))},Ro=e=>{let t=Tr(e.inputs[0].dataType);e.compute(br(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,Zs(t)))},Vi=(e,t)=>{let r=Tr(e.inputs[0].dataType);e.compute(br(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},No=e=>{e.compute(br(e.inputs[0],"Not",t=>`!${t}`))},jo=e=>{e.compute(br(e.inputs[0],"Neg",t=>`-${t}`))},Vo=e=>{e.compute(br(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Ui=e=>{let t=Tr(e.inputs[0].dataType);e.compute(br(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Uo=e=>{e.compute(br(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},Wo=e=>qt(e),Wi=(e,t)=>{let r=Tr(e.inputs[0].dataType);e.compute(br(e.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${n} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Go=e=>{e.compute(br(e.inputs[0],"Sin","sin"))},qo=e=>{e.compute(br(e.inputs[0],"Sinh","sinh"))},Gi=e=>{e.compute(br(e.inputs[0],"Sqrt","sqrt"))},Ho=e=>{e.compute(br(e.inputs[0],"Tan","tan"))},qi=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Hi=e=>{e.compute(br(e.inputs[0],"Tanh",qi))},Ki=(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 ${qi("v")};
}
`,Xi=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Qi=e=>{let t=Tr(e.inputs[0].dataType);e.compute(br(e.inputs[0],"FastGelu",Xi,Ki(t),void 0,e.inputs[0].dataType))},Ko=(e,t)=>{let r=Tr(e.inputs[0].dataType);return e.compute(br(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},Xo=e=>{e.compute(br(e.inputs[0],"Log","log"))},Yi=(e,t)=>`
const alpha = vec4<${e}>(${t});
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<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,Qo=e=>`quick_gelu_impl(${e})`,Yo=(e,t)=>{let r=Tr(e.inputs[0].dataType);e.compute(br(e.inputs[0],"QuickGelu",Qo,Yi(r,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),Zo,Js,Jo,Iu=R(()=>{Xt(),sr(),Zi(),Zo=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")},Js=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=Ze("input",e[0].dataType,e[0].dims,4),n=Ze("bias",e[0].dataType,[e[0].dims[2]],4),s=Vt("output",e[0].dataType,t,4),i=Ie.size(t)/4,a=Er(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)}}),getShaderSource:u=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

  ${u.declareVariables(r,n,s)}

  ${Zs(a)}

  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes(i)}
    let biasIdx = global_idx % halfChannels;
    let batchIndex = global_idx / halfChannels;
    let inputOffset = biasIdx + batchIndex * halfChannels * 2;
    let valueLeft = input[inputOffset] + bias[biasIdx];
    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];
    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);

    ${s.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Jo=e=>{Zo(e.inputs),e.compute(Js(e.inputs))}}),el,Ji,Tn,tl,rl,nl,ea,sl,il,ta,al,ol,ra,Fu=R(()=>{Kt(),Xt(),sr(),el=(e,t,r,n,s,i,a,u,d,c,g,l)=>{let y,v;typeof u=="string"?y=v=(k,Z)=>`${u}((${k}),(${Z}))`:typeof u=="function"?y=v=u:(y=u.scalar,v=u.vector);let S=Vt("outputData",g,n.length,4),x=Ze("aData",d,t.length,4),G=Ze("bData",c,r.length,4),q;if(s)if(i){let k=Ie.size(t)===1,Z=Ie.size(r)===1,ee=t.length>0&&t[t.length-1]%4===0,fe=r.length>0&&r[r.length-1]%4===0;k||Z?q=S.setByOffset("global_idx",v(k?`${x.type.value}(${x.getByOffset("0")}.x)`:x.getByOffset("global_idx"),Z?`${G.type.value}(${G.getByOffset("0")}.x)`:G.getByOffset("global_idx"))):q=`
            let outputIndices = ${S.offsetToIndices("global_idx * 4u")};
            let offsetA = ${x.broadcastedIndicesToOffset("outputIndices",S)};
            let offsetB = ${G.broadcastedIndicesToOffset("outputIndices",S)};
            ${S.setByOffset("global_idx",v(a||ee?x.getByOffset("offsetA / 4u"):`${x.type.value}(${x.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||fe?G.getByOffset("offsetB / 4u"):`${G.type.value}(${G.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else q=S.setByOffset("global_idx",v(x.getByOffset("global_idx"),G.getByOffset("global_idx")));else{if(!i)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let k=(Z,ee,fe="")=>{let We=`aData[indexA${ee}][componentA${ee}]`,Ne=`bData[indexB${ee}][componentB${ee}]`;return`
            let outputIndices${ee} = ${S.offsetToIndices(`global_idx * 4u + ${ee}u`)};
            let offsetA${ee} = ${x.broadcastedIndicesToOffset(`outputIndices${ee}`,S)};
            let offsetB${ee} = ${G.broadcastedIndicesToOffset(`outputIndices${ee}`,S)};
            let indexA${ee} = offsetA${ee} / 4u;
            let indexB${ee} = offsetB${ee} / 4u;
            let componentA${ee} = offsetA${ee} % 4u;
            let componentB${ee} = offsetB${ee} % 4u;
            ${Z}[${ee}] = ${fe}(${y(We,Ne)});
          `};g===9?q=`
            var data = vec4<u32>(0);
            ${k("data",0,"u32")}
            ${k("data",1,"u32")}
            ${k("data",2,"u32")}
            ${k("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:q=`
            ${k("outputData[global_idx]",0)}
            ${k("outputData[global_idx]",1)}
            ${k("outputData[global_idx]",2)}
            ${k("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(x,G,S)}

        ${l??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${q}
      }`},Ji=(e,t,r,n,s,i,a=r.dataType)=>{let u=!Ie.areEqual(r.dims,n.dims),d=r.dims,c=Ie.size(r.dims),g=!1,l=!1,y=[u];if(u){let v=Rr.calcShape(r.dims,n.dims,!1);if(!v)throw new Error("Can't perform binary op on the given tensors");d=v,c=Ie.size(d);let S=Ie.size(r.dims)===1,x=Ie.size(n.dims)===1,G=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,q=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;y.push(S),y.push(x),y.push(G),y.push(q);let k=1;for(let Z=1;Z<d.length;Z++){let ee=r.dims[r.dims.length-Z]??1,fe=n.dims[n.dims.length-Z]??1;if(ee===fe)k*=ee;else break}k%4===0?(l=!0,g=!0):(S||x||G||q)&&(g=!0)}else g=!0;return y.push(g),{name:e,shaderCache:{hint:t+y.map(v=>v.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:v=>el(v,r.dims,n.dims,d,g,u,l,s,r.dataType,n.dataType,a,i),getRunData:()=>({outputs:[{dims:d,dataType:a}],dispatchGroup:{x:Math.ceil(c/64/4)},programUniforms:[{type:12,data:Math.ceil(Ie.size(d)/4)},...Pt(r.dims,n.dims,d)]})}},Tn=(e,t,r,n,s,i)=>{e.compute(Ji(t,s??"",e.inputs[0],e.inputs[1],r,n,i))},tl=e=>{Tn(e,"Add",(t,r)=>`${t}+${r}`)},rl=e=>{Tn(e,"Div",(t,r)=>`${t}/${r}`)},nl=e=>{Tn(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},ea=e=>{Tn(e,"Mul",(t,r)=>`${t}*${r}`)},sl=e=>{let t=Ze("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Tn(e,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},`
    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {
      if (b == ${t}(0.0)) {
        return ${t}(1.0);
      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {
        return ${t}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {
      // TODO: implement vectorized pow
      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},il=e=>{Tn(e,"Sub",(t,r)=>`${t}-${r}`)},ta=e=>{Tn(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},al=e=>{Tn(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},ol=e=>{Tn(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},ra=e=>{Tn(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}}),ll,na,ul,dl,cl,pl,Ou=R(()=>{Kt(),Xt(),Cr(),sr(),ll=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],s=n.dataType,i=n.dims.length;e.forEach((a,u)=>{if(u!==r){if(a.dataType!==s)throw new Error("input tensors should be one type");if(a.dims.length!==i)throw new Error("input tensors should have the same shape");a.dims.forEach((d,c)=>{if(c!==t&&d!==n.dims[c])throw new Error("non concat dimensions must match")})}})},na=(e,t)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${e}u>(${t});
    for (var i: u32 = 0u; i < ${e}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${e}u;
  }`,ul=(e,t)=>{let r=e.length,n=[];for(let s=0;s<r;++s){let i=t.setByOffset("global_idx",e[s].getByIndices("indices"));r===1?n.push(i):s===0?n.push(`if (inputIndex == ${s}u) { ${i} }`):s===r-1?n.push(`else { ${i} }`):n.push(`else if (inputIndex == ${s}) { ${i} }`)}return n.join(`
`)},dl=(e,t,r,n)=>{let s=Ie.size(r),i=new Array(e.length),a=new Array(e.length),u=0,d=[],c=[],g=[{type:12,data:s}];for(let x=0;x<e.length;++x)u+=e[x].dims[t],i[x]=u,c.push(e[x].dims.length),a[x]=Ze(`input${x}`,n,c[x]),d.push("rank"),g.push({type:12,data:i[x]});for(let x=0;x<e.length;++x)g.push(...Pt(e[x].dims));g.push(...Pt(r));let l=Vt("output",n,r.length),y=l.indicesGet("indices",t),v=Array.from(Array(i.length).keys()).map(x=>`uniforms.sizeInConcatAxis${x}`).join(","),S=x=>`

  ${(()=>{x.registerUniform("outputSize","u32");for(let G=0;G<e.length;G++)x.registerUniform(`sizeInConcatAxis${G}`,"u32");return x.declareVariables(...a,l)})()}

  ${na(i.length,v)}

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

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

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

    ${ul(a,l)}
  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:g}),getShaderSource:S}},cl=(e,t)=>{let r=e.inputs,n=r[0].dims,s=Ie.normalizeAxis(t.axis,n.length);ll(r,s);let i=n.slice();i[s]=r.reduce((u,d)=>u+(d.dims.length>s?d.dims[s]:0),0);let a=r.filter(u=>Ie.size(u.dims)>0);e.compute(dl(a,s,i,r[0].dataType),{inputs:a})},pl=e=>qt({axis:e.axis})}),Gn,zn,qn,sa,Dn=R(()=>{Kt(),Xt(),Gn=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"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 ${e.activation}`)}},zn=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},qn=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},sa=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[r,n]=e?.activation_params||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=e?.activation_params||[Jr,dn];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=e?.activation_params||[.01];return{activation:t,alpha:r}}return{activation:t}}}),an,ia,Pn=R(()=>{an=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},ia=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),aa,hl=R(()=>{aa=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),fl,ml,ks,oa,_l,ei,gl,Ps,ti=R(()=>{Kt(),Xt(),sr(),Dn(),Pn(),fl=(e,t)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${t?", batchIndices":""});
        `,ml=(e,t)=>e?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${t===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];
          ${t===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];
          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,ks=(e,t,r="f32",n,s=!1,i=32,a=!1,u=32)=>{let d=t[1]*e[1],c=t[0]*e[0],g=s?d:i,l=s?i:d,y=g/t[0],v=i/t[1];if(!((s&&y===4&&e[1]===4||!s&&(y===3||y===4))&&g%t[0]===0&&i%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${s} is true, innerElementSize ${y} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${y} must be 3 or 4.
  tileAWidth ${g} must be divisible by workgroupSize[0]${t[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${y}<${r}>, ${g/y}>, ${l}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${c/e[0]}>, ${i}>;

const rowPerThread = ${e[1]};
const colPerThread = ${e[0]};
const innerElementSize = ${y};
const tileInner = ${i};

@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[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 = ${a?"0":"i32(globalId.z)"};
  ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
  let globalRowStart = i32(workgroupId.y) * ${d};

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

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

  // Loop over shared dimension.
  let tileRowB = localRow * ${v};
  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;
          ${fl(s,n)}
      }

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

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

          ${ml(s,y)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},oa=(e,t)=>e?`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              kStart + inputRow,
              globalRowStart + inputCol${t?", batchIndices":""});
            `:`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              globalRowStart + inputRow,
              kStart + inputCol${t?", batchIndices":""});
            `,_l=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",ei=(e,t,r="f32",n,s=!1,i=32,a=!1,u=32,d=!1)=>{let c=e[1]*t[1],g=e[0]*t[0],l=s?c:i,y=s?i:c;if(!(y%t[1]===0&&l%t[0]===0&&i%t[1]===0))throw new Error(`tileAHight ${y} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${l} must be divisible by workgroupSize[0]${t[0]}, tileInner ${i} must be divisible by workgroupSize[1]${t[1]}`);let v=y/t[1],S=l/t[0],x=i/t[1],G=d?`
    let localRow = i32(localId.y);
    let localCol = i32(localId.x);
    let globalRowStart = i32(workgroupId.y) * ${c};
    let globalColStart = i32(workgroupId.x) * ${g};

    // 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 < ${y}; inputRow = inputRow + ${t[1]}) {
        for (var inputCol = localCol; inputCol < ${l}; inputCol = inputCol + ${t[0]}) {
          ${oa(s,n)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${i}; inputRow = inputRow + ${t[1]}) {
            for (var inputCol = localCol; inputCol < ${g}; inputCol = inputCol + ${t[0]}) {
          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
            kStart + inputRow,
            globalColStart + inputCol${n?", batchIndices":""});
        }
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

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

let tileRowA = i32(localId.y) * ${v};
let tileColA = i32(localId.x) * ${S};
let tileRowB = i32(localId.y) * ${x};
// 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 < ${v}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${S}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${oa(s,n)}
    }
  }

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

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

    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      ${_l(s)}
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
      }
    }
  }

  workgroupBarrier();
}

for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
    mm_write(batch, globalRow + innerRow, globalCol + innerCol,
        acc[innerRow][innerCol]);
  }
}
`;return`
  var<workgroup> mm_Asub : array<array<${r}, ${l}>, ${y}>;
  var<workgroup> mm_Bsub : array<array<${r}, ${g}>, ${i}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${i};

@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[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 = ${a?"0":"i32(globalId.z)"};
    ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
    let num_tiles = ${a?`${Math.ceil(u/i)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
    var kStart = ${a?`i32(globalId.z) * ${u}`:"0"};

    var acc : array<array<${r}, colPerThread>, rowPerThread>;
    ${G}
  }
`},gl=(e,t,r,n,s,i=!1)=>{let[a,u,d]=s,[c,g,l,y]=n,v=cs(a,d),S=cs(u,d),x=Er(n[0].type.tensor),G=()=>{let k=g.rank,Z=c.rank,ee=`var aIndices: ${g.type.indices};`;for(let fe=k-2-1,We=Z-1;fe>=0;fe--,We--)ee+=`
aIndices[${fe}] = ${Z>1?`batchIndices[${We}]`:"batchIndices"};`;return v.forEach(fe=>{ee+=`
aIndices[${fe}] = 0;`}),ee+=`
aIndices[${k-2}] = u32(row);
                   aIndices[${k-1}] = u32(colIn);`,ee},q=()=>{let k=l.rank,Z=c.rank,ee=`var bIndices: ${l.type.indices};`;for(let fe=k-2-1,We=Z-1;fe>=0;fe--,We--)ee+=`
bIndices[${fe}] = ${Z>1?`batchIndices[${We}]`:"batchIndices"};`;return S.forEach(fe=>{ee+=`
bIndices[${fe}] = 0;`}),ee+=`
bIndices[${k-2}] = u32(row);
                   bIndices[${k-1}] = u32(colIn);`,ee};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${c.type.indices}) -> ${an(e,x)} {
      var value = ${an(e,x)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${G()}
        value = ${g.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${c.type.indices}) -> ${an(e,x)} {
      var value = ${an(e,x)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
      {
        ${q()}
        value = ${l.getByIndices("bIndices")};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${an(e,x)}) {
      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);
        ${t?`value = value + ${i?"bias[colIn]":`${an(e,x)}(bias[row])`};`:""}
        ${r}
        ${y.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},Ps=(e,t,r,n,s=!1,i)=>{let a=e[0].dims,u=e[1].dims,d=a.slice(0,-2),c=u.slice(0,-2),g=n?n.slice(0,-2):r.slice(0,-2),l=Ie.size(g),y=a[a.length-2],v=a[a.length-1],S=u[u.length-1],x=v%4===0&&S%4===0,G=y<=8?[4,1,1]:[4,4,1],q=[8,8,1],k=[Math.ceil(S/q[0]/G[0]),Math.ceil(y/q[1]/G[1]),Math.ceil(l/q[2]/G[2])],Z=x?4:1,ee=[...d,y,v/Z],fe=ee.length,We=[...c,v,S/Z],Ne=We.length,mt=[l,y,S/Z],jt=[{type:6,data:y},{type:6,data:S},{type:6,data:v}];zn(t,jt),jt.push(...Pt(g,ee,We));let Lt=["rank","rank"],dr=e.length>2;dr&&(jt.push(...Pt(e[2].dims)),Lt.push("rank")),jt.push(...Pt(mt));let ar=_r=>{let Pr=g.length,hr=wi("batchDims",e[0].dataType,Pr,1),Yt=Er(e[0].dataType),Ar=Ze("a",e[0].dataType,fe,Z),Br=Ze("b",e[1].dataType,Ne,Z),tr=Vt("result",e[0].dataType,mt.length,Z),gr=[Ar,Br];if(dr){let Ur=s?Z:1;gr.push(Ze("bias",e[2].dataType,e[2].dims.length,Ur))}let Ke=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];qn(t,Ke);let $t=Er(tr.type.tensor),cr=Gn(t,tr.type.value,$t),Vr=gl(Z,dr,cr,[hr,Ar,Br,tr],[d,c,g],s);return`
  ${_r.registerUniforms(Ke).registerInternalVariables(hr).declareVariables(...gr,tr)}
  ${Vr}
  ${x?ks(G,q,Yt,hr):ei(G,q,Yt,hr)}
                   `};return{name:"MatMul",shaderCache:{hint:`${G};${t.activation};${x};${s}`,inputDependencies:Lt},getRunData:()=>({outputs:[{dims:i?i(r):r,dataType:e[0].dataType}],dispatchGroup:{x:k[0],y:k[1],z:k[2]},programUniforms:jt}),getShaderSource:ar}}}),wl,yl,ri=R(()=>{Kt(),wn(),sr(),Dn(),Pn(),hl(),ti(),wl=(e,t,r,n,s=!1,i,a=4,u=4,d=4,c="f32")=>{let g=jt=>{switch(jt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${c}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${jt} is not supported.`)}},l=jt=>{switch(jt){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 ${jt} is not supported.`)}},y=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,v=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,S=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",x=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",G=e?"row":"col",q=e?"col":"row",k=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${G} / outWidth;
    let outCol = ${G} % outWidth;

    let WRow = ${q} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${q} / 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 = ${q} % inChannels;
    var resData = ${an(a,c)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${S} && xCol >= 0 && xCol < ${x}) {
      ${y}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${g(a)}
    }
    return resData;`,Z=e?t&&n?`
    let col = colIn * ${a};
    ${k}`:`
    let col = colIn * ${a};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${k}
    }
    return ${an(a,c)}(0.0);`:n&&r?`
    let col = colIn * ${a};
    ${k}`:`
    let col = colIn * ${a};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${k}
    }
    return ${an(a,c)}(0.0);`,ee=`${l(u)}`,fe=an(d,c),We=an(e?a:u,c),Ne=an(e?u:a,c),mt=Gn(i,fe,c);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${We} {
      ${e?Z:ee}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Ne} {
      ${e?ee:Z}
    }

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${fe}) {
      let col = colIn * ${d};
      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])"};
      ${v}
      ${ia(s)}
      ${mt}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},yl=(e,t,r,n,s,i,a,u,d)=>{let c=t.format==="NHWC",g=c?e[0].dims[3]:e[0].dims[1],l=r[0],y=c?r[2]:r[3],v=c?r[1]:r[2],S=c?r[3]:r[1],x=c&&(g%4===0||g%3===0)&&S%4===0,G=c?S:y*v,q=c?y*v:S,k=[8,8,1],Z=n<=8?[4,1,1]:[4,4,1],ee=[Math.ceil(G/k[0]/Z[0]),Math.ceil(q/k[1]/Z[1]),Math.ceil(l/k[2]/Z[2])];Nr("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${ee}`);let fe=x?c&&g%4!==0?3:4:1,We=k[1]*Z[1],Ne=k[0]*Z[0],mt=Math.max(k[0]*fe,k[1]),jt=n%We===0,Lt=s%Ne===0,dr=i%mt===0,ar=x?[fe,4,4]:[1,1,1],_r=[{type:6,data:n},{type:6,data:s},{type:6,data:i},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];zn(t,_r),_r.push(...Pt(e[0].dims,e[1].dims));let Pr=["rank","rank"];a&&(_r.push(...Pt(e[2].dims)),Pr.push("rank")),_r.push(...Pt(r));let hr=Yt=>{let Ar=[{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}];qn(t,Ar);let Br=x?4:1,tr=Er(e[0].dataType),gr=`
      fn setOutputAtIndex(flatIndex : i32, value : ${x?`vec4<${tr}>`:tr}) {
        result[flatIndex] = ${x?`vec4<${tr}>`:tr}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${x?`vec4<${tr}>`:tr}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${x?"/ 4":""}, value);
      }`,Ke=Ze("x",e[0].dataType,e[0].dims.length,fe===3?1:fe),$t=Ze("w",e[1].dataType,e[1].dims.length,Br),cr=[Ke,$t],Vr=Vt("result",e[0].dataType,r.length,Br);if(a){let Ur=Ze("bias",e[2].dataType,e[2].dims.length,Br);cr.push(Ur),gr+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${x?`vec4<${tr}>`:tr} {
          return bias[coords.${c?"w":"y"}${x?"/ 4":""}];
        }`}return`
        ${aa("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 };
        ${Yt.registerUniforms(Ar).declareVariables(...cr,Vr)}
        ${gr}
        ${wl(c,jt,Lt,dr,a,t,ar[0],ar[1],ar[2],tr)}
        ${x?ks(Z,k,tr,void 0,!c,mt):ei(Z,k,tr,void 0,!c,mt,!1,void 0,u)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${fe};${x};${jt};${Lt};${dr};${We};${Ne};${mt}`,inputDependencies:Pr},getRunData:()=>({outputs:[{dims:d?d(r):r,dataType:e[0].dataType}],dispatchGroup:{x:ee[0],y:ee[1],z:ee[2]},programUniforms:_r}),getShaderSource:hr}}}),bl,la,As,Ml,Is,vl,xl,Tl,Wd=R(()=>{Kt(),wn(),Xt(),sr(),Dn(),Pn(),bl=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t},la=e=>typeof e=="number"?[e,e,e]:e,As=(e,t)=>t<=1?e:e+(e-1)*(t-1),Ml=(e,t,r,n=1)=>{let s=As(t,n);return Math.floor((e[0]*(r-1)-r+s)/2)},Is=(e,t,r,n,s)=>{s==null&&(s=Ml(e,t[0],n[0]));let i=[0,0,0,r];for(let a=0;a<3;a++)e[a]+2*s>=t[a]&&(i[a]=Math.trunc((e[a]-t[a]+2*s)/n[a]+1));return i},vl=(e,t,r,n,s,i,a,u,d,c)=>{let g,l,y,v;if(e==="VALID"&&(e=0),typeof e=="number"){g={top:e,bottom:e,left:e,right:e,front:e,back:e};let S=Is([t,r,n,1],[u,d,c],1,[s,i,a],e);l=S[0],y=S[1],v=S[2]}else if(Array.isArray(e)){if(!e.every((x,G,q)=>x===q[0]))throw Error(`Unsupported padding parameter: ${e}`);g={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let S=Is([t,r,n,1],[u,d,c],1,[s,i,a],e[0]);l=S[0],y=S[1],v=S[2]}else if(e==="SAME_UPPER"){l=Math.ceil(t/s),y=Math.ceil(r/i),v=Math.ceil(n/a);let S=(l-1)*s+u-t,x=(y-1)*i+d-r,G=(v-1)*a+c-n,q=Math.floor(S/2),k=S-q,Z=Math.floor(x/2),ee=x-Z,fe=Math.floor(G/2),We=G-fe;g={top:Z,bottom:ee,left:fe,right:We,front:q,back:k}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:g,outDepth:l,outHeight:y,outWidth:v}},xl=(e,t,r,n,s,i=!1,a="channelsLast")=>{let u,d,c,g,l;if(a==="channelsLast")[u,d,c,g,l]=e;else if(a==="channelsFirst")[u,l,d,c,g]=e;else throw new Error(`Unknown dataFormat ${a}`);let[y,,v,S,x]=t,[G,q,k]=la(r),[Z,ee,fe]=la(n),We=As(v,Z),Ne=As(S,ee),mt=As(x,fe),{padInfo:jt,outDepth:Lt,outHeight:dr,outWidth:ar}=vl(s,d,c,g,G,q,k,We,Ne,mt),_r=i?y*l:y,Pr=[0,0,0,0,0];return a==="channelsFirst"?Pr=[u,_r,Lt,dr,ar]:a==="channelsLast"&&(Pr=[u,Lt,dr,ar,_r]),{batchSize:u,dataFormat:a,inDepth:d,inHeight:c,inWidth:g,inChannels:l,outDepth:Lt,outHeight:dr,outWidth:ar,outChannels:_r,padInfo:jt,strideDepth:G,strideHeight:q,strideWidth:k,filterDepth:v,filterHeight:S,filterWidth:x,effectiveFilterDepth:We,effectiveFilterHeight:Ne,effectiveFilterWidth:mt,dilationDepth:Z,dilationHeight:ee,dilationWidth:fe,inShape:e,outShape:Pr,filterShape:t}},Tl=(e,t,r,n,s,i)=>{let a=i==="channelsLast";a?e[0].dims[3]:e[0].dims[1];let u=[64,1,1],d={x:r.map((G,q)=>q)},c=[Math.ceil(bl(d.x.map(G=>r[G]))/u[0]),1,1];Nr("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${c}`);let g=1,l=Ie.size(r),y=[{type:12,data:l},{type:12,data:n},{type:12,data:s},{type:12,data:t.strides},{type:12,data:t.dilations}];zn(t,y),y.push(...Pt(e[0].dims,e[1].dims));let v=["rank","rank"],S=e.length===3;S&&(y.push(...Pt(e[2].dims)),v.push("rank")),y.push(...Pt(r));let x=G=>{let q=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:n.length},{name:"pads",type:"u32",length:s.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];qn(t,q);let k=1,Z=Er(e[0].dataType),ee=Ze("x",e[0].dataType,e[0].dims.length,g),fe=Ze("W",e[1].dataType,e[1].dims.length,k),We=[ee,fe],Ne=Vt("result",e[0].dataType,r.length,k),mt="";if(S){let dr=Ze("bias",e[2].dataType,e[2].dims.length,k);We.push(dr),mt+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${Z} {
          return bias[${a?Nt("coords",4,5):Nt("coords",1,5)}];
        }`}let jt=an(g,Z),Lt=Gn(t,jt,Z);return`
            ${mt}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${ee.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 ${fe.getByIndices("aIndices")};
            }
          ${G.registerUniforms(q).declareVariables(...We,Ne)}
          ${G.mainStart()}
          ${G.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${Ne.offsetToIndices("global_idx")};
              let batch = ${Nt("coords",0,ee.rank)};
              let d2 = ${a?Nt("coords",ee.rank-1,ee.rank):Nt("coords",1,ee.rank)};
              let xFRCCorner = vec3<u32>(${a?Nt("coords",1,ee.rank):Nt("coords",2,ee.rank)},
              ${a?Nt("coords",2,ee.rank):Nt("coords",3,ee.rank)},
              ${a?Nt("coords",3,ee.rank):Nt("coords",4,ee.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${a?Nt("uniforms.x_shape",1,ee.rank):Nt("uniforms.x_shape",2,ee.rank)};
              let xShapeZ = ${a?Nt("uniforms.x_shape",2,ee.rank):Nt("uniforms.x_shape",3,ee.rank)};
              let xShapeW = ${a?Nt("uniforms.x_shape",3,ee.rank):Nt("uniforms.x_shape",4,ee.rank)};
              let xShapeU = ${a?Nt("uniforms.x_shape",4,ee.rank):Nt("uniforms.x_shape",1,ee.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) {
                      ${a?`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) {
                        ${a?`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) {
                      ${a?`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) {
                      ${a?`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);
                    }
                  }
                }
              }
              ${S?"value = value + getBiasByOutputCoords(coords)":""};
              ${Lt}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${a};${g};${S}`,inputDependencies:v},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:c[0],y:c[1],z:c[2]},programUniforms:y}),getShaderSource:x}}}),Sl,$l,zu=R(()=>{Kt(),Xt(),sr(),ha(),Dn(),Sl=(e,t,r)=>{let n=e.length>2,s=n?"value += b[output_channel];":"",i=e[0].dims,a=e[1].dims,u=a[0]/t.group,d=t.format==="NHWC",c=ni(i,a,t.dilations,t.pads,t.strides,d),g=Ie.size(c),l=[{type:12,data:g},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:u}];zn(t,l),l.push(...Pt(i,a));let y=["rank","rank"];n&&(l.push(...Pt(e[2].dims)),y.push("rank")),l.push(...Pt(c));let v=S=>{let x=Vt("output",e[0].dataType,c.length),G=Er(x.type.tensor),q=Gn(t,x.type.value,G),k=Ze("x",e[0].dataType,i.length),Z=Ze("w",e[1].dataType,a.length),ee=[k,Z];n&&ee.push(Ze("b",e[2].dataType,e[2].dims.length));let fe=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return qn(t,fe),`
  ${S.registerUniforms(fe).declareVariables(...ee,x)}

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

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

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

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

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

          let xVal = ${d?k.get("batch","xHeight","xWidth","input_channel"):k.get("batch","input_channel","xHeight","xWidth")};
          let wVal = ${Z.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${s}
    ${q}
    ${x.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:y},getRunData:()=>({outputs:[{dims:r?r(c):c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:l}),getShaderSource:v}},$l=(e,t,r,n)=>{let s=e.length>2,i=Sr(r[3]),a=Sr(r[2]),u=Ie.size(r)/i/a,d=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/i],c=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/i],g=[r[0],r[1],r[2],r[3]/i],l=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];zn(t,l),l.push(...Pt(d,c,g));let y=(a-1)*t.strides[1]+c[1],v=S=>{let x=Vt("output",e[0].dataType,g.length,i),G=Er(x.type.tensor),q=Gn(t,x.type.value,G),k=Ze("x",e[0].dataType,d.length,i),Z=Ze("w",e[1].dataType,c.length,i),ee=[k,Z];s&&ee.push(Ze("b",e[2].dataType,e[2].dims,i));let fe=s?"value += b[output_channel];":"",We=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return qn(t,We),`
  ${S.registerUniforms(We).declareVariables(...ee,x)}
  ${S.mainStart()}
    ${S.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] / ${a}u;
    let col = (index1 % width1) * ${a}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;

    var x_vals: array<${k.type.value}, ${y}>;
    var values: array<${x.type.value}, ${a}>;
    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 < ${c[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 < ${y}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${k.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${k.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${c[1]}; w_width++) {
          let w_val = ${Z.get("w_height","w_width","0","output_channel")};
          for (var i = 0u; i < ${a}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${a}u; i++) {
      var value = values[i];
      ${fe}
      ${q}
      ${x.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${i};${a};${y};${c[0]};${c[1]}`,inputDependencies:s?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:v}}}),Jn,El,Cl,kl=R(()=>{Kt(),Xt(),ti(),sr(),Dn(),Jn=(e,t,r,n,s=!1,i)=>{let a=e[0].dims,u=e[1].dims,d=a[a.length-2],c=u[u.length-1],g=a[a.length-1],l=Sr(c),y=Sr(g),v=Sr(d),S=Ie.size(r)/l/v,x=e.length>2,G=n?n.slice(0,-2):r.slice(0,-2),q=[Ie.size(G),d,c],k=[{type:12,data:S},{type:12,data:d},{type:12,data:c},{type:12,data:g}];zn(t,k),k.push(...Pt(G,a,u)),x&&k.push(...Pt(e[2].dims)),k.push(...Pt(q));let Z=ee=>{let fe=wi("batch_dims",e[0].dataType,G.length),We=Ze("a",e[0].dataType,a.length,y),Ne=Ze("b",e[1].dataType,u.length,l),mt=Vt("output",e[0].dataType,q.length,l),jt=Er(mt.type.tensor),Lt=Gn(t,mt.type.value,jt),dr=[We,Ne],ar="";if(x){let gr=s?l:1;dr.push(Ze("bias",e[2].dataType,e[2].dims.length,gr)),ar=`${s?`value += bias[col / ${gr}];`:`value += ${mt.type.value}(bias[row + i]);`}`}let _r=a.slice(0,-2),Pr=u.slice(0,-2),hr=cs(_r,G),Yt=cs(Pr,G),Ar=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];qn(t,Ar);let Br=(gr,Ke)=>{let $t=gr.rank,cr=gr.name;if($t===2)return`var ${cr}_indices = ${gr.type.indices}(0u, 0u);`;let Vr=fe.rank,Ur=`var ${cr}_indices: ${gr.type.indices};`;for(let Rn=$t-2-1,Kn=Vr-1;Rn>=0;Rn--,Kn--)Ur+=`
${cr}_indices[${Rn}] = ${Vr>1?`batch_indices[${Kn}]`:"batch_indices"};`;return Ke.forEach(Rn=>{Ur+=`
${cr}_indices[${Rn}] = 0;`}),Ur+=`${cr}_indices[${$t-2}] = 0u;
                     ${cr}_indices[${$t-1}] = 0u;`,Ur},tr=()=>{let gr=`var a_data: ${We.type.value};`;for(let Ke=0;Ke<y;Ke++)gr+=`
              let b_data${Ke} = b[(b_offset + (k + ${Ke}) * uniforms.N + col) / ${l}];`;for(let Ke=0;Ke<v;Ke++){gr+=`a_data = a[(a_offset + (row + ${Ke}) * uniforms.K + k) / ${y}];`;for(let $t=0;$t<y;$t++)gr+=`
            values[${Ke}] = fma(${Ne.type.value}(a_data${y===1?"":`[${$t}]`}), b_data${$t}, values[${Ke}]);
`}return gr};return`
  ${ee.registerUniforms(Ar).registerInternalVariables(fe).declareVariables(...dr,mt)}
  ${ee.mainStart()}
    ${ee.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${l})) * ${l};
    var index1 = global_idx / (uniforms.N / ${l});
    let stride1 = uniforms.M / ${v};
    let row = (index1 % stride1) * ${v};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${fe.offsetToIndices("batch")};`}
    ${Br(We,hr)}
    let a_offset = ${We.indicesToOffset("a_indices")};
    ${Br(Ne,Yt)}
    let b_offset = ${Ne.indicesToOffset("b_indices")};
    var values: array<${mt.type.value}, ${v}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${y}) {
      ${tr()}
    }
    for (var i = 0u; i < ${v}u; i++) {
      var value = values[i];
      ${ar}
      ${Lt}
      let cur_indices = ${mt.type.indices}(batch, row + i, col);
      let offset = ${mt.indicesToOffset("cur_indices")};
      ${mt.setByOffset(`offset / ${l}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${l};${y};${v};${s}`,inputDependencies:x?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:i?i(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(S/64)},programUniforms:k}),getShaderSource:Z}},El=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.")},Cl=e=>{El(e.inputs);let t=Rr.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let r=t[t.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&n<8?e.compute(Jn(e.inputs,{activation:""},t)):e.compute(Ps(e.inputs,{activation:""},t))}}),ni,si,ii,ua,da,ca,Pl,Al,pa,ha=R(()=>{Xt(),ri(),Wd(),ti(),zu(),Dn(),kl(),ps(),ni=(e,t,r,n,s,i)=>{let a=e[0],u=e.slice(i?1:2,i?3:4),d=u.length,c=t[0],g=t.slice(2).map((y,v)=>y+(y-1)*(r[v]-1)),l=u.map((y,v)=>y+n[v]+n[v+d]).map((y,v)=>Math.floor((y-g[v]+s[v])/s[v]));return l.splice(0,0,a),l.splice(i?3:1,0,c),l},si=[2,3,1,0],ii=(e,t)=>{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 r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[1]*t.group;if(r!==n)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 s=e[0].dims.length-2;if(t.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(t.strides.length!==s)throw new Error(`strides should be ${s}D`);if(t.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ua=(e,t)=>{let r=e.kernelShape.slice();for(let i=2;i<t[1].dims.length;++i)r[i-2]===0&&(r[i-2]=t[1].dims[i]);let n=e.pads.slice();On.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,n,e.format==="NHWC",e.autoPad);let s=Object.assign({},e);return Object.assign(s,{kernelShape:r,pads:n}),s},da=e=>{let t=sa(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],s=e.dilations,i=e.group,a=e.kernel_shape,u=e.pads,d=e.strides,c=e.w_is_const();return{autoPad:n,format:r,dilations:s,group:i,kernelShape:a,pads:u,strides:d,wIsConst:c,...t,cacheKey:`${e.format};${t.activation};`}},ca=(e,t,r,n)=>{let s=r.format==="NHWC";if(r.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&s&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let We=ni(t[0].dims,t[1].dims,r.dilations,r.pads,r.strides,s),Ne=e.kernelCustomData.wT??e.compute(kn(t[1],si),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ne);let mt=[t[0],Ne];t.length===3&&mt.push(t[2]),e.compute($l(mt,r,We,n),{inputs:mt})}else e.compute(Sl(t,r,n));return}let i=t.length===3,a=t[0].dims[s?1:2],u=t[0].dims[s?2:3],d=t[0].dims[s?3:1],c=t[1].dims[2],g=t[1].dims[3],l=ni(t[0].dims,t[1].dims,r.dilations,r.pads,r.strides,s),y=l[s?1:2],v=l[s?2:3],S=l[s?3:1],x=s&&c===a&&g===u&&r.pads[0]===0&&r.pads[1]===0;if(x||c===1&&g===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let We=l[0],Ne,mt,jt,Lt=[];if(s){let _r=e.kernelCustomData.wT??e.compute(kn(t[1],si),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=_r),x){let Pr=a*u*d;Ne=t[0].reshape([1,We,Pr]),mt=_r.reshape([1,Pr,S]),jt=[1,We,S]}else Ne=t[0].reshape([We,a*u,d]),mt=_r.reshape([1,d,S]),jt=[We,y*v,S];Lt.push(Ne),Lt.push(mt)}else Ne=t[0].reshape([We,d,a*u]),mt=t[1].reshape([1,S,d]),jt=[We,S,y*v],Lt.push(mt),Lt.push(Ne);i&&Lt.push(t[2]);let dr=jt[2],ar=Lt[0].dims[Lt[0].dims.length-1];dr<8&&ar<8?e.compute(Jn(Lt,r,l,jt,s,n),{inputs:Lt}):e.compute(Ps(Lt,r,l,jt,s,n),{inputs:Lt});return}let G=!0,q=e.kernelCustomData.wT??e.compute(kn(t[1],si),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=q);let k=[t[0],q];i&&k.push(t[2]);let Z=s?y*v:S,ee=s?S:y*v,fe=c*g*d;e.compute(yl(k,r,l,Z,ee,fe,i,G,n),{inputs:k})},Pl=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let s=[0,t.pads[0],0,t.pads[1]],i=[1].concat(t.strides),a=[1].concat(t.dilations),u=[1].concat(t.kernelShape),d=ua({...t,pads:s,strides:i,dilations:a,kernelShape:u},n);ca(e,n,d,c=>r?[c[0],c[2],c[3]]:[c[0],c[1],c[3]])},Al=(e,t,r)=>{let n=r.format==="NHWC"?"channelsLast":"channelsFirst",s=ua(r,t),i=r.autoPad==="NOTSET"?r.pads:r.autoPad,a=xl(t[0].dims,t[1].dims,r.strides,r.dilations,i,!1,n);e.compute(Tl(t,s,a.outShape,[a.filterDepth,a.filterHeight,a.filterWidth],[a.padInfo.front,a.padInfo.top,a.padInfo.left],n))},pa=(e,t)=>{if(ii(e.inputs,t),e.inputs[0].dims.length===3)Pl(e,t);else if(e.inputs[0].dims.length===5)Al(e,e.inputs,t);else{let r=ua(t,e.inputs);ca(e,e.inputs,r)}}}),Il,Fl,Ol=R(()=>{Kt(),wn(),sr(),Dn(),Pn(),hl(),ti(),Il=(e,t=!1,r,n,s=4)=>{let i=G=>{switch(G){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`
            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];
            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];
            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];
            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];
            return ${n}(v0, v1, v2, v3);
            `;default:throw new Error(`innerElementSize ${G} is not supported.`)}},a=e?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,u=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,d=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",c=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",g=e?"row":"col",l=e?"col":"row",y=`
      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${g} / outWidth;
      let outCol = ${g} % outWidth;

      let WRow = ${l} / (uniforms.filter_dims[1] * inChannels);
      let WCol = ${l} / inChannels % uniforms.filter_dims[1];
      let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);
      let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);
      if (xR < 0.0 || xR >= f32(${d}) || fract(xR) > 0.0) {
        return ${n}(0.0);
      }
      if (xC < 0.0 || xC >= f32(${c}) || fract(xC) > 0.0) {
        return ${n}(0.0);
      }
      let iXR = i32(xR);
      let iXC = i32(xC);
      let xCh = ${l} % inChannels;
      ${a}
      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${s}];`,v=e?`
      let col = colIn * ${s};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
        ${y}
      }
      return ${n}(0.0);`:`
      let col = colIn * ${s};
      if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
        ${y}
      }
      return ${n}(0.0);`,S=`
      let col = colIn * ${s};
      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);
      let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];
      if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"}  && coordX >= 0 && coordY >= 0) {
        let rowInner = row % inChannels;
        let coord = vec4<i32>(coordX, coordY, col, rowInner);
        ${i(s)}
      }
      return ${n}(0.0);
      `,x=Gn(r,n);return`
  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} {
    ${e?v:S}
  }

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

  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) {
    let col = colIn * ${s};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
      var value = valueInput;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${u}
      ${ia(t)}
      ${x}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${s}] = value;
    }
  }`},Fl=(e,t,r,n,s,i,a,u)=>{let d=t.format==="NHWC",c=d?e[0].dims[3]:e[0].dims[1],g=r[0],l=d?r[2]:r[3],y=d?r[1]:r[2],v=d?r[3]:r[1],S=d&&c%4===0&&c%3&&v%4===0,x=d?v:l*y,G=d?l*y:v,q=[8,8,1],k=n<=8?[4,1,1]:[4,4,1],Z=[Math.ceil(x/q[0]/k[0]),Math.ceil(G/q[1]/k[1]),Math.ceil(g/q[2]/k[2])];Nr("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${Z}`);let ee=S?4:1,fe=Math.max(q[0]*ee,q[1]),We=S?4:1,Ne=[t.kernelShape[d?1:2],t.kernelShape[d?2:3]],mt=[Ne[0]+(t.dilations[0]<=1?0:(Ne[0]-1)*(t.dilations[0]-1)),Ne[1]+(t.dilations[1]<=1?0:(Ne[1]-1)*(t.dilations[1]-1))],jt=[mt[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),mt[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Lt=[{type:6,data:n},{type:6,data:s},{type:6,data:i},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:Ne},{type:6,data:jt}];zn(t,Lt),Lt.push(...Pt(e[0].dims,e[1].dims));let dr=["rank","rank"];a&&(Lt.push(...Pt(e[2].dims)),dr.push("rank")),Lt.push(...Pt(r));let ar=_r=>{let Pr=Ze("x",e[0].dataType,e[0].dims.length,We),hr=Ze("w",e[1].dataType,e[1].dims.length,1),Yt=Vt("result",e[0].dataType,r.length,We),Ar=[Pr,hr],Br="";if(a){let Ke=Ze("bias",e[2].dataType,e[2].dims.length,We);Ar.push(Ke),Br+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${Ke.type.value} {
            return bias[coords.${d?"w":"y"}${S?"/ 4":""}];
          }`}let tr=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:Ne.length},{name:"pads",type:"i32",length:jt.length}];qn(t,tr);let gr=Er(e[0].dataType,1);if(gr!=="f16"&&gr!=="f32")throw new Error(`elemType ${gr} is not supported.`);return`
        ${aa("uniforms.result_strides")}
        ${_r.registerUniforms(tr).declareVariables(...Ar,Yt)};
        ${Br}
        ${Il(d,a,t,Pr.type.value,ee)}
        ${S?ks(k,q,gr,void 0,!d,fe):ei(k,q,gr,void 0,!d,fe,!1,void 0,u)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${k};${q};${S}`,inputDependencies:dr},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Z[0],y:Z[1],z:Z[2]},programUniforms:Lt}),getShaderSource:ar}}}),zl,fa,Dl=R(()=>{Kt(),wn(),Xt(),sr(),zl=(e,t,r,n,s,i=!1,a,u,d=!1)=>{let c=d?1:2,g=d?2:3,l=d?3:1,y=i?2:1,v=`
  fn setOutputAtIndex(flatIndex : u32, value : ${i?`vec4<${a}>`:a}) {
    result[flatIndex] = ${i?`vec4<${a}>`:a}(value);
  }`;n&&(v+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${i?`vec4<${a}>`:a} {
      return bias[coords.${d?"w":"y"}${i?"/ 4":""}];
    }`);let S=i?4:1,x=Ze("W",t[1].dataType,t[1].dims.length,S),G=Ze("Dy",t[0].dataType,t[0].dims.length,S),q=[G,x];n&&q.push(Ze("bias",t[2].dataType,[r[l]].length,S));let k=Vt("result",t[0].dataType,r.length,S),Z=`{
        let batch: u32 = ${s?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];
        let r = ${s?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];
        let c = ${s?"global_id.y":"workgroup_id.y"} * ${y};
        let d1: u32 = ${s?"global_id.x":"workgroup_id.x"} * 4;

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

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        var dotProd: array<vec4<${a}>, ${y}>;
        for (var i = 0; i < ${y}; i++) {
          dotProd[i] = vec4<${a}>(0.0);
        }
        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {
          var dyR = (${a}(dyCorner.x) + ${a}(wR)) / ${a}(uniforms.strides.x);
          let wRPerm = uniforms.filter_dims[0] - 1 - wR;
          if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[1]) ||
              fract(dyR) > 0.0 || wRPerm < 0) {
            continue;
          }
          let idyR: u32 = u32(dyR);

          for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {
            let dyC = (${a}(dyCorner.y) + ${a}(wC)) / ${a}(uniforms.strides.y);
            let dyC2 = (${a}(dyCorner.y) + 1.0 + ${a}(wC)) / ${a}(uniforms.strides.y);
            let wCPerm = uniforms.filter_dims[1] - 1 - wC;
            if (wCPerm < 0) {
              continue;
            }
            var bDyCVal = true;
            var bDyCVal2 = true;
            if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[2]) ||
                fract(dyC) > 0.0) {
              bDyCVal = false;
            }
            if (dyC2 < 0.0 || dyC2 >= ${a}(uniforms.Dy_shape[2]) ||
                fract(dyC2) > 0.0) {
              bDyCVal2 = false;
            }

            let idyC: u32 = u32(dyC);
            let idyC2: u32 = u32(dyC2);
            if (bDyCVal && bDyCVal2) {
              let d2Length = uniforms.Dy_shape[3];
              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {
                let wValue0 = ${x.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${x.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${x.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${x.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

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

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

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

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

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

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

            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
              if (wC % uniforms.dilations.y != 0) {
                continue;
              }
              let dyC = (${a}(dyCCorner) + ${a}(wC)) / ${a}(uniforms.strides.y);
              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
              if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[${g}]) ||
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                continue;
              }
              let idyC: u32 = u32(dyC);
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              for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {
                let xValue = ${d?G.get("batch","idyR","idyC","inputChannel"):G.get("batch","inputChannel","idyR","idyC")};
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                inputChannel = inputChannel + 1;
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d=i[s?1:2],c=i[s?2:3],g=t[1].dims[2],l=t[1].dims[3],y=s?d*c:a,v=s?a:d*c,S=g*l*u,x=!0,G=e.kernelCustomData.wT??e.compute(kn(t[1],jl),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=G);let q=[t[0],G],k=t.length===3;k&&(!s&&t[2].dims.length===1?q.push(t[2].reshape([t[2].dims[0],1,1])):q.push(t[2])),e.compute(Fl(q,n,i,y,v,S,k,x),{inputs:q})},oi=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let s=t.kernelShape;(s.length===0||s[0]===0)&&(s=[e.inputs[1].dims[2]]);let i=t.dilations;(i.length===0||i[0]===0)&&(i=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let u=t.pads;u.length===0&&(u=[0,0]),u=[0,u[0],0,u[1]],a=[1].concat(a),i=[1].concat(i),s=[1].concat(s);let d=ma({...t,pads:u,strides:a,dilations:i,kernelShape:s},n);e.compute(fa(n,d,c=>r?[c[0],c[2],c[3]]:[c[0],c[1],c[3]]))},Du=(e,t)=>{Nl(e.inputs,t),e.inputs[0].dims.length===3?oi(e,t):_a(e,e.inputs,t)}}),Vl,ga,Ul,Lu=R(()=>{Kt(),Xt(),Cr(),sr(),Vl=(e,t,r,n)=>{let s=Ie.size(t),i=t.length,a=Ze("input",e,i),u=Vt("output",e,i),d=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),c=Ie.normalizeAxis(d,i),g=l=>{let y=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,v=Nt("uniforms.input_shape","uniforms.axis",i),S=n.reverse?y+(n.exclusive?" + 1":""):"0",x=n.reverse?v:y+(n.exclusive?"":" + 1");return`
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                ${l.mainStart()}
                  ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
                  var inputIndices = ${u.offsetToIndices("global_idx")};
                  var sum = ${u.type.value}(0);
                  let first : i32 = ${S};
                  let last : i32 = ${x};
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                    ${a.indicesSet("inputIndices","uniforms.axis","u32(i)")};
                    sum = sum + ${a.getByIndices("inputIndices")};
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  ${q.mainStart()}
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    let indices = ${x.offsetToIndices("global_idx")};
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    ${x.setByOffset("global_idx",S.getByIndices("aIndices"))}
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            ${c.registerUniforms(u.map(ee=>({name:`${li(ee)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...s,a)}

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`)}
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        ${v("data",2,"u32")}
        ${v("data",3,"u32")}
        ${l.setByOffset("global_idx","data")}
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    ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
    ${y}`},d=[{type:12,data:a},...Pt(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:u,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d})}},Uu=e=>{Yl(e.inputs),e.compute(Vu(e.inputs),{inputs:[0]})}}),Jl,eu,Wu=R(()=>{Kt(),Xt(),sr(),Zi(),Jl=e=>{let t=e[0].dataType,r=Ie.size(e[0].dims),n=Ie.size(e[1].dims),s=n%4===0,i=a=>{let u=Ze("x",t,[1],4),d=Ze("bias",t,[1],4),c=Vt("y",t,[1],4),g=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],l=v=>`
      let bias${v}_offset: u32 = (global_idx * 4 + ${v}) % uniforms.bias_size;
      let bias${v} = ${d.getByOffset(`bias${v}_offset / 4`)}[bias${v}_offset % 4];`,y=s?`
      let bias = ${d.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${l(0)}${l(1)}${l(2)}${l(3)}
      let bias = ${u.type.value}(bias0, bias1, bias2, bias3);`;return`${a.registerUniforms(g).declareVariables(u,d,c)}

    ${Ki(Tr(t))}

    ${a.mainStart(hn)}
      ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}

      let x = ${u.getByOffset("global_idx")};
      ${y}
      let x_in = x + bias;
      ${c.setByOffset("global_idx",Xi("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${s}`,inputDependencies:["type","type"]},getShaderSource:i,getRunData:a=>({outputs:[{dims:a[0].dims,dataType:a[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/hn/4)}})}},eu=e=>{e.inputs.length<2||Ie.size(e.inputs[1].dims)===0?Qi(e):e.compute(Jl(e.inputs))}}),tu,xa,Ta,ru,Gu=R(()=>{Kt(),Xt(),Cr(),sr(),tu=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},xa=(e,t)=>{let r=e[0].dims,n=e[1].dims,s=r.length,i=Ie.normalizeAxis(t.axis,s),a=r.slice(0);a.splice(i,1,...n);let u=r[i],d=e[0].dataType===9?4:1,c=Math.ceil(Ie.size(a)/d),g=[{type:12,data:c},{type:6,data:u},{type:12,data:i},...Pt(e[0].dims,e[1].dims,a)],l=y=>{let v=Ze("data",e[0].dataType,e[0].dims.length,d),S=Ze("inputIndices",e[1].dataType,e[1].dims.length),x=Vt("output",e[0].dataType,a.length,d),G=k=>{let Z=n.length,ee=`var indicesIndices${k}  = ${S.type.indices}(0);`;for(let fe=0;fe<Z;fe++)ee+=`${Z>1?`indicesIndices${k}[${fe}]`:`indicesIndices${k}`} = ${a.length>1?`outputIndices${k}[uniforms.axis + ${fe}]`:`outputIndices${k}`};`;ee+=`
          var idx${k} = ${S.getByIndices(`indicesIndices${k}`)};
          if (idx${k} < 0) {
            idx${k} = idx${k} + uniforms.axisDimLimit;
          }
          var dataIndices${k} : ${v.type.indices};
        `;for(let fe=0,We=0;fe<s;fe++)fe===i?(ee+=`${s>1?`dataIndices${k}[${fe}]`:`dataIndices${k}`} = u32(idx${k});`,We+=Z):(ee+=`${s>1?`dataIndices${k}[${fe}]`:`dataIndices${k}`} = ${a.length>1?`outputIndices${k}[${We}]`:`outputIndices${k}`};`,We++);return ee},q;if(e[0].dataType===9){let k=(Z,ee,fe="")=>`
          let outputIndices${ee} = ${x.offsetToIndices(`outputOffset + ${ee}u`)};
          ${G(ee)};
          let offset${ee} = ${v.indicesToOffset(`dataIndices${ee}`)};
          let index${ee} = offset${ee} / 4u;
          let component${ee} = offset${ee} % 4u;
          ${Z}[${ee}] = ${fe}(${v.getByOffset(`index${ee}`)}[component${ee}]);
        `;q=`
        let outputOffset = global_idx * ${d};
        var value = vec4<u32>(0);
        ${k("value",0,"u32")}
        ${k("value",1,"u32")}
        ${k("value",2,"u32")}
        ${k("value",3,"u32")}
        ${x.setByOffset("global_idx","value")}
      `}else q=`
      let outputIndices = ${x.offsetToIndices("global_idx")};
      ${G("")};
      let value = ${v.getByIndices("dataIndices")};
      ${x.setByOffset("global_idx","value")};
      `;return`
      ${y.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(v,S,x)}
      ${y.mainStart()}
        ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${q}
      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:l}},Ta=e=>qt({axis:e.axis}),ru=(e,t)=>{let r=e.inputs;tu(r),e.compute(xa(e.inputs,t))}}),Sa,qu,Hu,nu,Ku=R(()=>{Kt(),Xt(),Cr(),sr(),Sa=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let r=Ie.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,s=e[0],i=e[2],a=e.length===4?e[3]:void 0;if(i.dims.length!==s.dims.length||!s.dims.map((u,d)=>d===r?Math.ceil(u/n)===i.dims[d]:u===i.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(a){if(a.dataType!==s.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(a.dims.length!==i.dims.length||!a.dims.map((u,d)=>u===i.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},qu=(e,t)=>{let r=e[0].dims,n=e[1].dims,s=r.length,i=Ie.normalizeAxis(t.gatherAxis,s),a=Ie.normalizeAxis(t.quantizeAxis,s),u=r.slice(0);u.splice(i,1,...n);let d=Ie.size(u),c=e[2].dataType,g=e[0].dataType===22,l=[{type:12,data:d},{type:12,data:a},{type:12,data:i},{type:12,data:t.blockSize},...Pt(...e.map((v,S)=>v.dims),u)],y=v=>{let S=Ze("data",e[0].dataType,e[0].dims.length),x=Ze("inputIndices",e[1].dataType,e[1].dims.length),G=Ze("scales",e[2].dataType,e[2].dims.length),q=e.length>3?Ze("zeroPoint",e[3].dataType,e[3].dims.length):void 0,k=Vt("output",c,u.length),Z=[S,x,G];q&&Z.push(q);let ee=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${v.registerUniforms(ee).declareVariables(...Z,k)}
        ${v.mainStart()}
        let output_indices = ${k.offsetToIndices("global_idx")};
        var indices_indices = ${x.type.indices}(0);
        ${n.length>1?`
          for (var i: u32 = 0; i < ${n.length}; i++) {
            let index = ${k.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${x.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${k.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${S.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${k.indicesGet("output_indices","i")};
          ${S.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${x.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${r[i]};
        }
        ${S.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${u.length}; i++) {
          let index = ${k.indicesGet("output_indices",`i + ${n.length} - 1`)};
          ${S.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${S.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${S.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${g?"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 = ${G.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${G.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${G.getByIndices("scale_indices")};
        ${q?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${q.indicesToOffset("zero_point_indices")};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${q.getByOffset("zero_point_offset / 8")};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${g?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${Tr(c)}(quantized_data - zero_point) * scale;
        ${k.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((v,S)=>S!==1).map(v=>v.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(v,S)=>"rank")},getRunData:()=>({outputs:[{dims:u,dataType:c}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:l}),getShaderSource:y}},Hu=(e,t)=>{let r=e.inputs;Sa(r,t),e.compute(qu(e.inputs,t))},nu=e=>qt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),su,iu,au,ou,Xu=R(()=>{Kt(),Xt(),Cr(),sr(),su=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.`)},iu=(e,t)=>{let r=e[0].dims,n=e[0].dataType,s=r.length,i=e[1].dims,a=e[1].dataType,u=Ie.normalizeAxis(t.axis,s),d=r[u],c=i.slice(0),g=Ie.size(c),l=Ze("input",n,s),y=Ze("indicesInput",a,i.length),v=Vt("output",n,c.length),S=[{type:12,data:g},{type:6,data:d},{type:12,data:u}];return S.push(...Pt(r,i,c)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:S}),getShaderSource:x=>`
      ${x.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(l,y,v)}
      ${x.mainStart()}
      ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${v.setByOffset("global_idx","value")};
  }`}},au=e=>qt({axis:e.axis}),ou=(e,t)=>{let r=e.inputs;su(r),e.compute(iu(e.inputs,t))}}),lu,uu,du,cu,Qu=R(()=>{Kt(),Xt(),sr(),lu=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")},uu=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[s,i,a]=xr.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),u=[s,i];if(!u)throw new Error("Can't use gemm on the given tensors");let d=Ie.size(u),c=[{type:12,data:d},{type:12,data:s},{type:12,data:i},{type:12,data:a},{type:1,data:t.alpha},{type:1,data:t.beta}],g=["type","type"];e.length===3&&(c.push(...Pt(e[2].dims)),g.push("rank")),c.push(...Pt(u));let l=y=>{let v="";t.transA&&t.transB?v="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?v="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?v="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(v="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let S=t.alpha===1?"":"value *= uniforms.alpha;",x=Ze("a",e[0].dataType,e[0].dims),G=Ze("b",e[1].dataType,e[1].dims),q=x.type.value,k=null,Z=[x,G];e.length===3&&(k=Ze("c",e[2].dataType,e[2].dims.length),Z.push(k));let ee=Vt("output",e[0].dataType,u.length);Z.push(ee);let fe=[{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`
  ${y.registerUniforms(fe).declareVariables(...Z)}

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

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

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

    ${S}
    ${k!=null?`let cOffset = ${k.broadcastedIndicesToOffset("vec2(m, n)",ee)}; value += ${q}(uniforms.beta) * ${k.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:l}},du=e=>{let t=e.transA,r=e.transB,n=e.alpha,s=e.beta;return{transA:t,transB:r,alpha:n,beta:s,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},cu=(e,t)=>{lu(e.inputs),e.compute(uu(e.inputs,t))}}),ln,pu,hu,$a,Yu,ui,Zu,Ju=R(()=>{Kt(),Xt(),Cr(),z(),Qs(),sr(),ps(),ln=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,pu=(e,t)=>{let r=e[0],n=ln(e,1),s=ln(e,2),i=ln(e,3),a=ln(e,4),u=ln(e,5),d=ln(e,6),c=ln(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let g=r.dims[0],l=r.dims[1],y=r.dims.length===3?r.dims[2]:t.numHeads*r.dims[4],v=l,S=0,x=0,G=Math.floor(y/t.numHeads);if(d&&c&&Ie.size(d.dims)&&Ie.size(c.dims)){if(d.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims[0]!==g||d.dims[1]!==t.numHeads||d.dims[3]!==G)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(c.dims[0]!==g||c.dims[1]!==t.numHeads||c.dims[3]!==G)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[2]!==c.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(c.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');S=d.dims[2],x=d.dims[2]}else if(d&&Ie.size(d.dims)||c&&Ie.size(c.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let q;if(n&&Ie.size(n.dims)>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');q=2,v=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==G)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(s)throw new Error('Expect "value" be none when "key" has packed kv format.');q=5,v=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==G)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');q=0,v=n.dims[2]}}else{if(r.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(r.dims[2]!==t.numHeads||r.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');q=3}if(i&&Ie.size(i.dims)>0){if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let k=S+v,Z=0;if(a&&Ie.size(a.dims)>0){Z=8;let Ne=a.dims;throw Ne.length===1?Ne[0]===g?Z=1:Ne[0]===3*g+2&&(Z=3):Ne.length===2&&Ne[0]===g&&Ne[1]===k&&(Z=5),Z===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let ee=!1,fe=y;if(s&&Ie.size(s.dims)>0){if(s.dims.length!==3&&s.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==s.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(s.dims.length===3){if(v!==s.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');fe=s.dims[2]}else{if(v!==s.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');fe=s.dims[1]*s.dims[3],ee=!0}}let We=!1;if(a&&Ie.size(a.dims)>0)throw new Error("Key padding mask is not supported");if(u&&Ie.size(u.dims)>0){if(u.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(u.dims[0]!==g||u.dims[1]!==t.numHeads||u.dims[2]!==l||u.dims[3]!==k)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:g,sequenceLength:l,pastSequenceLength:S,kvSequenceLength:v,totalSequenceLength:k,maxSequenceLength:x,inputHiddenSize:0,hiddenSize:y,vHiddenSize:fe,headSize:G,vHeadSize:Math.floor(fe/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:Z,scale:t.scale,broadcastResPosBias:We,passPastInKv:ee,qkvFormat:q}},hu=e=>qt({...e}),$a=qt({perm:[0,2,1,3]}),Yu=(e,t,r,n,s,i,a)=>{let u=[n,s,i],d=Ie.size(u),c=[{type:12,data:d},{type:12,data:a},{type:12,data:i}],g=l=>{let y=Vt("qkv_with_bias",t.dataType,u),v=Ze("qkv",t.dataType,u),S=Ze("bias",r.dataType,u),x=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${l.registerUniforms(x).declareVariables(v,S,y)}
  ${l.mainStart()}
    ${l.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:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:g},{inputs:[t,r],outputs:[-1]})[0]},ui=(e,t,r,n,s,i,a,u)=>{let d=i;if(a&&Ie.size(a.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return d=Yu(e,i,a,t,n,r*s,u),d=d.reshape([t,n,r,s]),e.compute(kn(d,$a.perm),{inputs:[d],outputs:[-1]})[0]}else return i.dims.length===3&&(d=i.reshape([t,n,r,s])),e.compute(kn(d,$a.perm),{inputs:[d],outputs:[-1]})[0]},Zu=(e,t)=>{let r=pu(e.inputs,t),n=e.inputs[0],s=ln(e.inputs,1),i=ln(e.inputs,2),a=ln(e.inputs,3),u=ln(e.inputs,4),d=ln(e.inputs,5),c=ln(e.inputs,6),g=ln(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if(s?.dims.length===5)throw new Error("Packed KV is not implemented");let l=s&&i&&s.dims.length===4&&i.dims.length===4,y=ui(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,a,0);if(l)return hs(e,y,s,i,u,void 0,c,g,d,r,t);if(!s||!i)throw new Error("key and value must be provided");let v=ui(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,s,a,r.hiddenSize),S=ui(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,i,a,2*r.hiddenSize);hs(e,y,v,S,u,void 0,c,g,d,r,t)}}),fu,ed,td,mu,rd,nd=R(()=>{Kt(),Xt(),sr(),fu=e=>Array.from(e.getBigInt64Array(),Number),ed=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==10&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(fu(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},td=(e,t)=>{let r=[];for(let n=0;n<e.length;++n)r.push(e[n]*t[n]);return r},mu=(e,t)=>{let r=e[0].dims,n=t??fu(e[1]),s=td(r,n),i=Ie.size(s),a=e[0].dataType,u=Ze("input",a,r.length),d=Vt("output",a,s.length),c=g=>`
      const inputShape = ${u.indices(...r)};
      ${g.registerUniform("output_size","u32").declareVariables(u,d)}
      ${g.mainStart()}
      ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${d.offsetToIndices("global_idx")};
      var input_indices: ${u.type.indices};
      for (var i = 0; i < ${r.length}; i++) {
        let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${d.indicesGet("output_indices","i")}  % input_dim_i;

        ${u.indicesSet("input_indices","i","input_dim_value")}
      }
      ${d.setByOffset("global_idx",u.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},...Pt(e[0].dims,s)]}),getShaderSource:c}},rd=e=>{ed(e.inputs),e.compute(mu(e.inputs),{inputs:[0]})}}),sd,_u,id,ad,gu,od,qd=R(()=>{Kt(),Xt(),Cr(),Qs(),sr(),Ju(),nd(),ps(),sd=(e,t)=>{let r=e[0],n=e[1],s=e[2],i=e[3],a=e[4];if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let u=!1,d=r.dims[0],c=r.dims[1],g=r.dims.length===3?u?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],l=c,y=0,v=0,S=Math.floor(g/t.numHeads),x=i&&i.dims.length!==0,G=a&&a.dims.length!==0,q=!0;if(x&&G){if(i.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(a.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');y=i.dims[1],v=i.dims[1]}else if(x||G)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let k;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(r.dims[2]%n.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');k=2,l=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==S)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(s)throw new Error('Expect "value" be none when "key" has packed kv format.');k=5,l=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==S)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');k=0,l=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');k=3}let Z=0,ee=!1,fe=g;if(s){if(s.dims.length!==3&&s.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==s.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(s.dims.length===3){if(l!==s.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');fe=s.dims[2]}else{if(l!==s.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');fe=s.dims[1]*s.dims[3],ee=!0}}let We=y+l;return{batchSize:d,sequenceLength:c,pastSequenceLength:y,kvSequenceLength:l,totalSequenceLength:We,maxSequenceLength:v,inputHiddenSize:0,hiddenSize:g,vHiddenSize:fe,headSize:S,vHeadSize:Math.floor(fe/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:Z,scale:t.scale,broadcastResPosBias:!1,passPastInKv:ee,qkvFormat:k,isPastkvBSNH:q}},_u=(e,t,r,n)=>{let s=[n.batchSize,n.totalSequenceLength,n.kvNumHeads,n.headSize],i=4,a=Ie.size(s)/i,u=n.totalSequenceLength,d=Vt("present_kv",r,s.length,i),c=Ze("new_kv",e.dataType,e.dims.length,i),g=t?Ze("past_kv",t.dataType,t.dims.length,i):void 0,l=Math.ceil(n.headSize/i),y={x:u,y:e.dims[0],z:1},v=t?["rank","rank"]:["rank"],S=[{type:12,data:a},{type:12,data:n.pastSequenceLength},{type:12,data:n.kvSequenceLength},{type:12,data:n.totalSequenceLength}],x=[c];g?(S.push(...Pt(e.dims),...Pt(t.dims),...Pt(s)),x.push(g)):S.push(...Pt(e.dims),...Pt(s));let G=[{name:"output_size",type:"u32"},{name:"past_seqlen",type:"u32"},{name:"new_seqlen",type:"u32"},{name:"present_seqlen",type:"u32"}],q=`      let past_batch_stride = uniforms.past_seqlen * num_heads * H;
        var past_head_stride = uniforms.past_seqlen * H;
        if (is_bsnh) {
          past_head_stride = H;
        }
        let in_offset = b * past_batch_stride + s * row_stride + n * past_head_stride + h;
        present_kv[out_offset] = past_kv[in_offset];`,k=`      let new_batch_stride = uniforms.new_seqlen * num_heads * H;
        let new_row_stride = num_heads * H;
        let new_head_stride = H;
        let in_offset = b * new_batch_stride + (s - past_seqlen) * new_row_stride + n * new_head_stride + h;
        present_kv[out_offset] = new_kv[in_offset];`,Z=t?`if (s < past_seqlen) {
        ${q}
        } else if (s < past_seqlen + uniforms.new_seqlen) {
        ${k}
        }`:`if (s < past_seqlen + uniforms.new_seqlen) {
          ${k}
        }`,ee=fe=>`

  ${fe.registerUniforms(G).declareVariables(...x,d)}
  ${fe.mainStart([l,n.kvNumHeads,1])}
    ${fe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    var indices = ${d.offsetToIndices("global_idx")};
    let h = local_id.x;
    let n = local_id.y;
    let s = workgroup_id.x;
    let b = workgroup_id.y;
    let num_heads = ${n.kvNumHeads}u;
    let H = ${l}u;

    let present_seqlen = uniforms.present_seqlen;
    let present_batch_stride = present_seqlen * num_heads * H;
    var row_stride = H;
    let is_bsnh = ${n.isPastkvBSNH};

    if (is_bsnh) {
      row_stride = num_heads * H;
    }
    var present_head_stride = present_seqlen * H;
    if (is_bsnh) {
      present_head_stride = H;
    }

    let past_seqlen = uniforms.past_seqlen;

    let out_offset = b * present_batch_stride + s * row_stride + n * present_head_stride + h;
    ${Z}
  }`;return{name:"ConcatPastNew",shaderCache:{hint:`${n.kvNumHeads}${l}${!!t}`,inputDependencies:v},getRunData:()=>({outputs:[{dims:s,dataType:r}],dispatchGroup:y,programUniforms:S}),getShaderSource:ee}},id=e=>qt({...e}),ad=qt({perm:[0,2,1,3]}),gu=(e,t,r,n,s)=>{let i=t,a=n.kvNumHeads,u=n.nReps;return t.dims.length===3&&n.kvSequenceLength!==0&&(i=t.reshape([n.batchSize,n.kvSequenceLength,a,n.headSize])),r?i=e.compute(_u(i,r,i.dataType,n),{inputs:[i,r],outputs:[n.isPastkvBSNH?s:-1]})[0]:i=e.compute(_u(i,void 0,i.dataType,n),{inputs:[i],outputs:[n.isPastkvBSNH?s:-1]})[0],u!==1&&(i=e.compute(mu([i],[1,1,1,u]),{inputs:[i],outputs:[-1]})[0],i=i.reshape([n.batchSize,n.totalSequenceLength,a*u,n.headSize])),e.compute(kn(i,ad.perm),{inputs:[i],outputs:[-1]})[0]},od=(e,t)=>{let r=sd(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let n=ui(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,e.inputs[0],void 0,0),s=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,i=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,a=gu(e,e.inputs[1],s,r,1),u=gu(e,e.inputs[2],i,r,2);hs(e,n,a,u,void 0,void 0,void 0,void 0,void 0,r,t)}}),ld,ud,dd,cd,Hd=R(()=>{Kt(),Xt(),sr(),ld=(e,t)=>{let r=e[0].dims,n=r,s=2,i=Ie.sizeToDimension(r,s),a=Ie.sizeFromDimension(r,s),u=Sr(a),d=a/u,c=[r[0],r[1],d],g=["rank","type","type"],l=[{type:12,data:a},{type:12,data:d}];l.push(...Pt(c,c));let y=v=>{let S=Ze("x",e[0].dataType,c.length,u),x=Ze("scale",e[1].dataType,e[1].dims),G=Ze("bias",e[2].dataType,e[2].dims),q=Vt("output",e[0].dataType,c.length,u),k=[S,x,G,q],Z=S.type.value,ee=u===1?"f32":`vec${u}<f32>`,fe=64,We=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`
  var<workgroup> meanShared : f32;
  var<workgroup> squaredNormShared : f32;
  var<workgroup> workgroupShared : array<${ee}, ${fe}>;
  const workgroupSize = ${fe}u;
  ${v.registerUniforms(We).declareVariables(...k)}
  ${v.mainStart(fe)}
    let norm = global_idx / workgroupSize;
    let batch = norm / uniforms.x_shape[1];
    let channel = norm % uniforms.x_shape[1];
    let localIndex = local_id.x;

    // initialize workgroup memory
    var initial = ${ee}(0);
    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
      initial = initial + ${ee}(${S.get("batch","channel","h")});
    }
    workgroupShared[localIndex] = initial;
    workgroupBarrier();

    // Calculate the mean of current channel data.
    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {
      if (localIndex < currSize) {
        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];
      }
      workgroupBarrier();
    }
    if (localIndex == 0) {
      meanShared = ${fn("workgroupShared[0]",u)} / f32(uniforms.normSize);
    }
    workgroupBarrier();

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

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

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

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

    let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;
    var sum = ${Fr("f32",d)};
    var squaredSum = ${Fr("f32",d)};
    for (var i: u32 = wgOffset; i < wgMax; i++) {
        let value = ${l}(input[offset + i * uniforms.C]);
        sum += value;
        squaredSum += value * value;
    }
    output[global_idx] = ${y("sum","squaredSum")};
  }`},k=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${d}`,inputDependencies:x},getRunData:()=>({outputs:[{dims:[s,a,c,2],dataType:1}],dispatchGroup:{x:s*a/d},programUniforms:G}),getShaderSource:q},{inputs:[t],outputs:[-1]})[0],Z=[{type:12,data:v},{type:12,data:i},{type:12,data:Math.floor(a/d)},{type:12,data:Math.floor(c*a/d)}],ee=["type","type","type"],fe=We=>{let Ne=Ze("scale",r.dataType,r.dims,d),mt=Ze("bias",n.dataType,n.dims,d);return`
  @group(0) @binding(0) var<storage, read> input : array<${g}>;
  @group(0) @binding(1) var<storage, read> scale : array<${Ne.type.storage}>;
  @group(0) @binding(2) var<storage, read> bias : array<${mt.type.storage}>;
  @group(0) @binding(3) var<storage, read_write> output : array<${g}>;
  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};
  @group(0) @binding(4) var<uniform> uniforms: Uniforms;

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

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

    output[global_idx] = ${y("channelScale","channelShift")};
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${d};${u}`,inputDependencies:ee},getRunData:()=>({outputs:[{dims:[s,a,2],dataType:1}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:Z}),getShaderSource:fe},{inputs:[k,r,n],outputs:[-1]})[0]},dd=(e,t,r)=>{let n=t[0].dims,s=n,i=n[0],a=n[n.length-1],u=Ie.sizeFromDimension(n,1)/a,d=Sr(a),c=Ie.size(s)/d,g=[{type:12,data:u},{type:12,data:Math.floor(a/d)}],l=["type","type"],y=ud(e,t[0],t[1],t[2],i,u,a,r.epsilon),v=S=>{let x=Er(t[0].dataType),G=d===1?"vec2f":`mat2x${d}f`,q=d===1?x:`vec${d}<${x}>`,k=Ze("input",t[0].dataType,t[0].dims,d),Z=Vt("output",t[0].dataType,s,d);return`
  @group(0) @binding(0) var<storage, read> input : array<${k.type.storage}>;
  @group(0) @binding(1) var<storage, read> scaleInput : array<${G}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${Z.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${S.mainStart()}
    let currentImageNumber = global_idx / (uniforms.C * uniforms.H);
    let currentChannelNumber = global_idx % uniforms.C;

    let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;
    let scale = scaleInput[scaleOffset];
    output[global_idx] = fma(input[global_idx], ${q}(scale[0]), ${q}(scale[1]));
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${d}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:v},{inputs:[t[0],y]})},cd=(e,t)=>{t.format==="NHWC"?dd(e,e.inputs,t):e.compute(ld(e.inputs,t))}}),pd,hd,fd,Kd=R(()=>{Kt(),Xt(),sr(),pd=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},hd=(e,t,r)=>{let n=t.simplified,s=e[0].dims,i=e[1],a=!n&&e[2],u=s,d=Ie.normalizeAxis(t.axis,s.length),c=Ie.sizeToDimension(s,d),g=Ie.sizeFromDimension(s,d),l=Ie.size(i.dims),y=a?Ie.size(a.dims):0;if(l!==g||a&&y!==g)throw new Error(`Size of X.shape()[axis:] == ${g}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${l} and bias size of ${y}`);let v=[];for(let fe=0;fe<s.length;++fe)fe<d?v.push(s[fe]):v.push(1);let S=Sr(g),x=["type","type"],G=[{type:12,data:c},{type:1,data:g},{type:12,data:Math.floor(g/S)},{type:1,data:t.epsilon}];a&&x.push("type");let q=r>1,k=r>2,Z=fe=>{let We=Er(e[0].dataType),Ne=[Ze("x",e[0].dataType,e[0].dims,S),Ze("scale",i.dataType,i.dims,S)];a&&Ne.push(Ze("bias",a.dataType,a.dims,S)),Ne.push(Vt("output",e[0].dataType,u,S)),q&&Ne.push(Vt("mean_data_output",1,v)),k&&Ne.push(Vt("inv_std_output",1,v));let mt=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${fe.registerUniforms(mt).declareVariables(...Ne)}
  ${fe.mainStart()}
    ${fe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${Fr("f32",S)};
    var mean_square_vector = ${Fr("f32",S)};

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

    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
      let f32input = ${jr(We,S,"x[j + offset]")};
      let f32scale = ${jr(We,S,"scale[j]")};
      output[j + offset] = ${Ne[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale
        ${a?`+ ${jr(We,S,"bias[j]")}`:""}
      );
    }

    ${q?"mean_data_output[global_idx] = mean":""};
    ${k?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},ee=[{dims:u,dataType:e[0].dataType}];return q&&ee.push({dims:v,dataType:1}),k&&ee.push({dims:v,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${S};${r};${n}`,inputDependencies:x},getRunData:()=>({outputs:ee,dispatchGroup:{x:Math.ceil(c/64)},programUniforms:G}),getShaderSource:Z}},fd=(e,t)=>{pd(e.inputs),e.compute(hd(e.inputs,t,e.outputCount))}}),md,_d,gd,lr,wd=R(()=>{Kt(),Xt(),Cr(),sr(),md=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],n=r.dims.length;if(r.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let s=Math.floor((t.k+t.blockSize-1)/t.blockSize),i=t.blockSize/8*t.bits,a=e[1];if(!Ie.areEqual(a.dims,[t.n,s,i]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=e[2].dims;if(Ie.size(u)!==t.n*s)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,c=t.bits>4?t.n*s:t.n*Math.floor((s+1)/2);if(Ie.size(d)!==c)throw new Error("zeroPoints input size error.")}},_d=(e,t)=>{let r=e[0].dims,n=r.length,s=r[n-2],i=t.k,a=t.n,u=r.slice(0,n-2),d=Ie.size(u),c=e[1].dims[2]/4,g=e[0].dataType,l=Sr(t.k),y=Sr(c),v=Sr(a),S=u.concat([s,a]),x=s>1&&a/v%2===0?2:1,G=Ie.size(S)/v/x,q=64,k=[],Z=[d,s,i/l],ee=Ie.convertShape(e[1].dims).slice();ee.splice(-1,1,c/y),k.push(...Pt(Z)),k.push(...Pt(ee)),k.push(...Pt(e[2].dims)),e.length===4&&k.push(...Pt(Ie.convertShape(e[3].dims)));let fe=[d,s,a/v];k.push(...Pt(fe));let We=Ne=>{let mt=Z.length,jt=Ze("a",e[0].dataType,mt,l),Lt=Ze("b",12,ee.length,y),dr=Ze("scales",e[2].dataType,e[2].dims.length),ar=[jt,Lt,dr],_r=e.length===4?Ze("zero_points",12,e[3].dims.length):void 0;_r&&ar.push(_r);let Pr=fe.length,hr=Vt("output",e[0].dataType,Pr,v),Yt=Er(e[0].dataType),Ar=(()=>{switch(l){case 1:return`array<${Yt}, 8>`;case 2:return`mat4x2<${Yt}>`;case 4:return`mat2x4<${Yt}>`;default:throw new Error(`${l}-component is not supported.`)}})(),Br=()=>{let Ke=`
          // reuse a data
            var input_offset = ${jt.indicesToOffset(`${jt.type.indices}(batch, row, word_offset)`)};
            var a_data: ${Ar};
            for (var j: u32 = 0; j < ${8/l}; j++) {
              a_data[j] = ${jt.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let $t=0;$t<v*x;$t++)Ke+=`
            b_value = ${y===1?`b${$t}_data`:`b${$t}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${Ar}(${Array.from({length:4},(cr,Vr)=>`${Yt}(b_value_lower[${Vr}]), ${Yt}(b_value_upper[${Vr}])`).join(", ")});
            b_dequantized_values = ${l===1?`${Ar}(${Array.from({length:8},(cr,Vr)=>`(b_quantized_values[${Vr}] - ${_r?`zero_point${$t}`:"zero_point"}) * scale${$t}`).join(", ")});`:`(b_quantized_values - ${Ar}(${Array(8).fill(`${_r?`zero_point${$t}`:"zero_point"}`).join(",")})) * scale${$t};`};
            workgroup_shared[local_id.x * ${x} + ${Math.floor($t/v)}]${v>1?`[${$t%v}]`:""} += ${Array.from({length:8/l},(cr,Vr)=>`${l===1?`a_data[${Vr}] * b_dequantized_values[${Vr}]`:`dot(a_data[${Vr}], b_dequantized_values[${Vr}])`}`).join(" + ")};
          `;return Ke},tr=()=>{let Ke=`
            var col_index = col * ${v};
            ${_r?`
            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 = ${Yt}(8);`}
            `;for(let $t=0;$t<v*x;$t++)Ke+=`
            let scale${$t} = ${dr.getByOffset("col_index * nBlocksPerCol + block")};
            ${_r?`
            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 = ${_r.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${$t} = ${Yt}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return Ke},gr=()=>{let Ke=`col_index = col * ${v};`;for(let $t=0;$t<v*x;$t++)Ke+=`
            let b${$t}_data = ${Lt.getByIndices(`${Lt.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return Ke+=`
            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: ${Ar};
            var b_dequantized_values: ${Ar};`,Ke};return`
        var<workgroup> workgroup_shared: array<${hr.type.value}, ${x*q}>;
        ${Ne.declareVariables(...ar,hr)}
        ${Ne.mainStart([q,1,1])}
          let output_indices = ${hr.offsetToIndices(`(global_idx / ${q}) * ${x}`)};
          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 += ${q}) {
            //process one block
            var word_offset: u32 = block * ${t.blockSize/l};
            ${tr()}
            for (var word: u32 = 0; word < ${c}; word += ${y}) {
              ${gr()}
              for (var i: u32 = 0; i < ${y}; i++) {
                ${Br()}
                word_offset += ${8/l};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${x}) {
            var output_value: ${hr.type.value} = ${hr.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < ${q}u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${x};
            }
            ${hr.setByIndices(`${hr.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${l};${y};${v};${x};${q}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:S,dataType:g}],dispatchGroup:{x:G},programUniforms:k}),getShaderSource:We}},gd=(e,t)=>{md(e.inputs,t),e.compute(_d(e.inputs,t))},lr=e=>qt(e)}),en,rn,on,es,yd,wu,yu,_,h,D=R(()=>{Kt(),Xt(),sr(),en=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},rn=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
            k = i32(${e.indicesGet("indices",s)}) - ${Nt("uniforms.pads",s,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${Nt("uniforms.x_shape",s,t)})) {
              break;
            }
            offset += k * i32(${Nt("uniforms.x_strides",s,t)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${n}
            value = x[offset];
          }
      `},on=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
                k = i32(${e.indicesGet("indices",s)}) - ${Nt("uniforms.pads",s,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${Nt("uniforms.x_shape",s,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${Nt("uniforms.x_shape",s,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${Nt("uniforms.x_strides",s,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},es=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
                k = i32(${e.indicesGet("indices",s)}) - ${Nt("uniforms.pads",s,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${Nt("uniforms.x_shape",s,t)})) {
                  k = i32(${Nt("uniforms.x_shape",s,t)}) - 1;
                }
                offset += k * i32(${Nt("uniforms.x_strides",s,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},yd=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
                k = i32(${e.indicesGet("indices",s)}) - ${Nt("uniforms.pads",s,r)};
                if (k < 0)  {
                  k += i32(${Nt("uniforms.x_shape",s,t)}]);
                }
                if (k >= i32(${Nt("uniforms.x_shape",s,t)})) {
                  k -= i32(${Nt("uniforms.x_shape",s,t)});
                }
                offset += k * i32(${Nt("uniforms.x_strides",s,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},wu=(e,t,r)=>{switch(r.mode){case 0:return rn(e,t,r.pads.length);case 1:return on(e,t,r.pads.length);case 2:return es(e,t,r.pads.length);case 3:return yd(e,t,r.pads.length);default:throw new Error("Invalid mode")}},yu=(e,t)=>{let r=Ie.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,s=Ie.size(r),i=[{type:12,data:s},{type:6,data:t.pads}],a=e.length>=3&&e[2].data;t.mode===0&&i.push({type:a?e[2].dataType:1,data:t.value}),i.push(...Pt(e[0].dims,r));let u=["rank"],d=c=>{let g=Vt("output",e[0].dataType,r.length),l=Ze("x",e[0].dataType,n.length),y=l.type.value,v=wu(g,n.length,t),S=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&S.push({name:"constant_value",type:a?y:"f32"}),`
            ${c.registerUniforms(S).declareVariables(l,g)}
            ${c.mainStart()}
            ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${y}(0);
            ${v}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${a}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Ie.size(r)/64)},programUniforms:i}),getShaderSource:d}},_=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,s=e[0].dims.length,i=new Int32Array(2*s).fill(0);if(e.length>=4){let u=e[3].getBigInt64Array();for(let d=0;d<u.length;d++)i[Number(u[d])]=Number(r[d]),i[Number(u[d])+s]=Number(r[d+u.length])}else r.forEach((u,d)=>i[Number(d)]=Number(u));let a=[];return i.forEach(u=>a.push(u)),{mode:t.mode,value:n,pads:a}}else return t},h=(e,t)=>{en(e.inputs);let r=_(e.inputs,t);e.compute(yu(e.inputs,r),{inputs:[0]})}}),re,Oe,Ye,wt,At,Zt,mr,kr,Ir,pr,er,Dr,ir,Mr,qr,tn,Hn,Sn,mn,Qr=R(()=>{Bt(),Kt(),Xt(),sr(),re=e=>{if(C.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Oe=(e,t,r)=>{let n=t.format==="NHWC",s=e.dims.slice();n&&s.splice(1,0,s.pop());let i=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),u=t.strides.slice(),d=i?t.dilations.slice():[],c=t.pads.slice();On.adjustPoolAttributes(r,s,a,u,d,c);let g=On.computePoolOutputShape(r,s,u,d,a,c,t.autoPad),l=Object.assign({},t);i?Object.assign(l,{kernelShape:a,strides:u,pads:c,dilations:d,cacheKey:t.cacheKey}):Object.assign(l,{kernelShape:a,strides:u,pads:c,cacheKey:t.cacheKey});let y=g.slice();return y.push(y.splice(1,1)[0]),[l,n?y:g]},Ye=(e,t)=>{let r=t.format==="NHWC",n=Ie.size(e),s=Ie.size(t.kernelShape),i=[{type:12,data:n},{type:12,data:s}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let u=t.kernelShape[t.kernelShape.length-1],d=t.strides[t.strides.length-1],c=t.pads[t.pads.length/2-1],g=t.pads[t.pads.length-1],l=!!(c+g);i.push({type:12,data:u},{type:12,data:d},{type:12,data:c},{type:12,data:g}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let y=!1;if(t.kernelShape.length===2){let v=t.kernelShape[t.kernelShape.length-2],S=t.strides[t.strides.length-2],x=t.pads[t.pads.length/2-2],G=t.pads[t.pads.length-2];y=!!(x+G),i.push({type:12,data:v},{type:12,data:S},{type:12,data:x},{type:12,data:G}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,a,!0,l,y]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let u=Ie.computeStrides(t.kernelShape);i.push({type:12,data:u},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:u.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let d=t.pads.reduce((c,g)=>c+g);return[i,a,!!d,!1,!1]}},wt=(e,t,r,n,s,i,a,u,d,c,g,l)=>{let y=s.format==="NHWC",v=t.type.value,S=Vt("output",t.type.tensor,n);if(s.kernelShape.length<=2){let x="",G="",q="",k=r-(y?2:1);if(g?x=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${k}] = indices[${k}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${k}] < 0 || xIndices[${k}]
                      >= uniforms.x_shape[${k}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${i}
                }`:x=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${k}] = indices[${k}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${i}
                }`,s.kernelShape.length===2){let Z=r-(y?3:2);l?G=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${Z}] = indices[${Z}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${Z}] < 0 || xIndices[${Z}] >= uniforms.x_shape[${Z}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:G=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${Z}] = indices[${Z}] * uniforms.sh - uniforms.phStart + j;
                `,q=`
              }
            `}return`
            ${e.registerUniforms(d).declareVariables(t,S)}

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

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

              var value = ${v}(${u});
              var pad = 0;
              ${G}
              ${x}
              ${q}
              ${a}

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

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

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

              var value = ${v}(${u});
              var pad = 0;
              var isPad = false;

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

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

              output[global_idx] = value;
            }`}},At=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Zt=e=>`${At(e)};${e.countIncludePad}`,mr=e=>`${At(e)};${e.storageOrder};${e.dilations}`,kr=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}),Ir=(e,t,r,n)=>{let[s,i]=Oe(t,n,r),a=Ze("x",t.dataType,t.dims.length),u=a.type.value,d="value += x_val;",c="";s.countIncludePad?c+=`value /= ${u}(uniforms.kernelSize);`:c+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[g,l,y,v,S]=Ye(i,s);g.push(...Pt(t.dims,i));let x=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${y};${v};${S}`,inputDependencies:x},getRunData:()=>({outputs:[{dims:i,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Ie.size(i)/64)},programUniforms:g}),getShaderSource:G=>wt(G,a,t.dims.length,i.length,s,d,c,0,l,y,v,S)}},pr=e=>{let t=e.count_include_pad!==0,r=kr(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...r,cacheKey:""};return{...n,cacheKey:Zt(n)}},er=(e,t)=>{re(e.inputs),e.compute(Ir("AveragePool",e.inputs[0],!1,t))},Dr={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},ir=e=>{let t=e.format;return{format:t,...Dr,cacheKey:t}},Mr=(e,t)=>{re(e.inputs),e.compute(Ir("GlobalAveragePool",e.inputs[0],!0,t))},qr=(e,t,r,n)=>{let[s,i]=Oe(t,n,r),a=`
      value = max(x_val, value);
    `,u="",d=Ze("x",t.dataType,t.dims.length),c=["rank"],[g,l,y,v,S]=Ye(i,s);return g.push(...Pt(t.dims,i)),{name:e,shaderCache:{hint:`${n.cacheKey};${y};${v};${S}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:i,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Ie.size(i)/64)},programUniforms:g}),getShaderSource:x=>wt(x,d,t.dims.length,i.length,s,a,u,t.dataType===10?-65504:-1e5,l,y,v,S)}},tn=(e,t)=>{re(e.inputs),e.compute(qr("MaxPool",e.inputs[0],!1,t))},Hn=e=>{let t=e.storage_order,r=e.dilations,n=kr(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let s={storageOrder:t,dilations:r,...n,cacheKey:""};return{...s,cacheKey:mr(s)}},Sn=e=>{let t=e.format;return{format:t,...Dr,cacheKey:t}},mn=(e,t)=>{re(e.inputs),e.compute(qr("GlobalMaxPool",e.inputs[0],!0,t))}}),_n,di,Os,Xd,Bn=R(()=>{Kt(),Xt(),Cr(),sr(),_n=(e,t)=>{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((r,n)=>r===e[2].dims[n]).reduce((r,n)=>r&&n,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(t.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((s,i)=>i===t.axis||s===e[0].dims[i]).reduce((s,i)=>s&&i,!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 r=e[0].dims[t.axis],n=e[1].dims[t.axis];if(t.blockSize<Math.ceil(r/n)||t.blockSize>Math.ceil(r/(n-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},di=(e,t)=>{let r=Ie.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,s=n===3,i=e[0].dims,a=e[1].dataType,u=Ie.size(i),d=n===3||n===2,c=d?[Math.ceil(Ie.size(e[0].dims)/4)]:e[0].dims,g=e[1].dims,l=e.length>2?e[2]:void 0,y=l?d?[Math.ceil(Ie.size(l.dims)/4)]:l.dims:void 0,v=g.length===0||g.length===1&&g[0]===1,S=v===!1&&g.length===1,x=Sr(u),G=v&&(!d||x===4),q=G?x:1,k=G&&!d?x:1,Z=Ze("input",d?12:n,c.length,k),ee=Ze("scale",a,g.length),fe=l?Ze("zero_point",d?12:n,y.length):void 0,We=Vt("output",a,i.length,q),Ne=[Z,ee];fe&&Ne.push(fe);let mt=[c,g];l&&mt.push(y);let jt=[{type:12,data:u/q},{type:12,data:r},{type:12,data:t.blockSize},...Pt(...mt,i)],Lt=dr=>{let ar=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${dr.registerUniforms(ar).declareVariables(...Ne,We)}
      ${dr.mainStart()}
          ${dr.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${We.offsetToIndices("global_idx")};

          // Set input x
          ${d?`
            let input = ${Z.getByOffset("global_idx / 4")};
            let x_vec = ${s?"unpack4xI8(input)":"unpack4xU8(input)"};
            let x_value = ${q===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${Z.getByOffset("global_idx")};`};

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

          // Set zero-point input
          ${fe?v?d?`
                let zero_point_input = ${fe.getByOffset("0")};
                let zero_point_vec =  ${s?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${fe.getByOffset("0")}`:S?d?`
                let zero_point_index = ${We.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${fe.getByOffset("zero_point_index / 4")};
                let zero_point_vec =  ${s?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${We.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${fe.getByOffset("zero_point_index")};`:d?`
                let zero_point_offset = ${ee.indicesToOffset("scale_indices")};
                let zero_point_input = ${fe.getByOffset("zero_point_offset / 4")};
                let zero_point_vec = ${s?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${fe.getByIndices("scale_indices")};`:`let zero_point_value = ${d?s?"i32":"u32":Z.type.value}(0);`};
      // Compute and write output
      ${We.setByOffset("global_idx",`${We.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:fe?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Lt,getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(u/q/64),y:1,z:1},programUniforms:jt})}},Os=(e,t)=>{_n(e.inputs,t),e.compute(di(e.inputs,t))},Xd=e=>qt({axis:e.axis,blockSize:e.blockSize})}),ms,bd,Md,Qd=R(()=>{Bt(),Kt(),sr(),ms=(e,t,r)=>{let n=e===t,s=e<t&&r<0,i=e>t&&r>0;if(n||s||i)throw new Error("Range these inputs' contents are invalid.")},bd=(e,t,r,n)=>{let s=Math.abs(Math.ceil((t-e)/r)),i=[s],a=s,u=[{type:12,data:a},{type:n,data:e},{type:n,data:r},...Pt(i)],d=c=>{let g=Vt("output",n,i.length),l=g.type.value,y=[{name:"outputSize",type:"u32"},{name:"start",type:l},{name:"delta",type:l}];return`
        ${c.registerUniforms(y).declareVariables(g)}
        ${c.mainStart()}
        ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${l}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:u})}},Md=e=>{let t=0,r=0,n=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),C.webgpu.validateInputContent&&ms(t,r,n),e.compute(bd(t,r,n,e.inputs[0].dataType),{inputs:[]})}}),bu,jc,Vc,Uc,Wc,Gc,qc,Hc,Kc,Xc,Qc,Yd,Yc,Zc,Jc,ep,tp,rp,np,zf=R(()=>{Kt(),Xt(),Cr(),sr(),bu=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},jc=(e,t,r)=>{t.every(s=>s>=0&&s<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return t.forEach((s,i)=>n[s]=e[i]),n},Vc=(e,t,r,n,s,i)=>{let[a,u,d]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],c=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(g=>i.push(g));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(u>0&&e.length>u&&e[u].dims.length>0){if(e[u].getFloat32Array().forEach(g=>n.push(g)),n.length!==0&&n.length!==c&&r>=18&&n.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");bu(n,t),t.axes.length>0&&jc(n,t.axes,c).forEach((g,l)=>n[l]=g)}if(d>0&&e.length>d&&(e[d].getBigInt64Array().forEach(g=>s.push(Number(g))),s.length!==c||r>=18&&s.length===t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(n.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(s.length!==t.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof s<"u"&&n.length>0&&s.length>c)throw new Error("Resize requires only of scales or sizes to be specified")},Uc=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating
                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
                    // offset-by-one error later in floor().
                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));
                    let fract =
                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);
                    return whole + fract;
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +
                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /
                        ${t}(lengthResized - 1);
                  } else {
                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);
                  const adjustment = ${t}(lengthResized) / outputWidth;
                  const center = ${t}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",Wc=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Gc=(e,t,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),s=e.length===0?n:e.slice();return t.length>0?(t.forEach((i,a)=>{n[i]=s[a],n[a+r]=s[t.length+a]}),n):s},qc=(e,t,r,n)=>{let s=[];if(r.length>0)if(n.length>0){if(e.forEach(i=>s.push(i)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((i,a)=>s[i]=r[a])}else r.forEach(i=>s.push(i));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");s=e.map((i,a)=>Math.round(i*t[a]))}return s},Hc=(e,t,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(i=>t[i]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(i=>t[i]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let s=e.slice();return r.axes.length>0?(r.axes.forEach(i=>t[i]=n),r.axes.forEach(i=>s[i]=Math.round(e[i]*t[i]))):(t.fill(n,0,t.length),s.forEach((i,a)=>s[a]=Math.round(i*t[a]))),s},Kc=(e,t,r,n,s)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {
      var original_indices: array<${e.type.value}, ${r.length}>;
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var scale = ${Nt("uniforms.scales","i",n)};
        var roi_low = ${Nt("uniforms.roi","i",s)};
        var roi_hi = ${Nt("uniforms.roi",`i + ${t.length}`,s)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${Nt("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${Nt("uniforms.output_shape","i",r.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,Xc=(e,t,r,n,s,i,a)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
      var input_indices: ${e.type.indices};
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${t.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${Nt("uniforms.scales","i",s)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${Nt("uniforms.roi","i",i)};
          var roi_hi = ${Nt("uniforms.roi",`i + ${r.length}`,i)};
          var input_shape_i = ${Nt("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${Nt("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${a} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${t.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;
    }`,Qc=(e,t)=>`
    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var input_index = ${e.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${Nt("uniforms.input_shape","i",t.length)}) {
          return false;
        }
      }
      return true;
    }`,Yd=(e,t,r,n)=>e.rank>n?`
    ${e.indicesSet("input_indices",t,"channel")};
    ${e.indicesSet("input_indices",r,"batch")};
`:"",Yc=(e,t,r,n,s)=>{let[i,a,u,d]=r.length===2?[-1,0,1,-1]:[0,2,3,1],c=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${c} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",a,`max(0, min(row, ${r[a]} - 1))`)};
      ${e.indicesSet("input_indices",u,`max(0, min(col, ${r[u]} - 1))`)};
      ${Yd(e,d,i,2)}
      return ${e.getByIndices("input_indices")};
    }

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

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

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

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

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

      var x111: ${g} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${g} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${g} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${g} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${g} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${g} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${g} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${g} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${g} = abs(depth - ${g}(depth1));
      var dx2: ${g} = abs(${g}(depth2) - depth);
      var dy1: ${g} = abs(height - ${g}(height1));
      var dy2: ${g} = abs(${g}(height2) - height);
      var dz1: ${g} = abs(width - ${g}(width1));
      var dz2: ${g} = abs(${g}(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);
    }`},ep=(e,t,r,n,s,i)=>{let a=e.dims,u=Gc(i,t.axes,a.length),d=qc(a,n,s,t.axes),c=n.slice();n.length===0&&(c=a.map((k,Z)=>k===0?1:d[Z]/k),t.keepAspectRatioPolicy!=="stretch"&&(d=Hc(a,c,t)));let g=Vt("output",e.dataType,d.length),l=Ze("input",e.dataType,a.length),y=Ie.size(d),v=a.length===d.length&&a.every((k,Z)=>k===d[Z]),S=t.coordinateTransformMode==="tf_crop_and_resize",x=t.extrapolationValue,G=l.type.value,q=k=>`
      ${v?"":`
      ${Uc(t.coordinateTransformMode,G)};
      ${(()=>{switch(t.mode){case"nearest":return`
              ${Qc(l,a)};
              ${Wc(t.nearestMode,r,G)};
              ${Xc(l,g,a,d,c.length,u.length,S)};
              `;case"linear":return`
              ${Kc(g,a,d,c.length,u.length)};
              ${(()=>{if(a.length===2||a.length===4)return`${Yc(l,g,a,S,x)}`;if(a.length===3||a.length===5)return`${Jc(l,g,a,S,x)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(a.length===2||a.length===4)return`${Zc(l,g,a,d,c,u,t.cubicCoeffA,S,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${k.registerUniform("output_size","u32").registerUniform("scales","f32",c.length).registerUniform("roi","f32",u.length).declareVariables(l,g)}
      ${k.mainStart()}
        ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${v?"output[global_idx] = input[global_idx];":`
        let output_indices = ${g.offsetToIndices("global_idx")};
        var input_indices: ${l.type.indices};
        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${l.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${t.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${c.length>0?c:""}|${s.length>0?s:""}|${u.length>0?u:""}|${v}|${a}`,inputDependencies:["rank"]},getShaderSource:q,getRunData:()=>({outputs:[{dims:d,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:[{type:12,data:y},{type:1,data:c},{type:1,data:u},...Pt(a,d)]})}},tp=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},rp=(e,t)=>{let r=[],n=[],s=[],i=tp(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Vc(e.inputs,t,i,r,n,s),e.compute(ep(e.inputs[0],t,i,r,n,s),{inputs:[0]})},np=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,s=e.cubicCoeffA,i=e.excludeOutside!==0,a=e.extrapolationValue,u=e.keepAspectRatioPolicy,d=e.mode,c=e.nearestMode===""?"simple":e.nearestMode;return qt({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:s,excludeOutside:i,extrapolationValue:a,keepAspectRatioPolicy:u,mode:d,nearestMode:c})}}),sp,ip,ap,Df=R(()=>{Kt(),Xt(),Cr(),sr(),sp=(e,t)=>{let[r,n,s,i]=e,{numHeads:a,rotaryEmbeddingDim:u}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!Ie.areEqual(n.dims,[])&&!Ie.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(s.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${s.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(!Ie.areEqual(s.dims,i.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(u>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let d=r.dims[0],c=r.dims[r.dims.length-2],g=s.dims[0],l=Ie.sizeFromDimension(r.dims,1)/c,y=u===0?s.dims[1]*2:l/a;if(u>y)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(d!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(c!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(y/2!==s.dims[1]&&u/2!==s.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${s.dims[1]}`);if(c>g)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},ip=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:s,scale:i}=t,a=e[0].dims[0],u=Ie.sizeFromDimension(e[0].dims,1),d=e[0].dims[e[0].dims.length-2],c=u/d,g=e[2].dims[1],l=s===0?g*2:c/n,y=new Array(a,d,c/l,l-g),v=Ie.computeStrides(y),S=[{type:1,data:i},{type:12,data:y},{type:12,data:v},...e[0].dims.length===3?new Array({type:12,data:[u,c,l,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[u,l,d*l,1]}):[],...Pt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],x=G=>{let q=Ze("input",e[0].dataType,e[0].dims.length),k=Ze("position_ids",e[1].dataType,e[1].dims.length),Z=Ze("cos_cache",e[2].dataType,e[2].dims.length),ee=Ze("sin_cache",e[3].dataType,e[3].dims.length),fe=Vt("output",e[0].dataType,e[0].dims.length);return G.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:y.length},{name:"global_strides",type:"u32",length:v.length},{name:"input_output_strides",type:"u32",length:v.length}]),`
        ${G.declareVariables(q,k,Z,ee,fe)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${k.broadcastedIndicesToOffset("bsnh.xy",Vt("",k.type.tensor,2))};
            let position_id =
                u32(${k.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r});
            let j = i + select(half_rotary_emb_dim, 1, ${r});
            let re = ${q.getByOffset("i")} * ${Z.get("position_id","bsnh[3]")} -
                ${q.getByOffset("j")} * ${ee.get("position_id","bsnh[3]")};
            ${fe.setByOffset("i","re")}
            let im = ${q.getByOffset("i")} * ${ee.get("position_id","bsnh[3]")} +
                ${q.getByOffset("j")} * ${Z.get("position_id","bsnh[3]")};
            ${fe.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${fe.setByOffset("k",q.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:qt({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:x,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Ie.size(y)/hn)},programUniforms:S})}},ap=(e,t)=>{sp(e.inputs,t),e.compute(ip(e.inputs,t))}}),op,lp,up,Bf=R(()=>{Kt(),Xt(),sr(),op=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],n=e[2];if(t.dataType!==r.dataType||t.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let s=t.dims[t.dims.length-1],i=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==s)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==i)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==s)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==s)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==s)throw new Error("Bias must have the same hidden size as input")}},lp=(e,t,r,n)=>{let s=t.simplified,i=e[0].dims,a=Ie.size(i),u=i,d=a,c=i.slice(-1)[0],g=n?i.slice(0,-1).concat(1):[],l=!s&&e.length>3,y=e.length>4,v=n&&r>1,S=n&&r>2,x=r>3,G=64,q=Sr(c),k=[{type:12,data:d},{type:12,data:q},{type:12,data:c},{type:1,data:t.epsilon}],Z=fe=>{let We=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Ne=[Ze("x",e[0].dataType,e[0].dims,q),Ze("skip",e[1].dataType,e[1].dims,q),Ze("gamma",e[2].dataType,e[2].dims,q)];l&&Ne.push(Ze("beta",e[3].dataType,e[3].dims,q)),y&&Ne.push(Ze("bias",e[4].dataType,e[4].dims,q)),Ne.push(Vt("output",e[0].dataType,u,q)),v&&Ne.push(Vt("mean_output",1,g)),S&&Ne.push(Vt("inv_std_output",1,g)),x&&Ne.push(Vt("input_skip_bias_sum",e[0].dataType,u,q));let mt=Er(e[0].dataType),jt=Er(1,q);return`

      ${fe.registerUniforms(We).declareVariables(...Ne)}
      var<workgroup> sum_shared : array<${jt}, ${G}>;
      var<workgroup> sum_squared_shared : array<${jt}, ${G}>;

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

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

        var reduce_size : u32 = ${G};
        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 = ${fn("sum",q)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${fn("square_sum",q)} / f32(uniforms.hidden_size) ${s?"":"- mean * mean"} + uniforms.epsilon);
        ${v?"mean_output[global_idx] = mean;":""}
        ${S?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${s?"":`- ${mt}(mean)`}) *
            ${mt}(inv_std_dev) * gamma[offset1d + i]
            ${l?"+ beta[offset1d + i]":""};
        }
      }`},ee=[{dims:u,dataType:e[0].dataType}];return r>1&&ee.push({dims:g,dataType:1}),r>2&&ee.push({dims:g,dataType:1}),r>3&&ee.push({dims:i,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${q};${v};${S};${x}`,inputDependencies:e.map((fe,We)=>"type")},getShaderSource:Z,getRunData:()=>({outputs:ee,dispatchGroup:{x:Math.ceil(d/c)},programUniforms:k})}},up=(e,t)=>{op(e.inputs);let r=[0];e.outputCount>1&&r.push(-3),e.outputCount>2&&r.push(-3),e.outputCount>3&&r.push(3),e.compute(lp(e.inputs,t,e.outputCount,!1),{outputs:r})}}),dp,Mu,cp,Zd,pp,hp,fp,mp,Lf=R(()=>{Kt(),Xt(),Cr(),sr(),dp=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,n)=>{if(e[n+1].dataType!==6&&e[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},Mu=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},cp=(e,t)=>{if(e.length>1){let r=Mu(e,1),n=Mu(e,2),s=Mu(e,3);return s.length===0&&(s=[...Array(e[0].dims.length).keys()]),qt({starts:r,ends:n,axes:s})}else return t},Zd=(e,t,r,n,s)=>{let i=e;return e<0&&(i+=r[n[t]]),s[t]<0?Math.max(0,Math.min(i,r[n[t]]-1)):Math.max(0,Math.min(i,r[n[t]]))},pp=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${r.length}; i >= 0; i--) {
            let input_shape_i = ${Nt("uniforms.input_shape","i",r.length)};
            let steps_i = ${Nt("uniforms.steps","i",r.length)};
            let signs_i = ${Nt("uniforms.signs","i",r.length)};
            let starts_i = ${Nt("uniforms.starts","i",r.length)};
            var output_index = ${t.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;
      }`,hp=(e,t)=>{let r=e[0].dims,n=Ie.size(r),s=t.axes.length>0?Ie.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],i=Mu(e,4);i.forEach(q=>q!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(s.length).fill(1));let a=t.starts.map((q,k)=>Zd(q,k,r,s,i)),u=t.ends.map((q,k)=>Zd(q,k,r,s,i));if(s.length!==a.length||s.length!==u.length)throw new Error("start, ends and axes should have the same number of elements");if(s.length!==r.length)for(let q=0;q<r.length;++q)s.includes(q)||(a.splice(q,0,0),u.splice(q,0,r[q]),i.splice(q,0,1));let d=i.map(q=>Math.sign(q));i.forEach((q,k,Z)=>{if(q<0){let ee=(u[k]-a[k])/q,fe=a[k],We=fe+ee*i[k];a[k]=We,u[k]=fe,Z[k]=-q}});let c=r.slice(0);s.forEach((q,k)=>{c[q]=Math.ceil((u[q]-a[q])/i[q])});let g={dims:c,dataType:e[0].dataType},l=Vt("output",e[0].dataType,c.length),y=Ze("input",e[0].dataType,e[0].dims.length),v=Ie.size(c),S=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:a.length},{name:"signs",type:"i32",length:d.length},{name:"steps",type:"u32",length:i.length}],x=[{type:12,data:v},{type:12,data:a},{type:6,data:d},{type:12,data:i},...Pt(e[0].dims,c)],G=q=>`
      ${q.registerUniforms(S).declareVariables(y,l)}
        ${pp(y,l,r)}
        ${q.mainStart()}
          ${q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${l.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${l.setByOffset("global_idx",y.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${d.length}_${a.length}_${i.length}`,inputDependencies:["rank"]},getShaderSource:G,getRunData:()=>({outputs:[g],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:x})}},fp=(e,t)=>{dp(e.inputs,t);let r=cp(e.inputs,t);e.compute(hp(e.inputs,r),{inputs:[0]})},mp=e=>{let t=e.starts,r=e.ends,n=e.axes;return qt({starts:t,ends:r,axes:n})}}),_p,gp,wp,yp,Rf=R(()=>{Kt(),Xt(),Cr(),sr(),_p=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},gp=(e,t)=>{let r=e.dims,n=Ie.size(r),s=64,i=t.axis;if(i<0&&(i=r.length+i),i<r.length-1)throw new Error("softmax only supports last axis for now.");let a=r[i],u=n/a,d=Sr(a),c=a/d,g=(G,q)=>q===4?`max(max(${G}.x, ${G}.y), max(${G}.z, ${G}.w))`:q===2?`max(${G}.x, ${G}.y)`:q===3?`max(max(${G}.x, ${G}.y), ${G}.z)`:G,l=Ze("x",e.dataType,e.dims,d),y=Vt("result",e.dataType,e.dims,d),v=l.type.value,S=Er(e.dataType)==="f32"?`var threadMax = ${v}(-3.402823e+38f);`:`var threadMax = ${v}(-65504.0h);`,x=G=>`
      var<workgroup> rowMaxShared : ${v};
      var<workgroup> rowSumShared : ${v};
      var<workgroup> threadShared : array<${v}, ${s}>;

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

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${v}) {
        let index = row * row_stride + col;
        result[index] = value;
      }
      ${G.registerUniform("packedCols","i32").declareVariables(l,y)}
      ${G.mainStart()}
        let gindex = i32(global_idx);
        let lindex = i32(local_idx);
        const wg = ${s};
        let row = gindex / wg;
        let cols = uniforms.packedCols;
        let row_stride : i32 = uniforms.packedCols;

        // find the rows max
        ${S}
        for (var col = lindex; col < cols; col += wg) {
          let value = getValue(row, col, row_stride);
          threadMax = max(threadMax, value);
        }
        if (lindex < cols) {
          threadShared[lindex] = threadMax;
        }
        workgroupBarrier();

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

        // find the rows sum
        var threadSum = ${v}(0.0);
        for (var col = lindex; col < cols; col += wg) {
          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);
          threadSum += subExp;
        }
        threadShared[lindex] = threadSum;
        workgroupBarrier();

        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
          if (lindex < currSize) {
            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
          rowSumShared = ${v}(${fn("threadShared[0]",d)});
        }
        workgroupBarrier();

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          setValue(row, col, row_stride, value);
        }
      }`;return{name:"Softmax",shaderCache:{hint:`${d}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:u},programUniforms:[{type:6,data:c}]}),getShaderSource:x}},wp=(e,t)=>{_p(e.inputs),e.compute(gp(e.inputs[0],t))},yp=e=>qt({axis:e.axis})}),bp,Mp,vp,xp,Tp,Sp,$p,Nf=R(()=>{Kt(),Xt(),Cr(),sr(),bp=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Mp=(e,t)=>{let r=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(s=>r.push(Number(s))),n=r.length),qt({numOutputs:n,axis:t.axis,splitSizes:r})},vp=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${Nt("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,xp=e=>{let t=e.length,r=[];for(let n=0;n<t;++n){let s=e[n].setByIndices("indices","input[global_idx]");t===1?r.push(s):n===0?r.push(`if (output_number == ${n}u) { ${s} }`):n===t-1?r.push(`else { ${s} }`):r.push(`else if (output_number == ${n}) { ${s} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},Tp=(e,t)=>{let r=e[0].dims,n=Ie.size(r),s=e[0].dataType,i=Ie.normalizeAxis(t.axis,r.length),a=new Array(t.numOutputs),u=Ze("input",s,r.length),d=new Array(t.numOutputs),c=[],g=[],l=0,y=[{type:12,data:n}];for(let S=0;S<t.numOutputs;S++){l+=t.splitSizes[S],d[S]=l;let x=r.slice();x[i]=t.splitSizes[S],g.push(x),a[S]=Vt(`output${S}`,s,x.length),c.push({dims:g[S],dataType:e[0].dataType})}y.push({type:12,data:d},...Pt(r,...g));let v=S=>`
  ${S.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",d.length).declareVariables(u,...a)}
  ${vp(d.length)}
  ${xp(a)}

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

    var indices = ${u.offsetToIndices("global_idx")};
    var index = ${u.indicesGet("indices",i)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${Nt("uniforms.size_in_split_axis","output_number - 1u",d.length)};
      ${u.indicesSet("indices",i,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:v,getRunData:()=>({outputs:c,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:y})}},Sp=(e,t)=>{bp(e.inputs);let r=e.inputs.length===1?t:Mp(e.inputs,t);e.compute(Tp(e.inputs,r),{inputs:[0]})},$p=e=>{let t=e.axis,r=e.splitSizes,n=e.numOutputs<0?r.length:e.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return qt({axis:t,numOutputs:n,splitSizes:r})}}),Ep,Cp,kp,jf=R(()=>{Kt(),Xt(),sr(),Ep=(e,t,r,n,s)=>{let i=Vt("output_data",s,r.length,4),a=Ze("a_data",t[1].dataType,t[1].dims.length,4),u=Ze("b_data",t[2].dataType,t[2].dims.length,4),d=Ze("c_data",t[0].dataType,t[0].dims.length,4),c,g=(l,y,v)=>`select(${y}, ${l}, ${v})`;if(!n)c=i.setByOffset("global_idx",g(a.getByOffset("global_idx"),u.getByOffset("global_idx"),d.getByOffset("global_idx")));else{let l=(y,v,S="")=>{let x=`a_data[index_a${v}][component_a${v}]`,G=`b_data[index_b${v}][component_b${v}]`,q=`bool(c_data[index_c${v}] & (0xffu << (component_c${v} * 8)))`;return`
            let output_indices${v} = ${i.offsetToIndices(`global_idx * 4u + ${v}u`)};
            let offset_a${v} = ${a.broadcastedIndicesToOffset(`output_indices${v}`,i)};
            let offset_b${v} = ${u.broadcastedIndicesToOffset(`output_indices${v}`,i)};
            let offset_c${v} = ${d.broadcastedIndicesToOffset(`output_indices${v}`,i)};
            let index_a${v} = offset_a${v} / 4u;
            let index_b${v} = offset_b${v} / 4u;
            let index_c${v} = offset_c${v} / 4u;
            let component_a${v} = offset_a${v} % 4u;
            let component_b${v} = offset_b${v} % 4u;
            let component_c${v} = offset_c${v} % 4u;
            ${y}[${v}] = ${S}(${g(x,G,q)});
          `};s===9?c=`
            var data = vec4<u32>(0);
            ${l("data",0,"u32")}
            ${l("data",1,"u32")}
            ${l("data",2,"u32")}
            ${l("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:c=`
            ${l("output_data[global_idx]",0)}
            ${l("output_data[global_idx]",1)}
            ${l("output_data[global_idx]",2)}
            ${l("output_data[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(d,a,u,i)}
        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${c}
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`)}
${s.additionalImplementations}
${i}`,u=r.createShaderModule({code:a,label:e.name});Nr("verbose",()=>`[WebGPU] ${e.name} shader code: ${a}`);let d=r.createComputePipeline({compute:{module:u,entryPoint:"main"},layout:"auto",label:e.name});return Ve(e.name),{programInfo:e,computePipeline:d,uniformVariablesInfo:s.variablesInfo}}normalizeDispatchGroupSize(e){let t=typeof e=="number"?e:e.x,r=typeof e=="number"?1:e.y||1,n=typeof e=="number"?1:e.z||1,s=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(t<=s&&r<=s&&n<=s)return[t,r,n];let i=t*r*n,a=Math.ceil(Math.sqrt(i));if(a>s){if(a=Math.ceil(Math.cbrt(i)),a>s)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[a,a,a]}else return[a,a,1]}}}),Ip,Fp,Op,zp,Wf=R(()=>{Bt(),Kt(),wn(),m(),or(),Vf(),Uf(),Ip=(e,t)=>{if(t.length!==e.length)throw new Error(`inputDependencies length ${t.length} is not equal to inputTensors length ${e.length}.`);let r=[];for(let n=0;n<e.length;++n){let s=e[n].dataType;switch(t[n]){case"none":{r.push("");break}case"type":{r.push(`${s}`);break}case"rank":{let i=e[n].dims.length;r.push(`${s};${i}`);break}case"dims":{let i=e[n].dims.join(",");r.push(`${s};${i}`);break}default:throw new Error(`unsupported input dependency: ${t[n]}`)}}return r.join("|")},Fp=(e,t,r)=>{let n=e.name;return e.shaderCache?.hint&&(n+="["+e.shaderCache.hint+"]"),n+=":"+r+`:${Ip(t,e.shaderCache?.inputDependencies??new Array(t.length).fill("dims"))}`,n},Op=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},zp=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. 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t.requestDevice(n),this.adapterInfo=new Op(t.info||await t.requestAdapterInfo()),this.gpuDataManager=fr(this),this.programManager=new Ap(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,ds(e.logLevel,!!e.debug),this.device.onuncapturederror=s=>{s.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${s.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:t,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),t={};this.queryType==="at-passes"&&(t.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(t)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;je(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{let t=new BigUint64Array(e.getMappedRange()),r=this.pendingQueries.get(e);for(let n=0;n<t.length/2;n++){let s=r[n],i=s.kernelId,a=this.kernels.get(i),u=a.kernelType,d=a.kernelName,c=s.programName,g=s.inputTensorViews,l=s.outputTensorViews,y=t[n*2],v=t[n*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=y);let S=Number(y-this.queryTimeBase),x=Number(v-this.queryTimeBase);if(!Number.isSafeInteger(S)||!Number.isSafeInteger(x))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:g.map(G=>({dims:G.dims,dataType:Cn(G.dataType)})),outputsMetadata:l.map(G=>({dims:G.dims,dataType:Cn(G.dataType)})),kernelId:i,kernelType:u,kernelName:d,programName:c,startTime:S,endTime:x});else{let G="";g.forEach((k,Z)=>{G+=`input[${Z}]: [${k.dims}] | ${Cn(k.dataType)}, `});let q="";l.forEach((k,Z)=>{q+=`output[${Z}]: [${k.dims}] | ${Cn(k.dataType)}, `}),console.log(`[profiling] kernel "${i}|${u}|${d}|${c}" ${G}${q}execution time: ${x-S} ns`)}Ee("GPU",`${c}::${y}::${v}`)}e.unmap(),this.pendingQueries.delete(e)}),Ve()}run(e,t,r,n,s,i){je(e.name);let a=[];for(let k=0;k<t.length;++k){let Z=t[k].data;if(Z===0)continue;let ee=this.gpuDataManager.get(Z);if(!ee)throw new Error(`no GPU data for input: ${Z}`);a.push(ee)}let{outputs:u,dispatchGroup:d,programUniforms:c}=e.getRunData(t),g=r.length===0?u.map((k,Z)=>Z):r;if(g.length!==u.length)throw new Error(`Output size ${g.length} must be equal to ${u.length}.`);let l=[],y=[];for(let k=0;k<u.length;++k){if(!Number.isInteger(g[k])||g[k]<-3||g[k]>=i)throw new Error(`Invalid output index: ${g[k]}`);if(g[k]===-3)continue;let Z=g[k]===-1,ee=g[k]===-2,fe=Z||ee?s(u[k].dataType,u[k].dims):n(g[k],u[k].dataType,u[k].dims);if(l.push(fe),fe.data===0)continue;let We=this.gpuDataManager.get(fe.data);if(!We)throw new Error(`no GPU data for output: ${fe.data}`);if(Z&&this.temporaryData.push(We),ee){let Ne=this.kernelPersistentData.get(this.currentKernelId);Ne||(Ne=[],this.kernelPersistentData.set(this.currentKernelId,Ne)),Ne.push(We)}y.push(We)}if(a.length!==t.length||y.length!==l.length){if(y.length===0)return Ve(e.name),l;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let v;if(c){let k=0,Z=[];c.forEach(Ne=>{let mt=typeof Ne.data=="number"?[Ne.data]:Ne.data;if(mt.length===0)return;let jt=Ne.type===10?2:4,Lt,dr;Ne.type===10?(dr=mt.length>4?16:mt.length>2?8:mt.length*jt,Lt=mt.length>4?16:jt*mt.length):(dr=mt.length<=2?mt.length*jt:16,Lt=16),k=Math.ceil(k/dr)*dr,Z.push(k);let ar=Ne.type===10?8:4;k+=mt.length>4?Math.ceil(mt.length/ar)*Lt:mt.length*jt});let ee=16;k=Math.ceil(k/ee)*ee;let fe=new ArrayBuffer(k);c.forEach((Ne,mt)=>{let jt=Z[mt],Lt=typeof Ne.data=="number"?[Ne.data]:Ne.data;if(Ne.type===6)new Int32Array(fe,jt,Lt.length).set(Lt);else if(Ne.type===12)new Uint32Array(fe,jt,Lt.length).set(Lt);else if(Ne.type===10)new Uint16Array(fe,jt,Lt.length).set(Lt);else if(Ne.type===1)new Float32Array(fe,jt,Lt.length).set(Lt);else throw new Error(`Unsupported uniform type: ${Cn(Ne.type)}`)});let We=this.gpuDataManager.create(k,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(We.buffer,0,fe,0,k),this.gpuDataManager.release(We.id),v={offset:0,size:k,buffer:We.buffer}}let S=this.programManager.normalizeDispatchGroupSize(d),x=S[1]===1&&S[2]===1,G=Fp(e,t,x),q=this.programManager.getArtifact(G);if(q||(q=this.programManager.build(e,S),this.programManager.setArtifact(G,q),Nr("info",()=>`[artifact] key: ${G}, programName: ${e.name}`)),c&&q.uniformVariablesInfo){if(c.length!==q.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${q.uniformVariablesInfo.length}, got ${c.length} in program "${q.programInfo.name}".`);for(let k=0;k<c.length;k++){let Z=c[k],ee=Z.type,fe=typeof Z.data=="number"?1:Z.data.length,[We,Ne]=q.uniformVariablesInfo[k];if(ee!==We||fe!==Ne)throw new Error(`Uniform variable ${k} mismatch: expect type ${We} with size ${Ne}, got type ${ee} with size ${fe} in program "${q.programInfo.name}".`)}}if(Nr("info",()=>`[ProgramManager] run "${e.name}" (key=${G}) with ${S[0]}x${S[1]}x${S[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let k={kernelId:this.currentKernelId,programName:q.programInfo.name,inputTensorViews:t,outputTensorViews:l};this.pendingKernels.push(k),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(k)}return this.programManager.run(q,a,y,S,v),Ve(e.name),l}upload(e,t){this.gpuDataManager.upload(e,t)}memcpy(e,t){this.gpuDataManager.memcpy(e,t)}async download(e,t){await this.gpuDataManager.download(e,t)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,t,r,n){let s=Pp.get(e);if(!s)throw new Error(`kernel not implemented: ${e}`);let i={kernelType:e,kernelName:n,kernelEntry:s[0],attributes:[s[1],r]};this.kernels.set(t,i)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let r of t)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,r){let n=this.kernels.get(e);if(!n)throw new Error(`kernel not created: ${e}`);let s=n.kernelType,i=n.kernelName,a=n.kernelEntry,u=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${s}] ${i}" is not allowed to be called recursively`);this.currentKernelId=e,u[0]&&(u[1]=u[0](u[1]),u[0]=void 0),Nr("info",()=>`[WebGPU] Start to run kernel "[${s}] ${i}"...`);let d=this.env.debug;this.temporaryData=[];try{return d&&this.device.pushErrorScope("validation"),a(t,u[1]),0}catch(c){return r.push(Promise.resolve(`[WebGPU] Kernel "[${s}] ${i}" failed. ${c}`)),1}finally{d&&r.push(this.device.popErrorScope().then(c=>c?`GPU validation error for kernel "[${s}] ${i}": ${c.message}`:null));for(let c of this.temporaryData)this.gpuDataManager.release(c.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,r,n){let s=this.sessionExternalDataMapping.get(e);s||(s=new Map,this.sessionExternalDataMapping.set(e,s));let i=s.get(t),a=this.gpuDataManager.registerExternalBuffer(r,n,i?.[1]);return s.set(t,[a,r]),a}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[1])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let t=this.gpuDataManager.get(e);if(!t)throw new Error(`no GPU data for buffer: ${e}`);return t.buffer}createDownloader(e,t,r){return async()=>{let n=await kt(this,e,t);return we(n.buffer,r)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){this.queryType="none",(this.env.webgpu.profiling?.mode==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){Nr("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(){Nr("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){Nr("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),r=e.length;this.pendingKernels=[];for(let n=0;n<r;n++){let s=this.getComputePassEncoder(),i=e[n];this.writeTimestamp(this.pendingDispatchNumber*2),s.setPipeline(i.computePipeline),s.setBindGroup(0,i.bindGroup),s.dispatchWorkgroups(...i.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(t[n]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}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()}}}),Dp={};$(Dp,{init:()=>Lp});var vd,Bp,Lp,Gf=R(()=>{Kt(),Wf(),wn(),Xt(),vd=class kf{constructor(t,r,n,s){this.module=t,this.dataType=r,this.data=n,this.dims=s}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let t=Ie.size(this.dims);return t===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,t)}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let t=Ie.size(this.dims);return t===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,t)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let t=Ie.size(this.dims);return t===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let t=Ie.size(this.dims);return t===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(Ie.size(t)!==Ie.size(this.dims))throw new Error("Invalid new shape");return new kf(this.module,this.dataType,this.data,t)}},Bp=class{constructor(e,t,r){this.module=e,this.backend=t,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=t.adapterInfo;let n=e.HEAPU32,s=r>>>2;this.opKernelContext=n[s++];let i=n[s++];this.outputCount=n[s++],this.customDataOffset=n[s++],this.customDataSize=n[s++];let a=[];for(let u=0;u<i;u++){let d=n[s++],c=n[s++],g=n[s++],l=[];for(let y=0;y<g;y++)l.push(n[s++]);a.push(new vd(e,d,c,l))}this.inputs=a}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}getMaxComputeWorkgroupSizes(){return[this.backend.device.limits.maxComputeWorkgroupSizeX,this.backend.device.limits.maxComputeWorkgroupSizeY,this.backend.device.limits.maxComputeWorkgroupSizeZ]}getMaxComputeWorkgroupStoragesize(){return this.backend.device.limits.maxComputeWorkgroupStorageSize}compute(e,t){let r=t?.inputs?.map(a=>typeof a=="number"?this.inputs[a]:a)??this.inputs,n=t?.outputs??[],s=(a,u,d)=>new vd(this.module,u,this.output(a,d),d),i=(a,u)=>{let d=Wn(a,u);if(!d)throw new Error(`Unsupported data type: ${a}`);let c=d>0?this.backend.gpuDataManager.create(d).id:0;return new vd(this.module,a,c,u)};return this.backend.run(e,r,n,s,i,this.outputCount)}output(e,t){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+t.length)*4),s=n>>2;this.module.HEAPU32[s++]=t.length;for(let i=0;i<t.length;i++)this.module.HEAPU32[s++]=t[i];return this.module._JsepOutput(this.opKernelContext,e,n)}catch(n){throw new Error(`Failed to generate kernel's output[${e}] with dims [${t}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(r)}}},Lp=async(e,t,r,n)=>{let s=t.jsepInit;if(!s)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(e==="webgpu"){let i=new zp;await i.initialize(r,n),s("webgpu",[i,a=>i.alloc(a),a=>i.free(a),(a,u,d,c=!1)=>{if(c)Nr("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${a}, dst=${u}, size=${d}`),i.memcpy(a,u);else{Nr("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${a}, gpuDataId=${u}, size=${d}`);let g=t.HEAPU8.subarray(a>>>0,(a>>>0)+d);i.upload(u,g)}},async(a,u,d)=>{Nr("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${a}, dataOffset=${u}, size=${d}`),await i.download(a,()=>t.HEAPU8.subarray(u>>>0,(u>>>0)+d))},(a,u,d)=>i.createKernel(a,u,d,t.UTF8ToString(t._JsepGetNodeName(u))),a=>i.releaseKernel(a),(a,u,d,c)=>{Nr("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${d}, kernel=${a}, contextDataOffset=${u}`);let g=new Bp(t,i,u);return i.computeKernel(a,g,c)},()=>i.captureBegin(),()=>i.captureEnd(),()=>i.replay()])}else s("webnn")}}),Rp,Jd,ec,zs,Np,xd,tc,rc,nc,sc,ic,ac,jp=R(()=>{Us(),Ws(),Kt(),pn(),Vn(),vs(),Rp=(e,t)=>{Hr()._OrtInit(e,t)!==0&&Lr("Can't initialize onnxruntime.")},Jd=async e=>{Rp(e.wasm.numThreads,Yn(e.logLevel))},ec=async(e,t)=>{{let r=(Gf(),P(Dp)).init;if(t==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let n=e.webgpu.adapter;if(n){if(typeof n.limits!="object"||typeof n.features!="object"||typeof n.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let s=e.webgpu.powerPreference;if(s!==void 0&&s!=="low-power"&&s!=="high-performance")throw new Error(`Invalid powerPreference setting: "${s}"`);let i=e.webgpu.forceFallbackAdapter;if(i!==void 0&&typeof i!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${i}"`);if(n=await navigator.gpu.requestAdapter({powerPreference:s,forceFallbackAdapter:i}),!n)throw new Error('Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.')}await r("webgpu",Hr(),e,n)}if(t==="webnn"){if(typeof navigator>"u"||!navigator.ml)throw new Error("WebNN is not supported in current environment");await r("webnn",Hr(),e)}}},zs=new Map,Np=e=>{let t=Hr(),r=t.stackSave();try{let n=t.stackAlloc(8);return t._OrtGetInputOutputCount(e,n,n+4)!==0&&Lr("Can't get session input/output count."),[t.HEAP32[n/4],t.HEAP32[n/4+1]]}finally{t.stackRestore(r)}},xd=e=>{let t=Hr(),r=t._malloc(e.byteLength);if(r===0)throw new Error(`Can't create a session. failed to allocate a buffer of size ${e.byteLength}.`);return t.HEAPU8.set(e,r),[r,e.byteLength]},tc=async(e,t)=>{let r,n,s=Hr();Array.isArray(e)?[r,n]=e:e.buffer===s.HEAPU8.buffer?[r,n]=[e.byteOffset,e.byteLength]:[r,n]=xd(e);let i=0,a=0,u=0,d=[],c=[],g=[];try{if([a,d]=Un(t),t?.externalData&&s.mountExternalData){let k=[];for(let Z of t.externalData){let ee=typeof Z=="string"?Z:Z.path;k.push(Zn(typeof Z=="string"?Z:Z.data).then(fe=>{s.mountExternalData(ee,fe)}))}await Promise.all(k)}for(let k of t?.executionProviders??[])if((typeof k=="string"?k:k.name)==="webnn"){if(s.currentContext)throw new Error("WebNN execution provider is already set.");if(typeof k!="string"){let Z=k,ee=Z?.context,fe=Z?.gpuDevice,We=Z?.deviceType,Ne=Z?.numThreads,mt=Z?.powerPreference;ee?s.currentContext=ee:fe?s.currentContext=await navigator.ml.createContext(fe):s.currentContext=await navigator.ml.createContext({deviceType:We,numThreads:Ne,powerPreference:mt})}else s.currentContext=await navigator.ml.createContext();break}i=await s._OrtCreateSession(r,n,a),i===0&&Lr("Can't create a session."),s.currentContext&&(s.currentContext=void 0);let[l,y]=Np(i),v=!!t?.enableGraphCapture,S=[],x=[],G=[];for(let k=0;k<l;k++){let Z=s._OrtGetInputName(i,k);Z===0&&Lr("Can't get an input name."),c.push(Z),S.push(s.UTF8ToString(Z))}for(let k=0;k<y;k++){let Z=s._OrtGetOutputName(i,k);Z===0&&Lr("Can't get an output name."),g.push(Z);let ee=s.UTF8ToString(Z);x.push(ee);{if(v&&t?.preferredOutputLocation===void 0){G.push("gpu-buffer");continue}let fe=typeof t?.preferredOutputLocation=="string"?t.preferredOutputLocation:t?.preferredOutputLocation?.[ee]??"cpu";if(fe!=="cpu"&&fe!=="cpu-pinned"&&fe!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${fe}.`);if(v&&fe!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${fe}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);G.push(fe)}}let q=null;return G.some(k=>k==="gpu-buffer")&&(u=s._OrtCreateBinding(i),u===0&&Lr("Can't create IO binding."),q={handle:u,outputPreferredLocations:G,outputPreferredLocationsEncoded:G.map(k=>ls(k))}),zs.set(i,[i,c,g,q,v,!1]),[i,S,x]}catch(l){throw c.forEach(y=>s._OrtFree(y)),g.forEach(y=>s._OrtFree(y)),u!==0&&s._OrtReleaseBinding(u),i!==0&&s._OrtReleaseSession(i),l}finally{s._free(r),a!==0&&s._OrtReleaseSessionOptions(a),d.forEach(l=>s._free(l)),s.unmountExternalData?.()}},rc=e=>{let t=Hr(),r=zs.get(e);if(!r)throw new Error(`cannot release session. invalid session id: ${e}`);let[n,s,i,a,u]=r;a&&(u&&t._OrtClearBoundOutputs(a.handle),t._OrtReleaseBinding(a.handle)),t.jsepOnReleaseSession?.(e),s.forEach(d=>t._OrtFree(d)),i.forEach(d=>t._OrtFree(d)),t._OrtReleaseSession(n),zs.delete(e)},nc=(e,t,r,n,s,i=!1)=>{if(!e){t.push(0);return}let a=Hr(),u=e[0],d=e[1],c=e[3],g,l;if(u==="string"&&c==="gpu-buffer")throw new Error("String tensor is not supported on GPU.");if(i&&c!=="gpu-buffer")throw new Error(`External buffer must be provided for input/output index ${s} when enableGraphCapture is true.`);if(c==="gpu-buffer"){let S=e[2].gpuBuffer;l=Wn(as(u),d);let x=a.jsepRegisterBuffer;if(!x)throw new Error('Tensor location "gpu-buffer" is not supported without using WebGPU.');g=x(n,s,S,l)}else{let S=e[2];if(Array.isArray(S)){l=4*S.length,g=a._malloc(l),r.push(g);let x=g/4;for(let G=0;G<S.length;G++){if(typeof S[G]!="string")throw new TypeError(`tensor data at index ${G} is not a string`);a.HEAPU32[x++]=Xr(S[G],r)}}else l=S.byteLength,g=a._malloc(l),r.push(g),a.HEAPU8.set(new Uint8Array(S.buffer,S.byteOffset,l),g)}let y=a.stackSave(),v=a.stackAlloc(4*d.length);try{let S=v/4;d.forEach(G=>a.HEAP32[S++]=G);let x=a._OrtCreateTensor(as(u),g,l,v,d.length,ls(c));x===0&&Lr(`Can't create tensor for input/output. session=${n}, 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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.
 * =============================================================================
 */},"./src/backends/onnx.js":(_t,Me,N)=>{var I;N.r(Me),N.d(Me,{Tensor:()=>be.Tensor,createInferenceSession:()=>_e,deviceToExecutionProviders:()=>ne,isONNXProxy:()=>se,isONNXTensor:()=>F});var ae=N("./src/env.js"),he=N("?2ce3"),xe=N("./node_modules/onnxruntime-web/dist/ort.webgpu.bundle.min.mjs"),be=N("./node_modules/onnxruntime-common/dist/esm/index.js");const R=Object.freeze({auto:null,gpu:null,cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",webnn:{name:"webnn",deviceType:"cpu"},"webnn-npu":{name:"webnn",deviceType:"npu"},"webnn-gpu":{name:"webnn",deviceType:"gpu"},"webnn-cpu":{name:"webnn",deviceType:"cpu"}}),$=[];let V,P;if(ae.apis.IS_NODE_ENV){switch(P=he??(I||(I=N.t(he,2))),process.platform){case"win32":$.push("dml");break;case"linux":process.arch==="x64"&&$.push("cuda");break}$.push("cpu"),V=["cpu"]}else P=xe,ae.apis.IS_WEBNN_AVAILABLE&&$.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),ae.apis.IS_WEBGPU_AVAILABLE&&$.push("webgpu"),$.push("wasm"),V=["wasm"];const K=P.InferenceSession;function ne(le=null){if(!le)return V;switch(le){case"auto":return $;case"gpu":return $.filter(ie=>["webgpu","cuda","dml","webnn-gpu"].includes(ie))}if($.includes(le))return[R[le]??le];throw new Error(`Unsupported device: "${le}". 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process<"u",ne=K&&process?.release?.name==="node",oe=!L(I),_e=!L(ae),F=Object.freeze({IS_BROWSER_ENV:be,IS_WEBWORKER_ENV:R,IS_WEB_CACHE_AVAILABLE:$,IS_WEBGPU_AVAILABLE:V,IS_WEBNN_AVAILABLE:P,IS_PROCESS_AVAILABLE:K,IS_NODE_ENV:ne,IS_FS_AVAILABLE:oe,IS_PATH_AVAILABLE:_e}),Q=oe&&_e,se=Q?ae.dirname(ae.dirname(he.fileURLToPath(import.meta.url))):"./",le=Q?ae.join(se,"/.cache/"):null,ie="/models/",j=Q?ae.join(se,ie):ie,A={version:xe,backends:{onnx:{}},allowRemoteModels:!0,remoteHost:"https://huggingface.co/",remotePathTemplate:"{model}/resolve/{revision}/",allowLocalModels:!be,localModelPath:j,useFS:oe,useBrowserCache:$,useFSCache:oe,cacheDir:le,useCustomCache:!1,customCache:null};function L(C){return Object.keys(C).length===0}},"./src/generation/configuration_utils.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{GenerationConfig:()=>ae});var I=N("./src/utils/core.js");class ae{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(xe){Object.assign(this,(0,I.pick)(xe,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{ClassifierFreeGuidanceLogitsProcessor:()=>Q,ForcedBOSTokenLogitsProcessor:()=>R,ForcedEOSTokenLogitsProcessor:()=>$,LogitsProcessor:()=>he,LogitsProcessorList:()=>be,LogitsWarper:()=>xe,MinLengthLogitsProcessor:()=>oe,MinNewTokensLengthLogitsProcessor:()=>_e,NoBadWordsLogitsProcessor:()=>F,NoRepeatNGramLogitsProcessor:()=>K,RepetitionPenaltyLogitsProcessor:()=>ne,SuppressTokensAtBeginLogitsProcessor:()=>V,TemperatureLogitsWarper:()=>se,TopKLogitsWarper:()=>ie,TopPLogitsWarper:()=>le,WhisperTimeStampLogitsProcessor:()=>P});var I=N("./src/utils/generic.js");N("./src/utils/tensor.js");var ae=N("./src/utils/maths.js");class he extends I.Callable{_call(A,L){throw Error("`_call` should be implemented in a subclass")}}class xe extends I.Callable{_call(A,L){throw Error("`_call` should be implemented in a subclass")}}class be extends I.Callable{constructor(){super(),this.processors=[]}push(A){this.processors.push(A)}extend(A){this.processors.push(...A)}_call(A,L){let C=L;for(const me of this.processors)C=me(A,C);return C}[Symbol.iterator](){return this.processors.values()}}class R extends he{constructor(A){super(),this.bos_token_id=A}_call(A,L){for(let C=0;C<A.length;++C)if(A[C].length===1){const me=L[C].data;me.fill(-1/0),me[this.bos_token_id]=0}return L}}class $ extends he{constructor(A,L){super(),this.max_length=A,this.eos_token_id=Array.isArray(L)?L:[L]}_call(A,L){for(let C=0;C<A.length;++C)if(A[C].length===this.max_length-1){const me=L[C].data;me.fill(-1/0);for(const ye of this.eos_token_id)me[ye]=0}return L}}class V extends he{constructor(A,L){super(),this.begin_suppress_tokens=A,this.begin_index=L}_call(A,L){for(let C=0;C<A.length;++C)if(A[C].length===this.begin_index){const me=L[C].data;for(const ye of this.begin_suppress_tokens)me[ye]=-1/0}return L}}class P extends he{constructor(A,L){super(),this.eos_token_id=Array.isArray(A.eos_token_id)?A.eos_token_id[0]:A.eos_token_id,this.no_timestamps_token_id=A.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=L.length,L.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=A.max_initial_timestamp_index}_call(A,L){for(let C=0;C<A.length;++C){const me=L[C].data;if(me[this.no_timestamps_token_id]=-1/0,A[C].length===this.begin_index-1){me.fill(-1/0),me[this.timestamp_begin]=0;continue}const ye=A[C].slice(this.begin_index),Se=ye.length>=1&&ye[ye.length-1]>=this.timestamp_begin,Pe=ye.length<2||ye[ye.length-2]>=this.timestamp_begin;if(Se&&(Pe?me.subarray(this.timestamp_begin).fill(-1/0):me.subarray(0,this.eos_token_id).fill(-1/0)),A[C].length===this.begin_index&&this.max_initial_timestamp_index!==null){const dt=this.timestamp_begin+this.max_initial_timestamp_index;me.subarray(dt+1).fill(-1/0)}const Ce=(0,ae.log_softmax)(me),rt=Math.log(Ce.subarray(this.timestamp_begin).map(Math.exp).reduce((dt,ge)=>dt+ge)),Xe=(0,ae.max)(Ce.subarray(0,this.timestamp_begin))[0];rt>Xe&&me.subarray(0,this.timestamp_begin).fill(-1/0)}return L}}class K extends he{constructor(A){super(),this.no_repeat_ngram_size=A}getNgrams(A){const L=A.length,C=[];for(let ye=0;ye<L+1-this.no_repeat_ngram_size;++ye){const Se=[];for(let Pe=0;Pe<this.no_repeat_ngram_size;++Pe)Se.push(A[ye+Pe]);C.push(Se.map(Number))}const me=new Map;for(const ye of C){const Se=ye.slice(0,ye.length-1),Pe=JSON.stringify(Se),Ce=me.get(Pe)??[];Ce.push(ye[ye.length-1]),me.set(Pe,Ce)}return me}getGeneratedNgrams(A,L){const C=L.slice(L.length+1-this.no_repeat_ngram_size,L.length);return A.get(JSON.stringify(C.map(Number)))??[]}calcBannedNgramTokens(A){const L=[];if(A.length+1<this.no_repeat_ngram_size)return L;{const C=this.getNgrams(A);return this.getGeneratedNgrams(C,A)}}_call(A,L){for(let C=0;C<A.length;++C){const me=L[C].data,ye=this.calcBannedNgramTokens(A[C]);for(const Se of ye)me[Se]=-1/0}return L}}class ne extends he{constructor(A){super(),this.penalty=A}_call(A,L){for(let C=0;C<A.length;++C){const me=L[C].data;for(const ye of A[C]){const Se=Number(ye);me[Se]<0?me[Se]*=this.penalty:me[Se]/=this.penalty}}return L}}class oe extends he{constructor(A,L){super(),this.min_length=A,this.eos_token_id=Array.isArray(L)?L:[L]}_call(A,L){for(let C=0;C<A.length;++C)if(A[C].length<this.min_length){const me=L[C].data;for(const ye of this.eos_token_id)me[ye]=-1/0}return L}}class _e extends he{constructor(A,L,C){super(),this.prompt_length_to_skip=A,this.min_new_tokens=L,this.eos_token_id=Array.isArray(C)?C:[C]}_call(A,L){for(let C=0;C<A.length;++C)if(A[C].length-this.prompt_length_to_skip<this.min_new_tokens){const ye=L[C].data;for(const Se of this.eos_token_id)ye[Se]=-1/0}return L}}class F extends he{constructor(A,L){super(),this.bad_words_ids=A,this.eos_token_id=Array.isArray(L)?L:[L]}_call(A,L){for(let C=0;C<A.length;++C){const me=L[C].data,ye=A[C];for(const Se of this.bad_words_ids){let Pe=!0;for(let Ce=1;Ce<=Se.length-1&&Se.length<ye.length;++Ce)if(Se.at(-Ce-1)!=ye.at(-Ce)){Pe=!1;break}Pe&&(me[Se.at(-1)]=-1/0)}}return L}}class Q extends he{constructor(A){if(super(),A<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${A}.`);this.guidance_scale=A}_call(A,L){if(L.dims[0]!==2*A.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 ${L.dims[0]} for the logits and ${A.length} for the input ids.`);const C=A.length,me=L.slice([0,C],null),ye=L.slice([C,L.dims[0]],null);for(let Se=0;Se<ye.data.length;++Se)ye.data[Se]+=(me.data[Se]-ye.data[Se])*this.guidance_scale;return ye}}class se extends xe{constructor(A){super(),this.temperature=A}_call(A,L){const C=L.data;for(let me=0;me<C.length;++me)C[me]/=this.temperature;return L}}class le extends xe{constructor(A,{filter_value:L=-1/0,min_tokens_to_keep:C=1}={}){if(super(),A<0||A>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${A}`);if(!Number.isInteger(C)||C<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${C}`);this.top_p=A,this.filter_value=L,this.min_tokens_to_keep=C}}class ie extends xe{constructor(A,{filter_value:L=-1/0,min_tokens_to_keep:C=1}={}){if(super(),!Number.isInteger(A)||A<0)throw new Error(`\`top_k\` must be a positive integer, but is ${A}`);this.top_k=Math.max(A,C),this.filter_value=L}}},"./src/generation/logits_sampler.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{LogitsSampler:()=>xe});var I=N("./src/utils/generic.js"),ae=N("./src/utils/tensor.js"),he=N("./src/utils/maths.js");N("./src/generation/configuration_utils.js");class xe extends I.Callable{constructor(P){super(),this.generation_config=P}async _call(P){return this.sample(P)}async sample(P){throw Error("sample should be implemented in subclasses.")}getLogits(P,K){let ne=P.dims.at(-1),oe=P.data;if(K===-1)oe=oe.slice(-ne);else{let _e=K*ne;oe=oe.slice(_e,_e+ne)}return oe}randomSelect(P){let K=0;for(let oe=0;oe<P.length;++oe)K+=P[oe];let ne=Math.random()*K;for(let oe=0;oe<P.length;++oe)if(ne-=P[oe],ne<=0)return oe;return 0}static getSampler(P){if(P.do_sample)return new R(P);if(P.num_beams>1)return new $(P);if(P.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${P.num_return_sequences}.`);return new be(P)}}class be extends xe{async sample(P){const K=(0,he.max)(P.data)[1];return[[BigInt(K),0]]}}class R extends xe{async sample(P){let K=P.dims.at(-1);this.generation_config.top_k>0&&(K=Math.min(this.generation_config.top_k,K));const[ne,oe]=await(0,ae.topk)(P,K),_e=(0,he.softmax)(ne.data);return Array.from({length:this.generation_config.num_beams},()=>{const F=this.randomSelect(_e);return[oe.data[F],Math.log(_e[F])]})}}class $ extends xe{async sample(P){let K=P.dims.at(-1);this.generation_config.top_k>0&&(K=Math.min(this.generation_config.top_k,K));const[ne,oe]=await(0,ae.topk)(P,K),_e=(0,he.softmax)(ne.data);return Array.from({length:this.generation_config.num_beams},(F,Q)=>[oe.data[Q],Math.log(_e[Q])])}}},"./src/generation/stopping_criteria.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{EosTokenCriteria:()=>be,InterruptableStoppingCriteria:()=>R,MaxLengthCriteria:()=>xe,StoppingCriteria:()=>ae,StoppingCriteriaList:()=>he});var I=N("./src/utils/generic.js");class ae extends I.Callable{_call(V,P){throw Error("StoppingCriteria needs to be subclassed")}}class he extends I.Callable{constructor(){super(),this.criteria=[]}push(V){this.criteria.push(V)}extend(V){V instanceof he?V=V.criteria:V instanceof ae&&(V=[V]),this.criteria.push(...V)}_call(V,P){const K=new Array(V.length).fill(!1);for(const ne of this.criteria){const oe=ne(V,P);for(let _e=0;_e<K.length;++_e)K[_e]||=oe[_e]}return K}[Symbol.iterator](){return this.criteria.values()}}class xe extends ae{constructor(V,P=null){super(),this.max_length=V,this.max_position_embeddings=P}_call(V){return V.map(P=>P.length>=this.max_length)}}class be extends ae{constructor(V){super(),Array.isArray(V)||(V=[V]),this.eos_token_id=V}_call(V,P){return V.map(K=>{const ne=K.at(-1);return this.eos_token_id.some(oe=>ne==oe)})}}class R extends ae{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(V,P){return new Array(V.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{BaseStreamer:()=>xe,TextStreamer:()=>R,WhisperTextStreamer:()=>$});var I=N("./src/utils/core.js"),ae=N("./src/tokenizers.js"),he=N("./src/env.js");class xe{put(P){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const be=he.apis.IS_PROCESS_AVAILABLE?V=>process.stdout.write(V):V=>console.log(V);class R extends xe{constructor(P,{skip_prompt:K=!1,callback_function:ne=null,token_callback_function:oe=null,decode_kwargs:_e={},...F}={}){super(),this.tokenizer=P,this.skip_prompt=K,this.callback_function=ne??be,this.token_callback_function=oe,this.decode_kwargs={..._e,...F},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(P){if(P.length>1)throw Error("TextStreamer only supports batch size of 1");if(this.skip_prompt&&this.next_tokens_are_prompt){this.next_tokens_are_prompt=!1;return}const K=P[0];this.token_callback_function?.(K),this.token_cache=(0,I.mergeArrays)(this.token_cache,K);const ne=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let oe;ne.endsWith(`
`)?(oe=ne.slice(this.print_len),this.token_cache=[],this.print_len=0):ne.length>0&&(0,ae.is_chinese_char)(ne.charCodeAt(ne.length-1))?(oe=ne.slice(this.print_len),this.print_len+=oe.length):(oe=ne.slice(this.print_len,ne.lastIndexOf(" ")+1),this.print_len+=oe.length),this.on_finalized_text(oe,!1)}end(){let P;this.token_cache.length>0?(P=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):P="",this.next_tokens_are_prompt=!0,this.on_finalized_text(P,!0)}on_finalized_text(P,K){P.length>0&&this.callback_function?.(P),K&&this.callback_function===be&&he.apis.IS_PROCESS_AVAILABLE&&this.callback_function?.(`
`)}}class $ extends R{constructor(P,{skip_prompt:K=!1,callback_function:ne=null,token_callback_function:oe=null,on_chunk_start:_e=null,on_chunk_end:F=null,on_finalize:Q=null,time_precision:se=.02,skip_special_tokens:le=!0,decode_kwargs:ie={}}={}){super(P,{skip_prompt:K,callback_function:ne,token_callback_function:oe,decode_kwargs:{skip_special_tokens:le,...ie}}),this.timestamp_begin=P.timestamp_begin,this.on_chunk_start=_e,this.on_chunk_end=F,this.on_finalize=Q,this.time_precision=se,this.waiting_for_timestamp=!1}put(P){if(P.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const K=P[0];if(K.length===1){const ne=Number(K[0])-this.timestamp_begin;if(ne>=0){const oe=ne*this.time_precision;this.waiting_for_timestamp?this.on_chunk_end?.(oe):this.on_chunk_start?.(oe),this.waiting_for_timestamp=!this.waiting_for_timestamp,P=[[]]}}return 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function Xe(_,h,D=!1){const re=_.sessions[D?"decoder_model_merged":"model"],{past_key_values:Oe,...Ye}=h;re.inputNames.includes("use_cache_branch")&&(Ye.use_cache_branch=Pe(!!Oe)),re.inputNames.includes("position_ids")&&Ye.attention_mask&&!Ye.position_ids&&(Ye.position_ids=ge(Ye,Oe)),_.addPastKeyValues(Ye,Oe);const wt=(0,be.pick)(Ye,re.inputNames);return await me(re,wt)}async function dt(_,{input_ids:h=null,attention_mask:D=null,pixel_values:re=null,position_ids:Oe=null,inputs_embeds:Ye=null,past_key_values:wt=null,generation_config:At=null,logits_processor:Zt=null,...mr}){if(!Ye){if(Ye=await _.encode_text({input_ids:h}),re&&h.dims[1]!==1){const Ir=await _.encode_image({pixel_values:re});({inputs_embeds:Ye,attention_mask:D}=_._merge_input_ids_with_image_features({image_features:Ir,inputs_embeds:Ye,input_ids:h,attention_mask:D}))}else if(wt&&re&&h.dims[1]===1){const Ir=h.dims[1],pr=Object.values(wt)[0].dims.at(-2);D=(0,P.cat)([(0,P.ones)([h.dims[0],pr]),D.slice(null,[D.dims[1]-Ir,D.dims[1]])],1)}}return await Xe(_,{inputs_embeds:Ye,past_key_values:wt,attention_mask:D,position_ids:Oe,generation_config:At,logits_processor:Zt},!0)}function ge(_,h=null){const{input_ids:D,inputs_embeds:re,attention_mask:Oe}=_,[Ye,wt]=Oe.dims,At=new BigInt64Array(Oe.data.length);for(let mr=0;mr<Ye;++mr){const kr=mr*wt;let Ir=BigInt(0);for(let pr=0;pr<wt;++pr){const er=kr+pr;Oe.data[er]===0n?At[er]=BigInt(1):(At[er]=Ir,Ir+=Oe.data[er])}}let Zt=new P.Tensor("int64",At,Oe.dims);if(h){const mr=-(D??re).dims.at(1);Zt=Zt.slice(null,[mr,null])}return Zt}function U(_,h,D,re){if(D.past_key_values){const Oe=Object.values(D.past_key_values)[0].dims.at(-2),{input_ids:Ye,attention_mask:wt}=D;if(!(wt&&wt.dims[1]>Ye.dims[1])){if(Oe<Ye.dims[1])D.input_ids=Ye.slice(null,[Oe,null]);else if(_.config.image_token_index!=null&&Ye.data.some(At=>At==_.config.image_token_index)){const At=_.config.num_image_tokens;if(!At)throw new Error("`num_image_tokens` is missing in the model configuration.");const Zt=Ye.dims[1]-(Oe-At);D.input_ids=Ye.slice(null,[-Zt,null]),D.attention_mask=(0,P.ones)([1,Oe+Zt])}}}return D}function de(_,h,D,re){return D.past_key_values&&(h=h.map(Oe=>[Oe.at(-1)])),{...D,decoder_input_ids:Se(h)}}function $e(_,...h){return _.config.is_encoder_decoder?de(_,...h):U(_,...h)}class X extends xe.Callable{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(h,D){super(),this.config=h,this.sessions=D;const re=j.get(this.constructor),Oe=le.get(re);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Oe){case se.DecoderOnly:this.can_generate=!0,this._forward=Xe,this._prepare_inputs_for_generation=U;break;case se.Seq2Seq:case se.Vision2Seq:case se.Musicgen:this.can_generate=!0,this._forward=Ce,this._prepare_inputs_for_generation=de;break;case se.EncoderDecoder:this._forward=Ce;break;case se.ImageTextToText:this.can_generate=!0,this._forward=dt,this._prepare_inputs_for_generation=$e;break;default:this._forward=rt;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const h=[];for(const D of Object.values(this.sessions))D?.handler?.dispose&&h.push(D.handler.dispose());return await Promise.all(h)}static async from_pretrained(h,{progress_callback:D=null,config:re=null,cache_dir:Oe=null,local_files_only:Ye=!1,revision:wt="main",model_file_name:At=null,subfolder:Zt="onnx",device:mr=null,dtype:kr=null,use_external_data_format:Ir=null,session_options:pr={}}={}){let er={progress_callback:D,config:re,cache_dir:Oe,local_files_only:Ye,revision:wt,model_file_name:At,subfolder:Zt,device:mr,dtype:kr,use_external_data_format:Ir,session_options:pr};const Dr=j.get(this),ir=le.get(Dr);re=er.config=await I.AutoConfig.from_pretrained(h,er);let Mr;if(ir===se.DecoderOnly)Mr=await Promise.all([L(h,{model:er.model_file_name??"model"},er),(0,R.getModelJSON)(h,"generation_config.json",!1,er)]);else if(ir===se.Seq2Seq||ir===se.Vision2Seq)Mr=await Promise.all([L(h,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},er),(0,R.getModelJSON)(h,"generation_config.json",!1,er)]);else if(ir===se.MaskGeneration)Mr=await Promise.all([L(h,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},er)]);else if(ir===se.EncoderDecoder)Mr=await Promise.all([L(h,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},er)]);else if(ir===se.ImageTextToText){const qr={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};re.is_encoder_decoder&&(qr.model="encoder_model"),Mr=await Promise.all([L(h,qr,er),(0,R.getModelJSON)(h,"generation_config.json",!1,er)])}else ir===se.Musicgen?Mr=await Promise.all([L(h,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},er),(0,R.getModelJSON)(h,"generation_config.json",!1,er)]):(ir!==se.EncoderOnly&&console.warn(`Model type for '${Dr??re?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),Mr=await Promise.all([L(h,{model:er.model_file_name??"model"},er)]));return new this(re,...Mr)}async _call(h){return await this.forward(h)}async forward(h){return await this._forward(this,h)}_get_logits_warper(h){const D=new $.LogitsProcessorList;return h.temperature!==null&&h.temperature!==1&&D.push(new $.TemperatureLogitsWarper(h.temperature)),h.top_k!==null&&h.top_k!==0&&D.push(new $.TopKLogitsWarper(h.top_k)),h.top_p!==null&&h.top_p<1&&D.push(new $.TopPLogitsWarper(h.top_p)),D}_get_logits_processor(h,D,re=null){const Oe=new $.LogitsProcessorList;if(h.repetition_penalty!==null&&h.repetition_penalty!==1&&Oe.push(new $.RepetitionPenaltyLogitsProcessor(h.repetition_penalty)),h.no_repeat_ngram_size!==null&&h.no_repeat_ngram_size>0&&Oe.push(new $.NoRepeatNGramLogitsProcessor(h.no_repeat_ngram_size)),h.bad_words_ids!==null&&Oe.push(new $.NoBadWordsLogitsProcessor(h.bad_words_ids,h.eos_token_id)),h.min_length!==null&&h.eos_token_id!==null&&h.min_length>0&&Oe.push(new $.MinLengthLogitsProcessor(h.min_length,h.eos_token_id)),h.min_new_tokens!==null&&h.eos_token_id!==null&&h.min_new_tokens>0&&Oe.push(new $.MinNewTokensLengthLogitsProcessor(D,h.min_new_tokens,h.eos_token_id)),h.forced_bos_token_id!==null&&Oe.push(new $.ForcedBOSTokenLogitsProcessor(h.forced_bos_token_id)),h.forced_eos_token_id!==null&&Oe.push(new $.ForcedEOSTokenLogitsProcessor(h.max_length,h.forced_eos_token_id)),h.begin_suppress_tokens!==null){const Ye=D>1||h.forced_bos_token_id===null?D:D+1;Oe.push(new $.SuppressTokensAtBeginLogitsProcessor(h.begin_suppress_tokens,Ye))}return h.guidance_scale!==null&&h.guidance_scale>1&&Oe.push(new $.ClassifierFreeGuidanceLogitsProcessor(h.guidance_scale)),re!==null&&Oe.extend(re),Oe}_prepare_generation_config(h,D,re=V.GenerationConfig){const Oe={...this.config};for(const wt of["decoder","generator","text_config"])wt in Oe&&Object.assign(Oe,Oe[wt]);const Ye=new re(Oe);return"generation_config"in this&&Object.assign(Ye,this.generation_config),h&&Object.assign(Ye,h),D&&Object.assign(Ye,(0,be.pick)(D,Object.getOwnPropertyNames(Ye))),Ye}_get_stopping_criteria(h,D=null){const re=new ne.StoppingCriteriaList;return h.max_length!==null&&re.push(new ne.MaxLengthCriteria(h.max_length,this.config.max_position_embeddings??null)),h.eos_token_id!==null&&re.push(new ne.EosTokenCriteria(h.eos_token_id)),D&&re.extend(D),re}_validate_model_class(){if(!this.can_generate){const h=[Ta,Sa,xa,Zl],D=j.get(this.constructor),re=new Set,Oe=this.config.model_type;for(const wt of h){const At=wt.get(Oe);At&&re.add(At[0])}let Ye=`The current model class (${D}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw re.size>0&&(Ye+=` Please use the following class instead: ${[...re].join(", ")}`),Error(Ye)}}prepare_inputs_for_generation(...h){return this._prepare_inputs_for_generation(this,...h)}_update_model_kwargs_for_generation({generated_input_ids:h,outputs:D,model_inputs:re,is_encoder_decoder:Oe}){return re.past_key_values=this.getPastKeyValues(D,re.past_key_values),re.input_ids=new P.Tensor("int64",h.flat(),[h.length,1]),Oe||(re.attention_mask=(0,P.cat)([re.attention_mask,(0,P.ones)([re.attention_mask.dims[0],1])],1)),re.position_ids=null,re}_prepare_model_inputs({inputs:h,bos_token_id:D,model_kwargs:re}){const Oe=(0,be.pick)(re,this.forward_params),Ye=this.main_input_name;if(Ye in Oe){if(h)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Oe[Ye]=h;return{inputs_tensor:Oe[Ye],model_inputs:Oe,model_input_name:Ye}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:h,model_inputs:D,model_input_name:re,generation_config:Oe}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!D.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:wt,pixel_values:At,attention_mask:Zt,...mr}=D,kr=await this._prepare_inputs_embeds(D);D={...mr,...(0,be.pick)(kr,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Ye}=await rt(this,D);if(Oe.guidance_scale!==null&&Oe.guidance_scale>1)Ye=(0,P.cat)([Ye,(0,P.full_like)(Ye,0)],0),"attention_mask"in D&&(D.attention_mask=(0,P.cat)([D.attention_mask,(0,P.zeros_like)(D.attention_mask)],0));else if(D.decoder_input_ids){const wt=Se(D.decoder_input_ids).dims[0];if(wt!==Ye.dims[0]){if(Ye.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Ye.dims[0]}) than the decoder inputs (${wt}).`);Ye=(0,P.cat)(Array.from({length:wt},()=>Ye),0)}}return D.encoder_outputs=Ye,D}_prepare_decoder_input_ids_for_generation({batch_size:h,model_input_name:D,model_kwargs:re,decoder_start_token_id:Oe,bos_token_id:Ye,generation_config:wt}){let{decoder_input_ids:At,...Zt}=re;if(At)Array.isArray(At[0])||(At=Array.from({length:h},()=>At));else if(Oe??=Ye,this.config.model_type==="musicgen")At=Array.from({length:h*this.config.decoder.num_codebooks},()=>[Oe]);else if(Array.isArray(Oe)){if(Oe.length!==h)throw new Error(`\`decoder_start_token_id\` expcted to have length ${h} but got ${Oe.length}`);At=Oe}else At=Array.from({length:h},()=>[Oe]);return At=Se(At),re.decoder_attention_mask=(0,P.ones_like)(At),{input_ids:At,model_inputs:Zt}}async generate({inputs:h=null,generation_config:D=null,logits_processor:re=null,stopping_criteria:Oe=null,streamer:Ye=null,...wt}){this._validate_model_class(),D=this._prepare_generation_config(D,wt);let{inputs_tensor:At,model_inputs:Zt,model_input_name:mr}=this._prepare_model_inputs({inputs:h,model_kwargs:wt});const kr=this.config.is_encoder_decoder;kr&&("encoder_outputs"in Zt||(Zt=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:At,model_inputs:Zt,model_input_name:mr,generation_config:D})));let Ir;kr?{input_ids:Ir,model_inputs:Zt}=this._prepare_decoder_input_ids_for_generation({batch_size:Zt[mr].dims.at(0),model_input_name:mr,model_kwargs:Zt,decoder_start_token_id:D.decoder_start_token_id,bos_token_id:D.bos_token_id,generation_config:D}):Ir=Zt[mr];let pr=Ir.dims.at(-1);D.max_new_tokens!==null&&(D.max_length=pr+D.max_new_tokens);const er=this._get_logits_processor(D,pr,re),Dr=this._get_stopping_criteria(D,Oe),ir=Zt[mr].dims.at(0),Mr=oe.LogitsSampler.getSampler(D),qr=new Array(ir).fill(0),tn=Ir.tolist();Ye&&Ye.put(tn);let Hn=null,Sn={};for(;;){Zt=this.prepare_inputs_for_generation(tn,Zt,D);const Qr=await this.forward(Zt);if(D.output_attentions&&D.return_dict_in_generate){const Bn=this.getAttentions(Qr);for(const ms in Bn)ms in Sn||(Sn[ms]=[]),Sn[ms].push(Bn[ms])}const _n=Qr.logits.slice(null,-1,null),di=er(tn,_n),Os=[];for(let Bn=0;Bn<di.dims.at(0);++Bn){const ms=di[Bn],bd=await Mr(ms);for(const[Md,Qd]of bd){const bu=BigInt(Md);qr[Bn]+=Qd,tn[Bn].push(bu),Os.push([bu]);break}}if(Ye&&Ye.put(Os),Dr(tn).every(Bn=>Bn)){D.return_dict_in_generate&&(Hn=this.getPastKeyValues(Qr,Zt.past_key_values,!1));break}Zt=this._update_model_kwargs_for_generation({generated_input_ids:Os,outputs:Qr,model_inputs:Zt,is_encoder_decoder:kr})}Ye&&Ye.end();const mn=new P.Tensor("int64",tn.flat(),[tn.length,tn[0].length]);return D.return_dict_in_generate?{sequences:mn,past_key_values:Hn,...Sn}:mn}getPastKeyValues(h,D,re=!0){const Oe=Object.create(null);for(const Ye in h)if(Ye.startsWith("present")){const wt=Ye.replace("present","past_key_values");if(D&&Ye.includes("encoder"))Oe[wt]=D[wt];else{if(re&&D){const At=D[wt];At.location==="gpu-buffer"&&At.dispose()}Oe[wt]=h[Ye]}}return Oe}getAttentions(h){const D={};for(const re of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Oe in h)Oe.startsWith(re)&&(re in D||(D[re]=[]),D[re].push(h[Oe]));return D}addPastKeyValues(h,D){if(D)Object.assign(h,D);else{const re=this.custom_config.kv_cache_dtype??"float32",Oe=re==="float16"?new Uint16Array:[],Ye=(0,I.getKeyValueShapes)(this.config);for(const wt in Ye)h[wt]=new P.Tensor(re,Oe,Ye[wt])}}async encode_image({pixel_values:h}){const D=(await me(this.sessions.vision_encoder,{pixel_values:h})).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 (${D.dims[1]}).`),this.config.num_image_tokens=D.dims[1]),D}async encode_text({input_ids:h}){return(await me(this.sessions.embed_tokens,{input_ids:h})).inputs_embeds}}class Ue{}class ct extends Ue{constructor({last_hidden_state:h,hidden_states:D=null,attentions:re=null}){super(),this.last_hidden_state=h,this.hidden_states=D,this.attentions=re}}class st extends X{}class ot extends st{}class De extends st{async _call(h){return new rn(await super._call(h))}}class at extends st{async _call(h){return new lr(await super._call(h))}}class Et extends st{async _call(h){return new en(await super._call(h))}}class Fe extends st{async _call(h){return new on(await super._call(h))}}class H extends X{}class Ee extends H{}class qe extends X{}class je extends qe{}class Ve extends qe{async _call(h){return new rn(await super._call(h))}}class Ge extends qe{async _call(h){return new lr(await super._call(h))}}class ut extends qe{async _call(h){return new en(await super._call(h))}}class ht extends qe{async _call(h){return new on(await super._call(h))}}class Tt extends X{}class St extends Tt{}class M extends Tt{async _call(h){return new rn(await super._call(h))}}class W extends Tt{async _call(h){return new lr(await super._call(h))}}class E extends Tt{async _call(h){return new en(await super._call(h))}}class te extends Tt{async _call(h){return new on(await super._call(h))}}class pe extends X{}class Je extends pe{}class He extends pe{async _call(h){return new rn(await super._call(h))}}class Mt extends pe{async _call(h){return new lr(await super._call(h))}}class nt extends pe{async _call(h){return new en(await super._call(h))}}class pt extends pe{async _call(h){return new on(await super._call(h))}}class Bt extends X{}class et extends Bt{}class B extends Bt{async _call(h){return new rn(await super._call(h))}}class ce extends Bt{async _call(h){return new lr(await super._call(h))}}class Te extends Bt{async _call(h){return new en(await super._call(h))}}class Re extends Bt{async _call(h){return new on(await super._call(h))}}class ke extends X{}class tt extends ke{}class ft extends ke{async _call(h){return new rn(await super._call(h))}}class lt extends ke{async _call(h){return new lr(await super._call(h))}}class Ct extends ke{async _call(h){return new en(await super._call(h))}}class gt extends ke{async _call(h){return new on(await super._call(h))}}class ze extends X{}class Rt extends ze{}class Ot extends ze{async _call(h){return new rn(await super._call(h))}}class Wt extends ze{async _call(h){return new lr(await super._call(h))}}class zt extends ze{async _call(h){return new en(await super._call(h))}}class Ut extends ze{async _call(h){return new on(await super._call(h))}}class Ht extends X{}class ur extends Ht{}class rr extends Ht{async _call(h){return new lr(await super._call(h))}}class wr extends Ht{async _call(h){return new en(await super._call(h))}}class Qe extends Ht{async _call(h){return new on(await super._call(h))}}class yt extends Ht{async _call(h){return new rn(await super._call(h))}}class Ft extends X{}class Zr extends Ft{}class Xn extends Ft{async _call(h){return new rn(await super._call(h))}}class is extends Ft{async _call(h){return new lr(await super._call(h))}}class Hr extends Ft{async _call(h){return new en(await super._call(h))}}class pn extends X{}class Xr extends pn{}class Qn extends pn{async _call(h){return new rn(await super._call(h))}}class Lr extends pn{async _call(h){return new lr(await super._call(h))}}class Vn extends pn{async _call(h){return new on(await super._call(h))}}class In extends X{}class Us extends In{}class ws extends In{async _call(h){return new rn(await super._call(h))}}class ys extends In{async _call(h){return new lr(await super._call(h))}}class bs extends In{async _call(h){return new en(await super._call(h))}}class Ms extends In{async _call(h){return new on(await super._call(h))}}class Un extends X{}class Ws extends Un{}class as extends Un{async _call(h){return new rn(await super._call(h))}}class Cn extends Un{async _call(h){return new lr(await super._call(h))}}class Wn extends Un{async _call(h){return new on(await super._call(h))}}class Fn extends X{}class Yn extends Fn{}class os extends Fn{async _call(h){return new lr(await super._call(h))}}class ls extends Fn{async _call(h){return new on(await super._call(h))}}class Kt extends Fn{async _call(h){return new rn(await super._call(h))}}class Zn extends X{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(h,D,re){super(h,D),this.generation_config=re}}class vs extends Zn{}class xs extends Zn{}class us extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Ts extends us{}class Ss extends us{}class ds extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class $s extends ds{}class Nr extends ds{}class wn extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class we extends wn{}class m extends wn{}class z extends wn{async _call(h){return new lr(await super._call(h))}}class Y extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class J extends Y{}class ve extends Y{}class Be extends Y{async _call(h){return new lr(await super._call(h))}}class xt extends Y{}class vt extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class kt extends vt{}class bt extends vt{}class fr extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class or extends fr{}class yr extends fr{}class qt extends X{}class Cr extends qt{}class yn extends qt{async _call(h){return new rn(await super._call(h))}}class Rr extends qt{async _call(h){return new lr(await super._call(h))}}class Ie extends qt{async _call(h){return new en(await super._call(h))}}class On extends qt{async _call(h){return new on(await super._call(h))}}class xr extends X{}class Jr extends xr{}class dn extends xr{async _call(h){return new rn(await super._call(h))}}class Xt extends xr{async _call(h){return new lr(await super._call(h))}}class hn extends xr{async _call(h){return new en(await super._call(h))}}class sn extends xr{async _call(h){return new on(await super._call(h))}}class Er extends X{}class Tr extends Er{}class Pt extends Er{async _call(h){return new rn(await super._call(h))}}class Sr extends Er{async _call(h){return new lr(await super._call(h))}}class Fr extends Er{async _call(h){return new en(await super._call(h))}}class jr extends Er{async _call(h){return new on(await super._call(h))}}class fn extends X{}class Nt extends fn{}class Gs extends fn{}class Ze extends X{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(h,D,re){super(h,D),this.generation_config=re}}class Vt extends Ze{}class wi extends Ze{_prepare_generation_config(h,D){return super._prepare_generation_config(h,D,F.WhisperGenerationConfig)}_retrieve_init_tokens(h){const D=[h.decoder_start_token_id];let re=h.language;const Oe=h.task;if(h.is_multilingual){re||(console.warn("No language specified - defaulting to English (en)."),re="en");const wt=`<|${(0,Q.whisper_language_to_code)(re)}|>`;D.push(h.lang_to_id[wt]),D.push(h.task_to_id[Oe??"transcribe"])}else if(re||Oe)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!h.return_timestamps&&h.no_timestamps_token_id&&D.at(-1)!==h.no_timestamps_token_id?D.push(h.no_timestamps_token_id):h.return_timestamps&&D.at(-1)===h.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(Ye=>Ye!=null)}async generate({inputs:h=null,generation_config:D=null,logits_processor:re=null,stopping_criteria:Oe=null,...Ye}){D=this._prepare_generation_config(D,Ye);const wt=Ye.decoder_input_ids??this._retrieve_init_tokens(D);if(D.return_timestamps&&(re??=new $.LogitsProcessorList,re.push(new $.WhisperTimeStampLogitsProcessor(D,wt))),D.begin_suppress_tokens&&(re??=new $.LogitsProcessorList,re.push(new $.SuppressTokensAtBeginLogitsProcessor(D.begin_suppress_tokens,wt.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 At=await super.generate({inputs:h,generation_config:D,logits_processor:re,decoder_input_ids:wt,...Ye});return D.return_token_timestamps&&(At.token_timestamps=this._extract_token_timestamps(At,D.alignment_heads,D.num_frames)),At}_extract_token_timestamps(h,D,re=null,Oe=.02){if(!h.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`.");re==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 Ye=this.config.median_filter_width;Ye===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Ye=7);const wt=h.cross_attentions,At=Array.from({length:this.config.decoder_layers},(ir,Mr)=>(0,P.cat)(wt.map(qr=>qr[Mr]),2)),Zt=(0,P.stack)(D.map(([ir,Mr])=>{if(ir>=At.length)throw new Error(`Layer index ${ir} is out of bounds for cross attentions (length ${At.length}).`);return re?At[ir].slice(null,Mr,null,[0,re]):At[ir].slice(null,Mr)})).transpose(1,0,2,3),[mr,kr]=(0,P.std_mean)(Zt,-2,0,!0),Ir=Zt.clone();for(let ir=0;ir<Ir.dims[0];++ir){const Mr=Ir[ir];for(let qr=0;qr<Mr.dims[0];++qr){const tn=Mr[qr],Hn=mr[ir][qr][0].data,Sn=kr[ir][qr][0].data;for(let mn=0;mn<tn.dims[0];++mn){let Qr=tn[mn].data;for(let _n=0;_n<Qr.length;++_n)Qr[_n]=(Qr[_n]-Sn[_n])/Hn[_n];Qr.set((0,K.medianFilter)(Qr,Ye))}}}const pr=[(0,P.mean)(Ir,1)],er=h.sequences.dims,Dr=new P.Tensor("float32",new Float32Array(er[0]*er[1]),er);for(let ir=0;ir<er[0];++ir){const Mr=pr[ir].neg().squeeze_(0),[qr,tn]=(0,K.dynamic_time_warping)(Mr.tolist()),Hn=Array.from({length:qr.length-1},(Qr,_n)=>qr[_n+1]-qr[_n]),Sn=(0,be.mergeArrays)([1],Hn).map(Qr=>!!Qr),mn=[];for(let Qr=0;Qr<Sn.length;++Qr)Sn[Qr]&&mn.push(tn[Qr]*Oe);Dr[ir].data.set(mn,1)}return Dr}}class yi extends X{main_input_name="pixel_values";forward_params=["pixel_values","input_ids","encoder_hidden_states","past_key_values"];constructor(h,D,re){super(h,D),this.generation_config=re}}class Fa extends X{forward_params=["input_ids","pixel_values","attention_mask","position_ids","past_key_values"];constructor(h,D,re){super(h,D),this.generation_config=re}}class cs extends Fa{_merge_input_ids_with_image_features({inputs_embeds:h,image_features:D,input_ids:re,attention_mask:Oe}){const Ye=this.config.image_token_index,At=re.tolist().map(pr=>pr.findIndex(er=>er==Ye)),Zt=At.every(pr=>pr===-1),mr=At.every(pr=>pr!==-1);if(!Zt&&!mr)throw new Error("Every input should contain either 0 or 1 image token.");if(Zt)return{inputs_embeds:h,attention_mask:Oe};const kr=[],Ir=[];for(let pr=0;pr<At.length;++pr){const er=At[pr],Dr=h[pr],ir=D[pr],Mr=Oe[pr];kr.push((0,P.cat)([Dr.slice([0,er]),ir,Dr.slice([er+1,Dr.dims[0]])],0)),Ir.push((0,P.cat)([Mr.slice([0,er]),(0,P.ones)([ir.dims[0]]),Mr.slice([er+1,Mr.dims[0]])],0))}return{inputs_embeds:(0,P.stack)(kr,0),attention_mask:(0,P.stack)(Ir,0)}}}class sr extends cs{}class Oa extends X{forward_params=["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"];main_input_name="inputs_embeds";constructor(h,D,re){super(h,D),this.generation_config=re}}class bi extends Oa{_merge_input_ids_with_image_features({inputs_embeds:h,image_features:D,input_ids:re,attention_mask:Oe}){return{inputs_embeds:(0,P.cat)([D,h],1),attention_mask:(0,P.cat)([(0,P.ones)(D.dims.slice(0,2)),Oe],1)}}async _prepare_inputs_embeds({input_ids:h,pixel_values:D,inputs_embeds:re,attention_mask:Oe}){if(!h&&!D)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let Ye,wt;return h&&(Ye=await this.encode_text({input_ids:h})),D&&(wt=await this.encode_image({pixel_values:D})),Ye&&wt?{inputs_embeds:re,attention_mask:Oe}=this._merge_input_ids_with_image_features({inputs_embeds:Ye,image_features:wt,input_ids:h,attention_mask:Oe}):re=Ye||wt,{inputs_embeds:re,attention_mask:Oe}}async forward({input_ids:h,pixel_values:D,attention_mask:re,decoder_input_ids:Oe,decoder_attention_mask:Ye,encoder_outputs:wt,past_key_values:At,inputs_embeds:Zt,decoder_inputs_embeds:mr}){if(Zt||({inputs_embeds:Zt,attention_mask:re}=await this._prepare_inputs_embeds({input_ids:h,pixel_values:D,inputs_embeds:Zt,attention_mask:re})),!wt){let{last_hidden_state:pr}=await rt(this,{inputs_embeds:Zt,attention_mask:re});wt=pr}if(!mr){if(!Oe)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");mr=await this.encode_text({input_ids:Oe})}return await Xe(this,{inputs_embeds:mr,attention_mask:Ye,encoder_attention_mask:re,encoder_hidden_states:wt,past_key_values:At},!0)}}class Es extends X{}class za extends Es{}class kn extends Es{static async from_pretrained(h,D={}){return D.model_file_name??="text_model",super.from_pretrained(h,D)}}class Da extends Es{static async from_pretrained(h,D={}){return D.model_file_name??="vision_model",super.from_pretrained(h,D)}}class Mi extends X{}class ps extends Mi{}class Ba extends Mi{static async from_pretrained(h,D={}){return D.model_file_name??="text_model",super.from_pretrained(h,D)}}class La extends Es{static async from_pretrained(h,D={}){return D.model_file_name??="vision_model",super.from_pretrained(h,D)}}class Ra extends X{}class Na extends Ra{}class vi extends X{}class ja extends vi{}class Va extends vi{}class xi extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Ua extends xi{}class Wa extends xi{}class bn extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Ga extends bn{}class qa extends bn{}class Ti extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Ha extends Ti{}class Ka extends Ti{}class Si extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Xa extends Si{}class Qa extends Si{}class $i extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Ya extends $i{}class Cu extends $i{}class Mn extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Za extends Mn{}class qs extends Mn{}class Hs extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class xn extends Hs{}class Ja extends Hs{}class Ei extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class eo extends Ei{}class to extends Ei{}class Ci extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class ro extends Ci{}class no extends Ci{}class ki extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class so extends ki{}class io extends ki{}class vn extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class ao extends vn{}class oo extends vn{}class Pi extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class lo extends Pi{}class uo extends Pi{}class Ai extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class co extends Ai{}class po extends Ai{}class Ii extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class ho extends Ii{}class Fi extends Ii{}class Ks extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class fo extends Ks{}class mo extends Ks{}class Xs extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class ku extends Xs{}class _o extends Xs{}class Oi extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class go extends Oi{}class wo extends Oi{}class hs extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class yo extends hs{}class bo extends hs{}class Qs extends X{}class Mo extends Qs{}class vo extends Qs{async _call(h){return new lr(await super._call(h))}}class zi extends X{}class xo extends zi{}class Pu extends zi{async _call(h){return new lr(await super._call(h))}}class To extends X{}class So extends To{async _call(h){return new wu(await super._call(h))}}class Di extends X{}class Au extends Di{}class $o extends Di{async _call(h){return new lr(await super._call(h))}}class br extends X{}class Eo extends br{}class Co extends br{async _call(h){return new lr(await super._call(h))}}class Bi extends X{}class ko extends Bi{}class Po extends Bi{}class Li extends X{}class Ao extends Li{}class Io extends Li{}class Ri extends X{}class Fo extends Ri{}class Oo extends Ri{async _call(h){return new lr(await super._call(h))}}class Ys extends X{}class zo extends Ys{}class Do extends Ys{async _call(h){return new Ni(await super._call(h))}}class Cs extends Ys{async _call(h){return new Zs(await super._call(h))}}class Ni extends Ue{constructor({logits:h,pred_boxes:D}){super(),this.logits=h,this.pred_boxes=D}}class Zs extends Ue{constructor({logits:h,pred_boxes:D,pred_masks:re}){super(),this.logits=h,this.pred_boxes=D,this.pred_masks=re}}class ji extends X{}class Bo extends ji{}class Lo extends ji{async _call(h){return new Ro(await super._call(h))}}class Ro extends Ue{constructor({logits:h,pred_boxes:D}){super(),this.logits=h,this.pred_boxes=D}}class Vi extends X{}class No extends Vi{}class jo extends Vi{async _call(h){return new Vo(await super._call(h))}}class Vo extends Ni{}class Ui extends X{}class Uo extends Ui{}class Wo extends Ui{async _call(h){return new lr(await super._call(h))}}class Wi extends X{}class Go extends Wi{}class qo extends Wi{async _call(h){return new lr(await super._call(h))}}class Gi extends X{}class Ho extends Gi{}class qi extends Gi{async _call(h){return new lr(await super._call(h))}}class Hi extends X{}class Ki extends Hi{}class Xi extends Hi{async _call(h){return new lr(await super._call(h))}}class Qi extends X{}class Ko extends Qi{}class Xo extends Qi{}class Yi extends X{}class Qo extends Yi{}class Yo extends Yi{}class Zi extends X{}class Zo extends Zi{}class Js extends X{}class Jo extends Js{}class Iu extends Js{}class el extends Js{}class Ji extends X{}class Tn extends Ji{}class tl extends Ji{}class rl extends X{}class nl extends rl{}class ea extends X{}class sl extends ea{}class il extends ea{async _call(h){return new lr(await super._call(h))}}class ta extends X{}class al extends ta{}class ol extends ta{async _call(h){return new lr(await super._call(h))}}class ra extends X{}class Fu extends ra{}class ll extends ra{async _call(h){return new lr(await super._call(h))}}class na extends X{}class ul extends na{}class dl extends na{async _call(h){return new cl(await super._call(h))}}class cl extends Ue{constructor({logits:h,pred_boxes:D}){super(),this.logits=h,this.pred_boxes=D}}class pl extends X{}class Ou extends pl{async get_image_embeddings({pixel_values:h}){return await rt(this,{pixel_values:h})}async forward(h){if((!h.image_embeddings||!h.image_positional_embeddings)&&(h={...h,...await this.get_image_embeddings(h)}),!h.input_labels&&h.input_points){const re=h.input_points.dims.slice(0,-1),Oe=re.reduce((Ye,wt)=>Ye*wt,1);h.input_labels=new P.Tensor("int64",new BigInt64Array(Oe).fill(1n),re)}const D={image_embeddings:h.image_embeddings,image_positional_embeddings:h.image_positional_embeddings};return h.input_points&&(D.input_points=h.input_points),h.input_labels&&(D.input_labels=h.input_labels),h.input_boxes&&(D.input_boxes=h.input_boxes),await me(this.sessions.prompt_encoder_mask_decoder,D)}async _call(h){return new Gn(await super._call(h))}}class Gn extends Ue{constructor({iou_scores:h,pred_masks:D}){super(),this.iou_scores=h,this.pred_masks=D}}class zn extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class qn extends zn{}class sa extends zn{}class Dn extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class an extends Dn{}class ia extends Dn{}class Pn extends X{}class aa extends Pn{}class hl extends Pn{async _call(h){return new es(await super._call(h))}}class fl extends Pn{async _call(h){return new lr(await super._call(h))}}class ml extends Pn{async _call(h){return new en(await super._call(h))}}class ks extends X{}class oa extends ks{}class _l extends ks{async _call(h){return new en(await super._call(h))}}class ei extends X{}class gl extends ei{}class Ps extends X{}class ti extends Ps{}class wl extends Ps{async _call(h){return new es(await super._call(h))}}class yl extends Ps{async _call(h){return new lr(await super._call(h))}}class ri extends X{}class bl extends ri{}class la extends ri{async _call(h){return new es(await super._call(h))}}class As extends ri{async _call(h){return new lr(await super._call(h))}}class Ml extends ri{async _call(h){return new en(await super._call(h))}}class Is extends X{}class vl extends Is{}class xl extends Is{async _call(h){return new es(await super._call(h))}}class Tl extends Is{async _call(h){return new lr(await super._call(h))}}class Wd extends X{}class Sl extends Pn{}class $l extends Pn{async _call(h){return new es(await super._call(h))}}class zu extends Pn{async _call(h){return new lr(await super._call(h))}}class Jn extends X{}class El extends Jn{}class Cl extends Jn{async _call(h){return new es(await super._call(h))}}class kl extends Jn{async _call(h){return new lr(await super._call(h))}}class ni extends Jn{async _call(h){return new wd(await super._call(h))}}class si extends Jn{async _call(h){return new en(await super._call(h))}}class ii extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class ua extends ii{}class da extends ii{}class ca extends ii{async generate_speech(h,D,{threshold:re=.5,minlenratio:Oe=0,maxlenratio:Ye=20,vocoder:wt=null}={}){const At={input_ids:h},{encoder_outputs:Zt,encoder_attention_mask:mr}=await rt(this,At),kr=Zt.dims[1]/this.config.reduction_factor,Ir=Math.floor(kr*Ye),pr=Math.floor(kr*Oe),er=this.config.num_mel_bins;let Dr=[],ir=null,Mr=null,qr=0;for(;;){++qr;const Sn=Pe(!!Mr);let mn;Mr?mn=Mr.output_sequence_out:mn=new P.Tensor("float32",new Float32Array(er),[1,1,er]);let Qr={use_cache_branch:Sn,output_sequence:mn,encoder_attention_mask:mr,speaker_embeddings:D,encoder_hidden_states:Zt};this.addPastKeyValues(Qr,ir),Mr=await me(this.sessions.decoder_model_merged,Qr),ir=this.getPastKeyValues(Mr,ir);const{prob:_n,spectrum:di}=Mr;if(Dr.push(di),qr>=pr&&(Array.from(_n.data).filter(Os=>Os>=re).length>0||qr>=Ir))break}const tn=(0,P.cat)(Dr),{waveform:Hn}=await me(wt.sessions.model,{spectrogram:tn});return{spectrogram:tn,waveform:Hn}}}class Pl extends X{main_input_name="spectrogram"}class Al extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class pa extends Al{}class ha extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Il extends ha{}class Fl extends ha{}class Ol extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class zl extends Ol{}class fa extends Ol{}class Dl extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Bl extends Dl{}class Ll extends Dl{}class ai extends X{}class ma extends ai{}class Rl extends ai{static async from_pretrained(h,D={}){return D.model_file_name??="text_model",super.from_pretrained(h,D)}}class Nl extends ai{static async from_pretrained(h,D={}){return D.model_file_name??="audio_model",super.from_pretrained(h,D)}}class jl extends X{}class _a extends jl{async _call(h){return new yu(await super._call(h))}}class oi extends X{}class Du extends oi{}class Bu extends oi{}class Vl extends oi{}class ga extends X{constructor(h,D,re){super(h,D),this.generation_config=re}}class Ul extends ga{}class Lu extends ga{}class wa extends X{}class Wl extends wa{}class Gl extends wa{async _call(h){return new lr(await super._call(h))}}class ya extends X{}class Ru extends ya{}class Gd extends ya{}class Fs extends X{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(h,D,re){super(h,D),this.generation_config=re}_apply_and_filter_by_delay_pattern_mask(h){const[D,re]=h.dims,Oe=this.config.decoder.num_codebooks,Ye=re-Oe;let wt=0;for(let mr=0;mr<h.size;++mr){if(h.data[mr]===this.config.decoder.pad_token_id)continue;const kr=mr%re,Ir=Math.floor(mr/re)%Oe,pr=kr-Ir;pr>0&&pr<=Ye&&(h.data[wt++]=h.data[mr])}const At=Math.floor(D/Oe),Zt=wt/(At*Oe);return new P.Tensor(h.type,h.data.slice(0,wt),[At,Oe,Zt])}prepare_inputs_for_generation(h,D,re){let Oe=structuredClone(h);for(let wt=0;wt<Oe.length;++wt)for(let At=0;At<Oe[wt].length;++At)wt%this.config.decoder.num_codebooks>=At&&(Oe[wt][At]=BigInt(this.config.decoder.pad_token_id));return re.guidance_scale!==null&&re.guidance_scale>1&&(Oe=Oe.concat(Oe)),super.prepare_inputs_for_generation(Oe,D,re)}async generate(h){const D=await super.generate(h),re=this._apply_and_filter_by_delay_pattern_mask(D).unsqueeze_(0),{audio_values:Oe}=await me(this.sessions.encodec_decode,{audio_codes:re});return Oe}}class fs extends X{}class ba extends fs{}class ql extends fs{async _call(h){return new lr(await super._call(h))}}class Ma extends X{}class Hl extends Ma{}class Kl extends Ma{async _call(h){return new lr(await super._call(h))}}class li extends X{}class Xl extends li{}class Ql extends li{async _call(h){return new lr(await super._call(h))}}class va extends X{}class Nu extends va{}class Yl extends va{async _call(h){return new lr(await super._call(h))}}class Or{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(h,{progress_callback:D=null,config:re=null,cache_dir:Oe=null,local_files_only:Ye=!1,revision:wt="main",model_file_name:At=null,subfolder:Zt="onnx",device:mr=null,dtype:kr=null,use_external_data_format:Ir=null,session_options:pr={}}={}){let er={progress_callback:D,config:re,cache_dir:Oe,local_files_only:Ye,revision:wt,model_file_name:At,subfolder:Zt,device:mr,dtype:kr,use_external_data_format:Ir,session_options:pr};if(er.config=await I.AutoConfig.from_pretrained(h,er),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let Dr of this.MODEL_CLASS_MAPPINGS){const ir=Dr.get(er.config.model_type);if(ir)return await ir[1].from_pretrained(h,er)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${er.config.model_type}", attempting to construct from base class.`),await X.from_pretrained(h,er);throw Error(`Unsupported model type: ${er.config.model_type}`)}}const ju=new Map([["bert",["BertModel",ot]],["nomic_bert",["NomicBertModel",Ee]],["roformer",["RoFormerModel",je]],["electra",["ElectraModel",Je]],["esm",["EsmModel",Zr]],["convbert",["ConvBertModel",St]],["camembert",["CamembertModel",et]],["deberta",["DebertaModel",tt]],["deberta-v2",["DebertaV2Model",Rt]],["mpnet",["MPNetModel",Us]],["albert",["AlbertModel",Yn]],["distilbert",["DistilBertModel",ur]],["roberta",["RobertaModel",Cr]],["xlm",["XLMModel",Jr]],["xlm-roberta",["XLMRobertaModel",Tr]],["clap",["ClapModel",ma]],["clip",["CLIPModel",za]],["clipseg",["CLIPSegModel",ja]],["chinese_clip",["ChineseCLIPModel",Na]],["siglip",["SiglipModel",ps]],["mobilebert",["MobileBertModel",Xr]],["squeezebert",["SqueezeBertModel",Ws]],["wav2vec2",["Wav2Vec2Model",aa]],["wav2vec2-bert",["Wav2Vec2BertModel",vl]],["unispeech",["UniSpeechModel",ti]],["unispeech-sat",["UniSpeechSatModel",bl]],["hubert",["HubertModel",Sl]],["wavlm",["WavLMModel",El]],["audio-spectrogram-transformer",["ASTModel",Nt]],["vits",["VitsModel",_a]],["pyannote",["PyAnnoteModel",oa]],["wespeaker-resnet",["WeSpeakerResNetModel",gl]],["detr",["DetrModel",zo]],["rt_detr",["RTDetrModel",Bo]],["table-transformer",["TableTransformerModel",No]],["vit",["ViTModel",Mo]],["fastvit",["FastViTModel",xo]],["mobilevit",["MobileViTModel",Au]],["mobilevitv2",["MobileViTV2Model",Eo]],["owlvit",["OwlViTModel",ko]],["owlv2",["Owlv2Model",Ao]],["beit",["BeitModel",Fo]],["deit",["DeiTModel",Uo]],["hiera",["HieraModel",Go]],["convnext",["ConvNextModel",sl]],["convnextv2",["ConvNextV2Model",al]],["dinov2",["Dinov2Model",Fu]],["resnet",["ResNetModel",Ho]],["swin",["SwinModel",Ki]],["swin2sr",["Swin2SRModel",Ko]],["donut-swin",["DonutSwinModel",nl]],["yolos",["YolosModel",ul]],["dpt",["DPTModel",Qo]],["glpn",["GLPNModel",Tn]],["hifigan",["SpeechT5HifiGan",Pl]],["efficientnet",["EfficientNetModel",Wl]],["mobilenet_v1",["MobileNetV1Model",ba]],["mobilenet_v2",["MobileNetV2Model",Hl]],["mobilenet_v3",["MobileNetV3Model",Xl]],["mobilenet_v4",["MobileNetV4Model",Nu]]]),Vu=new Map([["t5",["T5Model",vs]],["longt5",["LongT5Model",Ts]],["mt5",["MT5Model",$s]],["bart",["BartModel",we]],["mbart",["MBartModel",J]],["marian",["MarianModel",qn]],["whisper",["WhisperModel",Vt]],["m2m_100",["M2M100Model",an]],["blenderbot",["BlenderbotModel",kt]],["blenderbot-small",["BlenderbotSmallModel",or]]]),Uu=new Map([["bloom",["BloomModel",ku]],["jais",["JAISModel",Ga]],["gpt2",["GPT2Model",Ua]],["gptj",["GPTJModel",Ya]],["gpt_bigcode",["GPTBigCodeModel",Za]],["gpt_neo",["GPTNeoModel",Ha]],["gpt_neox",["GPTNeoXModel",Xa]],["codegen",["CodeGenModel",xn]],["llama",["LlamaModel",eo]],["cohere",["CohereModel",ro]],["gemma",["GemmaModel",so]],["gemma2",["Gemma2Model",ao]],["openelm",["OpenELMModel",lo]],["qwen2",["Qwen2Model",co]],["phi",["PhiModel",ho]],["phi3",["Phi3Model",fo]],["mpt",["MptModel",go]],["opt",["OPTModel",yo]],["mistral",["MistralModel",Il]],["starcoder2",["Starcoder2Model",zl]],["falcon",["FalconModel",Bl]],["stablelm",["StableLmModel",Ul]]]),Zl=new Map([["speecht5",["SpeechT5ForSpeechToText",da]],["whisper",["WhisperForConditionalGeneration",wi]]]),Jl=new Map([["speecht5",["SpeechT5ForTextToSpeech",ca]]]),eu=new Map([["vits",["VitsModel",_a]],["musicgen",["MusicgenForConditionalGeneration",Fs]]]),Wu=new Map([["bert",["BertForSequenceClassification",at]],["roformer",["RoFormerForSequenceClassification",Ge]],["electra",["ElectraForSequenceClassification",Mt]],["esm",["EsmForSequenceClassification",is]],["convbert",["ConvBertForSequenceClassification",W]],["camembert",["CamembertForSequenceClassification",ce]],["deberta",["DebertaForSequenceClassification",lt]],["deberta-v2",["DebertaV2ForSequenceClassification",Wt]],["mpnet",["MPNetForSequenceClassification",ys]],["albert",["AlbertForSequenceClassification",os]],["distilbert",["DistilBertForSequenceClassification",rr]],["roberta",["RobertaForSequenceClassification",Rr]],["xlm",["XLMForSequenceClassification",Xt]],["xlm-roberta",["XLMRobertaForSequenceClassification",Sr]],["bart",["BartForSequenceClassification",z]],["mbart",["MBartForSequenceClassification",Be]],["mobilebert",["MobileBertForSequenceClassification",Lr]],["squeezebert",["SqueezeBertForSequenceClassification",Cn]]]),tu=new Map([["bert",["BertForTokenClassification",Et]],["roformer",["RoFormerForTokenClassification",ut]],["electra",["ElectraForTokenClassification",nt]],["esm",["EsmForTokenClassification",Hr]],["convbert",["ConvBertForTokenClassification",E]],["camembert",["CamembertForTokenClassification",Te]],["deberta",["DebertaForTokenClassification",Ct]],["deberta-v2",["DebertaV2ForTokenClassification",zt]],["mpnet",["MPNetForTokenClassification",bs]],["distilbert",["DistilBertForTokenClassification",wr]],["roberta",["RobertaForTokenClassification",Ie]],["xlm",["XLMForTokenClassification",hn]],["xlm-roberta",["XLMRobertaForTokenClassification",Fr]]]),xa=new Map([["t5",["T5ForConditionalGeneration",xs]],["longt5",["LongT5ForConditionalGeneration",Ss]],["mt5",["MT5ForConditionalGeneration",Nr]],["bart",["BartForConditionalGeneration",m]],["mbart",["MBartForConditionalGeneration",ve]],["marian",["MarianMTModel",sa]],["m2m_100",["M2M100ForConditionalGeneration",ia]],["blenderbot",["BlenderbotForConditionalGeneration",bt]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",yr]]]),Ta=new Map([["bloom",["BloomForCausalLM",_o]],["gpt2",["GPT2LMHeadModel",Wa]],["jais",["JAISLMHeadModel",qa]],["gptj",["GPTJForCausalLM",Cu]],["gpt_bigcode",["GPTBigCodeForCausalLM",qs]],["gpt_neo",["GPTNeoForCausalLM",Ka]],["gpt_neox",["GPTNeoXForCausalLM",Qa]],["codegen",["CodeGenForCausalLM",Ja]],["llama",["LlamaForCausalLM",to]],["cohere",["CohereForCausalLM",no]],["gemma",["GemmaForCausalLM",io]],["gemma2",["Gemma2ForCausalLM",oo]],["openelm",["OpenELMForCausalLM",uo]],["qwen2",["Qwen2ForCausalLM",po]],["phi",["PhiForCausalLM",Fi]],["phi3",["Phi3ForCausalLM",mo]],["mpt",["MptForCausalLM",wo]],["opt",["OPTForCausalLM",bo]],["mbart",["MBartForCausalLM",xt]],["mistral",["MistralForCausalLM",Fl]],["starcoder2",["Starcoder2ForCausalLM",fa]],["falcon",["FalconForCausalLM",Ll]],["trocr",["TrOCRForCausalLM",pa]],["stablelm",["StableLmForCausalLM",Lu]]]),ru=new Map([["bert",["BertForMaskedLM",De]],["roformer",["RoFormerForMaskedLM",Ve]],["electra",["ElectraForMaskedLM",He]],["esm",["EsmForMaskedLM",Xn]],["convbert",["ConvBertForMaskedLM",M]],["camembert",["CamembertForMaskedLM",B]],["deberta",["DebertaForMaskedLM",ft]],["deberta-v2",["DebertaV2ForMaskedLM",Ot]],["mpnet",["MPNetForMaskedLM",ws]],["albert",["AlbertForMaskedLM",Kt]],["distilbert",["DistilBertForMaskedLM",yt]],["roberta",["RobertaForMaskedLM",yn]],["xlm",["XLMWithLMHeadModel",dn]],["xlm-roberta",["XLMRobertaForMaskedLM",Pt]],["mobilebert",["MobileBertForMaskedLM",Qn]],["squeezebert",["SqueezeBertForMaskedLM",as]]]),Gu=new Map([["bert",["BertForQuestionAnswering",Fe]],["roformer",["RoFormerForQuestionAnswering",ht]],["electra",["ElectraForQuestionAnswering",pt]],["convbert",["ConvBertForQuestionAnswering",te]],["camembert",["CamembertForQuestionAnswering",Re]],["deberta",["DebertaForQuestionAnswering",gt]],["deberta-v2",["DebertaV2ForQuestionAnswering",Ut]],["mpnet",["MPNetForQuestionAnswering",Ms]],["albert",["AlbertForQuestionAnswering",ls]],["distilbert",["DistilBertForQuestionAnswering",Qe]],["roberta",["RobertaForQuestionAnswering",On]],["xlm",["XLMForQuestionAnswering",sn]],["xlm-roberta",["XLMRobertaForQuestionAnswering",jr]],["mobilebert",["MobileBertForQuestionAnswering",Vn]],["squeezebert",["SqueezeBertForQuestionAnswering",Wn]]]),Sa=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",yi]]]),qu=new Map([["llava",["LlavaForConditionalGeneration",cs]],["moondream1",["Moondream1ForConditionalGeneration",sr]],["florence2",["Florence2ForConditionalGeneration",bi]]]),Hu=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",yi]]]),nu=new Map([["vit",["ViTForImageClassification",vo]],["fastvit",["FastViTForImageClassification",Pu]],["mobilevit",["MobileViTForImageClassification",$o]],["mobilevitv2",["MobileViTV2ForImageClassification",Co]],["beit",["BeitForImageClassification",Oo]],["deit",["DeiTForImageClassification",Wo]],["hiera",["HieraForImageClassification",qo]],["convnext",["ConvNextForImageClassification",il]],["convnextv2",["ConvNextV2ForImageClassification",ol]],["dinov2",["Dinov2ForImageClassification",ll]],["resnet",["ResNetForImageClassification",qi]],["swin",["SwinForImageClassification",Xi]],["segformer",["SegformerForImageClassification",Bu]],["efficientnet",["EfficientNetForImageClassification",Gl]],["mobilenet_v1",["MobileNetV1ForImageClassification",ql]],["mobilenet_v2",["MobileNetV2ForImageClassification",Kl]],["mobilenet_v3",["MobileNetV3ForImageClassification",Ql]],["mobilenet_v4",["MobileNetV4ForImageClassification",Yl]]]),Ku=new Map([["detr",["DetrForObjectDetection",Do]],["rt_detr",["RTDetrForObjectDetection",Lo]],["table-transformer",["TableTransformerForObjectDetection",jo]],["yolos",["YolosForObjectDetection",dl]]]),su=new Map([["owlvit",["OwlViTForObjectDetection",Po]],["owlv2",["Owlv2ForObjectDetection",Io]]]),iu=new Map([["detr",["DetrForSegmentation",Cs]],["clipseg",["CLIPSegForImageSegmentation",Va]]]),au=new Map([["segformer",["SegformerForSemanticSegmentation",Vl]],["sapiens",["SapiensForSemanticSegmentation",Jo]]]),ou=new Map([["sam",["SamModel",Ou]]]),Xu=new Map([["wav2vec2",["Wav2Vec2ForCTC",hl]],["wav2vec2-bert",["Wav2Vec2BertForCTC",xl]],["unispeech",["UniSpeechForCTC",wl]],["unispeech-sat",["UniSpeechSatForCTC",la]],["wavlm",["WavLMForCTC",Cl]],["hubert",["HubertForCTC",$l]]]),lu=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",fl]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Tl]],["unispeech",["UniSpeechForSequenceClassification",yl]],["unispeech-sat",["UniSpeechSatForSequenceClassification",As]],["wavlm",["WavLMForSequenceClassification",kl]],["hubert",["HubertForSequenceClassification",zu]],["audio-spectrogram-transformer",["ASTForAudioClassification",Gs]]]),uu=new Map([["wavlm",["WavLMForXVector",ni]]]),du=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Ml]],["wavlm",["WavLMForAudioFrameClassification",si]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",ml]],["pyannote",["PyAnnoteForAudioFrameClassification",_l]]]),cu=new Map([["vitmatte",["VitMatteForImageMatting",So]]]),Qu=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Xo]]]),ln=new Map([["dpt",["DPTForDepthEstimation",Yo]],["depth_anything",["DepthAnythingForDepthEstimation",Zo]],["glpn",["GLPNForDepthEstimation",tl]],["sapiens",["SapiensForDepthEstimation",Iu]]]),pu=new Map([["sapiens",["SapiensForNormalEstimation",el]]]),hu=new Map([["clip",["CLIPVisionModelWithProjection",Da]],["siglip",["SiglipVisionModel",La]]]),$a=[[ju,se.EncoderOnly],[Vu,se.EncoderDecoder],[Uu,se.DecoderOnly],[Wu,se.EncoderOnly],[tu,se.EncoderOnly],[xa,se.Seq2Seq],[Zl,se.Seq2Seq],[Ta,se.DecoderOnly],[ru,se.EncoderOnly],[Gu,se.EncoderOnly],[Sa,se.Vision2Seq],[qu,se.ImageTextToText],[nu,se.EncoderOnly],[iu,se.EncoderOnly],[au,se.EncoderOnly],[cu,se.EncoderOnly],[Qu,se.EncoderOnly],[ln,se.EncoderOnly],[pu,se.EncoderOnly],[Ku,se.EncoderOnly],[su,se.EncoderOnly],[ou,se.MaskGeneration],[Xu,se.EncoderOnly],[lu,se.EncoderOnly],[Jl,se.Seq2Seq],[eu,se.EncoderOnly],[uu,se.EncoderOnly],[du,se.EncoderOnly],[hu,se.EncoderOnly]];for(const[_,h]of $a)for(const[D,re]of _.values())le.set(D,h),j.set(re,D),ie.set(D,re);const Yu=[["MusicgenForConditionalGeneration",Fs,se.Musicgen],["CLIPTextModelWithProjection",kn,se.EncoderOnly],["SiglipTextModel",Ba,se.EncoderOnly],["ClapTextModelWithProjection",Rl,se.EncoderOnly],["ClapAudioModelWithProjection",Nl,se.EncoderOnly]];for(const[_,h,D]of Yu)le.set(_,D),j.set(h,_),ie.set(_,h);class ui extends Or{static MODEL_CLASS_MAPPINGS=$a.map(h=>h[0]);static BASE_IF_FAIL=!0}class Zu extends Or{static MODEL_CLASS_MAPPINGS=[Wu]}class Ju extends Or{static MODEL_CLASS_MAPPINGS=[tu]}class fu extends Or{static MODEL_CLASS_MAPPINGS=[xa]}class ed extends Or{static MODEL_CLASS_MAPPINGS=[Zl]}class td extends Or{static MODEL_CLASS_MAPPINGS=[Jl]}class mu extends Or{static MODEL_CLASS_MAPPINGS=[eu]}class rd extends Or{static MODEL_CLASS_MAPPINGS=[Ta]}class nd extends Or{static MODEL_CLASS_MAPPINGS=[ru]}class sd extends Or{static MODEL_CLASS_MAPPINGS=[Gu]}class _u extends Or{static MODEL_CLASS_MAPPINGS=[Sa]}class id extends Or{static MODEL_CLASS_MAPPINGS=[nu]}class ad extends Or{static MODEL_CLASS_MAPPINGS=[iu]}class gu extends Or{static MODEL_CLASS_MAPPINGS=[au]}class od extends Or{static MODEL_CLASS_MAPPINGS=[Ku]}class qd extends Or{static MODEL_CLASS_MAPPINGS=[su]}class ld extends Or{static MODEL_CLASS_MAPPINGS=[ou]}class ud extends Or{static MODEL_CLASS_MAPPINGS=[Xu]}class dd extends Or{static MODEL_CLASS_MAPPINGS=[lu]}class cd extends Or{static MODEL_CLASS_MAPPINGS=[uu]}class Hd extends Or{static MODEL_CLASS_MAPPINGS=[du]}class pd extends Or{static MODEL_CLASS_MAPPINGS=[Hu]}class hd extends Or{static MODEL_CLASS_MAPPINGS=[cu]}class fd extends Or{static MODEL_CLASS_MAPPINGS=[Qu]}class Kd extends Or{static MODEL_CLASS_MAPPINGS=[ln]}class md extends Or{static MODEL_CLASS_MAPPINGS=[pu]}class _d extends Or{static MODEL_CLASS_MAPPINGS=[hu]}class gd extends Ue{constructor({logits:h,past_key_values:D,encoder_outputs:re,decoder_attentions:Oe=null,cross_attentions:Ye=null}){super(),this.logits=h,this.past_key_values=D,this.encoder_outputs=re,this.decoder_attentions=Oe,this.cross_attentions=Ye}}class lr extends Ue{constructor({logits:h}){super(),this.logits=h}}class wd extends Ue{constructor({logits:h,embeddings:D}){super(),this.logits=h,this.embeddings=D}}class en extends Ue{constructor({logits:h}){super(),this.logits=h}}class rn extends Ue{constructor({logits:h}){super(),this.logits=h}}class on extends Ue{constructor({start_logits:h,end_logits:D}){super(),this.start_logits=h,this.end_logits=D}}class es extends Ue{constructor({logits:h}){super(),this.logits=h}}class yd extends Ue{constructor({logits:h,past_key_values:D}){super(),this.logits=h,this.past_key_values=D}}class wu extends Ue{constructor({alphas:h}){super(),this.alphas=h}}class yu extends Ue{constructor({waveform:h,spectrogram:D}){super(),this.waveform=h,this.spectrogram=D}}},"./src/models/whisper/common_whisper.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{WHISPER_LANGUAGE_MAPPING:()=>ae,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>he,whisper_language_to_code:()=>xe});const I=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian 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this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=he([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=he([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=he([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=he([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=he([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=he([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}}},"./src/pipelines.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{AudioClassificationPipeline:()=>Pe,AutomaticSpeechRecognitionPipeline:()=>rt,DepthEstimationPipeline:()=>st,DocumentQuestionAnsweringPipeline:()=>X,FeatureExtractionPipeline:()=>ye,FillMaskPipeline:()=>le,ImageClassificationPipeline:()=>dt,ImageFeatureExtractionPipeline:()=>Se,ImageSegmentationPipeline:()=>ge,ImageToImagePipeline:()=>ct,ImageToTextPipeline:()=>Xe,ObjectDetectionPipeline:()=>de,Pipeline:()=>_e,QuestionAnsweringPipeline:()=>se,SummarizationPipeline:()=>j,Text2TextGenerationPipeline:()=>ie,TextClassificationPipeline:()=>F,TextGenerationPipeline:()=>C,TextToAudioPipeline:()=>Ue,TokenClassificationPipeline:()=>Q,TranslationPipeline:()=>A,ZeroShotAudioClassificationPipeline:()=>Ce,ZeroShotClassificationPipeline:()=>me,ZeroShotImageClassificationPipeline:()=>U,ZeroShotObjectDetectionPipeline:()=>$e,pipeline:()=>at});var I=N("./src/tokenizers.js"),ae=N("./src/models.js"),he=N("./src/processors.js"),xe=N("./src/utils/generic.js"),be=N("./src/utils/core.js"),R=N("./src/utils/maths.js"),$=N("./src/utils/audio.js"),V=N("./src/utils/tensor.js"),P=N("./src/utils/image.js");async function K(Fe){return Array.isArray(Fe)||(Fe=[Fe]),await Promise.all(Fe.map(H=>P.RawImage.read(H)))}async function ne(Fe,H){return Array.isArray(Fe)||(Fe=[Fe]),await Promise.all(Fe.map(Ee=>typeof Ee=="string"||Ee instanceof URL?(0,$.read_audio)(Ee,H):Ee instanceof Float64Array?new Float32Array(Ee):Ee))}function oe(Fe,H){H&&(Fe=Fe.map(Ge=>Ge|0));const[Ee,qe,je,Ve]=Fe;return{xmin:Ee,ymin:qe,xmax:je,ymax:Ve}}class _e extends xe.Callable{constructor({task:H,model:Ee,tokenizer:qe=null,processor:je=null}){super(),this.task=H,this.model=Ee,this.tokenizer=qe,this.processor=je}async dispose(){await this.model.dispose()}}class F extends _e{constructor(H){super(H)}async _call(H,{top_k:Ee=1}={}){const qe=this.tokenizer(H,{padding:!0,truncation:!0}),je=await this.model(qe),Ve=this.model.config.problem_type==="multi_label_classification"?ht=>ht.sigmoid():ht=>new V.Tensor("float32",(0,R.softmax)(ht.data),ht.dims),Ge=this.model.config.id2label,ut=[];for(const ht of je.logits){const Tt=Ve(ht),St=await(0,V.topk)(Tt,Ee),M=St[0].tolist(),E=St[1].tolist().map((te,pe)=>({label:Ge?Ge[te]:`LABEL_${te}`,score:M[pe]}));Ee===1?ut.push(...E):ut.push(E)}return Array.isArray(H)||Ee===1?ut:ut[0]}}class Q extends _e{constructor(H){super(H)}async _call(H,{ignore_labels:Ee=["O"]}={}){const qe=Array.isArray(H),je=this.tokenizer(qe?H:[H],{padding:!0,truncation:!0}),Ge=(await this.model(je)).logits,ut=this.model.config.id2label,ht=[];for(let Tt=0;Tt<Ge.dims[0];++Tt){const St=je.input_ids[Tt],M=Ge[Tt],W=[];for(let E=0;E<M.dims[0];++E){const te=M[E],pe=(0,R.max)(te.data)[1],Je=ut?ut[pe]:`LABEL_${pe}`;if(Ee.includes(Je))continue;const He=this.tokenizer.decode([St[E].item()],{skip_special_tokens:!0});if(He==="")continue;const Mt=(0,R.softmax)(te.data);W.push({entity:Je,score:Mt[pe],index:E,word:He})}ht.push(W)}return qe?ht:ht[0]}}class se extends _e{constructor(H){super(H)}async _call(H,Ee,{top_k:qe=1}={}){const je=this.tokenizer(H,{text_pair:Ee,padding:!0,truncation:!0}),{start_logits:Ve,end_logits:Ge}=await this.model(je),ut=je.input_ids.tolist(),ht=je.attention_mask.tolist(),Tt=this.tokenizer.all_special_ids,St=[];for(let M=0;M<Ve.dims[0];++M){const W=ut[M],E=W.findIndex(nt=>nt==this.tokenizer.sep_token_id);ht[M].map((nt,pt)=>nt==1&&(pt===0||pt>E&&Tt.findIndex(Bt=>Bt==W[pt])===-1));const te=Ve[M].tolist(),pe=Ge[M].tolist();for(let nt=1;nt<te.length;++nt)(ht[M]==0||nt<=E||Tt.findIndex(pt=>pt==W[nt])!==-1)&&(te[nt]=-1/0,pe[nt]=-1/0);const Je=(0,R.softmax)(te).map((nt,pt)=>[nt,pt]),He=(0,R.softmax)(pe).map((nt,pt)=>[nt,pt]);Je[0][0]=0,He[0][0]=0;const Mt=(0,be.product)(Je,He).filter(nt=>nt[0][1]<=nt[1][1]).map(nt=>[nt[0][1],nt[1][1],nt[0][0]*nt[1][0]]).sort((nt,pt)=>pt[2]-nt[2]);for(let nt=0;nt<Math.min(Mt.length,qe);++nt){const[pt,Bt,et]=Mt[nt],B=W.slice(pt,Bt+1),ce=this.tokenizer.decode(B,{skip_special_tokens:!0});St.push({answer:ce,score:et})}}return qe===1?St[0]:St}}class le extends _e{constructor(H){super(H)}async _call(H,{top_k:Ee=5}={}){const qe=this.tokenizer(H,{padding:!0,truncation:!0}),{logits:je}=await this.model(qe),Ve=[],Ge=qe.input_ids.tolist();for(let ut=0;ut<Ge.length;++ut){const ht=Ge[ut],Tt=ht.findIndex(te=>te==this.tokenizer.mask_token_id);if(Tt===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const St=je[ut][Tt],M=await(0,V.topk)(new V.Tensor("float32",(0,R.softmax)(St.data),St.dims),Ee),W=M[0].tolist(),E=M[1].tolist();Ve.push(E.map((te,pe)=>{const Je=ht.slice();return Je[Tt]=te,{score:W[pe],token:Number(te),token_str:this.tokenizer.model.vocab[te],sequence:this.tokenizer.decode(Je,{skip_special_tokens:!0})}}))}return Array.isArray(H)?Ve:Ve[0]}}class ie extends _e{_key="generated_text";constructor(H){super(H)}async _call(H,Ee={}){Array.isArray(H)||(H=[H]),this.model.config.prefix&&(H=H.map(ht=>this.model.config.prefix+ht));const qe=this.model.config.task_specific_params;qe&&qe[this.task]&&qe[this.task].prefix&&(H=H.map(ht=>qe[this.task].prefix+ht));const je=this.tokenizer,Ve={padding:!0,truncation:!0};let Ge;this instanceof A&&"_build_translation_inputs"in je?Ge=je._build_translation_inputs(H,Ve,Ee):Ge=je(H,Ve);const ut=await this.model.generate({...Ge,...Ee});return je.batch_decode(ut,{skip_special_tokens:!0}).map(ht=>({[this._key]:ht}))}}class j extends ie{_key="summary_text";constructor(H){super(H)}}class A extends ie{_key="translation_text";constructor(H){super(H)}}function L(Fe){return Array.isArray(Fe)&&Fe.every(H=>"role"in H&&"content"in H)}class C extends _e{constructor(H){super(H)}async _call(H,Ee={}){let qe=!1,je=!1,Ve;if(typeof H=="string")Ve=H=[H];else if(Array.isArray(H)&&H.every(E=>typeof E=="string"))qe=!0,Ve=H;else{if(L(H))H=[H];else if(Array.isArray(H)&&H.every(L))qe=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");je=!0,Ve=H.map(E=>this.tokenizer.apply_chat_template(E,{tokenize:!1,add_generation_prompt:!0}))}const Ge=Ee.add_special_tokens??!1,ut=je?!1:Ee.return_full_text??!0;this.tokenizer.padding_side="left";const ht=this.tokenizer(Ve,{add_special_tokens:Ge,padding:!0,truncation:!0}),Tt=await this.model.generate({...ht,...Ee}),St=this.tokenizer.batch_decode(Tt,{skip_special_tokens:!0});let M;!ut&&ht.input_ids.dims.at(-1)>0&&(M=this.tokenizer.batch_decode(ht.input_ids,{skip_special_tokens:!0}).map(E=>E.length));const W=Array.from({length:H.length},E=>[]);for(let E=0;E<St.length;++E){const te=Math.floor(E/Tt.dims[0]*H.length);M&&(St[E]=St[E].slice(M[te])),W[te].push({generated_text:je?[...H[te],{role:"assistant",content:St[E]}]:St[E]})}return!qe&&W.length===1?W[0]:W}}class me extends _e{constructor(H){super(H),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([Ee,qe])=>[Ee.toLowerCase(),qe])),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(H,Ee,{hypothesis_template:qe="This example is {}.",multi_label:je=!1}={}){const Ve=Array.isArray(H);Ve||(H=[H]),Array.isArray(Ee)||(Ee=[Ee]);const Ge=Ee.map(Tt=>qe.replace("{}",Tt)),ut=je||Ee.length===1,ht=[];for(const Tt of H){const St=[];for(const E of Ge){const te=this.tokenizer(Tt,{text_pair:E,padding:!0,truncation:!0}),pe=await this.model(te);ut?St.push([pe.logits.data[this.contradiction_id],pe.logits.data[this.entailment_id]]):St.push(pe.logits.data[this.entailment_id])}const W=(ut?St.map(E=>(0,R.softmax)(E)[1]):(0,R.softmax)(St)).map((E,te)=>[E,te]).sort((E,te)=>te[0]-E[0]);ht.push({sequence:Tt,labels:W.map(E=>Ee[E[1]]),scores:W.map(E=>E[0])})}return Ve?ht:ht[0]}}class ye extends _e{constructor(H){super(H)}async _call(H,{pooling:Ee="none",normalize:qe=!1,quantize:je=!1,precision:Ve="binary"}={}){const Ge=this.tokenizer(H,{padding:!0,truncation:!0}),ut=await this.model(Ge);let ht=ut.last_hidden_state??ut.logits??ut.token_embeddings;if(Ee!=="none")if(Ee==="mean")ht=(0,V.mean_pooling)(ht,Ge.attention_mask);else if(Ee==="cls")ht=ht.slice(null,0);else throw Error(`Pooling method '${Ee}' not supported.`);return qe&&(ht=ht.normalize(2,-1)),je&&(ht=(0,V.quantize_embeddings)(ht,Ve)),ht}}class Se extends _e{constructor(H){super(H)}async _call(H,{pool:Ee=null}={}){const qe=await K(H),{pixel_values:je}=await this.processor(qe),Ve=await this.model({pixel_values:je});let Ge;if(Ee){if(!("pooler_output"in Ve))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Ge=Ve.pooler_output}else Ge=Ve.last_hidden_state??Ve.logits??Ve.image_embeds;return Ge}}class Pe extends _e{constructor(H){super(H)}async _call(H,{top_k:Ee=5}={}){const qe=this.processor.feature_extractor.config.sampling_rate,je=await ne(H,qe),Ve=this.model.config.id2label,Ge=[];for(const ut of je){const ht=await this.processor(ut),St=(await this.model(ht)).logits[0],M=await(0,V.topk)(new V.Tensor("float32",(0,R.softmax)(St.data),St.dims),Ee),W=M[0].tolist(),te=M[1].tolist().map((pe,Je)=>({label:Ve?Ve[pe]:`LABEL_${pe}`,score:W[Je]}));Ge.push(te)}return Array.isArray(H)?Ge:Ge[0]}}class Ce extends _e{constructor(H){super(H)}async _call(H,Ee,{hypothesis_template:qe="This is a sound of {}."}={}){const je=!Array.isArray(H);je&&(H=[H]);const Ve=Ee.map(St=>qe.replace("{}",St)),Ge=this.tokenizer(Ve,{padding:!0,truncation:!0}),ut=this.processor.feature_extractor.config.sampling_rate,ht=await ne(H,ut),Tt=[];for(const St of ht){const M=await this.processor(St),W=await this.model({...Ge,...M}),E=(0,R.softmax)(W.logits_per_audio.data);Tt.push([...E].map((te,pe)=>({score:te,label:Ee[pe]})))}return je?Tt[0]:Tt}}class rt extends _e{constructor(H){super(H)}async _call(H,Ee={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(H,Ee);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(H,Ee);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(H,Ee){Ee.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),Ee.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const qe=!Array.isArray(H);qe&&(H=[H]);const je=this.processor.feature_extractor.config.sampling_rate,Ve=await ne(H,je),Ge=[];for(const ut of Ve){const ht=await this.processor(ut),St=(await this.model(ht)).logits[0],M=[];for(const E of St)M.push((0,R.max)(E.data)[1]);const W=this.tokenizer.decode(M);Ge.push({text:W})}return qe?Ge[0]:Ge}async _call_whisper(H,Ee){const qe=Ee.return_timestamps??!1,je=Ee.chunk_length_s??0,Ve=Ee.force_full_sequences??!1;let Ge=Ee.stride_length_s??null;const ut={...Ee};qe==="word"&&(ut.return_token_timestamps=!0,ut.return_timestamps=!1);const ht=!Array.isArray(H);ht&&(H=[H]);const Tt=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,St=this.processor.feature_extractor.config.hop_length,M=this.processor.feature_extractor.config.sampling_rate,W=await ne(H,M),E=[];for(const te of W){let pe=[];if(je>0){if(Ge===null)Ge=je/6;else if(je<=Ge)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const Mt=M*je,nt=M*Ge,pt=Mt-2*nt;let Bt=0;for(;;){const et=Bt+Mt,B=te.subarray(Bt,et),ce=await this.processor(B),Te=Bt===0,Re=et>=te.length;if(pe.push({stride:[B.length,Te?0:nt,Re?0:nt],input_features:ce.input_features,is_last:Re}),Re)break;Bt+=pt}}else pe=[{stride:[te.length,0,0],input_features:(await this.processor(te)).input_features,is_last:!0}];for(const Mt of pe){ut.num_frames=Math.floor(Mt.stride[0]/St);const nt=await this.model.generate({inputs:Mt.input_features,...ut});qe==="word"?(Mt.tokens=nt.sequences.tolist()[0],Mt.token_timestamps=nt.token_timestamps.tolist()[0].map(pt=>(0,R.round)(pt,2))):Mt.tokens=nt[0].tolist(),Mt.stride=Mt.stride.map(pt=>pt/M)}const[Je,He]=this.tokenizer._decode_asr(pe,{time_precision:Tt,return_timestamps:qe,force_full_sequences:Ve});E.push({text:Je,...He})}return ht?E[0]:E}}class Xe extends _e{constructor(H){super(H)}async _call(H,Ee={}){const qe=Array.isArray(H),je=await K(H),{pixel_values:Ve}=await this.processor(je),Ge=[];for(const ut of Ve){ut.dims=[1,...ut.dims];const ht=await this.model.generate({inputs:ut,...Ee}),Tt=this.tokenizer.batch_decode(ht,{skip_special_tokens:!0}).map(St=>({generated_text:St.trim()}));Ge.push(Tt)}return qe?Ge:Ge[0]}}class dt extends _e{constructor(H){super(H)}async _call(H,{top_k:Ee=5}={}){const qe=await K(H),{pixel_values:je}=await this.processor(qe),Ve=await this.model({pixel_values:je}),Ge=this.model.config.id2label,ut=[];for(const ht of Ve.logits){const Tt=await(0,V.topk)(new V.Tensor("float32",(0,R.softmax)(ht.data),ht.dims),Ee),St=Tt[0].tolist(),W=Tt[1].tolist().map((E,te)=>({label:Ge?Ge[E]:`LABEL_${E}`,score:St[te]}));ut.push(W)}return Array.isArray(H)?ut:ut[0]}}class ge extends _e{constructor(H){super(H),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(H,{threshold:Ee=.5,mask_threshold:qe=.5,overlap_mask_area_threshold:je=.8,label_ids_to_fuse:Ve=null,target_sizes:Ge=null,subtask:ut=null}={}){if(Array.isArray(H)&&H.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const Tt=await K(H),St=Tt.map(He=>[He.height,He.width]),{pixel_values:M,pixel_mask:W}=await this.processor(Tt),E=await this.model({pixel_values:M,pixel_mask:W});let te=null;if(ut!==null)te=this.subtasks_mapping[ut];else for(let[He,Mt]of Object.entries(this.subtasks_mapping))if(Mt in this.processor.feature_extractor){te=this.processor.feature_extractor[Mt].bind(this.processor.feature_extractor),ut=He;break}const pe=this.model.config.id2label,Je=[];if(ut==="panoptic"||ut==="instance"){const He=te(E,Ee,qe,je,Ve,Ge??St)[0],Mt=He.segmentation;for(const nt of He.segments_info){const pt=new Uint8ClampedArray(Mt.data.length);for(let et=0;et<Mt.data.length;++et)Mt.data[et]===nt.id&&(pt[et]=255);const Bt=new P.RawImage(pt,Mt.dims[1],Mt.dims[0],1);Je.push({score:nt.score,label:pe[nt.label_id],mask:Bt})}}else if(ut==="semantic"){const{segmentation:He,labels:Mt}=te(E,Ge??St)[0];for(const nt of Mt){const pt=new Uint8ClampedArray(He.data.length);for(let et=0;et<He.data.length;++et)He.data[et]===nt&&(pt[et]=255);const Bt=new P.RawImage(pt,He.dims[1],He.dims[0],1);Je.push({score:null,label:pe[nt],mask:Bt})}}else throw Error(`Subtask ${ut} not supported.`);return Je}}class U extends _e{constructor(H){super(H)}async _call(H,Ee,{hypothesis_template:qe="This is a photo of {}"}={}){const je=Array.isArray(H),Ve=await K(H),Ge=Ee.map(W=>qe.replace("{}",W)),ut=this.tokenizer(Ge,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:ht}=await this.processor(Ve),Tt=await this.model({...ut,pixel_values:ht}),St=this.model.config.model_type==="siglip"?W=>W.sigmoid().data:W=>(0,R.softmax)(W.data),M=[];for(const W of Tt.logits_per_image){const te=[...St(W)].map((pe,Je)=>({score:pe,label:Ee[Je]}));te.sort((pe,Je)=>Je.score-pe.score),M.push(te)}return je?M:M[0]}}class de extends _e{constructor(H){super(H)}async _call(H,{threshold:Ee=.9,percentage:qe=!1}={}){const je=Array.isArray(H);if(je&&H.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ve=await K(H),Ge=qe?null:Ve.map(E=>[E.height,E.width]),{pixel_values:ut,pixel_mask:ht}=await this.processor(Ve),Tt=await this.model({pixel_values:ut,pixel_mask:ht}),St=this.processor.feature_extractor.post_process_object_detection(Tt,Ee,Ge),M=this.model.config.id2label,W=St.map(E=>E.boxes.map((te,pe)=>({score:E.scores[pe],label:M[E.classes[pe]],box:oe(te,!qe)})));return je?W:W[0]}}class $e extends _e{constructor(H){super(H)}async _call(H,Ee,{threshold:qe=.1,top_k:je=null,percentage:Ve=!1}={}){const Ge=Array.isArray(H),ut=await K(H),ht=this.tokenizer(Ee,{padding:!0,truncation:!0}),Tt=await this.processor(ut),St=[];for(let M=0;M<ut.length;++M){const W=ut[M],E=Ve?null:[[W.height,W.width]],te=Tt.pixel_values[M].unsqueeze_(0),pe=await this.model({...ht,pixel_values:te}),Je=this.processor.feature_extractor.post_process_object_detection(pe,qe,E,!0)[0];let He=Je.boxes.map((Mt,nt)=>({score:Je.scores[nt],label:Ee[Je.classes[nt]],box:oe(Mt,!Ve)})).sort((Mt,nt)=>nt.score-Mt.score);je!==null&&(He=He.slice(0,je)),St.push(He)}return Ge?St:St[0]}}class X extends _e{constructor(H){super(H)}async _call(H,Ee,qe={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class Ue extends _e{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(H){super(H),this.vocoder=H.vocoder??null}async _call(H,{speaker_embeddings:Ee=null}={}){return this.processor?this._call_text_to_spectrogram(H,{speaker_embeddings:Ee}):this._call_text_to_waveform(H)}async _call_text_to_waveform(H){const Ee=this.tokenizer(H,{padding:!0,truncation:!0}),{waveform:qe}=await this.model(Ee),je=this.model.config.sampling_rate;return{audio:qe.data,sampling_rate:je}}async _call_text_to_spectrogram(H,{speaker_embeddings:Ee}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await ae.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Ee=="string"||Ee instanceof URL)&&(Ee=new Float32Array(await(await fetch(Ee)).arrayBuffer())),Ee instanceof Float32Array)Ee=new V.Tensor("float32",Ee,[1,Ee.length]);else if(!(Ee instanceof V.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:qe}=this.tokenizer(H,{padding:!0,truncation:!0}),{waveform:je}=await this.model.generate_speech(qe,Ee,{vocoder:this.vocoder}),Ve=this.processor.feature_extractor.config.sampling_rate;return{audio:je.data,sampling_rate:Ve}}}class ct extends _e{constructor(H){super(H)}async _call(H){const Ee=await K(H),qe=await this.processor(Ee),je=await this.model(qe),Ve=[];for(const Ge of je.reconstruction){const ut=Ge.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ve.push(P.RawImage.fromTensor(ut))}return Ve.length>1?Ve:Ve[0]}}class st extends _e{constructor(H){super(H)}async _call(H){const Ee=await K(H),qe=await this.processor(Ee),{predicted_depth:je}=await this.model(qe),Ve=[];for(let Ge=0;Ge<Ee.length;++Ge){const ut=(0,V.interpolate)(je[Ge],Ee[Ge].size.reverse(),"bilinear",!1),ht=ut.mul_(255/(0,R.max)(ut.data)[0]).to("uint8");Ve.push({predicted_depth:je[Ge],depth:P.RawImage.fromTensor(ht)})}return Ve.length>1?Ve:Ve[0]}}const ot=Object.freeze({"text-classification":{tokenizer:I.AutoTokenizer,pipeline:F,model:ae.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:I.AutoTokenizer,pipeline:Q,model:ae.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:I.AutoTokenizer,pipeline:se,model:ae.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:I.AutoTokenizer,pipeline:le,model:ae.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:I.AutoTokenizer,pipeline:j,model:ae.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:I.AutoTokenizer,pipeline:A,model:ae.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:I.AutoTokenizer,pipeline:ie,model:ae.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:I.AutoTokenizer,pipeline:C,model:ae.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:I.AutoTokenizer,pipeline:me,model:ae.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:Pe,model:ae.AutoModelForAudioClassification,processor:he.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:I.AutoTokenizer,pipeline:Ce,model:ae.AutoModel,processor:he.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:I.AutoTokenizer,pipeline:rt,model:[ae.AutoModelForSpeechSeq2Seq,ae.AutoModelForCTC],processor:he.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:I.AutoTokenizer,pipeline:Ue,model:[ae.AutoModelForTextToWaveform,ae.AutoModelForTextToSpectrogram],processor:[he.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:I.AutoTokenizer,pipeline:Xe,model:ae.AutoModelForVision2Seq,processor:he.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:dt,model:ae.AutoModelForImageClassification,processor:he.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ge,model:[ae.AutoModelForImageSegmentation,ae.AutoModelForSemanticSegmentation],processor:he.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:I.AutoTokenizer,pipeline:U,model:ae.AutoModel,processor:he.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:de,model:ae.AutoModelForObjectDetection,processor:he.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:I.AutoTokenizer,pipeline:$e,model:ae.AutoModelForZeroShotObjectDetection,processor:he.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:I.AutoTokenizer,pipeline:X,model:ae.AutoModelForDocumentQuestionAnswering,processor:he.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:ct,model:ae.AutoModelForImageToImage,processor:he.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:st,model:ae.AutoModelForDepthEstimation,processor:he.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:I.AutoTokenizer,pipeline:ye,model:ae.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:he.AutoProcessor,pipeline:Se,model:[ae.AutoModelForImageFeatureExtraction,ae.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),De=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function at(Fe,H=null,{progress_callback:Ee=null,config:qe=null,cache_dir:je=null,local_files_only:Ve=!1,revision:Ge="main",device:ut=null,dtype:ht=null,model_file_name:Tt=null,session_options:St={}}={}){Fe=De[Fe]??Fe;const M=ot[Fe.split("_",1)[0]];if(!M)throw Error(`Unsupported pipeline: ${Fe}. Must be one of [${Object.keys(ot)}]`);H||(H=M.default.model,console.log(`No model specified. Using default model: "${H}".`));const W={progress_callback:Ee,config:qe,cache_dir:je,local_files_only:Ve,revision:Ge,device:ut,dtype:ht,model_file_name:Tt,session_options:St},E=new Map([["tokenizer",M.tokenizer],["model",M.model],["processor",M.processor]]),te=await Et(E,H,W);te.task=Fe,(0,be.dispatchCallback)(Ee,{status:"ready",task:Fe,model:H});const pe=M.pipeline;return new pe(te)}async function Et(Fe,H,Ee){const qe=Object.create(null),je=[];for(let[Ve,Ge]of Fe.entries()){if(!Ge)continue;let ut;Array.isArray(Ge)?ut=new Promise(async(ht,Tt)=>{let St;for(let M of Ge){if(M===null){ht(null);return}try{ht(await M.from_pretrained(H,Ee));return}catch(W){if(W.message?.includes("Unsupported model type"))St=W;else if(W.message?.includes("Could not locate file"))St=W;else{Tt(W);return}}}Tt(St)}):ut=Ge.from_pretrained(H,Ee),qe[Ve]=ut,je.push(ut)}await Promise.all(je);for(let[Ve,Ge]of Object.entries(qe))qe[Ve]=await Ge;return qe}},"./src/processors.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{ASTFeatureExtractor:()=>ht,AutoProcessor:()=>Bt,BeitFeatureExtractor:()=>De,BitImageProcessor:()=>j,CLIPFeatureExtractor:()=>L,CLIPImageProcessor:()=>C,ChineseCLIPFeatureExtractor:()=>me,ClapFeatureExtractor:()=>Tt,ConvNextFeatureExtractor:()=>Se,ConvNextImageProcessor:()=>Pe,DPTFeatureExtractor:()=>le,DPTImageProcessor:()=>ie,DeiTFeatureExtractor:()=>ot,DetrFeatureExtractor:()=>Fe,DonutFeatureExtractor:()=>at,EfficientNetImageProcessor:()=>Xe,FeatureExtractor:()=>_e,Florence2Processor:()=>pt,GLPNFeatureExtractor:()=>A,ImageFeatureExtractor:()=>F,MobileNetV1FeatureExtractor:()=>dt,MobileNetV2FeatureExtractor:()=>ge,MobileNetV3FeatureExtractor:()=>U,MobileNetV4FeatureExtractor:()=>de,MobileViTFeatureExtractor:()=>$e,MobileViTImageProcessor:()=>X,NougatImageProcessor:()=>Et,OwlViTFeatureExtractor:()=>Ue,OwlViTProcessor:()=>nt,Owlv2ImageProcessor:()=>ct,Processor:()=>E,PyAnnoteFeatureExtractor:()=>St,PyAnnoteProcessor:()=>He,RTDetrImageProcessor:()=>st,SamImageProcessor:()=>Ee,SamProcessor:()=>te,SapiensFeatureExtractor:()=>Q,SeamlessM4TFeatureExtractor:()=>ut,SegformerFeatureExtractor:()=>se,SiglipImageProcessor:()=>ye,SpeechT5FeatureExtractor:()=>W,SpeechT5Processor:()=>Mt,Swin2SRImageProcessor:()=>qe,ViTFeatureExtractor:()=>Ce,ViTImageProcessor:()=>rt,VitMatteImageProcessor:()=>je,Wav2Vec2FeatureExtractor:()=>Ge,Wav2Vec2ProcessorWithLM:()=>Je,WeSpeakerFeatureExtractor:()=>M,WhisperFeatureExtractor:()=>Ve,WhisperProcessor:()=>pe,YolosFeatureExtractor:()=>H});var I=N("./src/utils/generic.js"),ae=N("./src/utils/core.js"),he=N("./src/utils/hub.js"),xe=N("./src/utils/maths.js"),be=N("./src/utils/tensor.js");N("./src/utils/image.js");var R=N("./src/utils/audio.js");function $([et,B,ce,Te]){return[et-ce/2,B-Te/2,et+ce/2,B+Te/2]}function V(et,B=.5,ce=null,Te=!1){const Re=et.logits,ke=et.pred_boxes,[tt,ft,lt]=Re.dims;if(ce!==null&&ce.length!==tt)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let Ct=[];for(let gt=0;gt<tt;++gt){let ze=ce!==null?ce[gt]:null,Rt={boxes:[],classes:[],scores:[]},Ot=Re[gt],Wt=ke[gt];for(let zt=0;zt<ft;++zt){let Ut=Ot[zt],Ht=[],ur;if(Te){ur=Ut.sigmoid().data;for(let rr=0;rr<ur.length;++rr)ur[rr]>B&&Ht.push(rr)}else{let rr=(0,xe.max)(Ut.data)[1];if(rr===lt-1||(ur=(0,xe.softmax)(Ut.data),ur[rr]<B))continue;Ht.push(rr)}for(const rr of Ht){let wr=Wt[zt].data;wr=$(wr),ze!==null&&(wr=wr.map((Qe,yt)=>Qe*ze[(yt+1)%2])),Rt.boxes.push(wr),Rt.classes.push(rr),Rt.scores.push(ur[rr])}}Ct.push(Rt)}return Ct}function P(et,B=null){const ce=et.logits,Te=ce.dims[0];if(B!==null&&B.length!==Te)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const Re=[];for(let ke=0;ke<Te;++ke){const tt=B!==null?B[ke]:null;let ft=ce[ke];tt!==null&&(ft=(0,be.interpolate)(ft,tt,"bilinear",!1));const[lt,Ct]=tt??ft.dims.slice(-2),gt=new be.Tensor("int32",new Int32Array(lt*Ct),[lt,Ct]),ze=ft[0].data,Rt=gt.data;for(let zt=1;zt<ft.dims[0];++zt){const Ut=ft[zt].data;for(let Ht=0;Ht<Ut.length;++Ht)Ut[Ht]>ze[Ht]&&(ze[Ht]=Ut[Ht],Rt[Ht]=zt)}const Ot=new Array(ft.dims[0]);for(let zt=0;zt<Rt.length;++zt){const Ut=Rt[zt];Ot[Ut]=Ut}const Wt=Ot.filter(zt=>zt!==void 0);Re.push({segmentation:gt,labels:Wt})}return Re}function K(et,B){if(!(et instanceof Float32Array||et instanceof Float64Array))throw new Error(`${B} expects input to be a Float32Array or a Float64Array, but got ${et?.constructor?.name??typeof et} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function ne(et,B,ce=0,Te=null){const Re=et/B;let ke=(0,xe.bankers_round)(Re)*B;return Te!==null&&ke>Te&&(ke=Math.floor(Re)*B),ke<ce&&(ke=Math.ceil(Re)*B),ke}function oe([et,B],ce){return[Math.max(Math.floor(et/ce),1)*ce,Math.max(Math.floor(B/ce),1)*ce]}class _e extends I.Callable{constructor(B){super(),this.config=B}}class F extends _e{constructor(B){super(B),this.image_mean=this.config.image_mean??this.config.mean,this.image_std=this.config.image_std??this.config.std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.size_divisibility=this.config.size_divisibility??this.config.size_divisor,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=this.config.do_flip_channel_order??!1}async thumbnail(B,ce,Te=2){const Re=B.height,ke=B.width,tt=ce.height,ft=ce.width;let lt=Math.min(Re,tt),Ct=Math.min(ke,ft);return lt===Re&&Ct===ke?B:(Re>ke?Ct=Math.floor(ke*lt/Re):ke>Re&&(lt=Math.floor(Re*Ct/ke)),await B.resize(Ct,lt,{resample:Te}))}async crop_margin(B,ce=200){const Te=B.clone().grayscale(),Re=(0,xe.min)(Te.data)[0],tt=(0,xe.max)(Te.data)[0]-Re;if(tt===0)return B;const ft=ce/255;let lt=Te.width,Ct=Te.height,gt=0,ze=0;const Rt=Te.data;for(let Ot=0;Ot<Te.height;++Ot){const Wt=Ot*Te.width;for(let zt=0;zt<Te.width;++zt)(Rt[Wt+zt]-Re)/tt<ft&&(lt=Math.min(lt,zt),Ct=Math.min(Ct,Ot),gt=Math.max(gt,zt),ze=Math.max(ze,Ot))}return B=await B.crop([lt,Ct,gt,ze]),B}pad_image(B,ce,Te,{mode:Re="constant",center:ke=!1,constant_values:tt=0}={}){const[ft,lt,Ct]=ce;let gt,ze;if(typeof Te=="number"?(gt=Te,ze=Te):(gt=Te.width,ze=Te.height),gt!==lt||ze!==ft){const Rt=new Float32Array(gt*ze*Ct);if(Array.isArray(tt))for(let zt=0;zt<Rt.length;++zt)Rt[zt]=tt[zt%Ct];else tt!==0&&Rt.fill(tt);const[Ot,Wt]=ke?[Math.floor((gt-lt)/2),Math.floor((ze-ft)/2)]:[0,0];for(let zt=0;zt<ft;++zt){const Ut=(zt+Wt)*gt,Ht=zt*lt;for(let ur=0;ur<lt;++ur){const rr=(Ut+ur+Ot)*Ct,wr=(Ht+ur)*Ct;for(let Qe=0;Qe<Ct;++Qe)Rt[rr+Qe]=B[wr+Qe]}}if(Re==="symmetric"){if(ke)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const zt=ft-1,Ut=lt-1;for(let Ht=0;Ht<ze;++Ht){const ur=Ht*gt,rr=(0,ae.calculateReflectOffset)(Ht,zt)*lt;for(let wr=0;wr<gt;++wr){if(Ht<ft&&wr<lt)continue;const Qe=(ur+wr)*Ct,yt=(rr+(0,ae.calculateReflectOffset)(wr,Ut))*Ct;for(let Ft=0;Ft<Ct;++Ft)Rt[Qe+Ft]=B[yt+Ft]}}}B=Rt,ce=[ze,gt,Ct]}return[B,ce]}rescale(B){for(let ce=0;ce<B.length;++ce)B[ce]=this.rescale_factor*B[ce]}get_resize_output_image_size(B,ce){const[Te,Re]=B.size;let ke,tt;if(this.do_thumbnail){const{height:ft,width:lt}=ce;ke=Math.min(ft,lt)}else Number.isInteger(ce)?(ke=ce,tt=this.config.max_size??ke):ce!==void 0&&(ke=ce.shortest_edge,tt=ce.longest_edge);if(ke!==void 0||tt!==void 0){const ft=ke===void 0?1:Math.max(ke/Te,ke/Re),lt=Te*ft,Ct=Re*ft,gt=tt===void 0?1:Math.min(tt/lt,tt/Ct);let ze=Math.floor(Number((lt*gt).toFixed(2))),Rt=Math.floor(Number((Ct*gt).toFixed(2)));return this.size_divisibility!==void 0&&([ze,Rt]=oe([ze,Rt],this.size_divisibility)),[ze,Rt]}else if(ce!==void 0&&ce.width!==void 0&&ce.height!==void 0){let ft=ce.width,lt=ce.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let Ct=lt/Re,gt=ft/Te;Math.abs(1-gt)<Math.abs(1-Ct)?Ct=gt:gt=Ct,lt=ne(Ct*Re,this.config.ensure_multiple_of),ft=ne(gt*Te,this.config.ensure_multiple_of)}return[ft,lt]}else{if(this.size_divisibility!==void 0)return oe([Te,Re],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(ce)}`)}}async resize(B){const[ce,Te]=this.get_resize_output_image_size(B,this.size);return await B.resize(ce,Te,{resample:this.resample})}async preprocess(B,{do_normalize:ce=null,do_pad:Te=null,do_convert_rgb:Re=null,do_convert_grayscale:ke=null,do_flip_channel_order:tt=null}={}){this.do_crop_margin&&(B=await this.crop_margin(B));const[ft,lt]=B.size;if(Re??this.do_convert_rgb?B=B.rgb():ke&&(B=B.grayscale()),this.do_resize&&(B=await this.resize(B)),this.do_thumbnail&&(B=await this.thumbnail(B,this.size,this.resample)),this.do_center_crop){let Ot,Wt;Number.isInteger(this.crop_size)?(Ot=this.crop_size,Wt=this.crop_size):(Ot=this.crop_size.width,Wt=this.crop_size.height),B=await B.center_crop(Ot,Wt)}const Ct=[B.height,B.width];let gt=Float32Array.from(B.data),ze=[B.height,B.width,B.channels];if(this.do_rescale&&this.rescale(gt),ce??this.do_normalize){let Ot=this.image_mean;Array.isArray(this.image_mean)||(Ot=new Array(B.channels).fill(Ot));let Wt=this.image_std;if(Array.isArray(this.image_std)||(Wt=new Array(B.channels).fill(Ot)),Ot.length!==B.channels||Wt.length!==B.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${Ot.length}) and \`image_std\` (${Wt.length}) must match the number of channels in the image (${B.channels}).`);for(let zt=0;zt<gt.length;zt+=B.channels)for(let Ut=0;Ut<B.channels;++Ut)gt[zt+Ut]=(gt[zt+Ut]-Ot[Ut])/Wt[Ut]}if(Te??this.do_pad){if(this.pad_size)[gt,ze]=this.pad_image(gt,[B.height,B.width,B.channels],this.pad_size);else if(this.size_divisibility){const[Ot,Wt]=oe([ze[1],ze[0]],this.size_divisibility);[gt,ze]=this.pad_image(gt,ze,{width:Ot,height:Wt})}}if(tt??this.do_flip_channel_order){if(ze[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let Ot=0;Ot<gt.length;Ot+=3){const Wt=gt[Ot];gt[Ot]=gt[Ot+2],gt[Ot+2]=Wt}}const Rt=new be.Tensor("float32",gt,ze).permute(2,0,1);return{original_size:[lt,ft],reshaped_input_size:Ct,pixel_values:Rt}}async _call(B,...ce){Array.isArray(B)||(B=[B]);const Te=await Promise.all(B.map(ke=>this.preprocess(ke)));return{pixel_values:(0,be.stack)(Te.map(ke=>ke.pixel_values),0),original_sizes:Te.map(ke=>ke.original_size),reshaped_input_sizes:Te.map(ke=>ke.reshaped_input_size)}}}class Q extends F{post_process_semantic_segmentation(...B){return P(...B)}}class se extends F{post_process_semantic_segmentation(...B){return P(...B)}}class le extends F{}class ie extends le{}class j extends F{}class A extends F{}class L extends F{}class C extends L{}class me extends F{}class ye extends F{}class Se extends F{constructor(B){super(B),this.crop_pct=this.config.crop_pct??224/256}async resize(B){const ce=this.size?.shortest_edge;if(ce===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(ce<384){const Te=Math.floor(ce/this.crop_pct),[Re,ke]=this.get_resize_output_image_size(B,{shortest_edge:Te});B=await B.resize(Re,ke,{resample:this.resample}),B=await B.center_crop(ce,ce)}else B=await B.resize(ce,ce,{resample:this.resample});return B}}class Pe extends Se{}class Ce extends F{}class rt extends F{}class Xe extends F{constructor(B){super(B),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(ce=>ce*ce))}}class dt extends F{}class ge extends F{}class U extends F{}class de extends F{}class $e extends F{}class X extends $e{}class Ue extends F{post_process_object_detection(...B){return V(...B)}}class ct extends Ue{}class st extends F{post_process_object_detection(...B){return V(...B)}}class ot extends F{}class De extends F{}class at extends F{pad_image(B,ce,Te,Re={}){const[ke,tt,ft]=ce;let lt=this.image_mean;Array.isArray(this.image_mean)||(lt=new Array(ft).fill(lt));let Ct=this.image_std;Array.isArray(Ct)||(Ct=new Array(ft).fill(lt));const gt=lt.map((ze,Rt)=>-ze/Ct[Rt]);return super.pad_image(B,ce,Te,{center:!0,constant_values:gt,...Re})}}class Et extends at{}class Fe extends F{async _call(B){const ce=await super._call(B),Te=[ce.pixel_values.dims[0],64,64],Re=new be.Tensor("int64",new BigInt64Array(Te.reduce((ke,tt)=>ke*tt)).fill(1n),Te);return{...ce,pixel_mask:Re}}post_process_object_detection(...B){return V(...B)}remove_low_and_no_objects(B,ce,Te,Re){let ke=[],tt=[],ft=[];for(let lt=0;lt<B.dims[0];++lt){let Ct=B[lt],gt=ce[lt],ze=(0,xe.max)(Ct.data)[1];if(ze===Re)continue;let Ot=(0,xe.softmax)(Ct.data)[ze];Ot>Te&&(ke.push(gt),tt.push(Ot),ft.push(ze))}return[ke,tt,ft]}check_segment_validity(B,ce,Te,Re=.5,ke=.8){let tt=[],ft=0,lt=0;const Ct=ce[Te].data;for(let ze=0;ze<B.length;++ze)B[ze]===Te&&(tt.push(ze),++ft),Ct[ze]>=Re&&++lt;let gt=ft>0&&lt>0;return gt&&(gt=ft/lt>ke),[gt,tt]}compute_segments(B,ce,Te,Re,ke,tt=null,ft=null){let[lt,Ct]=ft??B[0].dims,gt=new be.Tensor("int32",new Int32Array(lt*Ct),[lt,Ct]),ze=[];if(ft!==null)for(let Ut=0;Ut<B.length;++Ut)B[Ut]=(0,be.interpolate)(B[Ut],ft,"bilinear",!1);let Rt=new Int32Array(B[0].data.length),Ot=new Float32Array(B[0].data.length);for(let Ut=0;Ut<B.length;++Ut){let Ht=ce[Ut];const ur=B[Ut].data;for(let rr=0;rr<ur.length;++rr)ur[rr]*=Ht,ur[rr]>Ot[rr]&&(Rt[rr]=Ut,Ot[rr]=ur[rr])}let Wt=0;const zt=gt.data;for(let Ut=0;Ut<Te.length;++Ut){let Ht=Te[Ut],[ur,rr]=this.check_segment_validity(Rt,B,Ut,Re,ke);if(ur){++Wt;for(let wr of rr)zt[wr]=Wt;ze.push({id:Wt,label_id:Ht,score:ce[Ut]})}}return[gt,ze]}post_process_panoptic_segmentation(B,ce=.5,Te=.5,Re=.8,ke=null,tt=null){ke===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),ke=new Set);const ft=B.logits,Ct=B.pred_masks.sigmoid();let[gt,ze,Rt]=ft.dims;if(Rt-=1,tt!==null&&tt.length!==gt)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let Ot=[];for(let Wt=0;Wt<gt;++Wt){let zt=tt!==null?tt[Wt]:null,Ut=ft[Wt],Ht=Ct[Wt],[ur,rr,wr]=this.remove_low_and_no_objects(Ut,Ht,ce,Rt);if(wr.length===0){let[Ft,Zr]=zt??Ht.dims.slice(-2),Xn=new be.Tensor("int32",new Int32Array(Ft*Zr).fill(-1),[Ft,Zr]);Ot.push({segmentation:Xn,segments_info:[]});continue}let[Qe,yt]=this.compute_segments(ur,rr,wr,Te,Re,ke,zt);Ot.push({segmentation:Qe,segments_info:yt})}return Ot}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class H extends F{post_process_object_detection(...B){return V(...B)}}class Ee extends F{reshape_input_points(B,ce,Te,Re=!1){B=structuredClone(B);let ke=(0,ae.calculateDimensions)(B);if(ke.length===3)Re||(ke=[1,...ke]),B=[B];else if(ke.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 tt=0;tt<B.length;++tt){let ft=ce[tt],lt=Te[tt],Ct=[lt[0]/ft[0],lt[1]/ft[1]];for(let gt=0;gt<B[tt].length;++gt)for(let ze=0;ze<B[tt][gt].length;++ze)for(let Rt=0;Rt<B[tt][gt][ze].length;++Rt)B[tt][gt][ze][Rt]*=Ct[Rt%2]}return new be.Tensor("float32",Float32Array.from(B.flat(1/0)),ke)}add_input_labels(B,ce){let Te=(0,ae.calculateDimensions)(B);if(Te.length===2)Te=[1,...Te],B=[B];else if(Te.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(Te.some((Re,ke)=>Re!==ce.dims[ke]))throw Error(`The first ${Te.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new be.Tensor("int64",B.flat(1/0).map(BigInt),Te)}async _call(B,{input_points:ce=null,input_labels:Te=null,input_boxes:Re=null}={}){const ke=await super._call(B);if(ce&&(ke.input_points=this.reshape_input_points(ce,ke.original_sizes,ke.reshaped_input_sizes)),Te){if(!ke.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");ke.input_labels=this.add_input_labels(Te,ke.input_points)}return Re&&(ke.input_boxes=this.reshape_input_points(Re,ke.original_sizes,ke.reshaped_input_sizes,!0)),ke}async post_process_masks(B,ce,Te,{mask_threshold:Re=0,binarize:ke=!0,pad_size:tt=null}={}){const ft=[];tt=tt??this.pad_size;const lt=[tt.height,tt.width];for(let Ct=0;Ct<ce.length;++Ct){const gt=ce[Ct],ze=Te[Ct];let Rt=await(0,be.interpolate_4d)(B[Ct],{mode:"bilinear",size:lt});if(Rt=Rt.slice(null,null,[0,ze[0]],[0,ze[1]]),Rt=await(0,be.interpolate_4d)(Rt,{mode:"bilinear",size:gt}),ke){const Ot=Rt.data,Wt=new Uint8Array(Ot.length);for(let zt=0;zt<Ot.length;++zt)Ot[zt]>Re&&(Wt[zt]=1);Rt=new be.Tensor("bool",Wt,Rt.dims)}ft.push(Rt)}return ft}generate_crop_boxes(B,ce,{crop_n_layers:Te=0,overlap_ratio:Re=512/1500,points_per_crop:ke=32,crop_n_points_downscale_factor:tt=1}={}){}}class qe extends F{pad_image(B,ce,Te,Re={}){const[ke,tt,ft]=ce;return super.pad_image(B,ce,{width:tt+(Te-tt%Te)%Te,height:ke+(Te-ke%Te)%Te},{mode:"symmetric",center:!1,constant_values:-1,...Re})}}class je extends F{async _call(B,ce){Array.isArray(B)||(B=[B]),Array.isArray(ce)||(ce=[ce]);const Te=await Promise.all(B.map(tt=>this.preprocess(tt))),Re=await Promise.all(ce.map(tt=>this.preprocess(tt,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,be.stack)(Te.map((tt,ft)=>(0,be.cat)([tt.pixel_values,Re[ft].pixel_values],0)),0),original_sizes:Te.map(tt=>tt.original_size),reshaped_input_sizes:Te.map(tt=>tt.reshaped_input_size)}}}class Ve extends _e{constructor(B){super(B),this.config.mel_filters??=(0,R.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=(0,R.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(B){const ce=await(0,R.spectrogram)(B,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),Te=ce.data,Re=(0,xe.max)(Te)[0];for(let ke=0;ke<Te.length;++ke)Te[ke]=(Math.max(Te[ke],Re-8)+4)/4;return ce}async _call(B){K(B,"WhisperFeatureExtractor");let ce;return B.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),ce=B.slice(0,this.config.n_samples)):(ce=new Float32Array(this.config.n_samples),ce.set(B)),{input_features:(await this._extract_fbank_features(ce)).unsqueeze_(0)}}}class Ge extends _e{_zero_mean_unit_var_norm(B){const Te=B.reduce((ke,tt)=>ke+tt,0)/B.length,Re=B.reduce((ke,tt)=>ke+(tt-Te)**2,0)/B.length;return B.map(ke=>(ke-Te)/Math.sqrt(Re+1e-7))}async _call(B){K(B,"Wav2Vec2FeatureExtractor"),B instanceof Float64Array&&(B=new Float32Array(B));let ce=B;this.config.do_normalize&&(ce=this._zero_mean_unit_var_norm(ce));const Te=[1,ce.length];return{input_values:new be.Tensor("float32",ce,Te),attention_mask:new be.Tensor("int64",new BigInt64Array(ce.length).fill(1n),Te)}}}class ut extends _e{constructor(B){super(B);const ce=this.config.sampling_rate,Te=(0,R.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ce/2),ce,null,"kaldi",!0);for(let Re=0;Re<Te.length;++Re)Te[Re].push(0);this.mel_filters=Te,this.window=(0,R.window_function)(400,"povey",{periodic:!1})}async _extract_fbank_features(B,ce){return B=B.map(Te=>Te*32768),(0,R.spectrogram)(B,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:ce,transpose:!0})}async _call(B,{padding:ce=!0,pad_to_multiple_of:Te=2,do_normalize_per_mel_bins:Re=!0,return_attention_mask:ke=!0}={}){K(B,"SeamlessM4TFeatureExtractor");let tt=await this._extract_fbank_features(B,this.config.max_length);if(Re){const[Wt,zt]=tt.dims,Ut=tt.data;for(let Ht=0;Ht<zt;++Ht){let ur=0;for(let yt=0;yt<Wt;++yt)ur+=Ut[yt*zt+Ht];const rr=ur/Wt;let wr=0;for(let yt=0;yt<Wt;++yt)wr+=(Ut[yt*zt+Ht]-rr)**2;wr/=Wt-1;const Qe=Math.sqrt(wr+1e-7);for(let yt=0;yt<Wt;++yt){const Ft=yt*zt+Ht;Ut[Ft]=(Ut[Ft]-rr)/Qe}}}let ft;if(ce){const[Wt,zt]=tt.dims,Ut=tt.data,Ht=Wt%Te;if(Ht>0){const ur=new Float32Array(zt*(Wt+Ht));ur.set(Ut),ur.fill(this.config.padding_value,Ut.length);const rr=Wt+Ht;tt=new be.Tensor(tt.type,ur,[rr,zt]),ke&&(ft=new be.Tensor("int64",new BigInt64Array(rr),[1,rr]),ft.data.fill(1n,0,Wt))}}const[lt,Ct]=tt.dims,gt=this.config.stride;if(lt%gt!==0)throw new Error(`The number of frames (${lt}) must be a multiple of the stride (${gt}).`);const Rt=tt.view(1,Math.floor(lt/gt),Ct*gt),Ot={input_features:Rt};if(ke){const Wt=Rt.dims[1],zt=new BigInt64Array(Wt);if(ft){const Ut=ft.data;for(let Ht=1,ur=0;Ht<lt;Ht+=gt,++ur)zt[ur]=Ut[Ht]}else zt.fill(1n);Ot.attention_mask=new be.Tensor("int64",zt,[1,Wt])}return Ot}}class ht extends _e{constructor(B){super(B);const ce=this.config.sampling_rate,Te=(0,R.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ce/2),ce,null,"kaldi",!0);for(let Re=0;Re<Te.length;++Re)Te[Re].push(0);this.mel_filters=Te,this.window=(0,R.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(B,ce){return(0,R.spectrogram)(B,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:ce,transpose:!0})}async _call(B){K(B,"ASTFeatureExtractor");const ce=await this._extract_fbank_features(B,this.config.max_length);if(this.config.do_normalize){const Te=this.std*2,Re=ce.data;for(let ke=0;ke<Re.length;++ke)Re[ke]=(Re[ke]-this.mean)/Te}return{input_values:ce.unsqueeze_(0)}}}class Tt extends _e{constructor(B){super(B),this.mel_filters=(0,R.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,R.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,R.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(B,ce,Te,Re){let ke;const tt=B.length-ce;if(tt>0)if(Te==="rand_trunc"){const ft=Math.floor(Math.random()*(tt+1));B=B.subarray(ft,ft+ce),ke=await this._extract_fbank_features(B,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${Te}" not implemented`);else{if(tt<0){let ft=new Float64Array(ce);if(ft.set(B),Re==="repeat")for(let lt=B.length;lt<ce;lt+=B.length)ft.set(B.subarray(0,Math.min(B.length,ce-lt)),lt);else if(Re==="repeatpad")for(let lt=B.length;lt<-tt;lt+=B.length)ft.set(B,lt);B=ft}if(Te==="fusion")throw new Error(`Truncation strategy "${Te}" not implemented`);ke=await this._extract_fbank_features(B,this.mel_filters_slaney,this.config.nb_max_samples)}return ke.unsqueeze_(0)}async _extract_fbank_features(B,ce,Te=null){return(0,R.spectrogram)(B,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:ce,log_mel:"dB",max_num_frames:Te,do_pad:!1,transpose:!0})}async _call(B,{max_length:ce=null}={}){return K(B,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(B,ce??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}class St extends _e{async _call(B){K(B,"PyAnnoteFeatureExtractor"),B instanceof Float64Array&&(B=new Float32Array(B));const ce=[1,1,B.length];return{input_values:new be.Tensor("float32",B,ce)}}samples_to_frames(B){return(B-this.config.offset)/this.config.step}post_process_speaker_diarization(B,ce){const Te=ce/this.samples_to_frames(ce)/this.config.sampling_rate,Re=[];for(const ke of B.tolist()){const tt=[];let ft=-1;for(let lt=0;lt<ke.length;++lt){const Ct=(0,xe.softmax)(ke[lt]),[gt,ze]=(0,xe.max)(Ct),[Rt,Ot]=[lt,lt+1];ze!==ft?(ft=ze,tt.push({id:ze,start:Rt,end:Ot,score:gt})):(tt.at(-1).end=Ot,tt.at(-1).score+=gt)}Re.push(tt.map(({id:lt,start:Ct,end:gt,score:ze})=>({id:lt,start:Ct*Te,end:gt*Te,confidence:ze/(gt-Ct)})))}return Re}}class M extends _e{constructor(B){super(B);const ce=this.config.sampling_rate,Te=(0,R.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ce/2),ce,null,"kaldi",!0);for(let Re=0;Re<Te.length;++Re)Te[Re].push(0);this.mel_filters=Te,this.window=(0,R.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(B){return B=B.map(ce=>ce*32768),(0,R.spectrogram)(B,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,transpose:!0,min_num_frames:this.min_num_frames})}async _call(B){K(B,"WeSpeakerFeatureExtractor");const ce=(await this._extract_fbank_features(B)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const Te=ce.mean(1).data,Re=ce.data,[ke,tt,ft]=ce.dims;for(let lt=0;lt<ke;++lt){const Ct=lt*tt*ft,gt=lt*ft;for(let ze=0;ze<tt;++ze){const Rt=Ct+ze*ft;for(let Ot=0;Ot<ft;++Ot)Re[Rt+Ot]-=Te[gt+Ot]}}}return{input_features:ce}}}class W extends _e{}class E extends I.Callable{constructor(B){super(),this.feature_extractor=B}async _call(B,...ce){return await this.feature_extractor(B,...ce)}}class te extends E{async _call(...B){return await this.feature_extractor(...B)}post_process_masks(...B){return this.feature_extractor.post_process_masks(...B)}reshape_input_points(...B){return this.feature_extractor.reshape_input_points(...B)}}class pe extends E{async _call(B){return await this.feature_extractor(B)}}class Je extends E{async _call(B){return await this.feature_extractor(B)}}class He extends E{async _call(B){return await this.feature_extractor(B)}post_process_speaker_diarization(...B){return this.feature_extractor.post_process_speaker_diarization(...B)}}class Mt extends E{async _call(B){return await this.feature_extractor(B)}}class nt extends E{}class pt extends E{constructor(B){super(B);const{tasks_answer_post_processing_type:ce,task_prompts_without_inputs:Te,task_prompts_with_input:Re}=B.config;this.tasks_answer_post_processing_type=new Map(Object.entries(ce??{})),this.task_prompts_without_inputs=new Map(Object.entries(Te??{})),this.task_prompts_with_input=new Map(Object.entries(Re??{})),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(B){typeof B=="string"&&(B=[B]);const ce=[];for(const Te of B)if(this.task_prompts_without_inputs.has(Te))ce.push(this.task_prompts_without_inputs.get(Te));else{for(const[Re,ke]of this.task_prompts_with_input)if(Te.includes(Re)){ce.push(ke.replaceAll("{input}",Te).replaceAll(Re,""));break}ce.length!==B.length&&ce.push(Te)}return ce}post_process_generation(B,ce,Te){const Re=this.tasks_answer_post_processing_type.get(ce)??"pure_text";B=B.replaceAll("<s>","").replaceAll("</s>","");let ke;switch(Re){case"pure_text":ke=B;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const tt=Re==="ocr"?"quad_boxes":"bboxes",ft=B.matchAll(this.regexes[tt]),lt=[],Ct=[];for(const[gt,ze,...Rt]of ft)lt.push(ze?ze.trim():lt.at(-1)??""),Ct.push(Rt.map((Ot,Wt)=>(Number(Ot)+.5)/this.size_per_bin*Te[Wt%2]));ke={labels:lt,[tt]:Ct};break;default:throw new Error(`Task "${ce}" (of type "${Re}") not yet implemented.`)}return{[ce]:ke}}}class Bt{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor:F,WhisperFeatureExtractor:Ve,ViTFeatureExtractor:Ce,MobileViTFeatureExtractor:$e,MobileViTImageProcessor:X,MobileNetV1FeatureExtractor:dt,MobileNetV2FeatureExtractor:ge,MobileNetV3FeatureExtractor:U,MobileNetV4FeatureExtractor:de,OwlViTFeatureExtractor:Ue,Owlv2ImageProcessor:ct,CLIPFeatureExtractor:L,CLIPImageProcessor:C,Florence2Processor:pt,ChineseCLIPFeatureExtractor:me,SiglipImageProcessor:ye,ConvNextFeatureExtractor:Se,ConvNextImageProcessor:Pe,SegformerFeatureExtractor:se,SapiensFeatureExtractor:Q,BitImageProcessor:j,DPTImageProcessor:ie,DPTFeatureExtractor:le,GLPNFeatureExtractor:A,BeitFeatureExtractor:De,DeiTFeatureExtractor:ot,DetrFeatureExtractor:Fe,RTDetrImageProcessor:st,YolosFeatureExtractor:H,DonutFeatureExtractor:at,NougatImageProcessor:Et,EfficientNetImageProcessor:Xe,ViTImageProcessor:rt,VitMatteImageProcessor:je,SamImageProcessor:Ee,Swin2SRImageProcessor:qe,Wav2Vec2FeatureExtractor:Ge,SeamlessM4TFeatureExtractor:ut,SpeechT5FeatureExtractor:W,ASTFeatureExtractor:ht,ClapFeatureExtractor:Tt,PyAnnoteFeatureExtractor:St,WeSpeakerFeatureExtractor:M};static PROCESSOR_CLASS_MAPPING={WhisperProcessor:pe,Wav2Vec2ProcessorWithLM:Je,PyAnnoteProcessor:He,SamProcessor:te,SpeechT5Processor:Mt,OwlViTProcessor:nt,Florence2Processor:pt};static async from_pretrained(B,{progress_callback:ce=null,config:Te=null,cache_dir:Re=null,local_files_only:ke=!1,revision:tt="main"}={}){let ft=Te??await(0,he.getModelJSON)(B,"preprocessor_config.json",!0,{progress_callback:ce,config:Te,cache_dir:Re,local_files_only:ke,revision:tt}),lt=ft.feature_extractor_type??ft.image_processor_type,Ct=this.FEATURE_EXTRACTOR_CLASS_MAPPING[lt];if(!Ct)if(ft.size!==void 0)console.warn(`Feature extractor type "${lt}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),Ct=F;else throw new Error(`Unknown Feature Extractor type: ${lt}`);let gt=this.PROCESSOR_CLASS_MAPPING[ft.processor_class]??E,ze=new Ct(ft);return new gt(ze)}}},"./src/tokenizers.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{AlbertTokenizer:()=>Ot,AutoTokenizer:()=>wn,BartTokenizer:()=>Hr,BertTokenizer:()=>Rt,BlenderbotSmallTokenizer:()=>Ts,BlenderbotTokenizer:()=>us,BloomTokenizer:()=>Lr,CLIPTokenizer:()=>Kt,CamembertTokenizer:()=>yt,CodeGenTokenizer:()=>ls,CodeLlamaTokenizer:()=>Us,CohereTokenizer:()=>Nr,ConvBertTokenizer:()=>rr,DebertaTokenizer:()=>Ut,DebertaV2Tokenizer:()=>Ht,DistilBertTokenizer:()=>Qe,ElectraTokenizer:()=>Zr,EsmTokenizer:()=>Un,FalconTokenizer:()=>bs,GPT2Tokenizer:()=>is,GPTNeoXTokenizer:()=>Ms,GemmaTokenizer:()=>as,Grok1Tokenizer:()=>Cn,HerbertTokenizer:()=>ur,LlamaTokenizer:()=>In,M2M100Tokenizer:()=>Yn,MBart50Tokenizer:()=>Xr,MBartTokenizer:()=>pn,MPNetTokenizer:()=>ys,MarianTokenizer:()=>vs,MobileBertTokenizer:()=>Wt,NllbTokenizer:()=>Fn,NougatTokenizer:()=>ds,PreTrainedTokenizer:()=>ze,Qwen2Tokenizer:()=>Ws,RoFormerTokenizer:()=>wr,RobertaTokenizer:()=>Qn,SiglipTokenizer:()=>Zn,SpeechT5Tokenizer:()=>Ss,SqueezeBertTokenizer:()=>zt,T5Tokenizer:()=>Xn,TokenizerModel:()=>Se,VitsTokenizer:()=>$s,Wav2Vec2CTCTokenizer:()=>xs,WhisperTokenizer:()=>os,XLMRobertaTokenizer:()=>ws,XLMTokenizer:()=>Ft,is_chinese_char:()=>ie});var I=N("./src/utils/generic.js"),ae=N("./src/utils/core.js"),he=N("./src/utils/hub.js"),xe=N("./src/utils/maths.js"),be=N("./src/utils/tensor.js"),R=N("./src/utils/data-structures.js"),$=N("./node_modules/@huggingface/jinja/dist/index.js"),V=N("./src/models/whisper/common_whisper.js"),P=N("./src/utils/constants.js");async function K(we,m){const z=await Promise.all([(0,he.getModelJSON)(we,"tokenizer.json",!0,m),(0,he.getModelJSON)(we,"tokenizer_config.json",!0,m)]);return m.legacy!==null&&(z[1].legacy=m.legacy),z}function ne(we,m){const z=[];let Y=0;for(const J of we.matchAll(m)){const ve=J[0];Y<J.index&&z.push(we.slice(Y,J.index)),ve.length>0&&z.push(ve),Y=J.index+ve.length}return Y<we.length&&z.push(we.slice(Y)),z}function oe(we,m=!0){if(we.Regex!==void 0){let z=we.Regex.replace(/\\([#&~])/g,"$1");for(const[Y,J]of me)z=z.replaceAll(Y,J);return new RegExp(z,"gu")}else if(we.String!==void 0){const z=(0,ae.escapeRegExp)(we.String);return new RegExp(m?z:`(${z})`,"gu")}else return console.warn("Unknown pattern type:",we),null}function _e(we){return new Map(Object.entries(we))}function F(we){const m=we.dims;switch(m.length){case 1:return we.tolist();case 2:if(m[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return we.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${m.length}.`)}}function Q(we){return we.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function se(we){return we.replace(/[\u0300-\u036f]/g,"")}function le(we){return se(we.toLowerCase())}function ie(we){return we>=19968&&we<=40959||we>=13312&&we<=19903||we>=131072&&we<=173791||we>=173824&&we<=177983||we>=177984&&we<=178207||we>=178208&&we<=183983||we>=63744&&we<=64255||we>=194560&&we<=195103}function j(we,m,z){const Y=[];let J=0;for(;J<we.length;){if(Y.push(we[J]),(z.get(we[J])??m)!==m){++J;continue}for(;J<we.length&&(z.get(we[J])??m)===m;)++J}return Y}function A(we){return we.match(/\S+/g)||[]}const L="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",C=new RegExp(`^[${L}]+$`,"gu"),me=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class ye{constructor(m){this.content=m.content,this.id=m.id,this.single_word=m.single_word??!1,this.lstrip=m.lstrip??!1,this.rstrip=m.rstrip??!1,this.special=m.special??!1,this.normalized=m.normalized??null}}class Se extends I.Callable{constructor(m){super(),this.config=m,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(m,...z){switch(m.type){case"WordPiece":return new Pe(m);case"Unigram":return new Ce(m,...z);case"BPE":return new dt(m);default:if(m.vocab)return new ge(m,...z);throw new Error(`Unknown TokenizerModel type: ${m.type}`)}}_call(m){let z=this.encode(m);return this.fuse_unk&&(z=j(z,this.unk_token_id,this.tokens_to_ids)),z}encode(m){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(m){return m.map(z=>this.tokens_to_ids.get(z)??this.unk_token_id)}convert_ids_to_tokens(m){return m.map(z=>this.vocab[z]??this.unk_token)}}class Pe extends Se{constructor(m){super(m),this.tokens_to_ids=_e(m.vocab),this.unk_token_id=this.tokens_to_ids.get(m.unk_token),this.unk_token=m.unk_token,this.max_input_chars_per_word=m.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[z,Y]of this.tokens_to_ids)this.vocab[Y]=z}encode(m){const z=[];for(const Y of m){const J=[...Y];if(J.length>this.max_input_chars_per_word){z.push(this.unk_token);continue}let ve=!1,Be=0;const xt=[];for(;Be<J.length;){let vt=J.length,kt=null;for(;Be<vt;){let bt=J.slice(Be,vt).join("");if(Be>0&&(bt=this.config.continuing_subword_prefix+bt),this.tokens_to_ids.has(bt)){kt=bt;break}--vt}if(kt===null){ve=!0;break}xt.push(kt),Be=vt}ve?z.push(this.unk_token):z.push(...xt)}return z}}class Ce extends Se{constructor(m,z){super(m);const Y=m.vocab.length;this.vocab=new Array(Y),this.scores=new Array(Y);for(let J=0;J<Y;++J){const ve=m.vocab[J];this.vocab[J]=ve[0],this.scores[J]=ve[1]}this.unk_token_id=m.unk_id,this.unk_token=this.vocab[m.unk_id],this.tokens_to_ids=new Map(this.vocab.map((J,ve)=>[J,ve])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=z.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=(0,xe.min)(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new R.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(m){const z=m.sentence,Y=z.length;let J=0;for(;J<Y;){let Be=!1;for(let xt of this.trie.commonPrefixSearch(z.slice(J))){const vt=this.tokens_to_ids.get(xt),kt=this.scores[vt],bt=xt.length;m.insert(J,bt,kt,vt),!Be&&bt===1&&(Be=!0)}Be||m.insert(J,1,this.unkScore,this.unk_token_id),J+=1}}tokenize(m){const z=new R.TokenLattice(m,this.bosTokenId,this.eosTokenId);return this.populateNodes(z),z.tokens()}encode(m){const z=[];for(const Y of m){const J=this.tokenize(Y);z.push(...J)}return z}}const rt=(()=>{const we=[...Array.from({length:94},(J,ve)=>ve+33),...Array.from({length:12},(J,ve)=>ve+161),...Array.from({length:82},(J,ve)=>ve+174)],m=we.slice();let z=0;for(let J=0;J<256;++J)we.includes(J)||(we.push(J),m.push(256+z),z+=1);const Y=m.map(J=>String.fromCharCode(J));return Object.fromEntries(we.map((J,ve)=>[J,Y[ve]]))})(),Xe=(0,ae.reverseDictionary)(rt);class dt extends Se{constructor(m){super(m),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=_e(m.vocab),this.unk_token_id=this.tokens_to_ids.get(m.unk_token),this.unk_token=m.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[z,Y]of this.tokens_to_ids)this.vocab[Y]=z;this.bpe_ranks=new Map(m.merges.map((z,Y)=>[z,Y])),this.merges=m.merges.map(z=>z.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=m.end_of_word_suffix,this.continuing_subword_suffix=m.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(m){if(m.length===0)return[];const z=this.cache.get(m);if(z!==void 0)return z;const Y=Array.from(m);this.end_of_word_suffix&&(Y[Y.length-1]+=this.end_of_word_suffix);let J=[];if(Y.length>1){const ve=new R.PriorityQueue((vt,kt)=>vt.score<kt.score);let Be={token:Y[0],bias:0,prev:null,next:null},xt=Be;for(let vt=1;vt<Y.length;++vt){const kt={bias:vt/Y.length,token:Y[vt],prev:xt,next:null};xt.next=kt,this._add_node(ve,xt),xt=kt}for(;!ve.isEmpty();){const vt=ve.pop();if(vt.deleted||!vt.next||vt.next.deleted)continue;if(vt.deleted=!0,vt.next.deleted=!0,vt.prev){const bt={...vt.prev};vt.prev.deleted=!0,vt.prev=bt,bt.prev?bt.prev.next=bt:Be=bt}const kt={token:vt.token+vt.next.token,bias:vt.bias,prev:vt.prev,next:vt.next.next};kt.prev?(kt.prev.next=kt,this._add_node(ve,kt.prev)):Be=kt,kt.next&&(kt.next.prev=kt,this._add_node(ve,kt))}for(let vt=Be;vt!==null;vt=vt.next)J.push(vt.token)}else J=Y;if(this.continuing_subword_suffix)for(let ve=0;ve<J.length-1;++ve)J[ve]+=this.continuing_subword_suffix;return this.cache.set(m,J),J}_add_node(m,z){const Y=this.bpe_ranks.get(z.token+this.BPE_SPLIT_TOKEN+z.next.token);Y!==void 0&&(z.score=Y+z.bias,m.push(z))}encode(m){const z=[];for(const Y of m){if(this.ignore_merges&&this.tokens_to_ids.has(Y)){z.push(Y);continue}const J=this.bpe(Y);for(const ve of J)this.tokens_to_ids.has(ve)?z.push(ve):this.byte_fallback?z.push(...Array.from(this.text_encoder.encode(ve)).map(Be=>`<0x${Be.toString(16).toUpperCase().padStart(2,"0")}>`)):z.push(this.unk_token)}return z}}class ge extends Se{constructor(m,z){super(m),this.tokens_to_ids=_e(z.target_lang?m.vocab[z.target_lang]:m.vocab),this.bos_token=z.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=z.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=z.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=z.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[Y,J]of this.tokens_to_ids)this.vocab[J]=Y}encode(m){return m}}class U extends I.Callable{constructor(m){super(),this.config=m}static fromConfig(m){if(m===null)return null;switch(m.type){case"BertNormalizer":return new Et(m);case"Precompiled":return new Te(m);case"Sequence":return new at(m);case"Replace":return new de(m);case"NFC":return new $e(m);case"NFKC":return new X(m);case"NFKD":return new Ue(m);case"Strip":return new ct(m);case"StripAccents":return new st(m);case"Lowercase":return new ot(m);case"Prepend":return new De(m);default:throw new Error(`Unknown Normalizer type: ${m.type}`)}}normalize(m){throw Error("normalize should be implemented in subclass.")}_call(m){return this.normalize(m)}}class de extends U{normalize(m){const z=oe(this.config.pattern);return z===null?m:m.replaceAll(z,this.config.content)}}class $e extends U{normalize(m){return m=m.normalize("NFC"),m}}class X extends U{normalize(m){return m=m.normalize("NFKC"),m}}class Ue extends U{normalize(m){return m=m.normalize("NFKD"),m}}class ct extends U{normalize(m){return this.config.strip_left&&this.config.strip_right?m=m.trim():(this.config.strip_left&&(m=m.trimStart()),this.config.strip_right&&(m=m.trimEnd())),m}}class st extends U{normalize(m){return m=se(m),m}}class ot extends U{normalize(m){return m=m.toLowerCase(),m}}class De extends U{normalize(m){return m=this.config.prepend+m,m}}class at extends U{constructor(m){super(m),this.normalizers=m.normalizers.map(z=>U.fromConfig(z))}normalize(m){return this.normalizers.reduce((z,Y)=>Y.normalize(z),m)}}class Et extends U{_tokenize_chinese_chars(m){const z=[];for(let Y=0;Y<m.length;++Y){const J=m[Y],ve=J.charCodeAt(0);ie(ve)?(z.push(" "),z.push(J),z.push(" ")):z.push(J)}return z.join("")}stripAccents(m){return m.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(m){switch(m){case"	":case`
`:case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(m)}}_clean_text(m){const z=[];for(const Y of m){const J=Y.charCodeAt(0);J===0||J===65533||this._is_control(Y)||(/^\s$/.test(Y)?z.push(" "):z.push(Y))}return z.join("")}normalize(m){return this.config.clean_text&&(m=this._clean_text(m)),this.config.handle_chinese_chars&&(m=this._tokenize_chinese_chars(m)),this.config.lowercase?(m=m.toLowerCase(),this.config.strip_accents!==!1&&(m=this.stripAccents(m))):this.config.strip_accents&&(m=this.stripAccents(m)),m}}class Fe extends I.Callable{static fromConfig(m){if(m===null)return null;switch(m.type){case"BertPreTokenizer":return new H(m);case"Sequence":return new Re(m);case"Whitespace":return new ke(m);case"WhitespaceSplit":return new tt(m);case"Metaspace":return new B(m);case"ByteLevel":return new Ee(m);case"Split":return new qe(m);case"Punctuation":return new je(m);case"Digits":return new Ve(m);case"Replace":return new ft(m);default:throw new Error(`Unknown PreTokenizer type: ${m.type}`)}}pre_tokenize_text(m,z){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(m,z){return(Array.isArray(m)?m.map(Y=>this.pre_tokenize_text(Y,z)):this.pre_tokenize_text(m,z)).flat()}_call(m,z){return this.pre_tokenize(m,z)}}class H extends Fe{constructor(m){super(),this.pattern=new RegExp(`[^\\s${L}]+|[${L}]`,"gu")}pre_tokenize_text(m,z){return m.trim().match(this.pattern)||[]}}class Ee extends Fe{constructor(m){super(),this.config=m,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=rt,this.text_encoder=new TextEncoder}pre_tokenize_text(m,z){return this.add_prefix_space&&!m.startsWith(" ")&&(m=" "+m),(this.use_regex?m.match(this.pattern)||[]:[m]).map(J=>Array.from(this.text_encoder.encode(J),ve=>this.byte_encoder[ve]).join(""))}}class qe extends Fe{constructor(m){super(),this.config=m,this.pattern=oe(this.config.pattern,this.config.invert)}pre_tokenize_text(m,z){return this.pattern===null?[]:this.config.invert?m.match(this.pattern)||[]:ne(m,this.pattern)}}class je extends Fe{constructor(m){super(),this.config=m,this.pattern=new RegExp(`[^${L}]+|[${L}]+`,"gu")}pre_tokenize_text(m,z){return m.match(this.pattern)||[]}}class Ve extends Fe{constructor(m){super(),this.config=m;const z=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(z,"gu")}pre_tokenize_text(m,z){return m.match(this.pattern)||[]}}class Ge extends I.Callable{constructor(m){super(),this.config=m}static fromConfig(m){if(m===null)return null;switch(m.type){case"TemplateProcessing":return new Tt(m);case"ByteLevel":return new St(m);case"RobertaProcessing":return new ht(m);case"BertProcessing":return new ut(m);case"Sequence":return new M(m);default:throw new Error(`Unknown PostProcessor type: ${m.type}`)}}post_process(m,...z){throw Error("post_process should be implemented in subclass.")}_call(m,...z){return this.post_process(m,...z)}}class ut extends Ge{constructor(m){super(m),this.cls=m.cls[0],this.sep=m.sep[0]}post_process(m,z=null,{add_special_tokens:Y=!0}={}){Y&&(m=(0,ae.mergeArrays)([this.cls],m,[this.sep]));let J=new Array(m.length).fill(0);if(z!==null){const ve=Y&&this instanceof ht?[this.sep]:[],Be=Y?[this.sep]:[];m=(0,ae.mergeArrays)(m,ve,z,Be),J=(0,ae.mergeArrays)(J,new Array(z.length+ve.length+Be.length).fill(1))}return{tokens:m,token_type_ids:J}}}class ht extends ut{}class Tt extends Ge{constructor(m){super(m),this.single=m.single,this.pair=m.pair}post_process(m,z=null,{add_special_tokens:Y=!0}={}){const J=z===null?this.single:this.pair;let ve=[],Be=[];for(const xt of J)"SpecialToken"in xt?Y&&(ve.push(xt.SpecialToken.id),Be.push(xt.SpecialToken.type_id)):"Sequence"in xt&&(xt.Sequence.id==="A"?(ve=(0,ae.mergeArrays)(ve,m),Be=(0,ae.mergeArrays)(Be,new Array(m.length).fill(xt.Sequence.type_id))):xt.Sequence.id==="B"&&(ve=(0,ae.mergeArrays)(ve,z),Be=(0,ae.mergeArrays)(Be,new Array(z.length).fill(xt.Sequence.type_id))));return{tokens:ve,token_type_ids:Be}}}class St extends Ge{post_process(m,z=null){return z&&(m=(0,ae.mergeArrays)(m,z)),{tokens:m}}}class M extends Ge{constructor(m){super(m),this.processors=m.processors.map(z=>Ge.fromConfig(z))}post_process(m,z=null,Y={}){let J;for(const ve of this.processors)if(ve instanceof St)m=ve.post_process(m).tokens,z&&(z=ve.post_process(z).tokens);else{const Be=ve.post_process(m,z,Y);m=Be.tokens,J=Be.token_type_ids}return{tokens:m,token_type_ids:J}}}class W extends I.Callable{constructor(m){super(),this.config=m,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=m.trim_offsets}static fromConfig(m){if(m===null)return null;switch(m.type){case"WordPiece":return new He(m);case"Metaspace":return new ce(m);case"ByteLevel":return new Mt(m);case"Replace":return new E(m);case"ByteFallback":return new te(m);case"Fuse":return new pe(m);case"Strip":return new Je(m);case"Sequence":return new pt(m);case"CTC":return new nt(m);case"BPEDecoder":return new Bt(m);default:throw new Error(`Unknown Decoder type: ${m.type}`)}}_call(m){return this.decode(m)}decode(m){return this.decode_chain(m).join("")}decode_chain(m){throw Error("`decode_chain` should be implemented in subclass.")}}class E extends W{decode_chain(m){const z=oe(this.config.pattern);return z===null?m:m.map(Y=>Y.replaceAll(z,this.config.content))}}class te extends W{constructor(m){super(m),this.text_decoder=new TextDecoder}decode_chain(m){const z=[];let Y=[];for(const J of m){let ve=null;if(J.length===6&&J.startsWith("<0x")&&J.endsWith(">")){const Be=parseInt(J.slice(3,5),16);isNaN(Be)||(ve=Be)}if(ve!==null)Y.push(ve);else{if(Y.length>0){const Be=this.text_decoder.decode(Uint8Array.from(Y));z.push(Be),Y=[]}z.push(J)}}if(Y.length>0){const J=this.text_decoder.decode(Uint8Array.from(Y));z.push(J),Y=[]}return z}}class pe extends W{decode_chain(m){return[m.join("")]}}class Je extends W{constructor(m){super(m),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(m){return m.map(z=>{let Y=0;for(let ve=0;ve<this.start&&z[ve]===this.content;++ve){Y=ve+1;continue}let J=z.length;for(let ve=0;ve<this.stop;++ve){const Be=z.length-ve-1;if(z[Be]===this.content){J=Be;continue}else break}return z.slice(Y,J)})}}class He extends W{constructor(m){super(m),this.cleanup=m.cleanup}decode_chain(m){return m.map((z,Y)=>(Y!==0&&(z.startsWith(this.config.prefix)?z=z.replace(this.config.prefix,""):z=" "+z),this.cleanup&&(z=Q(z)),z))}}class Mt extends W{constructor(m){super(m),this.byte_decoder=Xe,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(m){const z=m.join(""),Y=new Uint8Array([...z].map(ve=>this.byte_decoder[ve]));return this.text_decoder.decode(Y)}decode_chain(m){const z=[];let Y=[];for(const J of m)this.added_tokens.find(ve=>ve.content===J)!==void 0?(Y.length>0&&(z.push(this.convert_tokens_to_string(Y)),Y=[]),z.push(J)):Y.push(J);return Y.length>0&&z.push(this.convert_tokens_to_string(Y)),z}}class nt extends W{constructor(m){super(m),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(m){if(m.length===0)return"";const z=[m[0]];for(let ve=1;ve<m.length;++ve)m[ve]!==z.at(-1)&&z.push(m[ve]);let J=z.filter(ve=>ve!==this.pad_token).join("");return this.cleanup&&(J=Q(J).replaceAll(this.word_delimiter_token," ").trim()),J}decode_chain(m){return[this.convert_tokens_to_string(m)]}}class pt extends W{constructor(m){super(m),this.decoders=m.decoders.map(z=>W.fromConfig(z))}decode_chain(m){return this.decoders.reduce((z,Y)=>Y.decode_chain(z),m)}}class Bt extends W{constructor(m){super(m),this.suffix=this.config.suffix}decode_chain(m){return m.map((z,Y)=>z.replaceAll(this.suffix,Y===m.length-1?"":" "))}}class et extends W{decode_chain(m){let z="";for(let Y=1;Y<m.length;Y+=2)z+=m[Y];return[z]}}class B extends Fe{constructor(m){super(),this.addPrefixSpace=m.add_prefix_space,this.replacement=m.replacement,this.strRep=m.str_rep||this.replacement,this.prepend_scheme=m.prepend_scheme??"always"}pre_tokenize_text(m,{section_index:z=void 0}={}){let Y=m.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!Y.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&z===0)&&(Y=this.strRep+Y),[Y]}}class ce extends W{constructor(m){super(m),this.addPrefixSpace=m.add_prefix_space,this.replacement=m.replacement}decode_chain(m){const z=[];for(let Y=0;Y<m.length;++Y){let J=m[Y].replaceAll(this.replacement," ");this.addPrefixSpace&&Y==0&&J.startsWith(" ")&&(J=J.substring(1)),z.push(J)}return z}}class Te extends U{constructor(m){super(m),this.charsmap=m.precompiled_charsmap}normalize(m){return m=m.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),m=m.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),m.includes("～")?m=m.split("～").map(Y=>Y.normalize("NFKC")).join("～"):m=m.normalize("NFKC"),m}}class Re extends Fe{constructor(m){super(),this.tokenizers=m.pretokenizers.map(z=>Fe.fromConfig(z))}pre_tokenize_text(m,z){return this.tokenizers.reduce((Y,J)=>J.pre_tokenize(Y,z),[m])}}class ke extends Fe{constructor(m){super()}pre_tokenize_text(m,z){return m.match(/\w+|[^\w\s]+/g)||[]}}class tt extends Fe{constructor(m){super()}pre_tokenize_text(m,z){return A(m)}}class ft extends Fe{constructor(m){super(),this.config=m,this.pattern=oe(this.config.pattern),this.content=this.config.content}pre_tokenize_text(m,z){return this.pattern===null?[m]:[m.replaceAll(this.pattern,this.config.content)]}}const lt=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ct(we,m,z,Y){for(const J of Object.keys(we)){const ve=m-we[J].length,Be=z(J),xt=new Array(ve).fill(Be);we[J]=Y==="right"?(0,ae.mergeArrays)(we[J],xt):(0,ae.mergeArrays)(xt,we[J])}}function gt(we,m){for(const z of Object.keys(we))we[z].length=m}class ze extends I.Callable{return_token_type_ids=!1;padding_side="right";constructor(m,z){super(),this._tokenizer_config=z,this.normalizer=U.fromConfig(m.normalizer),this.pre_tokenizer=Fe.fromConfig(m.pre_tokenizer),this.model=Se.fromConfig(m.model,z),this.post_processor=Ge.fromConfig(m.post_processor),this.decoder=W.fromConfig(m.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const Y of m.added_tokens){const J=new ye(Y);this.added_tokens.push(J),this.model.tokens_to_ids.set(J.content,J.id),this.model.vocab[J.id]=J.content,J.special&&(this.special_tokens.push(J.content),this.all_special_ids.push(J.id))}if(this.additional_special_tokens=z.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.toSorted((Y,J)=>J.content.length-Y.content.length).map(Y=>`${Y.lstrip?"\\s*":""}(${(0,ae.escapeRegExp)(Y.content)})${Y.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=z.model_max_length,this.remove_space=z.remove_space,this.clean_up_tokenization_spaces=z.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=z.do_lowercase_and_remove_accent??!1,z.padding_side&&(this.padding_side=z.padding_side),this.legacy=!1,this.chat_template=z.chat_template??null,Array.isArray(this.chat_template)){const Y=Object.create(null);for(const{name:J,template:ve}of this.chat_template){if(typeof J!="string"||typeof ve!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');Y[J]=ve}this.chat_template=Y}this._compiled_template_cache=new Map}getToken(...m){for(const z of m){const Y=this._tokenizer_config[z];if(Y)if(typeof Y=="object"){if(Y.__type==="AddedToken")return Y.content;throw Error(`Unknown token: ${Y}`)}else return Y}return null}static async from_pretrained(m,{progress_callback:z=null,config:Y=null,cache_dir:J=null,local_files_only:ve=!1,revision:Be="main",legacy:xt=null}={}){const vt=await K(m,{progress_callback:z,config:Y,cache_dir:J,local_files_only:ve,revision:Be,legacy:xt});return new this(...vt)}_call(m,{text_pair:z=null,add_special_tokens:Y=!0,padding:J=!1,truncation:ve=null,max_length:Be=null,return_tensor:xt=!0,return_token_type_ids:vt=null}={}){const kt=Array.isArray(m);let bt;if(kt){if(m.length===0)throw Error("text array must be non-empty");if(z!==null){if(Array.isArray(z)){if(m.length!==z.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");bt=m.map((or,yr)=>this._encode_plus(or,{text_pair:z[yr],add_special_tokens:Y,return_token_type_ids:vt}))}else bt=m.map(or=>this._encode_plus(or,{add_special_tokens:Y,return_token_type_ids:vt}))}else{if(m==null)throw Error("text may not be null or undefined");if(Array.isArray(z))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");bt=[this._encode_plus(m,{text_pair:z,add_special_tokens:Y,return_token_type_ids:vt})]}if(Be===null?J==="max_length"?Be=this.model_max_length:Be=(0,xe.max)(bt.map(or=>or.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."),Be=Math.min(Be,this.model_max_length??1/0),J||ve)for(let or=0;or<bt.length;++or)bt[or].input_ids.length!==Be&&(bt[or].input_ids.length>Be?ve&&gt(bt[or],Be):J&&Ct(bt[or],Be,yr=>yr==="input_ids"?this.pad_token_id:0,this.padding_side));const fr={};if(xt){if(!(J&&ve)&&bt.some(yr=>{for(const qt of Object.keys(yr))if(yr[qt].length!==bt[0][qt]?.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 or=[bt.length,bt[0].input_ids.length];for(const yr of Object.keys(bt[0]))fr[yr]=new be.Tensor("int64",BigInt64Array.from(bt.flatMap(qt=>qt[yr]).map(BigInt)),or)}else{for(const or of Object.keys(bt[0]))fr[or]=bt.map(yr=>yr[or]);if(!kt)for(const or of Object.keys(fr))fr[or]=fr[or][0]}return fr}_encode_text(m){return m===null?null:(this.added_tokens_regex?m.split(this.added_tokens_regex).filter(J=>J):[m]).map((J,ve)=>{if(this.added_tokens.find(xt=>xt.content===J)!==void 0)return J;{if(this.remove_space===!0&&(J=J.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(J=le(J)),this.normalizer!==null&&(J=this.normalizer(J)),J.length===0)return[];const xt=this.pre_tokenizer!==null?this.pre_tokenizer(J,{section_index:ve}):[J];return this.model(xt)}}).flat()}_encode_plus(m,{text_pair:z=null,add_special_tokens:Y=!0,return_token_type_ids:J=null}={}){const{tokens:ve,token_type_ids:Be}=this._tokenize_helper(m,{pair:z,add_special_tokens:Y}),xt=this.model.convert_tokens_to_ids(ve),vt={input_ids:xt,attention_mask:new Array(xt.length).fill(1)};return(J??this.return_token_type_ids)&&Be&&(vt.token_type_ids=Be),vt}_tokenize_helper(m,{pair:z=null,add_special_tokens:Y=!1}={}){const J=this._encode_text(m),ve=this._encode_text(z);return this.post_processor?this.post_processor(J,ve,{add_special_tokens:Y}):{tokens:(0,ae.mergeArrays)(J??[],ve??[])}}tokenize(m,{pair:z=null,add_special_tokens:Y=!1}={}){return this._tokenize_helper(m,{pair:z,add_special_tokens:Y}).tokens}encode(m,{text_pair:z=null,add_special_tokens:Y=!0,return_token_type_ids:J=null}={}){return this._encode_plus(m,{text_pair:z,add_special_tokens:Y,return_token_type_ids:J}).input_ids}batch_decode(m,z={}){return m instanceof be.Tensor&&(m=m.tolist()),m.map(Y=>this.decode(Y,z))}decode(m,z={}){if(m instanceof be.Tensor&&(m=F(m)),!Array.isArray(m)||m.length===0||!(0,ae.isIntegralNumber)(m[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(m,z)}decode_single(m,{skip_special_tokens:z=!1,clean_up_tokenization_spaces:Y=null}){let J=this.model.convert_ids_to_tokens(m);z&&(J=J.filter(Be=>!this.special_tokens.includes(Be)));let ve=this.decoder?this.decoder(J):J.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(ve=ve.replaceAll(this.decoder.end_of_word_suffix," "),z&&(ve=ve.trim())),(Y??this.clean_up_tokenization_spaces)&&(ve=Q(ve)),ve}apply_chat_template(m,{tools:z=null,documents:Y=null,chat_template:J=null,add_generation_prompt:ve=!1,tokenize:Be=!0,padding:xt=!1,truncation:vt=!1,max_length:kt=null,return_tensor:bt=!0,return_dict:fr=!1,tokenizer_kwargs:or={},...yr}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null){const Rr=this.chat_template;if(J!==null&&Object.hasOwn(Rr,J))J=Rr[J];else if(J===null&&"default"in Rr)J=Rr.default;else if(J===null)throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(Rr).sort()}.`)}else if(this.chat_template)J=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");if(typeof J!="string")throw Error(`chat_template must be a string, but got ${typeof J}`);let qt=this._compiled_template_cache.get(J);qt===void 0&&(qt=new $.Template(J),this._compiled_template_cache.set(J,qt));const Cr=Object.create(null);for(const Rr of lt){const Ie=this.getToken(Rr);Ie&&(Cr[Rr]=Ie)}const yn=qt.render({messages:m,add_generation_prompt:ve,tools:z,documents:Y,...Cr,...yr});if(Be){const Rr=this._call(yn,{add_special_tokens:!1,padding:xt,truncation:vt,max_length:kt,return_tensor:bt,...or});return fr?Rr:Rr.input_ids}return yn}}class Rt extends ze{return_token_type_ids=!0}class Ot extends ze{return_token_type_ids=!0}class Wt extends ze{return_token_type_ids=!0}class zt extends ze{return_token_type_ids=!0}class Ut extends ze{return_token_type_ids=!0}class Ht extends ze{return_token_type_ids=!0}class ur extends ze{return_token_type_ids=!0}class rr extends ze{return_token_type_ids=!0}class wr extends ze{return_token_type_ids=!0}class Qe extends ze{}class yt extends ze{}class Ft extends ze{return_token_type_ids=!0;constructor(m,z){super(m,z),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Zr extends ze{return_token_type_ids=!0}class Xn extends ze{}class is extends ze{}class Hr extends ze{}class pn extends ze{constructor(m,z){super(m,z),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(Y=>this.languageRegex.test(Y)),this.lang_to_token=Y=>Y}_build_translation_inputs(m,z,Y){return Wn(this,m,z,Y)}}class Xr extends pn{}class Qn extends ze{}class Lr extends ze{constructor(m,z){const Y=".,!?…。，、।۔،",J=m.pre_tokenizer?.pretokenizers[0]?.pattern;J&&J.Regex===` ?[^(\\s|[${Y}])]+`&&(J.Regex=` ?[^\\s${Y}]+`),super(m,z)}}const Vn="▁";class In extends ze{padding_side="left";constructor(m,z){super(m,z),this.legacy=z.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new B({replacement:Vn,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(m){if(m===null)return null;if(this.legacy||m.length===0)return super._encode_text(m);let z=super._encode_text(Vn+m.replaceAll(Vn," "));return z.length>1&&z[0]===Vn&&this.special_tokens.includes(z[1])&&(z=z.slice(1)),z}}class Us extends ze{}class ws extends ze{}class ys extends ze{}class bs extends ze{}class Ms extends ze{}class Un extends ze{}class Ws extends ze{}class as extends ze{}class Cn extends ze{}function Wn(we,m,z,Y){if(!("language_codes"in we)||!Array.isArray(we.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in we)||!(we.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in we)||typeof we.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const J=Y.src_lang,ve=Y.tgt_lang;if(!we.language_codes.includes(ve))throw new Error(`Target language code "${ve}" is not valid. Must be one of: {${we.language_codes.join(", ")}}`);if(J!==void 0){if(!we.language_codes.includes(J))throw new Error(`Source language code "${J}" is not valid. Must be one of: {${we.language_codes.join(", ")}}`);for(const Be of we.post_processor.config.single)if("SpecialToken"in Be&&we.languageRegex.test(Be.SpecialToken.id)){Be.SpecialToken.id=we.lang_to_token(J);break}}return Y.forced_bos_token_id=we.model.convert_tokens_to_ids([we.lang_to_token(ve)])[0],we._call(m,z)}class Fn extends ze{constructor(m,z){super(m,z),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(Y=>this.languageRegex.test(Y)),this.lang_to_token=Y=>Y}_build_translation_inputs(m,z,Y){return Wn(this,m,z,Y)}}class Yn extends ze{constructor(m,z){super(m,z),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(Y=>this.languageRegex.test(Y)).map(Y=>Y.slice(2,-2)),this.lang_to_token=Y=>`__${Y}__`}_build_translation_inputs(m,z,Y){return Wn(this,m,z,Y)}}class os extends ze{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(m,{return_timestamps:z=!1,return_language:Y=!1,time_precision:J=null,force_full_sequences:ve=!0}={}){if(J===null)throw Error("Must specify time_precision");let Be=null;const xt=z==="word";function vt(){return{language:Be,timestamp:[null,null],text:""}}const kt=[];let bt=vt(),fr=0;const or=this.timestamp_begin;let yr=[],qt=[],Cr=!1,yn=null;const Rr=new Set(this.all_special_ids);for(const xr of m){const Jr=xr.tokens,dn=xt?xr.token_timestamps:null;let Xt=null,hn=or;if("stride"in xr){const[Tr,Pt,Sr]=xr.stride;if(fr-=Pt,yn=Tr-Sr,Pt&&(hn=Pt/J+or),Sr)for(let Fr=Jr.length-1;Fr>=0;--Fr){const jr=Number(Jr[Fr]);if(jr>=or){if(Xt!==null&&(jr-or)*J<yn)break;Xt=jr}}}let sn=[],Er=[];for(let Tr=0;Tr<Jr.length;++Tr){const Pt=Number(Jr[Tr]);if(Rr.has(Pt)){const Sr=this.decode([Pt]),Fr=V.WHISPER_LANGUAGE_MAPPING.get(Sr.slice(2,-2));if(Fr!==void 0){if(Be!==null&&Fr!==Be&&!z){yr.push(sn);const jr=this.findLongestCommonSequence(yr)[0],fn=this.decode(jr);bt.text=fn,kt.push(bt),yr=[],sn=[],bt=vt()}Be=bt.language=Fr}}else if(Pt>=or){const Sr=(Pt-or)*J+fr,Fr=(0,xe.round)(Sr,2);if(Xt!==null&&Pt>=Xt)Cr=!0;else if(Cr||yr.length>0&&Pt<hn)Cr=!1;else if(bt.timestamp[0]===null)bt.timestamp[0]=Fr;else if(Fr!==bt.timestamp[0]){bt.timestamp[1]=Fr,yr.push(sn),xt&&qt.push(Er);const[jr,fn]=this.findLongestCommonSequence(yr,qt),Nt=this.decode(jr);bt.text=Nt,xt&&(bt.words=this.collateWordTimestamps(jr,fn,Be)),kt.push(bt),yr=[],sn=[],qt=[],Er=[],bt=vt()}}else if(sn.push(Pt),xt){let Sr=(0,xe.round)(dn[Tr]+fr,2),Fr;if(Tr+1<dn.length){Fr=(0,xe.round)(dn[Tr+1]+fr,2);const jr=this.decode([Pt]);C.test(jr)&&(Fr=(0,xe.round)(Math.min(Sr+J,Fr),2))}else Fr=null;Er.push([Sr,Fr])}}if("stride"in xr){const[Tr,Pt,Sr]=xr.stride;fr+=Tr-Sr}sn.length>0?(yr.push(sn),xt&&qt.push(Er)):yr.every(Tr=>Tr.length===0)&&(bt=vt(),yr=[],sn=[],qt=[],Er=[])}if(yr.length>0){if(ve&&z)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[xr,Jr]=this.findLongestCommonSequence(yr,qt),dn=this.decode(xr);bt.text=dn,xt&&(bt.words=this.collateWordTimestamps(xr,Jr,Be)),kt.push(bt)}let Ie=Object.create(null);const On=kt.map(xr=>xr.text).join("");if(z||Y){for(let xr=0;xr<kt.length;++xr){const Jr=kt[xr];z||delete Jr.timestamp,Y||delete Jr.language}if(xt){const xr=[];for(const Jr of kt)for(const dn of Jr.words)xr.push(dn);Ie={chunks:xr}}else Ie={chunks:kt}}return[On,Ie]}findLongestCommonSequence(m,z=null){let Y=m[0],J=Y.length,ve=[];const Be=Array.isArray(z)&&z.length>0;let xt=Be?[]:null,vt=Be?z[0]:null;for(let kt=1;kt<m.length;++kt){const bt=m[kt];let fr=0,or=[J,J,0,0];const yr=bt.length;for(let xr=1;xr<J+yr;++xr){const Jr=Math.max(0,J-xr),dn=Math.min(J,J+yr-xr),Xt=Y.slice(Jr,dn),hn=Math.max(0,xr-J),sn=Math.min(yr,xr),Er=bt.slice(hn,sn);if(Xt.length!==Er.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let Tr;Be?Tr=Xt.filter((Fr,jr)=>Fr===Er[jr]&&vt[Jr+jr]<=z[kt][hn+jr]).length:Tr=Xt.filter((Fr,jr)=>Fr===Er[jr]).length;const Pt=xr/1e4,Sr=Tr/xr+Pt;Tr>1&&Sr>fr&&(fr=Sr,or=[Jr,dn,hn,sn])}const[qt,Cr,yn,Rr]=or,Ie=Math.floor((Cr+qt)/2),On=Math.floor((Rr+yn)/2);ve.push(...Y.slice(0,Ie)),Y=bt.slice(On),J=Y.length,Be&&(xt.push(...vt.slice(0,Ie)),vt=z[kt].slice(On))}return ve.push(...Y),Be?(xt.push(...vt),[ve,xt]):[ve,[]]}collateWordTimestamps(m,z,Y){const[J,ve,Be]=this.combineTokensIntoWords(m,Y),xt=[];for(let vt=0;vt<J.length;++vt){const kt=Be[vt];xt.push({text:J[vt],timestamp:[z[kt.at(0)][0],z[kt.at(-1)][1]]})}return xt}combineTokensIntoWords(m,z,Y=`"'“¡¿([{-`,J=`"'.。,，!！?？:：”)]}、`){z=z??"english";let ve,Be,xt;return["chinese","japanese","thai","lao","myanmar"].includes(z)?[ve,Be,xt]=this.splitTokensOnUnicode(m):[ve,Be,xt]=this.splitTokensOnSpaces(m),this.mergePunctuations(ve,Be,xt,Y,J)}decode(m,z){let Y;return z?.decode_with_timestamps?(m instanceof be.Tensor&&(m=F(m)),Y=this.decodeWithTimestamps(m,z)):Y=super.decode(m,z),Y}decodeWithTimestamps(m,z){const Y=z?.time_precision??.02,J=Array.from(this.all_special_ids).at(-1)+1;let ve=[[]];for(let Be of m)if(Be=Number(Be),Be>=J){const xt=((Be-J)*Y).toFixed(2);ve.push(`<|${xt}|>`),ve.push([])}else ve[ve.length-1].push(Be);return ve=ve.map(Be=>typeof Be=="string"?Be:super.decode(Be,z)),ve.join("")}splitTokensOnUnicode(m){const z=this.decode(m,{decode_with_timestamps:!0}),Y="�",J=[],ve=[],Be=[];let xt=[],vt=[],kt=0;for(let bt=0;bt<m.length;++bt){const fr=m[bt];xt.push(fr),vt.push(bt);const or=this.decode(xt,{decode_with_timestamps:!0});(!or.includes(Y)||z[kt+or.indexOf(Y)]===Y)&&(J.push(or),ve.push(xt),Be.push(vt),xt=[],vt=[],kt+=or.length)}return[J,ve,Be]}splitTokensOnSpaces(m){const[z,Y,J]=this.splitTokensOnUnicode(m),ve=[],Be=[],xt=[],vt=new RegExp(`^[${L}]$`,"gu");for(let kt=0;kt<z.length;++kt){const bt=z[kt],fr=Y[kt],or=J[kt],yr=fr[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),qt=bt.startsWith(" "),Cr=bt.trim(),yn=vt.test(Cr);if(yr||qt||yn||ve.length===0)ve.push(bt),Be.push(fr),xt.push(or);else{const Rr=ve.length-1;ve[Rr]+=bt,Be[Rr].push(...fr),xt[Rr].push(...or)}}return[ve,Be,xt]}mergePunctuations(m,z,Y,J,ve){const Be=structuredClone(m),xt=structuredClone(z),vt=structuredClone(Y);let kt=Be.length-2,bt=Be.length-1;for(;kt>=0;)Be[kt].startsWith(" ")&&J.includes(Be[kt].trim())?(Be[bt]=Be[kt]+Be[bt],xt[bt]=(0,ae.mergeArrays)(xt[kt],xt[bt]),vt[bt]=(0,ae.mergeArrays)(vt[kt],vt[bt]),Be[kt]="",xt[kt]=[],vt[kt]=[]):bt=kt,--kt;for(kt=0,bt=1;bt<Be.length;)!Be[kt].endsWith(" ")&&ve.includes(Be[bt])?(Be[kt]+=Be[bt],xt[kt]=(0,ae.mergeArrays)(xt[kt],xt[bt]),vt[kt]=(0,ae.mergeArrays)(vt[kt],vt[bt]),Be[bt]="",xt[bt]=[],vt[bt]=[]):kt=bt,++bt;return[Be.filter(fr=>fr),xt.filter(fr=>fr.length>0),vt.filter(fr=>fr.length>0)]}get_decoder_prompt_ids({language:m=null,task:z=null,no_timestamps:Y=!0}={}){const J=[];if(m){const ve=(0,V.whisper_language_to_code)(m),Be=this.model.tokens_to_ids.get(`<|${ve}|>`);if(Be===void 0)throw new Error(`Unable to find language "${ve}" in model vocabulary. Please report this issue at ${P.GITHUB_ISSUE_URL}.`);J.push(Be)}else J.push(null);if(z){if(z=z.toLowerCase(),z!=="transcribe"&&z!=="translate")throw new Error(`Task "${z}" is not supported. Must be one of: ["transcribe", "translate"]`);const ve=this.model.tokens_to_ids.get(`<|${z}|>`);if(ve===void 0)throw new Error(`Unable to find task "${z}" in model vocabulary. Please report this issue at ${P.GITHUB_ISSUE_URL}.`);J.push(ve)}else J.push(null);if(Y){const ve=this.model.tokens_to_ids.get("<|notimestamps|>");if(ve===void 0)throw new Error(`Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at ${P.GITHUB_ISSUE_URL}.`);J.push(ve)}return J.map((ve,Be)=>[Be+1,ve]).filter(ve=>ve[1]!==null)}}class ls extends ze{}class Kt extends ze{}class Zn extends ze{}class vs extends ze{constructor(m,z){super(m,z),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(Y=>this.languageRegex.test(Y)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(m){if(m===null)return null;const[z,...Y]=m.trim().split(this.languageRegex);if(Y.length===0)return super._encode_text(z);if(Y.length===2){const[J,ve]=Y;return this.supported_language_codes.includes(J)||console.warn(`Unsupported language code "${J}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,ae.mergeArrays)([J],super._encode_text(ve))}}}class xs extends ze{}class us extends ze{}class Ts extends ze{}class Ss extends ze{}class ds extends ze{}class $s extends ze{constructor(m,z){super(m,z),this.decoder=new et({})}}class Nr extends ze{}class wn{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:Xn,DistilBertTokenizer:Qe,CamembertTokenizer:yt,DebertaTokenizer:Ut,DebertaV2Tokenizer:Ht,BertTokenizer:Rt,HerbertTokenizer:ur,ConvBertTokenizer:rr,RoFormerTokenizer:wr,XLMTokenizer:Ft,ElectraTokenizer:Zr,MobileBertTokenizer:Wt,SqueezeBertTokenizer:zt,AlbertTokenizer:Ot,GPT2Tokenizer:is,BartTokenizer:Hr,MBartTokenizer:pn,MBart50Tokenizer:Xr,RobertaTokenizer:Qn,WhisperTokenizer:os,CodeGenTokenizer:ls,CLIPTokenizer:Kt,SiglipTokenizer:Zn,MarianTokenizer:vs,BloomTokenizer:Lr,NllbTokenizer:Fn,M2M100Tokenizer:Yn,LlamaTokenizer:In,CodeLlamaTokenizer:Us,XLMRobertaTokenizer:ws,MPNetTokenizer:ys,FalconTokenizer:bs,GPTNeoXTokenizer:Ms,EsmTokenizer:Un,Wav2Vec2CTCTokenizer:xs,BlenderbotTokenizer:us,BlenderbotSmallTokenizer:Ts,SpeechT5Tokenizer:Ss,NougatTokenizer:ds,VitsTokenizer:$s,Qwen2Tokenizer:Ws,GemmaTokenizer:as,Grok1Tokenizer:Cn,CohereTokenizer:Nr,PreTrainedTokenizer:ze};static async from_pretrained(m,{progress_callback:z=null,config:Y=null,cache_dir:J=null,local_files_only:ve=!1,revision:Be="main",legacy:xt=null}={}){const[vt,kt]=await K(m,{progress_callback:z,config:Y,cache_dir:J,local_files_only:ve,revision:Be,legacy:xt}),bt=kt.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let fr=this.TOKENIZER_CLASS_MAPPING[bt];return fr||(console.warn(`Unknown tokenizer class "${bt}", attempting to construct from base class.`),fr=ze),new fr(vt,kt)}}},"./src/utils/audio.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{hamming:()=>V,hanning:()=>$,mel_filter_bank:()=>Q,read_audio:()=>be,spectrogram:()=>A,window_function:()=>L});var I=N("./src/utils/hub.js"),ae=N("./src/utils/maths.js"),he=N("./src/utils/core.js"),xe=N("./src/utils/tensor.js");async function be(C,me){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const ye=await(await(0,I.getFile)(C)).arrayBuffer(),Se=new AudioContext({sampleRate:me});typeof me>"u"&&console.warn(`No sampling rate provided, using default of ${Se.sampleRate}Hz.`);const Pe=await Se.decodeAudioData(ye);let Ce;if(Pe.numberOfChannels===2){const rt=Math.sqrt(2),Xe=Pe.getChannelData(0),dt=Pe.getChannelData(1);Ce=new Float32Array(Xe.length);for(let ge=0;ge<Pe.length;++ge)Ce[ge]=rt*(Xe[ge]+dt[ge])/2}else Ce=Pe.getChannelData(0);return Ce}function R(C,me){if(C<1)return new Float64Array;if(C===1)return new Float64Array([1]);const ye=1-me,Se=2*Math.PI/(C-1),Pe=new Float64Array(C);for(let Ce=0;Ce<C;++Ce)Pe[Ce]=me-ye*Math.cos(Ce*Se);return Pe}function $(C){return R(C,.5)}function V(C){return R(C,.54)}const P={htk:C=>2595*Math.log10(1+C/700),kaldi:C=>1127*Math.log(1+C/700),slaney:(C,me=1e3,ye=15,Se=27/Math.log(6.4))=>C>=me?ye+Math.log(C/me)*Se:3*C/200};function K(C,me="htk"){const ye=P[me];if(!ye)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof C=="number"?ye(C):C.map(Se=>ye(Se))}const ne={htk:C=>700*(10**(C/2595)-1),kaldi:C=>700*(Math.exp(C/1127)-1),slaney:(C,me=1e3,ye=15,Se=Math.log(6.4)/27)=>C>=ye?me*Math.exp(Se*(C-ye)):200*C/3};function oe(C,me="htk"){const ye=ne[me];if(!ye)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof C=="number"?ye(C):C.map(Se=>ye(Se))}function _e(C,me){const ye=Float64Array.from({length:me.length-1},(rt,Xe)=>me[Xe+1]-me[Xe]),Se=Array.from({length:C.length},()=>new Array(me.length));for(let rt=0;rt<C.length;++rt){const Xe=Se[rt];for(let dt=0;dt<me.length;++dt)Xe[dt]=me[dt]-C[rt]}const Pe=me.length-2,Ce=Array.from({length:Pe},()=>new Array(C.length));for(let rt=0;rt<C.length;++rt){const Xe=Se[rt];for(let dt=0;dt<Pe;++dt){const ge=-Xe[dt]/ye[dt],U=Xe[dt+2]/ye[dt+1];Ce[dt][rt]=Math.max(0,Math.min(ge,U))}}return Ce}function F(C,me,ye){const Se=(me-C)/(ye-1);return Float64Array.from({length:ye},(Pe,Ce)=>C+Se*Ce)}function Q(C,me,ye,Se,Pe,Ce=null,rt="htk",Xe=!1){if(Ce!==null&&Ce!=="slaney")throw new Error('norm must be one of null or "slaney"');const dt=K(ye,rt),ge=K(Se,rt),U=F(dt,ge,me+2);let de=oe(U,rt),$e;if(Xe){const Ue=Pe/(C*2);$e=K(Float64Array.from({length:C},(ct,st)=>st*Ue),rt),de=U}else $e=F(0,Math.floor(Pe/2),C);const X=_e($e,de);if(Ce!==null&&Ce==="slaney")for(let Ue=0;Ue<me;++Ue){const ct=X[Ue],st=2/(de[Ue+2]-de[Ue]);for(let ot=0;ot<C;++ot)ct[ot]*=st}return X}function se(C,me,ye){const Se=new C.constructor(C.length+me+ye),Pe=C.length-1;for(let Ce=0;Ce<C.length;++Ce)Se[me+Ce]=C[Ce];for(let Ce=1;Ce<=me;++Ce)Se[me-Ce]=C[(0,he.calculateReflectOffset)(Ce,Pe)];for(let Ce=1;Ce<=ye;++Ce)Se[Pe+me+Ce]=C[(0,he.calculateReflectOffset)(Pe-Ce,Pe)];return Se}function le(C,me,ye,Se,Pe){if(ye<=0)throw new Error("reference must be greater than zero");if(Se<=0)throw new Error("min_value must be greater than zero");ye=Math.max(Se,ye);const Ce=Math.log10(ye);for(let rt=0;rt<C.length;++rt)C[rt]=me*Math.log10(Math.max(Se,C[rt])-Ce);if(Pe!==null){if(Pe<=0)throw new Error("db_range must be greater than zero");const rt=(0,ae.max)(C)[0]-Pe;for(let Xe=0;Xe<C.length;++Xe)C[Xe]=Math.max(C[Xe],rt)}return C}function ie(C,me=1,ye=1e-5,Se=null){return le(C,20,me,ye,Se)}function j(C,me=1,ye=1e-10,Se=null){return le(C,10,me,ye,Se)}async function A(C,me,ye,Se,{fft_length:Pe=null,power:Ce=1,center:rt=!0,pad_mode:Xe="reflect",onesided:dt=!0,preemphasis:ge=null,mel_filters:U=null,mel_floor:de=1e-10,log_mel:$e=null,reference:X=1,min_value:Ue=1e-10,db_range:ct=null,remove_dc_offset:st=null,min_num_frames:ot=null,max_num_frames:De=null,do_pad:at=!0,transpose:Et=!1}={}){const Fe=me.length;if(Pe===null&&(Pe=ye),ye>Pe)throw Error(`frame_length (${ye}) may not be larger than fft_length (${Pe})`);if(Fe!==ye)throw new Error(`Length of the window (${Fe}) must equal frame_length (${ye})`);if(Se<=0)throw new Error("hop_length must be greater than zero");if(Ce===null&&U!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. 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Using bilinear instead.`),le="bilinear");case"nearest":case"bilinear":case"bicubic":ie=ie.affine([F/this.width,0,0,Q/this.height],{interpolator:le});break;case"lanczos":ie=ie.resize({width:F,height:Q,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${le} is not supported.`)}return await P(ie)}}async pad([F,Q,se,le]){if(F=Math.max(F,0),Q=Math.max(Q,0),se=Math.max(se,0),le=Math.max(le,0),F===0&&Q===0&&se===0&&le===0)return this;if(be){const ie=this.channels,j=this.toCanvas(),A=this.width+F+Q,L=this.height+se+le,C=$(A,L).getContext("2d");return C.drawImage(j,0,0,this.width,this.height,F,se,A,L),new oe(C.getImageData(0,0,A,L).data,A,L,4).convert(ie)}else{const ie=this.toSharp().extend({left:F,right:Q,top:se,bottom:le});return await P(ie)}}async crop([F,Q,se,le]){if(F=Math.max(F,0),Q=Math.max(Q,0),se=Math.min(se,this.width-1),le=Math.min(le,this.height-1),F===0&&Q===0&&se===this.width-1&&le===this.height-1)return this;const ie=se-F+1,j=le-Q+1;if(be){const A=this.channels,L=this.toCanvas(),C=$(ie,j).getContext("2d");return C.drawImage(L,F,Q,ie,j,0,0,ie,j),new oe(C.getImageData(0,0,ie,j).data,ie,j,4).convert(A)}else{const A=this.toSharp().extract({left:F,top:Q,width:ie,height:j});return await P(A)}}async center_crop(F,Q){if(this.width===F&&this.height===Q)return this;const se=(this.width-F)/2,le=(this.height-Q)/2;if(be){const ie=this.channels,j=this.toCanvas(),A=$(F,Q).getContext("2d");let L=0,C=0,me=0,ye=0;return se>=0?L=se:me=-se,le>=0?C=le:ye=-le,A.drawImage(j,L,C,F,Q,me,ye,F,Q),new oe(A.getImageData(0,0,F,Q).data,F,Q,4).convert(ie)}else{let ie=this.toSharp();if(se>=0&&le>=0)ie=ie.extract({left:Math.floor(se),top:Math.floor(le),width:F,height:Q});else if(se<=0&&le<=0){const j=Math.floor(-le),A=Math.floor(-se);ie=ie.extend({top:j,left:A,right:F-this.width-A,bottom:Q-this.height-j})}else{let j=[0,0],A=0;le<0?(j[0]=Math.floor(-le),j[1]=Q-this.height-j[0]):A=Math.floor(le);let L=[0,0],C=0;se<0?(L[0]=Math.floor(-se),L[1]=F-this.width-L[0]):C=Math.floor(se),ie=ie.extend({top:j[0],bottom:j[1],left:L[0],right:L[1]}).extract({left:C,top:A,width:F,height:Q})}return await P(ie)}}async toBlob(F="image/png",Q=1){if(!be)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:F,quality:Q})}toTensor(F="CHW"){let Q=new he.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(F!=="HWC")if(F==="CHW")Q=Q.permute(2,0,1);else throw new Error(`Unsupported channel format: ${F}`);return Q}toCanvas(){if(!be)throw new Error("toCanvas() is only supported in browser environments.");const F=this.clone().rgba(),Q=$(F.width,F.height),se=new V(F.data,F.width,F.height);return Q.getContext("2d").putImageData(se,0,0),Q}_update(F,Q,se,le=null){return this.data=F,this.width=Q,this.height=se,le!==null&&(this.channels=le),this}clone(){return new oe(this.data.slice(),this.width,this.height,this.channels)}convert(F){if(this.channels===F)return this;switch(F){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: ${this.channels}`)}return this}async save(F){if(be){if(R)throw new Error("Unable to save an image from a Web Worker.");const Q=F.split(".").pop().toLowerCase(),se=ne.get(Q)??"image/png",le=await this.toBlob(se),ie=URL.createObjectURL(le),j=document.createElement("a");j.href=ie,j.download=F,j.click(),j.remove()}else{if(ae.env.useFS)return await this.toSharp().toFile(F);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(be)throw new Error("toSharp() is only supported in server-side environments.");return xe(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}},"./src/utils/maths.js":(_t,Me,N)=>{N.r(Me),N.d(Me,{FFT:()=>_e,bankers_round:()=>se,cos_sim:()=>R,dot:()=>be,dynamic_time_warping:()=>le,interpolate_data:()=>I,log_softmax:()=>xe,magnitude:()=>$,max:()=>P,medianFilter:()=>F,min:()=>V,permute_data:()=>ae,round:()=>Q,softmax:()=>he});function I(ie,[j,A,L],[C,me],ye="bilinear",Se=!1){const Pe=me/L,Ce=C/A,rt=new ie.constructor(C*me*j),Xe=A*L,dt=C*me;for(let ge=0;ge<C;++ge)for(let U=0;U<me;++U){const de=ge*me+U,$e=(U+.5)/Pe-.5,X=(ge+.5)/Ce-.5;let Ue=Math.floor($e),ct=Math.floor(X);const st=Math.min(Ue+1,L-1),ot=Math.min(ct+1,A-1);Ue=Math.max(Ue,0),ct=Math.max(ct,0);const De=$e-Ue,at=X-ct,Et=(1-De)*(1-at),Fe=De*(1-at),H=(1-De)*at,Ee=De*at,qe=ct*L,je=ot*L,Ve=qe+Ue,Ge=qe+st,ut=je+Ue,ht=je+st;for(let Tt=0;Tt<j;++Tt){const St=Tt*Xe;rt[Tt*dt+de]=Et*ie[St+Ve]+Fe*ie[St+Ge]+H*ie[St+ut]+Ee*ie[St+ht]}}return rt}function ae(ie,j,A){const L=new Array(A.length),C=new Array(A.length);for(let Se=A.length-1,Pe=1;Se>=0;--Se)C[Se]=Pe,L[Se]=j[A[Se]],Pe*=L[Se];const me=A.map((Se,Pe)=>C[A.indexOf(Pe)]),ye=new ie.constructor(ie.length);for(let Se=0;Se<ie.length;++Se){let Pe=0;for(let Ce=j.length-1,rt=Se;Ce>=0;--Ce)Pe+=rt%j[Ce]*me[Ce],rt=Math.floor(rt/j[Ce]);ye[Pe]=ie[Se]}return[ye,L]}function he(ie){const j=P(ie)[0],A=ie.map(me=>Math.exp(me-j)),L=A.reduce((me,ye)=>me+ye,0);return A.map(me=>me/L)}function xe(ie){return he(ie).map(L=>Math.log(L))}function be(ie,j){let A=0;for(let L=0;L<ie.length;++L)A+=ie[L]*j[L];return A}function R(ie,j){const A=be(ie,j),L=$(ie),C=$(j);return A/(L*C)}function $(ie){return Math.sqrt(ie.reduce((j,A)=>j+A*A,0))}function V(ie){if(ie.length===0)throw Error("Array must not be empty");let j=ie[0],A=0;for(let L=1;L<ie.length;++L)ie[L]<j&&(j=ie[L],A=L);return[j,A]}function P(ie){if(ie.length===0)throw Error("Array must not be empty");let j=ie[0],A=0;for(let L=1;L<ie.length;++L)ie[L]>j&&(j=ie[L],A=L);return[Number(j),A]}function K(ie){return ie>0&&(ie&ie-1)===0}class ne{constructor(j){if(this.size=j|0,this.size<=1||!K(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=j<<1,this.table=new Float64Array(this.size*2);for(let L=0;L<this.table.length;L+=2){const C=Math.PI*L/this.size;this.table[L]=Math.cos(C),this.table[L+1]=-Math.sin(C)}let A=0;for(let L=1;this.size>L;L<<=1)++A;this._width=A%2===0?A-1:A,this._bitrev=new Int32Array(1<<this._width);for(let L=0;L<this._bitrev.length;++L){this._bitrev[L]=0;for(let C=0;C<this._width;C+=2){const me=this._width-C-2;this._bitrev[L]|=(L>>>C&3)<<me}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(j,A){const L=A||new Array(j.length>>>1);for(let C=0;C<j.length;C+=2)L[C>>>1]=j[C];return L}toComplexArray(j,A){const L=A||this.createComplexArray();for(let C=0;C<L.length;C+=2)L[C]=j[C>>>1],L[C+1]=0;return L}transform(j,A){if(j===A)throw new Error("Input and output buffers must be different");this._transform4(j,A,1)}realTransform(j,A){if(j===A)throw new Error("Input and output buffers must be different");this._realTransform4(j,A,1)}inverseTransform(j,A){if(j===A)throw new Error("Input and output buffers must be different");this._transform4(j,A,-1);for(let L=0;L<j.length;++L)j[L]/=this.size}_transform4(j,A,L){const C=this._csize;let ye=1<<this._width,Se=C/ye<<1,Pe,Ce;const rt=this._bitrev;if(Se===4)for(Pe=0,Ce=0;Pe<C;Pe+=Se,++Ce){const dt=rt[Ce];this._singleTransform2(A,j,Pe,dt,ye)}else for(Pe=0,Ce=0;Pe<C;Pe+=Se,++Ce){const dt=rt[Ce];this._singleTransform4(A,j,Pe,dt,ye,L)}const Xe=this.table;for(ye>>=2;ye>=2;ye>>=2){Se=C/ye<<1;const dt=Se>>>2;for(Pe=0;Pe<C;Pe+=Se){const ge=Pe+dt-1;for(let U=Pe,de=0;U<ge;U+=2,de+=ye){const $e=U,X=$e+dt,Ue=X+dt,ct=Ue+dt,st=j[$e],ot=j[$e+1],De=j[X],at=j[X+1],Et=j[Ue],Fe=j[Ue+1],H=j[ct],Ee=j[ct+1],qe=Xe[de],je=L*Xe[de+1],Ve=De*qe-at*je,Ge=De*je+at*qe,ut=Xe[2*de],ht=L*Xe[2*de+1],Tt=Et*ut-Fe*ht,St=Et*ht+Fe*ut,M=Xe[3*de],W=L*Xe[3*de+1],E=H*M-Ee*W,te=H*W+Ee*M,pe=st+Tt,Je=ot+St,He=st-Tt,Mt=ot-St,nt=Ve+E,pt=Ge+te,Bt=L*(Ve-E),et=L*(Ge-te);j[$e]=pe+nt,j[$e+1]=Je+pt,j[X]=He+et,j[X+1]=Mt-Bt,j[Ue]=pe-nt,j[Ue+1]=Je-pt,j[ct]=He-et,j[ct+1]=Mt+Bt}}}}_singleTransform2(j,A,L,C,me){const ye=j[C],Se=j[C+1],Pe=j[C+me],Ce=j[C+me+1];A[L]=ye+Pe,A[L+1]=Se+Ce,A[L+2]=ye-Pe,A[L+3]=Se-Ce}_singleTransform4(j,A,L,C,me,ye){const Se=me*2,Pe=me*3,Ce=j[C],rt=j[C+1],Xe=j[C+me],dt=j[C+me+1],ge=j[C+Se],U=j[C+Se+1],de=j[C+Pe],$e=j[C+Pe+1],X=Ce+ge,Ue=rt+U,ct=Ce-ge,st=rt-U,ot=Xe+de,De=dt+$e,at=ye*(Xe-de),Et=ye*(dt-$e);A[L]=X+ot,A[L+1]=Ue+De,A[L+2]=ct+Et,A[L+3]=st-at,A[L+4]=X-ot,A[L+5]=Ue-De,A[L+6]=ct-Et,A[L+7]=st+at}_realTransform4(j,A,L){const C=this._csize;let ye=1<<this._width,Se=C/ye<<1,Pe,Ce;const rt=this._bitrev;if(Se===4)for(Pe=0,Ce=0;Pe<C;Pe+=Se,++Ce){const ge=rt[Ce];this._singleRealTransform2(A,j,Pe,ge>>>1,ye>>>1)}else for(Pe=0,Ce=0;Pe<C;Pe+=Se,++Ce){const ge=rt[Ce];this._singleRealTransform4(A,j,Pe,ge>>>1,ye>>>1,L)}const Xe=this.table;for(ye>>=2;ye>=2;ye>>=2){Se=C/ye<<1;const ge=Se>>>1,U=ge>>>1,de=U>>>1;for(Pe=0;Pe<C;Pe+=Se)for(let $e=0,X=0;$e<=de;$e+=2,X+=ye){const Ue=Pe+$e,ct=Ue+U,st=ct+U,ot=st+U,De=j[Ue],at=j[Ue+1],Et=j[ct],Fe=j[ct+1],H=j[st],Ee=j[st+1],qe=j[ot],je=j[ot+1],Ve=De,Ge=at,ut=Xe[X],ht=L*Xe[X+1],Tt=Et*ut-Fe*ht,St=Et*ht+Fe*ut,M=Xe[2*X],W=L*Xe[2*X+1],E=H*M-Ee*W,te=H*W+Ee*M,pe=Xe[3*X],Je=L*Xe[3*X+1],He=qe*pe-je*Je,Mt=qe*Je+je*pe,nt=Ve+E,pt=Ge+te,Bt=Ve-E,et=Ge-te,B=Tt+He,ce=St+Mt,Te=L*(Tt-He),Re=L*(St-Mt);if(j[Ue]=nt+B,j[Ue+1]=pt+ce,j[ct]=Bt+Re,j[ct+1]=et-Te,$e===0){j[st]=nt-B,j[st+1]=pt-ce;continue}if($e===de)continue;const ke=Pe+U-$e,tt=Pe+ge-$e;j[ke]=Bt-L*Re,j[ke+1]=-et-L*Te,j[tt]=nt-L*B,j[tt+1]=-pt+L*ce}}const dt=C>>>1;for(let ge=2;ge<dt;ge+=2)j[C-ge]=j[ge],j[C-ge+1]=-j[ge+1]}_singleRealTransform2(j,A,L,C,me){const ye=j[C],Se=j[C+me];A[L]=ye+Se,A[L+1]=0,A[L+2]=ye-Se,A[L+3]=0}_singleRealTransform4(j,A,L,C,me,ye){const Se=me*2,Pe=me*3,Ce=j[C],rt=j[C+me],Xe=j[C+Se],dt=j[C+Pe],ge=Ce+Xe,U=Ce-Xe,de=rt+dt,$e=ye*(rt-dt);A[L]=ge+de,A[L+1]=0,A[L+2]=U,A[L+3]=-$e,A[L+4]=ge-de,A[L+5]=0,A[L+6]=U,A[L+7]=$e}}class oe{constructor(j){const A=2*(j-1),L=2*(2*j-1),C=2**Math.ceil(Math.log2(L));this.bufferSize=C,this._a=A;const me=new Float64Array(L),ye=new Float64Array(C);this._chirpBuffer=new Float64Array(C),this._buffer1=new Float64Array(C),this._buffer2=new Float64Array(C),this._outBuffer1=new Float64Array(C),this._outBuffer2=new Float64Array(C);const Se=-2*Math.PI/j,Pe=Math.cos(Se),Ce=Math.sin(Se);for(let rt=0;rt<L>>1;++rt){const Xe=(rt+1-j)**2/2,dt=Math.sqrt(Pe**2+Ce**2)**Xe,ge=Xe*Math.atan2(Ce,Pe),U=2*rt;me[U]=dt*Math.cos(ge),me[U+1]=dt*Math.sin(ge),ye[U]=me[U],ye[U+1]=-me[U+1]}this._slicedChirpBuffer=me.subarray(A,L),this._f=new ne(C>>1),this._f.transform(this._chirpBuffer,ye)}_transform(j,A,L){const C=this._buffer1,me=this._buffer2,ye=this._outBuffer1,Se=this._outBuffer2,Pe=this._chirpBuffer,Ce=this._slicedChirpBuffer,rt=this._a;if(L)for(let Xe=0;Xe<Ce.length;Xe+=2){const dt=Xe+1,ge=Xe>>1,U=A[ge];C[Xe]=U*Ce[Xe],C[dt]=U*Ce[dt]}else for(let Xe=0;Xe<Ce.length;Xe+=2){const dt=Xe+1;C[Xe]=A[Xe]*Ce[Xe]-A[dt]*Ce[dt],C[dt]=A[Xe]*Ce[dt]+A[dt]*Ce[Xe]}this._f.transform(ye,C);for(let Xe=0;Xe<Pe.length;Xe+=2){const dt=Xe+1;me[Xe]=ye[Xe]*Pe[Xe]-ye[dt]*Pe[dt],me[dt]=ye[Xe]*Pe[dt]+ye[dt]*Pe[Xe]}this._f.inverseTransform(Se,me);for(let Xe=0;Xe<Se.length;Xe+=2){const 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  !*** ./src/transformers.js ***!
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