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 * ONNX Runtime Web v1.19.0
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
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computePoolOutputShape(t,r,n,s,a,i,d){if(r.length<=0)throw new Error("input shape must be of size greater than 0");let c=[r[0],r[1]];return Fu.computeShapeHelper(t,r,c,n,s,a,i,d),c}static computeConvOutputShape(t,r,n,s,a,i,d){if(t.length<=0||r.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let c=[t[0],r[0]];return Fu.computeShapeHelper(!1,t,c,n,s,a,i,d),c}static computeShapeHelper(t,r,n,s,a,i,d,c){if(t)for(let h=0;h<r.length-2;h++)n.push(1);else for(let h=0;h<r.length-2;h++)n.push(Fu.adjustPadAndReturnShape(r[h+2],s[h],a[h],i[h],d,h,h+r.length-2,c))}static adjustPadAndReturnShape(t,r,n,s,a,i,d,c){let h=n*(s-1)+1;if(c&&c!=="NOTSET")switch(c){case"VALID":return a[i]=0,a[d]=0,Math.floor((t-h)/r+1);case"SAME_LOWER":case"SAME_UPPER":if(n!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let w=((t+r-1)/r-1)*r+s-t;return a[i]=Math.floor(c==="SAME_LOWER"?(w+1)/2:w/2),a[d]=w-a[i],Math.floor((t+w-s)/r+1)}default:throw new 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    let dim${Le} = current / ${Ft(G,Le,i)};
    let rest${Le} = current % ${Ft(G,Le,i)};
    indices[${Le}] = dim${Le};
    current = rest${Le};
    `;L+=`indices[${i-1}] = current;`;let pe=i<2?"":`
  fn o2i_${e}(offset: u32) -> ${u.indices} {
    var indices: ${u.indices};
    var current = offset;
    ${L}
    return indices;
  }`,Z=Le=>(C.offsetToIndices=!0,i<2?Le:`o2i_${e}(${Le})`),oe=[];if(i>=2)for(let Le=i-1;Le>=0;Le--)oe.push(`${Ft(G,Le,i)} * (indices[${Le}])`);let et=i<2?"":`
  fn i2o_${e}(indices: ${u.indices}) -> u32 {
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  fn get_${e}ByIndices(indices: ${u.indices}) -> ${w} {
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  fn get_${e}(${Le}) -> ${w} {
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  fn set_${e}ByIndices(indices: ${u.indices}, value: ${w}) {
    ${rr(`i2o_${e}(indices)`,"value")}
  }`,_r=i<2?"":(()=>{let Le=d.map(tr=>`d${tr}: u32`).join(", "),Nt=d.map(tr=>`d${tr}`).join(", ");return`
  fn set_${e}(${Le}, value: ${w}) {
    set_${e}ByIndices(${ct(Nt)}, value);
  }`})();return{impl:()=>{let Le=[],Nt=!1;return C.offsetToIndices&&(Le.push(pe),Nt=!0),C.indicesToOffset&&(Le.push(et),Nt=!0),C.broadcastedIndicesToOffset&&(Object.values(hr).forEach(tr=>Le.push(tr)),Nt=!0),C.set&&(Le.push(_r),Nt=!0),C.setByIndices&&(Le.push(Zt),Nt=!0),C.get&&(Le.push(cr),Nt=!0),C.getByIndices&&(Le.push(Wr),Nt=!0),!a&&Nt&&Le.unshift(`const ${U} = ${u.indices}(${r.join(",")});`,`const ${G} = ${u.indices}(${qe.computeStrides(r).join(",")});`),Le.join(`
`)},type:u,offsetToIndices:Z,indicesToOffset:We,broadcastedIndicesToOffset:br,indices:ct,indicesGet:Ot,indicesSet:zt,set:(...Le)=>{if(Le.length!==i+1)throw new Error(`indices length must be ${i}`);let Nt=Le[i];if(typeof Nt!="string")throw new Error("value must be string");let tr=Le.slice(0,i).map(k).join(",");return i===0?rr("0u",Nt):i===1?rr(tr[0],Nt):(C.set=!0,C.setByIndices=!0,C.indicesToOffset=!0,`set_${e}(${tr}, ${Nt})`)},setByOffset:rr,setByIndices:(Le,Nt)=>i<2?rr(Le,Nt):(C.setByIndices=!0,C.indicesToOffset=!0,`set_${e}ByIndices(${Le}, ${Nt});`),get:Rr,getByOffset:Sr,getByIndices:Bt,usage:n,name:e,strides:G,shape:U,rank:i}},it=(e,t,r,n=1)=>Ws(e,t,r,"input",n),Ut=(e,t,r,n=1)=>Ws(e,t,r,"output",n),fi=(e,t,r,n=1)=>Ws(e,t,r,"internal",n),mi=class{constructor(e,t){this.normalizedDispatchGroup=e,this.limits=t,this.internalVariables=[],this.variables=[],this.uniforms=[],this.variableIndex=0}guardAgainstOutOfBoundsWorkgroupSizes(e){return`if (global_idx >= ${typeof e=="number"?`${e}u`:e}) { return; }`}mainStart(e=_n){let t=typeof e=="number"?e:e[0],r=typeof e=="number"?1:e[1],n=typeof e=="number"?1:e[2];if(t>this.limits.maxComputeWorkgroupSizeX||r>this.limits.maxComputeWorkgroupSizeY||n>this.limits.maxComputeWorkgroupSizeZ)throw new Error(`workgroup size [${t}, ${r}, ${n}] exceeds the maximum workgroup size [${this.limits.maxComputeWorkgroupSizeX}, ${this.limits.maxComputeWorkgroupSizeY}, ${this.limits.maxComputeWorkgroupSizeZ}].`);if(t*r*n>this.limits.maxComputeInvocationsPerWorkgroup)throw new Error(`workgroup size [${t}, ${r}, ${n}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let s=this.normalizedDispatchGroup[1]===1&&this.normalizedDispatchGroup[2]===1,a=s?`@builtin(global_invocation_id) global_id : vec3<u32>,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(local_invocation_id) local_id : vec3<u32>`:`@builtin(global_invocation_id) global_id : vec3<u32>,
                                             @builtin(local_invocation_id) local_id : vec3<u32>,
    @builtin(local_invocation_index) local_idx : u32,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(num_workgroups) num_workgroups : vec3<u32>`,i=s?"let global_idx = global_id.x; let local_idx = local_id.x;":`let global_idx = (workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
          workgroup_id.y * num_workgroups[0] + workgroup_id.x) * ${t*r*n}u + local_idx;`;return`@compute @workgroup_size(${t}, ${r}, ${n})
  fn main(${a}) {
    ${i}
  `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,t){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let r=e.usage==="input"?"read":"read_write",n=e.type.storage;return`@group(0) @binding(${t}) var<storage, ${r}> ${e.name}: array<${n}>;`}declareVariables(...e){return e.map(t=>this.declareVariable(t,this.variableIndex++)).join(`
`)}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])}},Ia=(e,t)=>new mi(e,t),us=(e,t)=>{let r=e.length,n=[];for(let s=0;s<r;s++){let a=r-1-s,i=e[a]||1;(t[t.length-1-s]||1)>1&&i===1&&n.unshift(a)}return n}}),Fa,_i,Cs,Oa,Pn,za,gi,ds=D(()=>{Qt(),Yt(),fr(),ir(),Fa=e=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.")},_i=(e,t)=>t&&t.length!==e?[...new Array(e).keys()].reverse():t,Cs=(e,t)=>qe.sortBasedOnPerm(e,_i(e.length,t)),Oa=(e,t,r,n)=>{let s=[];s.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
    var a: ${r.type.indices};`);for(let a=0;a<t;++a)s.push(r.indicesSet("a",e[a],`i[${a}]`));return s.push("return a;}"),s.join(`
`)},Pn=(e,t)=>{let r=e.dataType,n=e.dims.length,s=_i(n,t),a=Cs(e.dims,s),i=Ut("output",r,a.length),d=it("a",r,n),c;if(s.length===2&&s[0]===1&&s[1]===0){let h=i.type.value,w=[16,16,1];c=y=>`
  ${y.registerUniform("output_size","u32").declareVariables(d,i)}
  var<workgroup> tile : array<array<${h}, ${w[0]+1}>, ${w[0]}>;
  ${y.mainStart(w)}
    var x = workgroup_id.x * ${w[0]}u + local_id.x;
    var y = workgroup_id.y * ${w[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] = ${d.getByOffset("y * width + x")};
    }
    workgroupBarrier();
    x = workgroup_id.y * ${w[0]}u + local_id.x;
    y = workgroup_id.x * ${w[0]}u + local_id.y;
    if (x < height && y < width) {
      ${i.setByOffset("y * height + x","tile[local_id.x][local_id.y]")}
    }
  }`}else c=h=>`
  ${h.registerUniform("output_size","u32").declareVariables(d,i)}

  ${Oa(s,n,d,i)}

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

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

    ${i.setByOffset("global_idx",d.getByIndices("aIndices"))}
  }`;return{name:"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:h=>{let w=qe.size(a);return{outputs:[{dims:a,dataType:h[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:[{type:12,data:w},...kt(h[0].dims,a)]}},getShaderSource:c}},za=(e,t)=>{Fa(e.inputs),e.compute(Pn(e.inputs[0],t.perm))},gi=e=>Gt({perm:e.perm})}),Da,Ba,La,Ra,wi,Na,ja,yi,Va,Ua,bn,Wa,Ga,bi,qa,Ha,Mi,Ka,Xa,vi,Qa,zu=D(()=>{Qt(),Yt(),ir(),ki(),ds(),Da={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"},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",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},La={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},Ra={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},wi=(e,t)=>{let r=[];for(let n=t-e;n<t;++n)r.push(n);return r},Na=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++)t.indexOf(a)===-1&&r.push(e[a]);let s=t.map(a=>e[a]);return[r,s]},ja=(e,t)=>{let r=e.length+t.length,n=[],s=0;for(let a=0;a<r;a++)t.indexOf(a)===-1?n.push(e[s++]):n.push(1);return n},yi=(e,t)=>{for(let r=0;r<e.length;++r)if(e[e.length-r-1]!==t-1-r)return!1;return!0},Va=(e,t)=>{let r=[];if(!yi(e,t)){for(let n=0;n<t;++n)e.indexOf(n)===-1&&r.push(n);e.forEach(n=>r.push(n))}return r},Ua=(e,t,r,n,s,a,i)=>{let d=r[0].dims,c=qe.size(a),h=qe.size(i),w=it("_A",r[0].dataType,d),y=Ut("output",s,a),u=32,k=`
          var<workgroup> aBestValues : array<f32, ${u}>;
       `;return{name:e,shaderCache:t,getShaderSource:C=>`
        ${C.registerUniform("reduceSize","u32").declareVariables(w,y)}
        ${k}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${C.mainStart(u)}

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

          var bestValue = f32(${La[n]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${u}) {
           let candidate = f32(${w.getByOffset("offset + k")});
           bestValue = ${Da[n]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

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

         if (local_idx == 0u) {
          ${y.setByOffset("outputIndex",`${n==="mean"?`${y.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${y.type.storage}(${Ra[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:a,dataType:s}],dispatchGroup:{x:c},programUniforms:[{type:12,data:h}]})}},bn=(e,t,r,n)=>{let s=e.inputs.length===1?r:qs(e.inputs,r),a=s.axes;a.length===0&&!s.noopWithEmptyAxes&&(a=e.inputs[0].dims.map((k,C)=>C));let i=qe.normalizeAxes(a,e.inputs[0].dims.length),d=i,c=e.inputs[0],h=Va(d,e.inputs[0].dims.length);h.length>0&&(c=e.compute(Pn(e.inputs[0],h),{inputs:[0],outputs:[-1]})[0],d=wi(d.length,c.dims.length));let[w,y]=Na(c.dims,d),u=w;s.keepDims&&(u=ja(w,i)),e.compute(Ua(t,{hint:s.cacheKey,inputDependencies:["type"]},[c],n,e.inputs[0].dataType,u,y),{inputs:[c]})},Wa=(e,t)=>{bn(e,"ReduceMeanShared",t,"mean")},Ga=(e,t)=>{bn(e,"ReduceL1Shared",t,"l1")},bi=(e,t)=>{bn(e,"ReduceL2Shared",t,"l2")},qa=(e,t)=>{bn(e,"ReduceLogSumExpShared",t,"logSumExp")},Ha=(e,t)=>{bn(e,"ReduceMaxShared",t,"max")},Mi=(e,t)=>{bn(e,"ReduceMinShared",t,"min")},Ka=(e,t)=>{bn(e,"ReduceProdShared",t,"prod")},Xa=(e,t)=>{bn(e,"ReduceSumShared",t,"sum")},vi=(e,t)=>{bn(e,"ReduceSumSquareShared",t,"sumSquare")},Qa=(e,t)=>{bn(e,"ReduceLogSumShared",t,"logSum")}}),Mn,Ya,Gs,qs,Cn,Za,xi,Ja,eo,Ti,to,ro,Ci,no,so,vn,io,ao,$i,oo,lo,Ei,uo,co,Si,po,ki=D(()=>{Qt(),Yt(),fr(),ir(),zu(),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.")},Ya=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],Gs=(e,t,r,n,s,a,i=!1,d=!1)=>{let c=[],h=r[0].dims,w=h.length,y=qe.normalizeAxes(s,w),u=!d&&y.length===0;h.forEach((F,U)=>{u||y.indexOf(U)>=0?i&&c.push(1):c.push(F)});let k=c.length,C=qe.size(c);return{name:e,shaderCache:t,getShaderSource:F=>{let U=[],G=it("_A",r[0].dataType,w),L=Ut("output",a,k),pe=n(G,L,y),Z=pe[2];for(let oe=0,et=0;oe<w;oe++)u||y.indexOf(oe)>=0?(i&&et++,Z=`for(var j${oe}: u32 = 0; j${oe} < ${h[oe]}; j${oe}++) {
                  ${pe[2].includes("last_index")?`let last_index = j${oe};`:""}
                  ${G.indicesSet("input_indices",oe,`j${oe}`)}
                  ${Z}
                }`):(U.push(`${G.indicesSet("input_indices",oe,L.indicesGet("output_indices",et))};`),et++);return`

        ${F.registerUniform("output_size","u32").declareVariables(G,L)}

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

          ${U.join(`
`)}
          ${pe[0]}       // init ops for reduce max/min
          ${pe[1]}
          ${Z}
          ${pe[3]}
          ${pe.length===4?L.setByOffset("global_idx","value"):pe.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:c,dataType:a}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:[{type:12,data:C},...kt(h,c)]})}},qs=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),Gt({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},Cn=(e,t,r,n)=>{let s=e.inputs,a=s.length===1?r:qs(s,r);e.compute(Gs(t,{hint:a.cacheKey,inputDependencies:["rank"]},[s[0]],a.noopWithEmptyAxes&&a.axes.length===0?Ya:n,a.axes,s[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},Za=(e,t)=>{Mn(e.inputs),Cn(e,"ReduceLogSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},xi=(e,t)=>{Mn(e.inputs),Cn(e,"ReduceL1",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},Ja=(e,t)=>{Mn(e.inputs),Cn(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);"])},eo=(e,t)=>{Mn(e.inputs),Cn(e,"ReduceLogSumExp",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},Ti=(e,t)=>{Mn(e.inputs),Cn(e,"ReduceMax",t,(r,n,s)=>{let a=[];for(let i=0;i<r.rank;i++)(s.indexOf(i)>=0||s.length===0)&&a.push(r.indicesSet("input_indices",i,0));return[`${a.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = max(value, ${r.getByIndices("input_indices")});`,""]})},to=(e,t)=>{Mn(e.inputs),Cn(e,"ReduceMean",t,(r,n,s)=>{let a=1;for(let i=0;i<r.rank;i++)(s.indexOf(i)>=0||s.length===0)&&(a*=e.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${a});`]})},ro=(e,t)=>{Mn(e.inputs),Cn(e,"ReduceMin",t,(r,n,s)=>{let a=[];for(let i=0;i<r.rank;i++)(s.indexOf(i)>=0||s.length===0)&&a.push(`input_indices[${i}] = 0;`);return[`${a.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},Ci=(e,t)=>{Mn(e.inputs),Cn(e,"ReduceProd",t,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},no=(e,t)=>{Mn(e.inputs),Cn(e,"ReduceSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},so=(e,t)=>{Mn(e.inputs),Cn(e,"ReduceSumSquare",t,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},vn=(e,t,r)=>{if(t.length===0)return r;let n=1,s=1;for(let a=0;a<t.length;a++)t.indexOf(a)===-1?n*=e[a]:s*=e[a];return s<32&&n>1024},io=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?to(e,t):Wa(e,t)},ao=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?xi(e,t):Ga(e,t)},$i=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ja(e,t):bi(e,t)},oo=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?eo(e,t):qa(e,t)},lo=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ti(e,t):Ha(e,t)},Ei=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?ro(e,t):Mi(e,t)},uo=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ci(e,t):Ka(e,t)},co=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?no(e,t):Xa(e,t)},Si=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?so(e,t):vi(e,t)},po=(e,t)=>{vn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Za(e,t):Qa(e,t)}}),Hs,ho,fo,Ks,Du=D(()=>{Qt(),fr(),ki(),Hs=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.")},ho=(e,t)=>{Hs(e.inputs);let r=(n,s,a)=>{let i=[];for(let d=0;d<n.rank;d++)(a.indexOf(d)>=0||a.length===0)&&i.push(`input_indices[${d}] = 0;`);return[`${i.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",s.setByOffset("global_idx","best_index")]};e.compute(Gs("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},fo=(e,t)=>{Hs(e.inputs);let r=(n,s,a)=>{let i=[];for(let d=0;d<n.rank;d++)(a.indexOf(d)>=0||a.length===0)&&i.push(`input_indices[${d}] = 0;`);return[`${i.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(Gs("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Ks=e=>Gt(e)}),mo,Pi,_o,go,cs,wo,yo,Xs=D(()=>{Qt(),O(),ir(),mo=(e,t)=>{let r=e[0],n=e[1],s=e[2],a=e[3],i=e[4],d=e[5];if(i&&d)throw new Error("Attention cannot have both past and relative_position_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let c=r.dims[0],h=r.dims[1],w=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]!==w)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 y=s.dims[0]/3,u=y,k=u;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let pe of t.qkvHiddenSizes)if(pe%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");y=t.qkvHiddenSizes[0],u=t.qkvHiddenSizes[1],k=t.qkvHiddenSizes[2]}let C=h;if(y!==u)throw new Error("qkv_hidden_sizes first element should be same as the second");if(s.dims[0]!==y+u+k)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let F=0;if(i){if(u!==k)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==c)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==u/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(F=i.dims[3])}let U=C+F,G=-1,L=0;if(a)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");return{batchSize:c,sequenceLength:h,pastSequenceLength:F,kvSequenceLength:C,totalSequenceLength:U,maxSequenceLength:G,inputHiddenSize:w,hiddenSize:y,vHiddenSize:k,headSize:Math.floor(y/t.numHeads),vHeadSize:Math.floor(k/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:L,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Pi=(e,t,r,n)=>{let s=gr(n),a=64,i=n/s;i<a?a=1:i/8<64&&(a=Math.ceil(i/8));let d=Math.ceil(n/s/a),c=[{type:t.dataType,data:1/n},{type:12,data:i},{type:12,data:d}],h=vr(t.dataType,s),w=xr(1,s),y=u=>{let k=Ut("x",t.dataType,t.dims,s),C=[{name:"d_inv",type:xr(t.dataType)},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${a}>;
  var<workgroup> thread_sum: array<f32, ${a}>;
  ${u.registerUniforms(C).declareVariables(k)}
  ${u.mainStart([a,1,1])}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = workgroup_id.x * uniforms.d_comp + local_offset;

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

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

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

    var sum: f32 = 0;
    for (var i = 0u; i < ${a}; 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] = ${k.type.value}(uniforms.d_inv);
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        var f32input = ${w}(x[offset + i]);
        x[offset + i] = ${k.type.value}(exp(f32input - max_value) / sum);
      }
    }
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${a};${h};${s}`},getShaderSource:y,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:c})}},_o=(e,t,r,n,s,a,i,d)=>{let c=d+a.kvSequenceLength,h=[a.batchSize,a.numHeads,a.sequenceLength,c],w=a.kvNumHeads===void 0&&e.outputCount>1,y=w?[a.batchSize,a.numHeads,c,a.headSize]:void 0,u=i.scale===0?1/Math.sqrt(a.headSize):i.scale,k=gr(a.headSize),C=a.headSize/k,F=12,U={x:Math.ceil(c/F),y:Math.ceil(a.sequenceLength/F),z:a.batchSize*a.numHeads},G=[{type:12,data:a.sequenceLength},{type:12,data:C},{type:12,data:c},{type:12,data:a.numHeads},{type:1,data:u},{type:12,data:d},{type:12,data:a.kvSequenceLength}],L=["type","type"];n&&L.push("type"),s&&L.push("type");let pe=[{dims:h,dataType:t.dataType,gpuDataType:0}];w&&pe.push({dims:y,dataType:t.dataType,gpuDataType:0});let Z=oe=>{let et=it("q",t.dataType,t.dims,k),We=it("key",r.dataType,r.dims,k),ct=[et,We];if(n){let rr=it("past_key",n.dataType,n.dims,k);ct.push(rr)}s&&ct.push(it("relative_position_bias",s.dataType,s.dims));let Ot=Ut("output",t.dataType,h),zt=[Ot];w&&zt.push(Ut("present_key",t.dataType,y,k));let hr=xr(1,k),br=[{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 = ${F}u;

  var<workgroup> tileQ: array<${et.type.storage}, ${F*F}>;
  var<workgroup> tileK: array<${et.type.storage}, ${F*F}>;
  ${oe.registerUniforms(br).declareVariables(...ct,...zt)}
  ${oe.mainStart([F,F,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;
    ${n&&w?`
    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;`}
    ${w?"let presentKeyOffset = headIdx * uniforms.N * uniforms.K;":""}
    var value = ${hr}(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;
      ${n&&w?`
              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];"}
      ${w?"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 += ${hr}(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(k){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: ${k}`)}})()};
        output[outputIdx] = ${Ot.type.value} (sum * uniforms.alpha) + ${s?"relative_position_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${k};${s!==void 0};${n!==void 0};${e.outputCount}`,inputDependencies:L},getRunData:()=>({outputs:pe,dispatchGroup:U,programUniforms:G}),getShaderSource:Z}},go=(e,t,r,n,s,a)=>{let i=a+s.kvSequenceLength,d=s.nReps?s.nReps:1,c=s.vHiddenSize*d,h=s.kvNumHeads==null&&e.outputCount>1,w=h?[s.batchSize,s.numHeads,i,s.headSize]:void 0,y=[s.batchSize,s.sequenceLength,c],u=12,k={x:Math.ceil(s.vHeadSize/u),y:Math.ceil(s.sequenceLength/u),z:s.batchSize*s.numHeads},C=[{type:12,data:s.sequenceLength},{type:12,data:i},{type:12,data:s.vHeadSize},{type:12,data:s.numHeads},{type:12,data:c},{type:12,data:a},{type:12,data:s.kvSequenceLength}],F=n?["type","type","type"]:["type","type"],U=[{dims:y,dataType:t.dataType,gpuDataType:0}];h&&U.push({dims:w,dataType:t.dataType,gpuDataType:0});let G=L=>{let pe=it("probs",t.dataType,t.dims),Z=it("v",r.dataType,r.dims),oe=[pe,Z];n&&oe.push(it("past_value",n.dataType,n.dims));let et=[Ut("output",t.dataType,y)];h&&et.push(Ut("present_value",t.dataType,w));let We=[{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 = ${u}u;
  var<workgroup> tileQ: array<${pe.type.value}, ${u*u}>;
  var<workgroup> tileK: array<${pe.type.value}, ${u*u}>;
  ${L.registerUniforms(We).declareVariables(...oe,...et)}
  ${L.mainStart([u,u,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;
   ${n&&h?`
    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;
            `}
    ${h?"let presentValueOffset = headIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${pe.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;
        ${n&&h?`
        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];
      `}
        ${h?"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.outputCount}`,inputDependencies:F},getRunData:()=>({outputs:U,dispatchGroup:k,programUniforms:C}),getShaderSource:G}},cs=(e,t,r,n,s,a,i,d,c,h,w)=>{let y=e.outputCount,u=h.kvNumHeads!==void 0||y>1?h.pastSequenceLength:0,k=u+h.kvSequenceLength,C=h.kvNumHeads===void 0&&y>1&&i?[t,r,i]:[t,r];c&&C.push(c);let F=e.compute(_o(e,t,r,y>1?i:void 0,c,h,w,u),{inputs:C,outputs:h.kvNumHeads===void 0&&y>1?[-1,1]:[-1]})[0];e.compute(Pi(e,F,h.batchSize*h.numHeads*h.sequenceLength,k),{inputs:[F],outputs:[]});let U=h.kvNumHeads===void 0&&y>1&&d?[F,n,d]:[F,n];e.compute(go(e,F,n,y>1&&d?d:void 0,h,u),{inputs:U,outputs:h.kvNumHeads===void 0&&y>1?[0,2]:[0]})},wo=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,s=t.inputHiddenSize,a=t.headSize,i=12,d={x:Math.ceil(t.headSize/i),y:Math.ceil(t.sequenceLength/i),z:t.batchSize*t.numHeads},c=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:n},{type:12,data:s},{type:12,data:a},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],w=y=>{let u=Ut("output_q",c[0].dataType,r),k=Ut("output_k",c[0].dataType,r),C=Ut("output_v",c[0].dataType,r),F=it("input",c[0].dataType,c[0].dims),U=it("weight",c[1].dataType,c[1].dims),G=it("bias",c[2].dataType,c[2].dims),L=F.type.storage,pe=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
  const TILE_SIZE = ${i}u;
  var<workgroup> tileInput: array<${L}, ${i*i}>;
  var<workgroup> tileWeightQ: array<${L}, ${i*i}>;
  var<workgroup> tileWeightK: array<${L}, ${i*i}>;
  var<workgroup> tileWeightV: array<${L}, ${i*i}>;
  ${y.registerUniforms(pe).declareVariables(F,U,G,u,k,C)}
  ${y.mainStart([i,i,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_id.x;

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

    var valueQ = ${L}(0);
    var valueK = ${L}(0);
    var valueV = ${L}(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:d,programUniforms:h}),getShaderSource:w},{inputs:c,outputs:[-1,-1,-1]})},yo=(e,t)=>{let r=mo(e.inputs,t),[n,s,a]=wo(e,r);return cs(e,n,s,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}}),bo,Mo,vo,xo,To=D(()=>{$(),Qt(),Yt(),fr(),ir(),bo=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,s,a)=>{let i=s.length;if(i!==n.length)throw new Error(`${a}: num dimensions != ${i}`);s.forEach((d,c)=>{if(d!==n[c])throw new Error(`${a}: dim[${c}] 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")},Mo=(e,t)=>{let{epsilon:r,spatial:n,format:s}=t,a=e[0].dims,i=n?gr(a[a.length-1]):1,d=s==="NHWC"&&a.length>1?i:1,c=qe.size(a)/i,h=n,w=h?a.length:a,y=it("x",e[0].dataType,e[0].dims,i),u=it("scale",e[1].dataType,e[1].dims,d),k=it("bias",e[2].dataType,e[2].dims,d),C=it("inputMean",e[3].dataType,e[3].dims,d),F=it("inputVar",e[4].dataType,e[4].dims,d),U=Ut("y",e[0].dataType,w,i),G=()=>{let pe="";if(n)pe=`let cOffset = ${a.length===1?"0u":s==="NHWC"?`outputIndices[${a.length-1}] / ${i}`:"outputIndices[1]"};`;else if(s==="NCHW")pe=`
            ${U.indicesSet("outputIndices","0","0")}
            let cOffset = ${U.indicesToOffset("outputIndices")};`;else{pe=`var cIndices = ${u.type.indices}(0);
                       cIndices[0] = outputIndices[${a.length-1}];`;for(let Z=1;Z<u.rank;Z++)pe+=`cIndices[${Z}] = outputIndices[${Z}];`;pe+=`let cOffset = ${u.indicesToOffset("cIndices")};`}return pe},L=pe=>`
  const epsilon = ${r};
  ${pe.registerUniform("outputSize","u32").declareVariables(y,u,k,C,F,U)}
  ${pe.mainStart()}
  ${pe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${U.offsetToIndices(`global_idx * ${i}`)};
    ${G()}
    let scale = ${u.getByOffset("cOffset")};
    let bias = ${k.getByOffset("cOffset")};
    let inputMean = ${C.getByOffset("cOffset")};
    let inputVar = ${F.getByOffset("cOffset")};
    let x = ${y.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${U.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${i}`,inputDependencies:h?["rank","type","type","type","type"]:void 0},getShaderSource:L,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:h?[{type:12,data:c},...kt(a)]:[{type:12,data:c}]})}},vo=e=>Gt(e),xo=(e,t)=>{let{inputs:r,outputCount:n}=e,s=vo({...t,outputCount:n});if(A.webgpu.validateInputContent&&bo(r,s),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Mo(r,s))}}),Co,$o,Ai,Bu=D(()=>{Yt(),ir(),Co=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")},$o=e=>{let t=e[0].dims,r=e[0].dims[2],n=qe.size(t)/4,s=e[0].dataType,a=it("input",s,t,4),i=it("bias",s,[r],4),d=it("residual",s,t,4),c=Ut("output",s,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:h=>`
  const channels = ${r}u / 4;
  ${h.declareVariables(a,i,d,c)}

  ${h.mainStart()}
    ${h.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${a.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${d.getByOffset("global_idx")};
    ${c.setByOffset("global_idx","value")}
  }`}},Ai=e=>{Co(e.inputs),e.compute($o(e.inputs))}}),Eo,wr,So,ko,Ii,Po,Ao,Fi,Io,Fo,Qs,Oo,zo,Do,Oi,Bo,ps,Lo,Ys,Ro,zi,No,jo,Vo,Di,Uo,Wo,Bi,Go,qo,Li,Ho,Ko,Ri,Xo,Ni,ji,Vi,Ui,Qo,Yo,Wi,Zo,Jo,el,Gi=D(()=>{Qt(),Yt(),fr(),ir(),Eo=(e,t,r,n,s,a)=>{let i=Math.ceil(t/4),d="";typeof s=="string"?d=`${s}(a)`:d=s("a");let c=it("inputData",r,[i],4),h=Ut("outputData",n,[i],4);return`
      ${e.registerUniform("vec_size","u32").declareVariables(c,h)}

  ${a??""}

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

    let a = ${c.getByOffset("global_idx")};
    ${h.setByOffset("global_idx",d)}
  }`},wr=(e,t,r,n,s,a=e.dataType)=>({name:t,shaderCache:{hint:s,inputDependencies:["type"]},getShaderSource:i=>Eo(i,qe.size(e.dims),e.dataType,a,r,n),getRunData:i=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil(qe.size(i[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(qe.size(e.dims)/4)}]})}),So=e=>{e.compute(wr(e.inputs[0],"Abs","abs"))},ko=e=>{e.compute(wr(e.inputs[0],"Acos","acos"))},Ii=e=>{e.compute(wr(e.inputs[0],"Acosh","acosh"))},Po=e=>{e.compute(wr(e.inputs[0],"Asin","asin"))},Ao=e=>{e.compute(wr(e.inputs[0],"Asinh","asinh"))},Fi=e=>{e.compute(wr(e.inputs[0],"Atan","atan"))},Io=e=>{e.compute(wr(e.inputs[0],"Atanh","atanh"))},Fo=e=>Gt(e),Qs=(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): ${t.to}`)}e.compute(wr(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},Oo=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:Hr,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:cn;return Gt({min:t,max:r})},zo=(e,t)=>{let r=e.inputs.length===1?t:Oo(e.inputs),n=xr(e.inputs[0].dataType);e.compute(wr(e.inputs[0],"Clip",s=>`clamp(${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]})},Do=e=>{e.compute(wr(e.inputs[0],"Ceil","ceil"))},Oi=e=>{e.compute(wr(e.inputs[0],"Cos","cos"))},Bo=e=>{e.compute(wr(e.inputs[0],"Cosh","cosh"))},ps=e=>Gt(e),Lo=(e,t)=>{let r=xr(e.inputs[0].dataType);e.compute(wr(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))},Ys=(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));
}`,Ro=e=>{let t=xr(e.inputs[0].dataType);e.compute(wr(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,Ys(t)))},zi=e=>{e.compute(wr(e.inputs[0],"Exp","exp"))},No=e=>{e.compute(wr(e.inputs[0],"Floor","floor"))},jo=e=>{let t=xr(e.inputs[0].dataType);e.compute(wr(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,Ys(t)))},Vo=(e,t)=>{let r=xr(e.inputs[0].dataType);e.compute(wr(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))},Di=e=>{e.compute(wr(e.inputs[0],"Not",t=>`!${t}`))},Uo=e=>{e.compute(wr(e.inputs[0],"Neg",t=>`-${t}`))},Wo=e=>{e.compute(wr(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Bi=e=>{let t=xr(e.inputs[0].dataType);e.compute(wr(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Go=e=>{e.compute(wr(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},qo=e=>Gt(e),Li=(e,t)=>{let r=xr(e.inputs[0].dataType);e.compute(wr(e.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${n} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Ho=e=>{e.compute(wr(e.inputs[0],"Sin","sin"))},Ko=e=>{e.compute(wr(e.inputs[0],"Sinh","sinh"))},Ri=e=>{e.compute(wr(e.inputs[0],"Sqrt","sqrt"))},Xo=e=>{e.compute(wr(e.inputs[0],"Tan","tan"))},Ni=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,ji=e=>{e.compute(wr(e.inputs[0],"Tanh",Ni))},Vi=(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 ${Ni("v")};
}
`,Ui=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Qo=e=>{let t=xr(e.inputs[0].dataType);e.compute(wr(e.inputs[0],"FastGelu",Ui,Vi(t),void 0,e.inputs[0].dataType))},Yo=(e,t)=>{let r=xr(e.inputs[0].dataType);return e.compute(wr(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},Wi=e=>{e.compute(wr(e.inputs[0],"Log","log"))},Zo=(e,t)=>`
const alpha = vec4<${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<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,Jo=e=>`quick_gelu_impl(${e})`,el=(e,t)=>{let r=xr(e.inputs[0].dataType);e.compute(wr(e.inputs[0],"QuickGelu",Jo,Zo(r,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),qi,tl,rl,nl=D(()=>{Yt(),ir(),Gi(),qi=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")},tl=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=it("input",e[0].dataType,e[0].dims,4),n=it("bias",e[0].dataType,[e[0].dims[2]],4),s=Ut("output",e[0].dataType,t,4),a=qe.size(t)/4,i=vr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:d=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

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

  ${Ys(i)}

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

    ${s.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},rl=e=>{qi(e.inputs),e.compute(tl(e.inputs))}}),sl,il,xn,al,ol,Hi,ll,ul,dl,cl,pl,hl,Ki,Lu=D(()=>{Qt(),Yt(),ir(),sl=(e,t,r,n,s,a,i,d,c,h,w,y)=>{let u,k;typeof d=="string"?u=k=(L,pe)=>`${d}((${L}),(${pe}))`:typeof d=="function"?u=k=d:(u=d.scalar,k=d.vector);let C=Ut("outputData",w,n.length,4),F=it("aData",c,t.length,4),U=it("bData",h,r.length,4),G;if(s)if(a){let L=qe.size(t)===1,pe=qe.size(r)===1,Z=t.length>0&&t[t.length-1]%4===0,oe=r.length>0&&r[r.length-1]%4===0;L||pe?G=C.setByOffset("global_idx",k(L?`${F.type.value}(${F.getByOffset("0")}.x)`:F.getByOffset("global_idx"),pe?`${U.type.value}(${U.getByOffset("0")}.x)`:U.getByOffset("global_idx"))):G=`
            let outputIndices = ${C.offsetToIndices("global_idx * 4u")};
            let offsetA = ${F.broadcastedIndicesToOffset("outputIndices",C)};
            let offsetB = ${U.broadcastedIndicesToOffset("outputIndices",C)};
            ${C.setByOffset("global_idx",k(i||Z?F.getByOffset("offsetA / 4u"):`${F.type.value}(${F.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||oe?U.getByOffset("offsetB / 4u"):`${U.type.value}(${U.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else G=C.setByOffset("global_idx",k(F.getByOffset("global_idx"),U.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let L=(pe,Z,oe="")=>{let et=`aData[indexA${Z}][componentA${Z}]`,We=`bData[indexB${Z}][componentB${Z}]`;return`
            let outputIndices${Z} = ${C.offsetToIndices(`global_idx * 4u + ${Z}u`)};
            let offsetA${Z} = ${F.broadcastedIndicesToOffset(`outputIndices${Z}`,C)};
            let offsetB${Z} = ${U.broadcastedIndicesToOffset(`outputIndices${Z}`,C)};
            let indexA${Z} = offsetA${Z} / 4u;
            let indexB${Z} = offsetB${Z} / 4u;
            let componentA${Z} = offsetA${Z} % 4u;
            let componentB${Z} = offsetB${Z} % 4u;
            ${pe}[${Z}] = ${oe}(${u(et,We)});
          `};w===9?G=`
            var data = vec4<u32>(0);
            ${L("data",0,"u32")}
            ${L("data",1,"u32")}
            ${L("data",2,"u32")}
            ${L("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:G=`
            ${L("outputData[global_idx]",0)}
            ${L("outputData[global_idx]",1)}
            ${L("outputData[global_idx]",2)}
            ${L("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(F,U,C)}

        ${y??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${G}
      }`},il=(e,t,r,n,s,a,i=r.dataType)=>{let d=!qe.areEqual(r.dims,n.dims),c=r.dims,h=qe.size(r.dims),w=!1,y=!1,u=[d];if(d){let k=Yr.calcShape(r.dims,n.dims,!1);if(!k)throw new Error("Can't perform binary op on the given tensors");c=k,h=qe.size(c);let C=qe.size(r.dims)===1,F=qe.size(n.dims)===1,U=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,G=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;u.push(C),u.push(F),u.push(U),u.push(G);let L=1;for(let pe=1;pe<c.length;pe++){let Z=r.dims[r.dims.length-pe]??1,oe=n.dims[n.dims.length-pe]??1;if(Z===oe)L*=Z;else break}L%4===0?(y=!0,w=!0):(C||F||U||G)&&(w=!0)}else w=!0;return u.push(w),{name:e,shaderCache:{hint:t+u.map(k=>k.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:k=>sl(k,r.dims,n.dims,c,w,d,y,s,r.dataType,n.dataType,i,a),getRunData:()=>({outputs:[{dims:c,dataType:i}],dispatchGroup:{x:Math.ceil(h/64/4)},programUniforms:[{type:12,data:Math.ceil(qe.size(c)/4)},...kt(r.dims,n.dims,c)]})}},xn=(e,t,r,n,s,a)=>{e.compute(il(t,s??"",e.inputs[0],e.inputs[1],r,n,a))},al=e=>{xn(e,"Add",(t,r)=>`${t}+${r}`)},ol=e=>{xn(e,"Div",(t,r)=>`${t}/${r}`)},Hi=e=>{xn(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},ll=e=>{xn(e,"Mul",(t,r)=>`${t}*${r}`)},ul=e=>{let t=it("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;xn(e,"Pow",{scalar:(r,n)=>`pow_custom(${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));
    }
      `)},dl=e=>{xn(e,"Sub",(t,r)=>`${t}-${r}`)},cl=e=>{xn(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},pl=e=>{xn(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},hl=e=>{xn(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},Ki=e=>{xn(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}}),fl,Xi,ml,_l,qn,gl,Ru=D(()=>{Qt(),Yt(),fr(),ir(),fl=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],s=n.dataType,a=n.dims.length;e.forEach((i,d)=>{if(d!==r){if(i.dataType!==s)throw new Error("input tensors should be one type");if(i.dims.length!==a)throw new Error("input tensors should have the same shape");i.dims.forEach((c,h)=>{if(h!==t&&c!==n.dims[h])throw new Error("non concat dimensions must match")})}})},Xi=(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;
  }`,ml=(e,t)=>{let r=e.length,n=[];for(let s=0;s<r;++s){let a=t.setByOffset("global_idx",e[s].getByIndices("indices"));r===1?n.push(a):s===0?n.push(`if (inputIndex == ${s}u) { ${a} }`):s===r-1?n.push(`else { ${a} }`):n.push(`else if (inputIndex == ${s}) { ${a} }`)}return n.join(`
`)},_l=(e,t,r,n)=>{let s=qe.size(r),a=new Array(e.length),i=new Array(e.length),d=0,c=[],h=[],w=[{type:12,data:s}];for(let F=0;F<e.length;++F)d+=e[F].dims[t],a[F]=d,h.push(e[F].dims.length),i[F]=it(`input${F}`,n,h[F]),c.push("rank"),w.push({type:12,data:a[F]});for(let F=0;F<e.length;++F)w.push(...kt(e[F].dims));w.push(...kt(r));let y=Ut("output",n,r.length),u=y.indicesGet("indices",t),k=Array.from(Array(a.length).keys()).map(F=>`uniforms.sizeInConcatAxis${F}`).join(","),C=F=>`

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

  ${Xi(a.length,k)}

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

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

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

    ${ml(i,y)}
  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:w}),getShaderSource:C}},qn=(e,t)=>{let r=e.inputs,n=r[0].dims,s=qe.normalizeAxis(t.axis,n.length);fl(r,s);let a=n.slice();a[s]=r.reduce((d,c)=>d+(c.dims.length>s?c.dims[s]:0),0);let i=r.filter(d=>qe.size(d.dims)>0);e.compute(_l(i,s,a,r[0].dataType),{inputs:i})},gl=e=>Gt({axis:e.axis})}),Hn,Kn,Bn,Qi,Xn=D(()=>{Qt(),Yt(),Hn=(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 ${e.activation}`)}},Kn=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Bn=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Qi=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[r,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=(e==null?void 0:e.activation_params)||[Hr,cn];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:r}}return{activation:t}}}),en,Yi,hs=D(()=>{en=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Yi=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),Zi,wl=D(()=>{Zi=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),yl,$s,Zs,Ji,bl,Js,ei,ea,ti=D(()=>{Qt(),Yt(),ir(),Xn(),hs(),yl=(e,t)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${t?", batchIndices":""});
        `,$s=(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];"}
        }`,Zs=(e,t,r="f32",n,s=!1,a=32,i=!1,d=32)=>{let c=t[1]*e[1],h=t[0]*e[0],w=s?c:a,y=s?a:c,u=w/t[0],k=a/t[1];if(!((s&&u===4&&e[1]===4||!s&&(u===3||u===4))&&w%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${s} is true, innerElementSize ${u} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${u} must be 3 or 4.
  tileAWidth ${w} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${u}<${r}>, ${w/u}>, ${y}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${h/e[0]}>, ${a}>;

const rowPerThread = ${e[1]};
const colPerThread = ${e[0]};
const innerElementSize = ${u};
const tileInner = ${a};

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

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

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

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

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

          ${$s(s,u)}
      }

      workgroupBarrier();
  }

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

    // Loop over shared dimension.
    for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var inputRow = localRow; inputRow < ${u}; inputRow = inputRow + ${t[1]}) {
        for (var inputCol = localCol; inputCol < ${y}; inputCol = inputCol + ${t[0]}) {
          ${Ji(s,n)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) {
            for (var inputCol = localCol; inputCol < ${w}; 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) * ${h};

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

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

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

    var acc : array<array<${r}, colPerThread>, rowPerThread>;
    ${U}
  }
`},ei=(e,t,r,n,s,a=!1)=>{let[i,d,c]=s,[h,w,y,u]=n,k=us(i,c),C=us(d,c),F=vr(n[0].type.tensor),U=()=>{let L=w.rank,pe=h.rank,Z=`var aIndices: ${w.type.indices};`;for(let oe=L-2-1,et=pe-1;oe>=0;oe--,et--)Z+=`
aIndices[${oe}] = ${pe>1?`batchIndices[${et}]`:"batchIndices"};`;return k.forEach(oe=>{Z+=`
aIndices[${oe}] = 0;`}),Z+=`
aIndices[${L-2}] = u32(row);
                   aIndices[${L-1}] = u32(colIn);`,Z},G=()=>{let L=y.rank,pe=h.rank,Z=`var bIndices: ${y.type.indices};`;for(let oe=L-2-1,et=pe-1;oe>=0;oe--,et--)Z+=`
bIndices[${oe}] = ${pe>1?`batchIndices[${et}]`:"batchIndices"};`;return C.forEach(oe=>{Z+=`
bIndices[${oe}] = 0;`}),Z+=`
bIndices[${L-2}] = u32(row);
                   bIndices[${L-1}] = u32(colIn);`,Z};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${en(e,F)} {
      var value = ${en(e,F)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${U()}
        value = ${w.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${en(e,F)} {
      var value = ${en(e,F)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
      {
        ${G()}
        value = ${y.getByIndices("bIndices")};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${en(e,F)}) {
      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 + ${a?"bias[colIn]":`${en(e,F)}(bias[row])`};`:""}
        ${r}
        ${u.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},ea=(e,t,r,n,s=!1)=>{let a=e[0].dims,i=e[1].dims,d=a.slice(0,-2),c=i.slice(0,-2),h=n?n.slice(0,-2):r.slice(0,-2),w=qe.size(h),y=a[a.length-2],u=a[a.length-1],k=i[i.length-1],C=u%4===0&&k%4===0,F=y<=8?[4,1,1]:[4,4,1],U=[8,8,1],G=[Math.ceil(k/U[0]/F[0]),Math.ceil(y/U[1]/F[1]),Math.ceil(w/U[2]/F[2])],L=C?4:1,pe=[...d,y,u/L],Z=pe.length,oe=[...c,u,k/L],et=oe.length,We=[w,y,k/L],ct=[{type:6,data:y},{type:6,data:k},{type:6,data:u}];Kn(t,ct),ct.push(...kt(h,pe,oe));let Ot=["rank","rank"],zt=e.length>2;zt&&(ct.push(...kt(e[2].dims)),Ot.push("rank")),ct.push(...kt(We));let hr=br=>{let rr=h.length,Sr=fi("batchDims",e[0].dataType,rr,1),Wr=vr(e[0].dataType),cr=it("a",e[0].dataType,Z,L),Rr=it("b",e[1].dataType,et,L),Bt=Ut("result",e[0].dataType,We.length,L),Zt=[cr,Rr];if(zt){let Br=s?L:1;Zt.push(it("bias",e[2].dataType,e[2].dims.length,Br))}let _r=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Bn(t,_r);let Le=vr(Bt.type.tensor),Nt=Hn(t,Bt.type.value,Le),tr=ei(L,zt,Nt,[Sr,cr,Rr,Bt],[d,c,h],s);return`
  ${br.registerUniforms(_r).registerInternalVariables(Sr).declareVariables(...Zt,Bt)}
  ${tr}
  ${C?Zs(F,U,Wr,Sr):Js(F,U,Wr,Sr)}
                   `};return{name:"MatMul",shaderCache:{hint:`${F};${t.activation};${C};${s}`,inputDependencies:Ot},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:G[0],y:G[1],z:G[2]},programUniforms:ct}),getShaderSource:hr}}}),Ml,Nu,ju=D(()=>{Qt(),mn(),ir(),Xn(),hs(),wl(),ti(),Ml=(e,t,r,n,s=!1,a,i=4,d=4,c=4,h="f32")=>{let w=Ot=>{switch(Ot){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Ot} is not supported.`)}},y=Ot=>{switch(Ot){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 ${Ot} is not supported.`)}},u=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,k=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,C=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",F=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",U=e?"row":"col",G=e?"col":"row",L=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${U} / outWidth;
    let outCol = ${U} % outWidth;

    let WRow = ${G} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${G} / 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 = ${G} % inChannels;
    var resData = ${en(i,h)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${C} && xCol >= 0 && xCol < ${F}) {
      ${u}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${w(i)}
    }
    return resData;`,pe=e?t&&n?`
    let col = colIn * ${i};
    ${L}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${L}
    }
    return ${en(i,h)}(0.0);`:n&&r?`
    let col = colIn * ${i};
    ${L}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${L}
    }
    return ${en(i,h)}(0.0);`,Z=`${y(d)}`,oe=en(c,h),et=en(e?i:d,h),We=en(e?d:i,h),ct=Hn(a,oe,h);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${et} {
      ${e?pe:Z}
    }

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${oe}) {
      let col = colIn * ${c};
      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])"};
      ${k}
      ${Yi(s)}
      ${ct}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Nu=(e,t,r,n,s,a,i,d)=>{let c=t.format==="NHWC",h=c?e[0].dims[3]:e[0].dims[1],w=r[0],y=c?r[2]:r[3],u=c?r[1]:r[2],k=c?r[3]:r[1],C=c&&(h%4===0||h%3===0)&&k%4===0,F=c?k:y*u,U=c?y*u:k,G=[8,8,1],L=n<=8?[4,1,1]:[4,4,1],pe=[Math.ceil(F/G[0]/L[0]),Math.ceil(U/G[1]/L[1]),Math.ceil(w/G[2]/L[2])];Dr("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${pe}`);let Z=C?c&&h%4!==0?3:4:1,oe=G[1]*L[1],et=G[0]*L[0],We=Math.max(G[0]*Z,G[1]),ct=n%oe===0,Ot=s%et===0,zt=a%We===0,hr=C?[Z,4,4]:[1,1,1],br=[{type:6,data:n},{type:6,data:s},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Kn(t,br),br.push(...kt(e[0].dims,e[1].dims));let rr=["rank","rank"];i&&(br.push(...kt(e[2].dims)),rr.push("rank")),br.push(...kt(r));let Sr=Wr=>{let cr=[{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}];Bn(t,cr);let Rr=C?4:1,Bt=vr(e[0].dataType),Zt=`
      fn setOutputAtIndex(flatIndex : i32, value : ${C?`vec4<${Bt}>`:Bt}) {
        result[flatIndex] = ${C?`vec4<${Bt}>`:Bt}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${C?`vec4<${Bt}>`:Bt}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${C?"/ 4":""}, value);
      }`,_r=it("x",e[0].dataType,e[0].dims.length,Z===3?1:Z),Le=it("w",e[1].dataType,e[1].dims.length,Rr),Nt=[_r,Le],tr=Ut("result",e[0].dataType,r.length,Rr);if(i){let Br=it("bias",e[2].dataType,e[2].dims.length,Rr);Nt.push(Br),Zt+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${C?`vec4<${Bt}>`:Bt} {
          return bias[coords.${c?"w":"y"}${C?"/ 4":""}];
        }`}return`
        ${Zi("uniforms.result_strides")}
        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
        ${Wr.registerUniforms(cr).declareVariables(...Nt,tr)}
        ${Zt}
        ${Ml(c,ct,Ot,zt,i,t,hr[0],hr[1],hr[2],Bt)}
        ${C?Zs(L,G,Bt,void 0,!c,We):Js(L,G,Bt,void 0,!c,We,!1,void 0,d)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${Z};${C};${ct};${Ot};${zt};${oe};${et};${We}`,inputDependencies:rr},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:pe[0],y:pe[1],z:pe[2]},programUniforms:br}),getShaderSource:Sr}}}),vl,ta,Ln,xl,ra,Tl,Cl,$l,na=D(()=>{Qt(),mn(),Yt(),ir(),Xn(),hs(),vl=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t},ta=e=>typeof e=="number"?[e,e,e]:e,Ln=(e,t)=>t<=1?e:e+(e-1)*(t-1),xl=(e,t,r,n=1)=>{let s=Ln(t,n);return Math.floor((e[0]*(r-1)-r+s)/2)},ra=(e,t,r,n,s)=>{s==null&&(s=xl(e,t[0],n[0]));let a=[0,0,0,r];for(let i=0;i<3;i++)e[i]+2*s>=t[i]&&(a[i]=Math.trunc((e[i]-t[i]+2*s)/n[i]+1));return a},Tl=(e,t,r,n,s,a,i,d,c,h)=>{let w,y,u,k;if(e==="VALID"&&(e=0),typeof e=="number"){w={top:e,bottom:e,left:e,right:e,front:e,back:e};let C=ra([t,r,n,1],[d,c,h],1,[s,a,i],e);y=C[0],u=C[1],k=C[2]}else if(Array.isArray(e)){if(!e.every((F,U,G)=>F===G[0]))throw Error(`Unsupported padding parameter: ${e}`);w={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let C=ra([t,r,n,1],[d,c,h],1,[s,a,i],e[0]);y=C[0],u=C[1],k=C[2]}else if(e==="SAME_UPPER"){y=Math.ceil(t/s),u=Math.ceil(r/a),k=Math.ceil(n/i);let C=(y-1)*s+d-t,F=(u-1)*a+c-r,U=(k-1)*i+h-n,G=Math.floor(C/2),L=C-G,pe=Math.floor(F/2),Z=F-pe,oe=Math.floor(U/2),et=U-oe;w={top:pe,bottom:Z,left:oe,right:et,front:G,back:L}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:w,outDepth:y,outHeight:u,outWidth:k}},Cl=(e,t,r,n,s,a=!1,i="channelsLast")=>{let d,c,h,w,y;if(i==="channelsLast")[d,c,h,w,y]=e;else if(i==="channelsFirst")[d,y,c,h,w]=e;else throw new Error(`Unknown dataFormat ${i}`);let[u,,k,C,F]=t,[U,G,L]=ta(r),[pe,Z,oe]=ta(n),et=Ln(k,pe),We=Ln(C,Z),ct=Ln(F,oe),{padInfo:Ot,outDepth:zt,outHeight:hr,outWidth:br}=Tl(s,c,h,w,U,G,L,et,We,ct),rr=a?u*y:u,Sr=[0,0,0,0,0];return i==="channelsFirst"?Sr=[d,rr,zt,hr,br]:i==="channelsLast"&&(Sr=[d,zt,hr,br,rr]),{batchSize:d,dataFormat:i,inDepth:c,inHeight:h,inWidth:w,inChannels:y,outDepth:zt,outHeight:hr,outWidth:br,outChannels:rr,padInfo:Ot,strideDepth:U,strideHeight:G,strideWidth:L,filterDepth:k,filterHeight:C,filterWidth:F,effectiveFilterDepth:et,effectiveFilterHeight:We,effectiveFilterWidth:ct,dilationDepth:pe,dilationHeight:Z,dilationWidth:oe,inShape:e,outShape:Sr,filterShape:t}},$l=(e,t,r,n,s,a)=>{let i=a==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let d=[64,1,1],c={x:r.map((U,G)=>G)},h=[Math.ceil(vl(c.x.map(U=>r[U]))/d[0]),1,1];Dr("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${h}`);let w=1,y=qe.size(r),u=[{type:12,data:y},{type:12,data:n},{type:12,data:s},{type:12,data:t.strides},{type:12,data:t.dilations}];Kn(t,u),u.push(...kt(e[0].dims,e[1].dims));let k=["rank","rank"],C=e.length===3;C&&(u.push(...kt(e[2].dims)),k.push("rank")),u.push(...kt(r));let F=U=>{let G=[{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}];Bn(t,G);let L=1,pe=vr(e[0].dataType),Z=it("x",e[0].dataType,e[0].dims.length,w),oe=it("W",e[1].dataType,e[1].dims.length,L),et=[Z,oe],We=Ut("result",e[0].dataType,r.length,L),ct="";if(C){let hr=it("bias",e[2].dataType,e[2].dims.length,L);et.push(hr),ct+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${pe} {
          return bias[${i?Ft("coords",4,5):Ft("coords",1,5)}];
        }`}let Ot=en(w,pe),zt=Hn(t,Ot,pe);return`
            ${ct}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${Z.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 ${oe.getByIndices("aIndices")};
            }
          ${U.registerUniforms(G).declareVariables(...et,We)}
          ${U.mainStart()}
          ${U.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${We.offsetToIndices("global_idx")};
              let batch = ${Ft("coords",0,Z.rank)};
              let d2 = ${i?Ft("coords",Z.rank-1,Z.rank):Ft("coords",1,Z.rank)};
              let xFRCCorner = vec3<u32>(${i?Ft("coords",1,Z.rank):Ft("coords",2,Z.rank)},
              ${i?Ft("coords",2,Z.rank):Ft("coords",3,Z.rank)},
              ${i?Ft("coords",3,Z.rank):Ft("coords",4,Z.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${i?Ft("uniforms.x_shape",1,Z.rank):Ft("uniforms.x_shape",2,Z.rank)};
              let xShapeZ = ${i?Ft("uniforms.x_shape",2,Z.rank):Ft("uniforms.x_shape",3,Z.rank)};
              let xShapeW = ${i?Ft("uniforms.x_shape",3,Z.rank):Ft("uniforms.x_shape",4,Z.rank)};
              let xShapeU = ${i?Ft("uniforms.x_shape",4,Z.rank):Ft("uniforms.x_shape",1,Z.rank)};
              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
              let inputDepthVec4Remainder = xShapeU % 4;

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

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

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

                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
                      ${i?`let xValues = vec4<f32>(
                               getX(batch, xF, xR, xC, d1),
                               getX(batch, xF, xR, xC, d1 + 1),
                               getX(batch, xF, xR, xC, d1 + 2),
                               getX(batch, xF, xR, xC, d1 + 3));
                            `:`let xValues = vec4<f32>(
                               getX(batch, d1, xF, xR, xC),
                               getX(batch, d1 + 1, xF, xR, xC),
                               getX(batch, d1 + 2, xF, xR, xC),
                               getX(batch, d1 + 3, xF, xR, xC));
                            `}
                            let wValues = vec4<f32>(
                              getW(d2, d1, wF, wR, wC),
                              getW(d2, d1 + 1, wF, wR, wC),
                              getW(d2, d1 + 2, wF, wR, wC),
                              getW(d2, d1 + 3, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                    if (inputDepthVec4Remainder == 1) {
                        ${i?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
                    } else if (inputDepthVec4Remainder == 2) {
                      ${i?`let xValues = vec2<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
                      `:`let xValues = vec2<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
                    `}
                    let wValues = vec2<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
                      value += dot(xValues, wValues);
                    } else if (inputDepthVec4Remainder == 3) {
                      ${i?`let xValues = vec3<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
                      `:`let xValues = vec3<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
                    `}
                    let wValues = vec3<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                  }
                }
              }
              ${C?"value = value + getBiasByOutputCoords(coords)":""};
              ${zt}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${i};${w};${C}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:h[0],y:h[1],z:h[2]},programUniforms:u}),getShaderSource:F}}}),El,Sl,Vu=D(()=>{Qt(),Yt(),ir(),Fl(),Xn(),El=(e,t,r)=>{let n=e.length>2,s=n?"value += b[output_channel];":"",a=e[0].dims,i=e[1].dims,d=i[0]/t.group,c=t.format==="NHWC",h=ri(a,i,t.dilations,t.pads,t.strides,c),w=qe.size(h),y=[{type:12,data:w},{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:d}];Kn(t,y),y.push(...kt(a,i));let u=["rank","rank"];n&&(y.push(...kt(e[2].dims)),u.push("rank")),y.push(...kt(h));let k=C=>{let F=Ut("output",e[0].dataType,h.length),U=vr(F.type.tensor),G=Hn(t,F.type.value,U),L=it("x",e[0].dataType,a.length),pe=it("w",e[1].dataType,i.length),Z=[L,pe];n&&Z.push(it("b",e[2].dataType,e[2].dims.length));let oe=[{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 Bn(t,oe),`
  ${C.registerUniforms(oe).declareVariables(...Z,F)}

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

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

    var value: ${F.type.value} = ${F.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[${c?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[${c?2:3}]) {
            continue;
          }

          let xVal = ${c?L.get("batch","xHeight","xWidth","input_channel"):L.get("batch","input_channel","xHeight","xWidth")};
          let wVal = ${pe.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${s}
    ${G}
    ${F.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:u},getRunData:()=>({outputs:[{dims:r?r(h):h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:y}),getShaderSource:k}},Sl=(e,t,r)=>{let n=e.length>2,s=gr(r[3]),a=gr(r[2]),i=qe.size(r)/s/a,d=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/s],c=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/s],h=[r[0],r[1],r[2],r[3]/s],w=[{type:12,data:i},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Kn(t,w),w.push(...kt(d,c,h));let y=(a-1)*t.strides[1]+c[1],u=k=>{let C=Ut("output",e[0].dataType,h.length,s),F=vr(C.type.tensor),U=Hn(t,C.type.value,F),G=it("x",e[0].dataType,d.length,s),L=it("w",e[1].dataType,c.length,s),pe=[G,L];n&&pe.push(it("b",e[2].dataType,e[2].dims,s));let Z=n?"value += b[output_channel];":"",oe=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Bn(t,oe),`
  ${k.registerUniforms(oe).declareVariables(...pe,C)}
  ${k.mainStart()}
    ${k.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<${G.type.value}, ${y}>;
    var values: array<${C.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] = ${G.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${G.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${c[1]}; w_width++) {
          let w_val = ${L.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];
      ${Z}
      ${U}
      ${C.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${s};${a};${y};${c[0]};${c[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:w}),getShaderSource:u}}}),sa,kl,Pl,ia=D(()=>{Qt(),Yt(),ti(),ir(),Xn(),sa=(e,t,r,n,s=!1)=>{let a=e[0].dims,i=e[1].dims,d=a[a.length-2],c=i[i.length-1],h=a[a.length-1],w=gr(c),y=gr(h),u=gr(d),k=qe.size(r)/w/u,C=e.length>2,F=n?n.slice(0,-2):r.slice(0,-2),U=[qe.size(F),d,c],G=[{type:12,data:k},{type:12,data:d},{type:12,data:c},{type:12,data:h}];Kn(t,G),G.push(...kt(F,a,i)),C&&G.push(...kt(e[2].dims)),G.push(...kt(U));let L=pe=>{let Z=fi("batch_dims",e[0].dataType,F.length),oe=it("a",e[0].dataType,a.length,y),et=it("b",e[1].dataType,i.length,w),We=Ut("output",e[0].dataType,U.length,w),ct=vr(We.type.tensor),Ot=Hn(t,We.type.value,ct),zt=[oe,et],hr="";if(C){let Zt=s?w:1;zt.push(it("bias",e[2].dataType,e[2].dims.length,Zt)),hr=`${s?`value += bias[col / ${Zt}];`:`value += ${We.type.value}(bias[row + i]);`}`}let br=a.slice(0,-2),rr=i.slice(0,-2),Sr=us(br,F),Wr=us(rr,F),cr=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Bn(t,cr);let Rr=(Zt,_r)=>{let Le=Zt.rank,Nt=Zt.name;if(Le===2)return`var ${Nt}_indices = ${Zt.type.indices}(0u, 0u);`;let tr=Z.rank,Br=`var ${Nt}_indices: ${Zt.type.indices};`;for(let Xr=Le-2-1,an=tr-1;Xr>=0;Xr--,an--)Br+=`
${Nt}_indices[${Xr}] = ${tr>1?`batch_indices[${an}]`:"batch_indices"};`;return _r.forEach(Xr=>{Br+=`
${Nt}_indices[${Xr}] = 0;`}),Br+=`${Nt}_indices[${Le-2}] = 0u;
                     ${Nt}_indices[${Le-1}] = 0u;`,Br},Bt=()=>{let Zt=`var a_data: ${oe.type.value};`;for(let _r=0;_r<y;_r++)Zt+=`
              let b_data${_r} = b[(b_offset + (k + ${_r}) * uniforms.N + col) / ${w}];`;for(let _r=0;_r<u;_r++){Zt+=`a_data = a[(a_offset + (row + ${_r}) * uniforms.K + k) / ${y}];`;for(let Le=0;Le<y;Le++)Zt+=`
            values[${_r}] = fma(${et.type.value}(a_data${y===1?"":`[${Le}]`}), b_data${Le}, values[${_r}]);
`}return Zt};return`
  ${pe.registerUniforms(cr).registerInternalVariables(Z).declareVariables(...zt,We)}
  ${pe.mainStart()}
    ${pe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${w})) * ${w};
    var index1 = global_idx / (uniforms.N / ${w});
    let stride1 = uniforms.M / ${u};
    let row = (index1 % stride1) * ${u};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${Z.offsetToIndices("batch")};`}
    ${Rr(oe,Sr)}
    let a_offset = ${oe.indicesToOffset("a_indices")};
    ${Rr(et,Wr)}
    let b_offset = ${et.indicesToOffset("b_indices")};
    var values: array<${We.type.value}, ${u}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${y}) {
      ${Bt()}
    }
    for (var i = 0u; i < ${u}u; i++) {
      var value = values[i];
      ${hr}
      ${Ot}
      let cur_indices = ${We.type.indices}(batch, row + i, col);
      let offset = ${We.indicesToOffset("cur_indices")};
      ${We.setByOffset(`offset / ${w}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${w};${y};${u};${s}`,inputDependencies:C?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:G}),getShaderSource:L}},kl=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.")},Pl=e=>{kl(e.inputs);let t=Yr.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(sa(e.inputs,{activation:""},t)):e.compute(ea(e.inputs,{activation:""},t))}}),ri,ni,aa,si,oa,la,Al,Il,Es,Fl=D(()=>{Yt(),ju(),na(),ti(),Vu(),Xn(),ia(),ds(),ri=(e,t,r,n,s,a)=>{let i=e[0],d=e.slice(a?1:2,a?3:4),c=d.length,h=t[0],w=t.slice(2).map((u,k)=>u+(u-1)*(r[k]-1)),y=d.map((u,k)=>u+n[k]+n[k+c]).map((u,k)=>Math.floor((u-w[k]+s[k])/s[k]));return y.splice(0,0,i),y.splice(a?3:1,0,h),y},ni=[2,3,1,0],aa=(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 ${s*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},si=(e,t)=>{let r=e.kernelShape.slice();for(let a=2;a<t[1].dims.length;++a)r[a-2]===0&&(r[a-2]=t[1].dims[a]);let n=e.pads.slice();yn.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,n,e.format==="NHWC",e.autoPad);let s=Object.assign({},e);return Object.assign(s,{kernelShape:r,pads:n}),s},oa=e=>{let t=Qi(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],s=e.dilations,a=e.group,i=e.kernel_shape,d=e.pads,c=e.strides,h=e.w_is_const();return{autoPad:n,format:r,dilations:s,group:a,kernelShape:i,pads:d,strides:c,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},la=(e,t,r)=>{let n=si(r,t),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 et=ri(t[0].dims,t[1].dims,r.dilations,n.pads,r.strides,s),We=e.kernelCustomData.wT??e.compute(Pn(t[1],ni),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=We);let ct=[t[0],We];t.length===3&&ct.push(t[2]),e.compute(Sl(ct,n,et),{inputs:ct})}else e.compute(El(t,n));return}let a=t.length===3,i=t[0].dims[s?1:2],d=t[0].dims[s?2:3],c=t[0].dims[s?3:1],h=t[1].dims[2],w=t[1].dims[3],y=ri(t[0].dims,t[1].dims,r.dilations,n.pads,r.strides,s),u=y[s?1:2],k=y[s?2:3],C=y[s?3:1],F=s&&h===i&&w===d&&r.pads[0]===0&&r.pads[1]===0;if(F||h===1&&w===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 et=y[0],We,ct,Ot,zt=[];if(s){let rr=e.kernelCustomData.wT??e.compute(Pn(t[1],ni),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=rr),F){let Sr=i*d*c;We=t[0].reshape([1,et,Sr]),ct=rr.reshape([1,Sr,C]),Ot=[1,et,C]}else We=t[0].reshape([et,i*d,c]),ct=rr.reshape([1,c,C]),Ot=[et,u*k,C];zt.push(We),zt.push(ct)}else We=t[0].reshape([et,c,i*d]),ct=t[1].reshape([1,C,c]),Ot=[et,C,u*k],zt.push(ct),zt.push(We);a&&zt.push(t[2]);let hr=Ot[2],br=zt[0].dims[zt[0].dims.length-1];hr<8&&br<8?e.compute(sa(zt,n,y,Ot,s),{inputs:zt}):e.compute(ea(zt,n,y,Ot,s),{inputs:zt});return}let U=!0,G=e.kernelCustomData.wT??e.compute(Pn(t[1],ni),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=G);let L=[t[0],G];a&&L.push(t[2]);let pe=s?u*k:C,Z=s?C:u*k,oe=h*w*c;e.compute(Nu(L,n,y,pe,Z,oe,a,U),{inputs:L})},Al=(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]],a=[1].concat(t.strides),i=[1].concat(t.dilations),d=[1].concat(t.kernelShape),c=si({...t,pads:s,strides:a,dilations:i,kernelShape:d},n);e.compute(El(n,c,h=>r?[h[0],h[2],h[3]]:[]))},Il=(e,t,r)=>{let n=r.format==="NHWC"?"channelsLast":"channelsFirst",s=si(r,t),a=r.autoPad==="NOTSET"?r.pads:r.autoPad,i=Cl(t[0].dims,t[1].dims,r.strides,r.dilations,a,!1,n);e.compute($l(t,s,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],n))},Es=(e,t)=>{aa(e.inputs,t),e.inputs[0].dims.length===3?Al(e,t):e.inputs[0].dims.length===5?Il(e,e.inputs,t):la(e,e.inputs,t)}}),Ol,zl,Uu=D(()=>{Qt(),mn(),ir(),Xn(),hs(),wl(),ti(),Ol=(e,t=!1,r,n,s=4)=>{let a=U=>{switch(U){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 ${U} is not supported.`)}},i=e?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,d=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,c=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",h=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",w=e?"row":"col",y=e?"col":"row",u=`
      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 = ${w} / outWidth;
      let outCol = ${w} % outWidth;

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

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

  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])"};
      ${d}
      ${Yi(t)}
      ${F}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${s}] = value;
    }
  }`},zl=(e,t,r,n,s,a,i,d)=>{let c=t.format==="NHWC",h=c?e[0].dims[3]:e[0].dims[1],w=r[0],y=c?r[2]:r[3],u=c?r[1]:r[2],k=c?r[3]:r[1],C=c&&h%4===0&&h%3&&k%4===0,F=c?k:y*u,U=c?y*u:k,G=[8,8,1],L=n<=8?[4,1,1]:[4,4,1],pe=[Math.ceil(F/G[0]/L[0]),Math.ceil(U/G[1]/L[1]),Math.ceil(w/G[2]/L[2])];Dr("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${pe}`);let Z=C?4:1,oe=Math.max(G[0]*Z,G[1]),et=C?4:1,We=[t.kernelShape[c?1:2],t.kernelShape[c?2:3]],ct=[We[0]+(t.dilations[0]<=1?0:(We[0]-1)*(t.dilations[0]-1)),We[1]+(t.dilations[1]<=1?0:(We[1]-1)*(t.dilations[1]-1))],Ot=[ct[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),ct[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],zt=[{type:6,data:n},{type:6,data:s},{type:6,data:a},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:We},{type:6,data:Ot}];Kn(t,zt),zt.push(...kt(e[0].dims,e[1].dims));let hr=["rank","rank"];i&&(zt.push(...kt(e[2].dims)),hr.push("rank")),zt.push(...kt(r));let br=rr=>{let Sr=it("x",e[0].dataType,e[0].dims.length,et),Wr=it("w",e[1].dataType,e[1].dims.length,1),cr=Ut("result",e[0].dataType,r.length,et),Rr=[Sr,Wr],Bt="";if(i){let Le=it("bias",e[2].dataType,e[2].dims.length,et);Rr.push(Le),Bt+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${Le.type.value} {
            return bias[coords.${c?"w":"y"}${C?"/ 4":""}];
          }`}let Zt=[{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:We.length},{name:"pads",type:"i32",length:Ot.length}];Bn(t,Zt);let _r=vr(e[0].dataType,1);if(_r!=="f16"&&_r!=="f32")throw new Error(`elemType ${_r} is not supported.`);return`
        ${Zi("uniforms.result_strides")}
        ${rr.registerUniforms(Zt).declareVariables(...Rr,cr)};
        ${Bt}
        ${Ol(c,i,t,Sr.type.value,Z)}
        ${C?Zs(L,G,_r,void 0,!c,oe):Js(L,G,_r,void 0,!c,oe,!1,void 0,d)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${L};${G};${C}`,inputDependencies:hr},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:pe[0],y:pe[1],z:pe[2]},programUniforms:zt}),getShaderSource:br}}}),ua,Ss,kd=D(()=>{Qt(),mn(),Yt(),ir(),ua=(e,t,r,n,s,a=!1,i,d,c=!1)=>{let h=c?1:2,w=c?2:3,y=c?3:1,u=a?2:1,k=`
  fn setOutputAtIndex(flatIndex : u32, value : ${a?`vec4<${i}>`:i}) {
    result[flatIndex] = ${a?`vec4<${i}>`:i}(value);
  }`;n&&(k+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${a?`vec4<${i}>`:i} {
      return bias[coords.${c?"w":"y"}${a?"/ 4":""}];
    }`);let C=a?4:1,F=it("W",t[1].dataType,t[1].dims.length,C),U=it("Dy",t[0].dataType,t[0].dims.length,C),G=[U,F];n&&G.push(it("bias",t[2].dataType,[r[y]].length,C));let L=Ut("result",t[0].dataType,r.length,C),pe=`{
        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"} * ${u};
        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<${i}>, ${u}>;
        for (var i = 0; i < ${u}; i++) {
          dotProd[i] = vec4<${i}>(0.0);
        }
        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {
          var dyR = (${i}(dyCorner.x) + ${i}(wR)) / ${i}(uniforms.strides.x);
          let wRPerm = uniforms.filter_dims[0] - 1 - wR;
          if (dyR < 0.0 || dyR >= ${i}(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 = (${i}(dyCorner.y) + ${i}(wC)) / ${i}(uniforms.strides.y);
            let dyC2 = (${i}(dyCorner.y) + 1.0 + ${i}(wC)) / ${i}(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 >= ${i}(uniforms.Dy_shape[2]) ||
                fract(dyC) > 0.0) {
              bDyCVal = false;
            }
            if (dyC2 < 0.0 || dyC2 >= ${i}(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 = ${F.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${F.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${F.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${F.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

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

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

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

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

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

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

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

    ${e.mainStart()}
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a=e[0].dims.length-2;if(t.dilations.reduce((i,d)=>i+d,0)>0&&t.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(t.strides.reduce((i,d)=>i+d,0)>0&&t.strides.length!==a)throw new Error(`strides should be ${a}D`);if(t.pads.reduce((i,d)=>i+d,0)>0&&t.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(t.outputPadding.length!==a&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${a}D`);if(t.kernelShape.reduce((i,d)=>i+d,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Rl=[2,3,1,0],Nl=(e,t,r)=>{let n=ca(r,t),s=r.format==="NHWC",a=n.outputShape,i=a[s?3:1],d=t[0].dims[s?3:1];if(n.group!==1||i===1&&d===1){e.compute(Ss(t,n));return}let c=a[s?1:2],h=a[s?2:3],w=t[1].dims[2],y=t[1].dims[3],u=s?c*h:i,k=s?i:c*h,C=w*y*d,F=!0,U=e.kernelCustomData.wT??e.compute(Pn(t[1],Rl),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=U);let G=[t[0],U],L=t.length===3;L&&(!s&&t[2].dims.length===1?G.push(t[2].reshape([t[2].dims[0],1,1])):G.push(t[2])),e.compute(zl(G,n,a,u,k,C,L,F),{inputs:G})},ha=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let s=t.kernelShape;(s.length===0||s[0]===0)&&(s=[e.inputs[1].dims[2]]);let a=t.dilations;(a.length===0||a[0]===0)&&(a=[1]);let i=t.strides;(i.length===0||i[0]===0)&&(i=[1]);let d=t.pads;d.length===0&&(d=[0,0]),d=[0,d[0],0,d[1]],i=[1].concat(i),a=[1].concat(a),s=[1].concat(s);let c=ca({...t,pads:d,strides:i,dilations:a,kernelShape:s},n);e.compute(Ss(n,c,h=>r?[h[0],h[2],h[3]]:[h[0],h[1],h[3]]))},Wu=(e,t)=>{pa(e.inputs,t),e.inputs[0].dims.length===3?ha(e,t):Nl(e,e.inputs,t)}}),fa,ma,jl,Gu=D(()=>{Qt(),Yt(),fr(),ir(),fa=(e,t,r,n)=>{let s=qe.size(t),a=t.length,i=it("input",e,a),d=Ut("output",e,a),c=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),h=qe.normalizeAxis(c,a),w=y=>{let u=` i32(${i.indicesGet("inputIndices","uniforms.axis")}) `,k=Ft("uniforms.input_shape","uniforms.axis",a),C=n.reverse?u+(n.exclusive?" + 1":""):"0",F=n.reverse?k:u+(n.exclusive?"":" + 1");return`
                ${y.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(i,d)}
                ${y.mainStart()}
                  ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
                  var inputIndices = ${d.offsetToIndices("global_idx")};
                  var sum = ${d.type.value}(0);
                  let first : i32 = ${C};
                  let last : i32 = ${F};
                  for (var i : i32 = first; i < last; i++) {
                    ${i.indicesSet("inputIndices","uniforms.axis","u32(i)")};
                    sum = sum + ${i.getByIndices("inputIndices")};
                  }
                  ${d.setByOffset("global_idx","sum")};
                }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:[{type:12,data:s},{type:12,data:h},...kt(t,t)]}),getShaderSource:w}},ma=(e,t)=>{let r=e.inputs[0].dims,n=e.inputs[0].dataType,s=e.inputs[1];e.compute(fa(n,r,s,t),{inputs:[0]})},jl=e=>{let t=e.exclusive===1,r=e.reverse===1;return Gt({exclusive:t,reverse:r})}}),_a,Vl,Ul,ga,Wl,qu=D(()=>{Qt(),Yt(),fr(),ir(),_a=e=>{if(!e||e.length!==1)throw new Error("DepthToSpace requires 1 input.");if(e[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},Vl=(e,t,r,n)=>{let s=[];s.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
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`)},Ul=(e,t)=>{let r,n,s,a,i,d,c=t.format==="NHWC",h=t.blocksize,w=t.mode==="DCR";c?([r,n,s,a]=e.dims,i=w?[r,n,s,h,h,a/h**2]:[r,n,s,a/h**2,h,h],d=w?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([r,n,s,a]=[e.dims[0],e.dims[2],e.dims[3],e.dims[1]],i=w?[r,h,h,a/h**2,n,s]:[r,a/h**2,h,h,n,s],d=w?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let y=e.reshape(i),u=y.dims.length,k=e.dataType,C=it("a",k,u),F=Ut("output",k,u),U=G=>`
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  ${Vl(d,u,C,F)}

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

    ${F.setByOffset("global_idx",C.getByIndices("aIndices"))}
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            ${h.mainStart()}
            ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
            var outputIndices = ${i.offsetToIndices("global_idx")};
            ${s.map((Z,oe)=>`var input${oe}Indices: ${s[oe].type.indices};`).join(`
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            ${pe.join(`
`)};
            ${i.setByOffset("global_idx","sum")};
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          let outputIndices${F} = ${y.offsetToIndices(`outputOffset + ${F}u`)};
          let offset${F} = ${w.broadcastedIndicesToOffset(`outputIndices${F}`,y)};
          let index${F} = offset${F} / 4u;
          let component${F} = offset${F} % 4u;
          ${C}[${F}] = ${U}(${w.getByOffset(`index${F}`)}[component${F}]);
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        let outputOffset = global_idx * ${a};
        var data = vec4<u32>(0);
        ${k("data",0,"u32")}
        ${k("data",1,"u32")}
        ${k("data",2,"u32")}
        ${k("data",3,"u32")}
        ${y.setByOffset("global_idx","data")}
      }`}else u=`
        let outputIndices = ${y.offsetToIndices("global_idx")};
        let inputOffset = ${w.broadcastedIndicesToOffset("outputIndices",y)};
        ${y.setByOffset("global_idx",w.getByOffset("inputOffset"))}
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    ${h.registerUniform("vec_size","u32").declareVariables(w,y)}
    ${h.mainStart()}
    ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
    ${u}`},c=[{type:12,data:i},...kt(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:d,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:c})}},ba=e=>{ya(e.inputs),e.compute(Xl(e.inputs),{inputs:[0]})}}),Yu,Ql,Zu=D(()=>{Qt(),Yt(),ir(),Gi(),Yu=e=>{let t=e[0].dataType,r=qe.size(e[0].dims),n=qe.size(e[1].dims),s=n%4===0,a=i=>{let d=it("x",t,[1],4),c=it("bias",t,[1],4),h=Ut("y",t,[1],4),w=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],y=k=>`
      let bias${k}_offset: u32 = (global_idx * 4 + ${k}) % uniforms.bias_size;
      let bias${k} = ${c.getByOffset(`bias${k}_offset / 4`)}[bias${k}_offset % 4];`,u=s?`
      let bias = ${c.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${y(0)}${y(1)}${y(2)}${y(3)}
      let bias = ${d.type.value}(bias0, bias1, bias2, bias3);`;return`${i.registerUniforms(w).declareVariables(d,c,h)}

    ${Vi(xr(t))}

    ${i.mainStart(_n)}
      ${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}

      let x = ${d.getByOffset("global_idx")};
      ${u}
      let x_in = x + bias;
      ${h.setByOffset("global_idx",Ui("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${s}`,inputDependencies:["type","type"]},getShaderSource:a,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/_n/4)}})}},Ql=e=>{e.inputs.length<2||qe.size(e.inputs[1].dims)===0?Qo(e):e.compute(Yu(e.inputs))}}),Yl,Zl,Jl,eu,Ju=D(()=>{Qt(),Yt(),fr(),ir(),Yl=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},Zl=(e,t)=>{let r=e[0].dims,n=e[1].dims,s=r.length,a=qe.normalizeAxis(t.axis,s),i=r.slice(0);i.splice(a,1,...n);let d=r[a],c=e[0].dataType===9?4:1,h=Math.ceil(qe.size(i)/c),w=[{type:12,data:h},{type:6,data:d},{type:12,data:a},...kt(e[0].dims,e[1].dims,i)],y=u=>{let k=it("data",e[0].dataType,e[0].dims.length,c),C=it("inputIndices",e[1].dataType,e[1].dims.length),F=Ut("output",e[0].dataType,i.length,c),U=L=>{let pe=n.length,Z=`var indicesIndices${L}  = ${C.type.indices}(0);`;for(let oe=0;oe<pe;oe++)Z+=`${pe>1?`indicesIndices${L}[${oe}]`:`indicesIndices${L}`} = ${i.length>1?`outputIndices${L}[uniforms.axis + ${oe}]`:`outputIndices${L}`};`;Z+=`
          var idx${L} = ${C.getByIndices(`indicesIndices${L}`)};
          if (idx${L} < 0) {
            idx${L} = idx${L} + uniforms.axisDimLimit;
          }
          var dataIndices${L} : ${k.type.indices};
        `;for(let oe=0,et=0;oe<s;oe++)oe===a?(Z+=`${s>1?`dataIndices${L}[${oe}]`:`dataIndices${L}`} = u32(idx${L});`,et+=pe):(Z+=`${s>1?`dataIndices${L}[${oe}]`:`dataIndices${L}`} = ${i.length>1?`outputIndices${L}[${et}]`:`outputIndices${L}`};`,et++);return Z},G;if(e[0].dataType===9){let L=(pe,Z,oe="")=>`
          let outputIndices${Z} = ${F.offsetToIndices(`outputOffset + ${Z}u`)};
          ${U(Z)};
          let offset${Z} = ${k.indicesToOffset(`dataIndices${Z}`)};
          let index${Z} = offset${Z} / 4u;
          let component${Z} = offset${Z} % 4u;
          ${pe}[${Z}] = ${oe}(${k.getByOffset(`index${Z}`)}[component${Z}]);
        `;G=`
        let outputOffset = global_idx * ${c};
        var value = vec4<u32>(0);
        ${L("value",0,"u32")}
        ${L("value",1,"u32")}
        ${L("value",2,"u32")}
        ${L("value",3,"u32")}
        ${F.setByOffset("global_idx","value")}
      `}else G=`
      let outputIndices = ${F.offsetToIndices("global_idx")};
      ${U("")};
      let value = ${k.getByIndices("dataIndices")};
      ${F.setByOffset("global_idx","value")};
      `;return`
      ${u.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(k,C,F)}
      ${u.mainStart()}
        ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${G}
      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:w}),getShaderSource:y}},Jl=e=>Gt({axis:e.axis}),eu=(e,t)=>{let r=e.inputs;Yl(r),e.compute(Zl(e.inputs,t))}}),tu,ru,nu,su,ed=D(()=>{Qt(),Yt(),fr(),ir(),tu=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.`)},ru=(e,t)=>{let r=e[0].dims,n=e[0].dataType,s=r.length,a=e[1].dims,i=e[1].dataType,d=qe.normalizeAxis(t.axis,s),c=r[d],h=a.slice(0),w=qe.size(h),y=it("input",n,s),u=it("indicesInput",i,a.length),k=Ut("output",n,h.length),C=[{type:12,data:w},{type:6,data:c},{type:12,data:d}];return C.push(...kt(r,a,h)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:C}),getShaderSource:F=>`
      ${F.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(y,u,k)}
      ${F.mainStart()}
      ${F.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${k.setByOffset("global_idx","value")};
  }`}},nu=e=>Gt({axis:e.axis}),su=(e,t)=>{let r=e.inputs;tu(r),e.compute(ru(e.inputs,t))}}),iu,au,ou,td,lu=D(()=>{Qt(),Yt(),ir(),iu=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")},au=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[s,a,i]=Mr.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),d=[s,a];if(!d)throw new Error("Can't use gemm on the given tensors");let c=qe.size(d),h=[{type:12,data:c},{type:12,data:s},{type:12,data:a},{type:12,data:i},{type:1,data:t.alpha},{type:1,data:t.beta}],w=["type","type"];e.length===3&&(h.push(...kt(e[2].dims)),w.push("rank")),h.push(...kt(d));let y=u=>{let k="";t.transA&&t.transB?k="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?k="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?k="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(k="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let C=t.alpha===1?"":"value *= uniforms.alpha;",F=it("a",e[0].dataType,e[0].dims),U=it("b",e[1].dataType,e[1].dims),G=F.type.value,L=null,pe=[F,U];e.length===3&&(L=it("c",e[2].dataType,e[2].dims.length),pe.push(L));let Z=Ut("output",e[0].dataType,d.length);pe.push(Z);let oe=[{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`
  ${u.registerUniforms(oe).declareVariables(...pe)}

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

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

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

    ${C}
    ${L!=null?`let cOffset = ${L.broadcastedIndicesToOffset("vec2(m, n)",Z)}; value += ${G}(uniforms.beta) * ${L.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:h}),getShaderSource:y}},ou=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}`}},td=(e,t)=>{iu(e.inputs),e.compute(au(e.inputs,t))}}),ln,uu,du,Ma,cu,As,pu,hu=D(()=>{Qt(),Yt(),fr(),O(),Xs(),ir(),ds(),ln=(e,t)=>e.length>t&&e[t].dims.length>0&&qe.size(e[t].dims)>0?e[t]:void 0,uu=(e,t)=>{let r=e[0],n=ln(e,1),s=ln(e,2),a=ln(e,3),i=ln(e,4),d=ln(e,5),c=ln(e,6),h=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 w=!1,y=r.dims[0],u=r.dims[1],k=r.dims.length===3?w?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],C=u,F=0,U=0,G=Math.floor(k/t.numHeads);if(c&&h){if(c.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(c.dims[0]!==y||c.dims[1]!==t.numHeads||c.dims[3]!==G)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(h.dims[0]!==y||h.dims[1]!==t.numHeads||h.dims[3]!==G)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(c.dims[2]!==h.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(h.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');F=c.dims[2],U=c.dims[2]}else if(c||h)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let L;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');L=2,C=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.');L=5,C=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');L=0,C=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');L=3}if(a){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(s&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let pe=0;if(i){pe=8;let ct=i.dims;throw ct.length===1?ct[0]===y?pe=1:ct[0]===3*y+2&&(pe=3):ct.length===2&&ct[0]===y&&ct[1]===C&&(pe=5),pe===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let Z=!1,oe=k;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(C!==s.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');oe=s.dims[2]}else{if(C!==s.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');oe=s.dims[1]*s.dims[3],Z=!0}}let et=F+C,We=!1;if(i)throw new Error("Key padding mask is not supported");if(d){if(d.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(d.dims[0]!==y&&d.dims[0]!==1||d.dims[1]!==t.numHeads||d.dims[2]!==u||d.dims[3]!==et)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:y,sequenceLength:u,pastSequenceLength:F,kvSequenceLength:C,totalSequenceLength:et,maxSequenceLength:U,inputHiddenSize:0,hiddenSize:k,vHiddenSize:oe,headSize:G,vHeadSize:Math.floor(oe/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:pe,scale:t.scale,broadcastResPosBias:We,passPastInKv:Z,qkvFormat:L}},du=e=>Gt({...e}),Ma=Gt({perm:[0,2,1,3]}),cu=(e,t,r,n,s,a,i)=>{let d=[n,s,a],c=qe.size(d),h=[{type:12,data:c},{type:12,data:i},{type:12,data:a}],w=y=>{let u=Ut("qkv_with_bias",t.dataType,d),k=it("qkv",t.dataType,d),C=it("bias",r.dataType,d),F=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${y.registerUniforms(F).declareVariables(k,C,u)}
  ${y.mainStart()}
    ${y.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:d,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:h}),getShaderSource:w},{inputs:[t,r],outputs:[-1]})[0]},As=(e,t,r,n,s,a,i,d)=>{let c=a;if(i){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return c=cu(e,a,i,t,n,r*s,d),c=c.reshape([t,n,r,s]),e.compute(Pn(c,Ma.perm),{inputs:[c],outputs:[-1]})[0]}else return a.dims.length===3&&(c=a.reshape([t,n,r,s])),e.compute(Pn(c,Ma.perm),{inputs:[c],outputs:[-1]})[0]},pu=(e,t)=>{let r=uu(e.inputs,t),n=e.inputs[0],s=ln(e.inputs,1),a=ln(e.inputs,2),i=ln(e.inputs,3),d=ln(e.inputs,4),c=ln(e.inputs,5),h=ln(e.inputs,6),w=ln(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((s==null?void 0:s.dims.length)===5)throw new Error("Packed KV is not implemented");let y=s&&a&&s.dims.length===4&&a.dims.length===4,u=As(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(y)return cs(e,u,s,a,d,void 0,h,w,c,r,t);if(!s||!a)throw new Error("key and value must be provided");let k=As(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,s,i,r.hiddenSize),C=As(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,a,i,2*r.hiddenSize);cs(e,u,k,C,d,void 0,h,w,c,r,t)}}),va,fu,mu,xa,_u,gu=D(()=>{Qt(),Yt(),ir(),va=e=>Array.from(e.getBigInt64Array(),Number),fu=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(va(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")},mu=(e,t)=>{let r=[];for(let n=0;n<e.length;++n)r.push(e[n]*t[n]);return r},xa=(e,t)=>{let r=e[0].dims,n=t??va(e[1]),s=mu(r,n),a=qe.size(s),i=e[0].dataType,d=it("input",i,r.length),c=Ut("output",i,s.length),h=w=>`
      const inputShape = ${d.indices(...r)};
      ${w.registerUniform("output_size","u32").declareVariables(d,c)}
      ${w.mainStart()}
      ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${c.offsetToIndices("global_idx")};
      var input_indices: ${d.type.indices};
      for (var i = 0; i < ${r.length}; i++) {
        let input_dim_i = ${d.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${c.indicesGet("output_indices","i")}  % input_dim_i;

        ${d.indicesSet("input_indices","i","input_dim_value")}
      }
      ${c.setByOffset("global_idx",d.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...kt(e[0].dims,s)]}),getShaderSource:h}},_u=e=>{fu(e.inputs),e.compute(xa(e.inputs),{inputs:[0]})}}),wu,Ta,yu,bu,Ca,Mu,rd=D(()=>{Qt(),Yt(),fr(),Xs(),ir(),hu(),gu(),ds(),wu=(e,t)=>{let r=e[0],n=e[1],s=e[2],a=e[3],i=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 d=!1,c=r.dims[0],h=r.dims[1],w=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],y=h,u=0,k=0,C=Math.floor(w/t.numHeads),F=a&&a.dims.length!==0,U=i&&i.dims.length!==0,G=!0;if(F&&U){if(a.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(i.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');u=a.dims[1],k=a.dims[1]}else if(F||U)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let L;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"');L=2,y=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==C)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.');L=5,y=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==C)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');L=0,y=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==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');L=3}let pe=0,Z=!1,oe=w;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(y!==s.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');oe=s.dims[2]}else{if(y!==s.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');oe=s.dims[1]*s.dims[3],Z=!0}}let et=u+y;return{batchSize:c,sequenceLength:h,pastSequenceLength:u,kvSequenceLength:y,totalSequenceLength:et,maxSequenceLength:k,inputHiddenSize:0,hiddenSize:w,vHiddenSize:oe,headSize:C,vHeadSize:Math.floor(oe/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:pe,scale:t.scale,broadcastResPosBias:!1,passPastInKv:Z,qkvFormat:L,isPastkvBSNH:G}},Ta=(e,t,r,n)=>{let s=[n.batchSize,n.totalSequenceLength,n.kvNumHeads,n.headSize],a=4,i=qe.size(s)/a,d=n.totalSequenceLength,c=Ut("present_kv",r,s.length,a),h=it("new_kv",e.dataType,e.dims.length,a),w=t?it("past_kv",t.dataType,t.dims.length,a):void 0,y=Math.ceil(n.headSize/a),u={x:d,y:e.dims[0],z:1},k=t?["rank","rank"]:["rank"],C=[{type:12,data:i},{type:12,data:n.pastSequenceLength},{type:12,data:n.kvSequenceLength},{type:12,data:n.totalSequenceLength}],F=[h];w?(C.push(...kt(e.dims),...kt(t.dims),...kt(s)),F.push(w)):C.push(...kt(e.dims),...kt(s));let U=[{name:"output_size",type:"u32"},{name:"past_seqlen",type:"u32"},{name:"new_seqlen",type:"u32"},{name:"present_seqlen",type:"u32"}],G=`      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];`,L=`      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];`,pe=t?`if (s < past_seqlen) {
        ${G}
        } else if (s < past_seqlen + uniforms.new_seqlen) {
        ${L}
        }`:`if (s < past_seqlen + uniforms.new_seqlen) {
          ${L}
        }`,Z=oe=>`

  ${oe.registerUniforms(U).declareVariables(...F,c)}
  ${oe.mainStart([y,n.kvNumHeads,1])}
    ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    var indices = ${c.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 = ${y}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;
    ${pe}
  }`;return{name:"ConcatPastNew",shaderCache:{hint:`${n.kvNumHeads}${y}${!!t}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:s,dataType:r}],dispatchGroup:u,programUniforms:C}),getShaderSource:Z}},yu=e=>Gt({...e}),bu=Gt({perm:[0,2,1,3]}),Ca=(e,t,r,n,s)=>{let a=t,i=n.kvNumHeads,d=n.nReps;return t.dims.length===3&&n.kvSequenceLength!==0&&(a=t.reshape([n.batchSize,n.kvSequenceLength,i,n.headSize])),r?a=e.compute(Ta(a,r,a.dataType,n),{inputs:[a,r],outputs:[n.isPastkvBSNH?s:-1]})[0]:a=e.compute(Ta(a,void 0,a.dataType,n),{inputs:[a],outputs:[n.isPastkvBSNH?s:-1]})[0],d!==1&&(a=e.compute(xa([a],[1,1,1,d]),{inputs:[a],outputs:[-1]})[0],a=a.reshape([n.batchSize,n.totalSequenceLength,i*d,n.headSize])),e.compute(Pn(a,bu.perm),{inputs:[a],outputs:[-1]})[0]},Mu=(e,t)=>{var c;let r=wu(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((c=e.inputs[1])==null?void 0:c.dims.length)===5)throw new Error("Packed KV is not implemented");let n=As(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,a=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,i=Ca(e,e.inputs[1],s,r,1),d=Ca(e,e.inputs[2],a,r,2);cs(e,n,i,d,void 0,void 0,void 0,void 0,void 0,r,t)}}),vu,xu,Tu,Cu,Id=D(()=>{Qt(),Yt(),ir(),vu=(e,t)=>{let r=e[0].dims,n=r,s=2,a=qe.sizeToDimension(r,s),i=qe.sizeFromDimension(r,s),d=gr(i),c=i/d,h=[r[0],r[1],c],w=["rank","type","type"],y=[{type:12,data:i},{type:12,data:c}];y.push(...kt(h,h));let u=k=>{let C=it("x",e[0].dataType,h.length,d),F=it("scale",e[1].dataType,e[1].dims),U=it("bias",e[2].dataType,e[2].dims),G=Ut("output",e[0].dataType,h.length,d),L=[C,F,U,G],pe=C.type.value,Z=d===1?"f32":`vec${d}<f32>`,oe=64,et=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`
  var<workgroup> meanShared : f32;
  var<workgroup> squaredNormShared : f32;
  var<workgroup> workgroupShared : array<${Z}, ${oe}>;
  const workgroupSize = ${oe}u;
  ${k.registerUniforms(et).declareVariables(...L)}
  ${k.mainStart(oe)}
    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 = ${Z}(0);
    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
      initial = initial + ${Z}(${C.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 = ${gn("workgroupShared[0]",d)} / f32(uniforms.normSize);
    }
    workgroupBarrier();

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

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

  ${et.mainStart(h)}
    let currentImageNumber = global_idx / ${h} / uniforms.C;
    let currentChannelNumber = (global_idx / ${h}) % 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 = ${Ar("f32",c)};
    var squaredSum = ${Ar("f32",c)};
    for (var i: u32 = wgOffset; i < wgMax; i++) {
        let value = ${y}(input[offset + i * uniforms.C]);
        sum += value;
        squaredSum += value * value;
    }
    output[global_idx] = ${u("sum","squaredSum")};
  }`},L=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${c}`,inputDependencies:F},getRunData:()=>({outputs:[{dims:[s,i,h,2],dataType:1}],dispatchGroup:{x:s*i/c},programUniforms:U}),getShaderSource:G},{inputs:[t],outputs:[-1]})[0],pe=[{type:12,data:k},{type:12,data:a},{type:12,data:Math.floor(i/c)},{type:12,data:Math.floor(h*i/c)}],Z=["type","type","type"],oe=et=>{let We=it("scale",r.dataType,r.dims,c),ct=it("bias",n.dataType,n.dims,c);return`
  @group(0) @binding(0) var<storage, read> input : array<${w}>;
  @group(0) @binding(1) var<storage, read> scale : array<${We.type.storage}>;
  @group(0) @binding(2) var<storage, read> bias : array<${ct.type.storage}>;
  @group(0) @binding(3) var<storage, read_write> output : array<${w}>;
  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};
  @group(0) @binding(4) var<uniform> uniforms: Uniforms;

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

    output[global_idx] = ${u("channelScale","channelShift")};
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${c};${d}`,inputDependencies:Z},getRunData:()=>({outputs:[{dims:[s,i,2],dataType:1}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:pe}),getShaderSource:oe},{inputs:[L,r,n],outputs:[-1]})[0]},Tu=(e,t,r)=>{let n=t[0].dims,s=n,a=n[0],i=n[n.length-1],d=qe.sizeFromDimension(n,1)/i,c=gr(i),h=qe.size(s)/c,w=[{type:12,data:d},{type:12,data:Math.floor(i/c)}],y=["type","type"],u=xu(e,t[0],t[1],t[2],a,d,i,r.epsilon),k=C=>{let F=vr(t[0].dataType),U=c===1?"vec2f":`mat2x${c}f`,G=c===1?F:`vec${c}<${F}>`,L=it("input",t[0].dataType,t[0].dims,c),pe=Ut("output",t[0].dataType,s,c);return`
  @group(0) @binding(0) var<storage, read> input : array<${L.type.storage}>;
  @group(0) @binding(1) var<storage, read> scaleInput : array<${U}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${pe.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${C.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], ${G}(scale[0]), ${G}(scale[1]));
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${c}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:w}),getShaderSource:k},{inputs:[t[0],u]})},Cu=(e,t)=>{t.format==="NHWC"?Tu(e,e.inputs,t):e.compute(vu(e.inputs,t))}}),or,$u,Qr,tn=D(()=>{Qt(),Yt(),ir(),or=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},$u=(e,t,r)=>{let n=t.simplified,s=e[0].dims,a=e[1],i=!n&&e[2],d=s,c=qe.normalizeAxis(t.axis,s.length),h=qe.sizeToDimension(s,c),w=qe.sizeFromDimension(s,c),y=qe.size(a.dims),u=i?qe.size(i.dims):0;if(y!==w||i&&u!==w)throw new Error(`Size of X.shape()[axis:] == ${w}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${y} and bias size of ${u}`);let k=[];for(let oe=0;oe<s.length;++oe)oe<c?k.push(s[oe]):k.push(1);let C=gr(w),F=["type","type"],U=[{type:12,data:h},{type:1,data:w},{type:12,data:Math.floor(w/C)},{type:1,data:t.epsilon}];i&&F.push("type");let G=r>1,L=r>2,pe=oe=>{let et=vr(e[0].dataType),We=[it("x",e[0].dataType,e[0].dims,C),it("scale",a.dataType,a.dims,C)];i&&We.push(it("bias",i.dataType,i.dims,C)),We.push(Ut("output",e[0].dataType,d,C)),G&&We.push(Ut("mean_data_output",1,k)),L&&We.push(Ut("inv_std_output",1,k));let ct=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${oe.registerUniforms(ct).declareVariables(...We)}
  ${oe.mainStart()}
    ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${Ar("f32",C)};
    var mean_square_vector = ${Ar("f32",C)};

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

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

    ${G?"mean_data_output[global_idx] = mean":""};
    ${L?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},Z=[{dims:d,dataType:e[0].dataType}];return G&&Z.push({dims:k,dataType:1}),L&&Z.push({dims:k,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${C};${r};${n}`,inputDependencies:F},getRunData:()=>({outputs:Z,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:U}),getShaderSource:pe}},Qr=(e,t)=>{or(e.inputs),e.compute($u(e.inputs,t,e.outputCount))}}),rn,Zn,nd,Eu,sd=D(()=>{Qt(),Yt(),fr(),ir(),rn=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],n=r.dims.length;if(r.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let s=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,i=e[1];if(!qe.areEqual(i.dims,[t.n,s,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let d=e[2].dims;if(qe.size(d)!==t.n*s)throw new Error("scales input size error.");if(e.length===4){let c=e[3].dims,h=t.bits>4?t.n*s:t.n*Math.floor((s+1)/2);if(qe.size(c)!==h)throw new Error("zeroPoints input size error.")}},Zn=(e,t,r,n)=>{let s=e[0].dims,a=s.length,i=Math.floor((t.k+t.blockSize-1)/t.blockSize),d=s[a-2],c=t.k,h=t.n,w=s.slice(0,a-2),y=qe.size(w),u=t.blockSize/8*t.bits/4,k=e[0].dataType,C=gr(d),F=gr(t.k),U=gr(u),G=zn(k),L=d*i*G,pe=Math.floor(n/L),Z=i<=r[0]&&pe>0,oe=!Z||pe>=4?gr(h):pe>=2&&gr(h)>=2?2:1,et=w.concat([d,h]),We=qe.size(et)/oe/C,ct=Z?[]:[{type:12,data:We},{type:12,data:t.blockSize}],Ot=[y,d,c/F],zt=qe.convertShape(e[1].dims).slice();zt.splice(-1,1,u/U),ct.push(...kt(Ot)),ct.push(...kt(zt)),ct.push(...kt(e[2].dims)),e.length===4&&ct.push(...kt(qe.convertShape(e[3].dims)));let hr=[y,d,h/oe];ct.push(...kt(hr));let br=rr=>{let Sr=Ot.length,Wr=it("a",e[0].dataType,Sr,F),cr=it("b",12,zt.length,U),Rr=it("scales",e[2].dataType,e[2].dims.length),Bt=[Wr,cr,Rr],Zt=e.length===4?it("zero_points",12,e[3].dims.length):void 0;Zt&&Bt.push(Zt);let _r=hr.length,Le=Ut("output",e[0].dataType,_r,oe),Nt=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],tr=vr(e[0].dataType),Br=(()=>{switch(F){case 1:return`array<${tr}, 8>`;case 2:return`mat4x2<${tr}>`;case 4:return`mat2x4<${tr}>`;default:throw new Error(`${F}-component is not supported.`)}})(),Xr=`
        for (var word: u32 = 0; word < ${u}; word += ${U}) {
          ${cr.indicesSet("b_indices","2","word")};
          let b_data = ${cr.getByIndices("b_indices")};
          for (var i: u32 = 0; i < ${U}; i++) {
            let b_value: u32 = ${U===1?"b_data":"b_data[word + i]"};
            let b_mask: u32 = 0x0F0F0F0Fu;
            let b_value_lower: vec4<u32> = unpack4xU8(b_value & b_mask);
            let b_value_upper: vec4<u32> = unpack4xU8((b_value >> 4) & b_mask);
            let b_quantized_values = ${Br}(${Array.from({length:4},(zs,En)=>`${tr}(b_value_lower[${En}]), ${tr}(b_value_upper[${En}])`).join(", ")});
            let b_dequantized_values = ${F===1?`${Br}(${Array.from({length:8},(zs,En)=>`(b_quantized_values[${En}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Br}(${Array(8).fill("zero_point").join(",")})) * scale;`};
            // Number of B elements per 32-bit word is 32/bits = 32/4 = 8
            for (var m: u32 = 0; m < ${Z?d:C}u; m++) {
              ${Wr.indicesSet("a_indices",Sr-2,Z?"m":`row * ${C} + m`)};
              ${Wr.indicesSet("a_indices",Sr-1,"word_offset")};
              var input_offset = ${Wr.indicesToOffset("a_indices")};
              var a_data: ${Br};
              for (var j: u32 = 0; j < ${8/F}; j++) {
                a_data[j] = ${Wr.getByOffset("input_offset")};
                input_offset++;
              }
              ${Z?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${oe>1?"[c]":""} += ${Array.from({length:8/F},(zs,En)=>`${F===1?`a_data[${En}] * b_dequantized_values[${En}]`:`dot(a_data[${En}], b_dequantized_values[${En}])`}`).join(" + ")};
            }
            word_offset += ${8/F};
          }
        }`,an=Zt?`
          zero_point_offset += 4;
          if (zero_point_offset == 32) {
            zero_point_offset = 0;
            zero_point_index++;
            zero_point_word = ${Zt.getByOffset("zero_point_index")};
          }`:"";return Z?`
        var<workgroup> workgroup_shared: array<${Le.type.value}, ${d*i}>;
        ${rr.declareVariables(...Bt,Le)}
        ${rr.mainStart([i,1,1])}
          var a_indices: ${Wr.type.indices};
          var block = local_id.x;
          var col = workgroup_id.y;
          var batch = workgroup_id.z;
          ${Wr.indicesSet("a_indices","0","batch")};
          // Two zero points are packed into one byte when uniforms.bits is 4.
          for (var c: u32 = 0; c < ${oe}; c++) {
            let col_times_components_plus_c = col * ${oe} + c;
              ${Zt?`
            var zero_point_bytes_per_col: u32 = (${i} + 1) / 2;
            var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u);
            var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u;
            var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u;
            var zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            var zero_point_word: u32 = ${Zt.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}
            var b_indices: ${cr.type.indices};
            ${cr.indicesSet("b_indices","0","col_times_components_plus_c")};
            // The scale and zero points are computed per block.
            var scales_index = col_times_components_plus_c * ${i} + block;
            let scale = ${Rr.getByOffset("scales_index")};
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${tr}(${Zt?"(zero_point_word) & 0xFu":8});
            ${cr.indicesSet("b_indices","1","block")};
            var word_offset: u32 = block * ${t.blockSize/F};
            var workgroup_shared_offset: u32 = block * ${d};
            ${Xr}
          }
          workgroupBarrier();
          var output_indices: ${Le.type.indices};
          var elements_per_thread: u32 = ${Math.ceil(d/i)};
          ${Le.indicesSet("output_indices","0","batch")};
          ${Le.indicesSet("output_indices",_r-1,"col")};
          ${Le.indicesSet("output_indices",_r-2,"local_id.x * elements_per_thread")};
          var output_offset = ${Le.indicesToOffset("output_indices")};
          for (var m: u32 = 0u; m < elements_per_thread; m++) {
            var row = m + local_id.x * elements_per_thread;
            if (row < ${d}) {
              var output_value: ${Le.type.value} = ${Le.type.value}(0);
              var workgroup_shared_offset: u32 = row;
              for (var b: u32 = 0u; b < ${i}u; b++) {
                output_value += workgroup_shared[workgroup_shared_offset];
                workgroup_shared_offset += ${d};
              }
              ${Le.setByOffset("output_offset","output_value")};
              output_offset += ${h/oe};
            }
          }
        }`:`
        ${rr.registerUniforms(Nt).declareVariables(...Bt,Le)}
        ${rr.mainStart()}
          ${rr.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var output_values: array<${Le.type.value}, ${C}>;
          var output_indices = ${Le.offsetToIndices("global_idx")};
          var col = ${Le.indicesGet("output_indices",_r-1)};
          var row = ${Le.indicesGet("output_indices",_r-2)};
          var a_indices: ${Wr.type.indices} = output_indices;
          // Two zero points are packed into one byte because uniforms.bits <= 4.
          // zero_point_offset is either 0 or 4. It is bit offset within one byte.
          // TODO support zero_point_offset for bits > 4
          ${Zt?`
          var zero_point_abs_offset = col * ${oe} * ((${i} + 1) / 2);
          var zero_point_index: u32 = zero_point_abs_offset / 4;
          var zero_point_word: u32 = ${Zt.getByOffset("zero_point_index")};
          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}
          var scale_index = col * ${i*oe};
          var b_indices: ${cr.type.indices};
          for (var c: u32 = 0; c < ${oe}; c++) {
            ${cr.indicesSet("b_indices","0",`col * ${oe} + c`)};
            var block_offset: u32 = 0;
            for (var block: u32 = 0; block < ${i}; block++) {
              // The scale and zero points are computed per block.
              let scale = ${Rr.getByOffset("scale_index")};
              // The default zero point is 8 for unsigned 4-bit quantization.
              let zero_point = ${tr}(${Zt?"extractBits(zero_point_word, zero_point_offset, 4)":8});
              ${cr.indicesSet("b_indices","1","block")};
              var word_offset: u32 = block_offset;
              ${Xr}
              scale_index++;
              ${an}
              block_offset += uniforms.block_size / ${F};
            }
            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.
            ${Zt?`if (zero_point_offset % 8 > 0) {
                ${an}
              }`:""}
            }
            for (var k: u32 = 0u; k < ${C}u; k++) {
              ${Le.indicesSet("output_indices",_r-2,`${C} * row + k`)};
              ${Le.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:Z?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${d};${k};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:et,dataType:k}],name:Z?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:Z?{x:1,y:Math.ceil(h/oe),z:y}:{x:Math.ceil(We/64)},programUniforms:ct}),getShaderSource:br}},nd=(e,t)=>{rn(e.inputs,t);let r=e.getMaxComputeWorkgroupSizes(),n=e.getMaxComputeWorkgroupStoragesize();e.compute(Zn(e.inputs,t,r,n))},Eu=e=>Gt(e)}),m,g,E,K,Pe,De,at,$t,Vt,lr=D(()=>{Qt(),Yt(),ir(),m=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].")}},g=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
            k = i32(${e.indicesGet("indices",s)}) - ${Ft("uniforms.pads",s,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${Ft("uniforms.x_shape",s,t)})) {
              break;
            }
            offset += k * i32(${Ft("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;
            ${n}
            value = x[offset];
          }
      `},E=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
                k = i32(${e.indicesGet("indices",s)}) - ${Ft("uniforms.pads",s,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${Ft("uniforms.x_shape",s,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${Ft("uniforms.x_shape",s,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${Ft("uniforms.x_strides",s,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},K=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
                k = i32(${e.indicesGet("indices",s)}) - ${Ft("uniforms.pads",s,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${Ft("uniforms.x_shape",s,t)})) {
                  k = i32(${Ft("uniforms.x_shape",s,t)}) - 1;
                }
                offset += k * i32(${Ft("uniforms.x_strides",s,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},Pe=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
                k = i32(${e.indicesGet("indices",s)}) - ${Ft("uniforms.pads",s,r)};
                if (k < 0)  {
                  k += i32(${Ft("uniforms.x_shape",s,t)}]);
                }
                if (k >= i32(${Ft("uniforms.x_shape",s,t)})) {
                  k -= i32(${Ft("uniforms.x_shape",s,t)});
                }
                offset += k * i32(${Ft("uniforms.x_strides",s,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},De=(e,t,r)=>{switch(r.mode){case 0:return g(e,t,r.pads.length);case 1:return E(e,t,r.pads.length);case 2:return K(e,t,r.pads.length);case 3:return Pe(e,t,r.pads.length);default:throw new Error("Invalid mode")}},at=(e,t)=>{let r=qe.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,s=qe.size(r),a=[{type:12,data:s},{type:6,data:t.pads}];t.mode===0&&a.push({type:e[0].dataType,data:t.value}),a.push(...kt(e[0].dims,r));let i=["rank"],d=c=>{let h=Ut("output",e[0].dataType,r.length),w=it("x",e[0].dataType,n.length),y=w.type.value,u=De(h,n.length,t),k=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&k.push({name:"constant_value",type:y}),`
            ${c.registerUniforms(k).declareVariables(w,h)}
            ${c.mainStart()}
            ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${y}(0);
            ${u}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(qe.size(r)/64)},programUniforms:a}),getShaderSource:d}},$t=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,s=e[0].dims.length,a=new Int32Array(2*s).fill(0);if(e.length>=4){let d=e[3].getBigInt64Array();for(let c=0;c<d.length;c++)a[Number(d[c])]=Number(r[c]),a[Number(d[c])+s]=Number(r[c+d.length])}else r.forEach((d,c)=>a[Number(c)]=Number(d));let i=[];return a.forEach(d=>i.push(d)),{mode:t.mode,value:n,pads:i}}else return t},Vt=(e,t)=>{m(e.inputs);let r=$t(e.inputs,t);e.compute(at(e.inputs,r),{inputs:[0]})}}),ar,Er,ur,mr,dr,pr,yr,Or,un,pn,Rn,nn,Kr,sn,oi,li,$a,Fd,An,Is=D(()=>{$(),Qt(),Yt(),ir(),ar=e=>{if(A.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Er=(e,t,r)=>{let n=t.format==="NHWC",s=e.dims.slice();n&&s.splice(1,0,s.pop());let a=Object.hasOwnProperty.call(t,"dilations"),i=t.kernelShape.slice(),d=t.strides.slice(),c=a?t.dilations.slice():[],h=t.pads.slice();yn.adjustPoolAttributes(r,s,i,d,c,h);let w=yn.computePoolOutputShape(r,s,d,c,i,h,t.autoPad),y=Object.assign({},t);a?Object.assign(y,{kernelShape:i,strides:d,pads:h,dilations:c,cacheKey:t.cacheKey}):Object.assign(y,{kernelShape:i,strides:d,pads:h,cacheKey:t.cacheKey});let u=w.slice();return u.push(u.splice(1,1)[0]),[y,n?u:w]},ur=(e,t)=>{let r=t.format==="NHWC",n=qe.size(e),s=qe.size(t.kernelShape),a=[{type:12,data:n},{type:12,data:s}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let d=t.kernelShape[t.kernelShape.length-1],c=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],w=t.pads[t.pads.length-1],y=!!(h+w);a.push({type:12,data:d},{type:12,data:c},{type:12,data:h},{type:12,data:w}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let u=!1;if(t.kernelShape.length===2){let k=t.kernelShape[t.kernelShape.length-2],C=t.strides[t.strides.length-2],F=t.pads[t.pads.length/2-2],U=t.pads[t.pads.length-2];u=!!(F+U),a.push({type:12,data:k},{type:12,data:C},{type:12,data:F},{type:12,data:U}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,i,!0,y,u]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let d=qe.computeStrides(t.kernelShape);a.push({type:12,data:d},{type:12,data:t.pads},{type:12,data:t.strides}),i.push({name:"kernelStrides",type:"u32",length:d.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let c=t.pads.reduce((h,w)=>h+w);return[a,i,!!c,!1,!1]}},mr=(e,t,r,n,s,a,i,d,c,h,w,y)=>{let u=s.format==="NHWC",k=t.type.value,C=Ut("output",t.type.tensor,n);if(s.kernelShape.length<=2){let F="",U="",G="",L=r-(u?2:1);if(w?F=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${L}] = indices[${L}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${L}] < 0 || xIndices[${L}]
                      >= uniforms.x_shape[${L}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`:F=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${L}] = indices[${L}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`,s.kernelShape.length===2){let pe=r-(u?3:2);y?U=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${pe}] = indices[${pe}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${pe}] < 0 || xIndices[${pe}] >= uniforms.x_shape[${pe}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:U=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${pe}] = indices[${pe}] * uniforms.sh - uniforms.phStart + j;
                `,G=`
              }
            `}return`
            ${e.registerUniforms(c).declareVariables(t,C)}

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

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

              var value = ${k}(${d});
              var pad = 0;
              ${U}
              ${F}
              ${G}
              ${i}

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

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

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

              var value = ${k}(${d});
              var pad = 0;
              var isPad = false;

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

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

              output[global_idx] = value;
            }`}},dr=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,pr=e=>`${dr(e)};${e.countIncludePad}`,yr=e=>`${dr(e)};${e.storageOrder};${e.dilations}`,Or=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}),un=(e,t,r,n)=>{let[s,a]=Er(t,n,r),i=it("x",t.dataType,t.dims.length),d=i.type.value,c="value += x_val;",h="";s.countIncludePad?h+=`value /= ${d}(uniforms.kernelSize);`:h+=`value /= ${d}(i32(uniforms.kernelSize) - pad);`;let[w,y,u,k,C]=ur(a,s);w.push(...kt(t.dims,a));let F=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${u};${k};${C}`,inputDependencies:F},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(qe.size(a)/64)},programUniforms:w}),getShaderSource:U=>mr(U,i,t.dims.length,a.length,s,c,h,0,y,u,k,C)}},pn=e=>{let t=e.count_include_pad!==0,r=Or(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...r,cacheKey:""};return{...n,cacheKey:pr(n)}},Rn=(e,t)=>{ar(e.inputs),e.compute(un("AveragePool",e.inputs[0],!1,t))},nn={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Kr=e=>{let t=e.format;return{format:t,...nn,cacheKey:t}},sn=(e,t)=>{ar(e.inputs),e.compute(un("GlobalAveragePool",e.inputs[0],!0,t))},oi=(e,t,r,n)=>{let[s,a]=Er(t,n,r),i=`
      value = max(x_val, value);
    `,d="",c=it("x",t.dataType,t.dims.length),h=["rank"],[w,y,u,k,C]=ur(a,s);return w.push(...kt(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${u};${k};${C}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(qe.size(a)/64)},programUniforms:w}),getShaderSource:F=>mr(F,c,t.dims.length,a.length,s,i,d,t.dataType===10?-65504:-1e5,y,u,k,C)}},li=(e,t)=>{ar(e.inputs),e.compute(oi("MaxPool",e.inputs[0],!1,t))},$a=e=>{let t=e.storage_order,r=e.dilations,n=Or(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:yr(s)}},Fd=e=>{let t=e.format;return{format:t,...nn,cacheKey:t}},An=(e,t)=>{ar(e.inputs),e.compute(oi("GlobalMaxPool",e.inputs[0],!0,t))}}),id,ad,od,ld=D(()=>{$(),Qt(),ir(),id=(e,t,r)=>{let n=e===t,s=e<t&&r<0,a=e>t&&r>0;if(n||s||a)throw new Error("Range these inputs' contents are invalid.")},ad=(e,t,r,n)=>{let s=Math.abs(Math.ceil((t-e)/r)),a=[s],i=s,d=[{type:12,data:i},{type:n,data:e},{type:n,data:r},...kt(a)],c=h=>{let w=Ut("output",n,a.length),y=w.type.value,u=[{name:"outputSize",type:"u32"},{name:"start",type:y},{name:"delta",type:y}];return`
        ${h.registerUniforms(u).declareVariables(w)}
        ${h.mainStart()}
        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${y}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:c,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:d})}},od=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]),A.webgpu.validateInputContent&&id(t,r,n),e.compute(ad(t,r,n,e.inputs[0].dataType),{inputs:[]})}}),mc,_c,gc,wc,yc,bc,Mc,vc,xc,Tc,Cc,Od,$c,Ec,Sc,kc,Pc,Ac,Ic,of=D(()=>{Qt(),Yt(),fr(),ir(),mc=(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")}},_c=(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,a)=>n[s]=e[a]),n},gc=(e,t,r,n,s,a)=>{let[i,d,c]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(w=>a.push(w));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(d>0&&e.length>d&&e[d].dims.length>0){if(e[d].getFloat32Array().forEach(w=>n.push(w)),n.length!==0&&n.length!==h&&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");mc(n,t),t.axes.length>0&&_c(n,t.axes,h).forEach((w,y)=>n[y]=w)}if(c>0&&e.length>c&&(e[c].getBigInt64Array().forEach(w=>s.push(Number(w))),s.length!==h||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>h)throw new Error("Resize requires only of scales or sizes to be specified")},wc=(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`)}})()+"}",yc=(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`)}})()+"}",bc=(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((a,i)=>{n[a]=s[i],n[i+r]=s[t.length+i]}),n):s},Mc=(e,t,r,n)=>{let s=[];if(r.length>0)if(n.length>0){if(e.forEach(a=>s.push(a)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((a,i)=>s[a]=r[i])}else r.forEach(a=>s.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");s=e.map((a,i)=>Math.round(a*t[i]))}return s},vc=(e,t,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(a=>t[a]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(a=>t[a]),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(a=>t[a]=n),r.axes.forEach(a=>s[a]=Math.round(e[a]*t[a]))):(t.fill(n,0,t.length),s.forEach((a,i)=>s[i]=Math.round(a*t[i]))),s},xc=(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 = ${Ft("uniforms.scales","i",n)};
        var roi_low = ${Ft("uniforms.roi","i",s)};
        var roi_hi = ${Ft("uniforms.roi",`i + ${t.length}`,s)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${Ft("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${Ft("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;
    }`,Tc=(e,t,r,n,s,a,i)=>`
    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 = ${Ft("uniforms.scales","i",s)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${Ft("uniforms.roi","i",a)};
          var roi_hi = ${Ft("uniforms.roi",`i + ${r.length}`,a)};
          var input_shape_i = ${Ft("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${Ft("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${i} || (original_idx >= 0 && original_idx < ${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;
    }`,Cc=(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 >= ${Ft("uniforms.input_shape","i",t.length)}) {
          return false;
        }
      }
      return true;
    }`,Od=(e,t,r,n)=>e.rank>n?`
    ${e.indicesSet("input_indices",t,"channel")};
    ${e.indicesSet("input_indices",r,"batch")};
`:"",$c=(e,t,r,n,s)=>{let[a,i,d,c]=r.length===2?[-1,0,1,-1]:[0,2,3,1],h=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${h} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",i,`max(0, min(row, ${r[i]} - 1))`)};
      ${e.indicesSet("input_indices",d,`max(0, min(col, ${r[d]} - 1))`)};
      ${Od(e,c,a,2)}
      return ${e.getByIndices("input_indices")};
    }

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

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

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

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

    depth = max(0, min(depth, ${r[i]} - 1));
      height = max(0, min(height, ${r[d]} - 1));
      width = max(0, min(width, ${r[c]} - 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 = ${r.length>3?`u32(originalIndices[${h}])`:"0"};
      var batch: u32 =  ${r.length>3?`u32(originalIndices[${a}])`:"0"};

      var x111: ${w} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${w} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${w} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${w} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${w} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${w} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${w} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${w} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${w} = abs(depth - ${w}(depth1));
      var dx2: ${w} = abs(${w}(depth2) - depth);
      var dy1: ${w} = abs(height - ${w}(height1));
      var dy2: ${w} = abs(${w}(height2) - height);
      var dz1: ${w} = abs(width - ${w}(width1));
      var dz2: ${w} = abs(${w}(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);
    }`},kc=(e,t,r,n,s,a)=>{let i=e.dims,d=bc(a,t.axes,i.length),c=Mc(i,n,s,t.axes),h=n.slice();n.length===0&&(h=i.map((L,pe)=>L===0?1:c[pe]/L),t.keepAspectRatioPolicy!=="stretch"&&(c=vc(i,h,t)));let w=Ut("output",e.dataType,c.length),y=it("input",e.dataType,i.length),u=qe.size(c),k=i.length===c.length&&i.every((L,pe)=>L===c[pe]),C=t.coordinateTransformMode==="tf_crop_and_resize",F=t.extrapolationValue,U=y.type.value,G=L=>`
      ${k?"":`
      ${wc(t.coordinateTransformMode,U)};
      ${(()=>{switch(t.mode){case"nearest":return`
              ${Cc(y,i)};
              ${yc(t.nearestMode,r,U)};
              ${Tc(y,w,i,c,h.length,d.length,C)};
              `;case"linear":return`
              ${xc(w,i,c,h.length,d.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${$c(y,w,i,C,F)}`;if(i.length===3||i.length===5)return`${Sc(y,w,i,C,F)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${Ec(y,w,i,c,h,d,t.cubicCoeffA,C,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")}})()};
      `}
      ${L.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",d.length).declareVariables(y,w)}
      ${L.mainStart()}
        ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${k?"output[global_idx] = input[global_idx];":`
        let output_indices = ${w.offsetToIndices("global_idx")};
        var input_indices: ${y.type.indices};
        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${y.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${t.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${h.length>0?h:""}|${s.length>0?s:""}|${d.length>0?d:""}|${k}|${i}`,inputDependencies:["rank"]},getShaderSource:G,getRunData:()=>({outputs:[{dims:c,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:[{type:12,data:u},{type:1,data:h},{type:1,data:d},...kt(i,c)]})}},Pc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Ac=(e,t)=>{let r=[],n=[],s=[],a=Pc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");gc(e.inputs,t,a,r,n,s),e.compute(kc(e.inputs[0],t,a,r,n,s),{inputs:[0]})},Ic=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,s=e.cubicCoeffA,a=e.excludeOutside!==0,i=e.extrapolationValue,d=e.keepAspectRatioPolicy,c=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return Gt({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:s,excludeOutside:a,extrapolationValue:i,keepAspectRatioPolicy:d,mode:c,nearestMode:h})}}),Fc,Oc,zc,lf=D(()=>{Qt(),Yt(),fr(),ir(),Fc=(e,t)=>{let[r,n,s,a]=e,{numHeads:i,rotaryEmbeddingDim:d}=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(!qe.areEqual(n.dims,[])&&!qe.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(a.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!qe.areEqual(s.dims,a.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(d>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let c=r.dims[0],h=r.dims[r.dims.length-2],w=s.dims[0],y=qe.sizeFromDimension(r.dims,1)/h,u=d===0?s.dims[1]*2:y/i;if(d>u)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(c!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(h!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(u/2!==s.dims[1]&&d/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(h>w)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Oc=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:s,scale:a}=t,i=e[0].dims[0],d=qe.sizeFromDimension(e[0].dims,1),c=e[0].dims[e[0].dims.length-2],h=d/c,w=e[2].dims[1],y=s===0?w*2:h/n,u=new Array(i,c,h/y,y-w),k=qe.computeStrides(u),C=[{type:1,data:a},{type:12,data:u},{type:12,data:k},...e[0].dims.length===3?new Array({type:12,data:[d,h,y,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[d,y,c*y,1]}):[],...kt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],F=U=>{let G=it("input",e[0].dataType,e[0].dims.length),L=it("position_ids",e[1].dataType,e[1].dims.length),pe=it("cos_cache",e[2].dataType,e[2].dims.length),Z=it("sin_cache",e[3].dataType,e[3].dims.length),oe=Ut("output",e[0].dataType,e[0].dims.length);return U.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:u.length},{name:"global_strides",type:"u32",length:k.length},{name:"input_output_strides",type:"u32",length:k.length}]),`
        ${U.declareVariables(G,L,pe,Z,oe)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${L.broadcastedIndicesToOffset("bsnh.xy",Ut("",L.type.tensor,2))};
            let position_id =
                u32(${L.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 = ${G.getByOffset("i")} * ${pe.get("position_id","bsnh[3]")} -
                ${G.getByOffset("j")} * ${Z.get("position_id","bsnh[3]")};
            ${oe.setByOffset("i","re")}
            let im = ${G.getByOffset("i")} * ${Z.get("position_id","bsnh[3]")} +
                ${G.getByOffset("j")} * ${pe.get("position_id","bsnh[3]")};
            ${oe.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${oe.setByOffset("k",G.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:Gt({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:F,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(qe.size(u)/_n)},programUniforms:C})}},zc=(e,t)=>{Fc(e.inputs,t),e.compute(Oc(e.inputs,t))}}),Dc,Bc,Lc,uf=D(()=>{Qt(),Yt(),ir(),Dc=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],a=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]!==a)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 i=e[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==s)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let i=e[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==s)throw new Error("Bias must have the same hidden size as input")}},Bc=(e,t,r,n)=>{let s=t.simplified,a=e[0].dims,i=qe.size(a),d=a,c=i,h=a.slice(-1)[0],w=n?a.slice(0,-1).concat(1):[],y=!s&&e.length>3,u=e.length>4,k=n&&r>1,C=n&&r>2,F=r>3,U=64,G=gr(h),L=[{type:12,data:c},{type:12,data:G},{type:12,data:h},{type:1,data:t.epsilon}],pe=oe=>{let et=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],We=[it("x",e[0].dataType,e[0].dims,G),it("skip",e[1].dataType,e[1].dims,G),it("gamma",e[2].dataType,e[2].dims,G)];y&&We.push(it("beta",e[3].dataType,e[3].dims,G)),u&&We.push(it("bias",e[4].dataType,e[4].dims,G)),We.push(Ut("output",e[0].dataType,d,G)),k&&We.push(Ut("mean_output",1,w)),C&&We.push(Ut("inv_std_output",1,w)),F&&We.push(Ut("input_skip_bias_sum",e[0].dataType,d,G));let ct=vr(e[0].dataType),Ot=vr(1,G);return`

      ${oe.registerUniforms(et).declareVariables(...We)}
      var<workgroup> sum_shared : array<${Ot}, ${U}>;
      var<workgroup> sum_squared_shared : array<${Ot}, ${U}>;

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

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

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

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${s?"":`- ${ct}(mean)`}) *
            ${ct}(inv_std_dev) * gamma[offset1d + i]
            ${y?"+ beta[offset1d + i]":""};
        }
      }`},Z=[{dims:d,dataType:e[0].dataType}];return r>1&&Z.push({dims:w,dataType:1}),r>2&&Z.push({dims:w,dataType:1}),r>3&&Z.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${G};${k};${C};${F}`,inputDependencies:e.map((oe,et)=>"type")},getShaderSource:pe,getRunData:()=>({outputs:Z,dispatchGroup:{x:Math.ceil(c/h)},programUniforms:L})}},Lc=(e,t)=>{Dc(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(Bc(e.inputs,t,e.outputCount,!1),{outputs:r})}}),Rc,Su,Nc,zd,jc,Vc,Uc,Wc,df=D(()=>{Qt(),Yt(),fr(),ir(),Rc=(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`)})},Su=(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},Nc=(e,t)=>{if(e.length>1){let r=Su(e,1),n=Su(e,2),s=Su(e,3);return s.length===0&&(s=[...Array(e[0].dims.length).keys()]),Gt({starts:r,ends:n,axes:s})}else return t},zd=(e,t,r,n,s)=>{let a=e;return e<0&&(a+=r[n[t]]),s[t]<0?Math.max(0,Math.min(a,r[n[t]]-1)):Math.max(0,Math.min(a,r[n[t]]))},jc=(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 = ${Ft("uniforms.input_shape","i",r.length)};
            let steps_i = ${Ft("uniforms.steps","i",r.length)};
            let signs_i = ${Ft("uniforms.signs","i",r.length)};
            let starts_i = ${Ft("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;
      }`,Vc=(e,t)=>{let r=e[0].dims,n=qe.size(r),s=t.axes.length>0?qe.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],a=Su(e,4);a.forEach(G=>G!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(s.length).fill(1));let i=t.starts.map((G,L)=>zd(G,L,r,s,a)),d=t.ends.map((G,L)=>zd(G,L,r,s,a));if(s.length!==i.length||s.length!==d.length)throw new Error("start, ends and axes should have the same number of elements");if(s.length!==r.length)for(let G=0;G<r.length;++G)s.includes(G)||(i.splice(G,0,0),d.splice(G,0,r[G]),a.splice(G,0,1));let c=a.map(G=>Math.sign(G));a.forEach((G,L,pe)=>{if(G<0){let Z=(d[L]-i[L])/G,oe=i[L],et=oe+Z*a[L];i[L]=et,d[L]=oe,pe[L]=-G}});let h=r.slice(0);s.forEach((G,L)=>{h[G]=Math.ceil((d[G]-i[G])/a[G])});let w={dims:h,dataType:e[0].dataType},y=Ut("output",e[0].dataType,h.length),u=it("input",e[0].dataType,e[0].dims.length),k=qe.size(h),C=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:c.length},{name:"steps",type:"u32",length:a.length}],F=[{type:12,data:k},{type:12,data:i},{type:6,data:c},{type:12,data:a},...kt(e[0].dims,h)],U=G=>`
      ${G.registerUniforms(C).declareVariables(u,y)}
        ${jc(u,y,r)}
        ${G.mainStart()}
          ${G.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${y.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${y.setByOffset("global_idx",u.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${c.length}_${i.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:U,getRunData:()=>({outputs:[w],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:F})}},Uc=(e,t)=>{Rc(e.inputs,t);let r=Nc(e.inputs,t);e.compute(Vc(e.inputs,r),{inputs:[0]})},Wc=e=>{let t=e.starts,r=e.ends,n=e.axes;return Gt({starts:t,ends:r,axes:n})}}),Gc,qc,Hc,Kc,cf=D(()=>{Qt(),Yt(),fr(),ir(),Gc=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},qc=(e,t)=>{let r=e.dims,n=qe.size(r),s=64,a=t.axis;if(a<0&&(a=r.length+a),a<r.length-1)throw new Error("softmax only supports last axis for now.");let i=r[a],d=n/i,c=gr(i),h=i/c,w=(U,G)=>G===4?`max(max(${U}.x, ${U}.y), max(${U}.z, ${U}.w))`:G===2?`max(${U}.x, ${U}.y)`:G===3?`max(max(${U}.x, ${U}.y), ${U}.z)`:U,y=it("x",e.dataType,e.dims,c),u=Ut("result",e.dataType,e.dims,c),k=y.type.value,C=vr(e.dataType)==="f32"?`var threadMax = ${k}(-3.402823e+38f);`:`var threadMax = ${k}(-65504.0h);`,F=U=>`
      var<workgroup> rowMaxShared : ${k};
      var<workgroup> rowSumShared : ${k};
      var<workgroup> threadShared : array<${k}, ${s}>;

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

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${k}) {
        let index = row * row_stride + col;
        result[index] = value;
      }
      ${U.registerUniform("packedCols","i32").declareVariables(y,u)}
      ${U.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
        ${C}
        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 = ${k}(${w("threadShared[0]",c)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${k}(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 = ${k}(${gn("threadShared[0]",c)});
        }
        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:`${c}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:d},programUniforms:[{type:6,data:h}]}),getShaderSource:F}},Hc=(e,t)=>{Gc(e.inputs),e.compute(qc(e.inputs[0],t))},Kc=e=>Gt({axis:e.axis})}),Xc,Qc,Yc,Zc,Jc,ep,tp,pf=D(()=>{Qt(),Yt(),fr(),ir(),Xc=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Qc=(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),Gt({numOutputs:n,axis:t.axis,splitSizes:r})},Yc=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${Ft("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,Zc=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) {
        ${r.join(`
`)}
      }`},Jc=(e,t)=>{let r=e[0].dims,n=qe.size(r),s=e[0].dataType,a=qe.normalizeAxis(t.axis,r.length),i=new Array(t.numOutputs),d=it("input",s,r.length),c=new Array(t.numOutputs),h=[],w=[],y=0,u=[{type:12,data:n}];for(let C=0;C<t.numOutputs;C++){y+=t.splitSizes[C],c[C]=y;let F=r.slice();F[t.axis]=t.splitSizes[C],w.push(F),i[C]=Ut(`output${C}`,s,F.length),h.push({dims:w[C],dataType:e[0].dataType})}u.push({type:12,data:c},...kt(r,...w));let k=C=>`
  ${C.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",c.length).declareVariables(d,...i)}
  ${Yc(c.length)}
  ${Zc(i)}

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

    var indices = ${d.offsetToIndices("global_idx")};
    var index = ${d.indicesGet("indices",a)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${Ft("uniforms.size_in_split_axis","output_number - 1u",c.length)};
      ${d.indicesSet("indices",a,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:k,getRunData:()=>({outputs:h,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:u})}},ep=(e,t)=>{Xc(e.inputs);let r=e.inputs.length===1?t:Qc(e.inputs,t);e.compute(Jc(e.inputs,r),{inputs:[0]})},tp=e=>{let t=e.axis,r=e.splitSizes,n=e.numOutputs<0?r.length:e.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Gt({axis:t,numOutputs:n,splitSizes:r})}}),rp,np,sp,hf=D(()=>{Qt(),Yt(),ir(),rp=(e,t,r,n,s)=>{let a=Ut("output_data",s,r.length,4),i=it("a_data",t[1].dataType,t[1].dims.length,4),d=it("b_data",t[2].dataType,t[2].dims.length,4),c=it("c_data",t[0].dataType,t[0].dims.length,4),h,w=(y,u,k)=>`select(${u}, ${y}, ${k})`;if(!n)h=a.setByOffset("global_idx",w(i.getByOffset("global_idx"),d.getByOffset("global_idx"),c.getByOffset("global_idx")));else{let y=(u,k,C="")=>{let F=`a_data[index_a${k}][component_a${k}]`,U=`b_data[index_b${k}][component_b${k}]`,G=`bool(c_data[index_c${k}] & (0xffu << (component_c${k} * 8)))`;return`
            let output_indices${k} = ${a.offsetToIndices(`global_idx * 4u + ${k}u`)};
            let offset_a${k} = ${i.broadcastedIndicesToOffset(`output_indices${k}`,a)};
            let offset_b${k} = ${d.broadcastedIndicesToOffset(`output_indices${k}`,a)};
            let offset_c${k} = ${c.broadcastedIndicesToOffset(`output_indices${k}`,a)};
            let index_a${k} = offset_a${k} / 4u;
            let index_b${k} = offset_b${k} / 4u;
            let index_c${k} = offset_c${k} / 4u;
            let component_a${k} = offset_a${k} % 4u;
            let component_b${k} = offset_b${k} % 4u;
            let component_c${k} = offset_c${k} % 4u;
            ${u}[${k}] = ${C}(${w(F,U,G)});
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            var data = vec4<u32>(0);
            ${y("data",0,"u32")}
            ${y("data",1,"u32")}
            ${y("data",2,"u32")}
            ${y("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:h=`
            ${y("output_data[global_idx]",0)}
            ${y("output_data[global_idx]",1)}
            ${y("output_data[global_idx]",2)}
            ${y("output_data[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(c,i,d,a)}
        ${e.mainStart()}
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        ${h}
      }`},np=e=>{let t=e[1].dims,r=e[2].dims,n=e[0].dims,s=e[1].dataType,a=!(qe.areEqual(t,r)&&qe.areEqual(r,n)),i=t,d=qe.size(t);if(a){let h=Yr.calcShape(Yr.calcShape(t,r,!1),n,!1);if(!h)throw new Error("Can't perform where op on the given tensors");i=h,d=qe.size(i)}let c=Math.ceil(d/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:h=>rp(h,e,i,a,s),getRunData:()=>({outputs:[{dims:i,dataType:s}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:c},...kt(n,t,r,i)]})}},sp=e=>{e.compute(np(e.inputs))}}),ip,ff=D(()=>{Du(),Xs(),To(),Bu(),nl(),Lu(),Ru(),Fl(),Pd(),Gu(),qu(),Qu(),Ad(),Zu(),Ju(),ed(),lu(),rd(),Id(),tn(),ia(),sd(),hu(),lr(),Is(),ld(),ki(),of(),lf(),uf(),df(),cf(),pf(),gu(),ds(),Gi(),hf(),ip=new Map([["Abs",[So]],["Acos",[ko]],["Acosh",[Ii]],["Add",[al]],["ArgMax",[fo,Ks]],["ArgMin",[ho,Ks]],["Asin",[Po]],["Asinh",[Ao]],["Atan",[Fi]],["Atanh",[Io]],["Attention",[yo]],["AveragePool",[Rn,pn]],["BatchNormalization",[xo]],["BiasAdd",[Ai]],["BiasSplitGelu",[rl]],["Cast",[Qs,Fo]],["Ceil",[Do]],["Clip",[zo]],["Concat",[qn,gl]],["Conv",[Es,oa]],["ConvTranspose",[Wu,Ll]],["Cos",[Oi]],["Cosh",[Bo]],["CumSum",[ma,jl]],["DepthToSpace",[ga,Wl]],["Div",[ol]],["Einsum",[ql,Hl]],["Elu",[Lo,ps]],["Equal",[Hi]],["Erf",[Ro]],["Exp",[zi]],["Expand",[ba]],["FastGelu",[Ql]],["Floor",[No]],["FusedConv",[Es,oa]],["Gather",[eu,Jl]],["GatherElements",[su,nu]],["Gelu",[jo]],["Gemm",[td,ou]],["GlobalAveragePool",[sn,Kr]],["GlobalMaxPool",[An,Fd]],["Greater",[cl]],["GreaterOrEqual",[hl]],["GroupQueryAttention",[Mu,yu]],["HardSigmoid",[Li,qo]],["InstanceNormalization",[Cu]],["LayerNormalization",[Qr]],["LeakyRelu",[Vo,ps]],["Less",[pl]],["LessOrEqual",[Ki]],["Log",[Wi]],["MatMul",[Pl]],["MatMulNBits",[nd,Eu]],["MaxPool",[li,$a]],["Mul",[ll]],["MultiHeadAttention",[pu,du]],["Neg",[Uo]],["Not",[Di]],["Pad",[Vt]],["Pow",[ul]],["QuickGelu",[el,ps]],["Range",[od]],["Reciprocal",[Wo]],["ReduceMin",[Ei]],["ReduceMean",[io]],["ReduceMax",[lo]],["ReduceSum",[co]],["ReduceProd",[uo]],["ReduceL1",[ao]],["ReduceL2",[$i]],["ReduceLogSum",[po]],["ReduceLogSumExp",[oo]],["ReduceSumSquare",[Si]],["Relu",[Bi]],["Resize",[Ac,Ic]],["RotaryEmbedding",[zc]],["Sigmoid",[Go]],["Sin",[Ho]],["Sinh",[Ko]],["Slice",[Uc,Wc]],["SkipLayerNormalization",[Lc]],["Split",[ep,tp]],["Sqrt",[Ri]],["Softmax",[Hc,Kc]],["Sub",[dl]],["Tan",[Xo]],["Tanh",[ji]],["ThresholdedRelu",[Yo,ps]],["Tile",[_u]],["Transpose",[za,gi]],["Where",[sp]]])}),ap,mf=D(()=>{$(),mn(),ir(),ap=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,t){this.repo.set(e,t)}run(e,t,r,n,s){Ge(e.programInfo.name);let a=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let d=[];for(let h of t)d.push({binding:d.length,resource:{buffer:h.buffer}});for(let h of r)d.push({binding:d.length,resource:{buffer:h.buffer}});s&&d.push({binding:d.length,resource:s});let c=a.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:d,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let h={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:c,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(h)}i.setPipeline(e.computePipeline),i.setBindGroup(0,c),i.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Ve(e.programInfo.name)}dispose(){}build(e,t){Ge(e.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let s=Ia(t,this.backend.device.limits),a=e.getShaderSource(s),i=`${n.join(`
`)}
${s.additionalImplementations}
${a}`,d=r.createShaderModule({code:i,label:e.name});Dr("verbose",()=>`[WebGPU] ${e.name} shader code: ${i}`);let c=r.createComputePipeline({compute:{module:d,entryPoint:"main"},layout:"auto",label:e.name});return Ve(e.name),{programInfo:e,computePipeline:c,uniformVariablesInfo:s.variablesInfo}}normalizeDispatchGroupSize(e){let t=typeof e=="number"?e:e.x,r=typeof e=="number"?1:e.y||1,n=typeof e=="number"?1:e.z||1,s=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(t<=s&&r<=s&&n<=s)return[t,r,n];let a=t*r*n,i=Math.ceil(Math.sqrt(a));if(i>s){if(i=Math.ceil(Math.cbrt(a)),i>s)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),op,lp,up,dp,_f=D(()=>{$(),Qt(),mn(),_(),$r(),ff(),mf(),op=(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 a=e[n].dims.length;r.push(`${s};${a}`);break}case"dims":{let a=e[n].dims.join(",");r.push(`${s};${a}`);break}default:throw new Error(`unsupported input dependency: ${t[n]}`)}}return r.join("|")},lp=(e,t,r)=>{var s,a;let n=e.name;return(s=e.shaderCache)!=null&&s.hint&&(n+="["+e.shaderCache.hint+"]"),n+=":"+r+`:${op(t,((a=e.shaderCache)==null?void 0:a.inputDependencies)??new Array(t.length).fill("dims"))}`,n},up=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},dp=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 up(t.info||await t.requestAdapterInfo()),this.gpuDataManager=Jt(this),this.programManager=new ap(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,ls(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;Ge(),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(()=>{var n;let t=new BigUint64Array(e.getMappedRange()),r=this.pendingQueries.get(e);for(let s=0;s<t.length/2;s++){let a=r[s],i=a.kernelId,d=this.kernels.get(i),c=d.kernelType,h=d.kernelName,w=a.programName,y=a.inputTensorViews,u=a.outputTensorViews,k=t[s*2],C=t[s*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=k);let F=Number(k-this.queryTimeBase),U=Number(C-this.queryTimeBase);if(!Number.isSafeInteger(F)||!Number.isSafeInteger(U))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:y.map(G=>({dims:G.dims,dataType:kn(G.dataType)})),outputsMetadata:u.map(G=>({dims:G.dims,dataType:kn(G.dataType)})),kernelId:i,kernelType:c,kernelName:h,programName:w,startTime:F,endTime:U});else{let G="";y.forEach((pe,Z)=>{G+=`input[${Z}]: [${pe.dims}] | ${kn(pe.dataType)}, `});let L="";u.forEach((pe,Z)=>{L+=`output[${Z}]: [${pe.dims}] | ${kn(pe.dataType)}, `}),console.log(`[profiling] kernel "${i}|${c}|${h}|${w}" ${G}${L}execution time: ${U-F} ns`)}Ee("GPU",`${w}::${k}::${C}`)}e.unmap(),this.pendingQueries.delete(e)}),Ve()}run(e,t,r,n,s,a){Ge(e.name);let i=[];for(let L=0;L<t.length;++L){let pe=t[L].data;if(pe===0)continue;let Z=this.gpuDataManager.get(pe);if(!Z)throw new Error(`no GPU data for input: ${pe}`);i.push(Z)}let{outputs:d,dispatchGroup:c,programUniforms:h}=e.getRunData(t),w=r.length===0?d.map((L,pe)=>pe):r;if(w.length!==d.length)throw new Error(`Output size ${w.length} must be equal to ${d.length}.`);let y=[],u=[];for(let L=0;L<d.length;++L){if(!Number.isInteger(w[L])||w[L]<-3||w[L]>=a)throw new Error(`Invalid output index: ${w[L]}`);if(w[L]===-3)continue;let pe=w[L]===-1,Z=w[L]===-2,oe=pe||Z?s(d[L].dataType,d[L].dims):n(w[L],d[L].dataType,d[L].dims);if(y.push(oe),oe.data===0)continue;let et=this.gpuDataManager.get(oe.data);if(!et)throw new Error(`no GPU data for output: ${oe.data}`);if(pe&&this.temporaryData.push(et),Z){let We=this.kernelPersistentData.get(this.currentKernelId);We||(We=[],this.kernelPersistentData.set(this.currentKernelId,We)),We.push(et)}u.push(et)}if(i.length!==t.length||u.length!==y.length){if(u.length===0)return Ve(e.name),y;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let k;if(h){let L=0,pe=[];h.forEach(We=>{let ct=typeof We.data=="number"?[We.data]:We.data;if(ct.length===0)return;let Ot=We.type===10?2:4,zt,hr;We.type===10?(hr=ct.length>4?16:ct.length>2?8:ct.length*Ot,zt=ct.length>4?16:Ot*ct.length):(hr=ct.length<=2?ct.length*Ot:16,zt=16),L=Math.ceil(L/hr)*hr,pe.push(L);let br=We.type===10?8:4;L+=ct.length>4?Math.ceil(ct.length/br)*zt:ct.length*Ot});let Z=16;L=Math.ceil(L/Z)*Z;let oe=new ArrayBuffer(L);h.forEach((We,ct)=>{let Ot=pe[ct],zt=typeof We.data=="number"?[We.data]:We.data;if(We.type===6)new Int32Array(oe,Ot,zt.length).set(zt);else if(We.type===12)new Uint32Array(oe,Ot,zt.length).set(zt);else if(We.type===10)new Uint16Array(oe,Ot,zt.length).set(zt);else if(We.type===1)new Float32Array(oe,Ot,zt.length).set(zt);else throw new Error(`Unsupported uniform type: ${kn(We.type)}`)});let et=this.gpuDataManager.create(L,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(et.buffer,0,oe,0,L),this.gpuDataManager.release(et.id),k={offset:0,size:L,buffer:et.buffer}}let C=this.programManager.normalizeDispatchGroupSize(c),F=C[1]===1&&C[2]===1,U=lp(e,t,F),G=this.programManager.getArtifact(U);if(G||(G=this.programManager.build(e,C),this.programManager.setArtifact(U,G),Dr("info",()=>`[artifact] key: ${U}, programName: ${e.name}`)),h&&G.uniformVariablesInfo){if(h.length!==G.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${G.uniformVariablesInfo.length}, got ${h.length} in program "${G.programInfo.name}".`);for(let L=0;L<h.length;L++){let pe=h[L],Z=pe.type,oe=typeof pe.data=="number"?1:pe.data.length,[et,We]=G.uniformVariablesInfo[L];if(Z!==et||oe!==We)throw new Error(`Uniform variable ${L} mismatch: expect type ${et} with size ${We}, got type ${Z} with size ${oe} in program "${G.programInfo.name}".`)}}if(Dr("info",()=>`[ProgramManager] run "${e.name}" (key=${U}) with ${C[0]}x${C[1]}x${C[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let L={kernelId:this.currentKernelId,programName:G.programInfo.name,inputTensorViews:t,outputTensorViews:y};this.pendingKernels.push(L),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(L)}return this.programManager.run(G,i,u,C,k),Ve(e.name),y}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=ip.get(e);if(!s)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:s[0],attributes:[s[1],r]};this.kernels.set(t,a)}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: ${e}`);let s=n.kernelType,a=n.kernelName,i=n.kernelEntry,d=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${s}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,d[0]&&(d[1]=d[0](d[1]),d[0]=void 0),Dr("info",()=>`[WebGPU] Start to run kernel "[${s}] ${a}"...`);let c=this.env.debug;this.temporaryData=[];try{return c&&this.device.pushErrorScope("validation"),i(t,d[1]),0}catch(h){return r.push(Promise.resolve(`[WebGPU] Kernel "[${s}] ${a}" failed. ${h}`)),1}finally{c&&r.push(this.device.popErrorScope().then(h=>h?`GPU validation error for kernel "[${s}] ${a}": ${h.message}`:null));for(let h of this.temporaryData)this.gpuDataManager.release(h.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 a=s.get(t),i=this.gpuDataManager.registerExternalBuffer(r,n,a==null?void 0:a[1]);return s.set(t,[i,r]),i}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 yt(this,e,t);return Me(n.buffer,r)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){var e;this.queryType="none",(((e=this.env.webgpu.profiling)==null?void 0:e.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){Dr("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(){Dr("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){Dr("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(),a=e[n];this.writeTimestamp(this.pendingDispatchNumber*2),s.setPipeline(a.computePipeline),s.setBindGroup(0,a.bindGroup),s.dispatchWorkgroups(...a.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()}}}),cp={};T(cp,{init:()=>hp});var ud,pp,hp,gf=D(()=>{Qt(),_f(),mn(),Yt(),ud=class Zh{constructor(t,r,n,s){this.module=t,this.dataType=r,this.data=n,this.dims=s}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let t=qe.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=qe.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=qe.size(this.dims);return t===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(qe.size(t)!==qe.size(this.dims))throw new Error("Invalid new shape");return new Zh(this.module,this.dataType,this.data,t)}},pp=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 a=n[s++];this.outputCount=n[s++],this.customDataOffset=n[s++],this.customDataSize=n[s++];let i=[];for(let d=0;d<a;d++){let c=n[s++],h=n[s++],w=n[s++],y=[];for(let u=0;u<w;u++)y.push(n[s++]);i.push(new ud(e,c,h,y))}this.inputs=i}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){var i;let r=((i=t==null?void 0:t.inputs)==null?void 0:i.map(d=>typeof d=="number"?this.inputs[d]:d))??this.inputs,n=(t==null?void 0:t.outputs)??[],s=(d,c,h)=>new ud(this.module,c,this.output(d,h),h),a=(d,c)=>{let h=zn(d);if(!h)throw new Error(`Unsupported data type: ${d}`);let w=h*qe.size(c),y=w>0?this.backend.gpuDataManager.create(w).id:0;return new ud(this.module,d,y,c)};return this.backend.run(e,r,n,s,a,this.outputCount)}output(e,t){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+t.length)*4),s=n>>2;this.module.HEAPU32[s++]=t.length;for(let a=0;a<t.length;a++)this.module.HEAPU32[s++]=t[a];return this.module._JsepOutput(this.opKernelContext,e,n)}catch(n){throw new Error(`Failed to generate kernel's output[${e}] with dims [${t}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(r)}}},hp=async(e,t,r,n)=>{let s=t.jsepInit;if(!s)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(e==="webgpu"){let a=new dp;await a.initialize(r,n),s("webgpu",[a,i=>a.alloc(i),i=>a.free(i),(i,d,c,h=!1)=>{if(h)Dr("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${i}, dst=${d}, size=${c}`),a.memcpy(i,d);else{Dr("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${i}, gpuDataId=${d}, size=${c}`);let w=t.HEAPU8.subarray(i>>>0,(i>>>0)+c);a.upload(d,w)}},async(i,d,c)=>{Dr("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${i}, dataOffset=${d}, size=${c}`),await a.download(i,()=>t.HEAPU8.subarray(d>>>0,(d>>>0)+c))},(i,d,c)=>a.createKernel(i,d,c,t.UTF8ToString(t._JsepGetNodeName(d))),i=>a.releaseKernel(i),(i,d,c,h)=>{Dr("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${c}, kernel=${i}, contextDataOffset=${d}`);let w=new pp(t,a,d);return a.computeKernel(i,w,h)},()=>a.captureBegin(),()=>a.captureEnd(),()=>a.replay()])}else s("webnn")}}),fp,Dd,Bd,Fs,mp,dd,Ld,Rd,Nd,jd,Vd,Ud,_p=D(()=>{Vs(),Us(),Qt(),Zr(),Wn(),bs(),fp=(e,t)=>{Lr()._OrtInit(e,t)!==0&&Pr("Can't initialize onnxruntime.")},Dd=async e=>{fp(e.wasm.numThreads,Qn(e.logLevel))},Bd=async(e,t)=>{{let r=(gf(),P(cp)).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`. 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i=Lr(),d=e[0],c=e[1],h=e[3],w,y;if(d==="string"&&h==="gpu-buffer")throw new Error("String tensor is not supported on GPU.");if(a&&h!=="gpu-buffer")throw new Error(`External buffer must be provided for input/output index ${s} when enableGraphCapture is true.`);if(h==="gpu-buffer"){let C=e[2].gpuBuffer,F=zn(ss(d));y=c.reduce((G,L)=>G*L,1)*F;let U=i.jsepRegisterBuffer;if(!U)throw new Error('Tensor location "gpu-buffer" is not supported without using WebGPU.');w=U(n,s,C,y)}else{let C=e[2];if(Array.isArray(C)){y=4*C.length,w=i._malloc(y),r.push(w);let F=w/4;for(let U=0;U<C.length;U++){if(typeof C[U]!="string")throw new TypeError(`tensor data at index ${U} is not a string`);i.HEAPU32[F++]=Nr(C[U],r)}}else y=C.byteLength,w=i._malloc(y),r.push(w),i.HEAPU8.set(new Uint8Array(C.buffer,C.byteOffset,y),w)}let u=i.stackSave(),k=i.stackAlloc(4*c.length);try{let C=k/4;c.forEach(U=>i.HEAP32[C++]=U);let F=i._OrtCreateTensor(ss(d),w,y,k,c.length,as(h));F===0&&Pr(`Can't create tensor for input/output. session=${n}, index=${s}.`),t.push(F)}finally{i.stackRestore(u)}},jd=async(e,t,r,n,s,a)=>{var zt,hr;let i=Lr(),d=Fs.get(e);if(!d)throw new Error(`cannot run inference. invalid session id: ${e}`);let c=d[0],h=d[1],w=d[2],y=d[3],u=d[4],k=d[5],C=t.length,F=n.length,U=0,G=[],L=[],pe=[],Z=[],oe=i.stackSave(),et=i.stackAlloc(C*4),We=i.stackAlloc(C*4),ct=i.stackAlloc(F*4),Ot=i.stackAlloc(F*4);try{[U,G]=On(a);for(let Bt=0;Bt<C;Bt++)Nd(r[Bt],L,Z,e,t[Bt],u);for(let Bt=0;Bt<F;Bt++)Nd(s[Bt],pe,Z,e,C+n[Bt],u);let br=et/4,rr=We/4,Sr=ct/4,Wr=Ot/4;for(let Bt=0;Bt<C;Bt++)i.HEAPU32[br++]=L[Bt],i.HEAPU32[rr++]=h[t[Bt]];for(let Bt=0;Bt<F;Bt++)i.HEAPU32[Sr++]=pe[Bt],i.HEAPU32[Wr++]=w[n[Bt]];if(y&&!k){let{handle:Bt,outputPreferredLocations:Zt,outputPreferredLocationsEncoded:_r}=y;if(h.length!==C)throw new Error(`input count from feeds (${C}) is expected to be always equal to model's input count (${h.length}).`);for(let Le=0;Le<C;Le++){let Nt=t[Le];await i._OrtBindInput(Bt,h[Nt],L[Le])!==0&&Pr(`Can't bind input[${Le}] for session=${e}.`)}for(let Le=0;Le<F;Le++){let Nt=n[Le];(zt=s[Le])!=null&&zt[3]?i._OrtBindOutput(Bt,w[Nt],pe[Le],0)!==0&&Pr(`Can't bind pre-allocated output[${Le}] for session=${e}.`):i._OrtBindOutput(Bt,w[Nt],0,_r[Nt])!==0&&Pr(`Can't bind output[${Le}] to ${Zt[Le]} for session=${e}.`)}Fs.set(e,[c,h,w,y,u,!0])}(hr=i.jsepOnRunStart)==null||hr.call(i,c);let cr;y?cr=await i._OrtRunWithBinding(c,y.handle,F,ct,U):cr=await i._OrtRun(c,We,et,C,Ot,F,ct,U),cr!==0&&Pr("failed to call OrtRun().");let Rr=[];for(let Bt=0;Bt<F;Bt++){let Zt=i.HEAPU32[ct/4+Bt];if(Zt===pe[Bt]){Rr.push(s[Bt]);continue}let _r=i.stackSave(),Le=i.stackAlloc(4*4),Nt=!1,tr,Br=0;try{i._OrtGetTensorData(Zt,Le,Le+4,Le+8,Le+12)!==0&&Pr(`Can't access output tensor data on index ${Bt}.`);let Xr=Le/4,an=i.HEAPU32[Xr++];Br=i.HEAPU32[Xr++];let zs=i.HEAPU32[Xr++],En=i.HEAPU32[Xr++],In=[];for(let fn=0;fn<En;fn++)In.push(i.HEAPU32[zs/4+fn]);i._OrtFree(zs);let hn=In.reduce((fn,Tn)=>fn*Tn,1);tr=kn(an);let Au=y==null?void 0:y.outputPreferredLocations[n[Bt]];if(tr==="string"){if(Au==="gpu-buffer")throw new Error("String tensor is not supported on GPU.");let fn=[],Tn=Br/4;for(let Nn=0;Nn<hn;Nn++){let Ds=i.HEAPU32[Tn++],hd=Nn===hn-1?void 0:i.HEAPU32[Tn]-Ds;fn.push(i.UTF8ToString(Ds,hd))}Rr.push([tr,In,fn,"cpu"])}else if(Au==="gpu-buffer"&&hn>0){let fn=i.jsepGetBuffer;if(!fn)throw new Error('preferredLocation "gpu-buffer" is not supported without using WebGPU.');let Tn=fn(Br),Nn=zn(an);if(Nn===void 0||!is(tr))throw new Error(`Unsupported data type: ${tr}`);Nt=!0,Rr.push([tr,In,{gpuBuffer:Tn,download:i.jsepCreateDownloader(Tn,hn*Nn,tr),dispose:()=>{i._OrtReleaseTensor(Zt)}},"gpu-buffer"])}else{let fn=Dn(tr),Tn=new fn(hn);new Uint8Array(Tn.buffer,Tn.byteOffset,Tn.byteLength).set(i.HEAPU8.subarray(Br,Br+Tn.byteLength)),Rr.push([tr,In,Tn,"cpu"])}}finally{i.stackRestore(_r),tr==="string"&&Br&&i._free(Br),Nt||i._OrtReleaseTensor(Zt)}}return 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 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
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x=l("./src/utils/generic.js");l("./src/utils/tensor.js");var H=l("./src/utils/maths.js");class ge extends x.Callable{_call(I,B){throw Error("`_call` should be implemented in a subclass")}}class ve extends x.Callable{_call(I,B){throw Error("`_call` should be implemented in a subclass")}}class xe extends x.Callable{constructor(){super(),this.processors=[]}push(I){this.processors.push(I)}extend(I){this.processors.push(...I)}_call(I,B){let A=B;for(const _e of this.processors)A=_e(I,A);return A}[Symbol.iterator](){return this.processors.values()}}class D extends ge{constructor(I){super(),this.bos_token_id=I}_call(I,B){for(let A=0;A<I.length;++A)if(I[A].length===1){const _e=B[A];_e.data.fill(-1/0),_e.data[this.bos_token_id]=0}return B}}class T extends ge{constructor(I,B){super(),this.max_length=I,this.eos_token_id=Array.isArray(B)?B:[B]}_call(I,B){for(let A=0;A<I.length;++A)if(I[A].length===this.max_length-1){const _e=B[A];_e.data.fill(-1/0);for(const ye of this.eos_token_id)_e.data[ye]=0}return B}}class j extends ge{constructor(I,B){super(),this.begin_suppress_tokens=I,this.begin_index=B}_call(I,B){for(let A=0;A<I.length;++A)if(I[A].length===this.begin_index){const _e=B[A];for(const ye of this.begin_suppress_tokens)_e.data[ye]=-1/0}return B}}class P extends ge{constructor(I,B){super(),this.eos_token_id=Array.isArray(I.eos_token_id)?I.eos_token_id[0]:I.eos_token_id,this.no_timestamps_token_id=I.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=B.length,B.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=I.max_initial_timestamp_index}_call(I,B){for(let A=0;A<I.length;++A){const ye=B[A].data;if(ye[this.no_timestamps_token_id]=-1/0,I[A].length===this.begin_index-1){ye.fill(-1/0),ye[this.timestamp_begin]=0;continue}const Ce=I[A].slice(this.begin_index),ke=Ce.length>=1&&Ce[Ce.length-1]>=this.timestamp_begin,Ie=Ce.length<2||Ce[Ce.length-2]>=this.timestamp_begin;if(ke&&(Ie?ye.subarray(this.timestamp_begin).fill(-1/0):ye.subarray(0,this.eos_token_id).fill(-1/0)),I[A].length===this.begin_index&&this.max_initial_timestamp_index!==null){const we=this.timestamp_begin+this.max_initial_timestamp_index;ye.subarray(we+1).fill(-1/0)}const tt=(0,H.log_softmax)(ye),Qe=Math.log(tt.subarray(this.timestamp_begin).map(Math.exp).reduce((we,V)=>we+V)),ht=(0,H.max)(tt.subarray(0,this.timestamp_begin))[0];Qe>ht&&ye.subarray(0,this.timestamp_begin).fill(-1/0)}return B}}class J extends ge{constructor(I){super(),this.no_repeat_ngram_size=I}getNgrams(I){const B=I.length,A=[];for(let ye=0;ye<B+1-this.no_repeat_ngram_size;++ye){const Ce=[];for(let ke=0;ke<this.no_repeat_ngram_size;++ke)Ce.push(I[ye+ke]);A.push(Ce.map(Number))}const _e=new Map;for(const ye of A){const Ce=ye.slice(0,ye.length-1),ke=JSON.stringify(Ce),Ie=_e.get(ke)??[];Ie.push(ye[ye.length-1]),_e.set(ke,Ie)}return _e}getGeneratedNgrams(I,B){const A=B.slice(B.length+1-this.no_repeat_ngram_size,B.length);return I.get(JSON.stringify(A.map(Number)))??[]}calcBannedNgramTokens(I){const B=[];if(I.length+1<this.no_repeat_ngram_size)return B;{const A=this.getNgrams(I);return this.getGeneratedNgrams(A,I)}}_call(I,B){for(let A=0;A<I.length;++A){const _e=B[A],ye=this.calcBannedNgramTokens(I[A]);for(const Ce of ye)_e.data[Ce]=-1/0}return B}}class te extends ge{constructor(I){super(),this.penalty=I}_call(I,B){for(let A=0;A<I.length;++A){const _e=B[A];for(const ye of I[A])_e.data[ye]<0?_e.data[ye]*=this.penalty:_e.data[ye]/=this.penalty}return B}}class ne extends ge{constructor(I,B){super(),this.min_length=I,this.eos_token_id=Array.isArray(B)?B:[B]}_call(I,B){for(let A=0;A<I.length;++A)if(I[A].length<this.min_length){const _e=B[A];for(const ye of this.eos_token_id)_e.data[ye]=-1/0}return B}}class ie extends ge{constructor(I,B,A){super(),this.prompt_length_to_skip=I,this.min_new_tokens=B,this.eos_token_id=Array.isArray(A)?A:[A]}_call(I,B){for(let A=0;A<I.length;++A)if(I[A].length-this.prompt_length_to_skip<this.min_new_tokens){const ye=B[A];for(const Ce of this.eos_token_id)ye[Ce]=-1/0}return B}}class R extends ge{constructor(I,B){super(),this.bad_words_ids=I,this.eos_token_id=Array.isArray(B)?B:[B]}_call(I,B){for(let A=0;A<I.length;++A){const _e=B[A];for(const ye of this.bad_words_ids){let Ce=!0;for(let ke=1;ke<=ye.length-1&&ye.length<I[ke].length;++ke)if(ye.at(-ke-1)!==Number(I[ke].at(-ke))){Ce=!1;break}Ce&&(_e[ye.at(-1)]=-1/0)}}return B}}class Y extends ge{constructor(I){if(super(),I<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${I}.`);this.guidance_scale=I}_call(I,B){if(B.dims[0]!==2*I.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 ${B.dims[0]} for the logits and ${I.length} for the input ids.`);const A=I.length,_e=B.slice([0,A],null),ye=B.slice([A,B.dims[0]],null);for(let Ce=0;Ce<ye.data.length;++Ce)ye.data[Ce]+=(_e.data[Ce]-ye.data[Ce])*this.guidance_scale;return ye}}class se extends ve{constructor(I){super(),this.temperature=I}_call(I,B){const A=B.data;for(let _e=0;_e<A.length;++_e)A[_e]/=this.temperature;return B}}class le extends ve{constructor(I,{filter_value:B=-1/0,min_tokens_to_keep:A=1}={}){if(super(),I<0||I>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${I}`);if(!Number.isInteger(A)||A<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${A}`);this.top_p=I,this.filter_value=B,this.min_tokens_to_keep=A}}class ae extends ve{constructor(I,{filter_value:B=-1/0,min_tokens_to_keep:A=1}={}){if(super(),!Number.isInteger(I)||I<0)throw new Error(`\`top_k\` must be a positive integer, but is ${I}`);this.top_k=Math.max(I,A),this.filter_value=B}}},"./src/generation/logits_sampler.js":(Mt,me,l)=>{l.r(me),l.d(me,{LogitsSampler:()=>ve});var x=l("./src/utils/generic.js"),H=l("./src/utils/tensor.js"),ge=l("./src/utils/maths.js");l("./src/generation/configuration_utils.js");class ve extends x.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,J){let te=P.dims.at(-1),ne=P.data;if(J===-1)ne=ne.slice(-te);else{let ie=J*te;ne=ne.slice(ie,ie+te)}return ne}randomSelect(P){let J=0;for(let ne=0;ne<P.length;++ne)J+=P[ne];let te=Math.random()*J;for(let ne=0;ne<P.length;++ne)if(te-=P[ne],te<=0)return ne;return 0}static getSampler(P){if(P.do_sample)return new D(P);if(P.num_beams>1)return new T(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 xe(P)}}class xe extends ve{async sample(P){const J=(0,ge.max)(P.data)[1];return[[BigInt(J),0]]}}class D extends ve{async sample(P){let J=P.dims.at(-1);this.generation_config.top_k>0&&(J=Math.min(this.generation_config.top_k,J));const[te,ne]=await(0,H.topk)(P,J),ie=(0,ge.softmax)(te.data);return Array.from({length:this.generation_config.num_beams},()=>{const R=this.randomSelect(ie);return[ne.data[R],Math.log(ie[R])]})}}class T extends ve{async sample(P){let J=P.dims.at(-1);this.generation_config.top_k>0&&(J=Math.min(this.generation_config.top_k,J));const[te,ne]=await(0,H.topk)(P,J),ie=(0,ge.softmax)(te.data);return Array.from({length:this.generation_config.num_beams},(R,Y)=>[ne.data[Y],Math.log(ie[Y])])}}},"./src/generation/stopping_criteria.js":(Mt,me,l)=>{l.r(me),l.d(me,{EosTokenCriteria:()=>xe,InterruptableStoppingCriteria:()=>D,MaxLengthCriteria:()=>ve,StoppingCriteria:()=>H,StoppingCriteriaList:()=>ge});var x=l("./src/utils/generic.js");class H extends x.Callable{_call(j,P){throw Error("StoppingCriteria needs to be subclassed")}}class ge extends x.Callable{constructor(){super(),this.criteria=[]}push(j){this.criteria.push(j)}extend(j){j instanceof ge?j=j.criteria:j instanceof H&&(j=[j]),this.criteria.push(...j)}_call(j,P){const J=new Array(j.length).fill(!1);for(const te of this.criteria){const ne=te(j,P);for(let ie=0;ie<J.length;++ie)J[ie]||(J[ie]=ne[ie])}return J}[Symbol.iterator](){return this.criteria.values()}}class ve extends H{constructor(j,P=null){super(),this.max_length=j,this.max_position_embeddings=P}_call(j){return j.map(P=>P.length>=this.max_length)}}class xe extends H{constructor(j){super(),Array.isArray(j)||(j=[j]),this.eos_token_id=j}_call(j,P){return j.map(J=>{const te=J.at(-1);return this.eos_token_id.some(ne=>te==ne)})}}class D extends H{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(j,P){return new Array(j.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(Mt,me,l)=>{l.r(me),l.d(me,{BaseStreamer:()=>ve,TextStreamer:()=>D,WhisperTextStreamer:()=>T});var x=l("./src/utils/core.js"),H=l("./src/tokenizers.js"),ge=l("./src/env.js");class ve{put(P){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const xe=ge.apis.IS_PROCESS_AVAILABLE?j=>process.stdout.write(j):j=>console.log(j);class D extends ve{constructor(P,{skip_prompt:J=!1,callback_function:te=null,token_callback_function:ne=null,decode_kwargs:ie={},...R}={}){super(),this.tokenizer=P,this.skip_prompt=J,this.callback_function=te??xe,this.token_callback_function=ne,this.decode_kwargs={...ie,...R},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(P){var ie;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 J=P[0];(ie=this.token_callback_function)==null||ie.call(this,J),this.token_cache=(0,x.mergeArrays)(this.token_cache,J);const te=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let ne;te.endsWith(`
`)?(ne=te.slice(this.print_len),this.token_cache=[],this.print_len=0):te.length>0&&(0,H.is_chinese_char)(te.charCodeAt(te.length-1))?(ne=te.slice(this.print_len),this.print_len+=ne.length):(ne=te.slice(this.print_len,te.lastIndexOf(" ")+1),this.print_len+=ne.length),this.on_finalized_text(ne,!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,J){var te,ne;P.length>0&&((te=this.callback_function)==null||te.call(this,P)),J&&this.callback_function===xe&&ge.apis.IS_PROCESS_AVAILABLE&&((ne=this.callback_function)==null||ne.call(this,`
`))}}class T extends D{constructor(P,{skip_prompt:J=!1,callback_function:te=null,token_callback_function:ne=null,on_chunk_start:ie=null,on_chunk_end:R=null,on_finalize:Y=null,time_precision:se=.02,skip_special_tokens:le=!0,decode_kwargs:ae={}}={}){super(P,{skip_prompt:J,callback_function:te,token_callback_function:ne,decode_kwargs:{skip_special_tokens:le,...ae}}),this.timestamp_begin=P.timestamp_begin,this.on_chunk_start=ie,this.on_chunk_end=R,this.on_finalize=Y,this.time_precision=se,this.waiting_for_timestamp=!1}put(P){var te,ne;if(P.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const J=P[0];if(J.length===1){const ie=Number(J[0])-this.timestamp_begin;if(ie>=0){const R=ie*this.time_precision;this.waiting_for_timestamp?(te=this.on_chunk_end)==null||te.call(this,R):(ne=this.on_chunk_start)==null||ne.call(this,R),this.waiting_for_timestamp=!this.waiting_for_timestamp,P=[[]]}}return super.put(P)}end(){var P;super.end(),(P=this.on_finalize)==null||P.call(this)}}},"./src/models.js":(Mt,me,l)=>{l.r(me),l.d(me,{ASTForAudioClassification:()=>Ws,ASTModel:()=>Ft,ASTPreTrainedModel:()=>gn,AlbertForMaskedLM:()=>Qt,AlbertForQuestionAnswering:()=>as,AlbertForSequenceClassification:()=>is,AlbertModel:()=>Qn,AlbertPreTrainedModel:()=>Dn,AutoModel:()=>lu,AutoModelForAudioClassification:()=>bu,AutoModelForAudioFrameClassification:()=>Mu,AutoModelForCTC:()=>yu,AutoModelForCausalLM:()=>pu,AutoModelForDepthEstimation:()=>Tu,AutoModelForDocumentQuestionAnswering:()=>rd,AutoModelForImageClassification:()=>mu,AutoModelForImageFeatureExtraction:()=>Cu,AutoModelForImageMatting:()=>vu,AutoModelForImageSegmentation:()=>xa,AutoModelForImageToImage:()=>xu,AutoModelForMaskGeneration:()=>Ta,AutoModelForMaskedLM:()=>hu,AutoModelForObjectDetection:()=>gu,AutoModelForQuestionAnswering:()=>va,AutoModelForSemanticSegmentation:()=>_u,AutoModelForSeq2SeqLM:()=>du,AutoModelForSequenceClassification:()=>ln,AutoModelForSpeechSeq2Seq:()=>Ma,AutoModelForTextToSpectrogram:()=>cu,AutoModelForTextToWaveform:()=>As,AutoModelForTokenClassification:()=>uu,AutoModelForVision2Seq:()=>fu,AutoModelForXVector:()=>Ca,AutoModelForZeroShotObjectDetection:()=>wu,BartForConditionalGeneration:()=>_,BartForSequenceClassification:()=>O,BartModel:()=>Me,BartPretrainedModel:()=>mn,BaseModelOutput:()=>pt,BeitForImageClassification:()=>Fo,BeitModel:()=>Io,BeitPreTrainedModel:()=>Fi,BertForMaskedLM:()=>Re,BertForQuestionAnswering:()=>ze,BertForSequenceClassification:()=>st,BertForTokenClassification:()=>Tt,BertModel:()=>lt,BertPreTrainedModel:()=>rt,BlenderbotForConditionalGeneration:()=>Pt,BlenderbotModel:()=>yt,BlenderbotPreTrainedModel:()=>bt,BlenderbotSmallForConditionalGeneration:()=>nr,BlenderbotSmallModel:()=>$r,BlenderbotSmallPreTrainedModel:()=>Jt,BloomForCausalLM:()=>fo,BloomModel:()=>ho,BloomPreTrainedModel:()=>Hs,CLIPModel:()=>Oa,CLIPPreTrainedModel:()=>Cs,CLIPSegForImageSegmentation:()=>ja,CLIPSegModel:()=>Na,CLIPSegPreTrainedModel:()=>wi,CLIPTextModelWithProjection:()=>Pn,CLIPVisionModelWithProjection:()=>za,CamembertForMaskedLM:()=>be,CamembertForQuestionAnswering:()=>Ne,CamembertForSequenceClassification:()=>Be,CamembertForTokenClassification:()=>Ae,CamembertModel:()=>q,CamembertPreTrainedModel:()=>$,CausalLMOutput:()=>Zn,CausalLMOutputWithPast:()=>nd,ChineseCLIPModel:()=>Ra,ChineseCLIPPreTrainedModel:()=>La,ClapAudioModelWithProjection:()=>zl,ClapModel:()=>Fl,ClapPreTrainedModel:()=>Es,ClapTextModelWithProjection:()=>Ol,CodeGenForCausalLM:()=>Gs,CodeGenModel:()=>Ya,CodeGenPreTrainedModel:()=>Mn,CohereForCausalLM:()=>eo,CohereModel:()=>Ja,CoherePreTrainedModel:()=>xi,ConvBertForMaskedLM:()=>M,ConvBertForQuestionAnswering:()=>X,ConvBertForSequenceClassification:()=>W,ConvBertForTokenClassification:()=>S,ConvBertModel:()=>xt,ConvBertPreTrainedModel:()=>wt,ConvNextForImageClassification:()=>rl,ConvNextModel:()=>tl,ConvNextPreTrainedModel:()=>qi,ConvNextV2ForImageClassification:()=>il,ConvNextV2Model:()=>sl,ConvNextV2PreTrainedModel:()=>nl,DPTForDepthEstimation:()=>Ui,DPTModel:()=>Vi,DPTPreTrainedModel:()=>ji,DebertaForMaskedLM:()=>vt,DebertaForQuestionAnswering:()=>Lt,DebertaForSequenceClassification:()=>ft,DebertaForTokenClassification:()=>Ct,DebertaModel:()=>nt,DebertaPreTrainedModel:()=>dt,DebertaV2ForMaskedLM:()=>Rt,DebertaV2ForQuestionAnswering:()=>er,DebertaV2ForSequenceClassification:()=>Ht,DebertaV2ForTokenClassification:()=>Xt,DebertaV2Model:()=>jt,DebertaV2PreTrainedModel:()=>Xe,DeiTForImageClassification:()=>Wo,DeiTModel:()=>Uo,DeiTPreTrainedModel:()=>Di,DepthAnythingForDepthEstimation:()=>Yo,DepthAnythingPreTrainedModel:()=>Qo,DetrForObjectDetection:()=>zo,DetrForSegmentation:()=>Do,DetrModel:()=>Oo,DetrObjectDetectionOutput:()=>Oi,DetrPreTrainedModel:()=>Qs,DetrSegmentationOutput:()=>Bo,Dinov2ForImageClassification:()=>ol,Dinov2Model:()=>al,Dinov2PreTrainedModel:()=>xn,DistilBertForMaskedLM:()=>St,DistilBertForQuestionAnswering:()=>Ze,DistilBertForSequenceClassification:()=>Ur,DistilBertForTokenClassification:()=>Cr,DistilBertModel:()=>Tr,DistilBertPreTrainedModel:()=>Wt,DonutSwinModel:()=>Gi,DonutSwinPreTrainedModel:()=>el,EfficientNetForImageClassification:()=>Nl,EfficientNetModel:()=>Rl,EfficientNetPreTrainedModel:()=>pa,ElectraForMaskedLM:()=>Je,ElectraForQuestionAnswering:()=>Se,ElectraForSequenceClassification:()=>At,ElectraForTokenClassification:()=>_t,ElectraModel:()=>Ye,ElectraPreTrainedModel:()=>fe,EsmForMaskedLM:()=>Un,EsmForSequenceClassification:()=>Fn,EsmForTokenClassification:()=>Lr,EsmModel:()=>qr,EsmPreTrainedModel:()=>Dt,FalconForCausalLM:()=>Il,FalconModel:()=>Al,FalconPreTrainedModel:()=>la,FastViTForImageClassification:()=>Mo,FastViTModel:()=>bo,FastViTPreTrainedModel:()=>Xs,Florence2ForConditionalGeneration:()=>_i,Florence2PreTrainedModel:()=>Fa,GLPNForDepthEstimation:()=>Jo,GLPNModel:()=>Zo,GLPNPreTrainedModel:()=>Wi,GPT2LMHeadModel:()=>Ua,GPT2Model:()=>Va,GPT2PreTrainedModel:()=>yi,GPTBigCodeForCausalLM:()=>zu,GPTBigCodeMo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PreTrainedModel:()=>kl,UniSpeechForCTC:()=>wl,UniSpeechForSequenceClassification:()=>yl,UniSpeechModel:()=>Zi,UniSpeechPreTrainedModel:()=>hs,UniSpeechSatForAudioFrameClassification:()=>Js,UniSpeechSatForCTC:()=>Ji,UniSpeechSatForSequenceClassification:()=>bl,UniSpeechSatModel:()=>Zs,UniSpeechSatPreTrainedModel:()=>$s,ViTForImageClassification:()=>yo,ViTModel:()=>wo,ViTPreTrainedModel:()=>cs,VisionEncoderDecoderModel:()=>mi,VitMatteForImageMatting:()=>xo,VitMattePreTrainedModel:()=>vo,VitsModel:()=>ua,VitsModelOutput:()=>sd,VitsPreTrainedModel:()=>Uu,Wav2Vec2BertForCTC:()=>ti,Wav2Vec2BertForSequenceClassification:()=>Ml,Wav2Vec2BertModel:()=>ea,Wav2Vec2BertPreTrainedModel:()=>ei,Wav2Vec2ForAudioFrameClassification:()=>Kn,Wav2Vec2ForCTC:()=>Ru,Wav2Vec2ForSequenceClassification:()=>Hn,Wav2Vec2Model:()=>gl,Wav2Vec2PreTrainedModel:()=>qn,WavLMForAudioFrameClassification:()=>$l,WavLMForCTC:()=>ra,WavLMForSequenceClassification:()=>Tl,WavLMForXVector:()=>Cl,WavLMModel:()=>xl,WavLMPreTrainedModel:()=>Ln,WeSpeakerResNetModel:()=>Yi,WeSpeakerResNetPreTrainedModel:()=>en,WhisperForConditionalGeneration:()=>fi,WhisperModel:()=>Ut,WhisperPreTrainedModel:()=>it,XLMForQuestionAnswering:()=>Jr,XLMForSequenceClassification:()=>Yt,XLMForTokenClassification:()=>_n,XLMModel:()=>Hr,XLMPreTrainedModel:()=>Mr,XLMRobertaForMaskedLM:()=>kt,XLMRobertaForQuestionAnswering:()=>jr,XLMRobertaForSequenceClassification:()=>gr,XLMRobertaForTokenClassification:()=>Ar,XLMRobertaModel:()=>xr,XLMRobertaPreTrainedModel:()=>vr,XLMWithLMHeadModel:()=>cn,XVectorOutput:()=>$u,YolosForObjectDetection:()=>ul,YolosModel:()=>ll,YolosObjectDetectionOutput:()=>dl,YolosPreTrainedModel:()=>Hi});var x=l("./src/configs.js"),H=l("./src/backends/onnx.js"),ge=l("./src/utils/dtypes.js"),ve=l("./src/utils/generic.js"),xe=l("./src/utils/core.js"),D=l("./src/utils/hub.js"),T=l("./src/generation/logits_process.js"),j=l("./src/generation/configuration_utils.js"),P=l("./src/utils/tensor.js"),J=l("./src/utils/maths.js"),te=l("./src/generation/stopping_criteria.js"),ne=l("./src/generation/logits_sampler.js"),ie=l("./src/env.js"),R=l("./src/models/whisper/generation_whisper.js"),Y=l("./src/models/whisper/common_whisper.js");const se={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},le=new Map,ae=new Map,N=new Map;async function I(m,g,E){let K=E.device;K&&typeof K!="string"&&(K.hasOwnProperty(g)?K=K[g]:(console.warn(`device not specified for "${g}". Using the default device.`),K=null));const Pe=K??(ie.apis.IS_NODE_ENV?"cpu":"wasm"),De=(0,H.deviceToExecutionProviders)(Pe);let at=E.dtype;typeof at!="string"&&(at&&at.hasOwnProperty(g)?at=at[g]:(at=ge.DEFAULT_DEVICE_DTYPE_MAPPING[Pe]??ge.DATA_TYPES.fp32,console.warn(`dtype not specified for "${g}". Using the default dtype (${at}) for this device (${Pe}).`)));const $t=at;if(ge.DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty($t)){if($t===ge.DATA_TYPES.fp16&&Pe==="webgpu"&&!await(0,ge.isWebGpuFp16Supported)())throw new Error(`The device (${Pe}) does not support fp16.`)}else throw new Error(`Invalid dtype: ${$t}. Should be one of: ${Object.keys(ge.DATA_TYPES).join(", ")}`);const Vt=ge.DEFAULT_DTYPE_SUFFIX_MAPPING[$t],lr=`${E.subfolder??""}/${g}${Vt}.onnx`,ar={...E.session_options};ar.executionProviders??(ar.executionProviders=De);const Er=(0,D.getModelFile)(m,lr,!0,E);let ur=[];if(E.use_external_data_format&&(E.use_external_data_format===!0||typeof E.use_external_data_format=="object"&&E.use_external_data_format.hasOwnProperty(g)&&E.use_external_data_format[g]===!0)){if(ie.apis.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const dr=`${g}${Vt}.onnx_data`,pr=`${E.subfolder??""}/${dr}`;ur.push(new Promise(async(yr,Or)=>{const un=await(0,D.getModelFile)(m,pr,!0,E);yr({path:dr,data:un})}))}else ar.externalData!==void 0&&(ur=ar.externalData.map(async dr=>{if(typeof dr.data=="string"){const pr=await(0,D.getModelFile)(m,dr.data,!0,E);return{...dr,data:pr}}return dr}));if(ur.length>0&&(ar.externalData=await Promise.all(ur)),Pe==="webgpu"){const dr=(0,x.getKeyValueShapes)(E.config,{prefix:"present"});if(Object.keys(dr).length>0&&!(0,H.isONNXProxy)()){const pr={};for(const yr in dr)pr[yr]="gpu-buffer";ar.preferredOutputLocation=pr}}return{buffer:await Er,session_options:ar}}async function B(m,g,E){return Object.fromEntries(await Promise.all(Object.keys(g).map(async K=>{const{buffer:Pe,session_options:De}=await I(m,g[K],E),at=await(0,H.createInferenceSession)(Pe,De);return[K,at]})))}function A(m,g){const E=Object.create(null),K=[];for(const at of m.inputNames){const $t=g[at];if(!($t instanceof P.Tensor)){K.push(at);continue}E[at]=(0,H.isONNXProxy)()?$t.clone():$t}if(K.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${K.join(", ")}.`);const Pe=Object.keys(g).length,De=m.inputNames.length;if(Pe>De){let at=Object.keys(g).filter($t=>!m.inputNames.includes($t));console.warn(`WARNING: Too many inputs were provided (${Pe} > ${De}). The following inputs will be ignored: "${at.join(", ")}".`)}return E}async function _e(m,g){const E=A(m,g);try{const K=Object.fromEntries(Object.entries(E).map(([De,at])=>[De,at.ort_tensor]));let Pe=await m.run(K);return Pe=ye(Pe),Pe}catch(K){throw console.error(`An error occurred during model execution: "${K}".`),console.error("Inputs given to model:",E),K}}function ye(m){for(let g in m)(0,H.isONNXTensor)(m[g])?m[g]=new P.Tensor(m[g]):typeof m[g]=="object"&&ye(m[g]);return m}function Ce(m){if(m instanceof P.Tensor)return m;if(m.length===0)throw Error("items must be non-empty");if(Array.isArray(m[0])){if(m.some(g=>g.length!==m[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new P.Tensor("int64",BigInt64Array.from(m.flat().map(g=>BigInt(g))),[m.length,m[0].length])}else return new P.Tensor("int64",BigInt64Array.from(m.map(g=>BigInt(g))),[1,m.length])}function ke(m){return new P.Tensor("bool",[m],[1])}async function Ie(m,g){let{encoder_outputs:E,input_ids:K,decoder_input_ids:Pe,...De}=g;if(!E){const $t=(0,xe.pick)(g,m.sessions.model.inputNames);E=(await tt(m,$t)).last_hidden_state}return De.input_ids=Pe,De.encoder_hidden_states=E,m.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(De.encoder_attention_mask=g.attention_mask),await Qe(m,De,!0)}async function tt(m,g){const E=m.sessions.model,K=(0,xe.pick)(g,E.inputNames);if(E.inputNames.includes("inputs_embeds")&&!K.inputs_embeds){if(!g.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");K.inputs_embeds=await m.encode_text({input_ids:g.input_ids})}return E.inputNames.includes("token_type_ids")&&!K.token_type_ids&&(K.token_type_ids=new P.Tensor("int64",new BigInt64Array(K.input_ids.data.length),K.input_ids.dims)),await _e(E,K)}async function Qe(m,g,E=!1){const K=m.sessions[E?"decoder_model_merged":"model"],{past_key_values:Pe,...De}=g;K.inputNames.includes("use_cache_branch")&&(De.use_cache_branch=ke(!!Pe)),K.inputNames.includes("position_ids")&&De.attention_mask&&!De.position_ids&&(De.position_ids=we(De,Pe)),m.addPastKeyValues(De,Pe);const at=(0,xe.pick)(De,K.inputNames);return await _e(K,at)}async function ht(m,{input_ids:g=null,attention_mask:E=null,pixel_values:K=null,position_ids:Pe=null,inputs_embeds:De=null,past_key_values:at=null,generation_config:$t=null,logits_processor:Vt=null,...lr}){if(!De){if(De=await m.encode_text({input_ids:g}),K&&g.dims[1]!==1){const Er=await m.encode_image({pixel_values:K});({inputs_embeds:De,attention_mask:E}=m._merge_input_ids_with_image_features({image_features:Er,inputs_embeds:De,input_ids:g,attention_mask:E}))}else if(at&&K&&g.dims[1]===1){const Er=g.dims[1],ur=Object.values(at)[0].dims.at(-2);E=(0,P.cat)([(0,P.ones)([g.dims[0],ur]),E.slice(null,[E.dims[1]-Er,E.dims[1]])],1)}}return await Qe(m,{inputs_embeds:De,past_key_values:at,attention_mask:E,position_ids:Pe,generation_config:$t,logits_processor:Vt},!0)}function we(m,g=null){const{input_ids:E,inputs_embeds:K,attention_mask:Pe}=m,[De,at]=Pe.dims,$t=new BigInt64Array(Pe.data.length);for(let lr=0;lr<De;++lr){const ar=lr*at;let Er=BigInt(0);for(let ur=0;ur<at;++ur){const mr=ar+ur;Pe.data[mr]===0n?$t[mr]=BigInt(1):($t[mr]=Er,Er+=Pe.data[mr])}}let Vt=new P.Tensor("int64",$t,Pe.dims);if(g){const lr=-(E??K).dims.at(1);Vt=Vt.slice(null,[lr,null])}return Vt}function V(m,g,E,K){if(E.past_key_values){const Pe=Object.values(E.past_key_values)[0].dims.at(-2),{input_ids:De,attention_mask:at}=E;if(!(at&&at.dims[1]>De.dims[1])){if(Pe<De.dims[1])E.input_ids=De.slice(null,[Pe,null]);else if(m.config.image_token_index!=null&&De.data.some($t=>$t==m.config.image_token_index)){const $t=m.config.num_image_tokens;if(!$t)throw new Error("`num_image_tokens` is missing in the model configuration.");const Vt=De.dims[1]-(Pe-$t);E.input_ids=De.slice(null,[-Vt,null]),E.attention_mask=(0,P.ones)([1,Pe+Vt])}}}return E}function he(m,g,E,K){return E.past_key_values&&(g=g.map(Pe=>[Pe.at(-1)])),{...E,decoder_input_ids:Ce(g)}}function $e(m,...g){return m.config.is_encoder_decoder?he(m,...g):V(m,...g)}class ee extends ve.Callable{constructor(E,K){super();Te(this,"main_input_name","input_ids");Te(this,"forward_params",["input_ids","attention_mask"]);this.config=E,this.sessions=K;const Pe=N.get(this.constructor),De=le.get(Pe);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,De){case se.DecoderOnly:this.can_generate=!0,this._forward=Qe,this._prepare_inputs_for_generation=V;break;case se.Seq2Seq:case se.Vision2Seq:case se.Musicgen:this.can_generate=!0,this._forward=Ie,this._prepare_inputs_for_generation=he;break;case se.EncoderDecoder:this._forward=Ie;break;case se.ImageTextToText:this.can_generate=!0,this._forward=ht,this._prepare_inputs_for_generation=$e;break;default:this._forward=tt;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var K;const E=[];for(const Pe of Object.values(this.sessions))(K=Pe==null?void 0:Pe.handler)!=null&&K.dispose&&E.push(Pe.handler.dispose());return await Promise.all(E)}static async from_pretrained(E,{progress_callback:K=null,config:Pe=null,cache_dir:De=null,local_files_only:at=!1,revision:$t="main",model_file_name:Vt=null,subfolder:lr="onnx",device:ar=null,dtype:Er=null,use_external_data_format:ur=null,session_options:mr={}}={}){let dr={progress_callback:K,config:Pe,cache_dir:De,local_files_only:at,revision:$t,model_file_name:Vt,subfolder:lr,device:ar,dtype:Er,use_external_data_format:ur,session_options:mr};const pr=N.get(this),yr=le.get(pr);Pe=dr.config=await x.AutoConfig.from_pretrained(E,dr);let Or;if(yr===se.DecoderOnly)Or=await Promise.all([B(E,{model:dr.model_file_name??"model"},dr),(0,D.getModelJSON)(E,"generation_config.json",!1,dr)]);else if(yr===se.Seq2Seq||yr===se.Vision2Seq)Or=await Promise.all([B(E,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},dr),(0,D.getModelJSON)(E,"generation_config.json",!1,dr)]);else if(yr===se.MaskGeneration)Or=await Promise.all([B(E,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},dr)]);else if(yr===se.EncoderDecoder)Or=await Promise.all([B(E,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},dr)]);else if(yr===se.ImageTextToText){const un={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Pe.is_encoder_decoder&&(un.model="encoder_model"),Or=await Promise.all([B(E,un,dr),(0,D.getModelJSON)(E,"generation_config.json",!1,dr)])}else yr===se.Musicgen?Or=await Promise.all([B(E,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},dr),(0,D.getModelJSON)(E,"generation_config.json",!1,dr)]):(yr!==se.EncoderOnly&&console.warn(`Model type for '${pr??(Pe==null?void 0:Pe.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),Or=await Promise.all([B(E,{model:dr.model_file_name??"model"},dr)]));return new this(Pe,...Or)}async _call(E){return await this.forward(E)}async forward(E){return await this._forward(this,E)}_get_logits_warper(E){const K=new T.LogitsProcessorList;return E.temperature!==null&&E.temperature!==1&&K.push(new T.TemperatureLogitsWarper(E.temperature)),E.top_k!==null&&E.top_k!==0&&K.push(new T.TopKLogitsWarper(E.top_k)),E.top_p!==null&&E.top_p<1&&K.push(new T.TopPLogitsWarper(E.top_p)),K}_get_logits_processor(E,K,Pe=null){const De=new T.LogitsProcessorList;if(E.repetition_penalty!==null&&E.repetition_penalty!==1&&De.push(new T.RepetitionPenaltyLogitsProcessor(E.repetition_penalty)),E.no_repeat_ngram_size!==null&&E.no_repeat_ngram_size>0&&De.push(new T.NoRepeatNGramLogitsProcessor(E.no_repeat_ngram_size)),E.bad_words_ids!==null&&De.push(new T.NoBadWordsLogitsProcessor(E.bad_words_ids,E.eos_token_id)),E.min_length!==null&&E.eos_token_id!==null&&E.min_length>0&&De.push(new T.MinLengthLogitsProcessor(E.min_length,E.eos_token_id)),E.min_new_tokens!==null&&E.eos_token_id!==null&&E.min_new_tokens>0&&De.push(new T.MinNewTokensLengthLogitsProcessor(K,E.min_new_tokens,E.eos_token_id)),E.forced_bos_token_id!==null&&De.push(new T.ForcedBOSTokenLogitsProcessor(E.forced_bos_token_id)),E.forced_eos_token_id!==null&&De.push(new T.ForcedEOSTokenLogitsProcessor(E.max_length,E.forced_eos_token_id)),E.begin_suppress_tokens!==null){const at=K>1||E.forced_bos_token_id===null?K:K+1;De.push(new T.SuppressTokensAtBeginLogitsProcessor(E.begin_suppress_tokens,at))}return E.guidance_scale!==null&&E.guidance_scale>1&&De.push(new T.ClassifierFreeGuidanceLogitsProcessor(E.guidance_scale)),Pe!==null&&De.extend(Pe),De}_prepare_generation_config(E,K,Pe=j.GenerationConfig){const De={...this.config};for(const $t of["decoder","generator","text_config"])$t in De&&Object.assign(De,De[$t]);const at=new Pe(De);return"generation_config"in this&&Object.assign(at,this.generation_config),E&&Object.assign(at,E),K&&Object.assign(at,(0,xe.pick)(K,Object.getOwnPropertyNames(at))),at}_get_stopping_criteria(E,K=null){const Pe=new te.StoppingCriteriaList;return E.max_length!==null&&Pe.push(new te.MaxLengthCriteria(E.max_length,this.config.max_position_embeddings??null)),E.eos_token_id!==null&&Pe.push(new te.EosTokenCriteria(E.eos_token_id)),K&&Pe.extend(K),Pe}_validate_model_class(){if(!this.can_generate){const E=[ai,ba,ya,ii],K=N.get(this.constructor),Pe=new Set,De=this.config.model_type;for(const $t of E){const Vt=$t.get(De);Vt&&Pe.add(Vt[0])}let at=`The current model class (${K}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Pe.size>0&&(at+=` Please use the following class instead: ${[...Pe].join(", ")}`),Error(at)}}prepare_inputs_for_generation(...E){return this._prepare_inputs_for_generation(this,...E)}_update_model_kwargs_for_generation({generated_input_ids:E,outputs:K,model_inputs:Pe,is_encoder_decoder:De}){return Pe.past_key_values=this.getPastKeyValues(K,Pe.past_key_values),Pe.input_ids=new P.Tensor("int64",E.flat(),[E.length,1]),De||(Pe.attention_mask=(0,P.cat)([Pe.attention_mask,(0,P.ones)([Pe.attention_mask.dims[0],1])],1)),Pe.position_ids=null,Pe}_prepare_model_inputs({inputs:E,bos_token_id:K,model_kwargs:Pe}){const De=(0,xe.pick)(Pe,this.forward_params),at=this.main_input_name;if(at in De){if(E)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else De[at]=E;return{inputs_tensor:De[at],model_inputs:De,model_input_name:at}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:E,model_inputs:K,model_input_name:Pe,generation_config:De}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!K.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:$t,pixel_values:Vt,attention_mask:lr,...ar}=K,Er=await this._prepare_inputs_embeds(K);K={...ar,...(0,xe.pick)(Er,["inputs_embeds","attention_mask"])}}let{last_hidden_state:at}=await tt(this,K);if(De.guidance_scale!==null&&De.guidance_scale>1)at=(0,P.cat)([at,(0,P.full_like)(at,0)],0),"attention_mask"in K&&(K.attention_mask=(0,P.cat)([K.attention_mask,(0,P.zeros_like)(K.attention_mask)],0));else if(K.decoder_input_ids){const $t=Ce(K.decoder_input_ids).dims[0];if($t!==at.dims[0]){if(at.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${at.dims[0]}) than the decoder inputs (${$t}).`);at=(0,P.cat)(Array.from({length:$t},()=>at),0)}}return K.encoder_outputs=at,K}_prepare_decoder_input_ids_for_generation({batch_size:E,model_input_name:K,model_kwargs:Pe,decoder_start_token_id:De,bos_token_id:at,generation_config:$t}){let{decoder_input_ids:Vt,...lr}=Pe;if(Vt)Array.isArray(Vt[0])||(Vt=Array.from({length:E},()=>Vt));else if(De??(De=at),this.config.model_type==="musicgen")Vt=Array.from({length:E*this.config.decoder.num_codebooks},()=>[De]);else if(Array.isArray(De)){if(De.length!==E)throw new Error(`\`decoder_start_token_id\` expcted to have length ${E} but got ${De.length}`);Vt=De}else Vt=Array.from({length:E},()=>[De]);return Vt=Ce(Vt),Pe.decoder_attention_mask=(0,P.ones_like)(Vt),{input_ids:Vt,model_inputs:lr}}async generate({inputs:E=null,generation_config:K=null,logits_processor:Pe=null,stopping_criteria:De=null,streamer:at=null,...$t}){this._validate_model_class(),K=this._prepare_generation_config(K,$t);let{inputs_tensor:Vt,model_inputs:lr,model_input_name:ar}=this._prepare_model_inputs({inputs:E,model_kwargs:$t});const Er=this.config.is_encoder_decoder;Er&&("encoder_outputs"in lr||(lr=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Vt,model_inputs:lr,model_input_name:ar,generation_config:K})));let ur;Er?{input_ids:ur,model_inputs:lr}=this._prepare_decoder_input_ids_for_generation({batch_size:lr[ar].dims.at(0),model_input_name:ar,model_kwargs:lr,decoder_start_token_id:K.decoder_start_token_id,bos_token_id:K.bos_token_id,generation_config:K}):ur=lr[ar];let mr=ur.dims.at(-1);K.max_new_tokens!==null&&(K.max_length=mr+K.max_new_tokens);const dr=this._get_logits_processor(K,mr,Pe),pr=this._get_stopping_criteria(K,De),yr=lr[ar].dims.at(0),Or=ne.LogitsSampler.getSampler(K),un=new Array(yr).fill(0),pn=ur.tolist();at&&at.put(pn);let Rn=null,nn={};for(;;){lr=this.prepare_inputs_for_generation(pn,lr,K);const sn=await this.forward(lr);if(K.output_attentions&&K.return_dict_in_generate){const An=this.getAttentions(sn);for(const Is in An)Is in nn||(nn[Is]=[]),nn[Is].push(An[Is])}const oi=sn.logits.slice(null,-1,null),li=dr(pn,oi),$a=[];for(let An=0;An<li.dims.at(0);++An){const Is=li[An],id=await Or(Is);for(const[ad,od]of id){const ld=BigInt(ad);un[An]+=od,pn[An].push(ld),$a.push([ld]);break}}if(at&&at.put($a),pr(pn).every(An=>An)){K.return_dict_in_generate&&(Rn=this.getPastKeyValues(sn,lr.past_key_values,!1));break}lr=this._update_model_kwargs_for_generation({generated_input_ids:$a,outputs:sn,model_inputs:lr,is_encoder_decoder:Er})}at&&at.end();const Kr=new P.Tensor("int64",pn.flat(),[pn.length,pn[0].length]);return K.return_dict_in_generate?{sequences:Kr,past_key_values:Rn,...nn}:Kr}getPastKeyValues(E,K,Pe=!0){const De=Object.create(null);for(const at in E)if(at.startsWith("present")){const $t=at.replace("present","past_key_values");if(K&&at.includes("encoder"))De[$t]=K[$t];else{if(Pe&&K){const Vt=K[$t];Vt.location==="gpu-buffer"&&Vt.dispose()}De[$t]=E[at]}}return De}getAttentions(E){const K={};for(const Pe of["cross_attentions","encoder_attentions","decoder_attentions"])for(const De in E)De.startsWith(Pe)&&(Pe in K||(K[Pe]=[]),K[Pe].push(E[De]));return K}addPastKeyValues(E,K){if(K)Object.assign(E,K);else{const Pe=this.custom_config.kv_cache_dtype??"float32",De=Pe==="float16"?new Uint16Array:[],at=(0,x.getKeyValueShapes)(this.config);for(const $t in at)E[$t]=new P.Tensor(Pe,De,at[$t])}}async encode_image({pixel_values:E}){const K=(await _e(this.sessions.vision_encoder,{pixel_values:E})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${K.dims[1]}).`),this.config.num_image_tokens=K.dims[1]),K}async encode_text({input_ids:E}){return(await _e(this.sessions.embed_tokens,{input_ids:E})).inputs_embeds}}class He{}class pt extends He{constructor({last_hidden_state:g,hidden_states:E=null,attentions:K=null}){super(),this.last_hidden_state=g,this.hidden_states=E,this.attentions=K}}class rt extends ee{}class lt extends rt{}class Re extends rt{async _call(g){return new tn(await super._call(g))}}class st extends rt{async _call(g){return new or(await super._call(g))}}class Tt extends rt{async _call(g){return new Qr(await super._call(g))}}class ze extends rt{async _call(g){return new rn(await super._call(g))}}class re extends ee{}class Ee extends re{}class je extends ee{}class Ge extends je{}class Ve extends je{async _call(g){return new tn(await super._call(g))}}class Ke extends je{async _call(g){return new or(await super._call(g))}}class ut extends je{async _call(g){return new Qr(await super._call(g))}}class mt extends je{async _call(g){return new rn(await super._call(g))}}class wt extends ee{}class xt extends wt{}class M extends wt{async _call(g){return new tn(await super._call(g))}}class W extends wt{async _call(g){return new or(await super._call(g))}}class S extends wt{async _call(g){return new Qr(await super._call(g))}}class X extends wt{async _call(g){return new rn(await super._call(g))}}class fe extends ee{}class Ye extends fe{}class Je extends fe{async _call(g){return new tn(await super._call(g))}}class At extends fe{async _call(g){return new or(await super._call(g))}}class _t extends fe{async _call(g){return new Qr(await super._call(g))}}class Se extends fe{async _call(g){return new rn(await super._call(g))}}class $ extends ee{}class q extends ${}class be extends ${async _call(g){return new tn(await super._call(g))}}class Be extends ${async _call(g){return new or(await super._call(g))}}class Ae extends ${async _call(g){return new Qr(await super._call(g))}}class Ne extends ${async _call(g){return new rn(await super._call(g))}}class dt extends ee{}class nt extends dt{}class vt extends dt{async _call(g){return new tn(await super._call(g))}}class ft extends dt{async _call(g){return new or(await super._call(g))}}class Ct extends dt{async _call(g){return new Qr(await super._call(g))}}class Lt extends dt{async _call(g){return new rn(await super._call(g))}}class Xe extends ee{}class jt extends Xe{}class Rt extends Xe{async _call(g){return new tn(await super._call(g))}}class Ht extends Xe{async _call(g){return new or(await super._call(g))}}class Xt extends Xe{async _call(g){return new Qr(await super._call(g))}}class er extends Xe{async _call(g){return new rn(await super._call(g))}}class Wt extends ee{}class Tr extends Wt{}class Ur extends Wt{async _call(g){return new or(await super._call(g))}}class Cr extends Wt{async _call(g){return new Qr(await super._call(g))}}class Ze extends Wt{async _call(g){return new rn(await super._call(g))}}class St extends Wt{async _call(g){return new tn(await super._call(g))}}class Dt extends ee{}class qr extends Dt{}class Un extends Dt{async _call(g){return new tn(await super._call(g))}}class Fn extends Dt{async _call(g){return new or(await super._call(g))}}class Lr extends Dt{async _call(g){return new Qr(await super._call(g))}}class Zr extends ee{}class Nr extends Zr{}class Sn extends Zr{async _call(g){return new tn(await super._call(g))}}class Pr extends Zr{async _call(g){return new or(await super._call(g))}}class Wn extends Zr{async _call(g){return new rn(await super._call(g))}}class On extends ee{}class Vs extends On{}class _s extends On{async _call(g){return new tn(await super._call(g))}}class gs extends On{async _call(g){return new or(await super._call(g))}}class ws extends On{async _call(g){return new Qr(await super._call(g))}}class ys extends On{async _call(g){return new rn(await super._call(g))}}class Gn extends ee{}class Us extends Gn{}class ss extends Gn{async _call(g){return new tn(await super._call(g))}}class kn extends Gn{async _call(g){return new or(await super._call(g))}}class zn extends Gn{async _call(g){return new rn(await super._call(g))}}class Dn extends ee{}class Qn extends Dn{}class is extends Dn{async _call(g){return new or(await super._call(g))}}class as extends Dn{async _call(g){return new rn(await super._call(g))}}class Qt extends Dn{async _call(g){return new tn(await super._call(g))}}class Yn extends ee{constructor(E,K,Pe){super(E,K);Te(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Pe}}class bs extends Yn{}class Ms extends Yn{}class os extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class vs extends os{}class xs extends os{}class ls extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Ts extends ls{}class Dr extends ls{}class mn extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Me extends mn{}class _ extends mn{}class O extends mn{async _call(g){return new or(await super._call(g))}}class Q extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class ue extends Q{}class de extends Q{}class Fe extends Q{async _call(g){return new or(await super._call(g))}}class gt extends Q{}class bt extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class yt extends bt{}class Pt extends bt{}class Jt extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class $r extends Jt{}class nr extends Jt{}class Gt extends ee{}class fr extends Gt{}class on extends Gt{async _call(g){return new tn(await super._call(g))}}class Yr extends Gt{async _call(g){return new or(await super._call(g))}}class qe extends Gt{async _call(g){return new Qr(await super._call(g))}}class yn extends Gt{async _call(g){return new rn(await super._call(g))}}class Mr extends ee{}class Hr extends Mr{}class cn extends Mr{async _call(g){return new tn(await super._call(g))}}class Yt extends Mr{async _call(g){return new or(await super._call(g))}}class _n extends Mr{async _call(g){return new Qr(await super._call(g))}}class Jr extends Mr{async _call(g){return new rn(await super._call(g))}}class vr extends ee{}class xr extends vr{}class kt extends vr{async _call(g){return new tn(await super._call(g))}}class gr extends vr{async _call(g){return new or(await super._call(g))}}class Ar extends vr{async _call(g){return new Qr(await super._call(g))}}class jr extends vr{async _call(g){return new rn(await super._call(g))}}class gn extends ee{}class Ft extends gn{}class Ws extends gn{}class it extends ee{constructor(E,K,Pe){super(E,K);Te(this,"requires_attention_mask",!1);Te(this,"main_input_name","input_features");Te(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Pe}}class Ut extends it{}class fi extends it{_prepare_generation_config(g,E){return super._prepare_generation_config(g,E,R.WhisperGenerationConfig)}_retrieve_init_tokens(g){const E=[g.decoder_start_token_id];let K=g.language;const Pe=g.task;if(g.is_multilingual){K||(console.warn("No language specified - defaulting to English (en)."),K="en");const at=`<|${(0,Y.whisper_language_to_code)(K)}|>`;E.push(g.lang_to_id[at]),E.push(g.task_to_id[Pe??"transcribe"])}else if(K||Pe)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!g.return_timestamps&&g.no_timestamps_token_id&&E.at(-1)!==g.no_timestamps_token_id?E.push(g.no_timestamps_token_id):g.return_timestamps&&E.at(-1)===g.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),E.pop()),E.filter(De=>De!=null)}async generate({inputs:g=null,generation_config:E=null,logits_processor:K=null,stopping_criteria:Pe=null,...De}){E=this._prepare_generation_config(E,De);const at=De.decoder_input_ids??this._retrieve_init_tokens(E);if(E.return_timestamps&&(K??(K=new T.LogitsProcessorList),K.push(new T.WhisperTimeStampLogitsProcessor(E,at))),E.begin_suppress_tokens&&(K??(K=new T.LogitsProcessorList),K.push(new T.SuppressTokensAtBeginLogitsProcessor(E.begin_suppress_tokens,at.length))),E.return_token_timestamps){if(!E.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.");E.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),E.output_attentions=!0,E.return_dict_in_generate=!0}const $t=await super.generate({inputs:g,generation_config:E,logits_processor:K,decoder_input_ids:at,...De});return E.return_token_timestamps&&($t.token_timestamps=this._extract_token_timestamps($t,E.alignment_heads,E.num_frames)),$t}_extract_token_timestamps(g,E,K=null,Pe=.02){if(!g.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`.");K==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 De=this.config.median_filter_width;De===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),De=7);const at=g.cross_attentions,$t=Array.from({length:this.config.decoder_layers},(pr,yr)=>(0,P.cat)(at.map(Or=>Or[yr]),2)),Vt=(0,P.stack)(E.map(([pr,yr])=>{if(pr>=$t.length)throw new Error(`Layer index ${pr} is out of bounds for cross attentions (length ${$t.length}).`);return K?$t[pr].slice(null,yr,null,[0,K]):$t[pr].slice(null,yr)})).transpose(1,0,2,3),[lr,ar]=(0,P.std_mean)(Vt,-2,0,!0),Er=Vt.clone();for(let pr=0;pr<Er.dims[0];++pr){const yr=Er[pr];for(let Or=0;Or<yr.dims[0];++Or){const un=yr[Or],pn=lr[pr][Or][0].data,Rn=ar[pr][Or][0].data;for(let nn=0;nn<un.dims[0];++nn){let Kr=un[nn].data;for(let sn=0;sn<Kr.length;++sn)Kr[sn]=(Kr[sn]-Rn[sn])/pn[sn];Kr.set((0,J.medianFilter)(Kr,De))}}}const ur=[(0,P.mean)(Er,1)],mr=g.sequences.dims,dr=new P.Tensor("float32",new Float32Array(mr[0]*mr[1]),mr);for(let pr=0;pr<mr[0];++pr){const yr=ur[pr].neg().squeeze_(0),[Or,un]=(0,J.dynamic_time_warping)(yr.tolist()),pn=Array.from({length:Or.length-1},(Kr,sn)=>Or[sn+1]-Or[sn]),Rn=(0,xe.mergeArrays)([1],pn).map(Kr=>!!Kr),nn=[];for(let Kr=0;Kr<Rn.length;++Kr)Rn[Kr]&&nn.push(un[Kr]*Pe);dr[pr].data.set(nn,1)}return dr}}class mi extends ee{constructor(E,K,Pe){super(E,K);Te(this,"main_input_name","pixel_values");Te(this,"forward_params",["pixel_values","input_ids","encoder_hidden_states","past_key_values"]);this.generation_config=Pe}}class Ia extends ee{constructor(E,K,Pe){super(E,K);Te(this,"forward_params",["input_ids","pixel_values","attention_mask","position_ids","past_key_values"]);this.generation_config=Pe}}class us extends Ia{_merge_input_ids_with_image_features({inputs_embeds:g,image_features:E,input_ids:K,attention_mask:Pe}){const De=this.config.image_token_index,$t=K.tolist().map(ur=>ur.findIndex(mr=>mr==De)),Vt=$t.every(ur=>ur===-1),lr=$t.every(ur=>ur!==-1);if(!Vt&&!lr)throw new Error("Every input should contain either 0 or 1 image token.");if(Vt)return{inputs_embeds:g,attention_mask:Pe};const ar=[],Er=[];for(let ur=0;ur<$t.length;++ur){const mr=$t[ur],dr=g[ur],pr=E[ur],yr=Pe[ur];ar.push((0,P.cat)([dr.slice([0,mr]),pr,dr.slice([mr+1,dr.dims[0]])],0)),Er.push((0,P.cat)([yr.slice([0,mr]),(0,P.ones)([pr.dims[0]]),yr.slice([mr+1,yr.dims[0]])],0))}return{inputs_embeds:(0,P.stack)(ar,0),attention_mask:(0,P.stack)(Er,0)}}}class ir extends us{}class Fa extends ee{constructor(E,K,Pe){super(E,K);Te(this,"forward_params",["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"]);Te(this,"main_input_name","inputs_embeds");this.generation_config=Pe}}class _i extends Fa{_merge_input_ids_with_image_features({inputs_embeds:g,image_features:E,input_ids:K,attention_mask:Pe}){return{inputs_embeds:(0,P.cat)([E,g],1),attention_mask:(0,P.cat)([(0,P.ones)(E.dims.slice(0,2)),Pe],1)}}async _prepare_inputs_embeds({input_ids:g,pixel_values:E,inputs_embeds:K,attention_mask:Pe}){if(!g&&!E)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let De,at;return g&&(De=await this.encode_text({input_ids:g})),E&&(at=await this.encode_image({pixel_values:E})),De&&at?{inputs_embeds:K,attention_mask:Pe}=this._merge_input_ids_with_image_features({inputs_embeds:De,image_features:at,input_ids:g,attention_mask:Pe}):K=De||at,{inputs_embeds:K,attention_mask:Pe}}async forward({input_ids:g,pixel_values:E,attention_mask:K,decoder_input_ids:Pe,decoder_attention_mask:De,encoder_outputs:at,past_key_values:$t,inputs_embeds:Vt,decoder_inputs_embeds:lr}){if(Vt||({inputs_embeds:Vt,attention_mask:K}=await this._prepare_inputs_embeds({input_ids:g,pixel_values:E,inputs_embeds:Vt,attention_mask:K})),!at){let{last_hidden_state:ur}=await tt(this,{inputs_embeds:Vt,attention_mask:K});at=ur}if(!lr){if(!Pe)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");lr=await this.encode_text({input_ids:Pe})}return await Qe(this,{inputs_embeds:lr,attention_mask:De,encoder_attention_mask:K,encoder_hidden_states:at,past_key_values:$t},!0)}}class Cs extends ee{}class Oa extends Cs{}class Pn extends Cs{static async from_pretrained(g,E={}){return E.model_file_name??(E.model_file_name="text_model"),super.from_pretrained(g,E)}}class za extends Cs{static async from_pretrained(g,E={}){return E.model_file_name??(E.model_file_name="vision_model"),super.from_pretrained(g,E)}}class gi extends ee{}class ds extends gi{}class Da extends gi{static async from_pretrained(g,E={}){return E.model_file_name??(E.model_file_name="text_model"),super.from_pretrained(g,E)}}class Ba extends Cs{static async from_pretrained(g,E={}){return E.model_file_name??(E.model_file_name="vision_model"),super.from_pretrained(g,E)}}class La extends ee{}class Ra extends La{}class wi extends ee{}class Na extends wi{}class ja extends wi{}class yi extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Va extends yi{}class Ua extends yi{}class bn extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Wa extends bn{}class Ga extends bn{}class bi extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class qa extends bi{}class Ha extends bi{}class Mi extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Ka extends Mi{}class Xa extends Mi{}class vi extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Qa extends vi{}class zu extends vi{}class Mn extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Ya extends Mn{}class Gs extends Mn{}class qs extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Cn extends qs{}class Za extends qs{}class xi extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Ja extends xi{}class eo extends xi{}class Ti extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class to extends Ti{}class ro extends Ti{}class Ci extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class no extends Ci{}class so extends Ci{}class vn extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class io extends vn{}class ao extends vn{}class $i extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class oo extends $i{}class lo extends $i{}class Ei extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class uo extends Ei{}class co extends Ei{}class Si extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class po extends Si{}class ki extends Si{}class Hs extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class ho extends Hs{}class fo extends Hs{}class Ks extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Du extends Ks{}class mo extends Ks{}class Pi extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class _o extends Pi{}class go extends Pi{}class cs extends ee{}class wo extends cs{}class yo extends cs{async _call(g){return new or(await super._call(g))}}class Xs extends ee{}class bo extends Xs{}class Mo extends Xs{async _call(g){return new or(await super._call(g))}}class vo extends ee{}class xo extends vo{async _call(g){return new Eu(await super._call(g))}}class To extends ee{}class Co extends To{}class $o extends To{async _call(g){return new or(await super._call(g))}}class Ai extends ee{}class Bu extends Ai{}class Eo extends Ai{async _call(g){return new or(await super._call(g))}}class wr extends ee{}class So extends wr{}class ko extends wr{}class Ii extends ee{}class Po extends Ii{}class Ao extends Ii{}class Fi extends ee{}class Io extends Fi{}class Fo extends Fi{async _call(g){return new or(await super._call(g))}}class Qs extends ee{}class Oo extends Qs{}class zo extends Qs{async _call(g){return new Oi(await super._call(g))}}class Do extends Qs{async _call(g){return new Bo(await super._call(g))}}class Oi extends He{constructor({logits:g,pred_boxes:E}){super(),this.logits=g,this.pred_boxes=E}}class Bo extends He{constructor({logits:g,pred_boxes:E,pred_masks:K}){super(),this.logits=g,this.pred_boxes=E,this.pred_masks=K}}class ps extends ee{}class Lo extends ps{}class Ys extends ps{async _call(g){return new Ro(await super._call(g))}}class Ro extends He{constructor({logits:g,pred_boxes:E}){super(),this.logits=g,this.pred_boxes=E}}class zi extends ee{}class No extends zi{}class jo extends zi{async _call(g){return new Vo(await super._call(g))}}class Vo extends Oi{}class Di extends ee{}class Uo extends Di{}class Wo extends Di{async _call(g){return new or(await super._call(g))}}class Bi extends ee{}class Go extends Bi{}class qo extends Bi{async _call(g){return new or(await super._call(g))}}class Li extends ee{}class Ho extends Li{}class Ko extends Li{async _call(g){return new or(await super._call(g))}}class Ri extends ee{}class Xo extends Ri{}class Ni extends Ri{}class ji extends ee{}class Vi extends ji{}class Ui extends ji{}class Qo extends ee{}class Yo extends Qo{}class Wi extends ee{}class Zo extends Wi{}class Jo extends Wi{}class el extends ee{}class Gi extends el{}class qi extends ee{}class tl extends qi{}class rl extends qi{async _call(g){return new or(await super._call(g))}}class nl extends ee{}class sl extends nl{}class il extends nl{async _call(g){return new or(await super._call(g))}}class xn extends ee{}class al extends xn{}class ol extends xn{async _call(g){return new or(await super._call(g))}}class Hi extends ee{}class ll extends Hi{}class ul extends Hi{async _call(g){return new dl(await super._call(g))}}class dl extends He{constructor({logits:g,pred_boxes:E}){super(),this.logits=g,this.pred_boxes=E}}class cl extends ee{}class pl extends cl{async get_image_embeddings({pixel_values:g}){return await tt(this,{pixel_values:g})}async forward(g){if((!g.image_embeddings||!g.image_positional_embeddings)&&(g={...g,...await this.get_image_embeddings(g)}),!g.input_labels&&g.input_points){const K=g.input_points.dims.slice(0,-1),Pe=K.reduce((De,at)=>De*at,1);g.input_labels=new P.Tensor("int64",new BigInt64Array(Pe).fill(1n),K)}const E={image_embeddings:g.image_embeddings,image_positional_embeddings:g.image_positional_embeddings};return g.input_points&&(E.input_points=g.input_points),g.input_labels&&(E.input_labels=g.input_labels),g.input_boxes&&(E.input_boxes=g.input_boxes),await _e(this.sessions.prompt_encoder_mask_decoder,E)}async _call(g){return new hl(await super._call(g))}}class hl extends He{constructor({iou_scores:g,pred_masks:E}){super(),this.iou_scores=g,this.pred_masks=E}}class Ki extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Lu extends Ki{}class fl extends Ki{}class Xi extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class ml extends Xi{}class _l extends Xi{}class qn extends ee{}class gl extends qn{}class Ru extends qn{async _call(g){return new Zn(await super._call(g))}}class Hn extends qn{async _call(g){return new or(await super._call(g))}}class Kn extends qn{async _call(g){return new Qr(await super._call(g))}}class Bn extends ee{}class Qi extends Bn{}class Xn extends Bn{async _call(g){return new Qr(await super._call(g))}}class en extends ee{}class Yi extends en{}class hs extends ee{}class Zi extends hs{}class wl extends hs{async _call(g){return new Zn(await super._call(g))}}class yl extends hs{async _call(g){return new or(await super._call(g))}}class $s extends ee{}class Zs extends $s{}class Ji extends $s{async _call(g){return new Zn(await super._call(g))}}class bl extends $s{async _call(g){return new or(await super._call(g))}}class Js extends $s{async _call(g){return new Qr(await super._call(g))}}class ei extends ee{}class ea extends ei{}class ti extends ei{async _call(g){return new Zn(await super._call(g))}}class Ml extends ei{async _call(g){return new or(await super._call(g))}}class Nu extends ee{}class ju extends qn{}class vl extends qn{async _call(g){return new Zn(await super._call(g))}}class ta extends qn{async _call(g){return new or(await super._call(g))}}class Ln extends ee{}class xl extends Ln{}class ra extends Ln{async _call(g){return new Zn(await super._call(g))}}class Tl extends Ln{async _call(g){return new or(await super._call(g))}}class Cl extends Ln{async _call(g){return new $u(await super._call(g))}}class $l extends Ln{async _call(g){return new Qr(await super._call(g))}}class na extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class El extends na{}class Sl extends na{}class Vu extends na{async generate_speech(g,E,{threshold:K=.5,minlenratio:Pe=0,maxlenratio:De=20,vocoder:at=null}={}){const $t={input_ids:g},{encoder_outputs:Vt,encoder_attention_mask:lr}=await tt(this,$t),ar=Vt.dims[1]/this.config.reduction_factor,Er=Math.floor(ar*De),ur=Math.floor(ar*Pe),mr=this.config.num_mel_bins;let dr=[],pr=null,yr=null,Or=0;for(;;){++Or;const Rn=ke(!!yr);let nn;yr?nn=yr.output_sequence_out:nn=new P.Tensor("float32",new Float32Array(mr),[1,1,mr]);let Kr={use_cache_branch:Rn,output_sequence:nn,encoder_attention_mask:lr,speaker_embeddings:E,encoder_hidden_states:Vt};this.addPastKeyValues(Kr,pr),yr=await _e(this.sessions.decoder_model_merged,Kr),pr=this.getPastKeyValues(yr,pr);const{prob:sn,spectrum:oi}=yr;if(dr.push(oi),Or>=ur&&(Array.from(sn.data).filter(li=>li>=K).length>0||Or>=Er))break}const un=(0,P.cat)(dr),{waveform:pn}=await _e(at.sessions.model,{spectrogram:un});return{spectrogram:un,waveform:pn}}}class sa extends ee{constructor(){super(...arguments);Te(this,"main_input_name","spectrogram")}}class kl extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Pl extends kl{}class ia extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class ri extends ia{}class ni extends ia{}class aa extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class si extends aa{}class oa extends aa{}class la extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class Al extends la{}class Il extends la{}class Es extends ee{}class Fl extends Es{}class Ol extends Es{static async from_pretrained(g,E={}){return E.model_file_name??(E.model_file_name="text_model"),super.from_pretrained(g,E)}}class zl extends Es{static async from_pretrained(g,E={}){return E.model_file_name??(E.model_file_name="audio_model"),super.from_pretrained(g,E)}}class Uu extends ee{}class ua extends Uu{async _call(g){return new sd(await super._call(g))}}class Ss extends ee{}class kd extends Ss{}class Dl extends Ss{}class Bl extends Ss{}class da extends ee{constructor(g,E,K){super(g,E),this.generation_config=K}}class ca extends da{}class Ll extends da{}class pa extends ee{}class Rl extends pa{}class Nl extends pa{async _call(g){return new or(await super._call(g))}}class ha extends ee{}class Wu extends ha{}class Pd extends ha{}class fa extends ee{constructor(E,K,Pe){super(E,K);Te(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Pe}_apply_and_filter_by_delay_pattern_mask(E){const[K,Pe]=E.dims,De=this.config.decoder.num_codebooks,at=Pe-De;let $t=0;for(let ar=0;ar<E.size;++ar){if(E.data[ar]===this.config.decoder.pad_token_id)continue;const Er=ar%Pe,ur=Math.floor(ar/Pe)%De,mr=Er-ur;mr>0&&mr<=at&&(E.data[$t++]=E.data[ar])}const Vt=Math.floor(K/De),lr=$t/(Vt*De);return new P.Tensor(E.type,E.data.slice(0,$t),[Vt,De,lr])}prepare_inputs_for_generation(E,K,Pe){let De=structuredClone(E);for(let $t=0;$t<De.length;++$t)for(let Vt=0;Vt<De[$t].length;++Vt)$t%this.config.decoder.num_codebooks>=Vt&&(De[$t][Vt]=BigInt(this.config.decoder.pad_token_id));return Pe.guidance_scale!==null&&Pe.guidance_scale>1&&(De=De.concat(De)),super.prepare_inputs_for_generation(De,K,Pe)}async generate(E){const K=await super.generate(E),Pe=this._apply_and_filter_by_delay_pattern_mask(K).unsqueeze_(0),{audio_values:De}=await _e(this.sessions.encodec_decode,{audio_codes:Pe});return De}}class ma extends ee{}class jl extends ma{}class Gu extends ma{async _call(g){return new or(await super._call(g))}}class _a extends ee{}class Vl extends _a{}class Ul extends _a{async _call(g){return new or(await super._call(g))}}class ga extends ee{}class Wl extends ga{}class qu extends ga{async _call(g){return new or(await super._call(g))}}class ks extends ee{}class Ps extends ks{}class wa extends ks{async _call(g){return new or(await super._call(g))}}class Ir{static async from_pretrained(g,{progress_callback:E=null,config:K=null,cache_dir:Pe=null,local_files_only:De=!1,revision:at="main",model_file_name:$t=null,subfolder:Vt="onnx",device:lr=null,dtype:ar=null,use_external_data_format:Er=null,session_options:ur={}}={}){let mr={progress_callback:E,config:K,cache_dir:Pe,local_files_only:De,revision:at,model_file_name:$t,subfolder:Vt,device:lr,dtype:ar,use_external_data_format:Er,session_options:ur};if(mr.config=await x.AutoConfig.from_pretrained(g,mr),!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 pr=dr.get(mr.config.model_type);if(pr)return await pr[1].from_pretrained(g,mr)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${mr.config.model_type}", attempting to construct from base class.`),await ee.from_pretrained(g,mr);throw Error(`Unsupported model type: ${mr.config.model_type}`)}}Te(Ir,"MODEL_CLASS_MAPPINGS",null),Te(Ir,"BASE_IF_FAIL",!1);const Hu=new Map([["bert",["BertModel",lt]],["nomic_bert",["NomicBertModel",Ee]],["roformer",["RoFormerModel",Ge]],["electra",["ElectraModel",Ye]],["esm",["EsmModel",qr]],["convbert",["ConvBertModel",xt]],["camembert",["CamembertModel",q]],["deberta",["DebertaModel",nt]],["deberta-v2",["DebertaV2Model",jt]],["mpnet",["MPNetModel",Vs]],["albert",["AlbertModel",Qn]],["distilbert",["DistilBertModel",Tr]],["roberta",["RobertaModel",fr]],["xlm",["XLMModel",Hr]],["xlm-roberta",["XLMRobertaModel",xr]],["clap",["ClapModel",Fl]],["clip",["CLIPModel",Oa]],["clipseg",["CLIPSegModel",Na]],["chinese_clip",["ChineseCLIPModel",Ra]],["siglip",["SiglipModel",ds]],["mobilebert",["MobileBertModel",Nr]],["squeezebert",["SqueezeBertModel",Us]],["wav2vec2",["Wav2Vec2Model",gl]],["wav2vec2-bert",["Wav2Vec2BertModel",ea]],["unispeech",["UniSpeechModel",Zi]],["unispeech-sat",["UniSpeechSatModel",Zs]],["hubert",["HubertModel",ju]],["wavlm",["WavLMModel",xl]],["audio-spectrogram-transformer",["ASTModel",Ft]],["vits",["VitsModel",ua]],["pyannote",["PyAnnoteModel",Qi]],["wespeaker-resnet",["WeSpeakerResNetModel",Yi]],["detr",["DetrModel",Oo]],["rt_detr",["RTDetrModel",Lo]],["table-transformer",["TableTransformerModel",No]],["vit",["ViTModel",wo]],["fastvit",["FastViTModel",bo]],["mobilevit",["MobileViTModel",Co]],["mobilevitv2",["MobileViTV2Model",Bu]],["owlvit",["OwlViTModel",So]],["owlv2",["Owlv2Model",Po]],["beit",["BeitModel",Io]],["deit",["DeiTModel",Uo]],["convnext",["ConvNextModel",tl]],["convnextv2",["ConvNextV2Model",sl]],["dinov2",["Dinov2Model",al]],["resnet",["ResNetModel",Go]],["swin",["SwinModel",Ho]],["swin2sr",["Swin2SRModel",Xo]],["donut-swin",["DonutSwinModel",Gi]],["yolos",["YolosModel",ll]],["dpt",["DPTModel",Vi]],["glpn",["GLPNModel",Zo]],["hifigan",["SpeechT5HifiGan",sa]],["efficientnet",["EfficientNetModel",Rl]],["mobilenet_v1",["MobileNetV1Model",jl]],["mobilenet_v2",["MobileNetV2Model",Vl]],["mobilenet_v3",["MobileNetV3Model",Wl]],["mobilenet_v4",["MobileNetV4Model",Ps]]]),Ku=new 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Map([["bert",["BertForSequenceClassification",st]],["roformer",["RoFormerForSequenceClassification",Ke]],["electra",["ElectraForSequenceClassification",At]],["esm",["EsmForSequenceClassification",Fn]],["convbert",["ConvBertForSequenceClassification",W]],["camembert",["CamembertForSequenceClassification",Be]],["deberta",["DebertaForSequenceClassification",ft]],["deberta-v2",["DebertaV2ForSequenceClassification",Ht]],["mpnet",["MPNetForSequenceClassification",gs]],["albert",["AlbertForSequenceClassification",is]],["distilbert",["DistilBertForSequenceClassification",Ur]],["roberta",["RobertaForSequenceClassification",Yr]],["xlm",["XLMForSequenceClassification",Yt]],["xlm-roberta",["XLMRobertaForSequenceClassification",gr]],["bart",["BartForSequenceClassification",O]],["mbart",["MBartForSequenceClassification",Fe]],["mobilebert",["MobileBertForSequenceClassification",Pr]],["squeezebert",["SqueezeBertForSequenceClassification",kn]]]),Qu=new Map([["bert",["BertForTokenClassification",Tt]],["roformer",["RoFormerForTokenClassification",ut]],["electra",["ElectraForTokenClassification",_t]],["esm",["EsmForTokenClassification",Lr]],["convbert",["ConvBertForTokenClassification",S]],["camembert",["CamembertForTokenClassification",Ae]],["deberta",["DebertaForTokenClassification",Ct]],["deberta-v2",["DebertaV2ForTokenClassification",Xt]],["mpnet",["MPNetForTokenClassification",ws]],["distilbert",["DistilBertForTokenClassification",Cr]],["roberta",["RobertaForTokenClassification",qe]],["xlm",["XLMForTokenClassification",_n]],["xlm-roberta",["XLMRobertaForTokenClassification",Ar]]]),ya=new Map([["t5",["T5ForConditionalGeneration",Ms]],["longt5",["LongT5ForConditionalGeneration",xs]],["mt5",["MT5ForConditionalGeneration",Dr]],["bart",["BartForConditionalGeneration",_]],["mbart",["MBartForConditionalGeneration",de]],["marian",["MarianMTModel",fl]],["m2m_100",["M2M100ForConditionalGeneration",_l]],["blenderbot",["BlenderbotForConditionalGeneration",Pt]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",nr]]]),ai=new Map([["bloom",["BloomForCausalLM",fo]],["gpt2",["GPT2LMHeadModel",Ua]],["gptj",["GPTJForCausalLM",Xa]],["gpt_bigcode",["GPTBigCodeForCausalLM",zu]],["gpt_neo",["GPTNeoForCausalLM",Ga]],["gpt_neox",["GPTNeoXForCausalLM",Ha]],["codegen",["CodeGenForCausalLM",Gs]],["llama",["LlamaForCausalLM",Za]],["cohere",["CohereForCausalLM",eo]],["gemma",["GemmaForCausalLM",ro]],["gemma2",["Gemma2ForCausalLM",so]],["openelm",["OpenELMForCausalLM",ao]],["qwen2",["Qwen2ForCausalLM",lo]],["phi",["PhiForCausalLM",co]],["phi3",["Phi3ForCausalLM",ki]],["mpt",["MptForCausalLM",mo]],["opt",["OPTForCausalLM",go]],["mbart",["MBartForCausalLM",gt]],["mistral",["MistralForCausalLM",ni]],["starcoder2",["Starcoder2ForCausalLM",oa]],["falcon",["FalconForCausalLM",Il]],["trocr",["TrOCRForCausalLM",Pl]],["stablelm",["StableLmForCausalLM",Ll]]]),Kl=new Map([["bert",["BertForMaskedLM",Re]],["roformer",["RoFormerForMaskedLM",Ve]],["electra",["ElectraForMaskedLM",Je]],["esm",["EsmForMaskedLM",Un]],["convbert",["ConvBertForMaskedLM",M]],["camembert",["CamembertForMaskedLM",be]],["deberta",["DebertaForMaskedLM",vt]],["deberta-v2",["DebertaV2ForMaskedLM",Rt]],["mpnet",["MPNetForMaskedLM",_s]],["albert",["AlbertForMaskedLM",Qt]],["distilbert",["DistilBertForMaskedLM",St]],["roberta",["RobertaForMaskedLM",on]],["xlm",["XLMWithLMHeadModel",cn]],["xlm-roberta",["XLMRobertaForMaskedLM",kt]],["mobilebert",["MobileBertForMaskedLM",Sn]],["squeezebert",["SqueezeBertForMaskedLM",ss]]]),Xl=new Map([["bert",["BertForQuestionAnswering",ze]],["roformer",["RoFormerForQuestionAnswering",mt]],["electra",["ElectraForQuestionAnswering",Se]],["convbert",["ConvBertForQuestionAnswering",X]],["camembert",["CamembertForQuestionAnswering",Ne]],["deberta",["DebertaForQuestionAnswering",Lt]],["deberta-v2",["DebertaV2ForQuestionAnswering",er]],["mpnet",["MPNetForQuestionAnswering",ys]],["albert",["AlbertForQuestionAnswering",as]],["distilbert",["DistilBertForQuestionAnswering",Ze]],["roberta",["RobertaForQuestionAnswering",yn]],["xlm",["XLMForQuestionAnswering",Jr]],["xlm-roberta",["XLMRobertaForQuestionAnswering",jr]],["mobilebert",["MobileBertForQuestionAnswering",Wn]],["squeezebert",["SqueezeBertForQuestionAnswering",zn]]]),ba=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",mi]]]),Ad=new Map([["llava",["LlavaForConditionalGeneration",us]],["moondream1",["Moondream1ForConditionalGeneration",ir]],["florence2",["Florence2ForConditionalGeneration",_i]]]),Yu=new 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Map([["detr",["DetrForObjectDetection",zo]],["rt_detr",["RTDetrForObjectDetection",Ys]],["table-transformer",["TableTransformerForObjectDetection",jo]],["yolos",["YolosForObjectDetection",ul]]]),Yl=new Map([["owlvit",["OwlViTForObjectDetection",ko]],["owlv2",["Owlv2ForObjectDetection",Ao]]]),Zl=new Map([["detr",["DetrForSegmentation",Do]],["clipseg",["CLIPSegForImageSegmentation",ja]]]),Jl=new Map([["segformer",["SegformerForSemanticSegmentation",Bl]]]),eu=new Map([["sam",["SamModel",pl]]]),Ju=new Map([["wav2vec2",["Wav2Vec2ForCTC",Ru]],["wav2vec2-bert",["Wav2Vec2BertForCTC",ti]],["unispeech",["UniSpeechForCTC",wl]],["unispeech-sat",["UniSpeechSatForCTC",Ji]],["wavlm",["WavLMForCTC",ra]],["hubert",["HubertForCTC",vl]]]),tu=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Hn]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Ml]],["unispeech",["UniSpeechForSequenceClassification",yl]],["unispeech-sat",["UniSpeechSatForSequenceClassification",bl]],["wavlm",["WavLMForSequenceClassification",Tl]],["hubert",["HubertForSequenceClassification",ta]],["audio-spectrogram-transformer",["ASTForAudioClassification",Ws]]]),ru=new Map([["wavlm",["WavLMForXVector",Cl]]]),nu=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Js]],["wavlm",["WavLMForAudioFrameClassification",$l]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Kn]],["pyannote",["PyAnnoteForAudioFrameClassification",Xn]]]),su=new Map([["vitmatte",["VitMatteForImageMatting",xo]]]),ed=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Ni]]]),iu=new Map([["dpt",["DPTForDepthEstimation",Ui]],["depth_anything",["DepthAnythingForDepthEstimation",Yo]],["glpn",["GLPNForDepthEstimation",Jo]]]),au=new 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td=[["MusicgenForConditionalGeneration",fa,se.Musicgen],["CLIPTextModelWithProjection",Pn,se.EncoderOnly],["SiglipTextModel",Da,se.EncoderOnly],["ClapTextModelWithProjection",Ol,se.EncoderOnly],["ClapAudioModelWithProjection",zl,se.EncoderOnly]];for(const[m,g,E]of td)le.set(m,E),N.set(g,m),ae.set(m,g);class lu extends Ir{}Te(lu,"MODEL_CLASS_MAPPINGS",ou.map(g=>g[0])),Te(lu,"BASE_IF_FAIL",!0);class ln extends Ir{}Te(ln,"MODEL_CLASS_MAPPINGS",[Hl]);class uu extends Ir{}Te(uu,"MODEL_CLASS_MAPPINGS",[Qu]);class du extends Ir{}Te(du,"MODEL_CLASS_MAPPINGS",[ya]);class Ma extends Ir{}Te(Ma,"MODEL_CLASS_MAPPINGS",[ii]);class cu extends Ir{}Te(cu,"MODEL_CLASS_MAPPINGS",[Gl]);class As extends Ir{}Te(As,"MODEL_CLASS_MAPPINGS",[ql]);class pu extends Ir{}Te(pu,"MODEL_CLASS_MAPPINGS",[ai]);class hu extends Ir{}Te(hu,"MODEL_CLASS_MAPPINGS",[Kl]);class va extends Ir{}Te(va,"MODEL_CLASS_MAPPINGS",[Xl]);class fu extends Ir{}Te(fu,"MODEL_CLASS_MAPPINGS",[ba]);class mu extends Ir{}Te(mu,"MODEL_CLASS_MAPPINGS",[Ql]);class xa extends Ir{}Te(xa,"MODEL_CLASS_MAPPINGS",[Zl]);class _u extends Ir{}Te(_u,"MODEL_CLASS_MAPPINGS",[Jl]);class gu extends Ir{}Te(gu,"MODEL_CLASS_MAPPINGS",[Zu]);class wu extends Ir{}Te(wu,"MODEL_CLASS_MAPPINGS",[Yl]);class Ta extends Ir{}Te(Ta,"MODEL_CLASS_MAPPINGS",[eu]);class yu extends Ir{}Te(yu,"MODEL_CLASS_MAPPINGS",[Ju]);class bu extends Ir{}Te(bu,"MODEL_CLASS_MAPPINGS",[tu]);class Ca extends Ir{}Te(Ca,"MODEL_CLASS_MAPPINGS",[ru]);class Mu extends Ir{}Te(Mu,"MODEL_CLASS_MAPPINGS",[nu]);class rd extends Ir{}Te(rd,"MODEL_CLASS_MAPPINGS",[Yu]);class vu extends Ir{}Te(vu,"MODEL_CLASS_MAPPINGS",[su]);class xu extends Ir{}Te(xu,"MODEL_CLASS_MAPPINGS",[ed]);class Tu extends Ir{}Te(Tu,"MODEL_CLASS_MAPPINGS",[iu]);class Cu extends Ir{}Te(Cu,"MODEL_CLASS_MAPPINGS",[au]);class Id extends He{constructor({logits:g,past_key_values:E,encoder_outputs:K,decoder_attentions:Pe=null,cross_attentions:De=null}){super(),this.logits=g,this.past_key_values=E,this.encoder_outputs=K,this.decoder_attentions=Pe,this.cross_attentions=De}}class or extends He{constructor({logits:g}){super(),this.logits=g}}class $u extends He{constructor({logits:g,embeddings:E}){super(),this.logits=g,this.embeddings=E}}class Qr extends He{constructor({logits:g}){super(),this.logits=g}}class tn extends He{constructor({logits:g}){super(),this.logits=g}}class rn extends He{constructor({start_logits:g,end_logits:E}){super(),this.start_logits=g,this.end_logits=E}}class Zn extends He{constructor({logits:g}){super(),this.logits=g}}class nd extends He{constructor({logits:g,past_key_values:E}){super(),this.logits=g,this.past_key_values=E}}class Eu extends He{constructor({alphas:g}){super(),this.alphas=g}}class sd extends 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x=[["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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j.run(J);return Array.isArray(T)?T.map(ne=>new H.Tensor(te[ne])):new H.Tensor(te[T])}};class ve{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=ge([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=ge([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=ge([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=ge([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=ge([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=ge([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}}Te(ve,"session_options",{})},"./src/pipelines.js":(Mt,me,l)=>{l.r(me),l.d(me,{AudioClassificationPipeline:()=>ke,AutomaticSpeechRecognitionPipeline:()=>tt,DepthEstimationPipeline:()=>rt,DocumentQuestionAnsweringPipeline:()=>ee,FeatureExtractionPipeline:()=>ye,FillMaskPipeline:()=>le,ImageClassificationPipeline:()=>ht,ImageFeatureExtractionPipeline:()=>Ce,ImageSegmentationPipeline:()=>we,ImageToImagePipeline:()=>pt,ImageToTextPipeline:()=>Qe,ObjectDetectionPipeline:()=>he,Pipeline:()=>ie,QuestionAnsweringPipeline:()=>se,SummarizationPipeline:()=>N,Text2TextGenerationPipeline:()=>ae,TextClassificationPipeline:()=>R,TextGenerationPipeline:()=>A,TextToAudioPipeline:()=>He,TokenClassificationPipeline:()=>Y,TranslationPipeline:()=>I,ZeroShotAudioClassificationPipeline:()=>Ie,ZeroShotClassificationPipeline:()=>_e,ZeroShotImageClassificationPipeline:()=>V,ZeroShotObjectDetectionPipeline:()=>$e,pipeline:()=>st});var x=l("./src/tokenizers.js"),H=l("./src/models.js"),ge=l("./src/processors.js"),ve=l("./src/utils/generic.js"),xe=l("./src/utils/core.js"),D=l("./src/utils/maths.js"),T=l("./src/utils/audio.js"),j=l("./src/utils/tensor.js"),P=l("./src/utils/image.js");async function J(ze){return Array.isArray(ze)||(ze=[ze]),await Promise.all(ze.map(re=>P.RawImage.read(re)))}async function te(ze,re){return Array.isArray(ze)||(ze=[ze]),await Promise.all(ze.map(Ee=>typeof Ee=="string"||Ee instanceof URL?(0,T.read_audio)(Ee,re):Ee instanceof Float64Array?new Float32Array(Ee):Ee))}function ne(ze,re){re&&(ze=ze.map(Ke=>Ke|0));const[Ee,je,Ge,Ve]=ze;return{xmin:Ee,ymin:je,xmax:Ge,ymax:Ve}}class ie extends ve.Callable{constructor({task:re,model:Ee,tokenizer:je=null,processor:Ge=null}){super(),this.task=re,this.model=Ee,this.tokenizer=je,this.processor=Ge}async dispose(){await this.model.dispose()}}class R extends ie{constructor(re){super(re)}async _call(re,{top_k:Ee=1}={}){const je=this.tokenizer(re,{padding:!0,truncation:!0}),Ge=await this.model(je),Ve=this.model.config.problem_type==="multi_label_classification"?mt=>mt.sigmoid():mt=>new j.Tensor("float32",(0,D.softmax)(mt.data),mt.dims),Ke=this.model.config.id2label,ut=[];for(const mt of Ge.logits){const wt=Ve(mt),xt=await(0,j.topk)(wt,Ee),M=xt[0].tolist(),S=xt[1].tolist().map((X,fe)=>({label:Ke?Ke[X]:`LABEL_${X}`,score:M[fe]}));Ee===1?ut.push(...S):ut.push(S)}return Array.isArray(re)||Ee===1?ut:ut[0]}}class Y extends ie{constructor(re){super(re)}async _call(re,{ignore_labels:Ee=["O"]}={}){const je=Array.isArray(re),Ge=this.tokenizer(je?re:[re],{padding:!0,truncation:!0}),Ke=(await this.model(Ge)).logits,ut=this.model.config.id2label,mt=[];for(let wt=0;wt<Ke.dims[0];++wt){const xt=Ge.input_ids[wt],M=Ke[wt],W=[];for(let S=0;S<M.dims[0];++S){const X=M[S],fe=(0,D.max)(X.data)[1],Ye=ut?ut[fe]:`LABEL_${fe}`;if(Ee.includes(Ye))continue;const Je=this.tokenizer.decode([xt[S].item()],{skip_special_tokens:!0});if(Je==="")continue;const At=(0,D.softmax)(X.data);W.push({entity:Ye,score:At[fe],index:S,word:Je})}mt.push(W)}return je?mt:mt[0]}}class se extends ie{constructor(re){super(re)}async _call(re,Ee,{top_k:je=1}={}){const Ge=this.tokenizer(re,{text_pair:Ee,padding:!0,truncation:!0}),{start_logits:Ve,end_logits:Ke}=await this.model(Ge),ut=Ge.input_ids.tolist(),mt=Ge.attention_mask.tolist(),wt=this.tokenizer.all_special_ids,xt=[];for(let M=0;M<Ve.dims[0];++M){const W=ut[M],S=W.findIndex(_t=>_t==this.tokenizer.sep_token_id);mt[M].map((_t,Se)=>_t==1&&(Se===0||Se>S&&wt.findIndex($=>$==W[Se])===-1));const X=Ve[M].tolist(),fe=Ke[M].tolist();for(let _t=1;_t<X.length;++_t)(mt[M]==0||_t<=S||wt.findIndex(Se=>Se==W[_t])!==-1)&&(X[_t]=-1/0,fe[_t]=-1/0);const Ye=(0,D.softmax)(X).map((_t,Se)=>[_t,Se]),Je=(0,D.softmax)(fe).map((_t,Se)=>[_t,Se]);Ye[0][0]=0,Je[0][0]=0;const At=(0,xe.product)(Ye,Je).filter(_t=>_t[0][1]<=_t[1][1]).map(_t=>[_t[0][1],_t[1][1],_t[0][0]*_t[1][0]]).sort((_t,Se)=>Se[2]-_t[2]);for(let _t=0;_t<Math.min(At.length,je);++_t){const[Se,$,q]=At[_t],be=W.slice(Se,$+1),Be=this.tokenizer.decode(be,{skip_special_tokens:!0});xt.push({answer:Be,score:q})}}return je===1?xt[0]:xt}}class le extends ie{constructor(re){super(re)}async _call(re,{top_k:Ee=5}={}){const je=this.tokenizer(re,{padding:!0,truncation:!0}),{logits:Ge}=await this.model(je),Ve=[],Ke=je.input_ids.tolist();for(let ut=0;ut<Ke.length;++ut){const mt=Ke[ut],wt=mt.findIndex(X=>X==this.tokenizer.mask_token_id);if(wt===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const xt=Ge[ut][wt],M=await(0,j.topk)(new j.Tensor("float32",(0,D.softmax)(xt.data),xt.dims),Ee),W=M[0].tolist(),S=M[1].tolist();Ve.push(S.map((X,fe)=>{const Ye=mt.slice();return Ye[wt]=X,{score:W[fe],token:Number(X),token_str:this.tokenizer.model.vocab[X],sequence:this.tokenizer.decode(Ye,{skip_special_tokens:!0})}}))}return Array.isArray(re)?Ve:Ve[0]}}class ae extends ie{constructor(Ee){super(Ee);Te(this,"_key","generated_text")}async _call(Ee,je={}){Array.isArray(Ee)||(Ee=[Ee]),this.model.config.prefix&&(Ee=Ee.map(wt=>this.model.config.prefix+wt));const Ge=this.model.config.task_specific_params;Ge&&Ge[this.task]&&Ge[this.task].prefix&&(Ee=Ee.map(wt=>Ge[this.task].prefix+wt));const Ve=this.tokenizer,Ke={padding:!0,truncation:!0};let ut;this instanceof I&&"_build_translation_inputs"in Ve?ut=Ve._build_translation_inputs(Ee,Ke,je):ut=Ve(Ee,Ke);const mt=await this.model.generate({...ut,...je});return Ve.batch_decode(mt,{skip_special_tokens:!0}).map(wt=>({[this._key]:wt}))}}class N extends ae{constructor(Ee){super(Ee);Te(this,"_key","summary_text")}}class I extends ae{constructor(Ee){super(Ee);Te(this,"_key","translation_text")}}function B(ze){return Array.isArray(ze)&&ze.every(re=>"role"in re&&"content"in re)}class A extends ie{constructor(re){super(re)}async _call(re,Ee={}){let je=!1,Ge=!1,Ve;if(typeof re=="string")Ve=re=[re];else if(Array.isArray(re)&&re.every(S=>typeof S=="string"))je=!0,Ve=re;else{if(B(re))re=[re];else if(Array.isArray(re)&&re.every(B))je=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");Ge=!0,Ve=re.map(S=>this.tokenizer.apply_chat_template(S,{tokenize:!1,add_generation_prompt:!0}))}const Ke=Ee.add_special_tokens??!1,ut=Ge?!1:Ee.return_full_text??!0;this.tokenizer.padding_side="left";const mt=this.tokenizer(Ve,{add_special_tokens:Ke,padding:!0,truncation:!0}),wt=await this.model.generate({...mt,...Ee}),xt=this.tokenizer.batch_decode(wt,{skip_special_tokens:!0});let M;!ut&&mt.input_ids.dims.at(-1)>0&&(M=this.tokenizer.batch_decode(mt.input_ids,{skip_special_tokens:!0}).map(S=>S.length));const W=Array.from({length:re.length},S=>[]);for(let S=0;S<xt.length;++S){const X=Math.floor(S/wt.dims[0]*re.length);M&&(xt[S]=xt[S].slice(M[X])),W[X].push({generated_text:Ge?[...re[X],{role:"assistant",content:xt[S]}]:xt[S]})}return!je&&W.length===1?W[0]:W}}class _e extends ie{constructor(re){super(re),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([Ee,je])=>[Ee.toLowerCase(),je])),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(re,Ee,{hypothesis_template:je="This example is {}.",multi_label:Ge=!1}={}){const Ve=Array.isArray(re);Ve||(re=[re]),Array.isArray(Ee)||(Ee=[Ee]);const Ke=Ee.map(wt=>je.replace("{}",wt)),ut=Ge||Ee.length===1,mt=[];for(const wt of re){const xt=[];for(const S of Ke){const X=this.tokenizer(wt,{text_pair:S,padding:!0,truncation:!0}),fe=await this.model(X);ut?xt.push([fe.logits.data[this.contradiction_id],fe.logits.data[this.entailment_id]]):xt.push(fe.logits.data[this.entailment_id])}const W=(ut?xt.map(S=>(0,D.softmax)(S)[1]):(0,D.softmax)(xt)).map((S,X)=>[S,X]).sort((S,X)=>X[0]-S[0]);mt.push({sequence:wt,labels:W.map(S=>Ee[S[1]]),scores:W.map(S=>S[0])})}return Ve?mt:mt[0]}}class ye extends ie{constructor(re){super(re)}async _call(re,{pooling:Ee="none",normalize:je=!1,quantize:Ge=!1,precision:Ve="binary"}={}){const Ke=this.tokenizer(re,{padding:!0,truncation:!0}),ut=await this.model(Ke);let mt=ut.last_hidden_state??ut.logits??ut.token_embeddings;if(Ee!=="none")if(Ee==="mean")mt=(0,j.mean_pooling)(mt,Ke.attention_mask);else if(Ee==="cls")mt=mt.slice(null,0);else throw Error(`Pooling method '${Ee}' not supported.`);return je&&(mt=mt.normalize(2,-1)),Ge&&(mt=(0,j.quantize_embeddings)(mt,Ve)),mt}}class Ce extends ie{constructor(re){super(re)}async _call(re,{pool:Ee=null}={}){const je=await J(re),{pixel_values:Ge}=await this.processor(je),Ve=await this.model({pixel_values:Ge});let Ke;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.");Ke=Ve.pooler_output}else Ke=Ve.last_hidden_state??Ve.logits??Ve.image_embeds;return Ke}}class ke extends ie{constructor(re){super(re)}async _call(re,{top_k:Ee=5}={}){const je=this.processor.feature_extractor.config.sampling_rate,Ge=await te(re,je),Ve=this.model.config.id2label,Ke=[];for(const ut of Ge){const mt=await this.processor(ut),xt=(await this.model(mt)).logits[0],M=await(0,j.topk)(new j.Tensor("float32",(0,D.softmax)(xt.data),xt.dims),Ee),W=M[0].tolist(),X=M[1].tolist().map((fe,Ye)=>({label:Ve?Ve[fe]:`LABEL_${fe}`,score:W[Ye]}));Ke.push(X)}return Array.isArray(re)?Ke:Ke[0]}}class Ie extends ie{constructor(re){super(re)}async _call(re,Ee,{hypothesis_template:je="This is a sound of {}."}={}){const Ge=!Array.isArray(re);Ge&&(re=[re]);const Ve=Ee.map(xt=>je.replace("{}",xt)),Ke=this.tokenizer(Ve,{padding:!0,truncation:!0}),ut=this.processor.feature_extractor.config.sampling_rate,mt=await te(re,ut),wt=[];for(const xt of mt){const M=await this.processor(xt),W=await this.model({...Ke,...M}),S=(0,D.softmax)(W.logits_per_audio.data);wt.push([...S].map((X,fe)=>({score:X,label:Ee[fe]})))}return Ge?wt[0]:wt}}class tt extends ie{constructor(re){super(re)}async _call(re,Ee={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(re,Ee);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(re,Ee);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(re,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 je=!Array.isArray(re);je&&(re=[re]);const Ge=this.processor.feature_extractor.config.sampling_rate,Ve=await te(re,Ge),Ke=[];for(const ut of Ve){const mt=await this.processor(ut),xt=(await this.model(mt)).logits[0],M=[];for(const S of xt)M.push((0,D.max)(S.data)[1]);const W=this.tokenizer.decode(M);Ke.push({text:W})}return je?Ke[0]:Ke}async _call_whisper(re,Ee){const je=Ee.return_timestamps??!1,Ge=Ee.chunk_length_s??0,Ve=Ee.force_full_sequences??!1;let Ke=Ee.stride_length_s??null;const ut={...Ee};je==="word"&&(ut.return_token_timestamps=!0,ut.return_timestamps=!1);const mt=!Array.isArray(re);mt&&(re=[re]);const wt=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,xt=this.processor.feature_extractor.config.hop_length,M=this.processor.feature_extractor.config.sampling_rate,W=await te(re,M),S=[];for(const X of W){let fe=[];if(Ge>0){if(Ke===null)Ke=Ge/6;else if(Ge<=Ke)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const At=M*Ge,_t=M*Ke,Se=At-2*_t;let $=0;for(;;){const q=$+At,be=X.subarray($,q),Be=await this.processor(be),Ae=$===0,Ne=q>=X.length;if(fe.push({stride:[be.length,Ae?0:_t,Ne?0:_t],input_features:Be.input_features,is_last:Ne}),Ne)break;$+=Se}}else fe=[{stride:[X.length,0,0],input_features:(await this.processor(X)).input_features,is_last:!0}];for(const At of fe){ut.num_frames=Math.floor(At.stride[0]/xt);const _t=await this.model.generate({inputs:At.input_features,...ut});je==="word"?(At.tokens=_t.sequences.tolist()[0],At.token_timestamps=_t.token_timestamps.tolist()[0].map(Se=>(0,D.round)(Se,2))):At.tokens=_t[0].tolist(),At.stride=At.stride.map(Se=>Se/M)}const[Ye,Je]=this.tokenizer._decode_asr(fe,{time_precision:wt,return_timestamps:je,force_full_sequences:Ve});S.push({text:Ye,...Je})}return mt?S[0]:S}}class Qe extends ie{constructor(re){super(re)}async _call(re,Ee={}){const je=Array.isArray(re),Ge=await J(re),{pixel_values:Ve}=await this.processor(Ge),Ke=[];for(const ut of Ve){ut.dims=[1,...ut.dims];const mt=await this.model.generate({inputs:ut,...Ee}),wt=this.tokenizer.batch_decode(mt,{skip_special_tokens:!0}).map(xt=>({generated_text:xt.trim()}));Ke.push(wt)}return je?Ke:Ke[0]}}class ht extends ie{constructor(re){super(re)}async _call(re,{top_k:Ee=5}={}){const je=await J(re),{pixel_values:Ge}=await this.processor(je),Ve=await this.model({pixel_values:Ge}),Ke=this.model.config.id2label,ut=[];for(const mt of Ve.logits){const wt=await(0,j.topk)(new j.Tensor("float32",(0,D.softmax)(mt.data),mt.dims),Ee),xt=wt[0].tolist(),W=wt[1].tolist().map((S,X)=>({label:Ke?Ke[S]:`LABEL_${S}`,score:xt[X]}));ut.push(W)}return Array.isArray(re)?ut:ut[0]}}class we extends ie{constructor(re){super(re),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(re,{threshold:Ee=.5,mask_threshold:je=.5,overlap_mask_area_threshold:Ge=.8,label_ids_to_fuse:Ve=null,target_sizes:Ke=null,subtask:ut=null}={}){if(Array.isArray(re)&&re.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const wt=await J(re),xt=wt.map(Je=>[Je.height,Je.width]),{pixel_values:M,pixel_mask:W}=await this.processor(wt),S=await this.model({pixel_values:M,pixel_mask:W});let X=null;if(ut!==null)X=this.subtasks_mapping[ut];else for(let[Je,At]of Object.entries(this.subtasks_mapping))if(At in this.processor.feature_extractor){X=this.processor.feature_extractor[At].bind(this.processor.feature_extractor),ut=Je;break}const fe=this.model.config.id2label,Ye=[];if(ut==="panoptic"||ut==="instance"){const Je=X(S,Ee,je,Ge,Ve,Ke??xt)[0],At=Je.segmentation;for(const _t of Je.segments_info){const Se=new Uint8ClampedArray(At.data.length);for(let q=0;q<At.data.length;++q)At.data[q]===_t.id&&(Se[q]=255);const $=new P.RawImage(Se,At.dims[1],At.dims[0],1);Ye.push({score:_t.score,label:fe[_t.label_id],mask:$})}}else if(ut==="semantic"){const{segmentation:Je,labels:At}=X(S,Ke??xt)[0];for(const _t of At){const Se=new Uint8ClampedArray(Je.data.length);for(let q=0;q<Je.data.length;++q)Je.data[q]===_t&&(Se[q]=255);const $=new P.RawImage(Se,Je.dims[1],Je.dims[0],1);Ye.push({score:null,label:fe[_t],mask:$})}}else throw Error(`Subtask ${ut} not supported.`);return Ye}}class V extends ie{constructor(re){super(re)}async _call(re,Ee,{hypothesis_template:je="This is a photo of {}"}={}){const Ge=Array.isArray(re),Ve=await J(re),Ke=Ee.map(W=>je.replace("{}",W)),ut=this.tokenizer(Ke,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:mt}=await this.processor(Ve),wt=await this.model({...ut,pixel_values:mt}),xt=this.model.config.model_type==="siglip"?W=>W.sigmoid().data:W=>(0,D.softmax)(W.data),M=[];for(const W of wt.logits_per_image){const X=[...xt(W)].map((fe,Ye)=>({score:fe,label:Ee[Ye]}));X.sort((fe,Ye)=>Ye.score-fe.score),M.push(X)}return Ge?M:M[0]}}class he extends ie{constructor(re){super(re)}async _call(re,{threshold:Ee=.9,percentage:je=!1}={}){const Ge=Array.isArray(re);if(Ge&&re.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ve=await J(re),Ke=je?null:Ve.map(S=>[S.height,S.width]),{pixel_values:ut,pixel_mask:mt}=await this.processor(Ve),wt=await this.model({pixel_values:ut,pixel_mask:mt}),xt=this.processor.feature_extractor.post_process_object_detection(wt,Ee,Ke),M=this.model.config.id2label,W=xt.map(S=>S.boxes.map((X,fe)=>({score:S.scores[fe],label:M[S.classes[fe]],box:ne(X,!je)})));return Ge?W:W[0]}}class $e extends ie{constructor(re){super(re)}async _call(re,Ee,{threshold:je=.1,top_k:Ge=null,percentage:Ve=!1}={}){const Ke=Array.isArray(re),ut=await J(re),mt=this.tokenizer(Ee,{padding:!0,truncation:!0}),wt=await this.processor(ut),xt=[];for(let M=0;M<ut.length;++M){const W=ut[M],S=Ve?null:[[W.height,W.width]],X=wt.pixel_values[M].unsqueeze_(0),fe=await this.model({...mt,pixel_values:X}),Ye=this.processor.feature_extractor.post_process_object_detection(fe,je,S,!0)[0];let Je=Ye.boxes.map((At,_t)=>({score:Ye.scores[_t],label:Ee[Ye.classes[_t]],box:ne(At,!Ve)})).sort((At,_t)=>_t.score-At.score);Ge!==null&&(Je=Je.slice(0,Ge)),xt.push(Je)}return Ke?xt:xt[0]}}class ee extends ie{constructor(re){super(re)}async _call(re,Ee,je={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class He extends ie{constructor(Ee){super(Ee);Te(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=Ee.vocoder??null}async _call(Ee,{speaker_embeddings:je=null}={}){return this.processor?this._call_text_to_spectrogram(Ee,{speaker_embeddings:je}):this._call_text_to_waveform(Ee)}async _call_text_to_waveform(Ee){const je=this.tokenizer(Ee,{padding:!0,truncation:!0}),{waveform:Ge}=await this.model(je),Ve=this.model.config.sampling_rate;return{audio:Ge.data,sampling_rate:Ve}}async _call_text_to_spectrogram(Ee,{speaker_embeddings:je}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await H.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof je=="string"||je instanceof URL)&&(je=new Float32Array(await(await fetch(je)).arrayBuffer())),je instanceof Float32Array)je=new j.Tensor("float32",je,[1,je.length]);else if(!(je instanceof j.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:Ge}=this.tokenizer(Ee,{padding:!0,truncation:!0}),{waveform:Ve}=await this.model.generate_speech(Ge,je,{vocoder:this.vocoder}),Ke=this.processor.feature_extractor.config.sampling_rate;return{audio:Ve.data,sampling_rate:Ke}}}class pt extends ie{constructor(re){super(re)}async _call(re){const Ee=await J(re),je=await this.processor(Ee),Ge=await this.model(je),Ve=[];for(const Ke of Ge.reconstruction){const ut=Ke.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ve.push(P.RawImage.fromTensor(ut))}return Ve.length>1?Ve:Ve[0]}}class rt extends ie{constructor(re){super(re)}async _call(re){const Ee=await J(re),je=await this.processor(Ee),{predicted_depth:Ge}=await this.model(je),Ve=[];for(let Ke=0;Ke<Ee.length;++Ke){const ut=(0,j.interpolate)(Ge[Ke],Ee[Ke].size.reverse(),"bilinear",!1),mt=ut.mul_(255/(0,D.max)(ut.data)[0]).to("uint8");Ve.push({predicted_depth:Ge[Ke],depth:P.RawImage.fromTensor(mt)})}return Ve.length>1?Ve:Ve[0]}}const lt=Object.freeze({"text-classification":{tokenizer:x.AutoTokenizer,pipeline:R,model:H.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:x.AutoTokenizer,pipeline:Y,model:H.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:x.AutoTokenizer,pipeline:se,model:H.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:x.AutoTokenizer,pipeline:le,model:H.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:x.AutoTokenizer,pipeline:N,model:H.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:x.AutoTokenizer,pipeline:I,model:H.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:x.AutoTokenizer,pipeline:ae,model:H.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:x.AutoTokenizer,pipeline:A,model:H.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:x.AutoTokenizer,pipeline:_e,model:H.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:ke,model:H.AutoModelForAudioClassification,processor:ge.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:x.AutoTokenizer,pipeline:Ie,model:H.AutoModel,processor:ge.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:x.AutoTokenizer,pipeline:tt,model:[H.AutoModelForSpeechSeq2Seq,H.AutoModelForCTC],processor:ge.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:x.AutoTokenizer,pipeline:He,model:[H.AutoModelForTextToWaveform,H.AutoModelForTextToSpectrogram],processor:[ge.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:x.AutoTokenizer,pipeline:Qe,model:H.AutoModelForVision2Seq,processor:ge.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:ht,model:H.AutoModelForImageClassification,processor:ge.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:we,model:[H.AutoModelForImageSegmentation,H.AutoModelForSemanticSegmentation],processor:ge.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:x.AutoTokenizer,pipeline:V,model:H.AutoModel,processor:ge.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:he,model:H.AutoModelForObjectDetection,processor:ge.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:x.AutoTokenizer,pipeline:$e,model:H.AutoModelForZeroShotObjectDetection,processor:ge.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:x.AutoTokenizer,pipeline:ee,model:H.AutoModelForDocumentQuestionAnswering,processor:ge.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:pt,model:H.AutoModelForImageToImage,processor:ge.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:rt,model:H.AutoModelForDepthEstimation,processor:ge.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:x.AutoTokenizer,pipeline:ye,model:H.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:ge.AutoProcessor,pipeline:Ce,model:[H.AutoModelForImageFeatureExtraction,H.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Re=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function st(ze,re=null,{progress_callback:Ee=null,config:je=null,cache_dir:Ge=null,local_files_only:Ve=!1,revision:Ke="main",device:ut=null,dtype:mt=null,model_file_name:wt=null,session_options:xt={}}={}){ze=Re[ze]??ze;const M=lt[ze.split("_",1)[0]];if(!M)throw Error(`Unsupported pipeline: ${ze}. Must be one of [${Object.keys(lt)}]`);re||(re=M.default.model,console.log(`No model specified. Using default model: "${re}".`));const W={progress_callback:Ee,config:je,cache_dir:Ge,local_files_only:Ve,revision:Ke,device:ut,dtype:mt,model_file_name:wt,session_options:xt},S=new Map([["tokenizer",M.tokenizer],["model",M.model],["processor",M.processor]]),X=await Tt(S,re,W);X.task=ze,(0,xe.dispatchCallback)(Ee,{status:"ready",task:ze,model:re});const fe=M.pipeline;return new fe(X)}async function Tt(ze,re,Ee){const je=Object.create(null),Ge=[];for(let[Ve,Ke]of ze.entries()){if(!Ke)continue;let ut;Array.isArray(Ke)?ut=new Promise(async(mt,wt)=>{var M,W;let xt;for(let S of Ke){if(S===null){mt(null);return}try{mt(await S.from_pretrained(re,Ee));return}catch(X){if((M=X.message)!=null&&M.includes("Unsupported model type"))xt=X;else if((W=X.message)!=null&&W.includes("Could not locate file"))xt=X;else{wt(X);return}}}wt(xt)}):ut=Ke.from_pretrained(re,Ee),je[Ve]=ut,Ge.push(ut)}await Promise.all(Ge);for(let[Ve,Ke]of Object.entries(je))je[Ve]=await Ke;return je}},"./src/processors.js":(Mt,me,l)=>{l.r(me),l.d(me,{ASTFeatureExtractor:()=>Ke,AutoProcessor:()=>_t,BeitFeatureExtractor:()=>rt,BitImageProcessor:()=>le,CLIPFeatureExtractor:()=>N,CLIPImageProcessor:()=>I,ChineseCLIPFeatureExtractor:()=>B,ClapFeatureExtractor:()=>ut,ConvNextFeatureExtractor:()=>_e,ConvNextImageProcessor:()=>ye,DPTFeatureExtractor:()=>Y,DPTImageProcessor:()=>se,DeiTFeatureExtractor:()=>pt,DetrFeatureExtractor:()=>st,DonutFeatureExtractor:()=>lt,EfficientNetImageProcessor:()=>Ie,FeatureExtractor:()=>ne,Florence2Processor:()=>At,GLPNFeatureExtractor:()=>ae,ImageFeatureExtractor:()=>ie,MobileNetV1FeatureExtractor:()=>tt,MobileNetV2FeatureExtractor:()=>Qe,MobileNetV3FeatureExtractor:()=>ht,MobileNetV4FeatureExtractor:()=>we,MobileViTFeatureExtractor:()=>V,MobileViTImageProcessor:()=>he,NougatImageProcessor:()=>Re,OwlViTFeatureExtractor:()=>$e,OwlViTProcessor:()=>Je,Owlv2ImageProcessor:()=>ee,Processor:()=>M,PyAnnoteFeatureExtractor:()=>mt,PyAnnoteProcessor:()=>fe,RTDetrImageProcessor:()=>He,SamImageProcessor:()=>ze,SamProcessor:()=>W,SeamlessM4TFeatureExtractor:()=>Ve,SegformerFeatureExtractor:()=>R,SiglipImageProcessor:()=>A,SpeechT5FeatureExtractor:()=>xt,SpeechT5Processor:()=>Ye,Swin2SRImageProcessor:()=>re,ViTFeatureExtractor:()=>Ce,ViTImageProcessor:()=>ke,VitMatteImageProcessor:()=>Ee,Wav2Vec2FeatureExtractor:()=>Ge,Wav2Vec2ProcessorWithLM:()=>X,WeSpeakerFeatureExtractor:()=>wt,WhisperFeatureExtractor:()=>je,WhisperProcessor:()=>S,YolosFeatureExtractor:()=>Tt});var x=l("./src/utils/generic.js"),H=l("./src/utils/core.js"),ge=l("./src/utils/hub.js"),ve=l("./src/utils/maths.js"),xe=l("./src/utils/tensor.js");l("./src/utils/image.js");var D=l("./src/utils/audio.js");function T([Se,$,q,be]){return[Se-q/2,$-be/2,Se+q/2,$+be/2]}function j(Se,$=.5,q=null,be=!1){const Be=Se.logits,Ae=Se.pred_boxes,[Ne,dt,nt]=Be.dims;if(q!==null&&q.length!==Ne)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let vt=[];for(let ft=0;ft<Ne;++ft){let Ct=q!==null?q[ft]:null,Lt={boxes:[],classes:[],scores:[]},Xe=Be[ft],jt=Ae[ft];for(let Rt=0;Rt<dt;++Rt){let Ht=Xe[Rt],Xt=[],er;if(be){er=Ht.sigmoid().data;for(let Wt=0;Wt<er.length;++Wt)er[Wt]>$&&Xt.push(Wt)}else{let Wt=(0,ve.max)(Ht.data)[1];if(Wt===nt-1||(er=(0,ve.softmax)(Ht.data),er[Wt]<$))continue;Xt.push(Wt)}for(const Wt of Xt){let Tr=jt[Rt].data;Tr=T(Tr),Ct!==null&&(Tr=Tr.map((Ur,Cr)=>Ur*Ct[(Cr+1)%2])),Lt.boxes.push(Tr),Lt.classes.push(Wt),Lt.scores.push(er[Wt])}}vt.push(Lt)}return vt}function P(Se,$){var q;if(!(Se instanceof Float32Array||Se instanceof Float64Array))throw new Error(`${$} expects input to be a Float32Array or a Float64Array, but got ${((q=Se==null?void 0:Se.constructor)==null?void 0:q.name)??typeof Se} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function J(Se,$,q=0,be=null){const Be=Se/$;let Ae=(0,ve.bankers_round)(Be)*$;return be!==null&&Ae>be&&(Ae=Math.floor(Be)*$),Ae<q&&(Ae=Math.ceil(Be)*$),Ae}function te([Se,$],q){return[Math.max(Math.floor(Se/q),1)*q,Math.max(Math.floor($/q),1)*q]}class ne extends x.Callable{constructor($){super(),this.config=$}}class ie extends ne{constructor($){super($),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??.00392156862745098,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($,q,be=2){const Be=$.height,Ae=$.width,Ne=q.height,dt=q.width;let nt=Math.min(Be,Ne),vt=Math.min(Ae,dt);return nt===Be&&vt===Ae?$:(Be>Ae?vt=Math.floor(Ae*nt/Be):Ae>Be&&(nt=Math.floor(Be*vt/Ae)),await $.resize(vt,nt,{resample:be}))}async crop_margin($,q=200){const be=$.clone().grayscale(),Be=(0,ve.min)(be.data)[0],Ne=(0,ve.max)(be.data)[0]-Be;if(Ne===0)return $;const dt=q/255;let nt=be.width,vt=be.height,ft=0,Ct=0;const Lt=be.data;for(let Xe=0;Xe<be.height;++Xe){const jt=Xe*be.width;for(let Rt=0;Rt<be.width;++Rt)(Lt[jt+Rt]-Be)/Ne<dt&&(nt=Math.min(nt,Rt),vt=Math.min(vt,Xe),ft=Math.max(ft,Rt),Ct=Math.max(Ct,Xe))}return $=await $.crop([nt,vt,ft,Ct]),$}pad_image($,q,be,{mode:Be="constant",center:Ae=!1,constant_values:Ne=0}={}){const[dt,nt,vt]=q;let ft,Ct;if(typeof be=="number"?(ft=be,Ct=be):(ft=be.width,Ct=be.height),ft!==nt||Ct!==dt){const Lt=new Float32Array(ft*Ct*vt);if(Array.isArray(Ne))for(let Rt=0;Rt<Lt.length;++Rt)Lt[Rt]=Ne[Rt%vt];else Ne!==0&&Lt.fill(Ne);const[Xe,jt]=Ae?[Math.floor((ft-nt)/2),Math.floor((Ct-dt)/2)]:[0,0];for(let Rt=0;Rt<dt;++Rt){const Ht=(Rt+jt)*ft,Xt=Rt*nt;for(let er=0;er<nt;++er){const Wt=(Ht+er+Xe)*vt,Tr=(Xt+er)*vt;for(let Ur=0;Ur<vt;++Ur)Lt[Wt+Ur]=$[Tr+Ur]}}if(Be==="symmetric"){if(Ae)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const Rt=dt-1,Ht=nt-1;for(let Xt=0;Xt<Ct;++Xt){const er=Xt*ft,Wt=(0,H.calculateReflectOffset)(Xt,Rt)*nt;for(let Tr=0;Tr<ft;++Tr){if(Xt<dt&&Tr<nt)continue;const Ur=(er+Tr)*vt,Cr=(Wt+(0,H.calculateReflectOffset)(Tr,Ht))*vt;for(let Ze=0;Ze<vt;++Ze)Lt[Ur+Ze]=$[Cr+Ze]}}}$=Lt,q=[Ct,ft,vt]}return[$,q]}rescale($){for(let q=0;q<$.length;++q)$[q]=this.rescale_factor*$[q]}get_resize_output_image_size($,q){const[be,Be]=$.size;let Ae,Ne;if(this.do_thumbnail){const{height:dt,width:nt}=q;Ae=Math.min(dt,nt)}else Number.isInteger(q)?(Ae=q,Ne=this.config.max_size??Ae):q!==void 0&&(Ae=q.shortest_edge,Ne=q.longest_edge);if(Ae!==void 0||Ne!==void 0){const dt=Ae===void 0?1:Math.max(Ae/be,Ae/Be),nt=be*dt,vt=Be*dt,ft=Ne===void 0?1:Math.min(Ne/nt,Ne/vt);let Ct=Math.floor(Number((nt*ft).toFixed(2))),Lt=Math.floor(Number((vt*ft).toFixed(2)));return this.size_divisibility!==void 0&&([Ct,Lt]=te([Ct,Lt],this.size_divisibility)),[Ct,Lt]}else if(q!==void 0&&q.width!==void 0&&q.height!==void 0){let dt=q.width,nt=q.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let vt=nt/Be,ft=dt/be;Math.abs(1-ft)<Math.abs(1-vt)?vt=ft:ft=vt,nt=J(vt*Be,this.config.ensure_multiple_of),dt=J(ft*be,this.config.ensure_multiple_of)}return[dt,nt]}else{if(this.size_divisibility!==void 0)return te([be,Be],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(q)}`)}}async resize($){const[q,be]=this.get_resize_output_image_size($,this.size);return await $.resize(q,be,{resample:this.resample})}async preprocess($,{do_normalize:q=null,do_pad:be=null,do_convert_rgb:Be=null,do_convert_grayscale:Ae=null,do_flip_channel_order:Ne=null}={}){this.do_crop_margin&&($=await this.crop_margin($));const[dt,nt]=$.size;if(Be??this.do_convert_rgb?$=$.rgb():Ae&&($=$.grayscale()),this.do_resize&&($=await this.resize($)),this.do_thumbnail&&($=await this.thumbnail($,this.size,this.resample)),this.do_center_crop){let Xe,jt;Number.isInteger(this.crop_size)?(Xe=this.crop_size,jt=this.crop_size):(Xe=this.crop_size.width,jt=this.crop_size.height),$=await $.center_crop(Xe,jt)}const vt=[$.height,$.width];let ft=Float32Array.from($.data),Ct=[$.height,$.width,$.channels];if(this.do_rescale&&this.rescale(ft),q??this.do_normalize){let Xe=this.image_mean;Array.isArray(this.image_mean)||(Xe=new Array($.channels).fill(Xe));let jt=this.image_std;if(Array.isArray(this.image_std)||(jt=new Array($.channels).fill(Xe)),Xe.length!==$.channels||jt.length!==$.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${Xe.length}) and \`image_std\` (${jt.length}) must match the number of channels in the image (${$.channels}).`);for(let Rt=0;Rt<ft.length;Rt+=$.channels)for(let Ht=0;Ht<$.channels;++Ht)ft[Rt+Ht]=(ft[Rt+Ht]-Xe[Ht])/jt[Ht]}if(be??this.do_pad){if(this.pad_size)[ft,Ct]=this.pad_image(ft,[$.height,$.width,$.channels],this.pad_size);else if(this.size_divisibility){const[Xe,jt]=te([Ct[1],Ct[0]],this.size_divisibility);[ft,Ct]=this.pad_image(ft,Ct,{width:Xe,height:jt})}}if(Ne??this.do_flip_channel_order){if(Ct[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let Xe=0;Xe<ft.length;Xe+=3){const jt=ft[Xe];ft[Xe]=ft[Xe+2],ft[Xe+2]=jt}}const Lt=new xe.Tensor("float32",ft,Ct).permute(2,0,1);return{original_size:[nt,dt],reshaped_input_size:vt,pixel_values:Lt}}async _call($,...q){Array.isArray($)||($=[$]);const be=await Promise.all($.map(Ae=>this.preprocess(Ae)));return{pixel_values:(0,xe.stack)(be.map(Ae=>Ae.pixel_values),0),original_sizes:be.map(Ae=>Ae.original_size),reshaped_input_sizes:be.map(Ae=>Ae.reshaped_input_size)}}}class R extends ie{post_process_semantic_segmentation($,q=null){const be=$.logits,Be=be.dims[0];if(q!==null&&q.length!==Be)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const Ae=[];for(let Ne=0;Ne<Be;++Ne){const dt=q!==null?q[Ne]:null;let nt=be[Ne];dt!==null&&(nt=(0,xe.interpolate)(nt,dt,"bilinear",!1));const[vt,ft]=dt??nt.dims.slice(-2),Ct=new xe.Tensor("int32",new Int32Array(vt*ft),[vt,ft]),Lt=nt[0].data,Xe=Ct.data;for(let Xt=1;Xt<nt.dims[0];++Xt){const er=nt[Xt].data;for(let Wt=0;Wt<er.length;++Wt)er[Wt]>Lt[Wt]&&(Lt[Wt]=er[Wt],Xe[Wt]=Xt)}const jt=new Array(nt.dims[0]),Rt=Ct.data;for(let Xt=0;Xt<Rt.length;++Xt){const er=Rt[Xt];jt[er]=er}const Ht=jt.filter(Xt=>Xt!==void 0);Ae.push({segmentation:Ct,labels:Ht})}return Ae}}class Y extends ie{}class se extends Y{}class le extends ie{}class ae extends ie{}class N extends ie{}class I extends N{}class B extends ie{}class A extends ie{}class _e extends ie{constructor($){super($),this.crop_pct=this.config.crop_pct??.875}async resize($){var be;const q=(be=this.size)==null?void 0:be.shortest_edge;if(q===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(q<384){const Be=Math.floor(q/this.crop_pct),[Ae,Ne]=this.get_resize_output_image_size($,{shortest_edge:Be});$=await $.resize(Ae,Ne,{resample:this.resample}),$=await $.center_crop(q,q)}else $=await $.resize(q,q,{resample:this.resample});return $}}class ye extends _e{}class Ce extends ie{}class ke extends ie{}class Ie extends ie{constructor($){super($),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(q=>q*q))}}class tt extends ie{}class Qe extends ie{}class ht extends ie{}class we extends ie{}class V extends ie{}class he extends V{}class $e extends ie{post_process_object_detection(...$){return j(...$)}}class ee extends $e{}class He extends ie{post_process_object_detection(...$){return j(...$)}}class pt extends ie{}class rt extends ie{}class lt extends ie{pad_image($,q,be,Be={}){const[Ae,Ne,dt]=q;let nt=this.image_mean;Array.isArray(this.image_mean)||(nt=new Array(dt).fill(nt));let vt=this.image_std;Array.isArray(vt)||(vt=new Array(dt).fill(nt));const ft=nt.map((Ct,Lt)=>-Ct/vt[Lt]);return super.pad_image($,q,be,{center:!0,constant_values:ft,...Be})}}class Re extends lt{}class st extends ie{async _call($){const q=await super._call($),be=[q.pixel_values.dims[0],64,64],Be=new xe.Tensor("int64",new BigInt64Array(be.reduce((Ae,Ne)=>Ae*Ne)).fill(1n),be);return{...q,pixel_mask:Be}}post_process_object_detection(...$){return j(...$)}remove_low_and_no_objects($,q,be,Be){let Ae=[],Ne=[],dt=[];for(let nt=0;nt<$.dims[0];++nt){let vt=$[nt],ft=q[nt],Ct=(0,ve.max)(vt.data)[1];if(Ct===Be)continue;let Xe=(0,ve.softmax)(vt.data)[Ct];Xe>be&&(Ae.push(ft),Ne.push(Xe),dt.push(Ct))}return[Ae,Ne,dt]}check_segment_validity($,q,be,Be=.5,Ae=.8){let Ne=[],dt=0,nt=0;const vt=q[be].data;for(let Ct=0;Ct<$.length;++Ct)$[Ct]===be&&(Ne.push(Ct),++dt),vt[Ct]>=Be&&++nt;let ft=dt>0&&nt>0;return ft&&(ft=dt/nt>Ae),[ft,Ne]}compute_segments($,q,be,Be,Ae,Ne=null,dt=null){let[nt,vt]=dt??$[0].dims,ft=new xe.Tensor("int32",new Int32Array(nt*vt),[nt,vt]),Ct=[];if(dt!==null)for(let Ht=0;Ht<$.length;++Ht)$[Ht]=(0,xe.interpolate)($[Ht],dt,"bilinear",!1);let Lt=new Int32Array($[0].data.length),Xe=new Float32Array($[0].data.length);for(let Ht=0;Ht<$.length;++Ht){let Xt=q[Ht];const er=$[Ht].data;for(let Wt=0;Wt<er.length;++Wt)er[Wt]*=Xt,er[Wt]>Xe[Wt]&&(Lt[Wt]=Ht,Xe[Wt]=er[Wt])}let jt=0;const Rt=ft.data;for(let Ht=0;Ht<be.length;++Ht){let Xt=be[Ht],[er,Wt]=this.check_segment_validity(Lt,$,Ht,Be,Ae);if(er){++jt;for(let Tr of Wt)Rt[Tr]=jt;Ct.push({id:jt,label_id:Xt,score:q[Ht]})}}return[ft,Ct]}post_process_panoptic_segmentation($,q=.5,be=.5,Be=.8,Ae=null,Ne=null){Ae===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),Ae=new Set);const dt=$.logits,vt=$.pred_masks.sigmoid();let[ft,Ct,Lt]=dt.dims;if(Lt-=1,Ne!==null&&Ne.length!==ft)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let Xe=[];for(let jt=0;jt<ft;++jt){let Rt=Ne!==null?Ne[jt]:null,Ht=dt[jt],Xt=vt[jt],[er,Wt,Tr]=this.remove_low_and_no_objects(Ht,Xt,q,Lt);if(Tr.length===0){let[Ze,St]=Rt??Xt.dims.slice(-2),Dt=new xe.Tensor("int32",new Int32Array(Ze*St).fill(-1),[Ze,St]);Xe.push({segmentation:Dt,segments_info:[]});continue}let[Ur,Cr]=this.compute_segments(er,Wt,Tr,be,Be,Ae,Rt);Xe.push({segmentation:Ur,segments_info:Cr})}return Xe}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class Tt extends ie{post_process_object_detection(...$){return j(...$)}}class ze extends ie{reshape_input_points($,q,be,Be=!1){$=structuredClone($);let Ae=(0,H.calculateDimensions)($);if(Ae.length===3)Be||(Ae=[1,...Ae]),$=[$];else if(Ae.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 Ne=0;Ne<$.length;++Ne){let dt=q[Ne],nt=be[Ne],vt=[nt[0]/dt[0],nt[1]/dt[1]];for(let ft=0;ft<$[Ne].length;++ft)for(let Ct=0;Ct<$[Ne][ft].length;++Ct)for(let Lt=0;Lt<$[Ne][ft][Ct].length;++Lt)$[Ne][ft][Ct][Lt]*=vt[Lt%2]}return new xe.Tensor("float32",Float32Array.from($.flat(1/0)),Ae)}add_input_labels($,q){let be=(0,H.calculateDimensions)($);if(be.length===2)be=[1,...be],$=[$];else if(be.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(be.some((Be,Ae)=>Be!==q.dims[Ae]))throw Error(`The first ${be.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new xe.Tensor("int64",$.flat(1/0).map(BigInt),be)}async _call($,{input_points:q=null,input_labels:be=null,input_boxes:Be=null}={}){const Ae=await super._call($);if(q&&(Ae.input_points=this.reshape_input_points(q,Ae.original_sizes,Ae.reshaped_input_sizes)),be){if(!Ae.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");Ae.input_labels=this.add_input_labels(be,Ae.input_points)}return Be&&(Ae.input_boxes=this.reshape_input_points(Be,Ae.original_sizes,Ae.reshaped_input_sizes,!0)),Ae}async post_process_masks($,q,be,{mask_threshold:Be=0,binarize:Ae=!0,pad_size:Ne=null}={}){const dt=[];Ne=Ne??this.pad_size;const nt=[Ne.height,Ne.width];for(let vt=0;vt<q.length;++vt){const ft=q[vt],Ct=be[vt];let Lt=await(0,xe.interpolate_4d)($[vt],{mode:"bilinear",size:nt});if(Lt=Lt.slice(null,null,[0,Ct[0]],[0,Ct[1]]),Lt=await(0,xe.interpolate_4d)(Lt,{mode:"bilinear",size:ft}),Ae){const Xe=Lt.data,jt=new Uint8Array(Xe.length);for(let Rt=0;Rt<Xe.length;++Rt)Xe[Rt]>Be&&(jt[Rt]=1);Lt=new xe.Tensor("bool",jt,Lt.dims)}dt.push(Lt)}return dt}generate_crop_boxes($,q,{crop_n_layers:be=0,overlap_ratio:Be=.3413333333333333,points_per_crop:Ae=32,crop_n_points_downscale_factor:Ne=1}={}){}}class re extends ie{pad_image($,q,be,Be={}){const[Ae,Ne,dt]=q;return super.pad_image($,q,{width:Ne+(be-Ne%be)%be,height:Ae+(be-Ae%be)%be},{mode:"symmetric",center:!1,constant_values:-1,...Be})}}class Ee extends ie{async _call($,q){Array.isArray($)||($=[$]),Array.isArray(q)||(q=[q]);const be=await Promise.all($.map(Ne=>this.preprocess(Ne))),Be=await Promise.all(q.map(Ne=>this.preprocess(Ne,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,xe.stack)(be.map((Ne,dt)=>(0,xe.cat)([Ne.pixel_values,Be[dt].pixel_values],0)),0),original_sizes:be.map(Ne=>Ne.original_size),reshaped_input_sizes:be.map(Ne=>Ne.reshaped_input_size)}}}class je extends ne{constructor($){var q;super($),(q=this.config).mel_filters??(q.mel_filters=(0,D.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,D.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features($){const q=await(0,D.spectrogram)($,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}),be=q.data,Be=(0,ve.max)(be)[0];for(let Ae=0;Ae<be.length;++Ae)be[Ae]=(Math.max(be[Ae],Be-8)+4)/4;return q}async _call($){P($,"WhisperFeatureExtractor");let q;return $.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),q=$.slice(0,this.config.n_samples)):(q=new Float32Array(this.config.n_samples),q.set($)),{input_features:(await this._extract_fbank_features(q)).unsqueeze_(0)}}}class Ge extends ne{_zero_mean_unit_var_norm($){const be=$.reduce((Ae,Ne)=>Ae+Ne,0)/$.length,Be=$.reduce((Ae,Ne)=>Ae+(Ne-be)**2,0)/$.length;return $.map(Ae=>(Ae-be)/Math.sqrt(Be+1e-7))}async _call($){P($,"Wav2Vec2FeatureExtractor"),$ instanceof Float64Array&&($=new Float32Array($));let q=$;this.config.do_normalize&&(q=this._zero_mean_unit_var_norm(q));const be=[1,q.length];return{input_values:new xe.Tensor("float32",q,be),attention_mask:new xe.Tensor("int64",new BigInt64Array(q.length).fill(1n),be)}}}class Ve extends ne{constructor($){super($);const q=this.config.sampling_rate,be=(0,D.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(q/2),q,null,"kaldi",!0);for(let Be=0;Be<be.length;++Be)be[Be].push(0);this.mel_filters=be,this.window=(0,D.window_function)(400,"povey",{periodic:!1})}async _extract_fbank_features($,q){return $=$.map(be=>be*32768),(0,D.spectrogram)($,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:q,transpose:!0})}async _call($,{padding:q=!0,pad_to_multiple_of:be=2,do_normalize_per_mel_bins:Be=!0,return_attention_mask:Ae=!0}={}){P($,"SeamlessM4TFeatureExtractor");let Ne=await this._extract_fbank_features($,this.config.max_length);if(Be){const[jt,Rt]=Ne.dims,Ht=Ne.data;for(let Xt=0;Xt<Rt;++Xt){let er=0;for(let Cr=0;Cr<jt;++Cr)er+=Ht[Cr*Rt+Xt];const Wt=er/jt;let Tr=0;for(let Cr=0;Cr<jt;++Cr)Tr+=(Ht[Cr*Rt+Xt]-Wt)**2;Tr/=jt-1;const Ur=Math.sqrt(Tr+1e-7);for(let Cr=0;Cr<jt;++Cr){const Ze=Cr*Rt+Xt;Ht[Ze]=(Ht[Ze]-Wt)/Ur}}}let dt;if(q){const[jt,Rt]=Ne.dims,Ht=Ne.data,Xt=jt%be;if(Xt>0){const er=new Float32Array(Rt*(jt+Xt));er.set(Ht),er.fill(this.config.padding_value,Ht.length);const Wt=jt+Xt;Ne=new xe.Tensor(Ne.type,er,[Wt,Rt]),Ae&&(dt=new xe.Tensor("int64",new BigInt64Array(Wt),[1,Wt]),dt.data.fill(1n,0,jt))}}const[nt,vt]=Ne.dims,ft=this.config.stride;if(nt%ft!==0)throw new Error(`The number of frames (${nt}) must be a multiple of the stride (${ft}).`);const Lt=Ne.view(1,Math.floor(nt/ft),vt*ft),Xe={input_features:Lt};if(Ae){const jt=Lt.dims[1],Rt=new BigInt64Array(jt);if(dt){const Ht=dt.data;for(let Xt=1,er=0;Xt<nt;Xt+=ft,++er)Rt[er]=Ht[Xt]}else Rt.fill(1n);Xe.attention_mask=new xe.Tensor("int64",Rt,[1,jt])}return Xe}}class Ke extends ne{constructor($){super($);const q=this.config.sampling_rate,be=(0,D.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(q/2),q,null,"kaldi",!0);for(let Be=0;Be<be.length;++Be)be[Be].push(0);this.mel_filters=be,this.window=(0,D.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features($,q){return(0,D.spectrogram)($,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:q,transpose:!0})}async _call($){P($,"ASTFeatureExtractor");const q=await this._extract_fbank_features($,this.config.max_length);if(this.config.do_normalize){const be=this.std*2,Be=q.data;for(let Ae=0;Ae<Be.length;++Ae)Be[Ae]=(Be[Ae]-this.mean)/be}return{input_values:q.unsqueeze_(0)}}}class ut extends ne{constructor($){super($),this.mel_filters=(0,D.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,D.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,D.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel($,q,be,Be){let Ae;const Ne=$.length-q;if(Ne>0)if(be==="rand_trunc"){const dt=Math.floor(Math.random()*(Ne+1));$=$.subarray(dt,dt+q),Ae=await this._extract_fbank_features($,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${be}" not implemented`);else{if(Ne<0){let dt=new Float64Array(q);if(dt.set($),Be==="repeat")for(let nt=$.length;nt<q;nt+=$.length)dt.set($.subarray(0,Math.min($.length,q-nt)),nt);else if(Be==="repeatpad")for(let nt=$.length;nt<-Ne;nt+=$.length)dt.set($,nt);$=dt}if(be==="fusion")throw new Error(`Truncation strategy "${be}" not implemented`);Ae=await this._extract_fbank_features($,this.mel_filters_slaney,this.config.nb_max_samples)}return Ae.unsqueeze_(0)}async _extract_fbank_features($,q,be=null){return(0,D.spectrogram)($,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:q,log_mel:"dB",max_num_frames:be,do_pad:!1,transpose:!0})}async _call($,{max_length:q=null}={}){return P($,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel($,q??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}class mt extends ne{async _call($){P($,"PyAnnoteFeatureExtractor"),$ instanceof Float64Array&&($=new Float32Array($));const q=[1,1,$.length];return{input_values:new xe.Tensor("float32",$,q)}}samples_to_frames($){return($-this.config.offset)/this.config.step}post_process_speaker_diarization($,q){const be=q/this.samples_to_frames(q)/this.config.sampling_rate,Be=[];for(const Ae of $.tolist()){const Ne=[];let dt=-1;for(let nt=0;nt<Ae.length;++nt){const vt=(0,ve.softmax)(Ae[nt]),[ft,Ct]=(0,ve.max)(vt),[Lt,Xe]=[nt,nt+1];Ct!==dt?(dt=Ct,Ne.push({id:Ct,start:Lt,end:Xe,score:ft})):(Ne.at(-1).end=Xe,Ne.at(-1).score+=ft)}Be.push(Ne.map(({id:nt,start:vt,end:ft,score:Ct})=>({id:nt,start:vt*be,end:ft*be,confidence:Ct/(ft-vt)})))}return Be}}class wt extends ne{constructor($){super($);const q=this.config.sampling_rate,be=(0,D.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(q/2),q,null,"kaldi",!0);for(let Be=0;Be<be.length;++Be)be[Be].push(0);this.mel_filters=be,this.window=(0,D.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features($){return $=$.map(q=>q*32768),(0,D.spectrogram)($,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call($){P($,"WeSpeakerFeatureExtractor");const q=(await this._extract_fbank_features($)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const be=q.mean(1).data,Be=q.data,[Ae,Ne,dt]=q.dims;for(let nt=0;nt<Ae;++nt){const vt=nt*Ne*dt,ft=nt*dt;for(let Ct=0;Ct<Ne;++Ct){const Lt=vt+Ct*dt;for(let Xe=0;Xe<dt;++Xe)Be[Lt+Xe]-=be[ft+Xe]}}}return{input_features:q}}}class xt extends ne{}class M extends x.Callable{constructor($){super(),this.feature_extractor=$}async _call($,...q){return await this.feature_extractor($,...q)}}class W extends M{async _call(...$){return await this.feature_extractor(...$)}post_process_masks(...$){return this.feature_extractor.post_process_masks(...$)}reshape_input_points(...$){return this.feature_extractor.reshape_input_points(...$)}}class S extends M{async _call($){return await this.feature_extractor($)}}class X extends M{async _call($){return await this.feature_extractor($)}}class fe extends M{async _call($){return await this.feature_extractor($)}post_process_speaker_diarization(...$){return this.feature_extractor.post_process_speaker_diarization(...$)}}class Ye extends M{async _call($){return await this.feature_extractor($)}}class Je extends M{}class At extends M{constructor($){super($);const{tasks_answer_post_processing_type:q,task_prompts_without_inputs:be,task_prompts_with_input:Be}=$.config;this.tasks_answer_post_processing_type=new Map(Object.entries(q??{})),this.task_prompts_without_inputs=new Map(Object.entries(be??{})),this.task_prompts_with_input=new Map(Object.entries(Be??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts($){typeof $=="string"&&($=[$]);const q=[];for(const be of $)if(this.task_prompts_without_inputs.has(be))q.push(this.task_prompts_without_inputs.get(be));else{for(const[Be,Ae]of this.task_prompts_with_input)if(be.includes(Be)){q.push(Ae.replaceAll("{input}",be).replaceAll(Be,""));break}q.length!==$.length&&q.push(be)}return q}post_process_generation($,q,be){const Be=this.tasks_answer_post_processing_type.get(q)??"pure_text";$=$.replaceAll("<s>","").replaceAll("</s>","");let Ae;switch(Be){case"pure_text":Ae=$;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const Ne=Be==="ocr"?"quad_boxes":"bboxes",dt=$.matchAll(this.regexes[Ne]),nt=[],vt=[];for(const[ft,Ct,...Lt]of dt)nt.push(Ct?Ct.trim():nt.at(-1)??""),vt.push(Lt.map((Xe,jt)=>(Number(Xe)+.5)/this.size_per_bin*be[jt%2]));Ae={labels:nt,[Ne]:vt};break;default:throw new Error(`Task "${q}" (of type "${Be}") not yet implemented.`)}return{[q]:Ae}}}class _t{static async from_pretrained($,{progress_callback:q=null,config:be=null,cache_dir:Be=null,local_files_only:Ae=!1,revision:Ne="main"}={}){let dt=be??await(0,ge.getModelJSON)($,"preprocessor_config.json",!0,{progress_callback:q,config:be,cache_dir:Be,local_files_only:Ae,revision:Ne}),nt=dt.feature_extractor_type??dt.image_processor_type,vt=this.FEATURE_EXTRACTOR_CLASS_MAPPING[nt];if(!vt)if(dt.size!==void 0)console.warn(`Feature extractor type "${nt}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),vt=ie;else throw new Error(`Unknown Feature Extractor type: ${nt}`);let ft=this.PROCESSOR_CLASS_MAPPING[dt.processor_class]??M,Ct=new vt(dt);return new ft(Ct)}}Te(_t,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:ie,WhisperFeatureExtractor:je,ViTFeatureExtractor:Ce,MobileViTFeatureExtractor:V,MobileViTImageProcessor:he,MobileNetV1FeatureExtractor:tt,MobileNetV2FeatureExtractor:Qe,MobileNetV3FeatureExtractor:ht,MobileNetV4FeatureExtractor:we,OwlViTFeatureExtractor:$e,Owlv2ImageProcessor:ee,CLIPFeatureExtractor:N,CLIPImageProcessor:I,Florence2Processor:At,ChineseCLIPFeatureExtractor:B,SiglipImageProcessor:A,ConvNextFeatureExtractor:_e,ConvNextImageProcessor:ye,SegformerFeatureExtractor:R,BitImageProcessor:le,DPTImageProcessor:se,DPTFeatureExtractor:Y,GLPNFeatureExtractor:ae,BeitFeatureExtractor:rt,DeiTFeatureExtractor:pt,DetrFeatureExtractor:st,RTDetrImageProcessor:He,YolosFeatureExtractor:Tt,DonutFeatureExtractor:lt,NougatImageProcessor:Re,EfficientNetImageProcessor:Ie,ViTImageProcessor:ke,VitMatteImageProcessor:Ee,SamImageProcessor:ze,Swin2SRImageProcessor:re,Wav2Vec2FeatureExtractor:Ge,SeamlessM4TFeatureExtractor:Ve,SpeechT5FeatureExtractor:xt,ASTFeatureExtractor:Ke,ClapFeatureExtractor:ut,PyAnnoteFeatureExtractor:mt,WeSpeakerFeatureExtractor:wt}),Te(_t,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:S,Wav2Vec2ProcessorWithLM:X,PyAnnoteProcessor:fe,SamProcessor:W,SpeechT5Processor:Ye,OwlViTProcessor:Je,Florence2Processor:At})},"./src/tokenizers.js":(Mt,me,l)=>{l.r(me),l.d(me,{AlbertTokenizer:()=>Rt,AutoTokenizer:()=>mn,BartTokenizer:()=>Lr,BertTokenizer:()=>jt,BlenderbotSmallTokenizer:()=>vs,BlenderbotTokenizer:()=>os,BloomTokenizer:()=>Pr,CLIPTokenizer:()=>Qt,CamembertTokenizer:()=>St,CodeGenTokenizer:()=>as,CodeLlamaTokenizer:()=>Vs,CohereTokenizer:()=>Dr,ConvBertTokenizer:()=>Ur,DebertaTokenizer:()=>er,DebertaV2Tokenizer:()=>Wt,DistilBertTokenizer:()=>Ze,ElectraTokenizer:()=>qr,EsmTokenizer:()=>Gn,FalconTokenizer:()=>ws,GPT2Tokenizer:()=>Fn,GPTNeoXTokenizer:()=>ys,GemmaTokenizer:()=>ss,Grok1Tokenizer:()=>kn,HerbertTokenizer:()=>Tr,LlamaTokenizer:()=>On,M2M100Tokenizer:()=>Qn,MBart50Tokenizer:()=>Nr,MBartTokenizer:()=>Zr,MPNetTokenizer:()=>gs,MarianTokenizer:()=>bs,MobileBertTokenizer:()=>Ht,NllbTokenizer:()=>Dn,NougatTokenizer:()=>ls,PreTrainedTokenizer:()=>Xe,Qwen2Tokenizer:()=>Us,RoFormerTokenizer:()=>Cr,RobertaTokenizer:()=>Sn,SiglipTokenizer:()=>Yn,SpeechT5Tokenizer:()=>xs,SqueezeBertTokenizer:()=>Xt,T5Tokenizer:()=>Un,TokenizerModel:()=>Ce,VitsTokenizer:()=>Ts,Wav2Vec2CTCTokenizer:()=>Ms,WhisperTokenizer:()=>is,XLMRobertaTokenizer:()=>_s,XLMTokenizer:()=>Dt,is_chinese_char:()=>ae});var x=l("./src/utils/generic.js"),H=l("./src/utils/core.js"),ge=l("./src/utils/hub.js"),ve=l("./src/utils/maths.js"),xe=l("./src/utils/tensor.js"),D=l("./src/utils/data-structures.js"),T=l("./node_modules/@huggingface/jinja/dist/index.js"),j=l("./src/models/whisper/common_whisper.js"),P=l("./src/utils/constants.js");async function J(Me,_){const O=await Promise.all([(0,ge.getModelJSON)(Me,"tokenizer.json",!0,_),(0,ge.getModelJSON)(Me,"tokenizer_config.json",!0,_)]);return _.legacy!==null&&(O[1].legacy=_.legacy),O}function te(Me,_){const O=[];let Q=0;for(const ue of Me.matchAll(_)){const de=ue[0];Q<ue.index&&O.push(Me.slice(Q,ue.index)),de.length>0&&O.push(de),Q=ue.index+de.length}return Q<Me.length&&O.push(Me.slice(Q)),O}function ne(Me,_=!0){if(Me.Regex!==void 0){let O=Me.Regex.replace(/\\([#&~])/g,"$1");for(const[Q,ue]of _e)O=O.replaceAll(Q,ue);return new RegExp(O,"gu")}else if(Me.String!==void 0){const O=(0,H.escapeRegExp)(Me.String);return new RegExp(_?O:`(${O})`,"gu")}else return console.warn("Unknown pattern type:",Me),null}function ie(Me){return new Map(Object.entries(Me))}function R(Me){const _=Me.dims;switch(_.length){case 1:return Me.tolist();case 2:if(_[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return Me.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${_.length}.`)}}function Y(Me){return Me.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function se(Me){return Me.replace(/[\u0300-\u036f]/g,"")}function le(Me){return se(Me.toLowerCase())}function ae(Me){return Me>=19968&&Me<=40959||Me>=13312&&Me<=19903||Me>=131072&&Me<=173791||Me>=173824&&Me<=177983||Me>=177984&&Me<=178207||Me>=178208&&Me<=183983||Me>=63744&&Me<=64255||Me>=194560&&Me<=195103}function N(Me,_,O){const Q=[];let ue=0;for(;ue<Me.length;){if(Q.push(Me[ue]),(O.get(Me[ue])??_)!==_){++ue;continue}for(;ue<Me.length&&(O.get(Me[ue])??_)===_;)++ue}return Q}function I(Me){return Me.match(/\S+/g)||[]}const B="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",A=new RegExp(`^[${B}]+$`,"gu"),_e=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class ye{constructor(_){this.content=_.content,this.id=_.id,this.single_word=_.single_word??!1,this.lstrip=_.lstrip??!1,this.rstrip=_.rstrip??!1,this.special=_.special??!1,this.normalized=_.normalized??null}}class Ce extends x.Callable{constructor(_){super(),this.config=_,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(_,...O){switch(_.type){case"WordPiece":return new ke(_);case"Unigram":return new Ie(_,...O);case"BPE":return new ht(_);default:if(_.vocab)return new we(_,...O);throw new Error(`Unknown TokenizerModel type: ${_.type}`)}}_call(_){let O=this.encode(_);return this.fuse_unk&&(O=N(O,this.unk_token_id,this.tokens_to_ids)),O}encode(_){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(_){return _.map(O=>this.tokens_to_ids.get(O)??this.unk_token_id)}convert_ids_to_tokens(_){return _.map(O=>this.vocab[O]??this.unk_token)}}class ke extends Ce{constructor(_){super(_),this.tokens_to_ids=ie(_.vocab),this.unk_token_id=this.tokens_to_ids.get(_.unk_token),this.unk_token=_.unk_token,this.max_input_chars_per_word=_.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[O,Q]of this.tokens_to_ids)this.vocab[Q]=O}encode(_){const O=[];for(const Q of _){const ue=[...Q];if(ue.length>this.max_input_chars_per_word){O.push(this.unk_token);continue}let de=!1,Fe=0;const gt=[];for(;Fe<ue.length;){let bt=ue.length,yt=null;for(;Fe<bt;){let Pt=ue.slice(Fe,bt).join("");if(Fe>0&&(Pt=this.config.continuing_subword_prefix+Pt),this.tokens_to_ids.has(Pt)){yt=Pt;break}--bt}if(yt===null){de=!0;break}gt.push(yt),Fe=bt}de?O.push(this.unk_token):O.push(...gt)}return O}}class Ie extends Ce{constructor(_,O){super(_);const Q=_.vocab.length;this.vocab=new Array(Q),this.scores=new Array(Q);for(let ue=0;ue<Q;++ue){const de=_.vocab[ue];this.vocab[ue]=de[0],this.scores[ue]=de[1]}this.unk_token_id=_.unk_id,this.unk_token=this.vocab[_.unk_id],this.tokens_to_ids=new Map(this.vocab.map((ue,de)=>[ue,de])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=O.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=(0,ve.min)(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new D.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(_){const O=_.sentence,Q=O.length;let ue=0;for(;ue<Q;){let Fe=!1;for(let gt of this.trie.commonPrefixSearch(O.slice(ue))){const bt=this.tokens_to_ids.get(gt),yt=this.scores[bt],Pt=gt.length;_.insert(ue,Pt,yt,bt),!Fe&&Pt===1&&(Fe=!0)}Fe||_.insert(ue,1,this.unkScore,this.unk_token_id),ue+=1}}tokenize(_){const O=new D.TokenLattice(_,this.bosTokenId,this.eosTokenId);return this.populateNodes(O),O.tokens()}encode(_){const O=[];for(const Q of _){const ue=this.tokenize(Q);O.push(...ue)}return O}}const tt=(()=>{const Me=[...Array.from({length:94},(ue,de)=>de+33),...Array.from({length:12},(ue,de)=>de+161),...Array.from({length:82},(ue,de)=>de+174)],_=Me.slice();let O=0;for(let ue=0;ue<256;++ue)Me.includes(ue)||(Me.push(ue),_.push(256+O),O+=1);const Q=_.map(ue=>String.fromCharCode(ue));return Object.fromEntries(Me.map((ue,de)=>[ue,Q[de]]))})(),Qe=(0,H.reverseDictionary)(tt);class ht extends Ce{constructor(_){super(_),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=ie(_.vocab),this.unk_token_id=this.tokens_to_ids.get(_.unk_token),this.unk_token=_.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[O,Q]of this.tokens_to_ids)this.vocab[Q]=O;this.bpe_ranks=new Map(_.merges.map((O,Q)=>[O,Q])),this.merges=_.merges.map(O=>O.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=_.end_of_word_suffix,this.continuing_subword_suffix=_.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(_){if(_.length===0)return[];const O=this.cache.get(_);if(O!==void 0)return O;const Q=Array.from(_);this.end_of_word_suffix&&(Q[Q.length-1]+=this.end_of_word_suffix);let ue=[];if(Q.length>1){const de=new D.PriorityQueue((bt,yt)=>bt.score<yt.score);let Fe={token:Q[0],bias:0,prev:null,next:null},gt=Fe;for(let bt=1;bt<Q.length;++bt){const yt={bias:bt/Q.length,token:Q[bt],prev:gt,next:null};gt.next=yt,this._add_node(de,gt),gt=yt}for(;!de.isEmpty();){const bt=de.pop();if(bt.deleted||!bt.next||bt.next.deleted)continue;if(bt.deleted=!0,bt.next.deleted=!0,bt.prev){const Pt={...bt.prev};bt.prev.deleted=!0,bt.prev=Pt,Pt.prev?Pt.prev.next=Pt:Fe=Pt}const yt={token:bt.token+bt.next.token,bias:bt.bias,prev:bt.prev,next:bt.next.next};yt.prev?(yt.prev.next=yt,this._add_node(de,yt.prev)):Fe=yt,yt.next&&(yt.next.prev=yt,this._add_node(de,yt))}for(let bt=Fe;bt!==null;bt=bt.next)ue.push(bt.token)}else ue=Q;if(this.continuing_subword_suffix)for(let de=0;de<ue.length-1;++de)ue[de]+=this.continuing_subword_suffix;return this.cache.set(_,ue),ue}_add_node(_,O){const Q=this.bpe_ranks.get(O.token+this.BPE_SPLIT_TOKEN+O.next.token);Q!==void 0&&(O.score=Q+O.bias,_.push(O))}encode(_){const O=[];for(const Q of _){if(this.ignore_merges&&this.tokens_to_ids.has(Q)){O.push(Q);continue}const ue=this.bpe(Q);for(const de of ue)this.tokens_to_ids.has(de)?O.push(de):this.byte_fallback?O.push(...Array.from(this.text_encoder.encode(de)).map(Fe=>`<0x${Fe.toString(16).toUpperCase().padStart(2,"0")}>`)):O.push(this.unk_token)}return O}}class we extends Ce{constructor(_,O){super(_),this.tokens_to_ids=ie(O.target_lang?_.vocab[O.target_lang]:_.vocab),this.bos_token=O.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=O.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=O.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=O.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[Q,ue]of this.tokens_to_ids)this.vocab[ue]=Q}encode(_){return _}}class V extends x.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"BertNormalizer":return new Tt(_);case"Precompiled":return new Ae(_);case"Sequence":return new st(_);case"Replace":return new he(_);case"NFC":return new $e(_);case"NFKC":return new ee(_);case"NFKD":return new He(_);case"Strip":return new pt(_);case"StripAccents":return new rt(_);case"Lowercase":return new lt(_);case"Prepend":return new Re(_);default:throw new Error(`Unknown Normalizer type: ${_.type}`)}}normalize(_){throw Error("normalize should be implemented in subclass.")}_call(_){return this.normalize(_)}}class he extends V{normalize(_){const O=ne(this.config.pattern);return O===null?_:_.replaceAll(O,this.config.content)}}class $e extends V{normalize(_){return _=_.normalize("NFC"),_}}class ee extends V{normalize(_){return _=_.normalize("NFKC"),_}}class He extends V{normalize(_){return _=_.normalize("NFKD"),_}}class pt extends V{normalize(_){return this.config.strip_left&&this.config.strip_right?_=_.trim():(this.config.strip_left&&(_=_.trimStart()),this.config.strip_right&&(_=_.trimEnd())),_}}class rt extends V{normalize(_){return _=se(_),_}}class lt extends V{normalize(_){return _=_.toLowerCase(),_}}class Re extends V{normalize(_){return _=this.config.prepend+_,_}}class st extends V{constructor(_){super(_),this.normalizers=_.normalizers.map(O=>V.fromConfig(O))}normalize(_){return this.normalizers.reduce((O,Q)=>Q.normalize(O),_)}}class Tt extends V{_tokenize_chinese_chars(_){const O=[];for(let Q=0;Q<_.length;++Q){const ue=_[Q],de=ue.charCodeAt(0);ae(de)?(O.push(" "),O.push(ue),O.push(" ")):O.push(ue)}return O.join("")}stripAccents(_){return _.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(_){switch(_){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(_)}}_clean_text(_){const O=[];for(const Q of _){const ue=Q.charCodeAt(0);ue===0||ue===65533||this._is_control(Q)||(/^\s$/.test(Q)?O.push(" "):O.push(Q))}return O.join("")}normalize(_){return this.config.clean_text&&(_=this._clean_text(_)),this.config.handle_chinese_chars&&(_=this._tokenize_chinese_chars(_)),this.config.lowercase?(_=_.toLowerCase(),this.config.strip_accents!==!1&&(_=this.stripAccents(_))):this.config.strip_accents&&(_=this.stripAccents(_)),_}}class ze extends x.Callable{static fromConfig(_){if(_===null)return null;switch(_.type){case"BertPreTokenizer":return new re(_);case"Sequence":return new Ne(_);case"Whitespace":return new dt(_);case"WhitespaceSplit":return new nt(_);case"Metaspace":return new be(_);case"ByteLevel":return new Ee(_);case"Split":return new je(_);case"Punctuation":return new Ge(_);case"Digits":return new Ve(_);case"Replace":return new vt(_);default:throw new Error(`Unknown PreTokenizer type: ${_.type}`)}}pre_tokenize_text(_,O){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(_,O){return(Array.isArray(_)?_.map(Q=>this.pre_tokenize_text(Q,O)):this.pre_tokenize_text(_,O)).flat()}_call(_,O){return this.pre_tokenize(_,O)}}class re extends ze{constructor(_){super(),this.pattern=new RegExp(`[^\\s${B}]+|[${B}]`,"gu")}pre_tokenize_text(_,O){return _.trim().match(this.pattern)||[]}}class Ee extends ze{constructor(_){super(),this.config=_,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=tt,this.text_encoder=new TextEncoder}pre_tokenize_text(_,O){return this.add_prefix_space&&!_.startsWith(" ")&&(_=" "+_),(this.use_regex?_.match(this.pattern)||[]:[_]).map(ue=>Array.from(this.text_encoder.encode(ue),de=>this.byte_encoder[de]).join(""))}}class je extends ze{constructor(_){super(),this.config=_,this.pattern=ne(this.config.pattern,this.config.invert)}pre_tokenize_text(_,O){return this.pattern===null?[]:this.config.invert?_.match(this.pattern)||[]:te(_,this.pattern)}}class Ge extends ze{constructor(_){super(),this.config=_,this.pattern=new RegExp(`[^${B}]+|[${B}]+`,"gu")}pre_tokenize_text(_,O){return _.match(this.pattern)||[]}}class Ve extends ze{constructor(_){super(),this.config=_;const O=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(O,"gu")}pre_tokenize_text(_,O){return _.match(this.pattern)||[]}}class Ke extends x.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"TemplateProcessing":return new wt(_);case"ByteLevel":return new xt(_);case"RobertaProcessing":return new mt(_);case"BertProcessing":return new ut(_);case"Sequence":return new M(_);default:throw new Error(`Unknown PostProcessor type: ${_.type}`)}}post_process(_,...O){throw Error("post_process should be implemented in subclass.")}_call(_,...O){return this.post_process(_,...O)}}class ut extends Ke{constructor(_){super(_),this.cls=_.cls[0],this.sep=_.sep[0]}post_process(_,O=null,{add_special_tokens:Q=!0}={}){Q&&(_=(0,H.mergeArrays)([this.cls],_,[this.sep]));let ue=new Array(_.length).fill(0);if(O!==null){const de=Q&&this instanceof mt?[this.sep]:[],Fe=Q?[this.sep]:[];_=(0,H.mergeArrays)(_,de,O,Fe),ue=(0,H.mergeArrays)(ue,new Array(O.length+de.length+Fe.length).fill(1))}return{tokens:_,token_type_ids:ue}}}class mt extends ut{}class wt extends Ke{constructor(_){super(_),this.single=_.single,this.pair=_.pair}post_process(_,O=null,{add_special_tokens:Q=!0}={}){const ue=O===null?this.single:this.pair;let de=[],Fe=[];for(const gt of ue)"SpecialToken"in gt?Q&&(de.push(gt.SpecialToken.id),Fe.push(gt.SpecialToken.type_id)):"Sequence"in gt&&(gt.Sequence.id==="A"?(de=(0,H.mergeArrays)(de,_),Fe=(0,H.mergeArrays)(Fe,new Array(_.length).fill(gt.Sequence.type_id))):gt.Sequence.id==="B"&&(de=(0,H.mergeArrays)(de,O),Fe=(0,H.mergeArrays)(Fe,new Array(O.length).fill(gt.Sequence.type_id))));return{tokens:de,token_type_ids:Fe}}}class xt extends Ke{post_process(_,O=null){return O&&(_=(0,H.mergeArrays)(_,O)),{tokens:_}}}class M extends Ke{constructor(_){super(_),this.processors=_.processors.map(O=>Ke.fromConfig(O))}post_process(_,O=null,Q={}){let ue;for(const de of this.processors)if(de instanceof xt)_=de.post_process(_).tokens,O&&(O=de.post_process(O).tokens);else{const Fe=de.post_process(_,O,Q);_=Fe.tokens,ue=Fe.token_type_ids}return{tokens:_,token_type_ids:ue}}}class W extends x.Callable{constructor(_){super(),this.config=_,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=_.trim_offsets}static fromConfig(_){if(_===null)return null;switch(_.type){case"WordPiece":return new Je(_);case"Metaspace":return new Be(_);case"ByteLevel":return new At(_);case"Replace":return new S(_);case"ByteFallback":return new X(_);case"Fuse":return new fe(_);case"Strip":return new Ye(_);case"Sequence":return new Se(_);case"CTC":return new _t(_);case"BPEDecoder":return new $(_);default:throw new Error(`Unknown Decoder type: ${_.type}`)}}_call(_){return this.decode(_)}decode(_){return this.decode_chain(_).join("")}decode_chain(_){throw Error("`decode_chain` should be implemented in subclass.")}}class S extends W{decode_chain(_){const O=ne(this.config.pattern);return O===null?_:_.map(Q=>Q.replaceAll(O,this.config.content))}}class X extends W{constructor(_){super(_),this.text_decoder=new TextDecoder}decode_chain(_){const O=[];let Q=[];for(const ue of _){let de=null;if(ue.length===6&&ue.startsWith("<0x")&&ue.endsWith(">")){const Fe=parseInt(ue.slice(3,5),16);isNaN(Fe)||(de=Fe)}if(de!==null)Q.push(de);else{if(Q.length>0){const Fe=this.text_decoder.decode(Uint8Array.from(Q));O.push(Fe),Q=[]}O.push(ue)}}if(Q.length>0){const ue=this.text_decoder.decode(Uint8Array.from(Q));O.push(ue),Q=[]}return O}}class fe extends W{decode_chain(_){return[_.join("")]}}class Ye extends W{constructor(_){super(_),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(_){return _.map(O=>{let Q=0;for(let de=0;de<this.start&&O[de]===this.content;++de){Q=de+1;continue}let ue=O.length;for(let de=0;de<this.stop;++de){const Fe=O.length-de-1;if(O[Fe]===this.content){ue=Fe;continue}else break}return O.slice(Q,ue)})}}class Je extends W{constructor(_){super(_),this.cleanup=_.cleanup}decode_chain(_){return _.map((O,Q)=>(Q!==0&&(O.startsWith(this.config.prefix)?O=O.replace(this.config.prefix,""):O=" "+O),this.cleanup&&(O=Y(O)),O))}}class At extends W{constructor(_){super(_),this.byte_decoder=Qe,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(_){const O=_.join(""),Q=new Uint8Array([...O].map(de=>this.byte_decoder[de]));return this.text_decoder.decode(Q)}decode_chain(_){const O=[];let Q=[];for(const ue of _)this.added_tokens.find(de=>de.content===ue)!==void 0?(Q.length>0&&(O.push(this.convert_tokens_to_string(Q)),Q=[]),O.push(ue)):Q.push(ue);return Q.length>0&&O.push(this.convert_tokens_to_string(Q)),O}}class _t extends W{constructor(_){super(_),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(_){if(_.length===0)return"";const O=[_[0]];for(let de=1;de<_.length;++de)_[de]!==O.at(-1)&&O.push(_[de]);let ue=O.filter(de=>de!==this.pad_token).join("");return this.cleanup&&(ue=Y(ue).replaceAll(this.word_delimiter_token," ").trim()),ue}decode_chain(_){return[this.convert_tokens_to_string(_)]}}class Se extends W{constructor(_){super(_),this.decoders=_.decoders.map(O=>W.fromConfig(O))}decode_chain(_){return this.decoders.reduce((O,Q)=>Q.decode_chain(O),_)}}class $ extends W{constructor(_){super(_),this.suffix=this.config.suffix}decode_chain(_){return _.map((O,Q)=>O.replaceAll(this.suffix,Q===_.length-1?"":" "))}}class q extends W{decode_chain(_){let O="";for(let Q=1;Q<_.length;Q+=2)O+=_[Q];return[O]}}class be extends ze{constructor(_){super(),this.addPrefixSpace=_.add_prefix_space,this.replacement=_.replacement,this.strRep=_.str_rep||this.replacement,this.prepend_scheme=_.prepend_scheme??"always"}pre_tokenize_text(_,{section_index:O=void 0}={}){let Q=_.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!Q.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&O===0)&&(Q=this.strRep+Q),[Q]}}class Be extends W{constructor(_){super(_),this.addPrefixSpace=_.add_prefix_space,this.replacement=_.replacement}decode_chain(_){const O=[];for(let Q=0;Q<_.length;++Q){let ue=_[Q].replaceAll(this.replacement," ");this.addPrefixSpace&&Q==0&&ue.startsWith(" ")&&(ue=ue.substring(1)),O.push(ue)}return O}}class Ae extends V{constructor(_){super(_),this.charsmap=_.precompiled_charsmap}normalize(_){return _=_.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),_=_.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),_.includes("～")?_=_.split("～").map(Q=>Q.normalize("NFKC")).join("～"):_=_.normalize("NFKC"),_}}class Ne extends ze{constructor(_){super(),this.tokenizers=_.pretokenizers.map(O=>ze.fromConfig(O))}pre_tokenize_text(_,O){return this.tokenizers.reduce((Q,ue)=>ue.pre_tokenize(Q,O),[_])}}class dt extends ze{constructor(_){super()}pre_tokenize_text(_,O){return _.match(/\w+|[^\w\s]+/g)||[]}}class nt extends ze{constructor(_){super()}pre_tokenize_text(_,O){return I(_)}}class vt extends ze{constructor(_){super(),this.config=_,this.pattern=ne(this.config.pattern),this.content=this.config.content}pre_tokenize_text(_,O){return this.pattern===null?[_]:[_.replaceAll(this.pattern,this.config.content)]}}const ft=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ct(Me,_,O,Q){for(const ue of Object.keys(Me)){const de=_-Me[ue].length,Fe=O(ue),gt=new Array(de).fill(Fe);Me[ue]=Q==="right"?(0,H.mergeArrays)(Me[ue],gt):(0,H.mergeArrays)(gt,Me[ue])}}function Lt(Me,_){for(const O of Object.keys(Me))Me[O].length=_}class Xe extends x.Callable{constructor(O,Q){super();Te(this,"return_token_type_ids",!1);Te(this,"padding_side","right");this._tokenizer_config=Q,this.normalizer=V.fromConfig(O.normalizer),this.pre_tokenizer=ze.fromConfig(O.pre_tokenizer),this.model=Ce.fromConfig(O.model,Q),this.post_processor=Ke.fromConfig(O.post_processor),this.decoder=W.fromConfig(O.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ue of O.added_tokens){const de=new ye(ue);this.added_tokens.push(de),this.model.tokens_to_ids.set(de.content,de.id),this.model.vocab[de.id]=de.content,de.special&&(this.special_tokens.push(de.content),this.all_special_ids.push(de.id))}if(this.additional_special_tokens=Q.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.toSorted((ue,de)=>de.content.length-ue.content.length).map(ue=>`${ue.lstrip?"\\s*":""}(${(0,H.escapeRegExp)(ue.content)})${ue.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=Q.model_max_length,this.remove_space=Q.remove_space,this.clean_up_tokenization_spaces=Q.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=Q.do_lowercase_and_remove_accent??!1,Q.padding_side&&(this.padding_side=Q.padding_side),this.legacy=!1,this.chat_template=Q.chat_template??null,Array.isArray(this.chat_template)){const ue=Object.create(null);for(const{name:de,template:Fe}of this.chat_template){if(typeof de!="string"||typeof Fe!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ue[de]=Fe}this.chat_template=ue}this._compiled_template_cache=new Map}getToken(...O){for(const Q of O){const ue=this._tokenizer_config[Q];if(ue)if(typeof ue=="object"){if(ue.__type==="AddedToken")return ue.content;throw Error(`Unknown token: ${ue}`)}else return ue}return null}static async from_pretrained(O,{progress_callback:Q=null,config:ue=null,cache_dir:de=null,local_files_only:Fe=!1,revision:gt="main",legacy:bt=null}={}){const yt=await J(O,{progress_callback:Q,config:ue,cache_dir:de,local_files_only:Fe,revision:gt,legacy:bt});return new this(...yt)}_call(O,{text_pair:Q=null,add_special_tokens:ue=!0,padding:de=!1,truncation:Fe=null,max_length:gt=null,return_tensor:bt=!0,return_token_type_ids:yt=null}={}){const Pt=Array.isArray(O);let Jt;if(Pt){if(O.length===0)throw Error("text array must be non-empty");if(Q!==null){if(Array.isArray(Q)){if(O.length!==Q.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");Jt=O.map((nr,Gt)=>this._encode_plus(nr,{text_pair:Q[Gt],add_special_tokens:ue,return_token_type_ids:yt}))}else Jt=O.map(nr=>this._encode_plus(nr,{add_special_tokens:ue,return_token_type_ids:yt}))}else{if(O==null)throw Error("text may not be null or undefined");if(Array.isArray(Q))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");Jt=[this._encode_plus(O,{text_pair:Q,add_special_tokens:ue,return_token_type_ids:yt})]}if(gt===null?de==="max_length"?gt=this.model_max_length:gt=(0,ve.max)(Jt.map(nr=>nr.input_ids.length))[0]:Fe||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."),gt=Math.min(gt,this.model_max_length??1/0),de||Fe)for(let nr=0;nr<Jt.length;++nr)Jt[nr].input_ids.length!==gt&&(Jt[nr].input_ids.length>gt?Fe&&Lt(Jt[nr],gt):de&&Ct(Jt[nr],gt,Gt=>Gt==="input_ids"?this.pad_token_id:0,this.padding_side));const $r={};if(bt){if(!(de&&Fe)&&Jt.some(Gt=>{var fr;for(const on of Object.keys(Gt))if(Gt[on].length!==((fr=Jt[0][on])==null?void 0:fr.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const nr=[Jt.length,Jt[0].input_ids.length];for(const Gt of Object.keys(Jt[0]))$r[Gt]=new xe.Tensor("int64",BigInt64Array.from(Jt.flatMap(fr=>fr[Gt]).map(BigInt)),nr)}else{for(const nr of Object.keys(Jt[0]))$r[nr]=Jt.map(Gt=>Gt[nr]);if(!Pt)for(const nr of Object.keys($r))$r[nr]=$r[nr][0]}return $r}_encode_text(O){return O===null?null:(this.added_tokens_regex?O.split(this.added_tokens_regex).filter(de=>de):[O]).map((de,Fe)=>{if(this.added_tokens.find(bt=>bt.content===de)!==void 0)return de;{if(this.remove_space===!0&&(de=de.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(de=le(de)),this.normalizer!==null&&(de=this.normalizer(de)),de.length===0)return[];const bt=this.pre_tokenizer!==null?this.pre_tokenizer(de,{section_index:Fe}):[de];return this.model(bt)}}).flat()}_encode_plus(O,{text_pair:Q=null,add_special_tokens:ue=!0,return_token_type_ids:de=null}={}){const{tokens:Fe,token_type_ids:gt}=this._tokenize_helper(O,{pair:Q,add_special_tokens:ue}),bt=this.model.convert_tokens_to_ids(Fe),yt={input_ids:bt,attention_mask:new Array(bt.length).fill(1)};return(de??this.return_token_type_ids)&&gt&&(yt.token_type_ids=gt),yt}_tokenize_helper(O,{pair:Q=null,add_special_tokens:ue=!1}={}){const de=this._encode_text(O),Fe=this._encode_text(Q);return this.post_processor?this.post_processor(de,Fe,{add_special_tokens:ue}):{tokens:(0,H.mergeArrays)(de??[],Fe??[])}}tokenize(O,{pair:Q=null,add_special_tokens:ue=!1}={}){return this._tokenize_helper(O,{pair:Q,add_special_tokens:ue}).tokens}encode(O,{text_pair:Q=null,add_special_tokens:ue=!0,return_token_type_ids:de=null}={}){return this._encode_plus(O,{text_pair:Q,add_special_tokens:ue,return_token_type_ids:de}).input_ids}batch_decode(O,Q={}){return O instanceof xe.Tensor&&(O=O.tolist()),O.map(ue=>this.decode(ue,Q))}decode(O,Q={}){if(O instanceof xe.Tensor&&(O=R(O)),!Array.isArray(O)||O.length===0||!(0,H.isIntegralNumber)(O[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(O,Q)}decode_single(O,{skip_special_tokens:Q=!1,clean_up_tokenization_spaces:ue=null}){let de=this.model.convert_ids_to_tokens(O);Q&&(de=de.filter(gt=>!this.special_tokens.includes(gt)));let Fe=this.decoder?this.decoder(de):de.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Fe=Fe.replaceAll(this.decoder.end_of_word_suffix," "),Q&&(Fe=Fe.trim())),(ue??this.clean_up_tokenization_spaces)&&(Fe=Y(Fe)),Fe}apply_chat_template(O,{tools:Q=null,documents:ue=null,chat_template:de=null,add_generation_prompt:Fe=!1,tokenize:gt=!0,padding:bt=!1,truncation:yt=!1,max_length:Pt=null,return_tensor:Jt=!0,return_dict:$r=!1,tokenizer_kwargs:nr={},...Gt}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null){const qe=this.chat_template;if(de!==null&&Object.hasOwn(qe,de))de=qe[de];else if(de===null&&"default"in qe)de=qe.default;else if(de===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(qe).sort()}.`)}else if(this.chat_template)de=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 de!="string")throw Error(`chat_template must be a string, but got ${typeof de}`);let fr=this._compiled_template_cache.get(de);fr===void 0&&(fr=new T.Template(de),this._compiled_template_cache.set(de,fr));const on=Object.create(null);for(const qe of ft){const yn=this.getToken(qe);yn&&(on[qe]=yn)}const Yr=fr.render({messages:O,add_generation_prompt:Fe,tools:Q,documents:ue,...on,...Gt});if(gt){const qe=this._call(Yr,{add_special_tokens:!1,padding:bt,truncation:yt,max_length:Pt,return_tensor:Jt,...nr});return $r?qe:qe.input_ids}return Yr}}class jt extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class Rt extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class Ht extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class Xt extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class er extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class Wt extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class Tr extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class Ur extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class Cr extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class Ze extends Xe{}class St extends Xe{}class Dt extends Xe{constructor(O,Q){super(O,Q);Te(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class qr extends Xe{constructor(){super(...arguments);Te(this,"return_token_type_ids",!0)}}class Un extends Xe{}class Fn extends Xe{}class Lr extends Xe{}class Zr extends Xe{constructor(_,O){super(_,O),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)),this.lang_to_token=Q=>Q}_build_translation_inputs(_,O,Q){return zn(this,_,O,Q)}}class Nr extends Zr{}class Sn extends Xe{}class Pr extends Xe{constructor(_,O){var de,Fe;const Q=".,!?…。，、।۔،",ue=(Fe=(de=_.pre_tokenizer)==null?void 0:de.pretokenizers[0])==null?void 0:Fe.pattern;ue&&ue.Regex===` ?[^(\\s|[${Q}])]+`&&(ue.Regex=` ?[^\\s${Q}]+`),super(_,O)}}const Wn="▁";class On extends Xe{constructor(O,Q){super(O,Q);Te(this,"padding_side","left");this.legacy=Q.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new be({replacement:Wn,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(O){if(O===null)return null;if(this.legacy||O.length===0)return super._encode_text(O);let Q=super._encode_text(Wn+O.replaceAll(Wn," "));return Q.length>1&&Q[0]===Wn&&this.special_tokens.includes(Q[1])&&(Q=Q.slice(1)),Q}}class Vs extends Xe{}class _s extends Xe{}class gs extends Xe{}class ws extends Xe{}class ys extends Xe{}class Gn extends Xe{}class Us extends Xe{}class ss extends Xe{}class kn extends Xe{}function zn(Me,_,O,Q){if(!("language_codes"in Me)||!Array.isArray(Me.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Me)||!(Me.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Me)||typeof Me.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const ue=Q.src_lang,de=Q.tgt_lang;if(!Me.language_codes.includes(de))throw new Error(`Target language code "${de}" is not valid. Must be one of: {${Me.language_codes.join(", ")}}`);if(ue!==void 0){if(!Me.language_codes.includes(ue))throw new Error(`Source language code "${ue}" is not valid. Must be one of: {${Me.language_codes.join(", ")}}`);for(const Fe of Me.post_processor.config.single)if("SpecialToken"in Fe&&Me.languageRegex.test(Fe.SpecialToken.id)){Fe.SpecialToken.id=Me.lang_to_token(ue);break}}return Q.forced_bos_token_id=Me.model.convert_tokens_to_ids([Me.lang_to_token(de)])[0],Me._call(_,O)}class Dn extends Xe{constructor(_,O){super(_,O),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)),this.lang_to_token=Q=>Q}_build_translation_inputs(_,O,Q){return zn(this,_,O,Q)}}class Qn extends Xe{constructor(_,O){super(_,O),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)).map(Q=>Q.slice(2,-2)),this.lang_to_token=Q=>`__${Q}__`}_build_translation_inputs(_,O,Q){return zn(this,_,O,Q)}}class is extends Xe{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(_,{return_timestamps:O=!1,return_language:Q=!1,time_precision:ue=null,force_full_sequences:de=!0}={}){if(ue===null)throw Error("Must specify time_precision");let Fe=null;const gt=O==="word";function bt(){return{language:Fe,timestamp:[null,null],text:""}}const yt=[];let Pt=bt(),Jt=0;const $r=this.timestamp_begin;let nr=[],Gt=[],fr=!1,on=null;const Yr=new Set(this.all_special_ids);for(const Mr of _){const Hr=Mr.tokens,cn=gt?Mr.token_timestamps:null;let Yt=null,_n=$r;if("stride"in Mr){const[xr,kt,gr]=Mr.stride;if(Jt-=kt,on=xr-gr,kt&&(_n=kt/ue+$r),gr)for(let Ar=Hr.length-1;Ar>=0;--Ar){const jr=Number(Hr[Ar]);if(jr>=$r){if(Yt!==null&&(jr-$r)*ue<on)break;Yt=jr}}}let Jr=[],vr=[];for(let xr=0;xr<Hr.length;++xr){const kt=Number(Hr[xr]);if(Yr.has(kt)){const gr=this.decode([kt]),Ar=j.WHISPER_LANGUAGE_MAPPING.get(gr.slice(2,-2));if(Ar!==void 0){if(Fe!==null&&Ar!==Fe&&!O){nr.push(Jr);const jr=this.findLongestCommonSequence(nr)[0],gn=this.decode(jr);Pt.text=gn,yt.push(Pt),nr=[],Jr=[],Pt=bt()}Fe=Pt.language=Ar}}else if(kt>=$r){const gr=(kt-$r)*ue+Jt,Ar=(0,ve.round)(gr,2);if(Yt!==null&&kt>=Yt)fr=!0;else if(fr||nr.length>0&&kt<_n)fr=!1;else if(Pt.timestamp[0]===null)Pt.timestamp[0]=Ar;else if(Ar!==Pt.timestamp[0]){Pt.timestamp[1]=Ar,nr.push(Jr),gt&&Gt.push(vr);const[jr,gn]=this.findLongestCommonSequence(nr,Gt),Ft=this.decode(jr);Pt.text=Ft,gt&&(Pt.words=this.collateWordTimestamps(jr,gn,Fe)),yt.push(Pt),nr=[],Jr=[],Gt=[],vr=[],Pt=bt()}}else if(Jr.push(kt),gt){let gr=(0,ve.round)(cn[xr]+Jt,2),Ar;if(xr+1<cn.length){Ar=(0,ve.round)(cn[xr+1]+Jt,2);const jr=this.decode([kt]);A.test(jr)&&(Ar=(0,ve.round)(Math.min(gr+ue,Ar),2))}else Ar=null;vr.push([gr,Ar])}}if("stride"in Mr){const[xr,kt,gr]=Mr.stride;Jt+=xr-gr}Jr.length>0?(nr.push(Jr),gt&&Gt.push(vr)):nr.every(xr=>xr.length===0)&&(Pt=bt(),nr=[],Jr=[],Gt=[],vr=[])}if(nr.length>0){if(de&&O)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[Mr,Hr]=this.findLongestCommonSequence(nr,Gt),cn=this.decode(Mr);Pt.text=cn,gt&&(Pt.words=this.collateWordTimestamps(Mr,Hr,Fe)),yt.push(Pt)}let qe=Object.create(null);const yn=yt.map(Mr=>Mr.text).join("");if(O||Q){for(let Mr=0;Mr<yt.length;++Mr){const Hr=yt[Mr];O||delete Hr.timestamp,Q||delete Hr.language}if(gt){const Mr=[];for(const Hr of yt)for(const cn of Hr.words)Mr.push(cn);qe={chunks:Mr}}else qe={chunks:yt}}return[yn,qe]}findLongestCommonSequence(_,O=null){let Q=_[0],ue=Q.length,de=[];const Fe=Array.isArray(O)&&O.length>0;let gt=Fe?[]:null,bt=Fe?O[0]:null;for(let yt=1;yt<_.length;++yt){const Pt=_[yt];let Jt=0,$r=[ue,ue,0,0];const nr=Pt.length;for(let Mr=1;Mr<ue+nr;++Mr){const Hr=Math.max(0,ue-Mr),cn=Math.min(ue,ue+nr-Mr),Yt=Q.slice(Hr,cn),_n=Math.max(0,Mr-ue),Jr=Math.min(nr,Mr),vr=Pt.slice(_n,Jr);if(Yt.length!==vr.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let xr;Fe?xr=Yt.filter((Ar,jr)=>Ar===vr[jr]&&bt[Hr+jr]<=O[yt][_n+jr]).length:xr=Yt.filter((Ar,jr)=>Ar===vr[jr]).length;const kt=Mr/1e4,gr=xr/Mr+kt;xr>1&&gr>Jt&&(Jt=gr,$r=[Hr,cn,_n,Jr])}const[Gt,fr,on,Yr]=$r,qe=Math.floor((fr+Gt)/2),yn=Math.floor((Yr+on)/2);de.push(...Q.slice(0,qe)),Q=Pt.slice(yn),ue=Q.length,Fe&&(gt.push(...bt.slice(0,qe)),bt=O[yt].slice(yn))}return de.push(...Q),Fe?(gt.push(...bt),[de,gt]):[de,[]]}collateWordTimestamps(_,O,Q){const[ue,de,Fe]=this.combineTokensIntoWords(_,Q),gt=[];for(let bt=0;bt<ue.length;++bt){const yt=Fe[bt];gt.push({text:ue[bt],timestamp:[O[yt.at(0)][0],O[yt.at(-1)][1]]})}return gt}combineTokensIntoWords(_,O,Q=`"'“¡¿([{-`,ue=`"'.。,，!！?？:：”)]}、`){O=O??"english";let de,Fe,gt;return["chinese","japanese","thai","lao","myanmar"].includes(O)?[de,Fe,gt]=this.splitTokensOnUnicode(_):[de,Fe,gt]=this.splitTokensOnSpaces(_),this.mergePunctuations(de,Fe,gt,Q,ue)}decode(_,O){let Q;return O!=null&&O.decode_with_timestamps?(_ instanceof xe.Tensor&&(_=R(_)),Q=this.decodeWithTimestamps(_,O)):Q=super.decode(_,O),Q}decodeWithTimestamps(_,O){const Q=(O==null?void 0:O.time_precision)??.02,ue=Array.from(this.all_special_ids).at(-1)+1;let de=[[]];for(let Fe of _)if(Fe=Number(Fe),Fe>=ue){const gt=((Fe-ue)*Q).toFixed(2);de.push(`<|${gt}|>`),de.push([])}else de[de.length-1].push(Fe);return de=de.map(Fe=>typeof Fe=="string"?Fe:super.decode(Fe,O)),de.join("")}splitTokensOnUnicode(_){const O=this.decode(_,{decode_with_timestamps:!0}),Q="�",ue=[],de=[],Fe=[];let gt=[],bt=[],yt=0;for(let Pt=0;Pt<_.length;++Pt){const Jt=_[Pt];gt.push(Jt),bt.push(Pt);const $r=this.decode(gt,{decode_with_timestamps:!0});(!$r.includes(Q)||O[yt+$r.indexOf(Q)]===Q)&&(ue.push($r),de.push(gt),Fe.push(bt),gt=[],bt=[],yt+=$r.length)}return[ue,de,Fe]}splitTokensOnSpaces(_){const[O,Q,ue]=this.splitTokensOnUnicode(_),de=[],Fe=[],gt=[],bt=new RegExp(`^[${B}]$`,"gu");for(let yt=0;yt<O.length;++yt){const Pt=O[yt],Jt=Q[yt],$r=ue[yt],nr=Jt[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),Gt=Pt.startsWith(" "),fr=Pt.trim(),on=bt.test(fr);if(nr||Gt||on||de.length===0)de.push(Pt),Fe.push(Jt),gt.push($r);else{const Yr=de.length-1;de[Yr]+=Pt,Fe[Yr].push(...Jt),gt[Yr].push(...$r)}}return[de,Fe,gt]}mergePunctuations(_,O,Q,ue,de){const Fe=structuredClone(_),gt=structuredClone(O),bt=structuredClone(Q);let yt=Fe.length-2,Pt=Fe.length-1;for(;yt>=0;)Fe[yt].startsWith(" ")&&ue.includes(Fe[yt].trim())?(Fe[Pt]=Fe[yt]+Fe[Pt],gt[Pt]=(0,H.mergeArrays)(gt[yt],gt[Pt]),bt[Pt]=(0,H.mergeArrays)(bt[yt],bt[Pt]),Fe[yt]="",gt[yt]=[],bt[yt]=[]):Pt=yt,--yt;for(yt=0,Pt=1;Pt<Fe.length;)!Fe[yt].endsWith(" ")&&de.includes(Fe[Pt])?(Fe[yt]+=Fe[Pt],gt[yt]=(0,H.mergeArrays)(gt[yt],gt[Pt]),bt[yt]=(0,H.mergeArrays)(bt[yt],bt[Pt]),Fe[Pt]="",gt[Pt]=[],bt[Pt]=[]):yt=Pt,++Pt;return[Fe.filter(Jt=>Jt),gt.filter(Jt=>Jt.length>0),bt.filter(Jt=>Jt.length>0)]}get_decoder_prompt_ids({language:_=null,task:O=null,no_timestamps:Q=!0}={}){const ue=[];if(_){const de=(0,j.whisper_language_to_code)(_),Fe=this.model.tokens_to_ids.get(`<|${de}|>`);if(Fe===void 0)throw new Error(`Unable to find language "${de}" in model vocabulary. 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Using bilinear instead.`),le="bilinear");case"nearest":case"bilinear":case"bicubic":ae=ae.affine([R/this.width,0,0,Y/this.height],{interpolator:le});break;case"lanczos":ae=ae.resize({width:R,height:Y,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${le} is not supported.`)}return await P(ae)}}async pad([R,Y,se,le]){if(R=Math.max(R,0),Y=Math.max(Y,0),se=Math.max(se,0),le=Math.max(le,0),R===0&&Y===0&&se===0&&le===0)return this;if(xe){const ae=this.channels,N=this.toCanvas(),I=this.width+R+Y,B=this.height+se+le,A=T(I,B).getContext("2d");return A.drawImage(N,0,0,this.width,this.height,R,se,I,B),new ne(A.getImageData(0,0,I,B).data,I,B,4).convert(ae)}else{const ae=this.toSharp().extend({left:R,right:Y,top:se,bottom:le});return await P(ae)}}async crop([R,Y,se,le]){if(R=Math.max(R,0),Y=Math.max(Y,0),se=Math.min(se,this.width-1),le=Math.min(le,this.height-1),R===0&&Y===0&&se===this.width-1&&le===this.height-1)return this;const ae=se-R+1,N=le-Y+1;if(xe){const I=this.channels,B=this.toCanvas(),A=T(ae,N).getContext("2d");return A.drawImage(B,R,Y,ae,N,0,0,ae,N),new ne(A.getImageData(0,0,ae,N).data,ae,N,4).convert(I)}else{const I=this.toSharp().extract({left:R,top:Y,width:ae,height:N});return await P(I)}}async center_crop(R,Y){if(this.width===R&&this.height===Y)return this;const se=(this.width-R)/2,le=(this.height-Y)/2;if(xe){const ae=this.channels,N=this.toCanvas(),I=T(R,Y).getContext("2d");let B=0,A=0,_e=0,ye=0;return se>=0?B=se:_e=-se,le>=0?A=le:ye=-le,I.drawImage(N,B,A,R,Y,_e,ye,R,Y),new ne(I.getImageData(0,0,R,Y).data,R,Y,4).convert(ae)}else{let ae=this.toSharp();if(se>=0&&le>=0)ae=ae.extract({left:Math.floor(se),top:Math.floor(le),width:R,height:Y});else if(se<=0&&le<=0){const N=Math.floor(-le),I=Math.floor(-se);ae=ae.extend({top:N,left:I,right:R-this.width-I,bottom:Y-this.height-N})}else{let N=[0,0],I=0;le<0?(N[0]=Math.floor(-le),N[1]=Y-this.height-N[0]):I=Math.floor(le);let B=[0,0],A=0;se<0?(B[0]=Math.floor(-se),B[1]=R-this.width-B[0]):A=Math.floor(se),ae=ae.extend({top:N[0],bottom:N[1],left:B[0],right:B[1]}).extract({left:A,top:I,width:R,height:Y})}return await P(ae)}}async toBlob(R="image/png",Y=1){if(!xe)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:R,quality:Y})}toTensor(R="CHW"){let Y=new ge.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(R!=="HWC")if(R==="CHW")Y=Y.permute(2,0,1);else throw new Error(`Unsupported channel format: ${R}`);return Y}toCanvas(){if(!xe)throw new Error("toCanvas() is only supported in browser environments.");const R=this.clone().rgba(),Y=T(R.width,R.height),se=new j(R.data,R.width,R.height);return Y.getContext("2d").putImageData(se,0,0),Y}_update(R,Y,se,le=null){return this.data=R,this.width=Y,this.height=se,le!==null&&(this.channels=le),this}clone(){return new ne(this.data.slice(),this.width,this.height,this.channels)}convert(R){if(this.channels===R)return this;switch(R){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: ${this.channels}`)}return this}async save(R){if(xe){if(D)throw new Error("Unable to save an image from a Web Worker.");const Y=R.split(".").pop().toLowerCase(),se=te.get(Y)??"image/png",le=await this.toBlob(se),ae=URL.createObjectURL(le),N=document.createElement("a");N.href=ae,N.download=R,N.click(),N.remove()}else{if(H.env.useFS)return await this.toSharp().toFile(R);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(xe)throw new Error("toSharp() is only supported in server-side environments.");return ve(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}},"./src/utils/maths.js":(Mt,me,l)=>{l.r(me),l.d(me,{FFT:()=>ie,bankers_round:()=>se,cos_sim:()=>D,dot:()=>xe,dynamic_time_warping:()=>le,interpolate_data:()=>x,log_softmax:()=>ve,magnitude:()=>T,max:()=>P,medianFilter:()=>R,min:()=>j,permute_data:()=>H,round:()=>Y,softmax:()=>ge});function x(ae,[N,I,B],[A,_e],ye="bilinear",Ce=!1){const ke=_e/B,Ie=A/I,tt=new ae.constructor(A*_e*N),Qe=I*B,ht=A*_e;for(let we=0;we<A;++we)for(let V=0;V<_e;++V){const he=we*_e+V,$e=(V+.5)/ke-.5,ee=(we+.5)/Ie-.5;let He=Math.floor($e),pt=Math.floor(ee);const rt=Math.min(He+1,B-1),lt=Math.min(pt+1,I-1);He=Math.max(He,0),pt=Math.max(pt,0);const Re=$e-He,st=ee-pt,Tt=(1-Re)*(1-st),ze=Re*(1-st),re=(1-Re)*st,Ee=Re*st,je=pt*B,Ge=lt*B,Ve=je+He,Ke=je+rt,ut=Ge+He,mt=Ge+rt;for(let wt=0;wt<N;++wt){const xt=wt*Qe;tt[wt*ht+he]=Tt*ae[xt+Ve]+ze*ae[xt+Ke]+re*ae[xt+ut]+Ee*ae[xt+mt]}}return tt}function H(ae,N,I){const B=new Array(I.length),A=new Array(I.length);for(let Ce=I.length-1,ke=1;Ce>=0;--Ce)A[Ce]=ke,B[Ce]=N[I[Ce]],ke*=B[Ce];const _e=I.map((Ce,ke)=>A[I.indexOf(ke)]),ye=new ae.constructor(ae.length);for(let Ce=0;Ce<ae.length;++Ce){let ke=0;for(let Ie=N.length-1,tt=Ce;Ie>=0;--Ie)ke+=tt%N[Ie]*_e[Ie],tt=Math.floor(tt/N[Ie]);ye[ke]=ae[Ce]}return[ye,B]}function ge(ae){const N=P(ae)[0],I=ae.map(_e=>Math.exp(_e-N)),B=I.reduce((_e,ye)=>_e+ye,0);return I.map(_e=>_e/B)}function ve(ae){return ge(ae).map(B=>Math.log(B))}function xe(ae,N){let I=0;for(let B=0;B<ae.length;++B)I+=ae[B]*N[B];return I}function D(ae,N){const I=xe(ae,N),B=T(ae),A=T(N);return I/(B*A)}function T(ae){return Math.sqrt(ae.reduce((N,I)=>N+I*I,0))}function j(ae){if(ae.length===0)throw Error("Array must not be empty");let N=ae[0],I=0;for(let B=1;B<ae.length;++B)ae[B]<N&&(N=ae[B],I=B);return[N,I]}function P(ae){if(ae.length===0)throw Error("Array must not be empty");let N=ae[0],I=0;for(let B=1;B<ae.length;++B)ae[B]>N&&(N=ae[B],I=B);return[Number(N),I]}function J(ae){return ae>0&&(ae&ae-1)===0}class te{constructor(N){if(this.size=N|0,this.size<=1||!J(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=N<<1,this.table=new Float64Array(this.size*2);for(let B=0;B<this.table.length;B+=2){const A=Math.PI*B/this.size;this.table[B]=Math.cos(A),this.table[B+1]=-Math.sin(A)}let I=0;for(let B=1;this.size>B;B<<=1)++I;this._width=I%2===0?I-1:I,this._bitrev=new Int32Array(1<<this._width);for(let B=0;B<this._bitrev.length;++B){this._bitrev[B]=0;for(let A=0;A<this._width;A+=2){const _e=this._width-A-2;this._bitrev[B]|=(B>>>A&3)<<_e}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(N,I){const B=I||new Array(N.length>>>1);for(let A=0;A<N.length;A+=2)B[A>>>1]=N[A];return B}toComplexArray(N,I){const B=I||this.createComplexArray();for(let A=0;A<B.length;A+=2)B[A]=N[A>>>1],B[A+1]=0;return B}transform(N,I){if(N===I)throw new Error("Input and output buffers must be different");this._transform4(N,I,1)}realTransform(N,I){if(N===I)throw new Error("Input and output buffers must be different");this._realTransform4(N,I,1)}inverseTransform(N,I){if(N===I)throw new Error("Input and output buffers must be different");this._transform4(N,I,-1);for(let B=0;B<N.length;++B)N[B]/=this.size}_transform4(N,I,B){const A=this._csize;let ye=1<<this._width,Ce=A/ye<<1,ke,Ie;const tt=this._bitrev;if(Ce===4)for(ke=0,Ie=0;ke<A;ke+=Ce,++Ie){const ht=tt[Ie];this._singleTransform2(I,N,ke,ht,ye)}else for(ke=0,Ie=0;ke<A;ke+=Ce,++Ie){const ht=tt[Ie];this._singleTransform4(I,N,ke,ht,ye,B)}const Qe=this.table;for(ye>>=2;ye>=2;ye>>=2){Ce=A/ye<<1;const ht=Ce>>>2;for(ke=0;ke<A;ke+=Ce){const we=ke+ht-1;for(let V=ke,he=0;V<we;V+=2,he+=ye){const $e=V,ee=$e+ht,He=ee+ht,pt=He+ht,rt=N[$e],lt=N[$e+1],Re=N[ee],st=N[ee+1],Tt=N[He],ze=N[He+1],re=N[pt],Ee=N[pt+1],je=Qe[he],Ge=B*Qe[he+1],Ve=Re*je-st*Ge,Ke=Re*Ge+st*je,ut=Qe[2*he],mt=B*Qe[2*he+1],wt=Tt*ut-ze*mt,xt=Tt*mt+ze*ut,M=Qe[3*he],W=B*Qe[3*he+1],S=re*M-Ee*W,X=re*W+Ee*M,fe=rt+wt,Ye=lt+xt,Je=rt-wt,At=lt-xt,_t=Ve+S,Se=Ke+X,$=B*(Ve-S),q=B*(Ke-X);N[$e]=fe+_t,N[$e+1]=Ye+Se,N[ee]=Je+q,N[ee+1]=At-$,N[He]=fe-_t,N[He+1]=Ye-Se,N[pt]=Je-q,N[pt+1]=At+$}}}}_singleTransform2(N,I,B,A,_e){const ye=N[A],Ce=N[A+1],ke=N[A+_e],Ie=N[A+_e+1];I[B]=ye+ke,I[B+1]=Ce+Ie,I[B+2]=ye-ke,I[B+3]=Ce-Ie}_singleTransform4(N,I,B,A,_e,ye){const Ce=_e*2,ke=_e*3,Ie=N[A],tt=N[A+1],Qe=N[A+_e],ht=N[A+_e+1],we=N[A+Ce],V=N[A+Ce+1],he=N[A+ke],$e=N[A+ke+1],ee=Ie+we,He=tt+V,pt=Ie-we,rt=tt-V,lt=Qe+he,Re=ht+$e,st=ye*(Qe-he),Tt=ye*(ht-$e);I[B]=ee+lt,I[B+1]=He+Re,I[B+2]=pt+Tt,I[B+3]=rt-st,I[B+4]=ee-lt,I[B+5]=He-Re,I[B+6]=pt-Tt,I[B+7]=rt+st}_realTransform4(N,I,B){const A=this._csize;let ye=1<<this._width,Ce=A/ye<<1,ke,Ie;const tt=this._bitrev;if(Ce===4)for(ke=0,Ie=0;ke<A;ke+=Ce,++Ie){const we=tt[Ie];this._singleRealTransform2(I,N,ke,we>>>1,ye>>>1)}else for(ke=0,Ie=0;ke<A;ke+=Ce,++Ie){const we=tt[Ie];this._singleRealTransform4(I,N,ke,we>>>1,ye>>>1,B)}const Qe=this.table;for(ye>>=2;ye>=2;ye>>=2){Ce=A/ye<<1;const we=Ce>>>1,V=we>>>1,he=V>>>1;for(ke=0;ke<A;ke+=Ce)for(let $e=0,ee=0;$e<=he;$e+=2,ee+=ye){const He=ke+$e,pt=He+V,rt=pt+V,lt=rt+V,Re=N[He],st=N[He+1],Tt=N[pt],ze=N[pt+1],re=N[rt],Ee=N[rt+1],je=N[lt],Ge=N[lt+1],Ve=Re,Ke=st,ut=Qe[ee],mt=B*Qe[ee+1],wt=Tt*ut-ze*mt,xt=Tt*mt+ze*ut,M=Qe[2*ee],W=B*Qe[2*ee+1],S=re*M-Ee*W,X=re*W+Ee*M,fe=Qe[3*ee],Ye=B*Qe[3*ee+1],Je=je*fe-Ge*Ye,At=je*Ye+Ge*fe,_t=Ve+S,Se=Ke+X,$=Ve-S,q=Ke-X,be=wt+Je,Be=xt+At,Ae=B*(wt-Je),Ne=B*(xt-At);if(N[He]=_t+be,N[He+1]=Se+Be,N[pt]=$+Ne,N[pt+1]=q-Ae,$e===0){N[rt]=_t-be,N[rt+1]=Se-Be;continue}if($e===he)continue;const dt=ke+V-$e,nt=ke+we-$e;N[dt]=$-B*Ne,N[dt+1]=-q-B*Ae,N[nt]=_t-B*be,N[nt+1]=-Se+B*Be}}const ht=A>>>1;for(let we=2;we<ht;we+=2)N[A-we]=N[we],N[A-we+1]=-N[we+1]}_singleRealTransform2(N,I,B,A,_e){const ye=N[A],Ce=N[A+_e];I[B]=ye+Ce,I[B+1]=0,I[B+2]=ye-Ce,I[B+3]=0}_singleRealTransform4(N,I,B,A,_e,ye){const Ce=_e*2,ke=_e*3,Ie=N[A],tt=N[A+_e],Qe=N[A+Ce],ht=N[A+ke],we=Ie+Qe,V=Ie-Qe,he=tt+ht,$e=ye*(tt-ht);I[B]=we+he,I[B+1]=0,I[B+2]=V,I[B+3]=-$e,I[B+4]=we-he,I[B+5]=0,I[B+6]=V,I[B+7]=$e}}class ne{constructor(N){const I=2*(N-1),B=2*(2*N-1),A=2**Math.ceil(Math.log2(B));this.bufferSize=A,this._a=I;const _e=new Float64Array(B),ye=new Float64Array(A);this._chirpBuffer=new Float64Array(A),this._buffer1=new Float64Array(A),this._buffer2=new Float64Array(A),this._outBuffer1=new Float64Array(A),this._outBuffer2=new Float64Array(A);const Ce=-2*Math.PI/N,ke=Math.cos(Ce),Ie=Math.sin(Ce);for(let tt=0;tt<B>>1;++tt){const Qe=(tt+1-N)**2/2,ht=Math.sqrt(ke**2+Ie**2)**Qe,we=Qe*Math.atan2(Ie,ke),V=2*tt;_e[V]=ht*Math.cos(we),_e[V+1]=ht*Math.sin(we),ye[V]=_e[V],ye[V+1]=-_e[V+1]}this._slicedChirpBuffer=_e.subarray(I,B),this._f=new te(A>>1),this._f.transform(this._chirpBuffer,ye)}_transform(N,I,B){const A=this._buffer1,_e=this._buffer2,ye=this._outBuffer1,Ce=this._outBuffer2,ke=this._chirpBuffer,Ie=this._slicedChirpBuffer,tt=this._a;if(B)for(let Qe=0;Qe<Ie.length;Qe+=2){const ht=Qe+1,we=Qe>>1,V=I[we];A[Qe]=V*Ie[Qe],A[ht]=V*Ie[ht]}else for(let Qe=0;Qe<Ie.length;Qe+=2){const ht=Qe+1;A[Qe]=I[Qe]*Ie[Qe]-I[ht]*Ie[ht],A[ht]=I[Qe]*Ie[ht]+I[ht]*Ie[Qe]}this._f.transform(ye,A);for(let Qe=0;Qe<ke.length;Qe+=2){const ht=Qe+1;_e[Qe]=ye[Qe]*ke[Qe]-ye[ht]*ke[ht],_e[ht]=ye[Qe]*ke[ht]+ye[ht]*ke[Qe]}this._f.inverseTransform(Ce,_e);for(let Qe=0;Qe<Ce.length;Qe+=2){const ht=Ce[Qe+tt],we=Ce[Qe+tt+1],V=Ie[Qe],he=Ie[Qe+1];N[Qe]=ht*V-we*he,N[Qe+1]=ht*he+we*V}}transform(N,I){this._transform(N,I,!1)}realTransform(N,I){this._transform(N,I,!0)}}class ie{constructor(N){this.fft_length=N,this.isPowerOfTwo=J(N),this.isPowerOfTwo?(this.fft=new te(N),this.outputBufferSize=2*N):(this.fft=new ne(N),this.outputBufferSize=this.fft.bufferSize)}realTransform(N,I){this.fft.realTransform(N,I)}transform(N,I){this.fft.transform(N,I)}}function R(ae,N){if(N%2===0||N<=0)throw new Error("Window size must be a positive odd number");const I=new ae.constructor(ae.length),B=new ae.constructor(N),A=Math.floor(N/2);for(let _e=0;_e<ae.length;++_e){let ye=0;for(let Ce=-A;Ce<=A;++Ce){let ke=_e+Ce;ke<0?ke=Math.abs(ke):ke>=ae.length&&(ke=2*(ae.length-1)-ke),B[ye++]=ae[ke]}B.sort(),I[_e]=B[A]}return I}function Y(ae,N){const I=Math.pow(10,N);return Math.round(ae*I)/I}function se(ae){const N=Math.round(ae);return Math.abs(ae)%1===.5?N%2===0?N:N-1:N}function le(ae){const N=ae.length,I=ae[0].length,B=[N+1,I+1],A=Array.from({length:B[0]},()=>Array(B[1]).fill(1/0));A[0][0]=0;const _e=Array.from({length:B[0]},()=>Array(B[1]).fill(-1));for(let tt=1;tt<B[1];++tt)for(let Qe=1;Qe<B[0];++Qe){const ht=A[Qe-1][tt-1],we=A[Qe-1][tt],V=A[Qe][tt-1];let he,$e;ht<we&&ht<V?(he=ht,$e=0):we<ht&&we<V?(he=we,$e=1):(he=V,$e=2),A[Qe][tt]=ae[Qe-1][tt-1]+he,_e[Qe][tt]=$e}for(let tt=0;tt<B[1];++tt)_e[0][tt]=2;for(let tt=0;tt<B[0];++tt)_e[tt][0]=1;let ye=N,Ce=I,ke=[],Ie=[];for(;ye>0||Ce>0;)switch(ke.push(ye-1),Ie.push(Ce-1),_e[ye][Ce]){case 0:--ye,--Ce;break;case 1:--ye;break;case 2:--Ce;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${ye}, ${Ce}]. Please file a bug report.`)}return ke.reverse(),Ie.reverse(),[ke,Ie]}},"./src/utils/tensor.js":(Mt,me,l)=>{l.r(me),l.d(me,{Tensor:()=>xe,cat:()=>ae,full:()=>ye,full_like:()=>Ce,interpolate:()=>j,interpolate_4d:()=>P,layer_norm:()=>R,matmul:()=>J,mean:()=>B,mean_pooling:()=>ie,ones:()=>ke,ones_like:()=>Ie,permute:()=>T,quantize_embeddings:()=>ht,rfft:()=>te,stack:()=>N,std_mean:()=>I,topk:()=>ne,zeros:()=>tt,zeros_like:()=>Qe});var x=l("./src/utils/maths.js"),H=l("./src/backends/onnx.js"),ge=l("./src/ops/registry.js");const ve=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array});class xe{constructor(...V){Te(this,"ort_tensor");return(0,H.isONNXTensor)(V[0])?this.ort_tensor=V[0]:this.ort_tensor=new H.Tensor(V[0],V[1],V[2]),new Proxy(this,{get:(he,$e)=>{if(typeof $e=="string"){let 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ef=p.AutoTokenizer;p.AutomaticSpeechRecognitionPipeline,p.BartForConditionalGeneration,p.BartForSequenceClassification,p.BartModel,p.BartPretrainedModel,p.BartTokenizer,p.BaseModelOutput,p.BaseStreamer,p.BeitFeatureExtractor,p.BeitForImageClassification,p.BeitModel,p.BeitPreTrainedModel,p.BertForMaskedLM,p.BertForQuestionAnswering,p.BertForSequenceClassification,p.BertForTokenClassification,p.BertModel,p.BertPreTrainedModel,p.BertTokenizer,p.BitImageProcessor,p.BlenderbotForConditionalGeneration,p.BlenderbotModel,p.BlenderbotPreTrainedModel,p.BlenderbotSmallForConditionalGeneration,p.BlenderbotSmallModel,p.BlenderbotSmallPreTrainedModel,p.BlenderbotSmallTokenizer,p.BlenderbotTokenizer,p.BloomForCausalLM,p.BloomModel,p.BloomPreTrainedModel,p.BloomTokenizer,p.CLIPFeatureExtractor,p.CLIPImageProcessor,p.CLIPModel,p.CLIPPreTrainedModel,p.CLIPSegForImageSegmentation,p.CLIPSegModel,p.CLIPSegPreTrainedModel,p.CLIPTextModelWithProjection,p.CLIPTokenizer,p.CLIPVisionModelWithProjection,p.CamembertForMaskedLM,p.CamembertForQuestionAnswering,p.CamembertForSequenceClassification,p.CamembertForTokenClassification,p.CamembertModel,p.CamembertPreTrainedModel,p.CamembertTokenizer,p.CausalLMOutput,p.CausalLMOutputWithPast,p.ChineseCLIPFeatureExtractor,p.ChineseCLIPModel,p.ChineseCLIPPreTrainedModel,p.ClapAudioModelWithProjection,p.ClapFeatureExtractor,p.ClapModel,p.ClapPreTrainedModel,p.ClapTextModelWithProjection,p.CodeGenForCausalLM,p.CodeGenModel,p.CodeGenPreTrainedModel,p.CodeGenTokenizer,p.CodeLlamaTokenizer,p.CohereForCausalLM,p.CohereModel,p.CoherePreTrainedModel,p.CohereTokenizer,p.ConvBertForMaskedLM,p.ConvBertForQuestionAnswering,p.ConvBertForSequenceClassification,p.ConvBertForTokenClassification,p.ConvBertModel,p.ConvBertPreTrainedModel,p.ConvBertTokenizer,p.ConvNextFeatureExtractor,p.ConvNextForImageClassification,p.ConvNextImageProcessor,p.ConvNextModel,p.ConvNextPreTrainedModel,p.ConvNextV2ForImageClassification,p.ConvNextV2Model,p.ConvNextV2PreTrainedModel,p.DPTFeatureExtractor,p.DPTForDepthEstimation,p.DPTImageProcessor,p.DPTModel,p.DPTPreTrainedModel,p.DebertaForMaskedLM,p.DebertaForQuestionAnswering,p.DebertaForSequenceClassification,p.DebertaForTokenClassification,p.DebertaModel,p.DebertaPreTrainedModel,p.DebertaTokenizer,p.DebertaV2ForMaskedLM,p.DebertaV2ForQuestionAnswering,p.DebertaV2ForSequenceClassification,p.DebertaV2ForTokenClassification,p.DebertaV2Model,p.DebertaV2PreTrainedModel,p.DebertaV2Tokenizer,p.DeiTFeatureExtractor,p.DeiTForImageClassification,p.DeiTModel,p.DeiTPreTrainedModel,p.DepthAnythingForDepthEstimation,p.DepthAnythingPreTrainedModel,p.DepthEstimationPipeline,p.DetrFeatureExtractor,p.DetrForObjectDetection,p.DetrForSegmentation,p.DetrModel,p.DetrObjectDetectionOutput,p.DetrPreTrainedModel,p.DetrSegmentationOutput,p.Dinov2ForImageClassification,p.Dinov2Model,p.Dinov2PreTrainedModel,p.DistilBertForMaskedLM,p.DistilBertForQuestionAnswering,p.DistilBertForSequenceClassification,p.DistilBertForTokenClassification,p.DistilBertModel,p.DistilBertPreTrainedModel,p.DistilBertTokenizer,p.DocumentQuestionAnsweringPipeline,p.DonutFeatureExtractor,p.DonutSwinModel,p.DonutSwinPreTrainedModel,p.EfficientNetForImageClassification,p.EfficientNetImageProcessor,p.EfficientNetModel,p.EfficientNetPreTrainedModel,p.ElectraForMaskedLM,p.ElectraForQuestionAnswering,p.ElectraForSequenceClassification,p.ElectraForTokenClassification,p.ElectraModel,p.ElectraPreTrainedModel,p.ElectraTokenizer,p.EosTokenCriteria,p.EsmForMaskedLM,p.EsmForSequenceClassification,p.EsmForTokenClassification,p.EsmModel,p.EsmPreTrainedModel,p.EsmTokenizer,p.FFT,p.FalconForCausalLM,p.FalconModel,p.FalconPreTrainedModel,p.FalconTokenizer,p.FastViTForImageClassification,p.FastViTModel,p.FastViTPreTrainedModel,p.FeatureExtractionPipeline,p.FeatureExtractor,p.FillMaskPipeline,p.Florence2ForConditionalGeneration,p.Florence2PreTrainedModel,p.Florence2Processor,p.GLPNFeatureExtractor,p.GLPNForDepthEstimation,p.GLPNModel,p.GLPNPreTrainedModel,p.GPT2LMHeadModel,p.GPT2Model,p.GPT2PreTrainedModel,p.GPT2Tokenizer,p.GPTBigCodeForCausalLM,p.GPTBigCodeModel,p.GPTBigCodePreTrainedModel,p.GPTJForCausalLM,p.GPTJModel,p.GPTJPreTrainedModel,p.GPTNeoForCausalLM,p.GPTNeoModel,p.GPTNeoPreTrainedModel,p.GPTNeoXForCausalLM,p.GPTNeoXModel,p.GPTNeoXPreTrainedModel,p.GPTNeoXTokenizer,p.Gemma2ForCausalLM,p.Gemma2Model,p.Gemma2PreTrainedModel,p.GemmaForCausalLM,p.GemmaModel,p.GemmaPreTrainedModel,p.GemmaTokenizer,p.Grok1Tokenizer,p.HerbertTokenizer,p.HubertForCTC,p.HubertForSequenceClassification,p.HubertModel,p.HubertPreTrainedModel,p.ImageClassificationPipeline,p.ImageFeatureExtractionPipeline,p.ImageFeatureExtractor,p.ImageMattingOutput,p.ImageSegmentationPipeline,p.ImageToImagePipeline,p.ImageToTextPipeline;var tf=p.InterruptableStoppingCriteria;p.LlamaForCausalLM,p.LlamaModel,p.LlamaPreTrainedModel,p.LlamaTokenizer,p.LlavaForConditionalGeneration,p.LlavaPreTrainedModel,p.LongT5ForConditionalGeneration,p.LongT5Model,p.LongT5PreTrainedModel,p.M2M100ForConditionalGeneration,p.M2M100Model,p.M2M100PreTrainedModel,p.M2M100Tokenizer,p.MBart50Tokenizer,p.MBartForCausalLM,p.MBartForConditionalGeneration,p.MBartForSequenceClassification,p.MBartModel,p.MBartPreTrainedModel,p.MBartTokenizer,p.MPNetForMaskedLM,p.MPNetForQuestionAnswering,p.MPNetForSequenceClassification,p.MPNetForTokenClassification,p.MPNetModel,p.MPNetPreTrainedModel,p.MPNetTokenizer,p.MT5ForConditionalGeneration,p.MT5Model,p.MT5PreTrainedModel,p.MarianMTModel,p.MarianModel,p.MarianPreTrainedModel,p.MarianTokenizer,p.MaskedLMOutput,p.MaxLengthCriteria,p.MistralForCausalLM,p.MistralModel,p.MistralPreTrainedModel,p.MobileBertForMaskedLM,p.MobileBertForQuestionAnswering,p.MobileBertForSequenceClassification,p.MobileBertModel,p.MobileBertPreTrainedModel,p.MobileBertTokenizer,p.MobileNetV1FeatureExtractor,p.MobileNetV1ForImageClassification,p.MobileNetV1Model,p.MobileNetV1PreTrainedModel,p.MobileNetV2FeatureExtractor,p.MobileNetV2ForImageClassification,p.MobileNetV2Model,p.MobileNetV2PreTrainedModel,p.MobileNetV3FeatureExtractor,p.MobileNetV3ForImageClassification,p.MobileNetV3Model,p.MobileNetV3PreTrainedModel,p.MobileNetV4FeatureExtractor,p.MobileNetV4ForImageClassification,p.MobileNetV4Model,p.MobileNetV4PreTrainedModel,p.MobileViTFeatureExtractor,p.MobileViTForImageClassification,p.MobileViTImageProcessor,p.MobileViTModel,p.MobileViTPreTrainedModel,p.MobileViTV2ForImageClassification,p.MobileViTV2Model,p.MobileViTV2PreTrainedModel,p.ModelOutput,p.Moondream1ForConditionalGeneration,p.MptForCausalLM,p.MptModel,p.MptPreTrainedModel,p.MusicgenForCausalLM,p.MusicgenForConditionalGeneration,p.MusicgenModel,p.MusicgenPreTrainedModel,p.NllbTokenizer,p.NomicBertModel,p.NomicBertPreTrainedModel,p.NougatImageProcessor,p.NougatTokenizer,p.OPTForCausalLM,p.OPTModel,p.OPTPreTrainedModel,p.ObjectDetectionPipeline,p.OpenELMForCausalLM,p.OpenELMModel,p.OpenELMPreTrainedModel,p.OwlViTFeatureExtractor,p.OwlViTForObjectDetection,p.OwlViTModel,p.OwlViTPreTrainedModel,p.OwlViTProcessor,p.Owlv2ForObjectDetection,p.Owlv2ImageProcessor,p.Owlv2Model,p.Owlv2PreTrainedModel,p.Phi3ForCausalLM,p.Phi3Model,p.Phi3PreTrainedModel,p.PhiForCausalLM,p.PhiModel,p.PhiPreTrainedModel,p.Pipeline,p.PreTrainedModel,p.PreTrainedTokenizer,p.PretrainedConfig,p.PretrainedMixin,p.Processor,p.PyAnnoteFeatureExtractor,p.PyAnnoteForAudioFrameClassification,p.PyAnnoteModel,p.PyAnnotePreTrainedModel,p.PyAnnoteProcessor,p.QuestionAnsweringModelOutput,p.QuestionAnsweringPipeline,p.Qwen2ForCausalLM,p.Qwen2Model,p.Qwen2PreTrainedModel,p.Qwen2Tokenizer,p.RTDetrForObjectDetection,p.RTDetrImageProcessor,p.RTDetrModel,p.RTDetrObjectDetectionOutput,p.RTDetrPreTrainedModel,p.RawImage,p.ResNetForImageClassification,p.ResNetModel,p.ResNetPreTrainedModel,p.RoFormerForMaskedLM,p.RoFormerForQuestionAnswering,p.RoFormerForSequenceClassification,p.RoFormerForTokenClassification,p.RoFormerModel,p.RoFormerPreTrainedModel,p.RoFormerTokenizer,p.RobertaForMaskedLM,p.RobertaForQuestionAnswering,p.RobertaForSequenceClassification,p.RobertaForTokenClassification,p.RobertaModel,p.RobertaPreTrainedModel,p.RobertaTokenizer,p.SamImageProcessor,p.SamImageSegmentationOutput,p.SamModel,p.SamPreTrainedModel,p.SamProcessor,p.SeamlessM4TFeatureExtractor,p.SegformerFeatureExtractor,p.SegformerForImageClassification,p.SegformerForSemanticSegmentation,p.SegformerModel,p.SegformerPreTrainedModel,p.Seq2SeqLMOutput,p.SequenceClassifierOutput,p.SiglipImageProcessor,p.SiglipModel,p.SiglipPreTrainedModel,p.SiglipTextModel,p.SiglipTokenizer,p.SiglipVisionModel,p.SpeechT5FeatureExtractor,p.SpeechT5ForSpeechToText,p.SpeechT5ForTextToSpeech,p.SpeechT5HifiGan,p.SpeechT5Model,p.SpeechT5PreTrainedModel,p.SpeechT5Processor,p.SpeechT5Tokenizer,p.SqueezeBertForMaskedLM,p.SqueezeBertForQuestionAnswering,p.SqueezeBertForSequenceClassification,p.SqueezeBertModel,p.SqueezeBertPreTrainedModel,p.SqueezeBertTokenizer,p.StableLmForCausalLM,p.StableLmModel,p.StableLmPreTrainedModel,p.Starcoder2ForCausalLM,p.Starcoder2Model,p.Starcoder2PreTrainedModel,p.StoppingCriteria,p.StoppingCriteriaList,p.SummarizationPipeline,p.Swin2SRForImageSuperResolution,p.Swin2SRImageProcessor,p.Swin2SRModel,p.Swin2SRPreTrainedModel,p.SwinForImageClassification,p.SwinModel,p.SwinPreTrainedModel,p.T5ForConditionalGeneration,p.T5Model,p.T5PreTrainedModel,p.T5Tokenizer,p.TableTransformerForObjectDetection,p.TableTransformerModel,p.TableTransformerObjectDetectionOutput,p.TableTransformerPreTrainedModel,p.Tensor,p.Text2TextGenerationPipeline,p.TextClassificationPipeline,p.TextGenerationPipeline;var rf=p.TextStreamer;p.TextToAudioPipeline,p.TokenClassificationPipeline,p.TokenClassifierOutput,p.TokenizerModel,p.TrOCRForCausalLM,p.TrOCRPreTrainedModel,p.TranslationPipeline,p.UniSpeechForCTC,p.UniSpeechForSequenceClassification,p.UniSpeechModel,p.UniSpeechPreTrainedModel,p.UniSpeechSatForAudioFrameClassification,p.UniSpeechSatForCTC,p.UniSpeechSatForSequenceClassification,p.UniSpeechSatModel,p.UniSpeechSatPreTrainedModel,p.ViTFeatureExtractor,p.ViTForImageClassification,p.ViTImageProcessor,p.ViTModel,p.ViTPreTrainedModel,p.VisionEncoderDecoderModel,p.VitMatteForImageMatting,p.VitMatteImageProcessor,p.VitMattePreTrainedModel,p.VitsModel,p.VitsModelOutput,p.VitsPreTrainedModel,p.VitsTokenizer,p.Wav2Vec2BertForCTC,p.Wav2Vec2BertForSequenceClassification,p.Wav2Vec2BertModel,p.Wav2Vec2BertPreTrainedModel,p.Wav2Vec2CTCTokenizer,p.Wav2Vec2FeatureExtractor,p.Wav2Vec2ForAudioFrameClassification,p.Wav2Vec2ForCTC,p.Wav2Vec2ForSequenceClassification,p.Wav2Vec2Model,p.Wav2Vec2PreTrainedModel,p.Wav2Vec2ProcessorWithLM,p.WavLMForAudioFrameClassification,p.WavLMForCTC,p.WavLMForSequenceClassification,p.WavLMForXVector,p.WavLMModel,p.WavLMPreTrainedModel,p.WeSpeakerFeatureExtractor,p.WeSpeakerResNetModel,p.WeSpeakerResNetPreTrainedModel,p.WhisperFeatureExtractor,p.WhisperForConditionalGeneration,p.WhisperModel,p.WhisperPreTrainedModel,p.WhisperProcessor,p.WhisperTextStreamer,p.WhisperTokenizer,p.XLMForQuestionAnswering,p.XLMForSequenceClassification,p.XLMForTokenClassification,p.XLMModel,p.XLMPreTrainedModel,p.XLMRobertaForMaskedLM,p.XLMRobertaForQuestionAnswering,p.XLMRobertaForSequenceClassification,p.XLMRobertaForTokenClassification,p.XLMRobertaModel,p.XLMRobertaPreTrainedModel,p.XLMRobertaTokenizer,p.XLMTokenizer,p.XLMWithLMHeadModel,p.XVectorOutput,p.YolosFeatureExtractor,p.YolosForObjectDetection,p.YolosModel,p.YolosObjectDetectionOutput,p.YolosPreTrainedModel,p.ZeroShotAudioClassificationPipeline,p.ZeroShotClassificationPipeline,p.ZeroShotImageClassificationPipeline,p.ZeroShotObjectDetectionPipeline,p.bankers_round,p.cat,p.cos_sim,p.dot,p.dynamic_time_warping,p.env,p.full,p.full_like,p.getKeyValueShapes,p.hamming,p.hanning,p.interpolate,p.interpolate_4d,p.interpolate_data,p.is_chinese_char,p.layer_norm,p.log_softmax,p.magnitude,p.matmul,p.max,p.mean,p.mean_pooling,p.medianFilter,p.mel_filter_bank,p.min,p.ones,p.ones_like,p.permute,p.permute_data,p.pipeline,p.quantize_embeddings,p.read_audio,p.rfft,p.round,p.softmax,p.spectrogram,p.stack,p.std_mean,p.topk,p.window_function,p.zeros,p.zeros_like;class Ed{static async getInstance(me=null){return this.tokenizer??(this.tokenizer=ef.from_pretrained(this.model_id,{progress_callback:me})),this.model??(this.model=Jh.from_pretrained(this.model_id,{dtype:"q4",device:"webgpu",progress_callback:me})),Promise.all([this.tokenizer,this.model])}}Te(Ed,"model_id","HuggingFaceTB/SmolLM-360M-Instruct");const Ou=new tf;let Sd=null;async function nf(Mt){const[me,l]=await Ed.getInstance(),x=me.apply_chat_template(Mt,{add_generation_prompt:!0,return_dict:!0});let H,ge=0,ve;const xe=()=>{H??(H=performance.now()),ge++>0&&(ve=ge/(performance.now()-H)*1e3)},D=te=>{self.postMessage({status:"update",output:te,tps:ve,numTokens:ge})},T=new rf(me,{skip_prompt:!0,skip_special_tokens:!0,callback_function:D,token_callback_function:xe});self.postMessage({status:"start"});const{past_key_values:j,sequences:P}=await l.generate({...x,past_key_values:Sd,do_sample:!0,top_k:3,temperature:.2,max_new_tokens:1024,streamer:T,stopping_criteria:Ou,return_dict_in_generate:!0});Sd=j;const J=me.batch_decode(P,{skip_special_tokens:!0});self.postMessage({status:"complete",output:J})}async function sf(){try{if(!await navigator.gpu.requestAdapter())throw new Error("WebGPU is not supported (no adapter found)")}catch(Mt){self.postMessage({status:"error",data:Mt.toString()})}}async function af(){self.postMessage({status:"loading",data:"Loading model..."});const[Mt,me]=await Ed.getInstance(x=>{self.postMessage(x)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const l=Mt("a");await me.generate({...l,max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async Mt=>{const{type:me,data:l}=Mt.data;switch(me){case"check":sf();break;case"load":af();break;case"generate":Ou.reset(),nf(l);break;case"interrupt":Ou.interrupt();break;case"reset":Sd=null,Ou.reset();break}})})();