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 * ONNX Runtime Web v1.21.0-dev.20241024-d9ca84ef96
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
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computePoolOutputShape(t,r,n,i,a,s,u){if(r.length<=0)throw new Error("input shape must be of size greater than 0");let d=[r[0],r[1]];return bd.computeShapeHelper(t,r,d,n,i,a,s,u),d}static computeConvOutputShape(t,r,n,i,a,s,u){if(t.length<=0||r.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let d=[t[0],r[0]];return bd.computeShapeHelper(!1,t,d,n,i,a,s,u),d}static computeShapeHelper(t,r,n,i,a,s,u,d){if(t)for(let c=0;c<r.length-2;c++)n.push(1);else for(let c=0;c<r.length-2;c++)n.push(bd.adjustPadAndReturnShape(r[c+2],i[c],a[c],s[c],u,c,c+r.length-2,d))}static adjustPadAndReturnShape(t,r,n,i,a,s,u,d){let c=n*(i-1)+1;if(d&&d!=="NOTSET")switch(d){case"VALID":return a[s]=0,a[u]=0,Math.floor((t-c)/r+1);case"SAME_LOWER":case"SAME_UPPER":if(n!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let g=((t+r-1)/r-1)*r+i-t;return a[s]=Math.floor(d==="SAME_LOWER"?(g+1)/2:g/2),a[u]=g-a[s],Math.floor((t+g-i)/r+1)}default:throw new 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    current = rest${qe};
    `;A+=`indices[${s-1}] = current;`;let ee=s<2?"":`
  fn o2i_${e}(offset: u32) -> ${l.indices} {
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  fn i2o_${e}(indices: ${l.indices}) -> u32 {
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             return ${Br.length>0?Br.join("+"):"0u"};
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  fn set_${e}ByIndices(indices: ${l.indices}, value: ${g}) {
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  fn set_${e}(${qe}, value: ${g}) {
    set_${e}ByIndices(${Ye(vt)}, value);
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    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(local_invocation_index) local_idx : 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>`,s=i?`let global_idx = global_id.x;
         let workgroup_index = workgroup_id.x;`:`let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
             workgroup_id.y * num_workgroups[0] + workgroup_id.x;
         let global_idx = workgroup_index * ${t*r*n}u + local_idx;`;return`@compute @workgroup_size(${t}, ${r}, ${n})
  fn main(${a}) {
    ${s}
  `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,t){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let r=e.usage==="input"?"read":"read_write",n=e.type.storage;return`@group(0) @binding(${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}>, ${Math.ceil(n/4)}>`);else{let i=n==null||n===1?r:`vec${n}<${r}>`;e.push(`${t}:${i}`)}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])}},oo=(e,t)=>new Ti(e,t),bs=(e,t)=>{let r=e.length,n=[];for(let i=0;i<r;i++){let a=r-1-i,s=e[a]||1;(t[t.length-1-i]||1)>1&&s===1&&n.unshift(a)}return n}}),lo,Ci,os,uo,Md,xn,vd,co,Vn=L(()=>{Yt(),Kt(),Pr(),pr(),lo=e=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.")},Ci=(e,t)=>t&&t.length!==e?[...new Array(e).keys()].reverse():t,os=(e,t)=>$e.sortBasedOnPerm(e,Ci(e.length,t)),uo=(e,t,r,n)=>{let i=`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
    var a: ${r.type.indices};`;for(let a=0;a<t;++a)i+=r.indicesSet("a",e[a],`i[${a}]`);return i+="return a;}"},Md=(e,t)=>{let r=[],n=[];for(let i=0;i<e.length;++i)e[i]!==1&&r.push(e[i]),e[t[i]]!==1&&n.push(t[i]);return{newShape:r,newPerm:n}},xn=(e,t)=>{let r=e.dataType,n=e.dims.length,i=Ci(n,t),a=os(e.dims,i),{newShape:s,newPerm:u}=Md(e.dims,i),d=$e.areEqual(u,[2,3,1]),c=$e.areEqual(u,[3,1,2]),g=s.length===2&&u[0]>u[1]||d||c,m=g?s:e.dims,l=a;g&&(m=d?[s[0],s[1]*s[2]]:c?[s[0]*s[1],s[2]]:s,l=[m[1],m[0]]);let T=Qe("a",r,m.length),x=qt("output",r,l.length),C=16,z;return g?z=U=>`
  ${U.registerUniform("output_size","u32").declareVariables(T,x)}
  var<workgroup> tile : array<array<${x.type.value}, ${C+1}>, ${C}>;
  ${U.mainStart([C,C,1])}
    let stride = (uniforms.output_shape[1] - 1) / ${C} + 1;
    let workgroup_id_x = workgroup_index % stride;
    let workgroup_id_y = workgroup_index / stride;
    let input_col = workgroup_id_y * ${C}u + local_id.x;
    let input_row = workgroup_id_x * ${C}u + local_id.y;
    if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
      tile[local_id.y][local_id.x] = ${T.getByIndices(`${T.type.indices}(input_row, input_col)`)};
    }
    workgroupBarrier();

    let output_col = workgroup_id_x * ${C}u + local_id.x;
    let output_row = workgroup_id_y * ${C}u + local_id.y;
    if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) {
      ${x.setByIndices(`${x.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
    }
  }`:z=U=>`
  ${U.registerUniform("output_size","u32").declareVariables(T,x)}

  ${uo(i,n,T,x)}

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

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

    ${x.setByOffset("global_idx",T.getByIndices("aIndices"))}
  }`,{name:g?"TransposeShared":"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:()=>{let U=$e.size(a);return{outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:g?{x:Math.ceil(l[1]/C),y:Math.ceil(l[0]/C)}:{x:Math.ceil(U/64)},programUniforms:[{type:12,data:U},...Et(m,l)]}},getShaderSource:z}},vd=(e,t)=>{lo(e.inputs),e.compute(xn(e.inputs[0],t.perm))},co=e=>or({perm:e.perm})}),po,ho,fo,mo,_o,$i,go,wo,Ei,yo,Fn,ki,bo,Mo,Si,vo,xo,Pi,To,Co,Ai,xd=L(()=>{Yt(),Kt(),pr(),ji(),Vn(),po={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"},ho={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"},fo={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},mo={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},_o=(e,t)=>{let r=[];for(let n=t-e;n<t;++n)r.push(n);return r},$i=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++)t.indexOf(a)===-1&&r.push(e[a]);let i=t.map(a=>e[a]);return[r,i]},go=(e,t)=>{let r=e.length+t.length,n=[],i=0;for(let a=0;a<r;a++)t.indexOf(a)===-1?n.push(e[i++]):n.push(1);return n},wo=(e,t)=>{for(let r=0;r<e.length;++r)if(e[e.length-r-1]!==t-1-r)return!1;return!0},Ei=(e,t)=>{let r=[];if(!wo(e,t)){for(let n=0;n<t;++n)e.indexOf(n)===-1&&r.push(n);e.forEach(n=>r.push(n))}return r},yo=(e,t,r,n,i,a,s)=>{let u=r[0].dims,d=$e.size(a),c=$e.size(s),g=Qe("_A",r[0].dataType,u),m=qt("output",i,a),l=32,T=`
          var<workgroup> aBestValues : array<f32, ${l}>;
       `;return{name:e,shaderCache:t,getShaderSource:x=>`
        ${x.registerUniform("reduceSize","u32").declareVariables(g,m)}
        ${T}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${x.mainStart(l)}

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

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

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

         if (local_idx == 0u) {
          ${m.setByOffset("outputIndex",`${n==="mean"?`${m.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${m.type.storage}(${mo[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:d},programUniforms:[{type:12,data:c}]})}},Fn=(e,t,r,n)=>{let i=e.inputs.length===1?r:Fi(e.inputs,r),a=i.axes;a.length===0&&!i.noopWithEmptyAxes&&(a=e.inputs[0].dims.map((T,x)=>x));let s=$e.normalizeAxes(a,e.inputs[0].dims.length),u=s,d=e.inputs[0],c=Ei(u,e.inputs[0].dims.length);c.length>0&&(d=e.compute(xn(e.inputs[0],c),{inputs:[0],outputs:[-1]})[0],u=_o(u.length,d.dims.length));let[g,m]=$i(d.dims,u),l=g;i.keepDims&&(l=go(g,s)),e.compute(yo(t,{hint:i.cacheKey,inputDependencies:["type"]},[d],n,e.inputs[0].dataType,l,m),{inputs:[d]})},ki=(e,t)=>{Fn(e,"ReduceMeanShared",t,"mean")},bo=(e,t)=>{Fn(e,"ReduceL1Shared",t,"l1")},Mo=(e,t)=>{Fn(e,"ReduceL2Shared",t,"l2")},Si=(e,t)=>{Fn(e,"ReduceLogSumExpShared",t,"logSumExp")},vo=(e,t)=>{Fn(e,"ReduceMaxShared",t,"max")},xo=(e,t)=>{Fn(e,"ReduceMinShared",t,"min")},Pi=(e,t)=>{Fn(e,"ReduceProdShared",t,"prod")},To=(e,t)=>{Fn(e,"ReduceSumShared",t,"sum")},Co=(e,t)=>{Fn(e,"ReduceSumSquareShared",t,"sumSquare")},Ai=(e,t)=>{Fn(e,"ReduceLogSumShared",t,"logSum")}}),On,Ii,Zs,Fi,En,$o,Eo,Oi,ko,So,zi,Po,Ao,Di,Io,zn,Li,Fo,Oo,Bi,zo,Do,Ri,Lo,Bo,Ni,ji=L(()=>{Yt(),Kt(),Pr(),pr(),xd(),On=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.")},Ii=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],Zs=(e,t,r,n,i,a,s=!1,u=!1)=>{let d=[],c=r[0].dims,g=c.length,m=$e.normalizeAxes(i,g),l=!u&&m.length===0;c.forEach((C,z)=>{l||m.indexOf(z)>=0?s&&d.push(1):d.push(C)});let T=d.length,x=$e.size(d);return{name:e,shaderCache:t,getShaderSource:C=>{let z=[],U=Qe("_A",r[0].dataType,g),A=qt("output",a,T),ee=n(U,A,m),te=ee[2];for(let ie=0,ke=0;ie<g;ie++)l||m.indexOf(ie)>=0?(s&&ke++,te=`for(var j${ie}: u32 = 0; j${ie} < ${c[ie]}; j${ie}++) {
                  ${ee[2].includes("last_index")?`let last_index = j${ie};`:""}
                  ${U.indicesSet("input_indices",ie,`j${ie}`)}
                  ${te}
                }`):(z.push(`${U.indicesSet("input_indices",ie,A.indicesGet("output_indices",ke))};`),ke++);return`

        ${C.registerUniform("output_size","u32").declareVariables(U,A)}

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

          ${z.join(`
`)}
          ${ee[0]}       // init ops for reduce max/min
          ${ee[1]}
          ${te}
          ${ee[3]}
          ${ee.length===4?A.setByOffset("global_idx","value"):ee.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:d,dataType:a}],dispatchGroup:{x:Math.ceil(x/64)},programUniforms:[{type:12,data:x},...Et(c,d)]})}},Fi=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),or({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},En=(e,t,r,n)=>{let i=e.inputs,a=i.length===1?r:Fi(i,r);e.compute(Zs(t,{hint:a.cacheKey,inputDependencies:["rank"]},[i[0]],a.noopWithEmptyAxes&&a.axes.length===0?Ii:n,a.axes,i[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},$o=(e,t)=>{On(e.inputs),En(e,"ReduceLogSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},Eo=(e,t)=>{On(e.inputs),En(e,"ReduceL1",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},Oi=(e,t)=>{On(e.inputs),En(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);"])},ko=(e,t)=>{On(e.inputs),En(e,"ReduceLogSumExp",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},So=(e,t)=>{On(e.inputs),En(e,"ReduceMax",t,(r,n,i)=>{let a=[];for(let s=0;s<r.rank;s++)(i.indexOf(s)>=0||i.length===0)&&a.push(r.indicesSet("input_indices",s,0));return[`${a.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = max(value, ${r.getByIndices("input_indices")});`,""]})},zi=(e,t)=>{On(e.inputs),En(e,"ReduceMean",t,(r,n,i)=>{let a=1;for(let s=0;s<r.rank;s++)(i.indexOf(s)>=0||i.length===0)&&(a*=e.inputs[0].dims[s]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${a});`]})},Po=(e,t)=>{On(e.inputs),En(e,"ReduceMin",t,(r,n,i)=>{let a=[];for(let s=0;s<r.rank;s++)(i.indexOf(s)>=0||i.length===0)&&a.push(`input_indices[${s}] = 0;`);return[`${a.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},Ao=(e,t)=>{On(e.inputs),En(e,"ReduceProd",t,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},Di=(e,t)=>{On(e.inputs),En(e,"ReduceSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},Io=(e,t)=>{On(e.inputs),En(e,"ReduceSumSquare",t,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},zn=(e,t,r)=>{if(t.length===0)return r;let n=1,i=1;for(let a=0;a<t.length;a++)t.indexOf(a)===-1?n*=e[a]:i*=e[a];return i<32&&n>1024},Li=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?zi(e,t):ki(e,t)},Fo=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Eo(e,t):bo(e,t)},Oo=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Oi(e,t):Mo(e,t)},Bi=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?ko(e,t):Si(e,t)},zo=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?So(e,t):vo(e,t)},Do=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Po(e,t):xo(e,t)},Ri=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ao(e,t):Pi(e,t)},Lo=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Di(e,t):To(e,t)},Bo=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Io(e,t):Co(e,t)},Ni=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?$o(e,t):Ai(e,t)}}),Vi,Ui,Ro,Wi,No=L(()=>{Yt(),Pr(),ji(),Vi=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.")},Ui=(e,t)=>{Vi(e.inputs);let r=(n,i,a)=>{let s=[];for(let u=0;u<n.rank;u++)(a.indexOf(u)>=0||a.length===0)&&s.push(`input_indices[${u}] = 0;`);return[`${s.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",i.setByOffset("global_idx","best_index")]};e.compute(Zs("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Ro=(e,t)=>{Vi(e.inputs);let r=(n,i,a)=>{let s=[];for(let u=0;u<n.rank;u++)(a.indexOf(u)>=0||a.length===0)&&s.push(`input_indices[${u}] = 0;`);return[`${s.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);
       }`,"",i.setByOffset("global_idx","best_index")]};e.compute(Zs("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Wi=e=>or(e)}),jo,Js,Gi,Vo,Uo,Ms,Wo,Go,ei=L(()=>{Yt(),Kt(),oe(),pr(),jo=(e,t)=>{let r=e[0],n=e[1],i=e[2],a=e[3],s=e[4],u=e[5];if(s&&u)throw new Error("Attention cannot have both past and attention_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let d=r.dims[0],c=r.dims[1],g=r.dims[2];if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==g)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(i.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let m=i.dims[0]/3,l=m,T=l;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let ee of t.qkvHiddenSizes)if(ee%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");m=t.qkvHiddenSizes[0],l=t.qkvHiddenSizes[1],T=t.qkvHiddenSizes[2]}let x=c;if(m!==l)throw new Error("qkv_hidden_sizes first element should be same as the second");if(i.dims[0]!==m+l+T)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let C=0;if(s){if(l!==T)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(s.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(s.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(s.dims[1]!==d)throw new Error('Input "past" second dimension must be batch_size');if(s.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(s.dims[4]!==l/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(C=s.dims[3])}let z=x+C,U=-1,A=0;if(a)throw new Error("Mask not supported");if(s)throw new Error("past is not supported");if(u){if(u.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(u.dims[0]!==d||u.dims[1]!==t.numHeads||u.dims[2]!==c||u.dims[3]!==z)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:d,sequenceLength:c,pastSequenceLength:C,kvSequenceLength:x,totalSequenceLength:z,maxSequenceLength:U,inputHiddenSize:g,hiddenSize:m,vHiddenSize:T,headSize:Math.floor(m/t.numHeads),vHeadSize:Math.floor(T/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:A,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Js=(e,t,r)=>t&&e?`
      let total_sequence_length_input = u32(${t.getByOffset("0")});
      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
      total_sequence_length = u32(${e==null?void 0:e.getByOffset("batchIdx")}) + 1;
      var past_sequence_length: u32 = 0;
      if (is_first_prompt == false) {
        past_sequence_length = total_sequence_length - sequence_length;
      }
       `:`
    ${r?"let past_sequence_length = uniforms.past_sequence_length":""};
    let present_sequence_length = total_sequence_length;
    `,Gi=(e,t,r,n,i,a,s,u)=>{let d=_r(s?1:a),c=64,g=a/d;g<c&&(c=32);let m=Math.ceil(a/d/c),l=[{type:12,data:t},{type:12,data:r},{type:12,data:n},{type:12,data:i},{type:12,data:g},{type:12,data:m}],T=fr(e.dataType,d),x=Fr(1,d),C=["type"];s&&C.push("type"),u&&C.push("type");let z=U=>{let A=qt("x",e.dataType,e.dims,d),ee=[A],te=s?Qe("seq_lens",s.dataType,s.dims):void 0;te&&ee.push(te);let ie=u?Qe("total_sequence_length_input",u.dataType,u.dims):void 0;ie&&ee.push(ie);let ke=Fr(e.dataType),Pe=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${c}>;
  var<workgroup> thread_sum: array<f32, ${c}>;
  ${U.registerUniforms(Pe).declareVariables(...ee)}
  ${U.mainStart([c,1,1])}
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let sequence_length = uniforms.sequence_length;
    var total_sequence_length = uniforms.total_sequence_length;
    ${Js(te,ie,!1)}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${c}) * uniforms.total_sequence_length + local_offset;
    let seq_causal_length = ${s?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
    var thread_max_vector = ${x}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${x}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(d){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: ${d}`)}})()};
    workgroupBarrier();

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

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

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

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        x[offset + i] = ${A.type.value}(${ke}(1.0) / ${ke}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${x}(x[offset + i]);
        x[offset + i] = ${A.type.value}(exp(f32input - max_value) / sum);
      }
    }
      ${s?`
        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
          x[offset + total_seq_id] = ${A.type.value}(${ke}(0));
        }`:""};
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${c};${T};${d}`,inputDependencies:C},getShaderSource:z,getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(a/c),y:i,z:t*r},programUniforms:l})}},Vo=(e,t,r,n,i,a,s,u,d)=>{let c=s+a.kvSequenceLength,g=[a.batchSize,a.numHeads,a.sequenceLength,c],m=e>1&&n,l=a.kvNumHeads?a.kvNumHeads:a.numHeads,T=m?[a.batchSize,l,c,a.headSize]:void 0,x=a.nReps?a.nReps:1,C=a.scale===0?1/Math.sqrt(a.headSize):a.scale,z=_r(a.headSize),U=a.headSize/z,A=12,ee={x:Math.ceil(c/A),y:Math.ceil(a.sequenceLength/A),z:a.batchSize*a.numHeads},te=[{type:12,data:a.sequenceLength},{type:12,data:U},{type:12,data:c},{type:12,data:a.numHeads},{type:12,data:a.headSize},{type:1,data:C},{type:12,data:s},{type:12,data:a.kvSequenceLength},{type:12,data:x}],ie=m&&n&&$e.size(n.dims)>0,ke=["type","type"];ie&&ke.push("type"),i&&ke.push("type"),u&&ke.push("type"),d&&ke.push("type");let Pe=[{dims:g,dataType:t.dataType,gpuDataType:0}];m&&Pe.push({dims:T,dataType:t.dataType,gpuDataType:0});let Ye=It=>{let Bt=Qe("q",t.dataType,t.dims,z),ar=Qe("key",r.dataType,r.dims,z),nr=[Bt,ar];if(ie){let Qt=Qe("past_key",n.dataType,n.dims,z);nr.push(Qt)}i&&nr.push(Qe("attention_bias",i.dataType,i.dims));let Ht=u?Qe("seq_lens",u.dataType,u.dims):void 0;Ht&&nr.push(Ht);let Er=d?Qe("total_sequence_length_input",d.dataType,d.dims):void 0;Er&&nr.push(Er);let jr=qt("output",t.dataType,g),hr=[jr];m&&hr.push(qt("present_key",t.dataType,T,z));let Ir=Fr(1,z),Gt=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${A}u;

  var<workgroup> tileQ: array<${Bt.type.storage}, ${A*A}>;
  var<workgroup> tileK: array<${Bt.type.storage}, ${A*A}>;
  ${It.registerUniforms(Gt).declareVariables(...nr,...hr)}
  ${It.mainStart([A,A,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${x===1?"headIdx":"headIdx / uniforms.n_reps"};
    let kv_num_heads = ${x===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let sequence_length = uniforms.M;
    var total_sequence_length = uniforms.N;
    ${Js(Ht,Er,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${ie&&m?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${m?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
    var value = ${Ir}(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;
      ${ie&&m?`
              if (n + local_id.y < past_sequence_length) {
                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
              }`:`
          if (n + local_id.y < uniforms.kv_sequence_length) {
            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
          }`}
      ${m?`if (n + local_id.y < present_sequence_length) {
        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
      }`:""}
      }
      workgroupBarrier();

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

      workgroupBarrier();
    }

    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(z){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: ${z}`)}})()};
        output[outputIdx] = ${jr.type.value} (sum * uniforms.alpha) + ${i?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${z};${i!==void 0};${n!==void 0};${e}`,inputDependencies:ke},getRunData:()=>({outputs:Pe,dispatchGroup:ee,programUniforms:te}),getShaderSource:Ye}},Uo=(e,t,r,n,i,a,s=void 0,u=void 0)=>{let d=a+i.kvSequenceLength,c=i.nReps?i.nReps:1,g=i.vHiddenSize*c,m=e>1&&n,l=i.kvNumHeads?i.kvNumHeads:i.numHeads,T=m?[i.batchSize,l,d,i.headSize]:void 0,x=[i.batchSize,i.sequenceLength,g],C=12,z={x:Math.ceil(i.vHeadSize/C),y:Math.ceil(i.sequenceLength/C),z:i.batchSize*i.numHeads},U=[{type:12,data:i.sequenceLength},{type:12,data:d},{type:12,data:i.vHeadSize},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:12,data:g},{type:12,data:a},{type:12,data:i.kvSequenceLength},{type:12,data:c}],A=m&&n&&$e.size(n.dims)>0,ee=["type","type"];A&&ee.push("type"),s&&ee.push("type"),u&&ee.push("type");let te=[{dims:x,dataType:t.dataType,gpuDataType:0}];m&&te.push({dims:T,dataType:t.dataType,gpuDataType:0});let ie=ke=>{let Pe=Qe("probs",t.dataType,t.dims),Ye=Qe("v",r.dataType,r.dims),It=[Pe,Ye];A&&It.push(Qe("past_value",n.dataType,n.dims));let Bt=s?Qe("seq_lens",s.dataType,s.dims):void 0;s&&It.push(Bt);let ar=u?Qe("total_sequence_length_input",u.dataType,u.dims):void 0;u&&It.push(ar);let nr=[qt("output",t.dataType,x)];m&&nr.push(qt("present_value",t.dataType,T));let Ht=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${C}u;
  var<workgroup> tileQ: array<${Pe.type.value}, ${C*C}>;
  var<workgroup> tileV: array<${Pe.type.value}, ${C*C}>;
  ${ke.registerUniforms(Ht).declareVariables(...It,...nr)}
  ${ke.mainStart([C,C,1])}
   let headIdx = workgroup_id.z % uniforms.num_heads;
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let kvHeadIdx = ${c===1?"headIdx":"headIdx / uniforms.n_reps"};
   let kv_num_heads = ${c===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
   let m = global_id.y;
   let n = global_id.x;
   let sequence_length = uniforms.M;
   var total_sequence_length = uniforms.K;
   ${Js(Bt,ar,!0)}
   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
   ${A&&m?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${m?"let presentValueOffset = absKvHeadIdx * 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;
        ${A&&m?`
        if (w + local_id.y < past_sequence_length) {
          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
        }
      `:`
            if (w + local_id.y < uniforms.kv_sequence_length) {
              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
            }`}
        ${m?`
            if (w + local_id.y < present_sequence_length) {
          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
        }`:""}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + headIdx * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:ee},getRunData:()=>({outputs:te,dispatchGroup:z,programUniforms:U}),getShaderSource:ie}},Ms=(e,t,r,n,i,a,s,u,d,c,g=void 0,m=void 0)=>{let l=Math.min(e.outputCount,1+(s?1:0)+(u?1:0)),T=l>1?c.pastSequenceLength:0,x=T+c.kvSequenceLength,C=d&&$e.size(d.dims)>0?d:void 0,z=[t,r];l>1&&s&&$e.size(s.dims)>0&&z.push(s),C&&z.push(C),g&&z.push(g),m&&z.push(m);let U=e.compute(Vo(l,t,r,s,C,c,T,g,m),{inputs:z,outputs:l>1?[-1,1]:[-1]})[0];e.compute(Gi(U,c.batchSize,c.numHeads,T,c.sequenceLength,x,g,m),{inputs:g&&m?[U,g,m]:[U],outputs:[]});let A=[U,n];l>1&&u&&$e.size(u.dims)>0&&A.push(u),g&&A.push(g),m&&A.push(m),e.compute(Uo(l,U,n,u,c,T,g,m),{inputs:A,outputs:l>1?[0,2]:[0]})},Wo=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,i=t.inputHiddenSize,a=t.headSize,s=12,u={x:Math.ceil(t.headSize/s),y:Math.ceil(t.sequenceLength/s),z:t.batchSize*t.numHeads},d=[e.inputs[0],e.inputs[1],e.inputs[2]],c=[{type:12,data:n},{type:12,data:i},{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}],g=m=>{let l=qt("output_q",d[0].dataType,r),T=qt("output_k",d[0].dataType,r),x=qt("output_v",d[0].dataType,r),C=Qe("input",d[0].dataType,d[0].dims),z=Qe("weight",d[1].dataType,d[1].dims),U=Qe("bias",d[2].dataType,d[2].dims),A=C.type.storage,ee=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
  const TILE_SIZE = ${s}u;
  var<workgroup> tileInput: array<${A}, ${s*s}>;
  var<workgroup> tileWeightQ: array<${A}, ${s*s}>;
  var<workgroup> tileWeightK: array<${A}, ${s*s}>;
  var<workgroup> tileWeightV: array<${A}, ${s*s}>;
  ${m.registerUniforms(ee).declareVariables(C,z,U,l,T,x)}
  ${m.mainStart([s,s,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 = ${A}(0);
    var valueK = ${A}(0);
    var valueV = ${A}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:c}),getShaderSource:g},{inputs:d,outputs:[-1,-1,-1]})},Go=(e,t)=>{let r=jo(e.inputs,t),[n,i,a]=Wo(e,r);return Ms(e,n,i,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r)}}),qo,Ho,qi,Ko,Td=L(()=>{Pt(),Yt(),Kt(),Pr(),pr(),qo=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,i,a)=>{let s=i.length;if(s!==n.length)throw new Error(`${a}: num dimensions != ${s}`);i.forEach((u,d)=>{if(u!==n[d])throw new Error(`${a}: dim[${d}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,n,"Invalid input scale"),r(e[2].dims,n,"Invalid input B"),r(e[3].dims,n,"Invalid input mean"),r(e[4].dims,n,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input var")},Ho=(e,t)=>{let{epsilon:r,spatial:n,format:i}=t,a=e[0].dims,s=n?_r(a[a.length-1]):1,u=i==="NHWC"&&a.length>1?s:1,d=$e.size(a)/s,c=n,g=c?a.length:a,m=Qe("x",e[0].dataType,e[0].dims,s),l=Qe("scale",e[1].dataType,e[1].dims,u),T=Qe("bias",e[2].dataType,e[2].dims,u),x=Qe("inputMean",e[3].dataType,e[3].dims,u),C=Qe("inputVar",e[4].dataType,e[4].dims,u),z=qt("y",e[0].dataType,g,s),U=()=>{let ee="";if(n)ee=`let cOffset = ${a.length===1?"0u":i==="NHWC"?`outputIndices[${a.length-1}] / ${s}`:"outputIndices[1]"};`;else if(i==="NCHW")ee=`
            ${z.indicesSet("outputIndices","0","0")}
            let cOffset = ${z.indicesToOffset("outputIndices")};`;else{ee=`var cIndices = ${l.type.indices}(0);
                       cIndices[0] = outputIndices[${a.length-1}];`;for(let te=1;te<l.rank;te++)ee+=`cIndices[${te}] = outputIndices[${te}];`;ee+=`let cOffset = ${l.indicesToOffset("cIndices")};`}return ee},A=ee=>`
  const epsilon = ${r};
  ${ee.registerUniform("outputSize","u32").declareVariables(m,l,T,x,C,z)}
  ${ee.mainStart()}
  ${ee.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${z.offsetToIndices(`global_idx * ${s}`)};
    ${U()}
    let scale = ${l.getByOffset("cOffset")};
    let bias = ${T.getByOffset("cOffset")};
    let inputMean = ${x.getByOffset("cOffset")};
    let inputVar = ${C.getByOffset("cOffset")};
    let x = ${m.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${z.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${s}`,inputDependencies:c?["rank","type","type","type","type"]:void 0},getShaderSource:A,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c?[{type:12,data:d},...Et(a)]:[{type:12,data:d}]})}},qi=e=>or(e),Ko=(e,t)=>{let{inputs:r,outputCount:n}=e,i=qi({...t,outputCount:n});if(E.webgpu.validateInputContent&&qo(r,i),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Ho(r,i))}}),Hi,Xo,Qo,Cd=L(()=>{Kt(),pr(),Hi=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Xo=e=>{let t=e[0].dims,r=e[0].dims[2],n=$e.size(t)/4,i=e[0].dataType,a=Qe("input",i,t,4),s=Qe("bias",i,[r],4),u=Qe("residual",i,t,4),d=qt("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:c=>`
  const channels = ${r}u / 4;
  ${c.declareVariables(a,s,u,d)}

  ${c.mainStart()}
    ${c.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${a.getByOffset("global_idx")}
      + ${s.getByOffset("global_idx % channels")} + ${u.getByOffset("global_idx")};
    ${d.setByOffset("global_idx","value")}
  }`}},Qo=e=>{Hi(e.inputs),e.compute(Xo(e.inputs))}}),Yo,Mr,Zo,Jo,el,tl,Ki,rl,nl,sl,il,Xi,al,ol,Qi,ll,Ls,Yi,ti,ul,Zi,dl,cl,Ji,pl,hl,ri,fl,ml,ea,ta,_l,ra,gl,wl,na,sa,ia,aa,yl,oa,bl,Ml,la,vl,ua=L(()=>{Yt(),Kt(),Pr(),pr(),Yo=(e,t,r,n,i,a,s)=>{let u=Math.ceil(t/4),d="";typeof i=="string"?d=`${i}(a)`:d=i("a");let c=Qe("inputData",r,[u],4),g=qt("outputData",n,[u],4),m=[{name:"vec_size",type:"u32"}];return s&&m.push(...s),`
      ${e.registerUniforms(m).declareVariables(c,g)}

  ${a??""}

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

    let a = ${c.getByOffset("global_idx")};
    ${g.setByOffset("global_idx",d)}
  }`},Mr=(e,t,r,n,i,a=e.dataType,s,u)=>{let d=[{type:12,data:Math.ceil($e.size(e.dims)/4)}];return s&&d.push(...s),{name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:c=>Yo(c,$e.size(e.dims),e.dataType,a,r,n,u),getRunData:c=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil($e.size(c[0].dims)/64/4)},programUniforms:d})}},Zo=e=>{e.compute(Mr(e.inputs[0],"Abs","abs"))},Jo=e=>{e.compute(Mr(e.inputs[0],"Acos","acos"))},el=e=>{e.compute(Mr(e.inputs[0],"Acosh","acosh"))},tl=e=>{e.compute(Mr(e.inputs[0],"Asin","asin"))},Ki=e=>{e.compute(Mr(e.inputs[0],"Asinh","asinh"))},rl=e=>{e.compute(Mr(e.inputs[0],"Atan","atan"))},nl=e=>{e.compute(Mr(e.inputs[0],"Atanh","atanh"))},sl=e=>or(e),il=(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): ${t.to}`)}e.compute(Mr(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},Xi=e=>{let t,r,n=e.length>=2&&e[1].data!==0,i=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=n?e[1].getFloat32Array()[0]:-34028234663852886e22,r=i?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=n?e[1].getUint16Array()[0]:64511,r=i?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return or({min:t,max:r})},al=(e,t)=>{let r=t||Xi(e.inputs),n=Fr(e.inputs[0].dataType);e.compute(Mr(e.inputs[0],"Clip",i=>`clamp(${i}, vec4<${n}>(uniforms.min), vec4<${n}>(uniforms.max))`,void 0,r.cacheKey,void 0,[{type:e.inputs[0].dataType,data:r.min},{type:e.inputs[0].dataType,data:r.max}],[{name:"min",type:n},{name:"max",type:n}]),{inputs:[0]})},ol=e=>{e.compute(Mr(e.inputs[0],"Ceil","ceil"))},Qi=e=>{e.compute(Mr(e.inputs[0],"Cos","cos"))},ll=e=>{e.compute(Mr(e.inputs[0],"Cosh","cosh"))},Ls=e=>or(e),Yi=(e,t)=>{let r=Fr(e.inputs[0].dataType);e.compute(Mr(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))},ti=(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));
}`,ul=e=>{let t=Fr(e.inputs[0].dataType);e.compute(Mr(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,ti(t)))},Zi=e=>{e.compute(Mr(e.inputs[0],"Exp","exp"))},dl=e=>{e.compute(Mr(e.inputs[0],"Floor","floor"))},cl=e=>{let t=Fr(e.inputs[0].dataType);e.compute(Mr(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,ti(t)))},Ji=(e,t)=>{let r=Fr(e.inputs[0].dataType);e.compute(Mr(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))},pl=e=>{e.compute(Mr(e.inputs[0],"Not",t=>`!${t}`))},hl=e=>{e.compute(Mr(e.inputs[0],"Neg",t=>`-${t}`))},ri=e=>{e.compute(Mr(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},fl=e=>{let t=Fr(e.inputs[0].dataType);e.compute(Mr(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},ml=e=>{e.compute(Mr(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},ea=e=>or(e),ta=(e,t)=>{let r=Fr(e.inputs[0].dataType);e.compute(Mr(e.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${n} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},_l=e=>{e.compute(Mr(e.inputs[0],"Sin","sin"))},ra=e=>{e.compute(Mr(e.inputs[0],"Sinh","sinh"))},gl=e=>{e.compute(Mr(e.inputs[0],"Sqrt","sqrt"))},wl=e=>{e.compute(Mr(e.inputs[0],"Tan","tan"))},na=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,sa=e=>{e.compute(Mr(e.inputs[0],"Tanh",na))},ia=(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 ${na("v")};
}
`,aa=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,yl=e=>{let t=Fr(e.inputs[0].dataType);e.compute(Mr(e.inputs[0],"FastGelu",aa,ia(t),void 0,e.inputs[0].dataType))},oa=(e,t)=>{let r=Fr(e.inputs[0].dataType);return e.compute(Mr(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},bl=e=>{e.compute(Mr(e.inputs[0],"Log","log"))},Ml=(e,t)=>`
const alpha = vec4<${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<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,la=e=>`quick_gelu_impl(${e})`,vl=(e,t)=>{let r=Fr(e.inputs[0].dataType);e.compute(Mr(e.inputs[0],"QuickGelu",la,Ml(r,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),da,xl,Tl,Cl=L(()=>{Kt(),pr(),ua(),da=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")},xl=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=Qe("input",e[0].dataType,e[0].dims,4),n=Qe("bias",e[0].dataType,[e[0].dims[2]],4),i=qt("output",e[0].dataType,t,4),a=$e.size(t)/4,s=fr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:u=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

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

  ${ti(s)}

  ${u.mainStart()}
    ${u.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);

    ${i.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Tl=e=>{da(e.inputs),e.compute(xl(e.inputs))}}),$l,El,kn,kl,Sl,ca,Pl,Al,Il,Fl,ni,Ol,zl,$d=L(()=>{Yt(),Kt(),pr(),$l=(e,t,r,n,i,a,s,u,d,c,g,m)=>{let l,T;typeof u=="string"?l=T=(A,ee)=>`${u}((${A}),(${ee}))`:typeof u=="function"?l=T=u:(l=u.scalar,T=u.vector);let x=qt("outputData",g,n.length,4),C=Qe("aData",d,t.length,4),z=Qe("bData",c,r.length,4),U;if(i)if(a){let A=$e.size(t)===1,ee=$e.size(r)===1,te=t.length>0&&t[t.length-1]%4===0,ie=r.length>0&&r[r.length-1]%4===0;A||ee?U=x.setByOffset("global_idx",T(A?`${C.type.value}(${C.getByOffset("0")}.x)`:C.getByOffset("global_idx"),ee?`${z.type.value}(${z.getByOffset("0")}.x)`:z.getByOffset("global_idx"))):U=`
            let outputIndices = ${x.offsetToIndices("global_idx * 4u")};
            let offsetA = ${C.broadcastedIndicesToOffset("outputIndices",x)};
            let offsetB = ${z.broadcastedIndicesToOffset("outputIndices",x)};
            ${x.setByOffset("global_idx",T(s||te?C.getByOffset("offsetA / 4u"):`${C.type.value}(${C.getByOffset("offsetA / 4u")}[offsetA % 4u])`,s||ie?z.getByOffset("offsetB / 4u"):`${z.type.value}(${z.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else U=x.setByOffset("global_idx",T(C.getByOffset("global_idx"),z.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let A=(ee,te,ie="")=>{let ke=`aData[indexA${te}][componentA${te}]`,Pe=`bData[indexB${te}][componentB${te}]`;return`
            let outputIndices${te} = ${x.offsetToIndices(`global_idx * 4u + ${te}u`)};
            let offsetA${te} = ${C.broadcastedIndicesToOffset(`outputIndices${te}`,x)};
            let offsetB${te} = ${z.broadcastedIndicesToOffset(`outputIndices${te}`,x)};
            let indexA${te} = offsetA${te} / 4u;
            let indexB${te} = offsetB${te} / 4u;
            let componentA${te} = offsetA${te} % 4u;
            let componentB${te} = offsetB${te} % 4u;
            ${ee}[${te}] = ${ie}(${l(ke,Pe)});
          `};g===9?U=`
            var data = vec4<u32>(0);
            ${A("data",0,"u32")}
            ${A("data",1,"u32")}
            ${A("data",2,"u32")}
            ${A("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:U=`
            ${A("outputData[global_idx]",0)}
            ${A("outputData[global_idx]",1)}
            ${A("outputData[global_idx]",2)}
            ${A("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(C,z,x)}

        ${m??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${U}
      }`},El=(e,t,r,n,i,a,s=r.dataType)=>{let u=!$e.areEqual(r.dims,n.dims),d=r.dims,c=$e.size(r.dims),g=!1,m=!1,l=[u];if(u){let T=Mn.calcShape(r.dims,n.dims,!1);if(!T)throw new Error("Can't perform binary op on the given tensors");d=T,c=$e.size(d);let x=$e.size(r.dims)===1,C=$e.size(n.dims)===1,z=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,U=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;l.push(x),l.push(C),l.push(z),l.push(U);let A=1;for(let ee=1;ee<d.length;ee++){let te=r.dims[r.dims.length-ee]??1,ie=n.dims[n.dims.length-ee]??1;if(te===ie)A*=te;else break}A%4===0?(m=!0,g=!0):(x||C||z||U)&&(g=!0)}else g=!0;return l.push(g),{name:e,shaderCache:{hint:t+l.map(T=>T.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:T=>$l(T,r.dims,n.dims,d,g,u,m,i,r.dataType,n.dataType,s,a),getRunData:()=>({outputs:[{dims:d,dataType:s}],dispatchGroup:{x:Math.ceil(c/64/4)},programUniforms:[{type:12,data:Math.ceil($e.size(d)/4)},...Et(r.dims,n.dims,d)]})}},kn=(e,t,r,n,i,a)=>{e.compute(El(t,i??"",e.inputs[0],e.inputs[1],r,n,a))},kl=e=>{kn(e,"Add",(t,r)=>`${t}+${r}`)},Sl=e=>{kn(e,"Div",(t,r)=>`${t}/${r}`)},ca=e=>{kn(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},Pl=e=>{kn(e,"Mul",(t,r)=>`${t}*${r}`)},Al=e=>{let t=Qe("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;kn(e,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},`
    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {
      if (b == ${t}(0.0)) {
        return ${t}(1.0);
      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {
        return ${t}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {
      // TODO: implement vectorized pow
      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},Il=e=>{kn(e,"Sub",(t,r)=>`${t}-${r}`)},Fl=e=>{kn(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},ni=e=>{kn(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},Ol=e=>{kn(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},zl=e=>{kn(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}}),Dl,Ll,pa,Bl,Rl,ha,Ed=L(()=>{Yt(),Kt(),Pr(),pr(),Dl=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],i=n.dataType,a=n.dims.length;e.forEach((s,u)=>{if(u!==r){if(s.dataType!==i)throw new Error("input tensors should be one type");if(s.dims.length!==a)throw new Error("input tensors should have the same shape");s.dims.forEach((d,c)=>{if(c!==t&&d!==n.dims[c])throw new Error("non concat dimensions must match")})}})},Ll=(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;
  }`,pa=(e,t)=>{let r=e.length,n=[];for(let i=0;i<r;++i){let a=t.setByOffset("global_idx",e[i].getByIndices("indices"));r===1?n.push(a):i===0?n.push(`if (inputIndex == ${i}u) { ${a} }`):i===r-1?n.push(`else { ${a} }`):n.push(`else if (inputIndex == ${i}) { ${a} }`)}return n.join(`
`)},Bl=(e,t,r,n)=>{let i=$e.size(r),a=new Array(e.length),s=new Array(e.length),u=0,d=[],c=[],g=[{type:12,data:i}];for(let C=0;C<e.length;++C)u+=e[C].dims[t],a[C]=u,c.push(e[C].dims.length),s[C]=Qe(`input${C}`,n,c[C]),d.push("rank"),g.push({type:12,data:a[C]});for(let C=0;C<e.length;++C)g.push(...Et(e[C].dims));g.push(...Et(r));let m=qt("output",n,r.length),l=m.indicesGet("indices",t),T=Array.from(Array(a.length).keys()).map(C=>`uniforms.sizeInConcatAxis${C}`).join(","),x=C=>`

  ${(()=>{C.registerUniform("outputSize","u32");for(let z=0;z<e.length;z++)C.registerUniform(`sizeInConcatAxis${z}`,"u32");return C.declareVariables(...s,m)})()}

  ${Ll(a.length,T)}

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

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

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

    ${pa(s,m)}
  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:g}),getShaderSource:x}},Rl=(e,t)=>{let r=e.inputs,n=r[0].dims,i=$e.normalizeAxis(t.axis,n.length);Dl(r,i);let a=n.slice();a[i]=r.reduce((u,d)=>u+(d.dims.length>i?d.dims[i]:0),0);let s=r.filter(u=>$e.size(u.dims)>0);e.compute(Bl(s,i,a,r[0].dataType),{inputs:s})},ha=e=>or({axis:e.axis})}),Xn,Qn,Yn,si,Zn=L(()=>{Yt(),Kt(),Xn=(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 ${e.activation}`)}},Qn=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Yn=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},si=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[r,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=(e==null?void 0:e.activation_params)||[In,Nn];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:r}}return{activation:t}}}),hn,ii,ai=L(()=>{hn=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},ii=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),fa,ma=L(()=>{fa=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),Nl,jl,Bs,_a,Vl,oi,Ul,ga,li=L(()=>{Yt(),Kt(),pr(),Zn(),ai(),Nl=(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":""});
        `,jl=(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];"}
        }`,Bs=(e,t,r="f32",n,i=!1,a=32,s=!1,u=32)=>{let d=t[1]*e[1],c=t[0]*e[0],g=i?d:a,m=i?a:d,l=g/t[0],T=a/t[1];if(!((i&&l===4&&e[1]===4||!i&&(l===3||l===4))&&g%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${l} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${l} must be 3 or 4.
  tileAWidth ${g} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${l}<${r}>, ${g/l}>, ${m}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${c/e[0]}>, ${a}>;

const rowPerThread = ${e[1]};
const colPerThread = ${e[0]};
const innerElementSize = ${l};
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 = ${s?"0":"i32(globalId.z)"};
  ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
  let globalRowStart = i32(workgroupId.y) * ${d};

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

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

  // Loop over shared dimension.
  let tileRowB = localRow * ${T};
  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;
          ${Nl(i,n)}
      }

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

          ${jl(i,l)}
      }

      workgroupBarrier();
  }

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

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

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

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

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

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${C}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${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) {
      ${Vl(i)}
      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}, ${m}>, ${l}>;
  var<workgroup> mm_Bsub : array<array<${r}, ${g}>, ${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 = ${s?"0":"i32(globalId.z)"};
    ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
    let num_tiles = ${s?`${Math.ceil(u/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
    var kStart = ${s?`i32(globalId.z) * ${u}`:"0"};

    var acc : array<array<${r}, colPerThread>, rowPerThread>;
    ${z}
  }
`},Ul=(e,t,r,n,i,a=!1)=>{let[s,u,d]=i,[c,g,m,l]=n,T=bs(s,d),x=bs(u,d),C=fr(n[0].type.tensor),z=()=>{let A=g.rank,ee=c.rank,te=`var aIndices: ${g.type.indices};`;for(let ie=A-2-1,ke=ee-1;ie>=0;ie--,ke--)te+=`
aIndices[${ie}] = ${ee>1?`batchIndices[${ke}]`:"batchIndices"};`;return T.forEach(ie=>{te+=`
aIndices[${ie}] = 0;`}),te+=`
aIndices[${A-2}] = u32(row);
                   aIndices[${A-1}] = u32(colIn);`,te},U=()=>{let A=m.rank,ee=c.rank,te=`var bIndices: ${m.type.indices};`;for(let ie=A-2-1,ke=ee-1;ie>=0;ie--,ke--)te+=`
bIndices[${ie}] = ${ee>1?`batchIndices[${ke}]`:"batchIndices"};`;return x.forEach(ie=>{te+=`
bIndices[${ie}] = 0;`}),te+=`
bIndices[${A-2}] = u32(row);
                   bIndices[${A-1}] = u32(colIn);`,te};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${c.type.indices}) -> ${hn(e,C)} {
      var value = ${hn(e,C)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${z()}
        value = ${g.getByIndices("aIndices")};
      }
      return value;
    }

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${hn(e,C)}) {
      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 + ${a?"bias[colIn]":`${hn(e,C)}(bias[row])`};`:""}
        ${r}
        ${l.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},ga=(e,t,r,n,i=!1,a)=>{let s=e[0].dims,u=e[1].dims,d=s.slice(0,-2),c=u.slice(0,-2),g=n?n.slice(0,-2):r.slice(0,-2),m=$e.size(g),l=s[s.length-2],T=s[s.length-1],x=u[u.length-1],C=T%4===0&&x%4===0,z=l<=8?[4,1,1]:[4,4,1],U=[8,8,1],A=[Math.ceil(x/U[0]/z[0]),Math.ceil(l/U[1]/z[1]),Math.ceil(m/U[2]/z[2])],ee=C?4:1,te=[...d,l,T/ee],ie=te.length,ke=[...c,T,x/ee],Pe=ke.length,Ye=[m,l,x/ee],It=[{type:6,data:l},{type:6,data:x},{type:6,data:T}];Qn(t,It),It.push(...Et(g,te,ke));let Bt=["rank","rank"],ar=e.length>2;ar&&(It.push(...Et(e[2].dims)),Bt.push("rank")),It.push(...Et(Ye));let nr=Ht=>{let Er=g.length,jr=xi("batchDims",e[0].dataType,Er,1),hr=fr(e[0].dataType),Ir=Qe("a",e[0].dataType,ie,ee),Gt=Qe("b",e[1].dataType,Pe,ee),Qt=qt("result",e[0].dataType,Ye.length,ee),xr=[Ir,Gt];if(ar){let sn=i?ee:1;xr.push(Qe("bias",e[2].dataType,e[2].dims.length,sn))}let qe=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Yn(t,qe);let vt=fr(Qt.type.tensor),rr=Xn(t,Qt.type.value,vt),Br=Ul(ee,ar,rr,[jr,Ir,Gt,Qt],[d,c,g],i);return`
  ${Ht.registerUniforms(qe).registerInternalVariables(jr).declareVariables(...xr,Qt)}
  ${Br}
  ${C?Bs(z,U,hr,jr):oi(z,U,hr,jr)}
                   `};return{name:"MatMul",shaderCache:{hint:`${z};${t.activation};${C};${i}`,inputDependencies:Bt},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:It}),getShaderSource:nr}}}),wa,Wl,kd=L(()=>{Yt(),_(),pr(),Zn(),ai(),ma(),li(),wa=(e,t,r,n,i=!1,a,s=4,u=4,d=4,c="f32")=>{let g=It=>{switch(It){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${c}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${It} is not supported.`)}},m=It=>{switch(It){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 ${It} is not supported.`)}},l=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,T=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,x=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",C=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",z=e?"row":"col",U=e?"col":"row",A=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${z} / outWidth;
    let outCol = ${z} % outWidth;

    let WRow = ${U} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${U} / 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 = ${U} % inChannels;
    var resData = ${hn(s,c)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${x} && xCol >= 0 && xCol < ${C}) {
      ${l}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${g(s)}
    }
    return resData;`,ee=e?t&&n?`
    let col = colIn * ${s};
    ${A}`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${A}
    }
    return ${hn(s,c)}(0.0);`:n&&r?`
    let col = colIn * ${s};
    ${A}`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${A}
    }
    return ${hn(s,c)}(0.0);`,te=`${m(u)}`,ie=hn(d,c),ke=hn(e?s:u,c),Pe=hn(e?u:s,c),Ye=Xn(a,ie,c);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${ke} {
      ${e?ee:te}
    }

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${ie}) {
      let col = colIn * ${d};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${T}
      ${ii(i)}
      ${Ye}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Wl=(e,t,r,n,i,a,s,u,d)=>{let c=t.format==="NHWC",g=c?e[0].dims[3]:e[0].dims[1],m=r[0],l=c?r[2]:r[3],T=c?r[1]:r[2],x=c?r[3]:r[1],C=c&&(g%4===0||g%3===0)&&x%4===0,z=c?x:l*T,U=c?l*T:x,A=[8,8,1],ee=n<=8?[4,1,1]:[4,4,1],te=[Math.ceil(z/A[0]/ee[0]),Math.ceil(U/A[1]/ee[1]),Math.ceil(m/A[2]/ee[2])];ae("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${te}`);let ie=C?c&&g%4!==0?3:4:1,ke=A[1]*ee[1],Pe=A[0]*ee[0],Ye=Math.max(A[0]*ie,A[1]),It=n%ke===0,Bt=i%Pe===0,ar=a%Ye===0,nr=C?[ie,4,4]:[1,1,1],Ht=[{type:6,data:n},{type:6,data:i},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Qn(t,Ht),Ht.push(...Et(e[0].dims,e[1].dims));let Er=["rank","rank"];s&&(Ht.push(...Et(e[2].dims)),Er.push("rank")),Ht.push(...Et(r));let jr=hr=>{let Ir=[{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}];Yn(t,Ir);let Gt=C?4:1,Qt=fr(e[0].dataType),xr=`
      fn setOutputAtIndex(flatIndex : i32, value : ${C?`vec4<${Qt}>`:Qt}) {
        result[flatIndex] = ${C?`vec4<${Qt}>`:Qt}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${C?`vec4<${Qt}>`:Qt}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${C?"/ 4":""}, value);
      }`,qe=Qe("x",e[0].dataType,e[0].dims.length,ie===3?1:ie),vt=Qe("w",e[1].dataType,e[1].dims.length,Gt),rr=[qe,vt],Br=qt("result",e[0].dataType,r.length,Gt);if(s){let sn=Qe("bias",e[2].dataType,e[2].dims.length,Gt);rr.push(sn),xr+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${C?`vec4<${Qt}>`:Qt} {
          return bias[coords.${c?"w":"y"}${C?"/ 4":""}];
        }`}return`
        ${fa("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 };
        ${hr.registerUniforms(Ir).declareVariables(...rr,Br)}
        ${xr}
        ${wa(c,It,Bt,ar,s,t,nr[0],nr[1],nr[2],Qt)}
        ${C?Bs(ee,A,Qt,void 0,!c,Ye):oi(ee,A,Qt,void 0,!c,Ye,!1,void 0,u)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${ie};${C};${It};${Bt};${ar};${ke};${Pe};${Ye}`,inputDependencies:Er},getRunData:()=>({outputs:[{dims:d?d(r):r,dataType:e[0].dataType}],dispatchGroup:{x:te[0],y:te[1],z:te[2]},programUniforms:Ht}),getShaderSource:jr}}}),ya,ba,Rs,Jn,Ma,Gl,ql,Hl,Kl=L(()=>{Yt(),_(),Kt(),pr(),Zn(),ai(),ya=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t},ba=e=>typeof e=="number"?[e,e,e]:e,Rs=(e,t)=>t<=1?e:e+(e-1)*(t-1),Jn=(e,t,r,n=1)=>{let i=Rs(t,n);return Math.floor((e[0]*(r-1)-r+i)/2)},Ma=(e,t,r,n,i)=>{i==null&&(i=Jn(e,t[0],n[0]));let a=[0,0,0,r];for(let s=0;s<3;s++)e[s]+2*i>=t[s]&&(a[s]=Math.trunc((e[s]-t[s]+2*i)/n[s]+1));return a},Gl=(e,t,r,n,i,a,s,u,d,c)=>{let g,m,l,T;if(e==="VALID"&&(e=0),typeof e=="number"){g={top:e,bottom:e,left:e,right:e,front:e,back:e};let x=Ma([t,r,n,1],[u,d,c],1,[i,a,s],e);m=x[0],l=x[1],T=x[2]}else if(Array.isArray(e)){if(!e.every((C,z,U)=>C===U[0]))throw Error(`Unsupported padding parameter: ${e}`);g={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let x=Ma([t,r,n,1],[u,d,c],1,[i,a,s],e[0]);m=x[0],l=x[1],T=x[2]}else if(e==="SAME_UPPER"){m=Math.ceil(t/i),l=Math.ceil(r/a),T=Math.ceil(n/s);let x=(m-1)*i+u-t,C=(l-1)*a+d-r,z=(T-1)*s+c-n,U=Math.floor(x/2),A=x-U,ee=Math.floor(C/2),te=C-ee,ie=Math.floor(z/2),ke=z-ie;g={top:ee,bottom:te,left:ie,right:ke,front:U,back:A}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:g,outDepth:m,outHeight:l,outWidth:T}},ql=(e,t,r,n,i,a=!1,s="channelsLast")=>{let u,d,c,g,m;if(s==="channelsLast")[u,d,c,g,m]=e;else if(s==="channelsFirst")[u,m,d,c,g]=e;else throw new Error(`Unknown dataFormat ${s}`);let[l,,T,x,C]=t,[z,U,A]=ba(r),[ee,te,ie]=ba(n),ke=Rs(T,ee),Pe=Rs(x,te),Ye=Rs(C,ie),{padInfo:It,outDepth:Bt,outHeight:ar,outWidth:nr}=Gl(i,d,c,g,z,U,A,ke,Pe,Ye),Ht=a?l*m:l,Er=[0,0,0,0,0];return s==="channelsFirst"?Er=[u,Ht,Bt,ar,nr]:s==="channelsLast"&&(Er=[u,Bt,ar,nr,Ht]),{batchSize:u,dataFormat:s,inDepth:d,inHeight:c,inWidth:g,inChannels:m,outDepth:Bt,outHeight:ar,outWidth:nr,outChannels:Ht,padInfo:It,strideDepth:z,strideHeight:U,strideWidth:A,filterDepth:T,filterHeight:x,filterWidth:C,effectiveFilterDepth:ke,effectiveFilterHeight:Pe,effectiveFilterWidth:Ye,dilationDepth:ee,dilationHeight:te,dilationWidth:ie,inShape:e,outShape:Er,filterShape:t}},Hl=(e,t,r,n,i,a)=>{let s=a==="channelsLast";s?e[0].dims[3]:e[0].dims[1];let u=[64,1,1],d={x:r.map((z,U)=>U)},c=[Math.ceil(ya(d.x.map(z=>r[z]))/u[0]),1,1];ae("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${c}`);let g=1,m=$e.size(r),l=[{type:12,data:m},{type:12,data:n},{type:12,data:i},{type:12,data:t.strides},{type:12,data:t.dilations}];Qn(t,l),l.push(...Et(e[0].dims,e[1].dims));let T=["rank","rank"],x=e.length===3;x&&(l.push(...Et(e[2].dims)),T.push("rank")),l.push(...Et(r));let C=z=>{let U=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:n.length},{name:"pads",type:"u32",length:i.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];Yn(t,U);let A=1,ee=fr(e[0].dataType),te=Qe("x",e[0].dataType,e[0].dims.length,g),ie=Qe("W",e[1].dataType,e[1].dims.length,A),ke=[te,ie],Pe=qt("result",e[0].dataType,r.length,A),Ye="";if(x){let ar=Qe("bias",e[2].dataType,e[2].dims.length,A);ke.push(ar),Ye+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${ee} {
          return bias[${s?Wt("coords",4,5):Wt("coords",1,5)}];
        }`}let It=hn(g,ee),Bt=Xn(t,It,ee);return`
            ${Ye}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${te.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 ${ie.getByIndices("aIndices")};
            }
          ${z.registerUniforms(U).declareVariables(...ke,Pe)}
          ${z.mainStart()}
          ${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${Pe.offsetToIndices("global_idx")};
              let batch = ${Wt("coords",0,te.rank)};
              let d2 = ${s?Wt("coords",te.rank-1,te.rank):Wt("coords",1,te.rank)};
              let xFRCCorner = vec3<u32>(${s?Wt("coords",1,te.rank):Wt("coords",2,te.rank)},
              ${s?Wt("coords",2,te.rank):Wt("coords",3,te.rank)},
              ${s?Wt("coords",3,te.rank):Wt("coords",4,te.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${s?Wt("uniforms.x_shape",1,te.rank):Wt("uniforms.x_shape",2,te.rank)};
              let xShapeZ = ${s?Wt("uniforms.x_shape",2,te.rank):Wt("uniforms.x_shape",3,te.rank)};
              let xShapeW = ${s?Wt("uniforms.x_shape",3,te.rank):Wt("uniforms.x_shape",4,te.rank)};
              let xShapeU = ${s?Wt("uniforms.x_shape",4,te.rank):Wt("uniforms.x_shape",1,te.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) {
                      ${s?`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) {
                        ${s?`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) {
                      ${s?`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) {
                      ${s?`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);
                    }
                  }
                }
              }
              ${x?"value = value + getBiasByOutputCoords(coords)":""};
              ${Bt}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${s};${g};${x}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:c[0],y:c[1],z:c[2]},programUniforms:l}),getShaderSource:C}}}),Xl,Ql,Sd=L(()=>{Yt(),Kt(),pr(),Zn(),Xl=(e,t,r,n)=>{let i=e.length>2,a=i?"value += b[output_channel];":"",s=e[0].dims,u=e[1].dims,d=t.format==="NHWC",c=d?r[3]:r[1],g=c/t.group,m=d&&g>=4?_r(c):1,l=$e.size(r)/m,T=[{type:12,data:l},{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:g}];Qn(t,T),T.push(...Et(s,[u[0],u[1],u[2],u[3]/m]));let x=i?["rank","rank","rank"]:["rank","rank"];T.push(...Et([r[0],r[1],r[2],r[3]/m]));let C=z=>{let U=qt("output",e[0].dataType,r.length,m),A=fr(U.type.tensor),ee=Xn(t,U.type.value,A),te=Qe("x",e[0].dataType,s.length),ie=Qe("w",e[1].dataType,u.length,m),ke=[te,ie];i&&ke.push(Qe("b",e[2].dataType,e[2].dims,m));let Pe=[{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"}];Yn(t,Pe);let Ye=d?`
      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

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

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

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

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

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

            let xVal = ${te.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${ie.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${z.registerUniforms(Pe).declareVariables(...ke,U)}

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

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

    var value: ${U.type.value} = ${U.type.value}(0);
    ${Ye}
    ${a}
    ${ee}
    ${U.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${m}`,inputDependencies:x},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:T}),getShaderSource:C}},Ql=(e,t,r,n)=>{let i=e.length>2,a=_r(r[3]),s=_r(r[2]),u=$e.size(r)/a/s,d=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],c=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],g=[r[0],r[1],r[2],r[3]/a],m=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Qn(t,m),m.push(...Et(d,c,g));let l=(s-1)*t.strides[1]+c[1],T=x=>{let C=qt("output",e[0].dataType,g.length,a),z=fr(C.type.tensor),U=Xn(t,C.type.value,z),A=Qe("x",e[0].dataType,d.length,a),ee=Qe("w",e[1].dataType,c.length,a),te=[A,ee];i&&te.push(Qe("b",e[2].dataType,e[2].dims,a));let ie=i?"value += b[output_channel];":"",ke=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Yn(t,ke),`
  ${x.registerUniforms(ke).declareVariables(...te,C)}
  ${x.mainStart()}
    ${x.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] / ${s}u;
    let col = (index1 % width1) * ${s}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<${A.type.value}, ${l}>;
    var values: array<${C.type.value}, ${s}>;
    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 < ${l}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${A.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${A.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${c[1]}; w_width++) {
          let w_val = ${ee.get("w_height","w_width","0","output_channel")};
          for (var i = 0u; i < ${s}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${s}u; i++) {
      var value = values[i];
      ${ie}
      ${U}
      ${C.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${a};${s};${l};${c[0]};${c[1]}`,inputDependencies:i?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:m}),getShaderSource:T}}}),va,ui,Yl,Zl=L(()=>{Yt(),Kt(),li(),pr(),Zn(),va=(e,t,r,n,i=!1,a)=>{let s=e[0].dims,u=e[1].dims,d=s[s.length-2],c=u[u.length-1],g=s[s.length-1],m=_r(c),l=_r(g),T=_r(d),x=$e.size(r)/m/T,C=e.length>2,z=n?n.slice(0,-2):r.slice(0,-2),U=[$e.size(z),d,c],A=[{type:12,data:x},{type:12,data:d},{type:12,data:c},{type:12,data:g}];Qn(t,A),A.push(...Et(z,s,u)),C&&A.push(...Et(e[2].dims)),A.push(...Et(U));let ee=te=>{let ie=xi("batch_dims",e[0].dataType,z.length),ke=Qe("a",e[0].dataType,s.length,l),Pe=Qe("b",e[1].dataType,u.length,m),Ye=qt("output",e[0].dataType,U.length,m),It=fr(Ye.type.tensor),Bt=Xn(t,Ye.type.value,It),ar=[ke,Pe],nr="";if(C){let xr=i?m:1;ar.push(Qe("bias",e[2].dataType,e[2].dims.length,xr)),nr=`${i?`value += bias[col / ${xr}];`:`value += ${Ye.type.value}(bias[row + i]);`}`}let Ht=s.slice(0,-2),Er=u.slice(0,-2),jr=bs(Ht,z),hr=bs(Er,z),Ir=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Yn(t,Ir);let Gt=(xr,qe)=>{let vt=xr.rank,rr=xr.name;if(vt===2)return`var ${rr}_indices = ${xr.type.indices}(0u, 0u);`;let Br=ie.rank,sn=`var ${rr}_indices: ${xr.type.indices};`;for(let an=vt-2-1,Ws=Br-1;an>=0;an--,Ws--)sn+=`
${rr}_indices[${an}] = ${Br>1?`batch_indices[${Ws}]`:"batch_indices"};`;return qe.forEach(an=>{sn+=`
${rr}_indices[${an}] = 0;`}),sn+=`${rr}_indices[${vt-2}] = 0u;
                     ${rr}_indices[${vt-1}] = 0u;`,sn},Qt=()=>{let xr=`var a_data: ${ke.type.value};`;for(let qe=0;qe<l;qe++)xr+=`
              let b_data${qe} = b[(b_offset + (k + ${qe}) * uniforms.N + col) / ${m}];`;for(let qe=0;qe<T;qe++){xr+=`a_data = a[(a_offset + (row + ${qe}) * uniforms.K + k) / ${l}];`;for(let vt=0;vt<l;vt++)xr+=`
            values[${qe}] = fma(${Pe.type.value}(a_data${l===1?"":`[${vt}]`}), b_data${vt}, values[${qe}]);
`}return xr};return`
  ${te.registerUniforms(Ir).registerInternalVariables(ie).declareVariables(...ar,Ye)}
  ${te.mainStart()}
    ${te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${m})) * ${m};
    var index1 = global_idx / (uniforms.N / ${m});
    let stride1 = uniforms.M / ${T};
    let row = (index1 % stride1) * ${T};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${ie.offsetToIndices("batch")};`}
    ${Gt(ke,jr)}
    let a_offset = ${ke.indicesToOffset("a_indices")};
    ${Gt(Pe,hr)}
    let b_offset = ${Pe.indicesToOffset("b_indices")};
    var values: array<${Ye.type.value}, ${T}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${l}) {
      ${Qt()}
    }
    for (var i = 0u; i < ${T}u; i++) {
      var value = values[i];
      ${nr}
      ${Bt}
      let cur_indices = ${Ye.type.indices}(batch, row + i, col);
      let offset = ${Ye.indicesToOffset("cur_indices")};
      ${Ye.setByOffset(`offset / ${m}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${m};${l};${T};${i}`,inputDependencies:C?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(x/64)},programUniforms:A}),getShaderSource:ee}},ui=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.")},Yl=e=>{ui(e.inputs);let t=Mn.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(va(e.inputs,{activation:""},t)):e.compute(ga(e.inputs,{activation:""},t))}}),Jl,ls,eu,di,xa,Ta,ci,tu,Ca,Pd=L(()=>{Kt(),kd(),Kl(),li(),Sd(),Zn(),Zl(),Vn(),Jl=(e,t,r,n,i,a)=>{let s=e[0],u=e.slice(a?1:2,a?3:4),d=u.length,c=t[0],g=t.slice(2).map((l,T)=>l+(l-1)*(r[T]-1)),m=u.map((l,T)=>l+n[T]+n[T+d]).map((l,T)=>Math.floor((l-g[T]+i[T])/i[T]));return m.splice(0,0,s),m.splice(a?3:1,0,c),m},ls=[2,3,1,0],eu=(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 i=e[0].dims.length-2;if(t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},di=(e,t)=>{let r=e.kernelShape.slice();r.length<t[1].dims.length-2&&r.push(...Array(t[1].dims.length-2-r.length).fill(0));for(let a=2;a<t[1].dims.length;++a)r[a-2]===0&&(r[a-2]=t[1].dims[a]);let n=e.pads.slice();tn.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,n,e.format==="NHWC",e.autoPad);let i=Object.assign({},e);return Object.assign(i,{kernelShape:r,pads:n}),i},xa=e=>{let t=si(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,a=e.group,s=e.kernel_shape,u=e.pads,d=e.strides,c=e.w_is_const();return{autoPad:n,format:r,dilations:i,group:a,kernelShape:s,pads:u,strides:d,wIsConst:c,...t,cacheKey:`${e.format};${t.activation};`}},Ta=(e,t,r,n)=>{let i=r.format==="NHWC",a=Jl(t[0].dims,t[1].dims,r.dilations,r.pads,r.strides,i);if(r.group!==1){let ke=[t[0]];if(i){let Pe=e.kernelCustomData.wT??e.compute(xn(t[1],ls),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Pe),ke.push(Pe)}else ke.push(t[1]);t.length===3&&ke.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&i&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1?e.compute(Ql(ke,r,a,n),{inputs:ke}):e.compute(Xl(ke,r,a,n),{inputs:ke});return}let s=t.length===3,u=t[0].dims[i?1:2],d=t[0].dims[i?2:3],c=t[0].dims[i?3:1],g=t[1].dims[2],m=t[1].dims[3],l=a[i?1:2],T=a[i?2:3],x=a[i?3:1],C=i&&g===u&&m===d&&r.pads[0]===0&&r.pads[1]===0;if(C||g===1&&m===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let ke=a[0],Pe,Ye,It,Bt=[];if(i){let Ht=e.kernelCustomData.wT??e.compute(xn(t[1],ls),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ht),C){let Er=u*d*c;Pe=t[0].reshape([1,ke,Er]),Ye=Ht.reshape([1,Er,x]),It=[1,ke,x]}else Pe=t[0].reshape([ke,u*d,c]),Ye=Ht.reshape([1,c,x]),It=[ke,l*T,x];Bt.push(Pe),Bt.push(Ye)}else Pe=t[0].reshape([ke,c,u*d]),Ye=t[1].reshape([1,x,c]),It=[ke,x,l*T],Bt.push(Ye),Bt.push(Pe);s&&Bt.push(t[2]);let ar=It[2],nr=Bt[0].dims[Bt[0].dims.length-1];ar<8&&nr<8?e.compute(va(Bt,r,a,It,i,n),{inputs:Bt}):e.compute(ga(Bt,r,a,It,i,n),{inputs:Bt});return}let z=!0,U=e.kernelCustomData.wT??e.compute(xn(t[1],ls),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=U);let A=[t[0],U];s&&A.push(t[2]);let ee=i?l*T:x,te=i?x:l*T,ie=g*m*c;e.compute(Wl(A,r,a,ee,te,ie,s,z,n),{inputs:A})},ci=(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 i=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),s=[1].concat(t.dilations),u=[1].concat(t.kernelShape),d=di({...t,pads:i,strides:a,dilations:s,kernelShape:u},n);Ta(e,n,d,c=>r?[c[0],c[2],c[3]]:[c[0],c[1],c[3]])},tu=(e,t,r)=>{let n=r.format==="NHWC"?"channelsLast":"channelsFirst",i=di(r,t),a=r.autoPad==="NOTSET"?r.pads:r.autoPad,s=ql(t[0].dims,t[1].dims,r.strides,r.dilations,a,!1,n);e.compute(Hl(t,i,s.outShape,[s.filterDepth,s.filterHeight,s.filterWidth],[s.padInfo.front,s.padInfo.top,s.padInfo.left],n))},Ca=(e,t)=>{if(eu(e.inputs,t),e.inputs[0].dims.length===3)ci(e,t);else if(e.inputs[0].dims.length===5)tu(e,e.inputs,t);else{let r=di(t,e.inputs);Ta(e,e.inputs,r)}}}),Ad,ru,Id=L(()=>{Yt(),_(),pr(),Zn(),ai(),ma(),li(),Ad=(e,t=!1,r,n,i=4)=>{let a=z=>{switch(z){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 ${z} is not supported.`)}},s=e?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,u=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,d=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",c=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",g=e?"row":"col",m=e?"col":"row",l=`
      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${g} / outWidth;
      let outCol = ${g} % outWidth;

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

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

  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) {
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
      var value = valueInput;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${u}
      ${ii(t)}
      ${C}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${i}] = value;
    }
  }`},ru=(e,t,r,n,i,a,s,u)=>{let d=t.format==="NHWC",c=d?e[0].dims[3]:e[0].dims[1],g=r[0],m=d?r[2]:r[3],l=d?r[1]:r[2],T=d?r[3]:r[1],x=d&&c%4===0&&c%3&&T%4===0,C=d?T:m*l,z=d?m*l:T,U=[8,8,1],A=n<=8?[4,1,1]:[4,4,1],ee=[Math.ceil(C/U[0]/A[0]),Math.ceil(z/U[1]/A[1]),Math.ceil(g/U[2]/A[2])];ae("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${ee}`);let te=x?4:1,ie=Math.max(U[0]*te,U[1]),ke=x?4:1,Pe=[t.kernelShape[d?1:2],t.kernelShape[d?2:3]],Ye=[Pe[0]+(t.dilations[0]<=1?0:(Pe[0]-1)*(t.dilations[0]-1)),Pe[1]+(t.dilations[1]<=1?0:(Pe[1]-1)*(t.dilations[1]-1))],It=[Ye[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),Ye[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Bt=[{type:6,data:n},{type:6,data:i},{type:6,data:a},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:Pe},{type:6,data:It}];Qn(t,Bt),Bt.push(...Et(e[0].dims,e[1].dims));let ar=["rank","rank"];s&&(Bt.push(...Et(e[2].dims)),ar.push("rank")),Bt.push(...Et(r));let nr=Ht=>{let Er=Qe("x",e[0].dataType,e[0].dims.length,ke),jr=Qe("w",e[1].dataType,e[1].dims.length,1),hr=qt("result",e[0].dataType,r.length,ke),Ir=[Er,jr],Gt="";if(s){let qe=Qe("bias",e[2].dataType,e[2].dims.length,ke);Ir.push(qe),Gt+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${qe.type.value} {
            return bias[coords.${d?"w":"y"}${x?"/ 4":""}];
          }`}let Qt=[{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:Pe.length},{name:"pads",type:"i32",length:It.length}];Yn(t,Qt);let xr=fr(e[0].dataType,1);if(xr!=="f16"&&xr!=="f32")throw new Error(`elemType ${xr} is not supported.`);return`
        ${fa("uniforms.result_strides")}
        ${Ht.registerUniforms(Qt).declareVariables(...Ir,hr)};
        ${Gt}
        ${Ad(d,s,t,Er.type.value,te)}
        ${x?Bs(A,U,xr,void 0,!d,ie):oi(A,U,xr,void 0,!d,ie,!1,void 0,u)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${A};${U};${x}`,inputDependencies:ar},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:ee[0],y:ee[1],z:ee[2]},programUniforms:Bt}),getShaderSource:nr}}}),nu,us,Fd=L(()=>{Yt(),_(),Kt(),pr(),nu=(e,t,r,n,i,a=!1,s,u,d=!1)=>{let c=d?1:2,g=d?2:3,m=d?3:1,l=a?2:1,T=`
  fn setOutputAtIndex(flatIndex : u32, value : ${a?`vec4<${s}>`:s}) {
    result[flatIndex] = ${a?`vec4<${s}>`:s}(value);
  }`;n&&(T+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${a?`vec4<${s}>`:s} {
      return bias[coords.${d?"w":"y"}${a?"/ 4":""}];
    }`);let x=a?4:1,C=Qe("W",t[1].dataType,t[1].dims.length,x),z=Qe("Dy",t[0].dataType,t[0].dims.length,x),U=[z,C];n&&U.push(Qe("bias",t[2].dataType,[r[m]].length,x));let A=qt("result",t[0].dataType,r.length,x),ee=`{
        let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];
        let r = ${i?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];
        let c = ${i?"global_id.y":"workgroup_id.y"} * ${l};
        let d1: u32 = ${i?"global_id.x":"workgroup_id.x"} * 4;

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

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

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

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

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

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

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

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

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

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

            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
              if (wC % uniforms.dilations.y != 0) {
                continue;
              }
              let dyC = (${s}(dyCCorner) + ${s}(wC)) / ${s}(uniforms.strides.y);
              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
              if (dyC < 0.0 || dyC >= ${s}(uniforms.Dy_shape[${g}]) ||
                  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 = ${d?z.get("batch","idyR","idyC","inputChannel"):z.get("batch","inputChannel","idyR","idyC")};
                let wValue = ${C.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};
                dotProd = dotProd + xValue * wValue;
                inputChannel = inputChannel + 1;
              }
            }
          }
          let value = dotProd + ${n?"bias[d1]":`${s}(0.0)`};
          ${A.setByOffset("global_idx","value")};
        `;return`
  ${e.registerUniforms(u).declareVariables(...U,A)}
  ${T}

    ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
  ${a?ee:te}}`},us=(e,t,r)=>{let n=e.length>2,i=t.outputShape,a=$e.size(i),s=[Math.ceil(a/64),1,1];ae("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${s}`);let u=t.format==="NHWC",d=["rank","rank"],c=[t.strides[0],t.strides[1]],g=[t.kernelShape[u?1:2],t.kernelShape[u?2:3]],m=[t.dilations[0],t.dilations[1]],l=[g[0]+(t.dilations[0]<=1?0:(t.kernelShape[u?1:2]-1)*(t.dilations[0]-1)),g[1]+(t.dilations[1]<=1?0:(t.kernelShape[u?2:3]-1)*(t.dilations[1]-1))],T=[l[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),l[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],x=!1,C=t.group,z=e[1].dims,U=z[0]/C,A=z[1],ee=[{type:12,data:a},{type:12,data:c},{type:12,data:g},{type:12,data:m},{type:12,data:l},{type:6,data:T},{type:12,data:U},{type:12,data:A},...Et(e[0].dims,e[1].dims)];n&&(ee.push(...Et(e[2].dims)),d.push("rank")),ee.push(...Et(i));let te=s[1]===1&&s[2]===1,ie=ke=>{let 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l=e[0],T=t[u?3:1]*i;for(let x=0,C=e.length-g-(u?1:0);x<g;++x,++C){let z=e[C],U=m?z*s[x]:c[x],A=su(z,s[x],a[x],t[C],r[x],U);iu(A,n,a,x,x+g),m&&c.push(s[x]*(z-1)+d[x]+(t[C]-1)*r[x]+1-a[x]-a[x+g])}c.splice(0,0,l),c.splice(u?3:1,0,T)},$a=(e,t)=>{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((m,l)=>m*l,1)===0){r.length=0;for(let m=2;m<t[1].dims.length;++m)r.push(t[1].dims[m])}let n=e.format==="NHWC";r.splice(0,0,t[1].dims[0]),r.splice(n?3:1,0,t[1].dims[1]);let i=e.pads.slice(),a=e.outputShape.slice(),s=e.outputPadding.slice(),u=t[0].dims,d=e.dilations.slice();if(d.reduce((m,l)=>m+l,0)===0){let m=t[0].dims.length-2;d=new Array(m).fill(1)}let c=e.strides.slice();if(c.reduce((m,l)=>m+l,0)===0){let m=t[0].dims.length-2;c=new Array(m).fill(1)}au(u,r,d,e.autoPad,e.group,i,c,n,s,a);let g=Object.assign({},e);return Object.assign(g,{kernelShape:r,pads:i,outputPadding:s,outputShape:a,dilations:d,strides:c}),g},pi=e=>{let t=si(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,a=e.group,s=e.kernelShape,u=e.pads,d=e.strides,c=e.wIsConst(),g=e.outputPadding,m=e.outputShape;return{autoPad:n,format:r,dilations:i,group:a,kernelShape:s,outputPadding:g,outputShape:m,pads:u,strides:d,wIsConst:c,...t,cacheKey:`${e.format};${t.activation};`}},Od=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");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[0];if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let i=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==i))throw new Error("invalid bias");let a=e[0].dims.length-2;if(t.dilations.reduce((s,u)=>s+u,0)>0&&t.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(t.strides.reduce((s,u)=>s+u,0)>0&&t.strides.length!==a)throw new Error(`strides should be ${a}D`);if(t.pads.reduce((s,u)=>s+u,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 ${a}D`);if(t.kernelShape.reduce((s,u)=>s+u,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},ou=[2,3,1,0],lu=(e,t,r)=>{let n=$a(r,t),i=r.format==="NHWC",a=n.outputShape,s=a[i?3:1],u=t[0].dims[i?3:1];if(n.group!==1||s===1&&u===1){e.compute(us(t,n));return}let d=a[i?1:2],c=a[i?2:3],g=t[1].dims[2],m=t[1].dims[3],l=i?d*c:s,T=i?s:d*c,x=g*m*u,C=!0,z=e.kernelCustomData.wT??e.compute(xn(t[1],ou),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=z);let U=[t[0],z],A=t.length===3;A&&(!i&&t[2].dims.length===1?U.push(t[2].reshape([t[2].dims[0],1,1])):U.push(t[2])),e.compute(ru(U,n,a,l,T,x,A,C),{inputs:U})},uu=(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 i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let a=t.dilations;(a.length===0||a[0]===0)&&(a=[1]);let s=t.strides;(s.length===0||s[0]===0)&&(s=[1]);let u=t.pads;u.length===0&&(u=[0,0]),u=[0,u[0],0,u[1]],s=[1].concat(s),a=[1].concat(a),i=[1].concat(i);let d=$a({...t,pads:u,strides:s,dilations:a,kernelShape:i},n);e.compute(us(n,d,c=>r?[c[0],c[2],c[3]]:[c[0],c[1],c[3]]))},du=(e,t)=>{Od(e.inputs,t),e.inputs[0].dims.length===3?uu(e,t):lu(e,e.inputs,t)}}),Ea,cu,pu,hu=L(()=>{Yt(),Kt(),Pr(),pr(),Ea=(e,t,r,n)=>{let i=$e.size(t),a=t.length,s=Qe("input",e,a),u=qt("output",e,a),d=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),c=$e.normalizeAxis(d,a),g=m=>{let l=` i32(${s.indicesGet("inputIndices","uniforms.axis")}) `,T=Wt("uniforms.input_shape","uniforms.axis",a),x=n.reverse?l+(n.exclusive?" + 1":""):"0",C=n.reverse?T:l+(n.exclusive?"":" + 1");return`
                ${m.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(s,u)}
                ${m.mainStart()}
                  ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
                  var inputIndices = ${u.offsetToIndices("global_idx")};
                  var sum = ${u.type.value}(0);
                  let first : i32 = ${x};
                  let last : i32 = ${C};
                  for (var i : i32 = first; i < last; i++) {
                    ${s.indicesSet("inputIndices","uniforms.axis","u32(i)")};
                    sum = sum + ${s.getByIndices("inputIndices")};
                  }
                  ${u.setByOffset("global_idx","sum")};
                }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},{type:12,data:c},...Et(t,t)]}),getShaderSource:g}},cu=(e,t)=>{let r=e.inputs[0].dims,n=e.inputs[0].dataType,i=e.inputs[1];e.compute(Ea(n,r,i,t),{inputs:[0]})},pu=e=>{let t=e.exclusive===1,r=e.reverse===1;return or({exclusive:t,reverse:r})}}),fu,mu,ka,_u,gu,Sa=L(()=>{Yt(),Kt(),Pr(),pr(),fu=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.")},mu=(e,t,r,n)=>{let i=[];i.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
    var a: ${r.type.indices};`);for(let a=0;a<t;++a)i.push(r.indicesSet("a",e[a],`i[${a}]`));return i.push("return a;}"),i.join(`
`)},ka=(e,t)=>{let r,n,i,a,s,u,d=t.format==="NHWC",c=t.blocksize,g=t.mode==="DCR";d?([r,n,i,a]=e.dims,s=g?[r,n,i,c,c,a/c**2]:[r,n,i,a/c**2,c,c],u=g?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([r,n,i,a]=[e.dims[0],e.dims[2],e.dims[3],e.dims[1]],s=g?[r,c,c,a/c**2,n,i]:[r,a/c**2,c,c,n,i],u=g?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let m=e.reshape(s),l=m.dims.length,T=e.dataType,x=Qe("a",T,l),C=qt("output",T,l),z=U=>`
  ${U.registerUniform("output_size","u32").declareVariables(x,C)}

  ${mu(u,l,x,C)}

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

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

    ${C.setByOffset("global_idx",x.getByIndices("aIndices"))}
  }`;return{name:"DepthToSpace",shaderCache:{hint:`${e.dims};${t.blocksize};${t.mode}`,inputDependencies:["rank"]},getRunData:U=>{let A=d?[r,n*c,i*c,a/c**2]:[r,a/c**2,n*c,i*c],ee=$e.size(A),te=m.dims,ie=$e.sortBasedOnPerm(te,u);return{outputs:[{dims:A,dataType:U[0].dataType}],dispatchGroup:{x:Math.ceil(ee/64)},programUniforms:[{type:12,data:ee},...Et(te,ie)]}},getShaderSource:z}},_u=(e,t)=>{fu(e.inputs),e.compute(ka(e.inputs[0],t))},gu=e=>or({blocksize:e.blocksize,mode:e.mode,format:e.format})}),hi,Ns,Pa,wu,Aa,fi,Dd,Ia,yu,Fa,bu,Ld=L(()=>{Yt(),Kt(),Pr(),pr(),hi="[a-zA-Z]|\\.\\.\\.",Ns="("+hi+")+",Pa="^"+Ns+"$",wu="("+Ns+",)*"+Ns,Aa="^"+wu+"$",fi=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,t){let r=this.symbolToIndices.get(e);r===void 0?r=[t]:r.push(t),this.symbolToIndices.set(e,r)}},Dd=class{constructor(e,t){var i;this.equation=t,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[r,n]=t.includes("->")?t.split("->",2):[t,""];if(!r.match(RegExp(Aa)))throw new Error("Invalid LHS term");if(r.split(",").forEach((a,s)=>{let u=e[s].dims.slice();if(!a.match(RegExp(Pa)))throw new Error("Invalid LHS term");let d=this.processTerm(a,!0,u,s);this.lhs.push(d)}),n==="")n+=[...this.symbolToInfo.entries()].filter(([a,s])=>s.count===1||a==="...").map(([a])=>a).join("");else if(!n.match(RegExp(Ns)))throw new Error("Invalid RHS");(i=n.match(RegExp(hi,"g")))==null||i.forEach(a=>{if(a==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let s=this.symbolToInfo.get(a);if(s===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(s.dimValue)}}),this.rhs=this.processTerm(n,!1,this.outputDims)}addSymbol(e,t,r){let n=this.symbolToInfo.get(e);if(n!==void 0){if(n.dimValue!==t&&n.count!==1)throw new Error("Dimension mismatch");n.count++,n.inputIndices.push(r)}else n={count:1,dimValue:t,inputIndices:[r]};this.symbolToInfo.set(e,n)}processTerm(e,t,r,n=-1){let i=r.length,a=!1,s=[],u=0;if(!e.match(RegExp(Pa))&&!t&&e!=="")throw new Error("Invalid LHS term");let d=e.match(RegExp(hi,"g")),c=new fi(n);return d==null||d.forEach((g,m)=>{if(g==="..."){if(a)throw new Error("Only one ellipsis is allowed per input term");a=!0;let l=i-d.length+1;if(l<0)throw new Error("Ellipsis out of bounds");if(s=r.slice(u,u+l),this.hasEllipsis){if(this.ellipsisDims.length!==s.length||this.ellipsisDims.toString()!==s.toString())throw new Error("Ellipsis dimensions mismatch")}else if(t)this.hasEllipsis=!0,this.ellipsisDims=s;else throw new Error("Ellipsis must be specified in the LHS");for(let T=0;T<s.length;T++){let x=String.fromCharCode(48+T);c.addSymbol(x,m+T),this.addSymbol(x,r[u++],n)}}else c.addSymbol(g,m+(this.hasEllipsis?this.ellipsisDims.length-1:0)),this.addSymbol(g,r[u++],n)}),c}},Ia=e=>e+"_max",yu=(e,t,r,n)=>{let i=e.map(c=>c.length).map((c,g)=>Qe(`input${g}`,t,c)),a=$e.size(n),s=qt("output",t,n.length),u=[...r.symbolToInfo.keys()].filter(c=>!r.rhs.symbolToIndices.has(c)),d=c=>{let g=[],m="var prod = 1.0;",l="var sum = 0.0;",T="sum += prod;",x=[],C=[],z=[],U=[],A=r.symbolToInfo.size===r.rhs.symbolToIndices.size;r.symbolToInfo.forEach((te,ie)=>{var ke;if(r.rhs.symbolToIndices.has(ie)){let Pe=(ke=r.rhs.symbolToIndices.get(ie))==null?void 0:ke[0];Pe!==void 0&&r.lhs.forEach((Ye,It)=>{if(te.inputIndices.includes(It)){let Bt=Ye.symbolToIndices.get(ie);if(Bt===void 0)throw new Error("Invalid symbol error");Bt.forEach(ar=>{g.push(`${i[It].indicesSet(`input${It}Indices`,ar,s.indicesGet("outputIndices",Pe))}`)})}})}else r.lhs.forEach((Pe,Ye)=>{if(te.inputIndices.includes(Ye)){let It=Pe.symbolToIndices.get(ie);if(It===void 0)throw new Error("Invalid symbol error");It.forEach(Bt=>{x.push(`${i[Ye].indicesSet(`input${Ye}Indices`,Bt,`${ie}`)}`)}),U.push(`prod *= ${i[Ye].getByIndices(`input${Ye}Indices`)};`)}}),C.push(`for(var ${ie}: u32 = 0; ${ie} < uniforms.${Ia(ie)}; ${ie}++) {`),z.push("}")});let ee=A?[...g,`let sum = ${i.map((te,ie)=>te.getByIndices(`input${ie}Indices`)).join(" * ")};`]:[...g,l,...C,...x,m,...U,T,...z];return`
            ${c.registerUniforms(u.map(te=>({name:`${Ia(te)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...i,s)}

            ${c.mainStart()}
            ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
            var outputIndices = ${s.offsetToIndices("global_idx")};
            ${i.map((te,ie)=>`var input${ie}Indices: ${i[ie].type.indices};`).join(`
`)}
            ${ee.join(`
`)};
            ${s.setByOffset("global_idx","sum")};
          }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let c=u.filter(m=>r.symbolToInfo.has(m)).map(m=>{var l;return{type:12,data:((l=r.symbolToInfo.get(m))==null?void 0:l.dimValue)||0}});c.push({type:12,data:a});let g=e.map((m,l)=>[...Et(m)]).reduce((m,l)=>m.concat(l),c);return g.push(...Et(n)),{outputs:[{dims:n,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:g}},getShaderSource:d}},Fa=(e,t)=>{let r=new Dd(e.inputs,t.equation),n=r.outputDims,i=e.inputs.map((a,s)=>a.dims);e.compute(yu(i,e.inputs[0].dataType,r,n))},bu=e=>{let t=e.equation.replace(/\s+/g,"");return or({equation:t})}}),Oa,za,Mu,Da,Bd,bc=L(()=>{Yt(),Kt(),pr(),Oa=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=r.length<t.length?0:r.length-t.length,i=t.length<r.length?0:t.length-r.length;for(;n<r.length&&i<t.length;++n,++i)if(r[n]!==t[i]&&r[n]!==1&&t[i]!==1)throw new Error("Expand requires shape to be broadcastable to input")},za=(e,t)=>{let r=e.length-t.length,n=[];for(let i=0;i<r;++i)n.push(e[i]);for(let i=0;i<t.length;++i)n.push(t[i]===1?e[i+r]:t[i]);return n},Mu=(e,t)=>e.length>t.length?za(e,t):za(t,e),Da=e=>{let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=Mu(t,r),i=e[0].dataType,a=i===9?4:1,s=Math.ceil($e.size(n)/a),u=c=>{let g=Qe("input",i,t.length,a),m=qt("output",i,n.length,a),l;if(i===9){let T=(x,C,z="")=>`
          let outputIndices${C} = ${m.offsetToIndices(`outputOffset + ${C}u`)};
          let offset${C} = ${g.broadcastedIndicesToOffset(`outputIndices${C}`,m)};
          let index${C} = offset${C} / 4u;
          let component${C} = offset${C} % 4u;
          ${x}[${C}] = ${z}(${g.getByOffset(`index${C}`)}[component${C}]);
        `;l=`
        let outputOffset = global_idx * ${a};
        var data = vec4<u32>(0);
        ${T("data",0,"u32")}
        ${T("data",1,"u32")}
        ${T("data",2,"u32")}
        ${T("data",3,"u32")}
        ${m.setByOffset("global_idx","data")}
      }`}else l=`
        let outputIndices = ${m.offsetToIndices("global_idx")};
        let inputOffset = ${g.broadcastedIndicesToOffset("outputIndices",m)};
        ${m.setByOffset("global_idx",g.getByOffset("inputOffset"))}
      }`;return`
    ${c.registerUniform("vec_size","u32").declareVariables(g,m)}
    ${c.mainStart()}
    ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
    ${l}`},d=[{type:12,data:s},...Et(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:u,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:d})}},Bd=e=>{Oa(e.inputs),e.compute(Da(e.inputs),{inputs:[0]})}}),La,Ba,Rd=L(()=>{Yt(),Kt(),pr(),ua(),La=e=>{let t=e[0].dataType,r=$e.size(e[0].dims),n=$e.size(e[1].dims),i=n%4===0,a=s=>{let u=Qe("x",t,[1],4),d=Qe("bias",t,[1],4),c=qt("y",t,[1],4),g=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],m=T=>`
      let bias${T}_offset: u32 = (global_idx * 4 + ${T}) % uniforms.bias_size;
      let bias${T} = ${d.getByOffset(`bias${T}_offset / 4`)}[bias${T}_offset % 4];`,l=i?`
      let bias = ${d.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${m(0)}${m(1)}${m(2)}${m(3)}
      let bias = ${u.type.value}(bias0, bias1, bias2, bias3);`;return`${s.registerUniforms(g).declareVariables(u,d,c)}

    ${ia(Fr(t))}

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

      let x = ${u.getByOffset("global_idx")};
      ${l}
      let x_in = x + bias;
      ${c.setByOffset("global_idx",aa("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${i}`,inputDependencies:["type","type"]},getShaderSource:a,getRunData:s=>({outputs:[{dims:s[0].dims,dataType:s[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/pn/4)}})}},Ba=e=>{e.inputs.length<2||$e.size(e.inputs[1].dims)===0?yl(e):e.compute(La(e.inputs))}}),vu,Ra,xu,Tu,Cu=L(()=>{Yt(),Kt(),Pr(),pr(),vu=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},Ra=(e,t)=>{let r=e[0].dims,n=e[1].dims,i=r.length,a=$e.normalizeAxis(t.axis,i),s=r.slice(0);s.splice(a,1,...n);let u=r[a],d=e[0].dataType===9?4:1,c=Math.ceil($e.size(s)/d),g=[{type:12,data:c},{type:6,data:u},{type:12,data:a},...Et(e[0].dims,e[1].dims,s)],m=l=>{let T=Qe("data",e[0].dataType,e[0].dims.length,d),x=Qe("inputIndices",e[1].dataType,e[1].dims.length),C=qt("output",e[0].dataType,s.length,d),z=A=>{let ee=n.length,te=`var indicesIndices${A}  = ${x.type.indices}(0);`;for(let ie=0;ie<ee;ie++)te+=`${ee>1?`indicesIndices${A}[${ie}]`:`indicesIndices${A}`} = ${s.length>1?`outputIndices${A}[uniforms.axis + ${ie}]`:`outputIndices${A}`};`;te+=`
          var idx${A} = ${x.getByIndices(`indicesIndices${A}`)};
          if (idx${A} < 0) {
            idx${A} = idx${A} + uniforms.axisDimLimit;
          }
          var dataIndices${A} : ${T.type.indices};
        `;for(let ie=0,ke=0;ie<i;ie++)ie===a?(te+=`${i>1?`dataIndices${A}[${ie}]`:`dataIndices${A}`} = u32(idx${A});`,ke+=ee):(te+=`${i>1?`dataIndices${A}[${ie}]`:`dataIndices${A}`} = ${s.length>1?`outputIndices${A}[${ke}]`:`outputIndices${A}`};`,ke++);return te},U;if(e[0].dataType===9){let A=(ee,te,ie="")=>`
          let outputIndices${te} = ${C.offsetToIndices(`outputOffset + ${te}u`)};
          ${z(te)};
          let offset${te} = ${T.indicesToOffset(`dataIndices${te}`)};
          let index${te} = offset${te} / 4u;
          let component${te} = offset${te} % 4u;
          ${ee}[${te}] = ${ie}(${T.getByOffset(`index${te}`)}[component${te}]);
        `;U=`
        let outputOffset = global_idx * ${d};
        var value = vec4<u32>(0);
        ${A("value",0,"u32")}
        ${A("value",1,"u32")}
        ${A("value",2,"u32")}
        ${A("value",3,"u32")}
        ${C.setByOffset("global_idx","value")}
      `}else U=`
      let outputIndices = ${C.offsetToIndices("global_idx")};
      ${z("")};
      let value = ${T.getByIndices("dataIndices")};
      ${C.setByOffset("global_idx","value")};
      `;return`
      ${l.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(T,x,C)}
      ${l.mainStart()}
        ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${U}
      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:m}},xu=e=>or({axis:e.axis}),Tu=(e,t)=>{let r=e.inputs;vu(r),e.compute(Ra(e.inputs,t))}}),$u,Eu,Na,ku,Nd=L(()=>{Yt(),Kt(),Pr(),pr(),$u=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let r=$e.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,i=e[0],a=e[2],s=e.length===4?e[3]:void 0;if(a.dims.length!==i.dims.length||!i.dims.map((u,d)=>d===r?Math.ceil(u/n)===a.dims[d]:u===a.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(s){if(s.dataType!==i.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(s.dims.length!==a.dims.length||!s.dims.map((u,d)=>u===a.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},Eu=(e,t)=>{let r=e[0].dims,n=e[1].dims,i=r.length,a=$e.normalizeAxis(t.gatherAxis,i),s=$e.normalizeAxis(t.quantizeAxis,i),u=r.slice(0);u.splice(a,1,...n);let d=$e.size(u),c=e[2].dataType,g=e[0].dataType===22,m=[{type:12,data:d},{type:12,data:s},{type:12,data:a},{type:12,data:t.blockSize},...Et(...e.map((T,x)=>T.dims),u)],l=T=>{let x=Qe("data",e[0].dataType,e[0].dims.length),C=Qe("inputIndices",e[1].dataType,e[1].dims.length),z=Qe("scales",e[2].dataType,e[2].dims.length),U=e.length>3?Qe("zeroPoint",e[3].dataType,e[3].dims.length):void 0,A=qt("output",c,u.length),ee=[x,C,z];U&&ee.push(U);let te=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${T.registerUniforms(te).declareVariables(...ee,A)}
        ${T.mainStart()}
        let output_indices = ${A.offsetToIndices("global_idx")};
        var indices_indices = ${C.type.indices}(0);
        ${n.length>1?`
          for (var i: u32 = 0; i < ${n.length}; i++) {
            let index = ${A.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${C.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${A.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${x.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${A.indicesGet("output_indices","i")};
          ${x.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${C.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${r[a]};
        }
        ${x.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${u.length}; i++) {
          let index = ${A.indicesGet("output_indices",`i + ${n.length} - 1`)};
          ${x.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${x.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${x.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${g?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
        let quantized_data = quantized_data_vec[data_index / 2];
        var scale_indices = data_indices;
        let quantize_axis_index = ${z.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${z.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${z.getByIndices("scale_indices")};
        ${U?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${U.indicesToOffset("zero_point_indices")};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${U.getByOffset("zero_point_offset / 8")};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${g?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${Fr(c)}(quantized_data - zero_point) * scale;
        ${A.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((T,x)=>x!==1).map(T=>T.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(T,x)=>"rank")},getRunData:()=>({outputs:[{dims:u,dataType:c}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:m}),getShaderSource:l}},Na=(e,t)=>{let r=e.inputs;$u(r,t),e.compute(Eu(e.inputs,t))},ku=e=>or({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),Su,Pu,Or,jd,Mc=L(()=>{Yt(),Kt(),Pr(),pr(),Su=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},Pu=(e,t)=>{let r=e[0].dims,n=e[0].dataType,i=r.length,a=e[1].dims,s=e[1].dataType,u=$e.normalizeAxis(t.axis,i),d=r[u],c=a.slice(0),g=$e.size(c),m=Qe("input",n,i),l=Qe("indicesInput",s,a.length),T=qt("output",n,c.length),x=[{type:12,data:g},{type:6,data:d},{type:12,data:u}];return x.push(...Et(r,a,c)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:x}),getShaderSource:C=>`
      ${C.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(m,l,T)}
      ${C.mainStart()}
      ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${T.setByOffset("global_idx","value")};
  }`}},Or=e=>or({axis:e.axis}),jd=(e,t)=>{let r=e.inputs;Su(r),e.compute(Pu(e.inputs,t))}}),Vd,ja,Au,Iu,Ud=L(()=>{Yt(),Kt(),pr(),Vd=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},ja=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[i,a,s]=ln.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),u=[i,a];if(!u)throw new Error("Can't use gemm on the given tensors");let d=$e.size(u),c=[{type:12,data:d},{type:12,data:i},{type:12,data:a},{type:12,data:s},{type:1,data:t.alpha},{type:1,data:t.beta}],g=["type","type"];e.length===3&&(c.push(...Et(e[2].dims)),g.push("rank")),c.push(...Et(u));let m=l=>{let T="";t.transA&&t.transB?T="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?T="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?T="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(T="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let x=t.alpha===1?"":"value *= uniforms.alpha;",C=Qe("a",e[0].dataType,e[0].dims),z=Qe("b",e[1].dataType,e[1].dims),U=C.type.value,A=null,ee=[C,z];e.length===3&&(A=Qe("c",e[2].dataType,e[2].dims.length),ee.push(A));let te=qt("output",e[0].dataType,u.length);ee.push(te);let ie=[{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`
  ${l.registerUniforms(ie).declareVariables(...ee)}

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

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

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

    ${x}
    ${A!=null?`let cOffset = ${A.broadcastedIndicesToOffset("vec2(m, n)",te)}; value += ${U}(uniforms.beta) * ${A.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:m}},Au=e=>{let t=e.transA,r=e.transB,n=e.alpha,i=e.beta;return{transA:t,transB:r,alpha:n,beta:i,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},Iu=(e,t)=>{Vd(e.inputs),e.compute(ja(e.inputs,t))}}),gn,Va,Ua,Wa,Fu,vs,Wd,Gd=L(()=>{Yt(),Kt(),Pr(),oe(),ei(),pr(),Vn(),gn=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,Va=(e,t)=>{let r=e[0],n=gn(e,1),i=gn(e,2),a=gn(e,3),s=gn(e,4),u=gn(e,5),d=gn(e,6),c=gn(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let g=r.dims[0],m=r.dims[1],l=r.dims.length===3?r.dims[2]:t.numHeads*r.dims[4],T=m,x=0,C=0,z=Math.floor(l/t.numHeads);if(d&&c&&$e.size(d.dims)&&$e.size(c.dims)){if(d.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims[0]!==g||d.dims[1]!==t.numHeads||d.dims[3]!==z)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(c.dims[0]!==g||c.dims[1]!==t.numHeads||c.dims[3]!==z)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[2]!==c.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(c.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');x=d.dims[2],C=d.dims[2]}else if(d&&$e.size(d.dims)||c&&$e.size(c.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let U;if(n&&$e.size(n.dims)>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');U=2,T=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==z)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(i)throw new Error('Expect "value" be none when "key" has packed kv format.');U=5,T=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==z)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');U=0,T=n.dims[2]}}else{if(r.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(r.dims[2]!==t.numHeads||r.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');U=3}if(a&&$e.size(a.dims)>0){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let A=x+T,ee=0;if(s&&$e.size(s.dims)>0){ee=8;let Pe=s.dims;throw Pe.length===1?Pe[0]===g?ee=1:Pe[0]===3*g+2&&(ee=3):Pe.length===2&&Pe[0]===g&&Pe[1]===A&&(ee=5),ee===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let te=!1,ie=l;if(i&&$e.size(i.dims)>0){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(T!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');ie=i.dims[2]}else{if(T!==i.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');ie=i.dims[1]*i.dims[3],te=!0}}let ke=!1;if(s&&$e.size(s.dims)>0)throw new Error("Key padding mask is not supported");if(u&&$e.size(u.dims)>0){if(u.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(u.dims[0]!==g||u.dims[1]!==t.numHeads||u.dims[2]!==m||u.dims[3]!==A)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:g,sequenceLength:m,pastSequenceLength:x,kvSequenceLength:T,totalSequenceLength:A,maxSequenceLength:C,inputHiddenSize:0,hiddenSize:l,vHiddenSize:ie,headSize:z,vHeadSize:Math.floor(ie/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:ee,scale:t.scale,broadcastResPosBias:ke,passPastInKv:te,qkvFormat:U}},Ua=e=>or({...e}),Wa=or({perm:[0,2,1,3]}),Fu=(e,t,r,n,i,a,s)=>{let u=[n,i,a],d=$e.size(u),c=[{type:12,data:d},{type:12,data:s},{type:12,data:a}],g=m=>{let l=qt("qkv_with_bias",t.dataType,u),T=Qe("qkv",t.dataType,u),x=Qe("bias",r.dataType,u),C=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${m.registerUniforms(C).declareVariables(T,x,l)}
  ${m.mainStart()}
    ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:g},{inputs:[t,r],outputs:[-1]})[0]},vs=(e,t,r,n,i,a,s,u)=>{let d=a;if(s&&$e.size(s.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return d=Fu(e,a,s,t,n,r*i,u),d=d.reshape([t,n,r,i]),r===1||n===1?d:e.compute(xn(d,Wa.perm),{inputs:[d],outputs:[-1]})[0]}else return a.dims.length===3&&(d=a.reshape([t,n,r,i])),r===1||n===1?d:e.compute(xn(d,Wa.perm),{inputs:[d],outputs:[-1]})[0]},Wd=(e,t)=>{let r=Va(e.inputs,t),n=e.inputs[0],i=gn(e.inputs,1),a=gn(e.inputs,2),s=gn(e.inputs,3),u=gn(e.inputs,4),d=gn(e.inputs,5),c=gn(e.inputs,6),g=gn(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((i==null?void 0:i.dims.length)===5)throw new Error("Packed KV is not implemented");let m=i&&a&&i.dims.length===4&&a.dims.length===4,l=vs(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,s,0);if(m)return Ms(e,l,i,a,u,void 0,c,g,d,r);if(!i||!a)throw new Error("key and value must be provided");let T=vs(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,i,s,r.hiddenSize),x=vs(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,a,s,2*r.hiddenSize);Ms(e,l,T,x,u,void 0,c,g,d,r)}}),Ou,zu,Du,Lu,Ga,Bu,Ru,Nu=L(()=>{Yt(),Kt(),Pr(),pr(),Ou=e=>{if(!e||e.length<1)throw new Error("too few inputs")},zu=(e,t)=>{let r=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(i=>r.push(Number(i))),n=r.length),or({numOutputs:n,axis:t.axis,splitSizes:r})},Du=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${Wt("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,Lu=e=>{let t=e.length,r=[];for(let n=0;n<t;++n){let i=e[n].setByIndices("indices","input[global_idx]");t===1?r.push(i):n===0?r.push(`if (output_number == ${n}u) { ${i} }`):n===t-1?r.push(`else { ${i} }`):r.push(`else if (output_number == ${n}) { ${i} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},Ga=(e,t)=>{let r=e[0].dims,n=$e.size(r),i=e[0].dataType,a=$e.normalizeAxis(t.axis,r.length),s=new Array(t.numOutputs),u=Qe("input",i,r.length),d=new Array(t.numOutputs),c=[],g=[],m=0,l=[{type:12,data:n}];for(let x=0;x<t.numOutputs;x++){m+=t.splitSizes[x],d[x]=m;let C=r.slice();C[a]=t.splitSizes[x],g.push(C),s[x]=qt(`output${x}`,i,C.length),c.push({dims:g[x],dataType:e[0].dataType})}l.push({type:12,data:d},...Et(r,...g));let T=x=>`
  ${x.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",d.length).declareVariables(u,...s)}
  ${Du(d.length)}
  ${Lu(s)}

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

    var indices = ${u.offsetToIndices("global_idx")};
    var index = ${u.indicesGet("indices",a)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${Wt("uniforms.size_in_split_axis","output_number - 1u",d.length)};
      ${u.indicesSet("indices",a,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:T,getRunData:()=>({outputs:c,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:l})}},Bu=(e,t)=>{Ou(e.inputs);let r=e.inputs.length===1?t:zu(e.inputs,t);e.compute(Ga(e.inputs,r),{inputs:[0]})},Ru=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 or({axis:t,numOutputs:n,splitSizes:r})}}),ju,Vu,qa,Uu,qd=L(()=>{Pr(),ei(),Gd(),Nu(),Vn(),ju=(e,t)=>{if(t.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let r=e[0],n=e[1],i=e[2],a=e[3],s=e[4];if(t.localWindowSize!==-1)throw new Error("Local attention is not supported");if(t.softcap!==0)throw new Error("Softcap is not supported");if(t.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(t.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let u=!1,d=r.dims[0],c=r.dims[1],g=r.dims.length===3?u?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],m=c,l=0,T=!n||n.dims.length===0,x=Math.floor(T?g/(t.numHeads+2*t.kvNumHeads):g/t.numHeads);T&&(g=x*t.numHeads);let C=a&&a.dims.length!==0,z=s&&s.dims.length!==0;if(C&&a.dims.length===4&&a.dims[0]===d&&a.dims[1]!==t.kvNumHeads&&a.dims[2]===t.kvNumHeads&&a.dims[3]===x)throw new Error("BSNH pastKey/pastValue is not supported");if(C&&z){if(a.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(s.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');l=a.dims[2]}else if(C||z)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let U=1;if(n&&n.dims.length>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(r.dims[2]%n.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');m=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==x)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(i)throw new Error('Expect "value" be none when "key" has packed kv format.');m=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==x)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');m=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');U=3}let A=0,ee=!1,te=t.kvNumHeads?x*t.kvNumHeads:g;if(i&&i.dims.length>0){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(m!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');te=i.dims[2]}else{if(m!==i.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');te=i.dims[1]*i.dims[3],ee=!0}}let ie=e.length>4?e[5]:void 0;if(ie&&ie.dims.length!==1&&ie.dims[0]!==d)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:d,sequenceLength:c,pastSequenceLength:l,kvSequenceLength:m,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:g,vHiddenSize:te,headSize:x,vHeadSize:Math.floor(te/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:A,scale:t.scale,broadcastResPosBias:!1,passPastInKv:ee,qkvFormat:U}},Vu=or({perm:[0,2,1,3]}),qa=(e,t,r)=>{let n=t,i=r.kvNumHeads;return t.dims.length===3&&r.kvSequenceLength!==0&&(n=t.reshape([r.batchSize,r.kvSequenceLength,i,r.headSize]),n=e.compute(xn(n,Vu.perm),{inputs:[n],outputs:[-1]})[0]),n},Uu=(e,t)=>{var z;let r=ju(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((z=e.inputs[1])==null?void 0:z.dims.length)===5)throw new Error("Packed KV is not implemented");let n=e.inputs[0],i=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,a=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,s=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,u=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,d=e.inputs.length>4?e.inputs[5]:void 0,c=e.inputs.length>5?e.inputs[6]:void 0,g=r.kvNumHeads?r.kvNumHeads:r.numHeads,m=or({axis:2,numOutputs:3,splitSizes:[r.numHeads*r.headSize,g*r.headSize,g*r.headSize]}),[l,T,x]=!i&&!a?e.compute(Ga([n],m),{inputs:[n],outputs:[-1,-1,-1]}):[n,i,a],C=vs(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,l,void 0,0);Ms(e,C,qa(e,T,r),qa(e,x,r),void 0,void 0,s,u,void 0,r,d,c)}}),Ha,Wu,Gu,qu,vc=L(()=>{Yt(),Kt(),Vn(),pr(),Ha=(e,t,r,n,i,a,s,u)=>{let d=_r(a),c=d===1?"f32":`vec${d}f`,g=d===1?"vec2f":`mat2x${d}f`,m=i*s,l=[i,s,a/d],T=[i,s,2],x=["rank","type","type"],C=[];C.push(...Et(l,T));let z=U=>{let A=Qe("x",t.dataType,3,d),ee=Qe("scale",r.dataType,r.dims),te=Qe("bias",n.dataType,n.dims),ie=qt("output",1,3,2),ke=[A,ee,te,ie],Pe=64;return`
  var<workgroup> workgroup_shared : array<${g}, ${Pe}>;
  const workgroup_size = ${Pe}u;
  ${U.declareVariables(...ke)}
  ${U.mainStart(Pe)}
    let batch = workgroup_index / uniforms.x_shape[1];
    let channel = workgroup_index % uniforms.x_shape[1];
    let hight = uniforms.x_shape[2];
    // initialize workgroup memory
    var sum = ${c}(0);
    var squared_sum = ${c}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${c}(${A.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${g}(sum, squared_sum);
    workgroupBarrier();

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

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${u}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${d};${u}`,inputDependencies:x},getRunData:()=>({outputs:[{dims:T,dataType:1}],dispatchGroup:{x:m},programUniforms:C}),getShaderSource:z},{inputs:[t,r,n],outputs:[-1]})[0]},Wu=(e,t,r)=>{let n=t[0].dims,i=n,a=2,s=n[0],u=n[1],d=$e.sizeFromDimension(n,a),c=_r(d),g=$e.size(i)/c,m=Ha(e,t[0],t[1],t[2],s,d,u,r.epsilon),l=[s,u,d/c],T=[s,u],x=["type","none"],C=z=>{let U=Qe("x",t[0].dataType,l.length,c),A=Qe("scale_shift",1,T.length,2),ee=qt("output",t[0].dataType,l.length,c),te=[U,A,ee];return`
  ${z.registerUniform("output_size","u32").declareVariables(...te)}
  ${z.mainStart()}
  ${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let outputIndices = ${ee.offsetToIndices("global_idx")};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${A.getByIndices("vec2<u32>(batch, channel)")};
      let value = ${U.getByOffset("global_idx")} * ${ee.type.value}(scale_shift.x) + ${ee.type.value}(scale_shift.y);
      ${ee.setByOffset("global_idx","value")};
  }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${c}`,inputDependencies:x},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:12,data:g},...Et(l,T,l)]}),getShaderSource:C},{inputs:[t[0],m]})},Gu=(e,t,r)=>{let n=t[0].dims,i=n,a=n[0],s=n[n.length-1],u=$e.sizeFromDimension(n,1)/s,d=_r(s),c=$e.size(i)/d,g=[{type:12,data:u},{type:12,data:Math.floor(s/d)}],m=["type","type"],l=[0,n.length-1];for(let z=0;z<n.length-2;z++)l.push(z+1);let T=e.compute(xn(e.inputs[0],l),{inputs:[e.inputs[0]],outputs:[-1]})[0],x=Ha(e,T,t[1],t[2],a,u,s,r.epsilon),C=z=>{let U=fr(t[0].dataType),A=d===1?"vec2f":`mat${d}x2f`,ee=ke=>{let Pe=ke===0?"x":"y",Ye=d===1?"f32":`vec${d}f`;switch(d){case 1:return`${U}(${Ye}(scale.${Pe}))`;case 2:return`vec2<${U}>(${Ye}(scale[0].${Pe}, scale[1].${Pe}))`;case 4:return`vec4<${U}>(${Ye}(scale[0].${Pe}, scale[1].${Pe}, scale[2].${Pe}, scale[3].${Pe}))`;default:throw new Error(`Not supported compoents ${d}`)}},te=Qe("input",t[0].dataType,t[0].dims,d),ie=qt("output",t[0].dataType,i,d);return`
  @group(0) @binding(0) var<storage, read> input : array<${te.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${A}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${ie.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

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

    let scale_offset = current_image_number * uniforms.C + current_channel_number;
    let scale = scale_input[scale_offset];
    output[global_idx] = fma(input[global_idx], ${ee(0)}, ${ee(1)});
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${d}`,inputDependencies:m},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:C},{inputs:[t[0],x]})},qu=(e,t)=>{t.format==="NHWC"?Gu(e,e.inputs,t):Wu(e,e.inputs,t)}}),Ka,Hu,Ku,Hd=L(()=>{Yt(),Kt(),pr(),Ka=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Hu=(e,t,r)=>{let n=t.simplified,i=e[0].dims,a=e[1],s=!n&&e[2],u=i,d=$e.normalizeAxis(t.axis,i.length),c=$e.sizeToDimension(i,d),g=$e.sizeFromDimension(i,d),m=$e.size(a.dims),l=s?$e.size(s.dims):0;if(m!==g||s&&l!==g)throw new Error(`Size of X.shape()[axis:] == ${g}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${m} and bias size of ${l}`);let T=[];for(let ie=0;ie<i.length;++ie)ie<d?T.push(i[ie]):T.push(1);let x=_r(g),C=["type","type"],z=[{type:12,data:c},{type:1,data:g},{type:12,data:Math.floor(g/x)},{type:1,data:t.epsilon}];s&&C.push("type");let U=r>1,A=r>2,ee=ie=>{let ke=fr(e[0].dataType),Pe=[Qe("x",e[0].dataType,e[0].dims,x),Qe("scale",a.dataType,a.dims,x)];s&&Pe.push(Qe("bias",s.dataType,s.dims,x)),Pe.push(qt("output",e[0].dataType,u,x)),U&&Pe.push(qt("mean_data_output",1,T)),A&&Pe.push(qt("inv_std_output",1,T));let Ye=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${ie.registerUniforms(Ye).declareVariables(...Pe)}
  ${ie.mainStart()}
    ${ie.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${as("f32",x)};
    var mean_square_vector = ${as("f32",x)};

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

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

    ${U?"mean_data_output[global_idx] = mean":""};
    ${A?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},te=[{dims:u,dataType:e[0].dataType}];return U&&te.push({dims:T,dataType:1}),A&&te.push({dims:T,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${x};${r};${n}`,inputDependencies:C},getRunData:()=>({outputs:te,dispatchGroup:{x:Math.ceil(c/64)},programUniforms:z}),getShaderSource:ee}},Ku=(e,t)=>{Ka(e.inputs),e.compute(Hu(e.inputs,t,e.outputCount))}}),Xu,Qu,Yu,Zu,Ju,Kd=L(()=>{Yt(),Kt(),Pr(),pr(),Xu=(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 i=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,s=e[1];if(!$e.areEqual(s.dims,[t.n,i,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=e[2].dims;if($e.size(u)!==t.n*i)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,c=t.bits>4?t.n*i:t.n*Math.floor((i+1)/2);if($e.size(d)!==c)throw new Error("zeroPoints input size error.")}},Qu=(e,t)=>{let r=e[0].dims,n=r.length,i=r[n-2],a=t.k,s=t.n,u=r.slice(0,n-2),d=$e.size(u),c=e[1].dims[2]/4,g=e[0].dataType,m=_r(t.k),l=_r(c),T=_r(s),x=u.concat([i,s]),C=i>1&&s/T%2===0?2:1,z=$e.size(x)/T/C,U=64,A=[],ee=[d,i,a/m],te=$e.convertShape(e[1].dims).slice();te.splice(-1,1,c/l),A.push(...Et(ee)),A.push(...Et(te)),A.push(...Et(e[2].dims)),e.length===4&&A.push(...Et($e.convertShape(e[3].dims)));let ie=[d,i,s/T];A.push(...Et(ie));let ke=Pe=>{let Ye=ee.length,It=Qe("a",e[0].dataType,Ye,m),Bt=Qe("b",12,te.length,l),ar=Qe("scales",e[2].dataType,e[2].dims.length),nr=[It,Bt,ar],Ht=e.length===4?Qe("zero_points",12,e[3].dims.length):void 0;Ht&&nr.push(Ht);let Er=ie.length,jr=qt("output",e[0].dataType,Er,T),hr=fr(e[0].dataType),Ir=(()=>{switch(m){case 1:return`array<${hr}, 8>`;case 2:return`mat4x2<${hr}>`;case 4:return`mat2x4<${hr}>`;default:throw new Error(`${m}-component is not supported.`)}})(),Gt=()=>{let qe=`
          // reuse a data
            var input_offset = ${It.indicesToOffset(`${It.type.indices}(batch, row, word_offset)`)};
            var a_data: ${Ir};
            for (var j: u32 = 0; j < ${8/m}; j++) {
              a_data[j] = ${It.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let vt=0;vt<T*C;vt++)qe+=`
            b_value = ${l===1?`b${vt}_data`:`b${vt}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${Ir}(${Array.from({length:4},(rr,Br)=>`${hr}(b_value_lower[${Br}]), ${hr}(b_value_upper[${Br}])`).join(", ")});
            b_dequantized_values = ${m===1?`${Ir}(${Array.from({length:8},(rr,Br)=>`(b_quantized_values[${Br}] - ${Ht?`zero_point${vt}`:"zero_point"}) * scale${vt}`).join(", ")});`:`(b_quantized_values - ${Ir}(${Array(8).fill(`${Ht?`zero_point${vt}`:"zero_point"}`).join(",")})) * scale${vt};`};
            workgroup_shared[local_id.x * ${C} + ${Math.floor(vt/T)}]${T>1?`[${vt%T}]`:""} += ${Array.from({length:8/m},(rr,Br)=>`${m===1?`a_data[${Br}] * b_dequantized_values[${Br}]`:`dot(a_data[${Br}], b_dequantized_values[${Br}])`}`).join(" + ")};
          `;return qe},Qt=()=>{let qe=`
            var col_index = col * ${T};
            ${Ht?`
            let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
            var zero_point_byte_count: u32;
            var zero_point_word_index: u32;
            var zero_point_byte_offset: u32;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32;
            var zero_point_word: u32;`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${hr}(8);`}
            `;for(let vt=0;vt<T*C;vt++)qe+=`
            let scale${vt} = ${ar.getByOffset("col_index * nBlocksPerCol + block")};
            ${Ht?`
            zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u);
            zero_point_word_index = zero_point_byte_count >> 0x2u;
            zero_point_byte_offset = zero_point_byte_count & 0x3u;
            zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            zero_point_word = ${Ht.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${vt} = ${hr}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return qe},xr=()=>{let qe=`col_index = col * ${T};`;for(let vt=0;vt<T*C;vt++)qe+=`
            let b${vt}_data = ${Bt.getByIndices(`${Bt.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return qe+=`
            var b_value: u32;
            let b_mask: u32 = 0x0F0F0F0Fu;
            var b_value_lower: vec4<u32>;
            var b_value_upper: vec4<u32>;
            var b_quantized_values: ${Ir};
            var b_dequantized_values: ${Ir};`,qe};return`
        var<workgroup> workgroup_shared: array<${jr.type.value}, ${C*U}>;
        ${Pe.declareVariables(...nr,jr)}
        ${Pe.mainStart([U,1,1])}
          let output_indices = ${jr.offsetToIndices(`(global_idx / ${U}) * ${C}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let nBlocksPerCol = uniforms.b_shape[1];

          for (var block = local_id.x; block < nBlocksPerCol; block += ${U}) {
            //process one block
            var word_offset: u32 = block * ${t.blockSize/m};
            ${Qt()}
            for (var word: u32 = 0; word < ${c}; word += ${l}) {
              ${xr()}
              for (var i: u32 = 0; i < ${l}; i++) {
                ${Gt()}
                word_offset += ${8/m};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${C}) {
            var output_value: ${jr.type.value} = ${jr.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < ${U}u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${C};
            }
            ${jr.setByIndices(`${jr.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${m};${l};${T};${C};${U}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:x,dataType:g}],dispatchGroup:{x:z},programUniforms:A}),getShaderSource:ke}},Yu=(e,t)=>{let r=e[0].dims,n=r.length,i=r[n-2],a=t.k,s=t.n,u=r.slice(0,n-2),d=$e.size(u),c=e[1].dims[2]/4,g=e[0].dataType,m=_r(t.k),l=_r(c),T=u.concat([i,s]),x=128,C=s%8===0?8:s%4===0?4:1,z=x/C,U=z*l*8,A=U/m,ee=U/t.blockSize,te=$e.size(T)/C,ie=[],ke=[d,i,a/m],Pe=$e.convertShape(e[1].dims).slice();Pe.splice(-1,1,c/l),ie.push(...Et(ke)),ie.push(...Et(Pe)),ie.push(...Et(e[2].dims)),e.length===4&&ie.push(...Et($e.convertShape(e[3].dims)));let Ye=[d,i,s];ie.push(...Et(Ye));let It=Bt=>{let ar=ke.length,nr=Qe("a",e[0].dataType,ar,m),Ht=Qe("b",12,Pe.length,l),Er=Qe("scales",e[2].dataType,e[2].dims.length),jr=[nr,Ht,Er],hr=e.length===4?Qe("zero_points",12,e[3].dims.length):void 0;hr&&jr.push(hr);let Ir=Ye.length,Gt=qt("output",e[0].dataType,Ir),Qt=fr(e[0].dataType),xr=()=>{switch(m){case 1:return`
          let a_data0 = vec4<${Qt}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
          let a_data1 = vec4<${Qt}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
          let a_data0 = vec4<${Qt}>(sub_a[word_offset], sub_a[word_offset + 1]);
          let a_data1 = vec4<${Qt}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${m}-component is not supported.`)}};return`
        var<workgroup> sub_a: array<${nr.type.value}, ${A}>;
        var<workgroup> inter_results: array<array<${Gt.type.value}, ${z}>, ${C}>;
        ${Bt.declareVariables(...jr,Gt)}
        ${Bt.mainStart([z,C,1])}
          let output_indices = ${Gt.offsetToIndices(`workgroup_index * ${C}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let n_blocks_per_col = uniforms.b_shape[1];
          let num_tiles =  (n_blocks_per_col - 1) / ${ee} + 1;

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

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

          if (local_idx < ${C}) {
            var output_value: ${Gt.type.value} = ${Gt.type.value}(0);
            for (var b = 0u; b < ${z}; b++) {
              output_value += inter_results[local_idx][b];
            }
            if (col + local_idx < uniforms.output_shape[2])
            {
              ${Gt.setByIndices(`${Gt.type.indices}(batch, row, col + local_idx)`,"output_value")}
            }
          }
        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${m};${l};${z};${C}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:T,dataType:g}],dispatchGroup:{x:te},programUniforms:ie}),getShaderSource:It}},Zu=(e,t)=>{Xu(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(Yu(e.inputs,t)):e.compute(Qu(e.inputs,t))},Ju=e=>or(e)}),ed,td,rd,nd,sd,id,ad,od,ld,Xd=L(()=>{Yt(),Kt(),pr(),ed=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},td=(e,t,r)=>{let n="";for(let i=t-1;i>=0;--i)n+=`
            k = i32(${e.indicesGet("indices",i)}) - ${Wt("uniforms.pads",i,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${Wt("uniforms.x_shape",i,t)})) {
              break;
            }
            offset += k * i32(${Wt("uniforms.x_strides",i,t)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${n}
            value = x[offset];
          }
      `},rd=(e,t,r)=>{let n="";for(let i=t-1;i>=0;--i)n+=`
                k = i32(${e.indicesGet("indices",i)}) - ${Wt("uniforms.pads",i,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${Wt("uniforms.x_shape",i,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${Wt("uniforms.x_shape",i,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${Wt("uniforms.x_strides",i,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},nd=(e,t,r)=>{let n="";for(let i=t-1;i>=0;--i)n+=`
                k = i32(${e.indicesGet("indices",i)}) - ${Wt("uniforms.pads",i,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${Wt("uniforms.x_shape",i,t)})) {
                  k = i32(${Wt("uniforms.x_shape",i,t)}) - 1;
                }
                offset += k * i32(${Wt("uniforms.x_strides",i,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},sd=(e,t,r)=>{let n="";for(let i=t-1;i>=0;--i)n+=`
                k = i32(${e.indicesGet("indices",i)}) - ${Wt("uniforms.pads",i,r)};
                if (k < 0)  {
                  k += i32(${Wt("uniforms.x_shape",i,t)}]);
                }
                if (k >= i32(${Wt("uniforms.x_shape",i,t)})) {
                  k -= i32(${Wt("uniforms.x_shape",i,t)});
                }
                offset += k * i32(${Wt("uniforms.x_strides",i,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},id=(e,t,r)=>{switch(r.mode){case 0:return td(e,t,r.pads.length);case 1:return rd(e,t,r.pads.length);case 2:return nd(e,t,r.pads.length);case 3:return sd(e,t,r.pads.length);default:throw new Error("Invalid mode")}},ad=(e,t)=>{let r=$e.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,i=$e.size(r),a=[{type:12,data:i},{type:6,data:t.pads}],s=e.length>=3&&e[2].data;t.mode===0&&a.push({type:s?e[2].dataType:1,data:t.value}),a.push(...Et(e[0].dims,r));let u=["rank"],d=c=>{let g=qt("output",e[0].dataType,r.length),m=Qe("x",e[0].dataType,n.length),l=m.type.value,T=id(g,n.length,t),x=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&x.push({name:"constant_value",type:s?l:"f32"}),`
            ${c.registerUniforms(x).declareVariables(m,g)}
            ${c.mainStart()}
            ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${l}(0);
            ${T}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${s}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil($e.size(r)/64)},programUniforms:a}),getShaderSource:d}},od=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,i=e[0].dims.length,a=new Int32Array(2*i).fill(0);if(e.length>=4){let u=e[3].getBigInt64Array();for(let d=0;d<u.length;d++)a[Number(u[d])]=Number(r[d]),a[Number(u[d])+i]=Number(r[d+u.length])}else r.forEach((u,d)=>a[Number(d)]=Number(u));let s=[];return a.forEach(u=>s.push(u)),{mode:t.mode,value:n,pads:s}}else return t},ld=(e,t)=>{ed(e.inputs);let r=od(e.inputs,t);e.compute(ad(e.inputs,r),{inputs:[0]})}}),js,Xa,Qa,Ya,Za,ud,dd,Ja,cd,cr,pd,nn,un,fn,es,Qd,hd,fd,f,b=L(()=>{Pt(),Yt(),Kt(),pr(),js=e=>{if(E.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Xa=(e,t,r)=>{let n=t.format==="NHWC",i=e.dims.slice();n&&i.splice(1,0,i.pop());let a=Object.hasOwnProperty.call(t,"dilations"),s=t.kernelShape.slice(),u=t.strides.slice(),d=a?t.dilations.slice():[],c=t.pads.slice();tn.adjustPoolAttributes(r,i,s,u,d,c);let g=tn.computePoolOutputShape(r,i,u,d,s,c,t.autoPad),m=Object.assign({},t);a?Object.assign(m,{kernelShape:s,strides:u,pads:c,dilations:d,cacheKey:t.cacheKey}):Object.assign(m,{kernelShape:s,strides:u,pads:c,cacheKey:t.cacheKey});let l=g.slice();return l.push(l.splice(1,1)[0]),[m,n?l:g]},Qa=(e,t)=>{let r=t.format==="NHWC",n=$e.size(e),i=$e.size(t.kernelShape),a=[{type:12,data:n},{type:12,data:i}],s=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let u=t.kernelShape[t.kernelShape.length-1],d=t.strides[t.strides.length-1],c=t.pads[t.pads.length/2-1],g=t.pads[t.pads.length-1],m=!!(c+g);a.push({type:12,data:u},{type:12,data:d},{type:12,data:c},{type:12,data:g}),s.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let l=!1;if(t.kernelShape.length===2){let T=t.kernelShape[t.kernelShape.length-2],x=t.strides[t.strides.length-2],C=t.pads[t.pads.length/2-2],z=t.pads[t.pads.length-2];l=!!(C+z),a.push({type:12,data:T},{type:12,data:x},{type:12,data:C},{type:12,data:z}),s.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,s,!0,m,l]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let u=$e.computeStrides(t.kernelShape);a.push({type:12,data:u},{type:12,data:t.pads},{type:12,data:t.strides}),s.push({name:"kernelStrides",type:"u32",length:u.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let d=t.pads.reduce((c,g)=>c+g);return[a,s,!!d,!1,!1]}},Ya=(e,t,r,n,i,a,s,u,d,c,g,m)=>{let l=i.format==="NHWC",T=t.type.value,x=qt("output",t.type.tensor,n);if(i.kernelShape.length<=2){let C="",z="",U="",A=r-(l?2:1);if(g?C=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${A}] = indices[${A}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${A}] < 0 || xIndices[${A}]
                      >= uniforms.x_shape[${A}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`:C=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${A}] = indices[${A}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`,i.kernelShape.length===2){let ee=r-(l?3:2);m?z=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${ee}] = indices[${ee}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${ee}] < 0 || xIndices[${ee}] >= uniforms.x_shape[${ee}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:z=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${ee}] = indices[${ee}] * uniforms.sh - uniforms.phStart + j;
                `,U=`
              }
            `}return`
            ${e.registerUniforms(d).declareVariables(t,x)}

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

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

              var value = ${T}(${u});
              var pad = 0;
              ${z}
              ${C}
              ${U}
              ${s}

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

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

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

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

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

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

              output[global_idx] = value;
            }`}},Za=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,ud=e=>`${Za(e)};${e.countIncludePad}`,dd=e=>`${Za(e)};${e.storageOrder};${e.dilations}`,Ja=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}),cd=(e,t,r,n)=>{let[i,a]=Xa(t,n,r),s=Qe("x",t.dataType,t.dims.length),u=s.type.value,d="value += x_val;",c="";i.countIncludePad?c+=`value /= ${u}(uniforms.kernelSize);`:c+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[g,m,l,T,x]=Qa(a,i);g.push(...Et(t.dims,a));let C=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${l};${T};${x}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil($e.size(a)/64)},programUniforms:g}),getShaderSource:z=>Ya(z,s,t.dims.length,a.length,i,d,c,0,m,l,T,x)}},cr=e=>{let t=e.count_include_pad!==0,r=Ja(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:ud(n)}},pd=(e,t)=>{js(e.inputs),e.compute(cd("AveragePool",e.inputs[0],!1,t))},nn={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},un=e=>{let t=e.format;return{format:t,...nn,cacheKey:t}},fn=(e,t)=>{js(e.inputs),e.compute(cd("GlobalAveragePool",e.inputs[0],!0,t))},es=(e,t,r,n)=>{let[i,a]=Xa(t,n,r),s=`
      value = max(x_val, value);
    `,u="",d=Qe("x",t.dataType,t.dims.length),c=["rank"],[g,m,l,T,x]=Qa(a,i);return g.push(...Et(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${l};${T};${x}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil($e.size(a)/64)},programUniforms:g}),getShaderSource:C=>Ya(C,d,t.dims.length,a.length,i,s,u,t.dataType===10?-65504:-1e5,m,l,T,x)}},Qd=(e,t)=>{js(e.inputs),e.compute(es("MaxPool",e.inputs[0],!1,t))},hd=e=>{let t=e.storage_order,r=e.dilations,n=Ja(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 i={storageOrder:t,dilations:r,...n,cacheKey:""};return{...i,cacheKey:dd(i)}},fd=e=>{let t=e.format;return{format:t,...nn,cacheKey:t}},f=(e,t)=>{js(e.inputs),e.compute(es("GlobalMaxPool",e.inputs[0],!0,t))}}),R,Me,Ue,Le,pt=L(()=>{Yt(),Kt(),Pr(),pr(),R=(e,t)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((r,n)=>r===e[2].dims[n]).reduce((r,n)=>r&&n,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(t.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((i,a)=>a===t.axis||i===e[0].dims[a]).reduce((i,a)=>i&&a,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let r=e[0].dims[t.axis],n=e[1].dims[t.axis];if(t.blockSize<Math.ceil(r/n)||t.blockSize>Math.ceil(r/(n-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},Me=(e,t)=>{let r=$e.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,i=n===3,a=e[0].dims,s=e[1].dataType,u=$e.size(a),d=n===3||n===2,c=d?[Math.ceil($e.size(e[0].dims)/4)]:e[0].dims,g=e[1].dims,m=e.length>2?e[2]:void 0,l=m?d?[Math.ceil($e.size(m.dims)/4)]:m.dims:void 0,T=g.length===0||g.length===1&&g[0]===1,x=T===!1&&g.length===1,C=_r(u),z=T&&(!d||C===4),U=z?C:1,A=z&&!d?C:1,ee=Qe("input",d?12:n,c.length,A),te=Qe("scale",s,g.length),ie=m?Qe("zero_point",d?12:n,l.length):void 0,ke=qt("output",s,a.length,U),Pe=[ee,te];ie&&Pe.push(ie);let Ye=[c,g];m&&Ye.push(l);let It=[{type:12,data:u/U},{type:12,data:r},{type:12,data:t.blockSize},...Et(...Ye,a)],Bt=ar=>{let nr=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${ar.registerUniforms(nr).declareVariables(...Pe,ke)}
      ${ar.mainStart()}
          ${ar.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${ke.offsetToIndices("global_idx")};

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

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

          // Set zero-point input
          ${ie?T?d?`
                let zero_point_input = ${ie.getByOffset("0")};
                let zero_point_vec =  ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${ie.getByOffset("0")}`:x?d?`
                let zero_point_index = ${ke.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${ie.getByOffset("zero_point_index / 4")};
                let zero_point_vec =  ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${ke.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${ie.getByOffset("zero_point_index")};`:d?`
                let zero_point_offset = ${te.indicesToOffset("scale_indices")};
                let zero_point_input = ${ie.getByOffset("zero_point_offset / 4")};
                let zero_point_vec = ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${ie.getByIndices("scale_indices")};`:`let zero_point_value = ${d?i?"i32":"u32":ee.type.value}(0);`};
      // Compute and write output
      ${ke.setByOffset("global_idx",`${ke.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:ie?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Bt,getRunData:()=>({outputs:[{dims:a,dataType:s}],dispatchGroup:{x:Math.ceil(u/U/64),y:1,z:1},programUniforms:It})}},Ue=(e,t)=>{R(e.inputs,t),e.compute(Me(e.inputs,t))},Le=e=>or({axis:e.axis,blockSize:e.blockSize})}),kt,Vt,tr,zr=L(()=>{Pt(),Yt(),pr(),kt=(e,t,r)=>{let n=e===t,i=e<t&&r<0,a=e>t&&r>0;if(n||i||a)throw new Error("Range these inputs' contents are invalid.")},Vt=(e,t,r,n)=>{let i=Math.abs(Math.ceil((t-e)/r)),a=[i],s=i,u=[{type:12,data:s},{type:n,data:e},{type:n,data:r},...Et(a)],d=c=>{let g=qt("output",n,a.length),m=g.type.value,l=[{name:"outputSize",type:"u32"},{name:"start",type:m},{name:"delta",type:m}];return`
        ${c.registerUniforms(l).declareVariables(g)}
        ${c.mainStart()}
        ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${m}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:u})}},tr=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]),E.webgpu.validateInputContent&&kt(t,r,n),e.compute(Vt(t,r,n,e.inputs[0].dataType),{inputs:[]})}}),Dr,wr,ir,Ar,$r,yr,Lr,Tn,dn,Gr,Qr,Kr,wn,ts,mi,eo,xc,Bn,xs,Tc=L(()=>{Yt(),Kt(),Pr(),pr(),Dr=(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")}},wr=(e,t,r)=>{t.every(i=>i>=0&&i<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((i,a)=>n[i]=e[a]),n},ir=(e,t,r,n,i,a)=>{let[s,u,d]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],c=e[0].dims.length;if(s>0&&e.length>s&&e[s].dims.length>0)e[s].getFloat32Array().forEach(g=>a.push(g));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(u>0&&e.length>u&&e[u].dims.length===1&&e[u].dims[0]>0){if(e[u].getFloat32Array().forEach(g=>n.push(g)),n.length!==0&&n.length!==c&&r>=18&&n.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");Dr(n,t),t.axes.length>0&&wr(n,t.axes,c).forEach((g,m)=>n[m]=g)}if(d>0&&e.length>d&&e[d].dims.length===1&&e[d].dims[0]>0&&(e[d].getBigInt64Array().forEach(g=>i.push(Number(g))),i.length!==0&&i.length!==c&&r>=18&&i.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!==0&&n.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(i.length!==0&&i.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 i<"u"&&n.length>0&&i.length>c)throw new Error("Resize requires only of scales or sizes to be specified")},Ar=(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 ${e} is not supported`)}})()+"}",$r=(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`)}})()+"}",yr=(e,t,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),i=e.length===0?n:e.slice();return t.length>0?(t.forEach((a,s)=>{n[a]=i[s],n[s+r]=i[t.length+s]}),n):i},Lr=(e,t,r,n)=>{let i=[];if(r.length>0)if(n.length>0){if(e.forEach(a=>i.push(a)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((a,s)=>i[a]=r[s])}else r.forEach(a=>i.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((a,s)=>Math.round(a*t[s]))}return i},Tn=(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 i=e.slice();return r.axes.length>0?(r.axes.forEach(a=>t[a]=n),r.axes.forEach(a=>i[a]=Math.round(e[a]*t[a]))):(t.fill(n,0,t.length),i.forEach((a,s)=>i[s]=Math.round(a*t[s]))),i},dn=(e,t,r,n,i)=>`
    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 = ${Wt("uniforms.scales","i",n)};
        var roi_low = ${Wt("uniforms.roi","i",i)};
        var roi_hi = ${Wt("uniforms.roi",`i + ${t.length}`,i)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${Wt("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${Wt("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;
    }`,Gr=(e,t,r,n,i,a,s)=>`
    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 = ${Wt("uniforms.scales","i",i)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${Wt("uniforms.roi","i",a)};
          var roi_hi = ${Wt("uniforms.roi",`i + ${r.length}`,a)};
          var input_shape_i = ${Wt("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${Wt("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${s} || (original_idx >= 0 && original_idx < ${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;
    }`,Qr=(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 >= ${Wt("uniforms.input_shape","i",t.length)}) {
          return false;
        }
      }
      return true;
    }`,Kr=(e,t,r,n)=>e.rank>n?`
    ${e.indicesSet("input_indices",t,"channel")};
    ${e.indicesSet("input_indices",r,"batch")};
`:"",wn=(e,t,r,n,i)=>{let[a,s,u,d]=r.length===2?[-1,0,1,-1]:[0,2,3,1],c=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${c} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",s,`max(0, min(row, ${r[s]} - 1))`)};
      ${e.indicesSet("input_indices",u,`max(0, min(col, ${r[u]} - 1))`)};
      ${Kr(e,d,a,2)}
      return ${e.getByIndices("input_indices")};
    }

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

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

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

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

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

      var x111: ${g} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${g} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${g} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${g} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${g} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${g} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${g} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${g} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${g} = abs(depth - ${g}(depth1));
      var dx2: ${g} = abs(${g}(depth2) - depth);
      var dy1: ${g} = abs(height - ${g}(height1));
      var dy2: ${g} = abs(${g}(height2) - height);
      var dz1: ${g} = abs(width - ${g}(width1));
      var dz2: ${g} = abs(${g}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},eo=(e,t,r,n,i,a)=>{let s=e.dims,u=yr(a,t.axes,s.length),d=Lr(s,n,i,t.axes),c=n.slice();n.length===0&&(c=s.map((A,ee)=>A===0?1:d[ee]/A),t.keepAspectRatioPolicy!=="stretch"&&(d=Tn(s,c,t)));let g=qt("output",e.dataType,d.length),m=Qe("input",e.dataType,s.length),l=$e.size(d),T=s.length===d.length&&s.every((A,ee)=>A===d[ee]),x=t.coordinateTransformMode==="tf_crop_and_resize",C=t.extrapolationValue,z=m.type.value,U=A=>`
      ${T?"":`
      ${Ar(t.coordinateTransformMode,z)};
      ${(()=>{switch(t.mode){case"nearest":return`
              ${Qr(m,s)};
              ${$r(t.nearestMode,r,z)};
              ${Gr(m,g,s,d,c.length,u.length,x)};
              `;case"linear":return`
              ${dn(g,s,d,c.length,u.length)};
              ${(()=>{if(s.length===2||s.length===4)return`${wn(m,g,s,x,C)}`;if(s.length===3||s.length===5)return`${mi(m,g,s,x,C)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(s.length===2||s.length===4)return`${ts(m,g,s,d,c,u,t.cubicCoeffA,x,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")}})()};
      `}
      ${A.registerUniform("output_size","u32").registerUniform("scales","f32",c.length).registerUniform("roi","f32",u.length).declareVariables(m,g)}
      ${A.mainStart()}
        ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${T?"output[global_idx] = input[global_idx];":`
        let output_indices = ${g.offsetToIndices("global_idx")};
        var input_indices: ${m.type.indices};
        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${m.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${t.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${s.length===2||s.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${c.length>0?c:""}|${i.length>0?i:""}|${u.length>0?u:""}|${T}|${s}`,inputDependencies:["rank"]},getShaderSource:U,getRunData:()=>({outputs:[{dims:d,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:[{type:12,data:l},{type:1,data:c},{type:1,data:u},...Et(s,d)]})}},xc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Bn=(e,t)=>{let r=[],n=[],i=[],a=xc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");ir(e.inputs,t,a,r,n,i),e.compute(eo(e.inputs[0],t,a,r,n,i),{inputs:[0]})},xs=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,i=e.cubicCoeffA,a=e.excludeOutside!==0,s=e.extrapolationValue,u=e.keepAspectRatioPolicy,d=e.mode,c=e.nearestMode===""?"simple":e.nearestMode;return or({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:i,excludeOutside:a,extrapolationValue:s,keepAspectRatioPolicy:u,mode:d,nearestMode:c})}}),Yd,Zd,md,Ff=L(()=>{Yt(),Kt(),Pr(),pr(),Yd=(e,t)=>{let[r,n,i,a]=e,{numHeads:s,rotaryEmbeddingDim:u}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!$e.areEqual(n.dims,[])&&!$e.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(i.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(a.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!$e.areEqual(i.dims,a.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(u>0&&s===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let d=r.dims[0],c=r.dims[r.dims.length-2],g=i.dims[0],m=$e.sizeFromDimension(r.dims,1)/c,l=u===0?i.dims[1]*2:m/s;if(u>l)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(d!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(c!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(l/2!==i.dims[1]&&u/2!==i.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${i.dims[1]}`);if(c>g)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Zd=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:i,scale:a}=t,s=e[0].dims[0],u=$e.sizeFromDimension(e[0].dims,1),d=e[0].dims[e[0].dims.length-2],c=u/d,g=e[2].dims[1],m=i===0?g*2:c/n,l=new Array(s,d,c/m,m-g),T=$e.computeStrides(l),x=[{type:1,data:a},{type:12,data:l},{type:12,data:T},...e[0].dims.length===3?new Array({type:12,data:[u,c,m,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[u,m,d*m,1]}):[],...Et(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],C=z=>{let U=Qe("input",e[0].dataType,e[0].dims.length),A=Qe("position_ids",e[1].dataType,e[1].dims.length),ee=Qe("cos_cache",e[2].dataType,e[2].dims.length),te=Qe("sin_cache",e[3].dataType,e[3].dims.length),ie=qt("output",e[0].dataType,e[0].dims.length);return z.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:l.length},{name:"global_strides",type:"u32",length:T.length},{name:"input_output_strides",type:"u32",length:T.length}]),`
        ${z.declareVariables(U,A,ee,te,ie)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${A.broadcastedIndicesToOffset("bsnh.xy",qt("",A.type.tensor,2))};
            let position_id =
                u32(${A.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 = ${U.getByOffset("i")} * ${ee.get("position_id","bsnh[3]")} -
                ${U.getByOffset("j")} * ${te.get("position_id","bsnh[3]")};
            ${ie.setByOffset("i","re")}
            let im = ${U.getByOffset("i")} * ${te.get("position_id","bsnh[3]")} +
                ${U.getByOffset("j")} * ${ee.get("position_id","bsnh[3]")};
            ${ie.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${ie.setByOffset("k",U.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:or({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:C,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil($e.size(l)/pn)},programUniforms:x})}},md=(e,t)=>{Yd(e.inputs,t),e.compute(Zd(e.inputs,t))}}),hp,fp,mp,Of=L(()=>{Yt(),Kt(),pr(),hp=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 i=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==i)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]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let s=e[3];if(s.dims.length!==1)throw new Error("Beta must be 1D");if(s.dims[s.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let s=e[4];if(s.dims.length!==1)throw new Error("Bias must be 1D");if(s.dims[s.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},fp=(e,t,r,n)=>{let i=t.simplified,a=e[0].dims,s=$e.size(a),u=a,d=s,c=a.slice(-1)[0],g=n?a.slice(0,-1).concat(1):[],m=!i&&e.length>3,l=e.length>4,T=n&&r>1,x=n&&r>2,C=r>3,z=64,U=_r(c),A=[{type:12,data:d},{type:12,data:U},{type:12,data:c},{type:1,data:t.epsilon}],ee=ie=>{let ke=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Pe=[Qe("x",e[0].dataType,e[0].dims,U),Qe("skip",e[1].dataType,e[1].dims,U),Qe("gamma",e[2].dataType,e[2].dims,U)];m&&Pe.push(Qe("beta",e[3].dataType,e[3].dims,U)),l&&Pe.push(Qe("bias",e[4].dataType,e[4].dims,U)),Pe.push(qt("output",e[0].dataType,u,U)),T&&Pe.push(qt("mean_output",1,g)),x&&Pe.push(qt("inv_std_output",1,g)),C&&Pe.push(qt("input_skip_bias_sum",e[0].dataType,u,U));let Ye=fr(e[0].dataType),It=fr(1,U);return`

      ${ie.registerUniforms(ke).declareVariables(...Pe)}
      var<workgroup> sum_shared : array<${It}, ${z}>;
      var<workgroup> sum_squared_shared : array<${It}, ${z}>;

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

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

        var reduce_size : u32 = ${z};
        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 = ${jn("sum",U)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${jn("square_sum",U)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon);
        ${T?"mean_output[global_idx] = mean;":""}
        ${x?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${i?"":`- ${Ye}(mean)`}) *
            ${Ye}(inv_std_dev) * gamma[offset1d + i]
            ${m?"+ beta[offset1d + i]":""};
        }
      }`},te=[{dims:u,dataType:e[0].dataType}];return r>1&&te.push({dims:g,dataType:1}),r>2&&te.push({dims:g,dataType:1}),r>3&&te.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${U};${T};${x};${C}`,inputDependencies:e.map((ie,ke)=>"type")},getShaderSource:ee,getRunData:()=>({outputs:te,dispatchGroup:{x:Math.ceil(d/c)},programUniforms:A})}},mp=(e,t)=>{hp(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(fp(e.inputs,t,e.outputCount,!1),{outputs:r})}}),_p,_d,gp,Cc,wp,yp,bp,Mp,zf=L(()=>{Yt(),Kt(),Pr(),pr(),_p=(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`)})},_d=(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},gp=(e,t)=>{if(e.length>1){let r=_d(e,1),n=_d(e,2),i=_d(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),or({starts:r,ends:n,axes:i})}else return t},Cc=(e,t,r,n,i)=>{let a=e;return e<0&&(a+=r[n[t]]),i[t]<0?Math.max(0,Math.min(a,r[n[t]]-1)):Math.max(0,Math.min(a,r[n[t]]))},wp=(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 = ${Wt("uniforms.input_shape","i",r.length)};
            let steps_i = ${Wt("uniforms.steps","i",r.length)};
            let signs_i = ${Wt("uniforms.signs","i",r.length)};
            let starts_i = ${Wt("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;
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          workgroupBarrier();
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        // find the rows sum
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        threadShared[lindex] = threadSum;
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        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
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        if (lindex == 0) {
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      var input_indices: ${u.type.indices};
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            let offset_c${T} = ${d.broadcastedIndicesToOffset(`output_indices${T}`,a)};
            let index_a${T} = offset_a${T} / 4u;
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            let index_c${T} = offset_c${T} / 4u;
            let component_a${T} = offset_a${T} % 4u;
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            ${m("data",1,"u32")}
            ${m("data",2,"u32")}
            ${m("data",3,"u32")}
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            ${m("output_data[global_idx]",3)}
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e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var n;let t=new BigUint64Array(e.getMappedRange()),r=this.pendingQueries.get(e);for(let i=0;i<t.length/2;i++){let a=r[i],s=a.kernelId,u=this.kernels.get(s),d=u.kernelType,c=u.kernelName,g=a.programName,m=a.inputTensorViews,l=a.outputTensorViews,T=t[i*2],x=t[i*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=T);let C=Number(T-this.queryTimeBase),z=Number(x-this.queryTimeBase);if(!Number.isSafeInteger(C)||!Number.isSafeInteger(z))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:m.map(U=>({dims:U.dims,dataType:An(U.dataType)})),outputsMetadata:l.map(U=>({dims:U.dims,dataType:An(U.dataType)})),kernelId:s,kernelType:d,kernelName:c,programName:g,startTime:C,endTime:z});else{let U="";m.forEach((ee,te)=>{U+=`input[${te}]: [${ee.dims}] | ${An(ee.dataType)}, `});let A="";l.forEach((ee,te)=>{A+=`output[${te}]: [${ee.dims}] | ${An(ee.dataType)}, `}),console.log(`[profiling] kernel "${s}|${d}|${c}|${g}" ${U}${A}execution time: ${z-C} ns`)}Ke("GPU",`${g}::${T}::${x}`)}e.unmap(),this.pendingQueries.delete(e)}),Ve()}run(e,t,r,n,i,a){je(e.name);let s=[];for(let A=0;A<t.length;++A){let ee=t[A].data;if(ee===0)continue;let te=this.gpuDataManager.get(ee);if(!te)throw new Error(`no GPU data for input: ${ee}`);s.push(te)}let{outputs:u,dispatchGroup:d,programUniforms:c}=e.getRunData(t),g=r.length===0?u.map((A,ee)=>ee):r;if(g.length!==u.length)throw new Error(`Output size ${g.length} must be equal to ${u.length}.`);let m=[],l=[];for(let A=0;A<u.length;++A){if(!Number.isInteger(g[A])||g[A]<-3||g[A]>=a)throw new Error(`Invalid output index: ${g[A]}`);if(g[A]===-3)continue;let ee=g[A]===-1,te=g[A]===-2,ie=ee||te?i(u[A].dataType,u[A].dims):n(g[A],u[A].dataType,u[A].dims);if(m.push(ie),ie.data===0)continue;let ke=this.gpuDataManager.get(ie.data);if(!ke)throw new Error(`no GPU data for output: ${ie.data}`);if(ee&&this.temporaryData.push(ke),te){let Pe=this.kernelPersistentData.get(this.currentKernelId);Pe||(Pe=[],this.kernelPersistentData.set(this.currentKernelId,Pe)),Pe.push(ke)}l.push(ke)}if(s.length!==t.length||l.length!==m.length){if(l.length===0)return Ve(e.name),m;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let T;if(c){let A=0,ee=[];c.forEach(Pe=>{let Ye=typeof Pe.data=="number"?[Pe.data]:Pe.data;if(Ye.length===0)return;let It=Pe.type===10?2:4,Bt,ar;Pe.type===10?(ar=Ye.length>4?16:Ye.length>2?8:Ye.length*It,Bt=Ye.length>4?16:It*Ye.length):(ar=Ye.length<=2?Ye.length*It:16,Bt=16),A=Math.ceil(A/ar)*ar,ee.push(A);let nr=Pe.type===10?8:4;A+=Ye.length>4?Math.ceil(Ye.length/nr)*Bt:Ye.length*It});let te=16;A=Math.ceil(A/te)*te;let ie=new ArrayBuffer(A);c.forEach((Pe,Ye)=>{let It=ee[Ye],Bt=typeof Pe.data=="number"?[Pe.data]:Pe.data;if(Pe.type===6)new Int32Array(ie,It,Bt.length).set(Bt);else if(Pe.type===12)new Uint32Array(ie,It,Bt.length).set(Bt);else if(Pe.type===10)new Uint16Array(ie,It,Bt.length).set(Bt);else if(Pe.type===1)new Float32Array(ie,It,Bt.length).set(Bt);else throw new Error(`Unsupported uniform type: ${An(Pe.type)}`)});let ke=this.gpuDataManager.create(A,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(ke.buffer,0,ie,0,A),this.gpuDataManager.release(ke.id),T={offset:0,size:A,buffer:ke.buffer}}let x=this.programManager.normalizeDispatchGroupSize(d),C=x[1]===1&&x[2]===1,z=Dp(e,t,C),U=this.programManager.getArtifact(z);if(U||(U=this.programManager.build(e,x),this.programManager.setArtifact(z,U),ae("info",()=>`[artifact] key: ${z}, programName: ${e.name}`)),c&&U.uniformVariablesInfo){if(c.length!==U.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${U.uniformVariablesInfo.length}, got ${c.length} in program "${U.programInfo.name}".`);for(let A=0;A<c.length;A++){let ee=c[A],te=ee.type,ie=typeof ee.data=="number"?1:ee.data.length,[ke,Pe]=U.uniformVariablesInfo[A];if(te!==ke||ie!==Pe)throw new Error(`Uniform variable ${A} mismatch: expect type ${ke} with size ${Pe}, got type ${te} with size ${ie} in program "${U.programInfo.name}".`)}}if(ae("info",()=>`[ProgramManager] run "${e.name}" (key=${z}) with ${x[0]}x${x[1]}x${x[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let A={kernelId:this.currentKernelId,programName:U.programInfo.name,inputTensorViews:t,outputTensorViews:m};this.pendingKernels.push(A),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(A)}return this.programManager.run(U,s,l,x,T),Ve(e.name),m}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 i=Fp.get(e);if(!i)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:i[0],attributes:[i[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: ${e}`);let i=n.kernelType,a=n.kernelName,s=n.kernelEntry,u=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${i}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,u[0]&&(u[1]=u[0](u[1]),u[0]=void 0),ae("info",()=>`[WebGPU] Start to run kernel "[${i}] ${a}"...`);let d=this.env.debug;this.temporaryData=[];try{return d&&this.device.pushErrorScope("validation"),s(t,u[1]),0}catch(c){return r.push(Promise.resolve(`[WebGPU] Kernel "[${i}] ${a}" failed. ${c}`)),1}finally{d&&r.push(this.device.popErrorScope().then(c=>c?`GPU validation error for kernel "[${i}] ${a}": ${c.message}`:null));for(let c of this.temporaryData)this.gpuDataManager.release(c.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,r,n){let i=this.sessionExternalDataMapping.get(e);i||(i=new Map,this.sessionExternalDataMapping.set(e,i));let a=i.get(t),s=this.gpuDataManager.registerExternalBuffer(r,n,a);return i.set(t,[s,r]),s}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[0])),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 er(this,e,t);return I(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(){ae("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(){ae("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){ae("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 i=this.getComputePassEncoder(),a=e[n];this.writeTimestamp(this.pendingDispatchNumber*2),i.setPipeline(a.computePipeline),i.setBindGroup(0,a.bindGroup),i.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"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),Rp,Ec,kc,Sc,Np,jp,Vf=L(()=>{_(),Rp=1,Ec=()=>Rp++,kc=class{constructor(e){this.sessionId=e.sessionId,this.mlContext=e.context,this.mlTensor=e.tensor,this.dataType=e.dataType,this.tensorShape=e.shape}get tensor(){return this.mlTensor}get type(){return this.dataType}get shape(){return this.tensorShape}destroy(){ae("verbose",()=>"[WebNN] TensorWrapper.destroy"),this.mlTensor.destroy()}write(e){this.mlContext.writeTensor(this.mlTensor,e)}async read(e){return e?this.mlContext.readTensor(this.mlTensor,e):this.mlContext.readTensor(this.mlTensor)}sameTypeAndShape(e,t){return this.dataType===e&&this.tensorShape.every((r,n)=>r===t[n])}},Sc=class{constructor(e,t){this.tensorManager=e,this.wrapper=t}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&this.tensorManager.releaseTensor(this.tensorWrapper)}async ensureTensor(e,t,r){if(this.wrapper){if(this.wrapper.sameTypeAndShape(e,t))return this.wrapper.tensor;r&&(this.activeUpload=new Uint8Array(await this.wrapper.read())),this.tensorManager.releaseTensor(this.wrapper)}let n=MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(e,t,n,!0,!0),r&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(e){if(this.wrapper){this.wrapper.write(e);return}this.activeUpload?this.activeUpload.set(e):this.activeUpload=new Uint8Array(e)}async download(e){if(this.activeUpload)if(e){e instanceof ArrayBuffer?new Uint8Array(e).set(this.activeUpload):new Uint8Array(e.buffer,e.byteOffset,e.byteLength).set(this.activeUpload);return}else return this.activeUpload.buffer;if(!this.wrapper)throw new Error("Tensor has not been created.");return e?this.wrapper.read(e):this.wrapper.read()}},Np=class{constructor(e){this.backend=e,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}reserveTensorId(){let e=Ec();return this.tensorTrackersById.set(e,new Sc(this)),e}releaseTensorId(e){let t=this.tensorTrackersById.get(e);t&&(this.tensorTrackersById.delete(e),t.tensorWrapper&&this.releaseTensor(t.tensorWrapper))}async ensureTensor(e,t,r,n){ae("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${e}, dataType: ${t}, shape: ${r}, copyOld: ${n}}`);let i=this.tensorTrackersById.get(e);if(!i)throw new Error("Tensor not found.");return i.ensureTensor(t,r,n)}upload(e,t){let r=this.tensorTrackersById.get(e);if(!r)throw new Error("Tensor not found.");r.upload(t)}async download(e,t){ae("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${t==null?void 0:t.byteLength}}`);let r=this.tensorTrackersById.get(e);if(!r)throw new Error("Tensor not found.");return r.download(t)}releaseTensorsForSession(e){for(let t of this.freeTensors)t.sessionId===e&&t.destroy();this.freeTensors=this.freeTensors.filter(t=>t.sessionId!==e)}registerTensor(e,t,r,n){let i=Ec(),a=new kc({sessionId:this.backend.currentSessionId,context:e,tensor:t,dataType:r,shape:n});return this.tensorTrackersById.set(i,new Sc(this,a)),this.externalTensors.add(a),i}async getCachedTensor(e,t,r,n,i){let a=this.backend.currentSessionId;for(let[d,c]of this.freeTensors.entries())if(c.sameTypeAndShape(e,t)){let g=this.freeTensors.splice(d,1)[0];return g.sessionId=a,g}let s=this.backend.currentContext;ae("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, shape: ${t}}`);let u=await s.createTensor({dataType:e,shape:t,dimensions:t,usage:r,writable:n,readable:i});return new kc({sessionId:a,context:s,tensor:u,dataType:e,shape:t})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},jp=(...e)=>new Np(...e)}),Pc,Vp,Uf=L(()=>{Yt(),kr(),Q(),Vf(),_(),Pc=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),Vp=class{constructor(e){this.tensorManager=jp(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,is(e.logLevel,!!e.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(e){this.activeSessionId=e}get currentContext(){let e=this.getMLContext(this.currentSessionId);if(!e)throw new Error(`No MLContext found for session ${this.currentSessionId}`);return e}registerMLContext(e,t){this.mlContextBySessionId.set(e,t);let r=this.sessionIdsByMLContext.get(t);r||(r=new Set,this.sessionIdsByMLContext.set(t,r)),r.add(e)}onReleaseSession(e){let t=this.mlContextBySessionId.get(e);if(!t)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e);let r=this.sessionIdsByMLContext.get(t);r.delete(e),r.size===0&&this.sessionIdsByMLContext.delete(t)}getMLContext(e){return this.mlContextBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){ae("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,t,r,n){let i=Pc.get(t);if(!i)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e,i,r,n)}uploadTensor(e,t){if(!mr().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");ae("verbose",()=>`[WebNN] uploadTensor {tensorId: ${e}, data: ${t.byteLength}}`),this.tensorManager.upload(e,t)}async downloadTensor(e,t){return this.tensorManager.download(e,t)}createMLTensorDownloader(e,t){return async()=>{let r=await this.tensorManager.download(e);return I(r,t)}}registerMLTensor(e,t,r){let n=Pc.get(t);if(!n)throw new Error(`Unsupported ONNX data type: ${t}`);let i=this.tensorManager.registerTensor(this.currentContext,e,n,r);return ae("verbose",()=>`[WebNN] registerMLTensor {tensor: ${e}, dataType: ${n}, dimensions: ${r}} -> {tensorId: ${i}}`),i}registerMLConstant(e,t,r,n,i,a){if(!a)throw new Error("External mounted files are not available.");let s=e;e.startsWith("./")&&(s=e.substring(2));let u=a.get(s);if(!u)throw new Error(`File with name ${s} not found in preloaded files.`);if(t+r>u.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let d=u.slice(t,t+r).buffer,c;switch(i.dataType){case"float32":c=new Float32Array(d);break;case"float16":c=new Uint16Array(d);break;case"int32":c=new Int32Array(d);break;case"uint32":c=new Uint32Array(d);break;case"int64":c=new BigInt64Array(d);break;case"uint64":c=new BigUint64Array(d);break;case"int8":c=new Int8Array(d);break;case"uint8":c=new Uint8Array(d);break;default:throw new Error(`Unsupported data type: ${i.dataType} in creating WebNN Constant from external data.`)}return ae("verbose",()=>`[WebNN] registerMLConstant {dataType: ${i.dataType}, shape: ${i.shape}}}`),n.constant(i,c)}flush(){}}}),Up={};P(Up,{init:()=>Gp});var Jd,Wp,Gp,Wf=L(()=>{Yt(),jf(),_(),Kt(),Uf(),Jd=class Sf{constructor(t,r,n,i){this.module=t,this.dataType=r,this.data=n,this.dims=i}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let t=$e.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=$e.size(this.dims);return t===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let t=$e.size(this.dims);return t===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let t=$e.size(this.dims);return t===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if($e.size(t)!==$e.size(this.dims))throw new Error("Invalid new shape");return new Sf(this.module,this.dataType,this.data,t)}},Wp=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,i=r>>>2;this.opKernelContext=n[i++];let a=n[i++];this.outputCount=n[i++],this.customDataOffset=n[i++],this.customDataSize=n[i++];let s=[];for(let u=0;u<a;u++){let d=n[i++],c=n[i++],g=n[i++],m=[];for(let l=0;l<g;l++)m.push(n[i++]);s.push(new Jd(e,d,c,m))}this.inputs=s}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 s;let r=((s=t==null?void 0:t.inputs)==null?void 0:s.map(u=>typeof u=="number"?this.inputs[u]:u))??this.inputs,n=(t==null?void 0:t.outputs)??[],i=(u,d,c)=>new Jd(this.module,d,this.output(u,c),c),a=(u,d)=>{let c=Rn(u,d);if(!c)throw new Error(`Unsupported data type: ${u}`);let g=c>0?this.backend.gpuDataManager.create(c).id:0;return new Jd(this.module,u,g,d)};return this.backend.run(e,r,n,i,a,this.outputCount)}output(e,t){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+t.length)*4),i=n>>2;this.module.HEAPU32[i++]=t.length;for(let a=0;a<t.length;a++)this.module.HEAPU32[i++]=t[a];return this.module._JsepOutput(this.opKernelContext,e,n)}catch(n){throw new Error(`Failed to generate kernel's output[${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)}}},Gp=async(e,t,r,n)=>{let i=t.jsepInit;if(!i)throw new Error("Failed to initialize JSEP. 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Ve(),g}startProfiling(){}endProfiling(){rh(this.sessionId)}}}),ah={};P(ah,{OnnxruntimeWebAssemblyBackend:()=>Vc,initializeFlags:()=>jc,wasmBackend:()=>oh});var jc,Vc,oh,qf=L(()=>{Pt(),nh(),Gf(),Ft(),jc=()=>{if((typeof E.wasm.initTimeout!="number"||E.wasm.initTimeout<0)&&(E.wasm.initTimeout=0),E.wasm.simd===!1&&console.warn('Deprecated property "env.wasm.simd" is set to false. non-SIMD build is no longer provided, and this setting will be ignored.'),typeof E.wasm.proxy!="boolean"&&(E.wasm.proxy=!1),typeof E.wasm.trace!="boolean"&&(E.wasm.trace=!1),typeof E.wasm.numThreads!="number"||!Number.isInteger(E.wasm.numThreads)||E.wasm.numThreads<=0)if(typeof self<"u"&&!self.crossOriginIsolated)E.wasm.numThreads=1;else{let e=typeof navigator>"u"?Te("node:os").cpus().length:navigator.hardwareConcurrency;E.wasm.numThreads=Math.min(4,Math.ceil((e||1)/2))}},Vc=class{async init(e){jc(),await Qp(),await Yp(e)}async createInferenceSessionHandler(e,t){let r=new ih;return await r.loadModel(e,t),Promise.resolve(r)}},oh=new Vc});Pt(),Pt(),Pt();var Hf="1.21.0-dev.20241024-d9ca84ef96",Kf=zt;{let e=(qf(),B(ah)).wasmBackend;fe("webgpu",e,5),fe("webnn",e,5),fe("cpu",e,10),fe("wasm",e,10)}Object.defineProperty(E.versions,"web",{value:Hf,enumerable:!0});/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
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N}}class D extends we{constructor(k,N){super(),this.begin_suppress_tokens=k,this.begin_index=N}_call(k,N){for(let E=0;E<k.length;++E)if(k[E].length===this.begin_index){const ue=N[E].data;for(const be of this.begin_suppress_tokens)ue[be]=-1/0}return N}}class B extends we{constructor(k,N){super(),this.eos_token_id=Array.isArray(k.eos_token_id)?k.eos_token_id[0]:k.eos_token_id,this.no_timestamps_token_id=k.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=N.length,N.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=k.max_initial_timestamp_index}_call(k,N){for(let E=0;E<k.length;++E){const ue=N[E].data;if(ue[this.no_timestamps_token_id]=-1/0,k[E].length===this.begin_index-1){ue.fill(-1/0),ue[this.timestamp_begin]=0;continue}const be=k[E].slice(this.begin_index),Ce=be.length>=1&&be[be.length-1]>=this.timestamp_begin,De=be.length<2||be[be.length-2]>=this.timestamp_begin;if(Ce&&(De?ue.subarray(this.timestamp_begin).fill(-1/0):ue.subarray(0,this.eos_token_id).fill(-1/0)),k[E].length===this.begin_index&&this.max_initial_timestamp_index!==null){const lt=this.timestamp_begin+this.max_initial_timestamp_index;ue.subarray(lt+1).fill(-1/0)}const ze=(0,ce.log_softmax)(ue),it=Math.log(ze.subarray(this.timestamp_begin).map(Math.exp).reduce((lt,me)=>lt+me)),rt=(0,ce.max)(ze.subarray(0,this.timestamp_begin))[0];it>rt&&ue.subarray(0,this.timestamp_begin).fill(-1/0)}return N}}class q extends we{constructor(k){super(),this.no_repeat_ngram_size=k}getNgrams(k){const N=k.length,E=[];for(let be=0;be<N+1-this.no_repeat_ngram_size;++be){const Ce=[];for(let De=0;De<this.no_repeat_ngram_size;++De)Ce.push(k[be+De]);E.push(Ce.map(Number))}const ue=new Map;for(const be of E){const Ce=be.slice(0,be.length-1),De=JSON.stringify(Ce),ze=ue.get(De)??[];ze.push(be[be.length-1]),ue.set(De,ze)}return ue}getGeneratedNgrams(k,N){const E=N.slice(N.length+1-this.no_repeat_ngram_size,N.length);return k.get(JSON.stringify(E.map(Number)))??[]}calcBannedNgramTokens(k){const N=[];if(k.length+1<this.no_repeat_ngram_size)return N;{const E=this.getNgrams(k);return this.getGeneratedNgrams(E,k)}}_call(k,N){for(let E=0;E<k.length;++E){const ue=N[E].data,be=this.calcBannedNgramTokens(k[E]);for(const Ce of be)ue[Ce]=-1/0}return N}}class re extends we{constructor(k){super(),this.penalty=k}_call(k,N){for(let E=0;E<k.length;++E){const ue=N[E].data;for(const be of k[E]){const Ce=Number(be);ue[Ce]<0?ue[Ce]*=this.penalty:ue[Ce]/=this.penalty}}return N}}class fe extends we{constructor(k,N){super(),this.min_length=k,this.eos_token_id=Array.isArray(N)?N:[N]}_call(k,N){for(let E=0;E<k.length;++E)if(k[E].length<this.min_length){const ue=N[E].data;for(const be of this.eos_token_id)ue[be]=-1/0}return N}}class le extends we{constructor(k,N,E){super(),this.prompt_length_to_skip=k,this.min_new_tokens=N,this.eos_token_id=Array.isArray(E)?E:[E]}_call(k,N){for(let E=0;E<k.length;++E)if(k[E].length-this.prompt_length_to_skip<this.min_new_tokens){const be=N[E].data;for(const Ce of this.eos_token_id)be[Ce]=-1/0}return N}}class O extends we{constructor(k,N){super(),this.bad_words_ids=k,this.eos_token_id=Array.isArray(N)?N:[N]}_call(k,N){for(let E=0;E<k.length;++E){const ue=N[E].data,be=k[E];for(const Ce of this.bad_words_ids){let De=!0;for(let ze=1;ze<=Ce.length-1&&Ce.length<be.length;++ze)if(Ce.at(-ze-1)!=be.at(-ze)){De=!1;break}De&&(ue[Ce.at(-1)]=-1/0)}}return N}}class J extends we{constructor(k){if(super(),k<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${k}.`);this.guidance_scale=k}_call(k,N){if(N.dims[0]!==2*k.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches 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Got batch size ${N.dims[0]} for the logits and ${k.length} for the input ids.`);const E=k.length,ue=N.slice([0,E],null),be=N.slice([E,N.dims[0]],null);for(let Ce=0;Ce<be.data.length;++Ce)be.data[Ce]+=(ue.data[Ce]-be.data[Ce])*this.guidance_scale;return be}}class pe extends ye{constructor(k){super(),this.temperature=k}_call(k,N){const E=N.data;for(let ue=0;ue<E.length;++ue)E[ue]/=this.temperature;return N}}class X extends ye{constructor(k,{filter_value:N=-1/0,min_tokens_to_keep:E=1}={}){if(super(),k<0||k>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${k}`);if(!Number.isInteger(E)||E<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${E}`);this.top_p=k,this.filter_value=N,this.min_tokens_to_keep=E}}class K extends ye{constructor(k,{filter_value:N=-1/0,min_tokens_to_keep:E=1}={}){if(super(),!Number.isInteger(k)||k<0)throw new Error(`\`top_k\` must be a positive integer, but is ${k}`);this.top_k=Math.max(k,E),this.filter_value=N}}},"./src/generation/logits_sampler.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{LogitsSampler:()=>ye});var F=V("./src/utils/generic.js"),ce=V("./src/utils/tensor.js"),we=V("./src/utils/maths.js");V("./src/generation/configuration_utils.js");class ye extends F.Callable{constructor(B){super(),this.generation_config=B}async _call(B){return this.sample(B)}async sample(B){throw Error("sample should be implemented in subclasses.")}getLogits(B,q){let re=B.dims.at(-1),fe=B.data;if(q===-1)fe=fe.slice(-re);else{let le=q*re;fe=fe.slice(le,le+re)}return fe}randomSelect(B){let q=0;for(let fe=0;fe<B.length;++fe)q+=B[fe];let re=Math.random()*q;for(let fe=0;fe<B.length;++fe)if(re-=B[fe],re<=0)return fe;return 0}static getSampler(B){if(B.do_sample)return new L(B);if(B.num_beams>1)return new P(B);if(B.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${B.num_return_sequences}.`);return new Te(B)}}class Te extends ye{async sample(B){const q=(0,we.max)(B.data)[1];return[[BigInt(q),0]]}}class L extends ye{async sample(B){let q=B.dims.at(-1);this.generation_config.top_k>0&&(q=Math.min(this.generation_config.top_k,q));const[re,fe]=await(0,ce.topk)(B,q),le=(0,we.softmax)(re.data);return Array.from({length:this.generation_config.num_beams},()=>{const O=this.randomSelect(le);return[fe.data[O],Math.log(le[O])]})}}class P extends ye{async sample(B){let q=B.dims.at(-1);this.generation_config.top_k>0&&(q=Math.min(this.generation_config.top_k,q));const[re,fe]=await(0,ce.topk)(B,q),le=(0,we.softmax)(re.data);return Array.from({length:this.generation_config.num_beams},(O,J)=>[fe.data[J],Math.log(le[J])])}}},"./src/generation/stopping_criteria.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{EosTokenCriteria:()=>Te,InterruptableStoppingCriteria:()=>L,MaxLengthCriteria:()=>ye,StoppingCriteria:()=>ce,StoppingCriteriaList:()=>we});var F=V("./src/utils/generic.js");class ce extends F.Callable{_call(D,B){throw Error("StoppingCriteria needs to be subclassed")}}class we extends F.Callable{constructor(){super(),this.criteria=[]}push(D){this.criteria.push(D)}extend(D){D instanceof we?D=D.criteria:D instanceof ce&&(D=[D]),this.criteria.push(...D)}_call(D,B){const q=new Array(D.length).fill(!1);for(const re of this.criteria){const fe=re(D,B);for(let le=0;le<q.length;++le)q[le]||(q[le]=fe[le])}return q}[Symbol.iterator](){return this.criteria.values()}}class ye extends ce{constructor(D,B=null){super(),this.max_length=D,this.max_position_embeddings=B}_call(D){return D.map(B=>B.length>=this.max_length)}}class Te extends ce{constructor(D){super(),Array.isArray(D)||(D=[D]),this.eos_token_id=D}_call(D,B){return D.map(q=>{const re=q.at(-1);return this.eos_token_id.some(fe=>re==fe)})}}class L extends ce{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(D,B){return new Array(D.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{BaseStreamer:()=>ye,TextStreamer:()=>L,WhisperTextStreamer:()=>P});var F=V("./src/utils/core.js"),ce=V("./src/tokenizers.js"),we=V("./src/env.js");class ye{put(B){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const Te=we.apis.IS_PROCESS_AVAILABLE?D=>process.stdout.write(D):D=>console.log(D);class L extends ye{constructor(B,{skip_prompt:q=!1,callback_function:re=null,token_callback_function:fe=null,decode_kwargs:le={},...O}={}){super(),this.tokenizer=B,this.skip_prompt=q,this.callback_function=re??Te,this.token_callback_function=fe,this.decode_kwargs={...le,...O},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(B){var le;if(B.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 q=B[0];(le=this.token_callback_function)==null||le.call(this,q),this.token_cache=(0,F.mergeArrays)(this.token_cache,q);const re=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let fe;re.endsWith(`
`)?(fe=re.slice(this.print_len),this.token_cache=[],this.print_len=0):re.length>0&&(0,ce.is_chinese_char)(re.charCodeAt(re.length-1))?(fe=re.slice(this.print_len),this.print_len+=fe.length):(fe=re.slice(this.print_len,re.lastIndexOf(" ")+1),this.print_len+=fe.length),this.on_finalized_text(fe,!1)}end(){let B;this.token_cache.length>0?(B=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):B="",this.next_tokens_are_prompt=!0,this.on_finalized_text(B,!0)}on_finalized_text(B,q){var re,fe;B.length>0&&((re=this.callback_function)==null||re.call(this,B)),q&&this.callback_function===Te&&we.apis.IS_PROCESS_AVAILABLE&&((fe=this.callback_function)==null||fe.call(this,`
`))}}class P extends L{constructor(B,{skip_prompt:q=!1,callback_function:re=null,token_callback_function:fe=null,on_chunk_start:le=null,on_chunk_end:O=null,on_finalize:J=null,time_precision:pe=.02,skip_special_tokens:X=!0,decode_kwargs:K={}}={}){super(B,{skip_prompt:q,callback_function:re,token_callback_function:fe,decode_kwargs:{skip_special_tokens:X,...K}}),this.timestamp_begin=B.timestamp_begin,this.on_chunk_start=le,this.on_chunk_end=O,this.on_finalize=J,this.time_precision=pe,this.waiting_for_timestamp=!1}put(B){var re,fe;if(B.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const q=B[0];if(q.length===1){const le=Number(q[0])-this.timestamp_begin;if(le>=0){const O=le*this.time_precision;this.waiting_for_timestamp?(re=this.on_chunk_end)==null||re.call(this,O):(fe=this.on_chunk_start)==null||fe.call(this,O),this.waiting_for_timestamp=!this.waiting_for_timestamp,B=[[]]}}return super.put(B)}end(){var B;super.end(),(B=this.on_finalize)==null||B.call(this)}}},"./src/models.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{ASTForAudioClassification:()=>Ys,ASTModel:()=>Wt,ASTPreTrainedModel:()=>jn,AlbertForMaskedLM:()=>Yt,AlbertForQuestionAnswering:()=>ws,AlbertForSequenceClassification:()=>gs,AlbertModel:()=>_s,AlbertPreTrainedModel:()=>Ln,AutoModel:()=>Ka,AutoModelForAudioClassification:()=>Xd,AutoModelForAudioFrameClassification:()=>Xa,AutoModelForCTC:()=>ld,AutoModelForCausalLM:()=>Zu,AutoModelForDepthEstimation:()=>ud,AutoModelForDocumentQuestionAnswering:()=>Qa,AutoModelForImageClassification:()=>td,AutoModelForImageFeatureExtraction:()=>Ja,AutoModelForImageMatting:()=>Ya,AutoModelForImageSegmentation:()=>rd,AutoModelForImageToImage:()=>Za,AutoModelForMaskGeneration:()=>od,AutoModelForMaskedLM:()=>Ju,AutoModelForNormalEstimation:()=>dd,AutoModelForObjectDetection:()=>id,AutoModelForQuestionAnswering:()=>Kd,AutoModelForSemanticSegmentation:()=>nd,AutoModelForSeq2SeqLM:()=>Hd,AutoModelForSequenceClassification:()=>Hu,AutoModelForSpeechSeq2Seq:()=>Xu,AutoModelForTextToSpectrogram:()=>Qu,AutoModelForTextToWaveform:()=>Yu,AutoModelForTokenClassification:()=>Ku,AutoModelForUniversalSegmentation:()=>sd,AutoModelForVision2Seq:()=>ed,AutoModelForXVector:()=>js,AutoModelForZeroShotObjectDetection:()=>ad,BartForConditionalGeneration:()=>Q,BartForSequenceClassification:()=>oe,BartModel:()=>I,BartPretrainedModel:()=>_,BaseModelOutput:()=>dt,BeitForImageClassification:()=>hl,BeitModel:()=>pl,BeitPreTrainedModel:()=>Ji,BertForMaskedLM:()=>Mt,BertForQuestionAnswering:()=>Ae,BertForSequenceClassification:()=>Xe,BertForTokenClassification:()=>Z,BertModel:()=>ht,BertPreTrainedModel:()=>Re,BlenderbotForConditionalGeneration:()=>Sr,BlenderbotModel:()=>er,BlenderbotPreTrainedModel:()=>Ot,BlenderbotSmallForConditionalGeneration:()=>en,BlenderbotSmallModel:()=>Wr,BlenderbotSmallPreTrainedModel:()=>ur,BloomForCausalLM:()=>Uo,BloomModel:()=>Vo,BloomPreTrainedModel:()=>Gi,CLIPModel:()=>uo,CLIPPreTrainedModel:()=>os,CLIPSegForImageSegmentation:()=>wo,CLIPSegModel:()=>go,CLIPSegPreTrainedModel:()=>$i,CLIPTextModel:()=>Md,CLIPTextModelWithProjection:()=>xn,CLIPVisionModel:()=>vd,CLIPVisionModelWithProjection:()=>co,CamembertForMaskedLM:()=>Yr,CamembertForQuestionAnswering:()=>bn,CamembertForSequenceClassification:()=>Rr,CamembertForTokenClassification:()=>Jr,CamembertModel:()=>Cr,CamembertPreTrainedModel:()=>dr,CausalLMOutput:()=>es,CausalLMOutputWithPast:()=>Qd,ChineseCLIPModel:()=>_o,ChineseCLIPPreTrainedModel:()=>mo,ClapAudioModelWithProjection:()=>Pa,ClapModel:()=>hi,ClapPreTrainedModel:()=>Sa,ClapTextModelWithProjection:()=>Ns,CodeGenForCausalLM:()=>Eo,CodeGenModel:()=>$o,CodeGenPreTrainedModel:()=>En,CohereForCausalLM:()=>zn,CohereModel:()=>Io,CoherePreTrainedModel:()=>Di,ConvBertForMaskedLM:()=>$,ConvBertForQuestionAnswering:()=>nt,ConvBertForSequenceClassification:()=>Y,ConvBertForTokenClassification:()=>he,ConvBertModel:()=>H,ConvBertPreTrainedModel:()=>v,ConvNextForImageClassification:()=>hn,ConvNextModel:()=>Zn,ConvNextPreTrainedModel:()=>si,ConvNextV2ForImageClassification:()=>fa,ConvNextV2Model:()=>ai,ConvNextV2PreTrainedModel:()=>ii,DPTForDepthEstimation:()=>Al,DPTModel:()=>Pl,DPTPreTrainedModel:()=>ca,DebertaForMaskedLM:()=>ge,DebertaForQuestionAnswering:()=>Ne,DebertaForSequenceClassification:()=>Ie,DebertaForTokenClassification:()=>Se,DebertaModel:()=>G,DebertaPreTrainedModel:()=>at,DebertaV2ForMaskedLM:()=>mt,DebertaV2ForQuestionAnswering:()=>Lt,DebertaV2ForSequenceClassification:()=>Ct,DebertaV2ForTokenClassification:()=>ft,DebertaV2Model:()=>wt,DebertaV2PreTrainedModel:()=>tt,DecisionTransformerModel:()=>Pu,DecisionTransformerPreTrainedModel:()=>Su,DeiTForImageClassification:()=>Ml,DeiTModel:()=>bl,DeiTPreTrainedModel:()=>oa,DepthAnythingForDepthEstimation:()=>Fl,DepthAnythingPreTrainedModel:()=>Il,DepthProForDepthEstimation:()=>Ll,DepthProPreTrainedModel:()=>Dl,DetrForObjectDetection:()=>ml,DetrForSegmentation:()=>ea,DetrModel:()=>fl,DetrObjectDetectionOutput:()=>ta,DetrPreTrainedModel:()=>ri,DetrSegmentationOutput:()=>_l,Dinov2ForImageClassification:()=>jl,Dinov2Model:()=>Nl,Dinov2PreTrainedModel:()=>ma,DistilBertForMaskedLM:()=>Ut,DistilBertForQuestionAnswering:()=>ct,DistilBertForSequenceClassification:()=>Fe,DistilBertForTokenClassification:()=>Oe,DistilBertModel:()=>Ft,DistilBertPreTrainedModel:()=>jt,DonutSwinModel:()=>Yn,DonutSwinPreTrainedModel:()=>Qn,EfficientNetForImageClassification:()=>Mu,EfficientNetModel:()=>za,EfficientNetPreTrainedModel:()=>Oa,ElectraForMaskedLM:()=>yt,ElectraForQuestionAnswering:()=>Pt,ElectraForSequenceClassification:()=>bt,ElectraForTokenClassification:()=>zt,ElectraModel:()=>Nt,ElectraPreTrainedModel:()=>Je,EsmForMaskedLM:()=>Nr,EsmForSequenceClassification:()=>mr,EsmForTokenClassification:()=>kr,EsmModel:()=>br,EsmPreTrainedModel:()=>sr,FalconForCausalLM:()=>gu,FalconModel:()=>_u,FalconPreTrainedModel:()=>ka,FastViTForImageClassification:()=>nl,FastViTModel:()=>rl,FastViTPreTrainedModel:()=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function me(f,b,R=!1){const Me=f.sessions[R?"decoder_model_merged":"model"],{past_key_values:Ue,...Le}=b;Me.inputNames.includes("use_cache_branch")&&(Le.use_cache_branch=it(!!Ue)),Me.inputNames.includes("position_ids")&&Le.attention_mask&&!Le.position_ids&&(Le.position_ids=de(Le,Ue)),f.addPastKeyValues(Le,Ue);const pt=(0,Te.pick)(Le,Me.inputNames);return await Ce(Me,pt)}async function W(f,{input_ids:b=null,attention_mask:R=null,pixel_values:Me=null,position_ids:Ue=null,inputs_embeds:Le=null,past_key_values:pt=null,generation_config:kt=null,logits_processor:Vt=null,...tr}){if(!Le){if(Le=await f.encode_text({input_ids:b}),Me&&b.dims[1]!==1){const Dr=await f.encode_image({pixel_values:Me});({inputs_embeds:Le,attention_mask:R}=f._merge_input_ids_with_image_features({image_features:Dr,inputs_embeds:Le,input_ids:b,attention_mask:R}))}else if(pt&&Me&&b.dims[1]===1){const 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F.AutoConfig.from_pretrained(R,Ar);let Lr;if(yr===X.DecoderOnly)Lr=await Promise.all([E(R,{model:Ar.model_file_name??"model"},Ar),ue(R,{generation_config:"generation_config.json"},Ar)]);else if(yr===X.Seq2Seq||yr===X.Vision2Seq)Lr=await Promise.all([E(R,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Ar),ue(R,{generation_config:"generation_config.json"},Ar)]);else if(yr===X.MaskGeneration)Lr=await Promise.all([E(R,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},Ar)]);else if(yr===X.EncoderDecoder)Lr=await Promise.all([E(R,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Ar)]);else if(yr===X.ImageTextToText){const Tn={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Ue.is_encoder_decoder&&(Tn.model="encoder_model"),Lr=await Promise.all([E(R,Tn,Ar),ue(R,{generation_config:"generation_config.json"},Ar)])}else yr===X.Musicgen?Lr=await Promise.all([E(R,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},Ar),ue(R,{generation_config:"generation_config.json"},Ar)]):(yr!==X.EncoderOnly&&console.warn(`Model type for '${$r??(Ue==null?void 0:Ue.model_type)}' not found, assuming encoder-only architecture. Please report this at ${P.GITHUB_ISSUE_URL}.`),Lr=await Promise.all([E(R,{model:Ar.model_file_name??"model"},Ar)]));return new this(Ue,...Lr)}async _call(R){return await this.forward(R)}async forward(R){return await this._forward(this,R)}get generation_config(){var R;return((R=this.configs)==null?void 0:R.generation_config)??null}_get_logits_warper(R){const Me=new D.LogitsProcessorList;return R.temperature!==null&&R.temperature!==1&&Me.push(new D.TemperatureLogitsWarper(R.temperature)),R.top_k!==null&&R.top_k!==0&&Me.push(new D.TopKLogitsWarper(R.top_k)),R.top_p!==null&&R.top_p<1&&Me.push(new D.TopPLogitsWarper(R.top_p)),Me}_get_logits_processor(R,Me,Ue=null){const Le=new D.LogitsProcessorList;if(R.repetition_penalty!==null&&R.repetition_penalty!==1&&Le.push(new D.RepetitionPenaltyLogitsProcessor(R.repetition_penalty)),R.no_repeat_ngram_size!==null&&R.no_repeat_ngram_size>0&&Le.push(new D.NoRepeatNGramLogitsProcessor(R.no_repeat_ngram_size)),R.bad_words_ids!==null&&Le.push(new D.NoBadWordsLogitsProcessor(R.bad_words_ids,R.eos_token_id)),R.min_length!==null&&R.eos_token_id!==null&&R.min_length>0&&Le.push(new D.MinLengthLogitsProcessor(R.min_length,R.eos_token_id)),R.min_new_tokens!==null&&R.eos_token_id!==null&&R.min_new_tokens>0&&Le.push(new D.MinNewTokensLengthLogitsProcessor(Me,R.min_new_tokens,R.eos_token_id)),R.forced_bos_token_id!==null&&Le.push(new D.ForcedBOSTokenLogitsProcessor(R.forced_bos_token_id)),R.forced_eos_token_id!==null&&Le.push(new D.ForcedEOSTokenLogitsProcessor(R.max_length,R.forced_eos_token_id)),R.begin_suppress_tokens!==null){const pt=Me>1||R.forced_bos_token_id===null?Me:Me+1;Le.push(new D.SuppressTokensAtBeginLogitsProcessor(R.begin_suppress_tokens,pt))}return R.guidance_scale!==null&&R.guidance_scale>1&&Le.push(new D.ClassifierFreeGuidanceLogitsProcessor(R.guidance_scale)),Ue!==null&&Le.extend(Ue),Le}_prepare_generation_config(R,Me,Ue=B.GenerationConfig){const Le={...this.config};for(const kt of["decoder","generator","text_config"])kt in Le&&Object.assign(Le,Le[kt]);const pt=new Ue(Le);return Object.assign(pt,this.generation_config??{}),R&&Object.assign(pt,R),Me&&Object.assign(pt,(0,Te.pick)(Me,Object.getOwnPropertyNames(pt))),pt}_get_stopping_criteria(R,Me=null){const Ue=new fe.StoppingCriteriaList;return R.max_length!==null&&Ue.push(new fe.MaxLengthCriteria(R.max_length,this.config.max_position_embeddings??null)),R.eos_token_id!==null&&Ue.push(new fe.EosTokenCriteria(R.eos_token_id)),Me&&Ue.extend(Me),Ue}_validate_model_class(){if(!this.can_generate){const R=[Ua,vs,Va,ja],Me=k.get(this.constructor),Ue=new Set,Le=this.config.model_type;for(const kt of R){const Vt=kt.get(Le);Vt&&Ue.add(Vt[0])}let pt=`The current model class (${Me}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Ue.size>0&&(pt+=` Please use the following class instead: ${[...Ue].join(", ")}`),Error(pt)}}prepare_inputs_for_generation(...R){return this._prepare_inputs_for_generation(this,...R)}_update_model_kwargs_for_generation({generated_input_ids:R,outputs:Me,model_inputs:Ue,is_encoder_decoder:Le}){return Ue.past_key_values=this.getPastKeyValues(Me,Ue.past_key_values),Ue.input_ids=new q.Tensor("int64",R.flat(),[R.length,1]),Le||(Ue.attention_mask=(0,q.cat)([Ue.attention_mask,(0,q.ones)([Ue.attention_mask.dims[0],1])],1)),Ue.position_ids=null,Ue}_prepare_model_inputs({inputs:R,bos_token_id:Me,model_kwargs:Ue}){const Le=(0,Te.pick)(Ue,this.forward_params),pt=this.main_input_name;if(pt in Le){if(R)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Le[pt]=R;return{inputs_tensor:Le[pt],model_inputs:Le,model_input_name:pt}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:R,model_inputs:Me,model_input_name:Ue,generation_config:Le}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!Me.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:kt,pixel_values:Vt,attention_mask:tr,...zr}=Me,Dr=await this._prepare_inputs_embeds(Me);Me={...zr,...(0,Te.pick)(Dr,["inputs_embeds","attention_mask"])}}let{last_hidden_state:pt}=await lt(this,Me);if(Le.guidance_scale!==null&&Le.guidance_scale>1)pt=(0,q.cat)([pt,(0,q.full_like)(pt,0)],0),"attention_mask"in Me&&(Me.attention_mask=(0,q.cat)([Me.attention_mask,(0,q.zeros_like)(Me.attention_mask)],0));else if(Me.decoder_input_ids){const kt=ze(Me.decoder_input_ids).dims[0];if(kt!==pt.dims[0]){if(pt.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${pt.dims[0]}) than the decoder inputs (${kt}).`);pt=(0,q.cat)(Array.from({length:kt},()=>pt),0)}}return Me.encoder_outputs=pt,Me}_prepare_decoder_input_ids_for_generation({batch_size:R,model_input_name:Me,model_kwargs:Ue,decoder_start_token_id:Le,bos_token_id:pt,generation_config:kt}){let{decoder_input_ids:Vt,...tr}=Ue;if(!(Vt instanceof q.Tensor)){if(Vt)Array.isArray(Vt[0])||(Vt=Array.from({length:R},()=>Vt));else if(Le??(Le=pt),this.config.model_type==="musicgen")Vt=Array.from({length:R*this.config.decoder.num_codebooks},()=>[Le]);else if(Array.isArray(Le)){if(Le.length!==R)throw new Error(`\`decoder_start_token_id\` expcted to have length ${R} but got ${Le.length}`);Vt=Le}else Vt=Array.from({length:R},()=>[Le]);Vt=ze(Vt)}return Ue.decoder_attention_mask=(0,q.ones_like)(Vt),{input_ids:Vt,model_inputs:tr}}async generate({inputs:R=null,generation_config:Me=null,logits_processor:Ue=null,stopping_criteria:Le=null,streamer:pt=null,...kt}){this._validate_model_class(),Me=this._prepare_generation_config(Me,kt);let{inputs_tensor:Vt,model_inputs:tr,model_input_name:zr}=this._prepare_model_inputs({inputs:R,model_kwargs:kt});const Dr=this.config.is_encoder_decoder;Dr&&("encoder_outputs"in tr||(tr=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Vt,model_inputs:tr,model_input_name:zr,generation_config:Me})));let wr;Dr?{input_ids:wr,model_inputs:tr}=this._prepare_decoder_input_ids_for_generation({batch_size:tr[zr].dims.at(0),model_input_name:zr,model_kwargs:tr,decoder_start_token_id:Me.decoder_start_token_id,bos_token_id:Me.bos_token_id,generation_config:Me}):wr=tr[zr];let ir=wr.dims.at(-1);Me.max_new_tokens!==null&&(Me.max_length=ir+Me.max_new_tokens);const Ar=this._get_logits_processor(Me,ir,Ue),$r=this._get_stopping_criteria(Me,Le),yr=tr[zr].dims.at(0),Lr=le.LogitsSampler.getSampler(Me),Tn=new Array(yr).fill(0),dn=wr.tolist();pt&&pt.put(dn);let Gr,Qr={};for(;;){if(tr=this.prepare_inputs_for_generation(dn,tr,Me),Gr=await this.forward(tr),Me.output_attentions&&Me.return_dict_in_generate){const Bn=this.getAttentions(Gr);for(const xs in Bn)xs in Qr||(Qr[xs]=[]),Qr[xs].push(Bn[xs])}const ts=Gr.logits.slice(null,-1,null),mi=Ar(dn,ts),eo=[];for(let Bn=0;Bn<mi.dims.at(0);++Bn){const xs=mi[Bn],Tc=await Lr(xs);for(const[Yd,Zd]of Tc){const md=BigInt(Yd);Tn[Bn]+=Zd,dn[Bn].push(md),eo.push([md]);break}}if(pt&&pt.put(eo),$r(dn).every(Bn=>Bn))break;tr=this._update_model_kwargs_for_generation({generated_input_ids:eo,outputs:Gr,model_inputs:tr,is_encoder_decoder:Dr})}pt&&pt.end();const Kr=this.getPastKeyValues(Gr,tr.past_key_values,!0),wn=new q.Tensor("int64",dn.flat(),[dn.length,dn[0].length]);if(Me.return_dict_in_generate)return{sequences:wn,past_key_values:Kr,...Qr};for(const ts of Object.values(Gr))ts.location==="gpu-buffer"&&ts.dispose();return wn}getPastKeyValues(R,Me,Ue=!1){const Le=Object.create(null);for(const pt in R)if(pt.startsWith("present")){const kt=pt.replace("present","past_key_values"),Vt=pt.includes("encoder");if(Vt&&Me?Le[kt]=Me[kt]:Le[kt]=R[pt],Me&&(!Vt||Ue)){const tr=Me[kt];tr.location==="gpu-buffer"&&tr.dispose()}}return Le}getAttentions(R){const Me={};for(const Ue of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Le in R)Le.startsWith(Ue)&&(Ue in Me||(Me[Ue]=[]),Me[Ue].push(R[Le]));return Me}addPastKeyValues(R,Me){var Ue;if(Me)Object.assign(R,Me);else{const Le=this.sessions.decoder_model_merged??this.sessions.model,pt=((Ue=Le==null?void 0:Le.config)==null?void 0:Ue.kv_cache_dtype)??"float32",kt=pt==="float16"?new Uint16Array:[],Vt=(0,F.getKeyValueShapes)(this.config);for(const tr in Vt)R[tr]=new q.Tensor(pt,kt,Vt[tr])}}async encode_image({pixel_values:R}){const Me=(await Ce(this.sessions.vision_encoder,{pixel_values:R})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${Me.dims[1]}).`),this.config.num_image_tokens=Me.dims[1]),Me}async encode_text({input_ids:R}){return(await Ce(this.sessions.embed_tokens,{input_ids:R})).inputs_embeds}}class Ze{}class dt extends Ze{constructor({last_hidden_state:b,hidden_states:R=null,attentions:Me=null}){super(),this.last_hidden_state=b,this.hidden_states=R,this.attentions=Me}}class Re extends se{}class ht extends Re{}class Mt extends Re{async _call(b){return new un(await super._call(b))}}class Xe extends Re{async _call(b){return new cr(await super._call(b))}}class Z extends Re{async _call(b){return new nn(await super._call(b))}}class Ae extends Re{async _call(b){return new fn(await super._call(b))}}class Ke extends se{}class et extends Ke{}class je extends se{}class Ve extends je{}class ut extends je{async _call(b){return new un(await super._call(b))}}class _t extends je{async _call(b){return new cr(await super._call(b))}}class St extends je{async _call(b){return new nn(await super._call(b))}}class xt extends je{async _call(b){return new fn(await super._call(b))}}class v extends se{}class H extends v{}class $ extends v{async _call(b){return new un(await super._call(b))}}class Y extends v{async _call(b){return new cr(await super._call(b))}}class he extends v{async _call(b){return new nn(await super._call(b))}}class nt extends v{async _call(b){return new fn(await super._call(b))}}class Je extends se{}class Nt extends Je{}class yt extends Je{async _call(b){return new un(await super._call(b))}}class bt extends Je{async _call(b){return new cr(await super._call(b))}}class zt extends Je{async _call(b){return new nn(await super._call(b))}}class Pt extends Je{async _call(b){return new fn(await super._call(b))}}class dr extends se{}class Cr extends dr{}class Yr extends dr{async _call(b){return new un(await super._call(b))}}class Rr extends dr{async _call(b){return new cr(await super._call(b))}}class Jr extends dr{async _call(b){return new nn(await super._call(b))}}class bn extends dr{async _call(b){return new fn(await super._call(b))}}class at extends se{}class G extends at{}class ge extends at{async _call(b){return new un(await super._call(b))}}class Ie extends at{async _call(b){return new cr(await super._call(b))}}class Se extends at{async _call(b){return new nn(await super._call(b))}}class Ne extends at{async _call(b){return new fn(await super._call(b))}}class tt extends se{}class wt extends tt{}class mt extends tt{async _call(b){return new un(await super._call(b))}}class Ct extends tt{async _call(b){return new cr(await super._call(b))}}class ft extends tt{async _call(b){return new nn(await super._call(b))}}class Lt extends tt{async _call(b){return new fn(await super._call(b))}}class jt extends se{}class Ft extends jt{}class Fe extends jt{async _call(b){return new cr(await super._call(b))}}class Oe extends jt{async _call(b){return new nn(await super._call(b))}}class ct extends jt{async _call(b){return new fn(await super._call(b))}}class Ut extends jt{async _call(b){return new un(await super._call(b))}}class sr extends se{}class br extends sr{}class Nr extends sr{async _call(b){return new un(await super._call(b))}}class mr extends sr{async _call(b){return new cr(await super._call(b))}}class kr extends sr{async _call(b){return new nn(await super._call(b))}}class gr extends se{}class $n extends gr{}class Ur extends gr{async _call(b){return new un(await super._call(b))}}class fs extends gr{async _call(b){return new cr(await super._call(b))}}class $s extends gr{async _call(b){return new fn(await super._call(b))}}class qn extends se{}class Es extends qn{}class ks extends qn{async _call(b){return new un(await super._call(b))}}class Ss extends qn{async _call(b){return new cr(await super._call(b))}}class Ps extends qn{async _call(b){return new nn(await super._call(b))}}class As extends qn{async _call(b){return new fn(await super._call(b))}}class ns extends se{}class Hn extends ns{}class An extends ns{async _call(b){return new un(await super._call(b))}}class Rn extends ns{async _call(b){return new cr(await super._call(b))}}class ms extends ns{async _call(b){return new fn(await super._call(b))}}class Ln extends se{}class _s extends Ln{}class gs extends Ln{async _call(b){return new cr(await super._call(b))}}class ws extends Ln{async _call(b){return new fn(await super._call(b))}}class Yt extends Ln{async _call(b){return new un(await super._call(b))}}class ss extends se{constructor(){super(...arguments);ve(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Is extends ss{}class Fs extends ss{}class ys extends se{}class Os extends ys{}class zs extends ys{}class is extends se{}class Ds extends is{}class ae extends is{}class _ extends se{}class I extends _{}class Q extends _{}class oe extends _{async _call(b){return new cr(await super._call(b))}}class _e extends se{}class Ge extends _e{}class gt extends _e{}class $t extends _e{async _call(b){return new cr(await super._call(b))}}class Tt extends _e{}class Ot extends se{}class er extends Ot{}class Sr extends Ot{}class ur extends se{}class Wr extends ur{}class en extends ur{}class or extends se{}class Pr extends or{}class _n extends or{async _call(b){return new un(await super._call(b))}}class Mn extends or{async _call(b){return new cr(await super._call(b))}}class $e extends or{async _call(b){return new nn(await super._call(b))}}class tn extends or{async _call(b){return new fn(await super._call(b))}}class ln extends se{}class In extends ln{}class Nn extends ln{async _call(b){return new un(await super._call(b))}}class Kt extends ln{async _call(b){return new cr(await super._call(b))}}class pn extends ln{async _call(b){return new nn(await super._call(b))}}class Xr extends ln{async _call(b){return new fn(await super._call(b))}}class fr extends se{}class Fr extends fr{}class Et extends fr{async _call(b){return new un(await super._call(b))}}class _r extends fr{async _call(b){return new cr(await super._call(b))}}class as extends fr{async _call(b){return new nn(await super._call(b))}}class Kn extends fr{async _call(b){return new fn(await super._call(b))}}class jn extends se{}class Wt extends jn{}class Ys extends jn{}class Qe extends se{constructor(){super(...arguments);ve(this,"requires_attention_mask",!1);ve(this,"main_input_name","input_features");ve(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class qt extends Qe{}class xi extends Qe{_prepare_generation_config(b,R){return super._prepare_generation_config(b,R,J.WhisperGenerationConfig)}_retrieve_init_tokens(b){const R=[b.decoder_start_token_id];let Me=b.language;const Ue=b.task;if(b.is_multilingual){Me||(console.warn("No language specified - defaulting to English (en)."),Me="en");const pt=`<|${(0,pe.whisper_language_to_code)(Me)}|>`;R.push(b.lang_to_id[pt]),R.push(b.task_to_id[Ue??"transcribe"])}else if(Me||Ue)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!b.return_timestamps&&b.no_timestamps_token_id&&R.at(-1)!==b.no_timestamps_token_id?R.push(b.no_timestamps_token_id):b.return_timestamps&&R.at(-1)===b.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),R.pop()),R.filter(Le=>Le!=null)}async generate({inputs:b=null,generation_config:R=null,logits_processor:Me=null,stopping_criteria:Ue=null,...Le}){R=this._prepare_generation_config(R,Le);const pt=Le.decoder_input_ids??this._retrieve_init_tokens(R);if(R.return_timestamps&&(Me??(Me=new D.LogitsProcessorList),Me.push(new D.WhisperTimeStampLogitsProcessor(R,pt))),R.begin_suppress_tokens&&(Me??(Me=new D.LogitsProcessorList),Me.push(new D.SuppressTokensAtBeginLogitsProcessor(R.begin_suppress_tokens,pt.length))),R.return_token_timestamps){if(!R.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.");R.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),R.output_attentions=!0,R.return_dict_in_generate=!0}const kt=await super.generate({inputs:b,generation_config:R,logits_processor:Me,decoder_input_ids:pt,...Le});return R.return_token_timestamps&&(kt.token_timestamps=this._extract_token_timestamps(kt,R.alignment_heads,R.num_frames)),kt}_extract_token_timestamps(b,R,Me=null,Ue=.02){if(!b.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`.");Me==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 Le=this.config.median_filter_width;Le===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Le=7);const pt=b.cross_attentions,kt=Array.from({length:this.config.decoder_layers},($r,yr)=>(0,q.cat)(pt.map(Lr=>Lr[yr]),2)),Vt=(0,q.stack)(R.map(([$r,yr])=>{if($r>=kt.length)throw new Error(`Layer index ${$r} is out of bounds for cross attentions (length ${kt.length}).`);return Me?kt[$r].slice(null,yr,null,[0,Me]):kt[$r].slice(null,yr)})).transpose(1,0,2,3),[tr,zr]=(0,q.std_mean)(Vt,-2,0,!0),Dr=Vt.clone();for(let $r=0;$r<Dr.dims[0];++$r){const yr=Dr[$r];for(let Lr=0;Lr<yr.dims[0];++Lr){const Tn=yr[Lr],dn=tr[$r][Lr][0].data,Gr=zr[$r][Lr][0].data;for(let Qr=0;Qr<Tn.dims[0];++Qr){let Kr=Tn[Qr].data;for(let wn=0;wn<Kr.length;++wn)Kr[wn]=(Kr[wn]-Gr[wn])/dn[wn];Kr.set((0,re.medianFilter)(Kr,Le))}}}const wr=[(0,q.mean)(Dr,1)],ir=b.sequences.dims,Ar=new q.Tensor("float32",new Float32Array(ir[0]*ir[1]),ir);for(let $r=0;$r<ir[0];++$r){const yr=wr[$r].neg().squeeze_(0),[Lr,Tn]=(0,re.dynamic_time_warping)(yr.tolist()),dn=Array.from({length:Lr.length-1},(Kr,wn)=>Lr[wn+1]-Lr[wn]),Gr=(0,Te.mergeArrays)([1],dn).map(Kr=>!!Kr),Qr=[];for(let Kr=0;Kr<Gr.length;++Kr)Gr[Kr]&&Qr.push(Tn[Kr]*Ue);Ar[$r].data.set(Qr,1)}return Ar}}class Ti extends se{constructor(){super(...arguments);ve(this,"main_input_name","pixel_values");ve(this,"forward_params",["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"])}}class oo extends se{constructor(){super(...arguments);ve(this,"forward_params",["input_ids","pixel_values","attention_mask","position_ids","past_key_values"])}}class bs extends oo{_merge_input_ids_with_image_features({inputs_embeds:b,image_features:R,input_ids:Me,attention_mask:Ue}){const Le=this.config.image_token_index,kt=Me.tolist().map(wr=>wr.findIndex(ir=>ir==Le)),Vt=kt.every(wr=>wr===-1),tr=kt.every(wr=>wr!==-1);if(!Vt&&!tr)throw new Error("Every input should contain either 0 or 1 image token.");if(Vt)return{inputs_embeds:b,attention_mask:Ue};const zr=[],Dr=[];for(let wr=0;wr<kt.length;++wr){const ir=kt[wr],Ar=b[wr],$r=R[wr],yr=Ue[wr];zr.push((0,q.cat)([Ar.slice([0,ir]),$r,Ar.slice([ir+1,Ar.dims[0]])],0)),Dr.push((0,q.cat)([yr.slice([0,ir]),(0,q.ones)([$r.dims[0]]),yr.slice([ir+1,yr.dims[0]])],0))}return{inputs_embeds:(0,q.stack)(zr,0),attention_mask:(0,q.stack)(Dr,0)}}}class pr extends bs{}class lo extends se{constructor(){super(...arguments);ve(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"]);ve(this,"main_input_name","inputs_embeds")}}class Ci extends lo{_merge_input_ids_with_image_features({inputs_embeds:b,image_features:R,input_ids:Me,attention_mask:Ue}){return{inputs_embeds:(0,q.cat)([R,b],1),attention_mask:(0,q.cat)([(0,q.ones)(R.dims.slice(0,2)),Ue],1)}}async _prepare_inputs_embeds({input_ids:b,pixel_values:R,inputs_embeds:Me,attention_mask:Ue}){if(!b&&!R)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let Le,pt;return b&&(Le=await this.encode_text({input_ids:b})),R&&(pt=await this.encode_image({pixel_values:R})),Le&&pt?{inputs_embeds:Me,attention_mask:Ue}=this._merge_input_ids_with_image_features({inputs_embeds:Le,image_features:pt,input_ids:b,attention_mask:Ue}):Me=Le||pt,{inputs_embeds:Me,attention_mask:Ue}}async forward({input_ids:b,pixel_values:R,attention_mask:Me,decoder_input_ids:Ue,decoder_attention_mask:Le,encoder_outputs:pt,past_key_values:kt,inputs_embeds:Vt,decoder_inputs_embeds:tr}){if(Vt||({inputs_embeds:Vt,attention_mask:Me}=await this._prepare_inputs_embeds({input_ids:b,pixel_values:R,inputs_embeds:Vt,attention_mask:Me})),!pt){let{last_hidden_state:wr}=await lt(this,{inputs_embeds:Vt,attention_mask:Me});pt=wr}if(!tr){if(!Ue)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");tr=await this.encode_text({input_ids:Ue})}return await me(this,{inputs_embeds:tr,attention_mask:Le,encoder_attention_mask:Me,encoder_hidden_states:pt,past_key_values:kt},!0)}}class os extends se{}class uo extends os{}class Md extends os{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="text_model"),super.from_pretrained(b,R)}}class xn extends os{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="text_model"),super.from_pretrained(b,R)}}class vd extends os{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="vision_model"),super.from_pretrained(b,R)}}class co extends os{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="vision_model"),super.from_pretrained(b,R)}}class Vn extends se{}class po extends Vn{}class ho extends Vn{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="text_model"),super.from_pretrained(b,R)}}class fo extends os{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="vision_model"),super.from_pretrained(b,R)}}class mo extends se{}class _o extends mo{}class $i extends se{}class go extends $i{}class wo extends $i{}class Ei extends se{}class yo extends Ei{}class Fn extends Ei{}class ki extends se{}class bo extends ki{}class Mo extends ki{}class Si extends se{}class vo extends Si{}class xo extends Si{}class Pi extends se{}class To extends Pi{}class Co extends Pi{}class Ai extends se{}class xd extends Ai{}class On extends Ai{}class Ii extends se{}class Zs extends Ii{}class Fi extends Ii{}class En extends se{}class $o extends En{}class Eo extends En{}class Oi extends se{}class ko extends Oi{}class So extends Oi{}class zi extends se{}class Po extends zi{}class Ao extends zi{}class Di extends se{}class Io extends Di{}class zn extends Di{}class Li extends se{}class Fo extends Li{}class Oo extends Li{}class Bi extends se{}class zo extends Bi{}class Do extends Bi{}class Ri extends se{}class Lo extends Ri{}class Bo extends Ri{}class Ni extends se{}class ji extends Ni{}class Vi extends Ni{}class Ui extends se{}class Ro extends Ui{}class Wi extends Ui{}class No extends se{}class jo extends No{}class Js extends No{}class Gi extends se{}class Vo extends Gi{}class Uo extends Gi{}class Ms extends se{}class Wo extends Ms{}class Go extends Ms{}class ei extends se{}class qo extends ei{}class Ho extends ei{}class qi extends se{}class Ko extends qi{}class Td extends qi{async _call(b){return new cr(await super._call(b))}}class Hi extends se{}class Xo extends Hi{}class Qo extends Hi{async _call(b){return new cr(await super._call(b))}}class Cd extends se{}class Yo extends Cd{}class Mr extends se{}class Zo extends Mr{}class Jo extends Mr{async _call(b){return new cr(await super._call(b))}}class el extends se{}class tl extends el{}class Ki extends se{}class rl extends Ki{}class nl extends Ki{async _call(b){return new cr(await super._call(b))}}class sl extends se{}class il extends sl{async _call(b){return new hd(await super._call(b))}}class Xi extends se{}class al extends Xi{}class ol extends Xi{async _call(b){return new cr(await super._call(b))}}class Qi extends se{}class ll extends Qi{}class Ls extends Qi{async _call(b){return new cr(await super._call(b))}}class Yi extends se{}class ti extends Yi{}class ul extends Yi{}class Zi extends se{}class dl extends Zi{}class cl extends Zi{}class Ji extends se{}class pl extends Ji{}class hl extends Ji{async _call(b){return new cr(await super._call(b))}}class ri extends se{}class fl extends ri{}class ml extends ri{async _call(b){return new ta(await super._call(b))}}class ea extends ri{async _call(b){return new _l(await super._call(b))}}class ta extends Ze{constructor({logits:b,pred_boxes:R}){super(),this.logits=b,this.pred_boxes=R}}class _l extends Ze{constructor({logits:b,pred_boxes:R,pred_masks:Me}){super(),this.logits=b,this.pred_boxes=R,this.pred_masks=Me}}class ra extends se{}class gl extends ra{}class wl extends ra{async _call(b){return new na(await super._call(b))}}class na extends Ze{constructor({logits:b,pred_boxes:R}){super(),this.logits=b,this.pred_boxes=R}}class sa extends se{}class ia extends sa{}class aa extends sa{async _call(b){return new yl(await super._call(b))}}class yl extends ta{}class oa extends se{}class bl extends oa{}class Ml extends oa{async _call(b){return new cr(await super._call(b))}}class la extends se{}class vl extends la{}class ua extends la{async _call(b){return new cr(await super._call(b))}}class da extends se{}class xl extends da{}class Tl extends da{async _call(b){return new cr(await super._call(b))}}class Cl extends se{}class $l extends Cl{}class El extends Cl{async _call(b){return new cr(await super._call(b))}}class kn extends se{}class kl extends kn{}class Sl extends kn{}class ca extends se{}class Pl extends ca{}class Al extends ca{}class Il extends se{}class Fl extends Il{}class ni extends se{}class Ol extends ni{}class zl extends ni{}class $d extends ni{}class Dl extends se{}class Ll extends Dl{}class pa extends se{}class Bl extends pa{}class Rl extends pa{}class ha extends se{}class Ed extends ha{}class Xn extends ha{}class Qn extends se{}class Yn extends Qn{}class si extends se{}class Zn extends si{}class hn extends si{async _call(b){return new cr(await super._call(b))}}class ii extends se{}class ai extends ii{}class fa extends ii{async _call(b){return new cr(await super._call(b))}}class ma extends se{}class Nl extends ma{}class jl extends ma{async _call(b){return new cr(await super._call(b))}}class Bs extends se{}class _a extends Bs{}class Vl extends Bs{async _call(b){return new oi(await super._call(b))}}class oi extends Ze{constructor({logits:b,pred_boxes:R}){super(),this.logits=b,this.pred_boxes=R}}class Ul extends se{}class ga extends Ul{async get_image_embeddings({pixel_values:b}){return await lt(this,{pixel_values:b})}async forward(b){if((!b.image_embeddings||!b.image_positional_embeddings)&&(b={...b,...await this.get_image_embeddings(b)}),!b.input_labels&&b.input_points){const Me=b.input_points.dims.slice(0,-1),Ue=Me.reduce((Le,pt)=>Le*pt,1);b.input_labels=new q.Tensor("int64",new BigInt64Array(Ue).fill(1n),Me)}const R={image_embeddings:b.image_embeddings,image_positional_embeddings:b.image_positional_embeddings};return b.input_points&&(R.input_points=b.input_points),b.input_labels&&(R.input_labels=b.input_labels),b.input_boxes&&(R.input_boxes=b.input_boxes),await Ce(this.sessions.prompt_encoder_mask_decoder,R)}async _call(b){return new li(await super._call(b))}}class li extends Ze{constructor({iou_scores:b,pred_masks:R}){super(),this.iou_scores=b,this.pred_masks=R}}class wa extends se{}class Wl extends wa{}class kd extends wa{}class ya extends se{}class ba extends ya{}class Rs extends ya{}class Jn extends se{}class Ma extends Jn{}class Gl extends Jn{async _call(b){return new es(await super._call(b))}}class ql extends Jn{async _call(b){return new cr(await super._call(b))}}class Hl extends Jn{async _call(b){return new nn(await super._call(b))}}class Kl extends se{}class Xl extends Kl{}class Ql extends Kl{async _call(b){return new nn(await super._call(b))}}class Sd extends se{}class va extends Sd{}class ui extends se{}class Yl extends ui{}class Zl extends ui{async _call(b){return new es(await super._call(b))}}class Jl extends ui{async _call(b){return new cr(await super._call(b))}}class ls extends se{}class eu extends ls{}class di extends ls{async _call(b){return new es(await super._call(b))}}class xa extends ls{async _call(b){return new cr(await super._call(b))}}class Ta extends ls{async _call(b){return new nn(await super._call(b))}}class ci extends se{}class tu extends ci{}class Ca extends ci{async _call(b){return new es(await super._call(b))}}class Pd extends ci{async _call(b){return new cr(await super._call(b))}}class Ad extends se{}class ru extends Jn{}class Id extends Jn{async _call(b){return new es(await super._call(b))}}class nu extends Jn{async _call(b){return new cr(await super._call(b))}}class us extends se{}class Fd extends us{}class su extends us{async _call(b){return new es(await super._call(b))}}class iu extends us{async _call(b){return new cr(await super._call(b))}}class au extends us{async _call(b){return new pd(await super._call(b))}}class $a extends us{async _call(b){return new nn(await super._call(b))}}class pi extends se{}class Od extends pi{}class ou extends pi{}class lu extends pi{async generate_speech(b,R,{threshold:Me=.5,minlenratio:Ue=0,maxlenratio:Le=20,vocoder:pt=null}={}){const kt={input_ids:b},{encoder_outputs:Vt,encoder_attention_mask:tr}=await lt(this,kt),zr=Vt.dims[1]/this.config.reduction_factor,Dr=Math.floor(zr*Le),wr=Math.floor(zr*Ue),ir=this.config.num_mel_bins;let Ar=[],$r=null,yr=null,Lr=0;for(;;){++Lr;const Gr=it(!!yr);let Qr;yr?Qr=yr.output_sequence_out:Qr=new q.Tensor("float32",new Float32Array(ir),[1,1,ir]);let Kr={use_cache_branch:Gr,output_sequence:Qr,encoder_attention_mask:tr,speaker_embeddings:R,encoder_hidden_states:Vt};this.addPastKeyValues(Kr,$r),yr=await Ce(this.sessions.decoder_model_merged,Kr),$r=this.getPastKeyValues(yr,$r);const{prob:wn,spectrum:ts}=yr;if(Ar.push(ts),Lr>=wr&&(Array.from(wn.data).filter(mi=>mi>=Me).length>0||Lr>=Dr))break}const Tn=(0,q.cat)(Ar),{waveform:dn}=await Ce(pt.sessions.model,{spectrogram:Tn});return{spectrogram:Tn,waveform:dn}}}class uu extends se{constructor(){super(...arguments);ve(this,"main_input_name","spectrogram")}}class du extends se{}class zd extends du{}class Ea extends se{}class cu extends Ea{}class pu extends Ea{}class hu extends se{}class fu extends hu{}class mu extends hu{}class ka extends se{}class _u extends ka{}class gu extends ka{}class Sa extends se{}class hi extends Sa{}class Ns extends Sa{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="text_model"),super.from_pretrained(b,R)}}class Pa extends Sa{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="audio_model"),super.from_pretrained(b,R)}}class wu extends se{}class Aa extends wu{async _call(b){return new fd(await super._call(b))}}class fi extends se{}class Dd extends fi{}class Ia extends fi{}class yu extends fi{}class Fa extends se{}class bu extends Fa{}class Ld extends Fa{}class Oa extends se{}class za extends Oa{}class Mu extends Oa{async _call(b){return new cr(await super._call(b))}}class Da extends se{}class Bd extends Da{}class bc extends Da{}class La extends se{constructor(){super(...arguments);ve(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}_apply_and_filter_by_delay_pattern_mask(R){const[Me,Ue]=R.dims,Le=this.config.decoder.num_codebooks,pt=Ue-Le;let kt=0;for(let zr=0;zr<R.size;++zr){if(R.data[zr]===this.config.decoder.pad_token_id)continue;const Dr=zr%Ue,wr=Math.floor(zr/Ue)%Le,ir=Dr-wr;ir>0&&ir<=pt&&(R.data[kt++]=R.data[zr])}const Vt=Math.floor(Me/Le),tr=kt/(Vt*Le);return new q.Tensor(R.type,R.data.slice(0,kt),[Vt,Le,tr])}prepare_inputs_for_generation(R,Me,Ue){let Le=structuredClone(R);for(let kt=0;kt<Le.length;++kt)for(let Vt=0;Vt<Le[kt].length;++Vt)kt%this.config.decoder.num_codebooks>=Vt&&(Le[kt][Vt]=BigInt(this.config.decoder.pad_token_id));return Ue.guidance_scale!==null&&Ue.guidance_scale>1&&(Le=Le.concat(Le)),super.prepare_inputs_for_generation(Le,Me,Ue)}async generate(R){const Me=await super.generate(R),Ue=this._apply_and_filter_by_delay_pattern_mask(Me).unsqueeze_(0),{audio_values:Le}=await Ce(this.sessions.encodec_decode,{audio_codes:Ue});return Le}}class Ba extends se{}class Rd extends Ba{}class vu extends Ba{async _call(b){return new cr(await super._call(b))}}class Ra extends se{}class xu extends Ra{}class Tu extends Ra{async _call(b){return new cr(await super._call(b))}}class Cu extends se{}class $u extends Cu{}class Eu extends Cu{async _call(b){return new cr(await super._call(b))}}class Na extends se{}class ku extends Na{}class Nd extends Na{async _call(b){return new cr(await super._call(b))}}class Su extends se{}class Pu extends Su{}class Or{static async from_pretrained(b,{progress_callback:R=null,config:Me=null,cache_dir:Ue=null,local_files_only:Le=!1,revision:pt="main",model_file_name:kt=null,subfolder:Vt="onnx",device:tr=null,dtype:zr=null,use_external_data_format:Dr=null,session_options:wr={}}={}){const ir={progress_callback:R,config:Me,cache_dir:Ue,local_files_only:Le,revision:pt,model_file_name:kt,subfolder:Vt,device:tr,dtype:zr,use_external_data_format:Dr,session_options:wr};if(ir.config=await F.AutoConfig.from_pretrained(b,ir),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const Ar of this.MODEL_CLASS_MAPPINGS){const $r=Ar.get(ir.config.model_type);if($r)return await $r[1].from_pretrained(b,ir)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${ir.config.model_type}", attempting to construct from base class.`),await se.from_pretrained(b,ir);throw Error(`Unsupported model type: ${ir.config.model_type}`)}}ve(Or,"MODEL_CLASS_MAPPINGS",null),ve(Or,"BASE_IF_FAIL",!1);const jd=new Map([["bert",["BertModel",ht]],["nomic_bert",["NomicBertModel",et]],["roformer",["RoFormerModel",Ve]],["electra",["ElectraModel",Nt]],["esm",["EsmModel",br]],["convbert",["ConvBertModel",H]],["camembert",["CamembertModel",Cr]],["deberta",["DebertaModel",G]],["deberta-v2",["DebertaV2Model",wt]],["mpnet",["MPNetModel",Es]],["albert",["AlbertModel",_s]],["distilbert",["DistilBertModel",Ft]],["roberta",["RobertaModel",Pr]],["xlm",["XLMModel",In]],["xlm-roberta",["XLMRobertaModel",Fr]],["clap",["ClapModel",hi]],["clip",["CLIPModel",uo]],["clipseg",["CLIPSegModel",go]],["chinese_clip",["ChineseCLIPModel",_o]],["siglip",["SiglipModel",po]],["mobilebert",["MobileBertModel",$n]],["squeezebert",["SqueezeBertModel",Hn]],["wav2vec2",["Wav2Vec2Model",Ma]],["wav2vec2-bert",["Wav2Vec2BertModel",tu]],["unispeech",["UniSpeechModel",Yl]],["unispeech-sat",["UniSpeechSatModel",eu]],["hubert",["HubertModel",ru]],["wavlm",["WavLMModel",Fd]],["audio-spectrogram-transformer",["ASTModel",Wt]],["vits",["VitsModel",Aa]],["pyannote",["PyAnnoteModel",Xl]],["wespeaker-resnet",["WeSpeakerResNetModel",va]],["detr",["DetrModel",fl]],["rt_detr",["RTDetrModel",gl]],["table-transformer",["TableTransformerModel",ia]],["vit",["ViTModel",Ko]],["pvt",["PvtModel",Xo]],["vit_msn",["ViTMSNModel",Zo]],["vit_mae",["ViTMAEModel",Yo]],["groupvit",["GroupViTModel",tl]],["fastvit",["FastViTModel",rl]],["mobilevit",["MobileViTModel",al]],["mobilevitv2",["MobileViTV2Model",ll]],["owlvit",["OwlViTModel",ti]],["owlv2",["Owlv2Model",dl]],["beit",["BeitModel",pl]],["deit",["DeiTModel",bl]],["hiera",["HieraModel",vl]],["convnext",["ConvNextModel",Zn]],["convnextv2",["ConvNextV2Model",ai]],["dinov2",["Dinov2Model",Nl]],["resnet",["ResNetModel",xl]],["swin",["SwinModel",$l]],["swin2sr",["Swin2SRModel",kl]],["donut-swin",["DonutSwinModel",Yn]],["yolos",["YolosModel",_a]],["dpt",["DPTModel",Pl]],["glpn",["GLPNModel",Ed]],["hifigan",["SpeechT5HifiGan",uu]],["efficientnet",["EfficientNetModel",za]],["decision_transformer",["DecisionTransformerModel",Pu]],["mobilenet_v1",["MobileNetV1Model",Rd]],["mobilenet_v2",["MobileNetV2Model",xu]],["mobilenet_v3",["MobileNetV3Model",$u]],["mobilenet_v4",["MobileNetV4Model",ku]],["maskformer",["MaskFormerModel",Bl]]]),Mc=new Map([["t5",["T5Model",Is]],["longt5",["LongT5Model",Os]],["mt5",["MT5Model",Ds]],["bart",["BartModel",I]],["mbart",["MBartModel",Ge]],["marian",["MarianModel",Wl]],["whisper",["WhisperModel",qt]],["m2m_100",["M2M100Model",ba]],["blenderbot",["BlenderbotModel",er]],["blenderbot-small",["BlenderbotSmallModel",Wr]]]),Vd=new Map([["bloom",["BloomModel",Vo]],["jais",["JAISModel",bo]],["gpt2",["GPT2Model",yo]],["gptj",["GPTJModel",xd]],["gpt_bigcode",["GPTBigCodeModel",Zs]],["gpt_neo",["GPTNeoModel",vo]],["gpt_neox",["GPTNeoXModel",To]],["codegen",["CodeGenModel",$o]],["llama",["LlamaModel",ko]],["granite",["GraniteModel",Po]],["cohere",["CohereModel",Io]],["gemma",["GemmaModel",Fo]],["gemma2",["Gemma2Model",zo]],["openelm",["OpenELMModel",Lo]],["qwen2",["Qwen2Model",ji]],["phi",["PhiModel",Ro]],["phi3",["Phi3Model",jo]],["mpt",["MptModel",Wo]],["opt",["OPTModel",qo]],["mistral",["MistralModel",cu]],["starcoder2",["Starcoder2Model",fu]],["falcon",["FalconModel",_u]],["stablelm",["StableLmModel",bu]]]),ja=new Map([["speecht5",["SpeechT5ForSpeechToText",ou]],["whisper",["WhisperForConditionalGeneration",xi]]]),Au=new Map([["speecht5",["SpeechT5ForTextToSpeech",lu]]]),Iu=new Map([["vits",["VitsModel",Aa]],["musicgen",["MusicgenForConditionalGeneration",La]]]),Ud=new Map([["bert",["BertForSequenceClassification",Xe]],["roformer",["RoFormerForSequenceClassification",_t]],["electra",["ElectraForSequenceClassification",bt]],["esm",["EsmForSequenceClassification",mr]],["convbert",["ConvBertForSequenceClassification",Y]],["camembert",["CamembertForSequenceClassification",Rr]],["deberta",["DebertaForSequenceClassification",Ie]],["deberta-v2",["DebertaV2ForSequenceClassification",Ct]],["mpnet",["MPNetForSequenceClassification",Ss]],["albert",["AlbertForSequenceClassification",gs]],["distilbert",["DistilBertForSequenceClassification",Fe]],["roberta",["RobertaForSequenceClassification",Mn]],["xlm",["XLMForSequenceClassification",Kt]],["xlm-roberta",["XLMRobertaForSequenceClassification",_r]],["bart",["BartForSequenceClassification",oe]],["mbart",["MBartForSequenceClassification",$t]],["mobilebert",["MobileBertForSequenceClassification",fs]],["squeezebert",["SqueezeBertForSequenceClassification",Rn]]]),gn=new Map([["bert",["BertForTokenClassification",Z]],["roformer",["RoFormerForTokenClassification",St]],["electra",["ElectraForTokenClassification",zt]],["esm",["EsmForTokenClassification",kr]],["convbert",["ConvBertForTokenClassification",he]],["camembert",["CamembertForTokenClassification",Jr]],["deberta",["DebertaForTokenClassification",Se]],["deberta-v2",["DebertaV2ForTokenClassification",ft]],["mpnet",["MPNetForTokenClassification",Ps]],["distilbert",["DistilBertForTokenClassification",Oe]],["roberta",["RobertaForTokenClassification",$e]],["xlm",["XLMForTokenClassification",pn]],["xlm-roberta",["XLMRobertaForTokenClassification",as]]]),Va=new Map([["t5",["T5ForConditionalGeneration",Fs]],["longt5",["LongT5ForConditionalGeneration",zs]],["mt5",["MT5ForConditionalGeneration",ae]],["bart",["BartForConditionalGeneration",Q]],["mbart",["MBartForConditionalGeneration",gt]],["marian",["MarianMTModel",kd]],["m2m_100",["M2M100ForConditionalGeneration",Rs]],["blenderbot",["BlenderbotForConditionalGeneration",Sr]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",en]]]),Ua=new Map([["bloom",["BloomForCausalLM",Uo]],["gpt2",["GPT2LMHeadModel",Fn]],["jais",["JAISLMHeadModel",Mo]],["gptj",["GPTJForCausalLM",On]],["gpt_bigcode",["GPTBigCodeForCausalLM",Fi]],["gpt_neo",["GPTNeoForCausalLM",xo]],["gpt_neox",["GPTNeoXForCausalLM",Co]],["codegen",["CodeGenForCausalLM",Eo]],["llama",["LlamaForCausalLM",So]],["granite",["GraniteForCausalLM",Ao]],["cohere",["CohereForCausalLM",zn]],["gemma",["GemmaForCausalLM",Oo]],["gemma2",["Gemma2ForCausalLM",Do]],["openelm",["OpenELMForCausalLM",Bo]],["qwen2",["Qwen2ForCausalLM",Vi]],["phi",["PhiForCausalLM",Wi]],["phi3",["Phi3ForCausalLM",Js]],["mpt",["MptForCausalLM",Go]],["opt",["OPTForCausalLM",Ho]],["mbart",["MBartForCausalLM",Tt]],["mistral",["MistralForCausalLM",pu]],["starcoder2",["Starcoder2ForCausalLM",mu]],["falcon",["FalconForCausalLM",gu]],["trocr",["TrOCRForCausalLM",zd]],["stablelm",["StableLmForCausalLM",Ld]]]),Wa=new Map([["bert",["BertForMaskedLM",Mt]],["roformer",["RoFormerForMaskedLM",ut]],["electra",["ElectraForMaskedLM",yt]],["esm",["EsmForMaskedLM",Nr]],["convbert",["ConvBertForMaskedLM",$]],["camembert",["CamembertForMaskedLM",Yr]],["deberta",["DebertaForMaskedLM",ge]],["deberta-v2",["DebertaV2ForMaskedLM",mt]],["mpnet",["MPNetForMaskedLM",ks]],["albert",["AlbertForMaskedLM",Yt]],["distilbert",["DistilBertForMaskedLM",Ut]],["roberta",["RobertaForMaskedLM",_n]],["xlm",["XLMWithLMHeadModel",Nn]],["xlm-roberta",["XLMRobertaForMaskedLM",Et]],["mobilebert",["MobileBertForMaskedLM",Ur]],["squeezebert",["SqueezeBertForMaskedLM",An]]]),Fu=new Map([["bert",["BertForQuestionAnswering",Ae]],["roformer",["RoFormerForQuestionAnswering",xt]],["electra",["ElectraForQuestionAnswering",Pt]],["convbert",["ConvBertForQuestionAnswering",nt]],["camembert",["CamembertForQuestionAnswering",bn]],["deberta",["DebertaForQuestionAnswering",Ne]],["deberta-v2",["DebertaV2ForQuestionAnswering",Lt]],["mpnet",["MPNetForQuestionAnswering",As]],["albert",["AlbertForQuestionAnswering",ws]],["distilbert",["DistilBertForQuestionAnswering",ct]],["roberta",["RobertaForQuestionAnswering",tn]],["xlm",["XLMForQuestionAnswering",Xr]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Kn]],["mobilebert",["MobileBertForQuestionAnswering",$s]],["squeezebert",["SqueezeBertForQuestionAnswering",ms]]]),vs=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ti]]]),Wd=new Map([["llava",["LlavaForConditionalGeneration",bs]],["moondream1",["Moondream1ForConditionalGeneration",pr]],["florence2",["Florence2ForConditionalGeneration",Ci]]]),Gd=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ti]]]),Ou=new Map([["vit",["ViTForImageClassification",Td]],["pvt",["PvtForImageClassification",Qo]],["vit_msn",["ViTMSNForImageClassification",Jo]],["fastvit",["FastViTForImageClassification",nl]],["mobilevit",["MobileViTForImageClassification",ol]],["mobilevitv2",["MobileViTV2ForImageClassification",Ls]],["beit",["BeitForImageClassification",hl]],["deit",["DeiTForImageClassification",Ml]],["hiera",["HieraForImageClassification",ua]],["convnext",["ConvNextForImageClassification",hn]],["convnextv2",["ConvNextV2ForImageClassification",fa]],["dinov2",["Dinov2ForImageClassification",jl]],["resnet",["ResNetForImageClassification",Tl]],["swin",["SwinForImageClassification",El]],["segformer",["SegformerForImageClassification",Ia]],["efficientnet",["EfficientNetForImageClassification",Mu]],["mobilenet_v1",["MobileNetV1ForImageClassification",vu]],["mobilenet_v2",["MobileNetV2ForImageClassification",Tu]],["mobilenet_v3",["MobileNetV3ForImageClassification",Eu]],["mobilenet_v4",["MobileNetV4ForImageClassification",Nd]]]),zu=new Map([["detr",["DetrForObjectDetection",ml]],["rt_detr",["RTDetrForObjectDetection",wl]],["table-transformer",["TableTransformerForObjectDetection",aa]],["yolos",["YolosForObjectDetection",Vl]]]),Du=new Map([["owlvit",["OwlViTForObjectDetection",ul]],["owlv2",["Owlv2ForObjectDetection",cl]]]),Lu=new Map([["detr",["DetrForSegmentation",ea]],["clipseg",["CLIPSegForImageSegmentation",wo]]]),Ga=new Map([["segformer",["SegformerForSemanticSegmentation",yu]],["sapiens",["SapiensForSemanticSegmentation",Ol]]]),Bu=new Map([["detr",["DetrForSegmentation",ea]],["maskformer",["MaskFormerForInstanceSegmentation",Rl]]]),Ru=new Map([["sam",["SamModel",ga]]]),Nu=new Map([["wav2vec2",["Wav2Vec2ForCTC",Gl]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Ca]],["unispeech",["UniSpeechForCTC",Zl]],["unispeech-sat",["UniSpeechSatForCTC",di]],["wavlm",["WavLMForCTC",su]],["hubert",["HubertForCTC",Id]]]),ju=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",ql]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Pd]],["unispeech",["UniSpeechForSequenceClassification",Jl]],["unispeech-sat",["UniSpeechSatForSequenceClassification",xa]],["wavlm",["WavLMForSequenceClassification",iu]],["hubert",["HubertForSequenceClassification",nu]],["audio-spectrogram-transformer",["ASTForAudioClassification",Ys]]]),Vu=new Map([["wavlm",["WavLMForXVector",au]]]),qa=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Ta]],["wavlm",["WavLMForAudioFrameClassification",$a]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Hl]],["pyannote",["PyAnnoteForAudioFrameClassification",Ql]]]),Uu=new Map([["vitmatte",["VitMatteForImageMatting",il]]]),qd=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Sl]]]),Ha=new Map([["dpt",["DPTForDepthEstimation",Al]],["depth_anything",["DepthAnythingForDepthEstimation",Fl]],["glpn",["GLPNForDepthEstimation",Xn]],["sapiens",["SapiensForDepthEstimation",zl]],["depth_pro",["DepthProForDepthEstimation",Ll]]]),Wu=new Map([["sapiens",["SapiensForNormalEstimation",$d]]]),Gu=new Map([["clip",["CLIPVisionModelWithProjection",co]],["siglip",["SiglipVisionModel",fo]]]),qu=[[jd,X.EncoderOnly],[Mc,X.EncoderDecoder],[Vd,X.DecoderOnly],[Ud,X.EncoderOnly],[gn,X.EncoderOnly],[Va,X.Seq2Seq],[ja,X.Seq2Seq],[Ua,X.DecoderOnly],[Wa,X.EncoderOnly],[Fu,X.EncoderOnly],[vs,X.Vision2Seq],[Wd,X.ImageTextToText],[Ou,X.EncoderOnly],[Lu,X.EncoderOnly],[Bu,X.EncoderOnly],[Ga,X.EncoderOnly],[Uu,X.EncoderOnly],[qd,X.EncoderOnly],[Ha,X.EncoderOnly],[Wu,X.EncoderOnly],[zu,X.EncoderOnly],[Du,X.EncoderOnly],[Ru,X.MaskGeneration],[Nu,X.EncoderOnly],[ju,X.EncoderOnly],[Au,X.Seq2Seq],[Iu,X.EncoderOnly],[Vu,X.EncoderOnly],[qa,X.EncoderOnly],[Gu,X.EncoderOnly]];for(const[f,b]of qu)for(const[R,Me]of f.values())K.set(R,b),k.set(Me,R),j.set(R,Me);const vc=[["MusicgenForConditionalGeneration",La,X.Musicgen],["CLIPTextModelWithProjection",xn,X.EncoderOnly],["SiglipTextModel",ho,X.EncoderOnly],["ClapTextModelWithProjection",Ns,X.EncoderOnly],["ClapAudioModelWithProjection",Pa,X.EncoderOnly]];for(const[f,b,R]of vc)K.set(f,R),k.set(b,f),j.set(f,b);class Ka extends Or{}ve(Ka,"MODEL_CLASS_MAPPINGS",qu.map(b=>b[0])),ve(Ka,"BASE_IF_FAIL",!0);class Hu extends Or{}ve(Hu,"MODEL_CLASS_MAPPINGS",[Ud]);class Ku extends Or{}ve(Ku,"MODEL_CLASS_MAPPINGS",[gn]);class Hd extends Or{}ve(Hd,"MODEL_CLASS_MAPPINGS",[Va]);class Xu extends Or{}ve(Xu,"MODEL_CLASS_MAPPINGS",[ja]);class Qu extends Or{}ve(Qu,"MODEL_CLASS_MAPPINGS",[Au]);class Yu extends Or{}ve(Yu,"MODEL_CLASS_MAPPINGS",[Iu]);class Zu extends Or{}ve(Zu,"MODEL_CLASS_MAPPINGS",[Ua]);class Ju extends Or{}ve(Ju,"MODEL_CLASS_MAPPINGS",[Wa]);class Kd extends Or{}ve(Kd,"MODEL_CLASS_MAPPINGS",[Fu]);class ed extends Or{}ve(ed,"MODEL_CLASS_MAPPINGS",[vs]);class td extends Or{}ve(td,"MODEL_CLASS_MAPPINGS",[Ou]);class rd extends Or{}ve(rd,"MODEL_CLASS_MAPPINGS",[Lu]);class nd extends Or{}ve(nd,"MODEL_CLASS_MAPPINGS",[Ga]);class sd extends Or{}ve(sd,"MODEL_CLASS_MAPPINGS",[Bu]);class id extends Or{}ve(id,"MODEL_CLASS_MAPPINGS",[zu]);class ad extends Or{}ve(ad,"MODEL_CLASS_MAPPINGS",[Du]);class od extends Or{}ve(od,"MODEL_CLASS_MAPPINGS",[Ru]);class ld extends Or{}ve(ld,"MODEL_CLASS_MAPPINGS",[Nu]);class Xd extends Or{}ve(Xd,"MODEL_CLASS_MAPPINGS",[ju]);class js extends Or{}ve(js,"MODEL_CLASS_MAPPINGS",[Vu]);class Xa extends Or{}ve(Xa,"MODEL_CLASS_MAPPINGS",[qa]);class Qa extends Or{}ve(Qa,"MODEL_CLASS_MAPPINGS",[Gd]);class Ya extends Or{}ve(Ya,"MODEL_CLASS_MAPPINGS",[Uu]);class Za extends Or{}ve(Za,"MODEL_CLASS_MAPPINGS",[qd]);class ud extends Or{}ve(ud,"MODEL_CLASS_MAPPINGS",[Ha]);class dd extends Or{}ve(dd,"MODEL_CLASS_MAPPINGS",[Wu]);class Ja extends Or{}ve(Ja,"MODEL_CLASS_MAPPINGS",[Gu]);class cd extends Ze{constructor({logits:b,past_key_values:R,encoder_outputs:Me,decoder_attentions:Ue=null,cross_attentions:Le=null}){super(),this.logits=b,this.past_key_values=R,this.encoder_outputs=Me,this.decoder_attentions=Ue,this.cross_attentions=Le}}class cr extends Ze{constructor({logits:b}){super(),this.logits=b}}class pd extends Ze{constructor({logits:b,embeddings:R}){super(),this.logits=b,this.embeddings=R}}class nn extends Ze{constructor({logits:b}){super(),this.logits=b}}class un extends Ze{constructor({logits:b}){super(),this.logits=b}}class fn extends Ze{constructor({start_logits:b,end_logits:R}){super(),this.start_logits=b,this.end_logits=R}}class es extends Ze{constructor({logits:b}){super(),this.logits=b}}class Qd extends Ze{constructor({logits:b,past_key_values:R}){super(),this.logits=b,this.past_key_values=R}}class hd extends Ze{constructor({alphas:b}){super(),this.alphas=b}}class fd extends Ze{constructor({waveform:b,spectrogram:R}){super(),this.waveform=b,this.spectrogram=R}}},"./src/models/whisper/common_whisper.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{WHISPER_LANGUAGE_MAPPING:()=>ce,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>we,whisper_language_to_code:()=>ye});const F=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],ce=new Map(F),we=new Map([...F.map(([Te,L])=>[L,Te]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function ye(Te){Te=Te.toLowerCase();let L=we.get(Te);if(L===void 0)if(ce.has(Te))L=Te;else{const D=Te.length===2?ce.keys():ce.values();throw new Error(`Language "${Te}" is not supported. Must be one of: ${JSON.stringify(D)}`)}return L}},"./src/models/whisper/generation_whisper.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{WhisperGenerationConfig:()=>ce});var F=V("./src/generation/configuration_utils.js");class ce extends F.GenerationConfig{constructor(){super(...arguments);ve(this,"return_timestamps",null);ve(this,"return_token_timestamps",null);ve(this,"num_frames",null);ve(this,"alignment_heads",null);ve(this,"task",null);ve(this,"language",null);ve(this,"no_timestamps_token_id",null);ve(this,"prompt_ids",null);ve(this,"is_multilingual",null);ve(this,"lang_to_id",null);ve(this,"task_to_id",null);ve(this,"max_initial_timestamp_index",1)}}},"./src/ops/registry.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{TensorOpRegistry:()=>ye});var F=V("./src/backends/onnx.js"),ce=V("./src/utils/tensor.js");const we=async(Te,L,P)=>{const D=await(0,F.createInferenceSession)(new Uint8Array(Te),L);return async B=>{const q=Object.fromEntries(Object.entries(B).map(([fe,le])=>[fe,le.ort_tensor])),re=await D.run(q);return Array.isArray(P)?P.map(fe=>new ce.Tensor(re[fe])):new ce.Tensor(re[P])}};class ye{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=we([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=we([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=we([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=we([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=we([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=we([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}}ve(ye,"session_options",{})},"./src/pipelines.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{AudioClassificationPipeline:()=>De,AutomaticSpeechRecognitionPipeline:()=>it,DepthEstimationPipeline:()=>Ze,DocumentQuestionAnsweringPipeline:()=>We,FeatureExtractionPipeline:()=>be,FillMaskPipeline:()=>X,ImageClassificationPipeline:()=>lt,ImageFeatureExtractionPipeline:()=>Ce,ImageSegmentationPipeline:()=>me,ImageToImagePipeline:()=>se,ImageToTextPipeline:()=>rt,ObjectDetectionPipeline:()=>de,Pipeline:()=>le,QuestionAnsweringPipeline:()=>pe,SummarizationPipeline:()=>j,Text2TextGenerationPipeline:()=>K,TextClassificationPipeline:()=>O,TextGenerationPipeline:()=>E,TextToAudioPipeline:()=>ot,TokenClassificationPipeline:()=>J,TranslationPipeline:()=>k,ZeroShotAudioClassificationPipeline:()=>ze,ZeroShotClassificationPipeline:()=>ue,ZeroShotImageClassificationPipeline:()=>W,ZeroShotObjectDetectionPipeline:()=>xe,pipeline:()=>ht});var F=V("./src/tokenizers.js"),ce=V("./src/models.js"),we=V("./src/processors.js"),ye=V("./src/utils/generic.js"),Te=V("./src/utils/core.js"),L=V("./src/utils/maths.js"),P=V("./src/utils/audio.js"),D=V("./src/utils/tensor.js"),B=V("./src/utils/image.js");async function q(Xe){return Array.isArray(Xe)||(Xe=[Xe]),await Promise.all(Xe.map(Z=>B.RawImage.read(Z)))}async function re(Xe,Z){return Array.isArray(Xe)||(Xe=[Xe]),await Promise.all(Xe.map(Ae=>typeof Ae=="string"||Ae instanceof URL?(0,P.read_audio)(Ae,Z):Ae instanceof Float64Array?new Float32Array(Ae):Ae))}function fe(Xe,Z){Z&&(Xe=Xe.map(Ve=>Ve|0));const[Ae,Ke,et,je]=Xe;return{xmin:Ae,ymin:Ke,xmax:et,ymax:je}}class le extends ye.Callable{constructor({task:Z,model:Ae,tokenizer:Ke=null,processor:et=null}){super(),this.task=Z,this.model=Ae,this.tokenizer=Ke,this.processor=et}async dispose(){await this.model.dispose()}}class O extends le{constructor(Z){super(Z)}async _call(Z,{top_k:Ae=1}={}){const Ke=this.tokenizer(Z,{padding:!0,truncation:!0}),et=await this.model(Ke),je=this.model.config.problem_type==="multi_label_classification"?_t=>_t.sigmoid():_t=>new D.Tensor("float32",(0,L.softmax)(_t.data),_t.dims),Ve=this.model.config.id2label,ut=[];for(const _t of et.logits){const St=je(_t),xt=await(0,D.topk)(St,Ae),v=xt[0].tolist(),$=xt[1].tolist().map((Y,he)=>({label:Ve?Ve[Y]:`LABEL_${Y}`,score:v[he]}));Ae===1?ut.push(...$):ut.push($)}return Array.isArray(Z)||Ae===1?ut:ut[0]}}class J extends le{constructor(Z){super(Z)}async _call(Z,{ignore_labels:Ae=["O"]}={}){const Ke=Array.isArray(Z),et=this.tokenizer(Ke?Z:[Z],{padding:!0,truncation:!0}),Ve=(await this.model(et)).logits,ut=this.model.config.id2label,_t=[];for(let St=0;St<Ve.dims[0];++St){const xt=et.input_ids[St],v=Ve[St],H=[];for(let $=0;$<v.dims[0];++$){const Y=v[$],he=(0,L.max)(Y.data)[1],nt=ut?ut[he]:`LABEL_${he}`;if(Ae.includes(nt))continue;const Je=this.tokenizer.decode([xt[$].item()],{skip_special_tokens:!0});if(Je==="")continue;const Nt=(0,L.softmax)(Y.data);H.push({entity:nt,score:Nt[he],index:$,word:Je})}_t.push(H)}return Ke?_t:_t[0]}}class pe extends le{constructor(Z){super(Z)}async _call(Z,Ae,{top_k:Ke=1}={}){const et=this.tokenizer(Z,{text_pair:Ae,padding:!0,truncation:!0}),{start_logits:je,end_logits:Ve}=await this.model(et),ut=et.input_ids.tolist(),_t=et.attention_mask.tolist(),St=this.tokenizer.all_special_ids,xt=[];for(let v=0;v<je.dims[0];++v){const H=ut[v],$=H.findIndex(yt=>yt==this.tokenizer.sep_token_id);_t[v].map((yt,bt)=>yt==1&&(bt===0||bt>$&&St.findIndex(zt=>zt==H[bt])===-1));const Y=je[v].tolist(),he=Ve[v].tolist();for(let yt=1;yt<Y.length;++yt)(_t[v]==0||yt<=$||St.findIndex(bt=>bt==H[yt])!==-1)&&(Y[yt]=-1/0,he[yt]=-1/0);const nt=(0,L.softmax)(Y).map((yt,bt)=>[yt,bt]),Je=(0,L.softmax)(he).map((yt,bt)=>[yt,bt]);nt[0][0]=0,Je[0][0]=0;const Nt=(0,Te.product)(nt,Je).filter(yt=>yt[0][1]<=yt[1][1]).map(yt=>[yt[0][1],yt[1][1],yt[0][0]*yt[1][0]]).sort((yt,bt)=>bt[2]-yt[2]);for(let yt=0;yt<Math.min(Nt.length,Ke);++yt){const[bt,zt,Pt]=Nt[yt],dr=H.slice(bt,zt+1),Cr=this.tokenizer.decode(dr,{skip_special_tokens:!0});xt.push({answer:Cr,score:Pt})}}return Ke===1?xt[0]:xt}}class X extends le{constructor(Z){super(Z)}async _call(Z,{top_k:Ae=5}={}){const Ke=this.tokenizer(Z,{padding:!0,truncation:!0}),{logits:et}=await this.model(Ke),je=[],Ve=Ke.input_ids.tolist();for(let ut=0;ut<Ve.length;++ut){const _t=Ve[ut],St=_t.findIndex(Y=>Y==this.tokenizer.mask_token_id);if(St===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const xt=et[ut][St],v=await(0,D.topk)(new D.Tensor("float32",(0,L.softmax)(xt.data),xt.dims),Ae),H=v[0].tolist(),$=v[1].tolist();je.push($.map((Y,he)=>{const nt=_t.slice();return nt[St]=Y,{score:H[he],token:Number(Y),token_str:this.tokenizer.model.vocab[Y],sequence:this.tokenizer.decode(nt,{skip_special_tokens:!0})}}))}return Array.isArray(Z)?je:je[0]}}class K extends le{constructor(Ae){super(Ae);ve(this,"_key","generated_text")}async _call(Ae,Ke={}){Array.isArray(Ae)||(Ae=[Ae]),this.model.config.prefix&&(Ae=Ae.map(St=>this.model.config.prefix+St));const et=this.model.config.task_specific_params;et&&et[this.task]&&et[this.task].prefix&&(Ae=Ae.map(St=>et[this.task].prefix+St));const je=this.tokenizer,Ve={padding:!0,truncation:!0};let ut;this instanceof k&&"_build_translation_inputs"in je?ut=je._build_translation_inputs(Ae,Ve,Ke):ut=je(Ae,Ve);const _t=await this.model.generate({...ut,...Ke});return je.batch_decode(_t,{skip_special_tokens:!0}).map(St=>({[this._key]:St}))}}class j extends K{constructor(Ae){super(Ae);ve(this,"_key","summary_text")}}class k extends K{constructor(Ae){super(Ae);ve(this,"_key","translation_text")}}function N(Xe){return Array.isArray(Xe)&&Xe.every(Z=>"role"in Z&&"content"in Z)}class E extends le{constructor(Z){super(Z)}async _call(Z,Ae={}){let Ke=!1,et=!1,je;if(typeof Z=="string")je=Z=[Z];else if(Array.isArray(Z)&&Z.every($=>typeof $=="string"))Ke=!0,je=Z;else{if(N(Z))Z=[Z];else if(Array.isArray(Z)&&Z.every(N))Ke=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");et=!0,je=Z.map($=>this.tokenizer.apply_chat_template($,{tokenize:!1,add_generation_prompt:!0}))}const Ve=Ae.add_special_tokens??!1,ut=et?!1:Ae.return_full_text??!0;this.tokenizer.padding_side="left";const _t=this.tokenizer(je,{add_special_tokens:Ve,padding:!0,truncation:!0}),St=await this.model.generate({..._t,...Ae}),xt=this.tokenizer.batch_decode(St,{skip_special_tokens:!0});let v;!ut&&_t.input_ids.dims.at(-1)>0&&(v=this.tokenizer.batch_decode(_t.input_ids,{skip_special_tokens:!0}).map($=>$.length));const H=Array.from({length:Z.length},$=>[]);for(let $=0;$<xt.length;++$){const Y=Math.floor($/St.dims[0]*Z.length);v&&(xt[$]=xt[$].slice(v[Y])),H[Y].push({generated_text:et?[...Z[Y],{role:"assistant",content:xt[$]}]:xt[$]})}return!Ke&&H.length===1?H[0]:H}}class ue extends le{constructor(Z){super(Z),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([Ae,Ke])=>[Ae.toLowerCase(),Ke])),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(Z,Ae,{hypothesis_template:Ke="This example is {}.",multi_label:et=!1}={}){const je=Array.isArray(Z);je||(Z=[Z]),Array.isArray(Ae)||(Ae=[Ae]);const Ve=Ae.map(St=>Ke.replace("{}",St)),ut=et||Ae.length===1,_t=[];for(const St of Z){const xt=[];for(const $ of Ve){const Y=this.tokenizer(St,{text_pair:$,padding:!0,truncation:!0}),he=await this.model(Y);ut?xt.push([he.logits.data[this.contradiction_id],he.logits.data[this.entailment_id]]):xt.push(he.logits.data[this.entailment_id])}const H=(ut?xt.map($=>(0,L.softmax)($)[1]):(0,L.softmax)(xt)).map(($,Y)=>[$,Y]).sort(($,Y)=>Y[0]-$[0]);_t.push({sequence:St,labels:H.map($=>Ae[$[1]]),scores:H.map($=>$[0])})}return je?_t:_t[0]}}class be extends le{constructor(Z){super(Z)}async _call(Z,{pooling:Ae="none",normalize:Ke=!1,quantize:et=!1,precision:je="binary"}={}){const Ve=this.tokenizer(Z,{padding:!0,truncation:!0}),ut=await this.model(Ve);let _t=ut.last_hidden_state??ut.logits??ut.token_embeddings;if(Ae!=="none")if(Ae==="mean")_t=(0,D.mean_pooling)(_t,Ve.attention_mask);else if(Ae==="cls")_t=_t.slice(null,0);else throw Error(`Pooling method '${Ae}' not supported.`);return Ke&&(_t=_t.normalize(2,-1)),et&&(_t=(0,D.quantize_embeddings)(_t,je)),_t}}class Ce extends le{constructor(Z){super(Z)}async _call(Z,{pool:Ae=null}={}){const Ke=await q(Z),{pixel_values:et}=await this.processor(Ke),je=await this.model({pixel_values:et});let Ve;if(Ae){if(!("pooler_output"in je))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Ve=je.pooler_output}else Ve=je.last_hidden_state??je.logits??je.image_embeds;return Ve}}class De extends le{constructor(Z){super(Z)}async _call(Z,{top_k:Ae=5}={}){const Ke=this.processor.feature_extractor.config.sampling_rate,et=await re(Z,Ke),je=this.model.config.id2label,Ve=[];for(const ut of et){const _t=await this.processor(ut),xt=(await this.model(_t)).logits[0],v=await(0,D.topk)(new D.Tensor("float32",(0,L.softmax)(xt.data),xt.dims),Ae),H=v[0].tolist(),Y=v[1].tolist().map((he,nt)=>({label:je?je[he]:`LABEL_${he}`,score:H[nt]}));Ve.push(Y)}return Array.isArray(Z)?Ve:Ve[0]}}class ze extends le{constructor(Z){super(Z)}async _call(Z,Ae,{hypothesis_template:Ke="This is a sound of {}."}={}){const et=!Array.isArray(Z);et&&(Z=[Z]);const je=Ae.map(xt=>Ke.replace("{}",xt)),Ve=this.tokenizer(je,{padding:!0,truncation:!0}),ut=this.processor.feature_extractor.config.sampling_rate,_t=await re(Z,ut),St=[];for(const xt of _t){const v=await this.processor(xt),H=await this.model({...Ve,...v}),$=(0,L.softmax)(H.logits_per_audio.data);St.push([...$].map((Y,he)=>({score:Y,label:Ae[he]})))}return et?St[0]:St}}class it extends le{constructor(Z){super(Z)}async _call(Z,Ae={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(Z,Ae);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(Z,Ae);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(Z,Ae){Ae.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),Ae.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Ke=!Array.isArray(Z);Ke&&(Z=[Z]);const et=this.processor.feature_extractor.config.sampling_rate,je=await re(Z,et),Ve=[];for(const ut of je){const _t=await this.processor(ut),xt=(await this.model(_t)).logits[0],v=[];for(const $ of xt)v.push((0,L.max)($.data)[1]);const H=this.tokenizer.decode(v);Ve.push({text:H})}return Ke?Ve[0]:Ve}async _call_whisper(Z,Ae){const Ke=Ae.return_timestamps??!1,et=Ae.chunk_length_s??0,je=Ae.force_full_sequences??!1;let Ve=Ae.stride_length_s??null;const ut={...Ae};Ke==="word"&&(ut.return_token_timestamps=!0,ut.return_timestamps=!1);const _t=!Array.isArray(Z);_t&&(Z=[Z]);const St=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,xt=this.processor.feature_extractor.config.hop_length,v=this.processor.feature_extractor.config.sampling_rate,H=await re(Z,v),$=[];for(const Y of H){let he=[];if(et>0){if(Ve===null)Ve=et/6;else if(et<=Ve)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const Nt=v*et,yt=v*Ve,bt=Nt-2*yt;let zt=0;for(;;){const Pt=zt+Nt,dr=Y.subarray(zt,Pt),Cr=await this.processor(dr),Yr=zt===0,Rr=Pt>=Y.length;if(he.push({stride:[dr.length,Yr?0:yt,Rr?0:yt],input_features:Cr.input_features,is_last:Rr}),Rr)break;zt+=bt}}else he=[{stride:[Y.length,0,0],input_features:(await this.processor(Y)).input_features,is_last:!0}];for(const Nt of he){ut.num_frames=Math.floor(Nt.stride[0]/xt);const yt=await this.model.generate({inputs:Nt.input_features,...ut});Ke==="word"?(Nt.tokens=yt.sequences.tolist()[0],Nt.token_timestamps=yt.token_timestamps.tolist()[0].map(bt=>(0,L.round)(bt,2))):Nt.tokens=yt[0].tolist(),Nt.stride=Nt.stride.map(bt=>bt/v)}const[nt,Je]=this.tokenizer._decode_asr(he,{time_precision:St,return_timestamps:Ke,force_full_sequences:je});$.push({text:nt,...Je})}return _t?$[0]:$}}class rt extends le{constructor(Z){super(Z)}async _call(Z,Ae={}){const Ke=Array.isArray(Z),et=await q(Z),{pixel_values:je}=await this.processor(et),Ve=[];for(const ut of je){ut.dims=[1,...ut.dims];const _t=await this.model.generate({inputs:ut,...Ae}),St=this.tokenizer.batch_decode(_t,{skip_special_tokens:!0}).map(xt=>({generated_text:xt.trim()}));Ve.push(St)}return Ke?Ve:Ve[0]}}class lt extends le{constructor(Z){super(Z)}async _call(Z,{top_k:Ae=5}={}){const Ke=await q(Z),{pixel_values:et}=await this.processor(Ke),je=await this.model({pixel_values:et}),Ve=this.model.config.id2label,ut=[];for(const _t of je.logits){const St=await(0,D.topk)(new D.Tensor("float32",(0,L.softmax)(_t.data),_t.dims),Ae),xt=St[0].tolist(),H=St[1].tolist().map(($,Y)=>({label:Ve?Ve[$]:`LABEL_${$}`,score:xt[Y]}));ut.push(H)}return Array.isArray(Z)?ut:ut[0]}}class me extends le{constructor(Z){super(Z),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(Z,{threshold:Ae=.5,mask_threshold:Ke=.5,overlap_mask_area_threshold:et=.8,label_ids_to_fuse:je=null,target_sizes:Ve=null,subtask:ut=null}={}){if(Array.isArray(Z)&&Z.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const St=await q(Z),xt=St.map(Je=>[Je.height,Je.width]),{pixel_values:v,pixel_mask:H}=await this.processor(St),$=await this.model({pixel_values:v,pixel_mask:H});let Y=null;if(ut!==null)Y=this.subtasks_mapping[ut];else for(let[Je,Nt]of Object.entries(this.subtasks_mapping))if(Nt in this.processor.feature_extractor){Y=this.processor.feature_extractor[Nt].bind(this.processor.feature_extractor),ut=Je;break}const he=this.model.config.id2label,nt=[];if(ut==="panoptic"||ut==="instance"){const Je=Y($,Ae,Ke,et,je,Ve??xt)[0],Nt=Je.segmentation;for(const yt of Je.segments_info){const bt=new Uint8ClampedArray(Nt.data.length);for(let Pt=0;Pt<Nt.data.length;++Pt)Nt.data[Pt]===yt.id&&(bt[Pt]=255);const zt=new B.RawImage(bt,Nt.dims[1],Nt.dims[0],1);nt.push({score:yt.score,label:he[yt.label_id],mask:zt})}}else if(ut==="semantic"){const{segmentation:Je,labels:Nt}=Y($,Ve??xt)[0];for(const yt of Nt){const bt=new Uint8ClampedArray(Je.data.length);for(let Pt=0;Pt<Je.data.length;++Pt)Je.data[Pt]===yt&&(bt[Pt]=255);const zt=new B.RawImage(bt,Je.dims[1],Je.dims[0],1);nt.push({score:null,label:he[yt],mask:zt})}}else throw Error(`Subtask ${ut} not supported.`);return nt}}class W extends le{constructor(Z){super(Z)}async _call(Z,Ae,{hypothesis_template:Ke="This is a photo of {}"}={}){const et=Array.isArray(Z),je=await q(Z),Ve=Ae.map(H=>Ke.replace("{}",H)),ut=this.tokenizer(Ve,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:_t}=await this.processor(je),St=await this.model({...ut,pixel_values:_t}),xt=this.model.config.model_type==="siglip"?H=>H.sigmoid().data:H=>(0,L.softmax)(H.data),v=[];for(const H of St.logits_per_image){const Y=[...xt(H)].map((he,nt)=>({score:he,label:Ae[nt]}));Y.sort((he,nt)=>nt.score-he.score),v.push(Y)}return et?v:v[0]}}class de extends le{constructor(Z){super(Z)}async _call(Z,{threshold:Ae=.9,percentage:Ke=!1}={}){const et=Array.isArray(Z);if(et&&Z.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const je=await q(Z),Ve=Ke?null:je.map($=>[$.height,$.width]),{pixel_values:ut,pixel_mask:_t}=await this.processor(je),St=await this.model({pixel_values:ut,pixel_mask:_t}),xt=this.processor.feature_extractor.post_process_object_detection(St,Ae,Ve),v=this.model.config.id2label,H=xt.map($=>$.boxes.map((Y,he)=>({score:$.scores[he],label:v[$.classes[he]],box:fe(Y,!Ke)})));return et?H:H[0]}}class xe extends le{constructor(Z){super(Z)}async _call(Z,Ae,{threshold:Ke=.1,top_k:et=null,percentage:je=!1}={}){const Ve=Array.isArray(Z),ut=await q(Z),_t=this.tokenizer(Ae,{padding:!0,truncation:!0}),St=await this.processor(ut),xt=[];for(let v=0;v<ut.length;++v){const H=ut[v],$=je?null:[[H.height,H.width]],Y=St.pixel_values[v].unsqueeze_(0),he=await this.model({..._t,pixel_values:Y}),nt=this.processor.feature_extractor.post_process_object_detection(he,Ke,$,!0)[0];let Je=nt.boxes.map((Nt,yt)=>({score:nt.scores[yt],label:Ae[nt.classes[yt]],box:fe(Nt,!je)})).sort((Nt,yt)=>yt.score-Nt.score);et!==null&&(Je=Je.slice(0,et)),xt.push(Je)}return Ve?xt:xt[0]}}class We extends le{constructor(Z){super(Z)}async _call(Z,Ae,Ke={}){const et=(await q(Z))[0],{pixel_values:je}=await this.processor(et),Ve=`<s_docvqa><s_question>${Ae}</s_question><s_answer>`,ut=this.tokenizer(Ve,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,_t=await this.model.generate({inputs:je,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:ut,...Ke}),xt=this.tokenizer.batch_decode(_t)[0].match(/<s_answer>(.*?)<\/s_answer>/);let v=null;return xt&&xt.length>=2&&(v=xt[1].trim()),[{answer:v}]}}class ot extends le{constructor(Ae){super(Ae);ve(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=Ae.vocoder??null}async _call(Ae,{speaker_embeddings:Ke=null}={}){return this.processor?this._call_text_to_spectrogram(Ae,{speaker_embeddings:Ke}):this._call_text_to_waveform(Ae)}async _call_text_to_waveform(Ae){const Ke=this.tokenizer(Ae,{padding:!0,truncation:!0}),{waveform:et}=await this.model(Ke),je=this.model.config.sampling_rate;return{audio:et.data,sampling_rate:je}}async _call_text_to_spectrogram(Ae,{speaker_embeddings:Ke}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await ce.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Ke=="string"||Ke instanceof URL)&&(Ke=new Float32Array(await(await fetch(Ke)).arrayBuffer())),Ke instanceof Float32Array)Ke=new D.Tensor("float32",Ke,[1,Ke.length]);else if(!(Ke instanceof D.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:et}=this.tokenizer(Ae,{padding:!0,truncation:!0}),{waveform:je}=await this.model.generate_speech(et,Ke,{vocoder:this.vocoder}),Ve=this.processor.feature_extractor.config.sampling_rate;return{audio:je.data,sampling_rate:Ve}}}class se extends le{constructor(Z){super(Z)}async _call(Z){const Ae=await q(Z),Ke=await this.processor(Ae),et=await this.model(Ke),je=[];for(const Ve of et.reconstruction){const ut=Ve.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");je.push(B.RawImage.fromTensor(ut))}return je.length>1?je:je[0]}}class Ze extends le{constructor(Z){super(Z)}async _call(Z){const Ae=await q(Z),Ke=await this.processor(Ae),{predicted_depth:et}=await this.model(Ke),je=[];for(let Ve=0;Ve<Ae.length;++Ve){const ut=(0,D.interpolate)(et[Ve],Ae[Ve].size.reverse(),"bilinear",!1),_t=ut.mul_(255/(0,L.max)(ut.data)[0]).to("uint8");je.push({predicted_depth:et[Ve],depth:B.RawImage.fromTensor(_t)})}return je.length>1?je:je[0]}}const dt=Object.freeze({"text-classification":{tokenizer:F.AutoTokenizer,pipeline:O,model:ce.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:F.AutoTokenizer,pipeline:J,model:ce.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:F.AutoTokenizer,pipeline:pe,model:ce.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:F.AutoTokenizer,pipeline:X,model:ce.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:F.AutoTokenizer,pipeline:j,model:ce.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:F.AutoTokenizer,pipeline:k,model:ce.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:F.AutoTokenizer,pipeline:K,model:ce.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:F.AutoTokenizer,pipeline:E,model:ce.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:F.AutoTokenizer,pipeline:ue,model:ce.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:De,model:ce.AutoModelForAudioClassification,processor:we.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:F.AutoTokenizer,pipeline:ze,model:ce.AutoModel,processor:we.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:F.AutoTokenizer,pipeline:it,model:[ce.AutoModelForSpeechSeq2Seq,ce.AutoModelForCTC],processor:we.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:F.AutoTokenizer,pipeline:ot,model:[ce.AutoModelForTextToWaveform,ce.AutoModelForTextToSpectrogram],processor:[we.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:F.AutoTokenizer,pipeline:rt,model:ce.AutoModelForVision2Seq,processor:we.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:lt,model:ce.AutoModelForImageClassification,processor:we.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:me,model:[ce.AutoModelForImageSegmentation,ce.AutoModelForSemanticSegmentation,ce.AutoModelForUniversalSegmentation],processor:we.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:F.AutoTokenizer,pipeline:W,model:ce.AutoModel,processor:we.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:de,model:ce.AutoModelForObjectDetection,processor:we.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:F.AutoTokenizer,pipeline:xe,model:ce.AutoModelForZeroShotObjectDetection,processor:we.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:F.AutoTokenizer,pipeline:We,model:ce.AutoModelForDocumentQuestionAnswering,processor:we.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:se,model:ce.AutoModelForImageToImage,processor:we.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Ze,model:ce.AutoModelForDepthEstimation,processor:we.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:F.AutoTokenizer,pipeline:be,model:ce.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:we.AutoProcessor,pipeline:Ce,model:[ce.AutoModelForImageFeatureExtraction,ce.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 ht(Xe,Z=null,{progress_callback:Ae=null,config:Ke=null,cache_dir:et=null,local_files_only:je=!1,revision:Ve="main",device:ut=null,dtype:_t=null,model_file_name:St=null,session_options:xt={}}={}){Xe=Re[Xe]??Xe;const v=dt[Xe.split("_",1)[0]];if(!v)throw Error(`Unsupported pipeline: ${Xe}. Must be one of [${Object.keys(dt)}]`);Z||(Z=v.default.model,console.log(`No model specified. Using default model: "${Z}".`));const H={progress_callback:Ae,config:Ke,cache_dir:et,local_files_only:je,revision:Ve,device:ut,dtype:_t,model_file_name:St,session_options:xt},$=new Map([["tokenizer",v.tokenizer],["model",v.model],["processor",v.processor]]),Y=await Mt($,Z,H);Y.task=Xe,(0,Te.dispatchCallback)(Ae,{status:"ready",task:Xe,model:Z});const he=v.pipeline;return new he(Y)}async function Mt(Xe,Z,Ae){const Ke=Object.create(null),et=[];for(const[je,Ve]of Xe.entries()){if(!Ve)continue;let ut;Array.isArray(Ve)?ut=new Promise(async(_t,St)=>{var v,H;let xt;for(const $ of Ve){if($===null){_t(null);return}try{_t(await $.from_pretrained(Z,Ae));return}catch(Y){if((v=Y.message)!=null&&v.includes("Unsupported model type"))xt=Y;else if((H=Y.message)!=null&&H.includes("Could not locate file"))xt=Y;else{St(Y);return}}}St(xt)}):ut=Ve.from_pretrained(Z,Ae),Ke[je]=ut,et.push(ut)}await Promise.all(et);for(const[je,Ve]of Object.entries(Ke))Ke[je]=await Ve;return Ke}},"./src/processors.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{ASTFeatureExtractor:()=>he,AutoProcessor:()=>bn,BeitFeatureExtractor:()=>Ae,BitImageProcessor:()=>be,CLIPFeatureExtractor:()=>De,CLIPImageProcessor:()=>ze,ChineseCLIPFeatureExtractor:()=>it,ClapFeatureExtractor:()=>nt,ConvNextFeatureExtractor:()=>lt,ConvNextImageProcessor:()=>me,DPTFeatureExtractor:()=>E,DPTImageProcessor:()=>ue,DeiTFeatureExtractor:()=>Z,DetrFeatureExtractor:()=>Ve,DonutFeatureExtractor:()=>Ke,DonutImageProcessor:()=>et,EfficientNetImageProcessor:()=>xe,FeatureExtractor:()=>X,Florence2Processor:()=>Jr,GLPNFeatureExtractor:()=>Ce,ImageFeatureExtractor:()=>K,MaskFormerFeatureExtractor:()=>ut,MobileNetV1FeatureExtractor:()=>We,MobileNetV2FeatureExtractor:()=>ot,MobileNetV3FeatureExtractor:()=>se,MobileNetV4FeatureExtractor:()=>Ze,MobileViTFeatureExtractor:()=>dt,MobileViTImageProcessor:()=>Re,NougatImageProcessor:()=>je,OwlViTFeatureExtractor:()=>ht,OwlViTProcessor:()=>Rr,Owlv2ImageProcessor:()=>Mt,Processor:()=>bt,PvtImageProcessor:()=>N,PyAnnoteFeatureExtractor:()=>Je,PyAnnoteProcessor:()=>Cr,RTDetrImageProcessor:()=>Xe,SamImageProcessor:()=>St,SamProcessor:()=>zt,SapiensFeatureExtractor:()=>j,SeamlessM4TFeatureExtractor:()=>Y,SegformerFeatureExtractor:()=>k,SiglipImageProcessor:()=>rt,SpeechT5FeatureExtractor:()=>yt,SpeechT5Processor:()=>Yr,Swin2SRImageProcessor:()=>xt,ViTFeatureExtractor:()=>W,ViTImageProcessor:()=>de,VitMatteImageProcessor:()=>v,Wav2Vec2FeatureExtractor:()=>$,Wav2Vec2ProcessorWithLM:()=>dr,WeSpeakerFeatureExtractor:()=>Nt,WhisperFeatureExtractor:()=>H,WhisperProcessor:()=>Pt,YolosFeatureExtractor:()=>_t});var F=V("./src/utils/generic.js"),ce=V("./src/utils/core.js"),we=V("./src/utils/hub.js"),ye=V("./src/utils/maths.js"),Te=V("./src/utils/tensor.js");V("./src/utils/image.js");var L=V("./src/utils/audio.js");function P([at,G,ge,Ie]){return[at-ge/2,G-Ie/2,at+ge/2,G+Ie/2]}function D(at,G=.5,ge=null,Ie=!1){const Se=at.logits,Ne=at.pred_boxes,[tt,wt,mt]=Se.dims;if(ge!==null&&ge.length!==tt)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let Ct=[];for(let ft=0;ft<tt;++ft){let Lt=ge!==null?ge[ft]:null,jt={boxes:[],classes:[],scores:[]},Ft=Se[ft],Fe=Ne[ft];for(let Oe=0;Oe<wt;++Oe){let ct=Ft[Oe],Ut=[],sr;if(Ie){sr=ct.sigmoid().data;for(let br=0;br<sr.length;++br)sr[br]>G&&Ut.push(br)}else{let br=(0,ye.max)(ct.data)[1];if(br===mt-1||(sr=(0,ye.softmax)(ct.data),sr[br]<G))continue;Ut.push(br)}for(const br of Ut){let Nr=Fe[Oe].data;Nr=P(Nr),Lt!==null&&(Nr=Nr.map((mr,kr)=>mr*Lt[(kr+1)%2])),jt.boxes.push(Nr),jt.classes.push(br),jt.scores.push(sr[br])}}Ct.push(jt)}return Ct}function B(at,G=null){const ge=at.logits,Ie=ge.dims[0];if(G!==null&&G.length!==Ie)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const Se=[];for(let Ne=0;Ne<Ie;++Ne){const tt=G!==null?G[Ne]:null;let wt=ge[Ne];tt!==null&&(wt=(0,Te.interpolate)(wt,tt,"bilinear",!1));const[mt,Ct]=tt??wt.dims.slice(-2),ft=new Te.Tensor("int32",new Int32Array(mt*Ct),[mt,Ct]),Lt=wt[0].data,jt=ft.data;for(let Oe=1;Oe<wt.dims[0];++Oe){const ct=wt[Oe].data;for(let Ut=0;Ut<ct.length;++Ut)ct[Ut]>Lt[Ut]&&(Lt[Ut]=ct[Ut],jt[Ut]=Oe)}const Ft=new Array(wt.dims[0]);for(let Oe=0;Oe<jt.length;++Oe){const ct=jt[Oe];Ft[ct]=ct}const Fe=Ft.filter(Oe=>Oe!==void 0);Se.push({segmentation:ft,labels:Fe})}return Se}function q(at,G,ge,Ie){const Se=[],Ne=[],tt=[];for(let wt=0;wt<at.dims[0];++wt){const mt=at[wt],Ct=G[wt],ft=(0,ye.max)(mt.data)[1];if(ft===Ie)continue;const jt=(0,ye.softmax)(mt.data)[ft];jt>ge&&(Se.push(Ct),Ne.push(jt),tt.push(ft))}return[Se,Ne,tt]}function re(at,G,ge,Ie=.5,Se=.8){const Ne=[];let tt=0,wt=0;const mt=G[ge].data;for(let ft=0;ft<at.length;++ft)at[ft]===ge&&(Ne.push(ft),++tt),mt[ft]>=Ie&&++wt;let Ct=tt>0&&wt>0;return Ct&&(Ct=tt/wt>Se),[Ct,Ne]}function fe(at,G,ge,Ie,Se,Ne=null,tt=null){const[wt,mt]=tt??at[0].dims,Ct=new Te.Tensor("int32",new Int32Array(wt*mt),[wt,mt]),ft=[];if(tt!==null)for(let Oe=0;Oe<at.length;++Oe)at[Oe]=(0,Te.interpolate)(at[Oe],tt,"bilinear",!1);const Lt=new Int32Array(at[0].data.length),jt=new Float32Array(at[0].data.length);for(let Oe=0;Oe<at.length;++Oe){let ct=G[Oe];const Ut=at[Oe].data;for(let sr=0;sr<Ut.length;++sr)Ut[sr]*=ct,Ut[sr]>jt[sr]&&(Lt[sr]=Oe,jt[sr]=Ut[sr])}let Ft=0;const Fe=Ct.data;for(let Oe=0;Oe<ge.length;++Oe){const ct=ge[Oe],[Ut,sr]=re(Lt,at,Oe,Ie,Se);if(Ut){++Ft;for(const br of sr)Fe[br]=Ft;ft.push({id:Ft,label_id:ct,score:G[Oe]})}}return[Ct,ft]}function le(at,G=.5,ge=.5,Ie=.8,Se=null,Ne=null){Se===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),Se=new Set);const tt=at.class_queries_logits??at.logits,mt=(at.masks_queries_logits??at.pred_masks).sigmoid();let[Ct,ft,Lt]=tt.dims;if(Lt-=1,Ne!==null&&Ne.length!==Ct)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let jt=[];for(let Ft=0;Ft<Ct;++Ft){let Fe=Ne!==null?Ne[Ft]:null,Oe=tt[Ft],ct=mt[Ft],[Ut,sr,br]=q(Oe,ct,G,Lt);if(br.length===0){let[kr,gr]=Fe??ct.dims.slice(-2),$n=new Te.Tensor("int32",new Int32Array(kr*gr).fill(-1),[kr,gr]);jt.push({segmentation:$n,segments_info:[]});continue}let[Nr,mr]=fe(Ut,sr,br,ge,Ie,Se,Fe);jt.push({segmentation:Nr,segments_info:mr})}return jt}function O(at,G){var ge;if(!(at instanceof Float32Array||at instanceof Float64Array))throw new Error(`${G} expects input to be a Float32Array or a Float64Array, but got ${((ge=at==null?void 0:at.constructor)==null?void 0:ge.name)??typeof at} 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(at,G,ge=0,Ie=null){const Se=at/G;let Ne=(0,ye.bankers_round)(Se)*G;return Ie!==null&&Ne>Ie&&(Ne=Math.floor(Se)*G),Ne<ge&&(Ne=Math.ceil(Se)*G),Ne}function pe([at,G],ge){return[Math.max(Math.floor(at/ge),1)*ge,Math.max(Math.floor(G/ge),1)*ge]}class X extends F.Callable{constructor(G){super(),this.config=G}}class K extends X{constructor(G){super(G),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(G,ge,Ie=2){const Se=G.height,Ne=G.width,tt=ge.height,wt=ge.width;let mt=Math.min(Se,tt),Ct=Math.min(Ne,wt);return mt===Se&&Ct===Ne?G:(Se>Ne?Ct=Math.floor(Ne*mt/Se):Ne>Se&&(mt=Math.floor(Se*Ct/Ne)),await G.resize(Ct,mt,{resample:Ie}))}async crop_margin(G,ge=200){const Ie=G.clone().grayscale(),Se=(0,ye.min)(Ie.data)[0],tt=(0,ye.max)(Ie.data)[0]-Se;if(tt===0)return G;const wt=ge/255;let mt=Ie.width,Ct=Ie.height,ft=0,Lt=0;const jt=Ie.data;for(let Ft=0;Ft<Ie.height;++Ft){const Fe=Ft*Ie.width;for(let Oe=0;Oe<Ie.width;++Oe)(jt[Fe+Oe]-Se)/tt<wt&&(mt=Math.min(mt,Oe),Ct=Math.min(Ct,Ft),ft=Math.max(ft,Oe),Lt=Math.max(Lt,Ft))}return G=await G.crop([mt,Ct,ft,Lt]),G}pad_image(G,ge,Ie,{mode:Se="constant",center:Ne=!1,constant_values:tt=0}={}){const[wt,mt,Ct]=ge;let ft,Lt;if(typeof Ie=="number"?(ft=Ie,Lt=Ie):(ft=Ie.width,Lt=Ie.height),ft!==mt||Lt!==wt){const jt=new Float32Array(ft*Lt*Ct);if(Array.isArray(tt))for(let Oe=0;Oe<jt.length;++Oe)jt[Oe]=tt[Oe%Ct];else tt!==0&&jt.fill(tt);const[Ft,Fe]=Ne?[Math.floor((ft-mt)/2),Math.floor((Lt-wt)/2)]:[0,0];for(let Oe=0;Oe<wt;++Oe){const ct=(Oe+Fe)*ft,Ut=Oe*mt;for(let sr=0;sr<mt;++sr){const br=(ct+sr+Ft)*Ct,Nr=(Ut+sr)*Ct;for(let mr=0;mr<Ct;++mr)jt[br+mr]=G[Nr+mr]}}if(Se==="symmetric"){if(Ne)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const Oe=wt-1,ct=mt-1;for(let Ut=0;Ut<Lt;++Ut){const sr=Ut*ft,br=(0,ce.calculateReflectOffset)(Ut,Oe)*mt;for(let Nr=0;Nr<ft;++Nr){if(Ut<wt&&Nr<mt)continue;const mr=(sr+Nr)*Ct,kr=(br+(0,ce.calculateReflectOffset)(Nr,ct))*Ct;for(let gr=0;gr<Ct;++gr)jt[mr+gr]=G[kr+gr]}}}G=jt,ge=[Lt,ft,Ct]}return[G,ge]}rescale(G){for(let ge=0;ge<G.length;++ge)G[ge]=this.rescale_factor*G[ge]}get_resize_output_image_size(G,ge){const[Ie,Se]=G.size;let Ne,tt;if(this.do_thumbnail){const{height:wt,width:mt}=ge;Ne=Math.min(wt,mt)}else Number.isInteger(ge)?(Ne=ge,tt=this.config.max_size??Ne):ge!==void 0&&(Ne=ge.shortest_edge,tt=ge.longest_edge);if(Ne!==void 0||tt!==void 0){const wt=Ne===void 0?1:Math.max(Ne/Ie,Ne/Se),mt=Ie*wt,Ct=Se*wt,ft=tt===void 0?1:Math.min(tt/mt,tt/Ct);let Lt=Math.floor(Number((mt*ft).toFixed(2))),jt=Math.floor(Number((Ct*ft).toFixed(2)));return this.size_divisibility!==void 0&&([Lt,jt]=pe([Lt,jt],this.size_divisibility)),[Lt,jt]}else if(ge!==void 0&&ge.width!==void 0&&ge.height!==void 0){let wt=ge.width,mt=ge.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let Ct=mt/Se,ft=wt/Ie;Math.abs(1-ft)<Math.abs(1-Ct)?Ct=ft:ft=Ct,mt=J(Ct*Se,this.config.ensure_multiple_of),wt=J(ft*Ie,this.config.ensure_multiple_of)}return[wt,mt]}else{if(this.size_divisibility!==void 0)return pe([Ie,Se],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(ge)}`)}}async resize(G){const[ge,Ie]=this.get_resize_output_image_size(G,this.size);return await G.resize(ge,Ie,{resample:this.resample})}async preprocess(G,{do_normalize:ge=null,do_pad:Ie=null,do_convert_rgb:Se=null,do_convert_grayscale:Ne=null,do_flip_channel_order:tt=null}={}){this.do_crop_margin&&(G=await this.crop_margin(G));const[wt,mt]=G.size;if(Se??this.do_convert_rgb?G=G.rgb():Ne&&(G=G.grayscale()),this.do_resize&&(G=await this.resize(G)),this.do_thumbnail&&(G=await this.thumbnail(G,this.size,this.resample)),this.do_center_crop){let Ft,Fe;Number.isInteger(this.crop_size)?(Ft=this.crop_size,Fe=this.crop_size):(Ft=this.crop_size.width,Fe=this.crop_size.height),G=await G.center_crop(Ft,Fe)}const Ct=[G.height,G.width];let ft=Float32Array.from(G.data),Lt=[G.height,G.width,G.channels];if(this.do_rescale&&this.rescale(ft),ge??this.do_normalize){let Ft=this.image_mean;Array.isArray(this.image_mean)||(Ft=new Array(G.channels).fill(Ft));let Fe=this.image_std;if(Array.isArray(this.image_std)||(Fe=new Array(G.channels).fill(Ft)),Ft.length!==G.channels||Fe.length!==G.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${Ft.length}) and \`image_std\` (${Fe.length}) must match the number of channels in the image (${G.channels}).`);for(let Oe=0;Oe<ft.length;Oe+=G.channels)for(let ct=0;ct<G.channels;++ct)ft[Oe+ct]=(ft[Oe+ct]-Ft[ct])/Fe[ct]}if(Ie??this.do_pad){if(this.pad_size)[ft,Lt]=this.pad_image(ft,[G.height,G.width,G.channels],this.pad_size);else if(this.size_divisibility){const[Ft,Fe]=pe([Lt[1],Lt[0]],this.size_divisibility);[ft,Lt]=this.pad_image(ft,Lt,{width:Ft,height:Fe})}}if(tt??this.do_flip_channel_order){if(Lt[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let Ft=0;Ft<ft.length;Ft+=3){const Fe=ft[Ft];ft[Ft]=ft[Ft+2],ft[Ft+2]=Fe}}const jt=new Te.Tensor("float32",ft,Lt).permute(2,0,1);return{original_size:[mt,wt],reshaped_input_size:Ct,pixel_values:jt}}async _call(G,...ge){Array.isArray(G)||(G=[G]);const Ie=await Promise.all(G.map(Ne=>this.preprocess(Ne)));return{pixel_values:(0,Te.stack)(Ie.map(Ne=>Ne.pixel_values),0),original_sizes:Ie.map(Ne=>Ne.original_size),reshaped_input_sizes:Ie.map(Ne=>Ne.reshaped_input_size)}}}class j extends K{post_process_semantic_segmentation(...G){return B(...G)}}class k extends K{post_process_semantic_segmentation(...G){return B(...G)}}class N extends K{}class E extends K{}class ue extends E{}class be extends K{}class Ce extends K{}class De extends K{}class ze extends De{}class it extends K{}class rt extends K{}class lt extends K{constructor(G){super(G),this.crop_pct=this.config.crop_pct??.875}async resize(G){var Ie;const ge=(Ie=this.size)==null?void 0:Ie.shortest_edge;if(ge===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(ge<384){const Se=Math.floor(ge/this.crop_pct),[Ne,tt]=this.get_resize_output_image_size(G,{shortest_edge:Se});G=await G.resize(Ne,tt,{resample:this.resample}),G=await G.center_crop(ge,ge)}else G=await G.resize(ge,ge,{resample:this.resample});return G}}class me extends lt{}class W extends K{}class de extends K{}class xe extends K{constructor(G){super(G),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(ge=>ge*ge))}}class We extends K{}class ot extends K{}class se extends K{}class Ze extends K{}class dt extends K{}class Re extends dt{}class ht extends K{post_process_object_detection(...G){return D(...G)}}class Mt extends ht{}class Xe extends K{post_process_object_detection(...G){return D(...G)}}class Z extends K{}class Ae extends K{}class Ke extends K{pad_image(G,ge,Ie,Se={}){const[Ne,tt,wt]=ge;let mt=this.image_mean;Array.isArray(this.image_mean)||(mt=new Array(wt).fill(mt));let Ct=this.image_std;Array.isArray(Ct)||(Ct=new Array(wt).fill(mt));const ft=mt.map((Lt,jt)=>-Lt/Ct[jt]);return super.pad_image(G,ge,Ie,{center:!0,constant_values:ft,...Se})}}class et extends Ke{}class je extends Ke{}class Ve extends K{async _call(G){const ge=await super._call(G),Ie=[ge.pixel_values.dims[0],64,64],Se=(0,Te.full)(Ie,1n);return{...ge,pixel_mask:Se}}post_process_object_detection(...G){return D(...G)}post_process_panoptic_segmentation(...G){return le(...G)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class ut extends K{post_process_panoptic_segmentation(...G){return le(...G)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class _t extends K{post_process_object_detection(...G){return D(...G)}}class St extends K{reshape_input_points(G,ge,Ie,Se=!1){G=structuredClone(G);let Ne=(0,ce.calculateDimensions)(G);if(Ne.length===3)Se||(Ne=[1,...Ne]),G=[G];else if(Ne.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let tt=0;tt<G.length;++tt){let wt=ge[tt],mt=Ie[tt],Ct=[mt[0]/wt[0],mt[1]/wt[1]];for(let ft=0;ft<G[tt].length;++ft)for(let Lt=0;Lt<G[tt][ft].length;++Lt)for(let jt=0;jt<G[tt][ft][Lt].length;++jt)G[tt][ft][Lt][jt]*=Ct[jt%2]}return new Te.Tensor("float32",Float32Array.from(G.flat(1/0)),Ne)}add_input_labels(G,ge){let Ie=(0,ce.calculateDimensions)(G);if(Ie.length===2)Ie=[1,...Ie],G=[G];else if(Ie.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(Ie.some((Se,Ne)=>Se!==ge.dims[Ne]))throw Error(`The first ${Ie.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Te.Tensor("int64",G.flat(1/0).map(BigInt),Ie)}async _call(G,{input_points:ge=null,input_labels:Ie=null,input_boxes:Se=null}={}){const Ne=await super._call(G);if(ge&&(Ne.input_points=this.reshape_input_points(ge,Ne.original_sizes,Ne.reshaped_input_sizes)),Ie){if(!Ne.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");Ne.input_labels=this.add_input_labels(Ie,Ne.input_points)}return Se&&(Ne.input_boxes=this.reshape_input_points(Se,Ne.original_sizes,Ne.reshaped_input_sizes,!0)),Ne}async post_process_masks(G,ge,Ie,{mask_threshold:Se=0,binarize:Ne=!0,pad_size:tt=null}={}){const wt=[];tt=tt??this.pad_size;const mt=[tt.height,tt.width];for(let Ct=0;Ct<ge.length;++Ct){const ft=ge[Ct],Lt=Ie[Ct];let jt=await(0,Te.interpolate_4d)(G[Ct],{mode:"bilinear",size:mt});if(jt=jt.slice(null,null,[0,Lt[0]],[0,Lt[1]]),jt=await(0,Te.interpolate_4d)(jt,{mode:"bilinear",size:ft}),Ne){const Ft=jt.data,Fe=new Uint8Array(Ft.length);for(let Oe=0;Oe<Ft.length;++Oe)Ft[Oe]>Se&&(Fe[Oe]=1);jt=new Te.Tensor("bool",Fe,jt.dims)}wt.push(jt)}return wt}generate_crop_boxes(G,ge,{crop_n_layers:Ie=0,overlap_ratio:Se=.3413333333333333,points_per_crop:Ne=32,crop_n_points_downscale_factor:tt=1}={}){}}class xt extends K{pad_image(G,ge,Ie,Se={}){const[Ne,tt,wt]=ge;return super.pad_image(G,ge,{width:tt+(Ie-tt%Ie)%Ie,height:Ne+(Ie-Ne%Ie)%Ie},{mode:"symmetric",center:!1,constant_values:-1,...Se})}}class v extends K{async _call(G,ge){Array.isArray(G)||(G=[G]),Array.isArray(ge)||(ge=[ge]);const Ie=await Promise.all(G.map(tt=>this.preprocess(tt))),Se=await Promise.all(ge.map(tt=>this.preprocess(tt,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,Te.stack)(Ie.map((tt,wt)=>(0,Te.cat)([tt.pixel_values,Se[wt].pixel_values],0)),0),original_sizes:Ie.map(tt=>tt.original_size),reshaped_input_sizes:Ie.map(tt=>tt.reshaped_input_size)}}}class H extends X{constructor(G){var ge;super(G),(ge=this.config).mel_filters??(ge.mel_filters=(0,L.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,L.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(G){const ge=await(0,L.spectrogram)(G,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}),Ie=ge.data,Se=(0,ye.max)(Ie)[0];for(let Ne=0;Ne<Ie.length;++Ne)Ie[Ne]=(Math.max(Ie[Ne],Se-8)+4)/4;return ge}async _call(G){O(G,"WhisperFeatureExtractor");let ge;return G.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`."),ge=G.slice(0,this.config.n_samples)):(ge=new Float32Array(this.config.n_samples),ge.set(G)),{input_features:(await this._extract_fbank_features(ge)).unsqueeze_(0)}}}class $ extends X{_zero_mean_unit_var_norm(G){const Ie=G.reduce((Ne,tt)=>Ne+tt,0)/G.length,Se=G.reduce((Ne,tt)=>Ne+(tt-Ie)**2,0)/G.length;return G.map(Ne=>(Ne-Ie)/Math.sqrt(Se+1e-7))}async _call(G){O(G,"Wav2Vec2FeatureExtractor"),G instanceof Float64Array&&(G=new Float32Array(G));let ge=G;this.config.do_normalize&&(ge=this._zero_mean_unit_var_norm(ge));const Ie=[1,ge.length];return{input_values:new Te.Tensor("float32",ge,Ie),attention_mask:new Te.Tensor("int64",new BigInt64Array(ge.length).fill(1n),Ie)}}}class Y extends X{constructor(G){super(G);const ge=this.config.sampling_rate,Ie=(0,L.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ge/2),ge,null,"kaldi",!0);for(let Se=0;Se<Ie.length;++Se)Ie[Se].push(0);this.mel_filters=Ie,this.window=(0,L.window_function)(400,"povey",{periodic:!1})}async _extract_fbank_features(G,ge){return G=G.map(Ie=>Ie*32768),(0,L.spectrogram)(G,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:ge,transpose:!0})}async _call(G,{padding:ge=!0,pad_to_multiple_of:Ie=2,do_normalize_per_mel_bins:Se=!0,return_attention_mask:Ne=!0}={}){O(G,"SeamlessM4TFeatureExtractor");let tt=await this._extract_fbank_features(G,this.config.max_length);if(Se){const[Fe,Oe]=tt.dims,ct=tt.data;for(let Ut=0;Ut<Oe;++Ut){let sr=0;for(let kr=0;kr<Fe;++kr)sr+=ct[kr*Oe+Ut];const br=sr/Fe;let Nr=0;for(let kr=0;kr<Fe;++kr)Nr+=(ct[kr*Oe+Ut]-br)**2;Nr/=Fe-1;const mr=Math.sqrt(Nr+1e-7);for(let kr=0;kr<Fe;++kr){const gr=kr*Oe+Ut;ct[gr]=(ct[gr]-br)/mr}}}let wt;if(ge){const[Fe,Oe]=tt.dims,ct=tt.data,Ut=Fe%Ie;if(Ut>0){const sr=new Float32Array(Oe*(Fe+Ut));sr.set(ct),sr.fill(this.config.padding_value,ct.length);const br=Fe+Ut;tt=new Te.Tensor(tt.type,sr,[br,Oe]),Ne&&(wt=new Te.Tensor("int64",new BigInt64Array(br),[1,br]),wt.data.fill(1n,0,Fe))}}const[mt,Ct]=tt.dims,ft=this.config.stride;if(mt%ft!==0)throw new Error(`The number of frames (${mt}) must be a multiple of the stride (${ft}).`);const jt=tt.view(1,Math.floor(mt/ft),Ct*ft),Ft={input_features:jt};if(Ne){const Fe=jt.dims[1],Oe=new BigInt64Array(Fe);if(wt){const ct=wt.data;for(let Ut=1,sr=0;Ut<mt;Ut+=ft,++sr)Oe[sr]=ct[Ut]}else Oe.fill(1n);Ft.attention_mask=new Te.Tensor("int64",Oe,[1,Fe])}return Ft}}class he extends X{constructor(G){super(G);const ge=this.config.sampling_rate,Ie=(0,L.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ge/2),ge,null,"kaldi",!0);for(let Se=0;Se<Ie.length;++Se)Ie[Se].push(0);this.mel_filters=Ie,this.window=(0,L.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(G,ge){return(0,L.spectrogram)(G,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:ge,transpose:!0})}async _call(G){O(G,"ASTFeatureExtractor");const ge=await this._extract_fbank_features(G,this.config.max_length);if(this.config.do_normalize){const Ie=this.std*2,Se=ge.data;for(let Ne=0;Ne<Se.length;++Ne)Se[Ne]=(Se[Ne]-this.mean)/Ie}return{input_values:ge.unsqueeze_(0)}}}class nt extends X{constructor(G){super(G),this.mel_filters=(0,L.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,L.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,L.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(G,ge,Ie,Se){let Ne;const tt=G.length-ge;if(tt>0)if(Ie==="rand_trunc"){const wt=Math.floor(Math.random()*(tt+1));G=G.subarray(wt,wt+ge),Ne=await this._extract_fbank_features(G,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${Ie}" not implemented`);else{if(tt<0){let wt=new Float64Array(ge);if(wt.set(G),Se==="repeat")for(let mt=G.length;mt<ge;mt+=G.length)wt.set(G.subarray(0,Math.min(G.length,ge-mt)),mt);else if(Se==="repeatpad")for(let mt=G.length;mt<-tt;mt+=G.length)wt.set(G,mt);G=wt}if(Ie==="fusion")throw new Error(`Truncation strategy "${Ie}" not implemented`);Ne=await this._extract_fbank_features(G,this.mel_filters_slaney,this.config.nb_max_samples)}return Ne.unsqueeze_(0)}async _extract_fbank_features(G,ge,Ie=null){return(0,L.spectrogram)(G,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:ge,log_mel:"dB",max_num_frames:Ie,do_pad:!1,transpose:!0})}async _call(G,{max_length:ge=null}={}){return O(G,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(G,ge??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}class Je extends X{async _call(G){O(G,"PyAnnoteFeatureExtractor"),G instanceof Float64Array&&(G=new Float32Array(G));const ge=[1,1,G.length];return{input_values:new Te.Tensor("float32",G,ge)}}samples_to_frames(G){return(G-this.config.offset)/this.config.step}post_process_speaker_diarization(G,ge){const Ie=ge/this.samples_to_frames(ge)/this.config.sampling_rate,Se=[];for(const Ne of G.tolist()){const tt=[];let wt=-1;for(let mt=0;mt<Ne.length;++mt){const Ct=(0,ye.softmax)(Ne[mt]),[ft,Lt]=(0,ye.max)(Ct),[jt,Ft]=[mt,mt+1];Lt!==wt?(wt=Lt,tt.push({id:Lt,start:jt,end:Ft,score:ft})):(tt.at(-1).end=Ft,tt.at(-1).score+=ft)}Se.push(tt.map(({id:mt,start:Ct,end:ft,score:Lt})=>({id:mt,start:Ct*Ie,end:ft*Ie,confidence:Lt/(ft-Ct)})))}return Se}}class Nt extends X{constructor(G){super(G);const ge=this.config.sampling_rate,Ie=(0,L.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ge/2),ge,null,"kaldi",!0);for(let Se=0;Se<Ie.length;++Se)Ie[Se].push(0);this.mel_filters=Ie,this.window=(0,L.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(G){return G=G.map(ge=>ge*32768),(0,L.spectrogram)(G,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(G){O(G,"WeSpeakerFeatureExtractor");const ge=(await this._extract_fbank_features(G)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const Ie=ge.mean(1).data,Se=ge.data,[Ne,tt,wt]=ge.dims;for(let mt=0;mt<Ne;++mt){const Ct=mt*tt*wt,ft=mt*wt;for(let Lt=0;Lt<tt;++Lt){const jt=Ct+Lt*wt;for(let Ft=0;Ft<wt;++Ft)Se[jt+Ft]-=Ie[ft+Ft]}}}return{input_features:ge}}}class yt extends X{}class bt extends F.Callable{constructor(G){super(),this.feature_extractor=G}async _call(G,...ge){return await this.feature_extractor(G,...ge)}}class zt extends bt{async _call(...G){return await this.feature_extractor(...G)}post_process_masks(...G){return this.feature_extractor.post_process_masks(...G)}reshape_input_points(...G){return this.feature_extractor.reshape_input_points(...G)}}class Pt extends bt{async _call(G){return await this.feature_extractor(G)}}class dr extends bt{async _call(G){return await this.feature_extractor(G)}}class Cr extends bt{async _call(G){return await this.feature_extractor(G)}post_process_speaker_diarization(...G){return this.feature_extractor.post_process_speaker_diarization(...G)}}class Yr extends bt{async _call(G){return await this.feature_extractor(G)}}class Rr extends bt{}class Jr extends bt{constructor(G){super(G);const{tasks_answer_post_processing_type:ge,task_prompts_without_inputs:Ie,task_prompts_with_input:Se}=G.config;this.tasks_answer_post_processing_type=new Map(Object.entries(ge??{})),this.task_prompts_without_inputs=new Map(Object.entries(Ie??{})),this.task_prompts_with_input=new Map(Object.entries(Se??{})),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(G){typeof G=="string"&&(G=[G]);const ge=[];for(const Ie of G)if(this.task_prompts_without_inputs.has(Ie))ge.push(this.task_prompts_without_inputs.get(Ie));else{for(const[Se,Ne]of this.task_prompts_with_input)if(Ie.includes(Se)){ge.push(Ne.replaceAll("{input}",Ie).replaceAll(Se,""));break}ge.length!==G.length&&ge.push(Ie)}return ge}post_process_generation(G,ge,Ie){const Se=this.tasks_answer_post_processing_type.get(ge)??"pure_text";G=G.replaceAll("<s>","").replaceAll("</s>","");let Ne;switch(Se){case"pure_text":Ne=G;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const tt=Se==="ocr"?"quad_boxes":"bboxes",wt=G.matchAll(this.regexes[tt]),mt=[],Ct=[];for(const[ft,Lt,...jt]of wt)mt.push(Lt?Lt.trim():mt.at(-1)??""),Ct.push(jt.map((Ft,Fe)=>(Number(Ft)+.5)/this.size_per_bin*Ie[Fe%2]));Ne={labels:mt,[tt]:Ct};break;default:throw new Error(`Task "${ge}" (of type "${Se}") not yet implemented.`)}return{[ge]:Ne}}}class bn{static async from_pretrained(G,{progress_callback:ge=null,config:Ie=null,cache_dir:Se=null,local_files_only:Ne=!1,revision:tt="main"}={}){let wt=Ie??await(0,we.getModelJSON)(G,"preprocessor_config.json",!0,{progress_callback:ge,config:Ie,cache_dir:Se,local_files_only:Ne,revision:tt}),mt=wt.feature_extractor_type??wt.image_processor_type,Ct=this.FEATURE_EXTRACTOR_CLASS_MAPPING[mt];if(!Ct)if(wt.size!==void 0)console.warn(`Feature extractor type "${mt}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),Ct=K;else throw new Error(`Unknown Feature Extractor type: ${mt}`);let ft=this.PROCESSOR_CLASS_MAPPING[wt.processor_class]??bt,Lt=new Ct(wt);return new ft(Lt)}}ve(bn,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:K,WhisperFeatureExtractor:H,ViTFeatureExtractor:W,MobileViTFeatureExtractor:dt,MobileViTImageProcessor:Re,MobileNetV1FeatureExtractor:We,MobileNetV2FeatureExtractor:ot,MobileNetV3FeatureExtractor:se,MobileNetV4FeatureExtractor:Ze,OwlViTFeatureExtractor:ht,Owlv2ImageProcessor:Mt,CLIPFeatureExtractor:De,CLIPImageProcessor:ze,Florence2Processor:Jr,ChineseCLIPFeatureExtractor:it,SiglipImageProcessor:rt,ConvNextFeatureExtractor:lt,ConvNextImageProcessor:me,SegformerFeatureExtractor:k,SapiensFeatureExtractor:j,BitImageProcessor:be,DPTImageProcessor:ue,DPTFeatureExtractor:E,PvtImageProcessor:N,GLPNFeatureExtractor:Ce,BeitFeatureExtractor:Ae,DeiTFeatureExtractor:Z,DetrFeatureExtractor:Ve,RTDetrImageProcessor:Xe,MaskFormerFeatureExtractor:ut,YolosFeatureExtractor:_t,DonutFeatureExtractor:Ke,DonutImageProcessor:et,NougatImageProcessor:je,EfficientNetImageProcessor:xe,ViTImageProcessor:de,VitMatteImageProcessor:v,SamImageProcessor:St,Swin2SRImageProcessor:xt,Wav2Vec2FeatureExtractor:$,SeamlessM4TFeatureExtractor:Y,SpeechT5FeatureExtractor:yt,ASTFeatureExtractor:he,ClapFeatureExtractor:nt,PyAnnoteFeatureExtractor:Je,WeSpeakerFeatureExtractor:Nt}),ve(bn,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:Pt,Wav2Vec2ProcessorWithLM:dr,PyAnnoteProcessor:Cr,SamProcessor:zt,SpeechT5Processor:Yr,OwlViTProcessor:Rr,Florence2Processor:Jr})},"./src/tokenizers.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{AlbertTokenizer:()=>tt,AutoTokenizer:()=>Ds,BartTokenizer:()=>Nr,BertTokenizer:()=>Ne,BlenderbotSmallTokenizer:()=>Fs,BlenderbotTokenizer:()=>Is,BloomTokenizer:()=>$n,CLIPTokenizer:()=>gs,CamembertTokenizer:()=>Oe,CodeGenTokenizer:()=>_s,CodeLlamaTokenizer:()=>$s,CohereTokenizer:()=>is,ConvBertTokenizer:()=>jt,DebertaTokenizer:()=>Ct,DebertaV2Tokenizer:()=>ft,DistilBertTokenizer:()=>Fe,ElectraTokenizer:()=>Ut,EsmTokenizer:()=>Ps,FalconTokenizer:()=>ks,GPT2Tokenizer:()=>br,GPTNeoXTokenizer:()=>Ss,GemmaTokenizer:()=>ns,Grok1Tokenizer:()=>Hn,HerbertTokenizer:()=>Lt,LlamaTokenizer:()=>fs,M2M100Tokenizer:()=>ms,MBart50Tokenizer:()=>kr,MBartTokenizer:()=>mr,MPNetTokenizer:()=>Es,MarianTokenizer:()=>Yt,MobileBertTokenizer:()=>wt,NllbTokenizer:()=>Rn,NougatTokenizer:()=>Os,PreTrainedTokenizer:()=>Se,Qwen2Tokenizer:()=>As,RoFormerTokenizer:()=>Ft,RobertaTokenizer:()=>gr,SiglipTokenizer:()=>ws,SpeechT5Tokenizer:()=>ys,SqueezeBertTokenizer:()=>mt,T5Tokenizer:()=>sr,TokenizerModel:()=>Ce,VitsTokenizer:()=>zs,Wav2Vec2CTCTokenizer:()=>ss,WhisperTokenizer:()=>Ln,XLMRobertaTokenizer:()=>qn,XLMTokenizer:()=>ct,is_chinese_char:()=>X});var F=V("./src/utils/generic.js"),ce=V("./src/utils/core.js"),we=V("./src/utils/hub.js"),ye=V("./src/utils/maths.js"),Te=V("./src/utils/tensor.js"),L=V("./src/utils/data-structures.js"),P=V("./node_modules/@huggingface/jinja/dist/index.js"),D=V("./src/models/whisper/common_whisper.js");V("./src/utils/constants.js");async function B(ae,_){const I=await Promise.all([(0,we.getModelJSON)(ae,"tokenizer.json",!0,_),(0,we.getModelJSON)(ae,"tokenizer_config.json",!0,_)]);return _.legacy!==null&&(I[1].legacy=_.legacy),I}function q(ae,_){const I=[];let Q=0;for(const oe of ae.matchAll(_)){const _e=oe[0];Q<oe.index&&I.push(ae.slice(Q,oe.index)),_e.length>0&&I.push(_e),Q=oe.index+_e.length}return Q<ae.length&&I.push(ae.slice(Q)),I}function re(ae,_=!0){if(ae.Regex!==void 0){let I=ae.Regex.replace(/\\([#&~])/g,"$1");for(const[Q,oe]of ue)I=I.replaceAll(Q,oe);return new RegExp(I,"gu")}else if(ae.String!==void 0){const I=(0,ce.escapeRegExp)(ae.String);return new RegExp(_?I:`(${I})`,"gu")}else return console.warn("Unknown pattern type:",ae),null}function fe(ae){return new Map(Object.entries(ae))}function le(ae){const _=ae.dims;switch(_.length){case 1:return ae.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 ae.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${_.length}.`)}}function O(ae){return ae.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function J(ae){return ae.replace(new RegExp("\\p{M}","gu"),"")}function pe(ae){return J(ae.toLowerCase())}function X(ae){return ae>=19968&&ae<=40959||ae>=13312&&ae<=19903||ae>=131072&&ae<=173791||ae>=173824&&ae<=177983||ae>=177984&&ae<=178207||ae>=178208&&ae<=183983||ae>=63744&&ae<=64255||ae>=194560&&ae<=195103}function K(ae,_,I){const Q=[];let oe=0;for(;oe<ae.length;){if(Q.push(ae[oe]),(_.get(ae[oe])??I)!==I){++oe;continue}for(;++oe<ae.length&&(_.get(ae[oe])??I)===I;)_.get(Q.at(-1))!==I&&(Q[Q.length-1]+=ae[oe])}return Q}function j(ae){return ae.match(/\S+/g)||[]}const k="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",N=new RegExp(`^[${k}]+$`,"gu"),E=".,!?…。，、।۔،",ue=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],[` ?[^(\\s|[${E}])]+`,` ?[^\\s${E}]+`]]);class be{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 F.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(_,...I){switch(_.type){case"WordPiece":return new De(_);case"Unigram":return new ze(_,...I);case"BPE":return new lt(_);default:if(_.vocab)return Array.isArray(_.vocab)?new ze(_,...I):new me(_,...I);throw new Error(`Unknown TokenizerModel type: ${_.type}`)}}_call(_){return _=this.encode(_),this.fuse_unk&&(_=K(_,this.tokens_to_ids,this.unk_token_id)),_}encode(_){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(_){return _.map(I=>this.tokens_to_ids.get(I)??this.unk_token_id)}convert_ids_to_tokens(_){return _.map(I=>this.vocab[I]??this.unk_token)}}class De extends Ce{constructor(_){super(_),this.tokens_to_ids=fe(_.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[I,Q]of this.tokens_to_ids)this.vocab[Q]=I}encode(_){const I=[];for(const Q of _){const oe=[...Q];if(oe.length>this.max_input_chars_per_word){I.push(this.unk_token);continue}let _e=!1,Ge=0;const gt=[];for(;Ge<oe.length;){let $t=oe.length,Tt=null;for(;Ge<$t;){let Ot=oe.slice(Ge,$t).join("");if(Ge>0&&(Ot=this.config.continuing_subword_prefix+Ot),this.tokens_to_ids.has(Ot)){Tt=Ot;break}--$t}if(Tt===null){_e=!0;break}gt.push(Tt),Ge=$t}_e?I.push(this.unk_token):I.push(...gt)}return I}}class ze extends Ce{constructor(_,I){super(_);const Q=_.vocab.length;this.vocab=new Array(Q),this.scores=new Array(Q);for(let oe=0;oe<Q;++oe){const _e=_.vocab[oe];this.vocab[oe]=_e[0],this.scores[oe]=_e[1]}this.unk_token_id=_.unk_id,this.unk_token=this.vocab[_.unk_id],this.tokens_to_ids=new Map(this.vocab.map((oe,_e)=>[oe,_e])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=I.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,ye.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new L.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(_){const I=_.chars,Q=1;let oe=0;for(;oe<I.length;){let _e=!1;const Ge=I.slice(oe).join(""),gt=this.trie.commonPrefixSearch(Ge);for(const $t of gt){const Tt=this.tokens_to_ids.get($t),Ot=this.scores[Tt],er=(0,ce.len)($t);_.insert(oe,er,Ot,Tt),!_e&&er===Q&&(_e=!0)}_e||_.insert(oe,Q,this.unk_score,this.unk_token_id),oe+=Q}}tokenize(_){const I=new L.TokenLattice(_,this.bos_token_id,this.eos_token_id);return this.populateNodes(I),I.tokens()}encode(_){const I=[];for(const Q of _){const oe=this.tokenize(Q);I.push(...oe)}return I}}const it=(()=>{const ae=[...Array.from({length:94},(oe,_e)=>_e+33),...Array.from({length:12},(oe,_e)=>_e+161),...Array.from({length:82},(oe,_e)=>_e+174)],_=ae.slice();let I=0;for(let oe=0;oe<256;++oe)ae.includes(oe)||(ae.push(oe),_.push(256+I),I+=1);const Q=_.map(oe=>String.fromCharCode(oe));return Object.fromEntries(ae.map((oe,_e)=>[oe,Q[_e]]))})(),rt=(0,ce.reverseDictionary)(it);class lt extends Ce{constructor(_){super(_),this.tokens_to_ids=fe(_.vocab),this.unk_token_id=this.tokens_to_ids.get(_.unk_token),this.unk_token=_.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[Q,oe]of this.tokens_to_ids)this.vocab[oe]=Q;const I=Array.isArray(_.merges[0]);this.merges=I?_.merges:_.merges.map(Q=>Q.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((Q,oe)=>[JSON.stringify(Q),oe])),this.end_of_word_suffix=_.end_of_word_suffix,this.continuing_subword_suffix=_.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(_){if(_.length===0)return[];const I=this.cache.get(_);if(I!==void 0)return I;const Q=Array.from(_);this.end_of_word_suffix&&(Q[Q.length-1]+=this.end_of_word_suffix);let oe=[];if(Q.length>1){const _e=new L.PriorityQueue(($t,Tt)=>$t.score<Tt.score);let Ge={token:Q[0],bias:0,prev:null,next:null},gt=Ge;for(let $t=1;$t<Q.length;++$t){const Tt={bias:$t/Q.length,token:Q[$t],prev:gt,next:null};gt.next=Tt,this._add_node(_e,gt),gt=Tt}for(;!_e.isEmpty();){const $t=_e.pop();if($t.deleted||!$t.next||$t.next.deleted)continue;if($t.deleted=!0,$t.next.deleted=!0,$t.prev){const Ot={...$t.prev};$t.prev.deleted=!0,$t.prev=Ot,Ot.prev?Ot.prev.next=Ot:Ge=Ot}const Tt={token:$t.token+$t.next.token,bias:$t.bias,prev:$t.prev,next:$t.next.next};Tt.prev?(Tt.prev.next=Tt,this._add_node(_e,Tt.prev)):Ge=Tt,Tt.next&&(Tt.next.prev=Tt,this._add_node(_e,Tt))}for(let $t=Ge;$t!==null;$t=$t.next)oe.push($t.token)}else oe=Q;if(this.continuing_subword_suffix)for(let _e=0;_e<oe.length-1;++_e)oe[_e]+=this.continuing_subword_suffix;return this.cache.set(_,oe),oe}_add_node(_,I){const Q=this.bpe_ranks.get(JSON.stringify([I.token,I.next.token]));Q!==void 0&&(I.score=Q+I.bias,_.push(I))}encode(_){const I=[];for(const Q of _){if(this.ignore_merges&&this.tokens_to_ids.has(Q)){I.push(Q);continue}const oe=this.bpe(Q);for(const _e of oe)if(this.tokens_to_ids.has(_e))I.push(_e);else if(this.byte_fallback){const Ge=Array.from(this.text_encoder.encode(_e)).map(gt=>`<0x${gt.toString(16).toUpperCase().padStart(2,"0")}>`);Ge.every(gt=>this.tokens_to_ids.has(gt))?I.push(...Ge):I.push(this.unk_token)}else I.push(this.unk_token)}return I}}class me extends Ce{constructor(_,I){super(_),this.tokens_to_ids=fe(I.target_lang?_.vocab[I.target_lang]:_.vocab),this.bos_token=I.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=I.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=I.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=I.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,oe]of this.tokens_to_ids)this.vocab[oe]=Q}encode(_){return _}}class W extends F.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"BertNormalizer":return new Mt(_);case"Precompiled":return new Yr(_);case"Sequence":return new ht(_);case"Replace":return new de(_);case"NFC":return new xe(_);case"NFKC":return new We(_);case"NFKD":return new ot(_);case"Strip":return new se(_);case"StripAccents":return new Ze(_);case"Lowercase":return new dt(_);case"Prepend":return new Re(_);default:throw new Error(`Unknown Normalizer type: ${_.type}`)}}normalize(_){throw Error("normalize should be implemented in subclass.")}_call(_){return this.normalize(_)}}class de extends W{normalize(_){const I=re(this.config.pattern);return I===null?_:_.replaceAll(I,this.config.content)}}class xe extends W{normalize(_){return _=_.normalize("NFC"),_}}class We extends W{normalize(_){return _=_.normalize("NFKC"),_}}class ot extends W{normalize(_){return _=_.normalize("NFKD"),_}}class se extends W{normalize(_){return this.config.strip_left&&this.config.strip_right?_=_.trim():(this.config.strip_left&&(_=_.trimStart()),this.config.strip_right&&(_=_.trimEnd())),_}}class Ze extends W{normalize(_){return _=J(_),_}}class dt extends W{normalize(_){return _=_.toLowerCase(),_}}class Re extends W{normalize(_){return _=this.config.prepend+_,_}}class ht extends W{constructor(_){super(_),this.normalizers=_.normalizers.map(I=>W.fromConfig(I))}normalize(_){return this.normalizers.reduce((I,Q)=>Q.normalize(I),_)}}class Mt extends W{_tokenize_chinese_chars(_){const I=[];for(let Q=0;Q<_.length;++Q){const oe=_[Q],_e=oe.charCodeAt(0);X(_e)?(I.push(" "),I.push(oe),I.push(" ")):I.push(oe)}return I.join("")}stripAccents(_){return _.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control(_){switch(_){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(_)}}_clean_text(_){const I=[];for(const Q of _){const oe=Q.charCodeAt(0);oe===0||oe===65533||this._is_control(Q)||(/^\s$/.test(Q)?I.push(" "):I.push(Q))}return I.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 Xe extends F.Callable{static fromConfig(_){if(_===null)return null;switch(_.type){case"BertPreTokenizer":return new Z(_);case"Sequence":return new Rr(_);case"Whitespace":return new Jr(_);case"WhitespaceSplit":return new bn(_);case"Metaspace":return new dr(_);case"ByteLevel":return new Ae(_);case"Split":return new Ke(_);case"Punctuation":return new et(_);case"Digits":return new je(_);case"Replace":return new at(_);default:throw new Error(`Unknown PreTokenizer type: ${_.type}`)}}pre_tokenize_text(_,I){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(_,I){return(Array.isArray(_)?_.map(Q=>this.pre_tokenize_text(Q,I)):this.pre_tokenize_text(_,I)).flat()}_call(_,I){return this.pre_tokenize(_,I)}}class Z extends Xe{constructor(_){super(),this.pattern=new RegExp(`[^\\s${k}]+|[${k}]`,"gu")}pre_tokenize_text(_,I){return _.trim().match(this.pattern)||[]}}class Ae extends Xe{constructor(_){super(),this.config=_,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=it,this.text_encoder=new TextEncoder}pre_tokenize_text(_,I){return this.add_prefix_space&&!_.startsWith(" ")&&(_=" "+_),(this.use_regex?_.match(this.pattern)||[]:[_]).map(oe=>Array.from(this.text_encoder.encode(oe),_e=>this.byte_encoder[_e]).join(""))}}class Ke extends Xe{constructor(_){super(),this.config=_,this.pattern=re(this.config.pattern,this.config.invert)}pre_tokenize_text(_,I){return this.pattern===null?[]:this.config.invert?_.match(this.pattern)||[]:q(_,this.pattern)}}class et extends Xe{constructor(_){super(),this.config=_,this.pattern=new RegExp(`[^${k}]+|[${k}]+`,"gu")}pre_tokenize_text(_,I){return _.match(this.pattern)||[]}}class je extends Xe{constructor(_){super(),this.config=_;const I=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(I,"gu")}pre_tokenize_text(_,I){return _.match(this.pattern)||[]}}class Ve extends F.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"TemplateProcessing":return new St(_);case"ByteLevel":return new xt(_);case"RobertaProcessing":return new _t(_);case"BertProcessing":return new ut(_);case"Sequence":return new v(_);default:throw new Error(`Unknown PostProcessor type: ${_.type}`)}}post_process(_,...I){throw Error("post_process should be implemented in subclass.")}_call(_,...I){return this.post_process(_,...I)}}class ut extends Ve{constructor(_){super(_),this.cls=_.cls[0],this.sep=_.sep[0]}post_process(_,I=null,{add_special_tokens:Q=!0}={}){Q&&(_=(0,ce.mergeArrays)([this.cls],_,[this.sep]));let oe=new Array(_.length).fill(0);if(I!==null){const _e=Q&&this instanceof _t?[this.sep]:[],Ge=Q?[this.sep]:[];_=(0,ce.mergeArrays)(_,_e,I,Ge),oe=(0,ce.mergeArrays)(oe,new Array(I.length+_e.length+Ge.length).fill(1))}return{tokens:_,token_type_ids:oe}}}class _t extends ut{}class St extends Ve{constructor(_){super(_),this.single=_.single,this.pair=_.pair}post_process(_,I=null,{add_special_tokens:Q=!0}={}){const oe=I===null?this.single:this.pair;let _e=[],Ge=[];for(const gt of oe)"SpecialToken"in gt?Q&&(_e.push(gt.SpecialToken.id),Ge.push(gt.SpecialToken.type_id)):"Sequence"in gt&&(gt.Sequence.id==="A"?(_e=(0,ce.mergeArrays)(_e,_),Ge=(0,ce.mergeArrays)(Ge,new Array(_.length).fill(gt.Sequence.type_id))):gt.Sequence.id==="B"&&(_e=(0,ce.mergeArrays)(_e,I),Ge=(0,ce.mergeArrays)(Ge,new Array(I.length).fill(gt.Sequence.type_id))));return{tokens:_e,token_type_ids:Ge}}}class xt extends Ve{post_process(_,I=null){return I&&(_=(0,ce.mergeArrays)(_,I)),{tokens:_}}}class v extends Ve{constructor(_){super(_),this.processors=_.processors.map(I=>Ve.fromConfig(I))}post_process(_,I=null,Q={}){let oe;for(const _e of this.processors)if(_e instanceof xt)_=_e.post_process(_).tokens,I&&(I=_e.post_process(I).tokens);else{const Ge=_e.post_process(_,I,Q);_=Ge.tokens,oe=Ge.token_type_ids}return{tokens:_,token_type_ids:oe}}}class H extends F.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 Cr(_);case"ByteLevel":return new Nt(_);case"Replace":return new $(_);case"ByteFallback":return new Y(_);case"Fuse":return new he(_);case"Strip":return new nt(_);case"Sequence":return new bt(_);case"CTC":return new yt(_);case"BPEDecoder":return new zt(_);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 $ extends H{decode_chain(_){const I=re(this.config.pattern);return I===null?_:_.map(Q=>Q.replaceAll(I,this.config.content))}}class Y extends H{constructor(_){super(_),this.text_decoder=new TextDecoder}decode_chain(_){const I=[];let Q=[];for(const oe of _){let _e=null;if(oe.length===6&&oe.startsWith("<0x")&&oe.endsWith(">")){const Ge=parseInt(oe.slice(3,5),16);isNaN(Ge)||(_e=Ge)}if(_e!==null)Q.push(_e);else{if(Q.length>0){const Ge=this.text_decoder.decode(Uint8Array.from(Q));I.push(Ge),Q=[]}I.push(oe)}}if(Q.length>0){const oe=this.text_decoder.decode(Uint8Array.from(Q));I.push(oe),Q=[]}return I}}class he extends H{decode_chain(_){return[_.join("")]}}class nt extends H{constructor(_){super(_),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(_){return _.map(I=>{let Q=0;for(let _e=0;_e<this.start&&I[_e]===this.content;++_e){Q=_e+1;continue}let oe=I.length;for(let _e=0;_e<this.stop;++_e){const Ge=I.length-_e-1;if(I[Ge]===this.content){oe=Ge;continue}else break}return I.slice(Q,oe)})}}class Je extends H{constructor(_){super(_),this.cleanup=_.cleanup}decode_chain(_){return _.map((I,Q)=>(Q!==0&&(I.startsWith(this.config.prefix)?I=I.replace(this.config.prefix,""):I=" "+I),this.cleanup&&(I=O(I)),I))}}class Nt extends H{constructor(_){super(_),this.byte_decoder=rt,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(_){const I=_.join(""),Q=new Uint8Array([...I].map(_e=>this.byte_decoder[_e]));return this.text_decoder.decode(Q)}decode_chain(_){const I=[];let Q=[];for(const oe of _)this.added_tokens.find(_e=>_e.content===oe)!==void 0?(Q.length>0&&(I.push(this.convert_tokens_to_string(Q)),Q=[]),I.push(oe)):Q.push(oe);return Q.length>0&&I.push(this.convert_tokens_to_string(Q)),I}}class yt extends H{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 I=[_[0]];for(let _e=1;_e<_.length;++_e)_[_e]!==I.at(-1)&&I.push(_[_e]);let oe=I.filter(_e=>_e!==this.pad_token).join("");return this.cleanup&&(oe=O(oe).replaceAll(this.word_delimiter_token," ").trim()),oe}decode_chain(_){return[this.convert_tokens_to_string(_)]}}class bt extends H{constructor(_){super(_),this.decoders=_.decoders.map(I=>H.fromConfig(I))}decode_chain(_){return this.decoders.reduce((I,Q)=>Q.decode_chain(I),_)}}class zt extends H{constructor(_){super(_),this.suffix=this.config.suffix}decode_chain(_){return _.map((I,Q)=>I.replaceAll(this.suffix,Q===_.length-1?"":" "))}}class Pt extends H{decode_chain(_){let I="";for(let Q=1;Q<_.length;Q+=2)I+=_[Q];return[I]}}class dr extends Xe{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:I=void 0}={}){let Q=_.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!Q.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&I===0)&&(Q=this.strRep+Q),[Q]}}class Cr extends H{constructor(_){super(_),this.addPrefixSpace=_.add_prefix_space,this.replacement=_.replacement}decode_chain(_){const I=[];for(let Q=0;Q<_.length;++Q){let oe=_[Q].replaceAll(this.replacement," ");this.addPrefixSpace&&Q==0&&oe.startsWith(" ")&&(oe=oe.substring(1)),I.push(oe)}return I}}class Yr extends W{constructor(_){super(_),this.charsmap=_.precompiled_charsmap}normalize(_){return _=_.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),_=_.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),_.includes("～")?_=_.split("～").map(Q=>Q.normalize("NFKC")).join("～"):_=_.normalize("NFKC"),_}}class Rr extends Xe{constructor(_){super(),this.tokenizers=_.pretokenizers.map(I=>Xe.fromConfig(I))}pre_tokenize_text(_,I){return this.tokenizers.reduce((Q,oe)=>oe.pre_tokenize(Q,I),[_])}}class Jr extends Xe{constructor(_){super()}pre_tokenize_text(_,I){return _.match(/\w+|[^\w\s]+/g)||[]}}class bn extends Xe{constructor(_){super()}pre_tokenize_text(_,I){return j(_)}}class at extends Xe{constructor(_){super(),this.config=_,this.pattern=re(this.config.pattern),this.content=this.config.content}pre_tokenize_text(_,I){return this.pattern===null?[_]:[_.replaceAll(this.pattern,this.config.content)]}}const G=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function ge(ae,_,I,Q){for(const oe of Object.keys(ae)){const _e=_-ae[oe].length,Ge=I(oe),gt=new Array(_e).fill(Ge);ae[oe]=Q==="right"?(0,ce.mergeArrays)(ae[oe],gt):(0,ce.mergeArrays)(gt,ae[oe])}}function Ie(ae,_){for(const I of Object.keys(ae))ae[I].length=_}class Se extends F.Callable{constructor(I,Q){super();ve(this,"return_token_type_ids",!1);ve(this,"padding_side","right");this._tokenizer_config=Q,this.normalizer=W.fromConfig(I.normalizer),this.pre_tokenizer=Xe.fromConfig(I.pre_tokenizer),this.model=Ce.fromConfig(I.model,Q),this.post_processor=Ve.fromConfig(I.post_processor),this.decoder=H.fromConfig(I.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const oe of I.added_tokens){const _e=new be(oe);this.added_tokens.push(_e),this.model.tokens_to_ids.set(_e.content,_e.id),this.model.vocab[_e.id]=_e.content,_e.special&&(this.special_tokens.push(_e.content),this.all_special_ids.push(_e.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.slice().sort((oe,_e)=>_e.content.length-oe.content.length).map(oe=>`${oe.lstrip?"\\s*":""}(${(0,ce.escapeRegExp)(oe.content)})${oe.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 oe=Object.create(null);for(const{name:_e,template:Ge}of this.chat_template){if(typeof _e!="string"||typeof Ge!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');oe[_e]=Ge}this.chat_template=oe}this._compiled_template_cache=new Map}getToken(...I){for(const Q of I){const oe=this._tokenizer_config[Q];if(oe)if(typeof oe=="object"){if(oe.__type==="AddedToken")return oe.content;throw Error(`Unknown token: ${oe}`)}else return oe}return null}static async from_pretrained(I,{progress_callback:Q=null,config:oe=null,cache_dir:_e=null,local_files_only:Ge=!1,revision:gt="main",legacy:$t=null}={}){const Tt=await B(I,{progress_callback:Q,config:oe,cache_dir:_e,local_files_only:Ge,revision:gt,legacy:$t});return new this(...Tt)}_call(I,{text_pair:Q=null,add_special_tokens:oe=!0,padding:_e=!1,truncation:Ge=null,max_length:gt=null,return_tensor:$t=!0,return_token_type_ids:Tt=null}={}){const Ot=Array.isArray(I);let er;if(Ot){if(I.length===0)throw Error("text array must be non-empty");if(Q!==null){if(Array.isArray(Q)){if(I.length!==Q.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");er=I.map((ur,Wr)=>this._encode_plus(ur,{text_pair:Q[Wr],add_special_tokens:oe,return_token_type_ids:Tt}))}else er=I.map(ur=>this._encode_plus(ur,{add_special_tokens:oe,return_token_type_ids:Tt}))}else{if(I==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).");er=[this._encode_plus(I,{text_pair:Q,add_special_tokens:oe,return_token_type_ids:Tt})]}if(gt===null?_e==="max_length"?gt=this.model_max_length:gt=(0,ye.max)(er.map(ur=>ur.input_ids.length))[0]:Ge||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),_e||Ge)for(let ur=0;ur<er.length;++ur)er[ur].input_ids.length!==gt&&(er[ur].input_ids.length>gt?Ge&&Ie(er[ur],gt):_e&&ge(er[ur],gt,Wr=>Wr==="input_ids"?this.pad_token_id:0,this.padding_side));const Sr={};if($t){if(!(_e&&Ge)&&er.some(Wr=>{var en;for(const or of Object.keys(Wr))if(Wr[or].length!==((en=er[0][or])==null?void 0:en.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const ur=[er.length,er[0].input_ids.length];for(const Wr of Object.keys(er[0]))Sr[Wr]=new Te.Tensor("int64",BigInt64Array.from(er.flatMap(en=>en[Wr]).map(BigInt)),ur)}else{for(const ur of Object.keys(er[0]))Sr[ur]=er.map(Wr=>Wr[ur]);if(!Ot)for(const ur of Object.keys(Sr))Sr[ur]=Sr[ur][0]}return Sr}_encode_text(I){return I===null?null:(this.added_tokens_regex?I.split(this.added_tokens_regex).filter(_e=>_e):[I]).map((_e,Ge)=>{if(this.added_tokens.find($t=>$t.content===_e)!==void 0)return _e;{if(this.remove_space===!0&&(_e=_e.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(_e=pe(_e)),this.normalizer!==null&&(_e=this.normalizer(_e)),_e.length===0)return[];const $t=this.pre_tokenizer!==null?this.pre_tokenizer(_e,{section_index:Ge}):[_e];return this.model($t)}}).flat()}_encode_plus(I,{text_pair:Q=null,add_special_tokens:oe=!0,return_token_type_ids:_e=null}={}){const{tokens:Ge,token_type_ids:gt}=this._tokenize_helper(I,{pair:Q,add_special_tokens:oe}),$t=this.model.convert_tokens_to_ids(Ge),Tt={input_ids:$t,attention_mask:new Array($t.length).fill(1)};return(_e??this.return_token_type_ids)&&gt&&(Tt.token_type_ids=gt),Tt}_tokenize_helper(I,{pair:Q=null,add_special_tokens:oe=!1}={}){const _e=this._encode_text(I),Ge=this._encode_text(Q);return this.post_processor?this.post_processor(_e,Ge,{add_special_tokens:oe}):{tokens:(0,ce.mergeArrays)(_e??[],Ge??[])}}tokenize(I,{pair:Q=null,add_special_tokens:oe=!1}={}){return this._tokenize_helper(I,{pair:Q,add_special_tokens:oe}).tokens}encode(I,{text_pair:Q=null,add_special_tokens:oe=!0,return_token_type_ids:_e=null}={}){return this._encode_plus(I,{text_pair:Q,add_special_tokens:oe,return_token_type_ids:_e}).input_ids}batch_decode(I,Q={}){return I instanceof Te.Tensor&&(I=I.tolist()),I.map(oe=>this.decode(oe,Q))}decode(I,Q={}){if(I instanceof Te.Tensor&&(I=le(I)),!Array.isArray(I)||I.length===0||!(0,ce.isIntegralNumber)(I[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(I,Q)}decode_single(I,{skip_special_tokens:Q=!1,clean_up_tokenization_spaces:oe=null}){let _e=this.model.convert_ids_to_tokens(I);Q&&(_e=_e.filter(gt=>!this.special_tokens.includes(gt)));let Ge=this.decoder?this.decoder(_e):_e.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Ge=Ge.replaceAll(this.decoder.end_of_word_suffix," "),Q&&(Ge=Ge.trim())),(oe??this.clean_up_tokenization_spaces)&&(Ge=O(Ge)),Ge}get_chat_template({chat_template:I=null,tools:Q=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const oe=this.chat_template;if(I!==null&&Object.hasOwn(oe,I))I=oe[I];else if(I===null)if(Q!==null&&"tool_use"in oe)I=oe.tool_use;else if("default"in oe)I=oe.default;else throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(oe).sort()}.`)}else if(I===null)if(this.chat_template)I=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return I}apply_chat_template(I,{tools:Q=null,documents:oe=null,chat_template:_e=null,add_generation_prompt:Ge=!1,tokenize:gt=!0,padding:$t=!1,truncation:Tt=!1,max_length:Ot=null,return_tensor:er=!0,return_dict:Sr=!1,tokenizer_kwargs:ur={},...Wr}={}){if(_e=this.get_chat_template({chat_template:_e,tools:Q}),typeof _e!="string")throw Error(`chat_template must be a string, but got ${typeof _e}`);let en=this._compiled_template_cache.get(_e);en===void 0&&(en=new P.Template(_e),this._compiled_template_cache.set(_e,en));const or=Object.create(null);for(const _n of G){const Mn=this.getToken(_n);Mn&&(or[_n]=Mn)}const Pr=en.render({messages:I,add_generation_prompt:Ge,tools:Q,documents:oe,...or,...Wr});if(gt){const _n=this._call(Pr,{add_special_tokens:!1,padding:$t,truncation:Tt,max_length:Ot,return_tensor:er,...ur});return Sr?_n:_n.input_ids}return Pr}}class Ne extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class tt extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class wt extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class mt extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class Ct extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class ft extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class Lt extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class jt extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class Ft extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class Fe extends Se{}class Oe extends Se{}class ct extends Se{constructor(I,Q){super(I,Q);ve(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 Ut extends Se{constructor(){super(...arguments);ve(this,"return_token_type_ids",!0)}}class sr extends Se{}class br extends Se{}class Nr extends Se{}class mr extends Se{constructor(_,I){super(_,I),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(_,I,Q){return An(this,_,I,Q)}}class kr extends mr{}class gr extends Se{}class $n extends Se{}const Ur="▁";class fs extends Se{constructor(I,Q){super(I,Q);ve(this,"padding_side","left");this.legacy=Q.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new dr({replacement:Ur,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(I){if(I===null)return null;if(this.legacy||I.length===0)return super._encode_text(I);let Q=super._encode_text(Ur+I.replaceAll(Ur," "));return Q.length>1&&Q[0]===Ur&&this.special_tokens.includes(Q[1])&&(Q=Q.slice(1)),Q}}class $s extends Se{}class qn extends Se{}class Es extends Se{}class ks extends Se{}class Ss extends Se{}class Ps extends Se{}class As extends Se{}class ns extends Se{}class Hn extends Se{}function An(ae,_,I,Q){if(!("language_codes"in ae)||!Array.isArray(ae.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in ae)||!(ae.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in ae)||typeof ae.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const oe=Q.src_lang,_e=Q.tgt_lang;if(!ae.language_codes.includes(_e))throw new Error(`Target language code "${_e}" is not valid. Must be one of: {${ae.language_codes.join(", ")}}`);if(oe!==void 0){if(!ae.language_codes.includes(oe))throw new Error(`Source language code "${oe}" is not valid. 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Using bilinear instead.`),X="bilinear");case"nearest":case"bilinear":case"bicubic":K=K.affine([O/this.width,0,0,J/this.height],{interpolator:X});break;case"lanczos":K=K.resize({width:O,height:J,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${X} is not supported.`)}return await B(K)}}async pad([O,J,pe,X]){if(O=Math.max(O,0),J=Math.max(J,0),pe=Math.max(pe,0),X=Math.max(X,0),O===0&&J===0&&pe===0&&X===0)return this;if(Te){const K=this.channels,j=this.toCanvas(),k=this.width+O+J,N=this.height+pe+X,E=P(k,N).getContext("2d");return E.drawImage(j,0,0,this.width,this.height,O,pe,k,N),new fe(E.getImageData(0,0,k,N).data,k,N,4).convert(K)}else{const K=this.toSharp().extend({left:O,right:J,top:pe,bottom:X});return await B(K)}}async crop([O,J,pe,X]){if(O=Math.max(O,0),J=Math.max(J,0),pe=Math.min(pe,this.width-1),X=Math.min(X,this.height-1),O===0&&J===0&&pe===this.width-1&&X===this.height-1)return this;const K=pe-O+1,j=X-J+1;if(Te){const k=this.channels,N=this.toCanvas(),E=P(K,j).getContext("2d");return E.drawImage(N,O,J,K,j,0,0,K,j),new fe(E.getImageData(0,0,K,j).data,K,j,4).convert(k)}else{const k=this.toSharp().extract({left:O,top:J,width:K,height:j});return await B(k)}}async center_crop(O,J){if(this.width===O&&this.height===J)return this;const pe=(this.width-O)/2,X=(this.height-J)/2;if(Te){const K=this.channels,j=this.toCanvas(),k=P(O,J).getContext("2d");let N=0,E=0,ue=0,be=0;return pe>=0?N=pe:ue=-pe,X>=0?E=X:be=-X,k.drawImage(j,N,E,O,J,ue,be,O,J),new fe(k.getImageData(0,0,O,J).data,O,J,4).convert(K)}else{let K=this.toSharp();if(pe>=0&&X>=0)K=K.extract({left:Math.floor(pe),top:Math.floor(X),width:O,height:J});else if(pe<=0&&X<=0){const j=Math.floor(-X),k=Math.floor(-pe);K=K.extend({top:j,left:k,right:O-this.width-k,bottom:J-this.height-j})}else{let j=[0,0],k=0;X<0?(j[0]=Math.floor(-X),j[1]=J-this.height-j[0]):k=Math.floor(X);let N=[0,0],E=0;pe<0?(N[0]=Math.floor(-pe),N[1]=O-this.width-N[0]):E=Math.floor(pe),K=K.extend({top:j[0],bottom:j[1],left:N[0],right:N[1]}).extract({left:E,top:k,width:O,height:J})}return await B(K)}}async toBlob(O="image/png",J=1){if(!Te)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:O,quality:J})}toTensor(O="CHW"){let J=new we.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(O!=="HWC")if(O==="CHW")J=J.permute(2,0,1);else throw new Error(`Unsupported channel format: ${O}`);return J}toCanvas(){if(!Te)throw new Error("toCanvas() is only supported in browser environments.");const O=this.clone().rgba(),J=P(O.width,O.height),pe=new D(O.data,O.width,O.height);return J.getContext("2d").putImageData(pe,0,0),J}_update(O,J,pe,X=null){return this.data=O,this.width=J,this.height=pe,X!==null&&(this.channels=X),this}clone(){return new fe(this.data.slice(),this.width,this.height,this.channels)}convert(O){if(this.channels===O)return this;switch(O){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(O){if(Te){if(L)throw new Error("Unable to save an image from a Web Worker.");const J=O.split(".").pop().toLowerCase(),pe=re.get(J)??"image/png",X=await this.toBlob(pe),K=URL.createObjectURL(X),j=document.createElement("a");j.href=K,j.download=O,j.click(),j.remove()}else{if(ce.env.useFS)return await this.toSharp().toFile(O);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Te)throw new Error("toSharp() is only supported in server-side environments.");return ye(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}},"./src/utils/maths.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{FFT:()=>le,bankers_round:()=>pe,cos_sim:()=>L,dot:()=>Te,dynamic_time_warping:()=>X,interpolate_data:()=>F,log_softmax:()=>ye,magnitude:()=>P,max:()=>B,medianFilter:()=>O,min:()=>D,permute_data:()=>ce,round:()=>J,softmax:()=>we});function F(K,[j,k,N],[E,ue],be="bilinear",Ce=!1){const De=ue/N,ze=E/k,it=new K.constructor(E*ue*j),rt=k*N,lt=E*ue;for(let me=0;me<E;++me)for(let W=0;W<ue;++W){const de=me*ue+W,xe=(W+.5)/De-.5,We=(me+.5)/ze-.5;let ot=Math.floor(xe),se=Math.floor(We);const Ze=Math.min(ot+1,N-1),dt=Math.min(se+1,k-1);ot=Math.max(ot,0),se=Math.max(se,0);const Re=xe-ot,ht=We-se,Mt=(1-Re)*(1-ht),Xe=Re*(1-ht),Z=(1-Re)*ht,Ae=Re*ht,Ke=se*N,et=dt*N,je=Ke+ot,Ve=Ke+Ze,ut=et+ot,_t=et+Ze;for(let St=0;St<j;++St){const xt=St*rt;it[St*lt+de]=Mt*K[xt+je]+Xe*K[xt+Ve]+Z*K[xt+ut]+Ae*K[xt+_t]}}return it}function ce(K,j,k){const N=new Array(k.length),E=new Array(k.length);for(let Ce=k.length-1,De=1;Ce>=0;--Ce)E[Ce]=De,N[Ce]=j[k[Ce]],De*=N[Ce];const ue=k.map((Ce,De)=>E[k.indexOf(De)]),be=new K.constructor(K.length);for(let Ce=0;Ce<K.length;++Ce){let De=0;for(let ze=j.length-1,it=Ce;ze>=0;--ze)De+=it%j[ze]*ue[ze],it=Math.floor(it/j[ze]);be[De]=K[Ce]}return[be,N]}function we(K){const j=B(K)[0],k=K.map(ue=>Math.exp(ue-j)),N=k.reduce((ue,be)=>ue+be,0);return k.map(ue=>ue/N)}function ye(K){const j=B(K)[0];let k=0;for(let ue=0;ue<K.length;++ue)k+=Math.exp(K[ue]-j);const N=Math.log(k);return K.map(ue=>ue-j-N)}function Te(K,j){let k=0;for(let N=0;N<K.length;++N)k+=K[N]*j[N];return k}function L(K,j){const k=Te(K,j),N=P(K),E=P(j);return k/(N*E)}function P(K){return Math.sqrt(K.reduce((j,k)=>j+k*k,0))}function D(K){if(K.length===0)throw Error("Array must not be empty");let j=K[0],k=0;for(let N=1;N<K.length;++N)K[N]<j&&(j=K[N],k=N);return[j,k]}function B(K){if(K.length===0)throw Error("Array must not be empty");let j=K[0],k=0;for(let N=1;N<K.length;++N)K[N]>j&&(j=K[N],k=N);return[Number(j),k]}function q(K){return K>0&&(K&K-1)===0}class re{constructor(j){if(this.size=j|0,this.size<=1||!q(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=j<<1,this.table=new Float64Array(this.size*2);for(let N=0;N<this.table.length;N+=2){const E=Math.PI*N/this.size;this.table[N]=Math.cos(E),this.table[N+1]=-Math.sin(E)}let k=0;for(let N=1;this.size>N;N<<=1)++k;this._width=k%2===0?k-1:k,this._bitrev=new Int32Array(1<<this._width);for(let N=0;N<this._bitrev.length;++N){this._bitrev[N]=0;for(let E=0;E<this._width;E+=2){const ue=this._width-E-2;this._bitrev[N]|=(N>>>E&3)<<ue}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(j,k){const N=k||new Array(j.length>>>1);for(let E=0;E<j.length;E+=2)N[E>>>1]=j[E];return N}toComplexArray(j,k){const N=k||this.createComplexArray();for(let E=0;E<N.length;E+=2)N[E]=j[E>>>1],N[E+1]=0;return N}transform(j,k){if(j===k)throw new Error("Input and output buffers must be different");this._transform4(j,k,1)}realTransform(j,k){if(j===k)throw new Error("Input and output buffers must be different");this._realTransform4(j,k,1)}inverseTransform(j,k){if(j===k)throw new Error("Input and output buffers must be different");this._transform4(j,k,-1);for(let N=0;N<j.length;++N)j[N]/=this.size}_transform4(j,k,N){const E=this._csize;let be=1<<this._width,Ce=E/be<<1,De,ze;const it=this._bitrev;if(Ce===4)for(De=0,ze=0;De<E;De+=Ce,++ze){const lt=it[ze];this._singleTransform2(k,j,De,lt,be)}else for(De=0,ze=0;De<E;De+=Ce,++ze){const lt=it[ze];this._singleTransform4(k,j,De,lt,be,N)}const rt=this.table;for(be>>=2;be>=2;be>>=2){Ce=E/be<<1;const lt=Ce>>>2;for(De=0;De<E;De+=Ce){const me=De+lt-1;for(let W=De,de=0;W<me;W+=2,de+=be){const xe=W,We=xe+lt,ot=We+lt,se=ot+lt,Ze=j[xe],dt=j[xe+1],Re=j[We],ht=j[We+1],Mt=j[ot],Xe=j[ot+1],Z=j[se],Ae=j[se+1],Ke=rt[de],et=N*rt[de+1],je=Re*Ke-ht*et,Ve=Re*et+ht*Ke,ut=rt[2*de],_t=N*rt[2*de+1],St=Mt*ut-Xe*_t,xt=Mt*_t+Xe*ut,v=rt[3*de],H=N*rt[3*de+1],$=Z*v-Ae*H,Y=Z*H+Ae*v,he=Ze+St,nt=dt+xt,Je=Ze-St,Nt=dt-xt,yt=je+$,bt=Ve+Y,zt=N*(je-$),Pt=N*(Ve-Y);j[xe]=he+yt,j[xe+1]=nt+bt,j[We]=Je+Pt,j[We+1]=Nt-zt,j[ot]=he-yt,j[ot+1]=nt-bt,j[se]=Je-Pt,j[se+1]=Nt+zt}}}}_singleTransform2(j,k,N,E,ue){const be=j[E],Ce=j[E+1],De=j[E+ue],ze=j[E+ue+1];k[N]=be+De,k[N+1]=Ce+ze,k[N+2]=be-De,k[N+3]=Ce-ze}_singleTransform4(j,k,N,E,ue,be){const Ce=ue*2,De=ue*3,ze=j[E],it=j[E+1],rt=j[E+ue],lt=j[E+ue+1],me=j[E+Ce],W=j[E+Ce+1],de=j[E+De],xe=j[E+De+1],We=ze+me,ot=it+W,se=ze-me,Ze=it-W,dt=rt+de,Re=lt+xe,ht=be*(rt-de),Mt=be*(lt-xe);k[N]=We+dt,k[N+1]=ot+Re,k[N+2]=se+Mt,k[N+3]=Ze-ht,k[N+4]=We-dt,k[N+5]=ot-Re,k[N+6]=se-Mt,k[N+7]=Ze+ht}_realTransform4(j,k,N){const E=this._csize;let be=1<<this._width,Ce=E/be<<1,De,ze;const it=this._bitrev;if(Ce===4)for(De=0,ze=0;De<E;De+=Ce,++ze){const me=it[ze];this._singleRealTransform2(k,j,De,me>>>1,be>>>1)}else for(De=0,ze=0;De<E;De+=Ce,++ze){const me=it[ze];this._singleRealTransform4(k,j,De,me>>>1,be>>>1,N)}const rt=this.table;for(be>>=2;be>=2;be>>=2){Ce=E/be<<1;const me=Ce>>>1,W=me>>>1,de=W>>>1;for(De=0;De<E;De+=Ce)for(let xe=0,We=0;xe<=de;xe+=2,We+=be){const ot=De+xe,se=ot+W,Ze=se+W,dt=Ze+W,Re=j[ot],ht=j[ot+1],Mt=j[se],Xe=j[se+1],Z=j[Ze],Ae=j[Ze+1],Ke=j[dt],et=j[dt+1],je=Re,Ve=ht,ut=rt[We],_t=N*rt[We+1],St=Mt*ut-Xe*_t,xt=Mt*_t+Xe*ut,v=rt[2*We],H=N*rt[2*We+1],$=Z*v-Ae*H,Y=Z*H+Ae*v,he=rt[3*We],nt=N*rt[3*We+1],Je=Ke*he-et*nt,Nt=Ke*nt+et*he,yt=je+$,bt=Ve+Y,zt=je-$,Pt=Ve-Y,dr=St+Je,Cr=xt+Nt,Yr=N*(St-Je),Rr=N*(xt-Nt);if(j[ot]=yt+dr,j[ot+1]=bt+Cr,j[se]=zt+Rr,j[se+1]=Pt-Yr,xe===0){j[Ze]=yt-dr,j[Ze+1]=bt-Cr;continue}if(xe===de)continue;const Jr=De+W-xe,bn=De+me-xe;j[Jr]=zt-N*Rr,j[Jr+1]=-Pt-N*Yr,j[bn]=yt-N*dr,j[bn+1]=-bt+N*Cr}}const lt=E>>>1;for(let me=2;me<lt;me+=2)j[E-me]=j[me],j[E-me+1]=-j[me+1]}_singleRealTransform2(j,k,N,E,ue){const be=j[E],Ce=j[E+ue];k[N]=be+Ce,k[N+1]=0,k[N+2]=be-Ce,k[N+3]=0}_singleRealTransform4(j,k,N,E,ue,be){const Ce=ue*2,De=ue*3,ze=j[E],it=j[E+ue],rt=j[E+Ce],lt=j[E+De],me=ze+rt,W=ze-rt,de=it+lt,xe=be*(it-lt);k[N]=me+de,k[N+1]=0,k[N+2]=W,k[N+3]=-xe,k[N+4]=me-de,k[N+5]=0,k[N+6]=W,k[N+7]=xe}}class fe{constructor(j){const k=2*(j-1),N=2*(2*j-1),E=2**Math.ceil(Math.log2(N));this.bufferSize=E,this._a=k;const ue=new Float64Array(N),be=new Float64Array(E);this._chirpBuffer=new Float64Array(E),this._buffer1=new Float64Array(E),this._buffer2=new Float64Array(E),this._outBuffer1=new Float64Array(E),this._outBuffer2=new Float64Array(E);const Ce=-2*Math.PI/j,De=Math.cos(Ce),ze=Math.sin(Ce);for(let it=0;it<N>>1;++it){const rt=(it+1-j)**2/2,lt=Math.sqrt(De**2+ze**2)**rt,me=rt*Math.atan2(ze,De),W=2*it;ue[W]=lt*Math.cos(me),ue[W+1]=lt*Math.sin(me),be[W]=ue[W],be[W+1]=-ue[W+1]}this._slicedChirpBuffer=ue.subarray(k,N),this._f=new re(E>>1),this._f.transform(this._chirpBuffer,be)}_transform(j,k,N){const E=this._buffer1,ue=this._buffer2,be=this._outBuffer1,Ce=this._outBuffer2,De=this._chirpBuffer,ze=this._slicedChirpBuffer,it=this._a;if(N)for(let rt=0;rt<ze.length;rt+=2){const lt=rt+1,me=rt>>1,W=k[me];E[rt]=W*ze[rt],E[lt]=W*ze[lt]}else for(let rt=0;rt<ze.length;rt+=2){const lt=rt+1;E[rt]=k[rt]*ze[rt]-k[lt]*ze[lt],E[lt]=k[rt]*ze[lt]+k[lt]*ze[rt]}this._f.transform(be,E);for(let rt=0;rt<De.length;rt+=2){const lt=rt+1;ue[rt]=be[rt]*De[rt]-be[lt]*De[lt],ue[lt]=be[rt]*De[lt]+be[lt]*De[rt]}this._f.inverseTransform(Ce,ue);for(let rt=0;rt<Ce.length;rt+=2){const lt=Ce[rt+it],me=Ce[rt+it+1],W=ze[rt],de=ze[rt+1];j[rt]=lt*W-me*de,j[rt+1]=lt*de+me*W}}transform(j,k){this._transform(j,k,!1)}realTransform(j,k){this._transform(j,k,!0)}}class le{constructor(j){this.fft_length=j,this.isPowerOfTwo=q(j),this.isPowerOfTwo?(this.fft=new re(j),this.outputBufferSize=2*j):(this.fft=new fe(j),this.outputBufferSize=this.fft.bufferSize)}realTransform(j,k){this.fft.realTransform(j,k)}transform(j,k){this.fft.transform(j,k)}}function O(K,j){if(j%2===0||j<=0)throw new Error("Window size must be a positive odd number");const k=new K.constructor(K.length),N=new K.constructor(j),E=Math.floor(j/2);for(let ue=0;ue<K.length;++ue){let be=0;for(let Ce=-E;Ce<=E;++Ce){let De=ue+Ce;De<0?De=Math.abs(De):De>=K.length&&(De=2*(K.length-1)-De),N[be++]=K[De]}N.sort(),k[ue]=N[E]}return k}function J(K,j){const k=Math.pow(10,j);return Math.round(K*k)/k}function pe(K){const j=Math.round(K);return Math.abs(K)%1===.5?j%2===0?j:j-1:j}function X(K){const j=K.length,k=K[0].length,N=[j+1,k+1],E=Array.from({length:N[0]},()=>Array(N[1]).fill(1/0));E[0][0]=0;const ue=Array.from({length:N[0]},()=>Array(N[1]).fill(-1));for(let it=1;it<N[1];++it)for(let rt=1;rt<N[0];++rt){const lt=E[rt-1][it-1],me=E[rt-1][it],W=E[rt][it-1];let de,xe;lt<me&&lt<W?(de=lt,xe=0):me<lt&&me<W?(de=me,xe=1):(de=W,xe=2),E[rt][it]=K[rt-1][it-1]+de,ue[rt][it]=xe}for(let it=0;it<N[1];++it)ue[0][it]=2;for(let it=0;it<N[0];++it)ue[it][0]=1;let be=j,Ce=k,De=[],ze=[];for(;be>0||Ce>0;)switch(De.push(be-1),ze.push(Ce-1),ue[be][Ce]){case 0:--be,--Ce;break;case 1:--be;break;case 2:--Ce;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${be}, ${Ce}]. Please file a bug report.`)}return De.reverse(),ze.reverse(),[De,ze]}},"./src/utils/tensor.js":(Dt,Ee,V)=>{V.r(Ee),V.d(Ee,{Tensor:()=>Te,cat:()=>K,full:()=>be,full_like:()=>Ce,interpolate:()=>D,interpolate_4d:()=>B,layer_norm:()=>O,matmul:()=>q,mean:()=>N,mean_pooling:()=>le,ones:()=>De,ones_like:()=>ze,permute:()=>P,quantize_embeddings:()=>lt,rfft:()=>re,stack:()=>j,std_mean:()=>k,topk:()=>fe,zeros:()=>it,zeros_like:()=>rt});var F=V("./src/utils/maths.js"),ce=V("./src/backends/onnx.js"),we=V("./src/ops/registry.js");const ye=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 Te{constructor(...W){ve(this,"ort_tensor");return(0,ce.isONNXTensor)(W[0])?this.ort_tensor=W[0]:this.ort_tensor=new ce.Tensor(W[0],W[1],W[2]),new Proxy(this,{get:(de,xe)=>{if(typeof xe=="string"){let We=Number(xe);if(Number.isInteger(We))return de._getitem(We)}return de[xe]},set:(de,xe,We)=>de[xe]=We})}get dims(){return this.ort_tensor.dims}set dims(W){this.ort_tensor.dims=W}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[W,...de]=this.dims;if(de.length>0){const xe=de.reduce((We,ot)=>We*ot);for(let We=0;We<W;++We)yield this._subarray(We,xe,de)}else yield*this.data}_getitem(W){const[de,...xe]=this.dims;if(W=X(W,de),xe.length>0){const We=xe.reduce((ot,se)=>ot*se);return this._subarray(W,We,xe)}else return new Te(this.type,[this.data[W]],xe)}indexOf(W){const de=this.data;for(let xe=0;xe<de.length;++xe)if(de[xe]==W)return xe;return-1}_subarray(W,de,xe){const We=W*de,ot=(W+1)*de,se="subarray"in this.data?this.data.subarray(We,ot):this.data.slice(We,ot);return new Te(this.type,se,xe)}item(){const W=this.data;if(W.length!==1)throw new Error(`a Tensor with ${W.length} elements cannot be converted to Scalar`);return W[0]}tolist(){return L(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){const W=this.data;for(let de=0;de<W.length;++de)W[de]=1/(1+Math.exp(-W[de]));return this}map(W){return this.clone().map_(W)}map_(W){const de=this.data;for(let xe=0;xe<de.length;++xe)de[xe]=W(de[xe],xe,de);return this}mul(W){return this.clone().mul_(W)}mul_(W){const de=this.data;for(let xe=0;xe<de.length;++xe)de[xe]*=W;return this}div(W){return this.clone().div_(W)}div_(W){const de=this.data;for(let xe=0;xe<de.length;++xe)de[xe]/=W;return this}add(W){return this.clone().add_(W)}add_(W){const de=this.data;for(let xe=0;xe<de.length;++xe)de[xe]+=W;return this}sub(W){return this.clone().sub_(W)}sub_(W){const de=this.data;for(let xe=0;xe<de.length;++xe)de[xe]-=W;return this}clone(){return new Te(this.type,this.data.slice(),this.dims.slice())}slice(...W){const de=[],xe=[];for(let Re=0;Re<this.dims.length;++Re){let ht=W[Re];if(ht==null)xe.push([0,this.dims[Re]]),de.push(this.dims[Re]);else if(typeof ht=="number")ht=X(ht,this.dims[Re],Re),xe.push([ht,ht+1]);else if(Array.isArray(ht)&&ht.length===2){let[Mt,Xe]=ht;if(Mt=Mt===null?0:X(Mt,this.dims[Re],Re,!1),Xe=Xe===null?this.dims[Re]:X(Xe,this.dims[Re],Re,!1),Mt>Xe)throw new Error(`Invalid slice: ${ht}`);const Z=[Math.max(Mt,0),Math.min(Xe,this.dims[Re])];xe.push(Z),de.push(Z[1]-Z[0])}else throw new Error(`Invalid slice: ${ht}`)}const We=xe.map(([Re,ht])=>ht-Re),ot=We.reduce((Re,ht)=>Re*ht),se=this.data,Ze=new se.constructor(ot),dt=this.stride();for(let Re=0;Re<ot;++Re){let ht=0;for(let Mt=We.length-1,Xe=Re;Mt>=0;--Mt){const Z=We[Mt];ht+=(Xe%Z+xe[Mt][0])*dt[Mt],Xe=Math.floor(Xe/Z)}Ze[Re]=se[ht]}return new Te(this.type,Ze,de)}permute(...W){return P(this,W)}transpose(...W){return this.permute(...W)}sum(W=null,de=!1){return this.norm(1,W,de)}norm(W="fro",de=null,xe=!1){if(W==="fro")W=2;else if(typeof W=="string")throw Error(`Unsupported norm: ${W}`);const We=this.data;if(de===null){let Ze=We.reduce((dt,Re)=>dt+Re**W,0)**(1/W);return new Te(this.type,[Ze],[])}de=X(de,this.dims.length);const ot=this.dims.slice();ot[de]=1;const se=new We.constructor(We.length/this.dims[de]);for(let Ze=0;Ze<We.length;++Ze){let dt=0;for(let Re=this.dims.length-1,ht=Ze,Mt=1;Re>=0;--Re){const Xe=this.dims[Re];if(Re!==de){const Z=ht%Xe;dt+=Z*Mt,Mt*=ot[Re]}ht=Math.floor(ht/Xe)}se[dt]+=We[Ze]**W}if(W!==1)for(let Ze=0;Ze<se.length;++Ze)se[Ze]=se[Ze]**(1/W);return xe||ot.splice(de,1),new Te(this.type,se,ot)}normalize_(W=2,de=1){de=X(de,this.dims.length);const xe=this.norm(W,de,!0),We=this.data,ot=xe.data;for(let se=0;se<We.length;++se){let Ze=0;for(let dt=this.dims.length-1,Re=se,ht=1;dt>=0;--dt){const Mt=this.dims[dt];if(dt!==de){const Xe=Re%Mt;Ze+=Xe*ht,ht*=this.dims[dt]}Re=Math.floor(Re/Mt)}We[se]/=ot[Ze]}return this}normalize(W=2,de=1){return this.clone().normalize_(W,de)}stride(){return E(this.dims)}squeeze(W=null){return new Te(this.type,this.data,J(this.dims,W))}squeeze_(W=null){return this.dims=J(this.dims,W),this}unsqueeze(W=null){return new Te(this.type,this.data,pe(this.dims,W))}unsqueeze_(W=null){return this.dims=pe(this.dims,W),this}flatten_(W=0,de=-1){de=(de+this.dims.length)%this.dims.length;let xe=this.dims.slice(0,W),We=this.dims.slice(W,de+1),ot=this.dims.slice(de+1);return this.dims=[...xe,We.reduce((se,Ze)=>se*Ze,1),...ot],this}flatten(W=0,de=-1){return this.clone().flatten_(W,de)}view(...W){let de=-1;for(let We=0;We<W.length;++We)if(W[We]===-1){if(de!==-1)throw new Error("Only one dimension can be inferred");de=We}const xe=this.data;if(de!==-1){const We=W.reduce((ot,se,Ze)=>Ze!==de?ot*se:ot,1);W[de]=xe.length/We}return new Te(this.type,xe,W)}neg_(){const W=this.data;for(let de=0;de<W.length;++de)W[de]=-W[de];return this}neg(){return this.clone().neg_()}clamp_(W,de){const xe=this.data;for(let We=0;We<xe.length;++We)xe[We]=Math.min(Math.max(xe[We],W),de);return this}clamp(W,de){return this.clone().clamp_(W,de)}round_(){const W=this.data;for(let de=0;de<W.length;++de)W[de]=Math.round(W[de]);return this}round(){return this.clone().round_()}mean(W=null,de=!1){return N(this,W,de)}to(W){if(this.type===W)return this;if(!ye.hasOwnProperty(W))throw new Error(`Unsupported type: ${W}`);return new Te(W,ye[W].from(this.data),this.dims)}}function L(me,W){const de=me.length,xe=W.reduce((ot,se)=>ot*se);if(de!==xe)throw Error(`cannot reshape array of size ${de} into shape (${W})`);let We=me;for(let ot=W.length-1;ot>=0;ot--)We=We.reduce((se,Ze)=>{let dt=se[se.length-1];return dt.length<W[ot]?dt.push(Ze):se.push([Ze]),se},[[]]);return We[0]}function P(me,W){const[de,xe]=(0,F.permute_data)(me.data,me.dims,W);return new Te(me.type,de,xe)}function D(me,[W,de],xe="bilinear",We=!1){const ot=me.dims.at(-3)??1,se=me.dims.at(-2),Ze=me.dims.at(-1);let dt=(0,F.interpolate_data)(me.data,[ot,se,Ze],[W,de],xe,We);return new Te(me.type,dt,[ot,W,de])}async function 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Te("int64",[BigInt(W)],[1])})}function le(me,W){const de=me.data,xe=W.data,We=[me.dims[0],me.dims[2]],ot=new de.constructor(We[0]*We[1]),[se,Ze,dt]=me.dims;let Re=0;for(let ht=0;ht<se;++ht){const Mt=ht*dt*Ze;for(let Xe=0;Xe<dt;++Xe){let Z=0,Ae=0;const Ke=ht*Ze,et=Mt+Xe;for(let Ve=0;Ve<Ze;++Ve){const ut=Number(xe[Ke+Ve]);Ae+=ut,Z+=de[et+Ve*dt]*ut}const je=Z/Ae;ot[Re++]=je}}return new Te(me.type,ot,We)}function O(me,W,{eps:de=1e-5}={}){if(me.dims.length!==2)throw new Error("`layer_norm` currently only supports 2D input.");const[xe,We]=me.dims;if(W.length!==1&&W[0]!==We)throw new Error("`normalized_shape` must be a 1D array with shape `[input.dims[1]]`.");const[ot,se]=k(me,1,0,!0),Ze=ot.data,dt=se.data,Re=me.data,ht=new Re.constructor(Re.length);for(let Mt=0;Mt<xe;++Mt){const Xe=Mt*We;for(let Z=0;Z<We;++Z){const Ae=Xe+Z;ht[Ae]=(Re[Ae]-dt[Mt])/(Ze[Mt]+de)}}return new Te(me.type,ht,me.dims)}function J(me,W){return me=me.slice(),W===null?me=me.filter(de=>de!==1):typeof W=="number"?me[W]===1&&me.splice(W,1):Array.isArray(W)&&(me=me.filter((de,xe)=>de!==1||!W.includes(xe))),me}function pe(me,W){return W=X(W,me.length+1),me=me.slice(),me.splice(W,0,1),me}function X(me,W,de=null,xe=!0){if(xe&&(me<-W||me>=W))throw new Error(`IndexError: index ${me} is out of bounds for dimension${de===null?"":" "+de} with size ${W}`);return me<0&&(me=(me%W+W)%W),me}function K(me,W=0){W=X(W,me[0].dims.length);const de=me[0].dims.slice();de[W]=me.reduce((se,Ze)=>se+Ze.dims[W],0);const xe=de.reduce((se,Ze)=>se*Ze,1),We=new me[0].data.constructor(xe),ot=me[0].type;if(W===0){let se=0;for(const Ze of me){const dt=Ze.data;We.set(dt,se),se+=dt.length}}else{let se=0;for(let Ze=0;Ze<me.length;++Ze){const{data:dt,dims:Re}=me[Ze];for(let ht=0;ht<dt.length;++ht){let Mt=0;for(let Xe=Re.length-1,Z=ht,Ae=1;Xe>=0;--Xe){const Ke=Re[Xe];let et=Z%Ke;Xe===W&&(et+=se),Mt+=et*Ae,Ae*=de[Xe],Z=Math.floor(Z/Ke)}We[Mt]=dt[ht]}se+=Re[W]}}return new Te(ot,We,de)}function j(me,W=0){return 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eMattingOutput,p.ImageSegmentationPipeline,p.ImageToImagePipeline,p.ImageToTextPipeline,p.InterruptableStoppingCriteria,p.JAISLMHeadModel,p.JAISModel,p.JAISPreTrainedModel,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.MaskFormerFeatureExtractor,p.MaskFormerForInstanceSegmentation,p.MaskFormerModel,p.MaskFormerPreTrainedModel,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.PvtForImageClassification,p.PvtImageProcessor,p.PvtModel,p.PvtPreTrainedModel,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.SapiensFeatureExtractor,p.SapiensForDepthEstimation,p.SapiensForNormalEstimation,p.SapiensForSemanticSegmentation,p.SapiensPreTrainedModel,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,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.ViTMAEModel,p.ViTMAEPreTrainedModel,p.ViTMSNForImageClassification,p.ViTMSNModel,p.ViTMSNPreTrainedModel,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;const pp=new Map;self.addEventListener("message",async Dt=>{const{model_id:Ee,text:V}=Dt.data;let F=pp.get(Ee);F||(F=If.from_pretrained(Ee).then(L=>{switch(L.constructor.name){case"LlamaTokenizer":case"Grok1Tokenizer":L.decoder.decoders.pop();break;case"T5Tokenizer":L.decoder.addPrefixSpace=!1;break}return L}),pp.set(Ee,F));const ce=await F,we=ce.encode(V);let ye=we.map(L=>ce.decode([L])),Te=[];switch(ce.constructor.name){case"BertTokenizer":Te=ye.map((L,P)=>P===0||L.startsWith("##")?0:8),ye=ye.map(L=>L.replace("##",""));break;case"T5Tokenizer":ye.length>0&&ye!==" "&&(ye[0]=ye[0].replace(/^ /,""));break}self.postMessage({token_ids:we,decoded:ye,margins:Te})})})();