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13:if(e>1)throw new Error("currently not supported vecX of uint64 yet");return["vec2<u32>","u32"];case 9:if(e!==4)throw new Error("bool must be vec4");return["u32","vec4<bool>"];default:throw new Error(`Unknown data type: ${t}`)}},mt=(t,e=1)=>{let r=vi(t,e);return typeof r=="string"?r:r[0]},Pt=(t,e=1)=>{let r=vi(t,e);return typeof r=="string"?r:r[1]},ye=(...t)=>{let e=[];return t.forEach(r=>{r.length!==0&&e.push({type:12,data:r},{type:12,data:K.computeStrides(r)})}),e},ot=t=>t%4===0?4:t%2===0?2:1,Sr=(t="f32",e,r="0")=>!e||e===1?`${t}(${r})`:`vec${e}<${t}>(${r})`,In=(t,e,r)=>t==="f32"?r:e===1?`f32(${r})`:`vec${e}<f32>(${r})`,Nr=(t,e)=>e===4?`(${t}.x + ${t}.y + ${t}.z + ${t}.w)`:e===2?`(${t}.x + ${t}.y)`:e===3?`(${t}.x + ${t}.y + ${t}.z)`:t,Se=(t,e,r,n)=>t.startsWith("uniforms.")&&r>4?typeof e=="string"?n==="f16"?`${t}[(${e}) / 8][(${e}) % 8 / 4][(${e}) % 8 % 4]`:`${t}[(${e}) / 4][(${e}) % 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    let dim${W} = current / ${Se(S,W,s)};
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    indices[${W}] = dim${W};
    current = rest${W};
    `;$+=`indices[${s-1}] = current;`;let E=s<2?"":`
  fn o2i_${t}(offset: u32) -> ${m.indices} {
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    var current = offset;
    ${$}
    return indices;
  }`,T=W=>(_.offsetToIndices=!0,s<2?W:`o2i_${t}(${W})`),A=[];if(s>=2)for(let W=s-1;W>=0;W--)A.push(`${Se(S,W,s)} * (indices[${W}])`);let P=s<2?"":`
  fn i2o_${t}(indices: ${m.indices}) -> u32 {
    return ${A.join("+")};
  }`,B=W=>(_.indicesToOffset=!0,s<2?W:`i2o_${t}(${W})`),D=(...W)=>s===0?"0u":`${m.indices}(${W.map(d).join(",")})`,q=(W,ae)=>s<2?`${W}`:`${Se(W,ae,s)}`,H=(W,ae,fe)=>s<2?`${W}=${fe};`:`${Se(W,ae,s)}=${fe};`,ie={},te=(W,ae)=>{_.broadcastedIndicesToOffset=!0;let fe=`${ae.name}broadcastedIndicesTo${t}Offset`;if(fe in ie)return`${fe}(${W})`;let Ce=[];for(let Be=s-1;Be>=0;Be--){let Ye=ae.indicesGet("outputIndices",Be+ae.rank-s);Ce.push(`${q(S,Be)} * (${Ye} % ${q(v,Be)})`)}return ie[fe]=`fn ${fe}(outputIndices: ${ae.type.indices}) -> u32 {
             return ${Ce.length>0?Ce.join("+"):"0u"};
           }`,`${fe}(${W})`},de=(W,ae)=>(()=>{if(m.storage===m.value)return`${t}[${W}]=${ae};`;if(m.storage==="vec2<u32>"&&m.value==="i32")return`${t}[${W}]=vec2<u32>(u32(${ae}), select(0u, 0xFFFFFFFFu, ${ae} < 0));`;if(m.storage==="vec2<u32>"&&m.value==="u32")return`${t}[${W}]=vec2<u32>(u32(${ae}), 0u);`;if(m.storage==="u32"&&m.value==="vec4<bool>")return`${t}[${W}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${ae}));`;throw new Error(`not supported combination of storage type ${m.storage} and value type ${m.value} yet`)})(),se=W=>(()=>{if(m.storage===m.value)return`${t}[${W}]`;if(m.storage==="vec2<u32>"&&m.value==="i32")return`i32(${t}[${W}].x)`;if(m.storage==="vec2<u32>"&&m.value==="u32")return`u32(${t}[${W}].x)`;if(m.storage==="u32"&&m.value==="vec4<bool>")return`vec4<bool>(bool(${t}[${W}] & 0xFFu), bool(${t}[${W}] & 0xFF00u), bool(${t}[${W}] & 0xFF0000u), bool(${t}[${W}] & 0xFF000000u))`;throw new Error(`not supported combination of storage type ${m.storage} and value type ${m.value} yet`)})(),M=s<2?"":`
  fn get_${t}ByIndices(indices: ${m.indices}) -> ${p} {
    return ${se(`i2o_${t}(indices)`)};
  }`,R=s<2?"":(()=>{let W=o.map(fe=>`d${fe}: u32`).join(", "),ae=o.map(fe=>`d${fe}`).join(", ");return`
  fn get_${t}(${W}) -> ${p} {
    return get_${t}ByIndices(${D(ae)});
  }`})(),G=(...W)=>{if(W.length!==s)throw new Error(`indices length must be ${s}`);let ae=W.map(d).join(",");return s===0?se("0u"):s===1?se(ae[0]):(_.get=!0,_.getByIndices=!0,_.indicesToOffset=!0,`get_${t}(${ae})`)},re=W=>s<2?se(W):(_.getByIndices=!0,_.indicesToOffset=!0,`get_${t}ByIndices(${W})`),ee=s<2?"":`
  fn set_${t}ByIndices(indices: ${m.indices}, value: ${p}) {
    ${de(`i2o_${t}(indices)`,"value")}
  }`,ne=s<2?"":(()=>{let W=o.map(fe=>`d${fe}: u32`).join(", "),ae=o.map(fe=>`d${fe}`).join(", ");return`
  fn set_${t}(${W}, value: ${p}) {
    set_${t}ByIndices(${D(ae)}, value);
  }`})();return{impl:()=>{let W=[],ae=!1;return _.offsetToIndices&&(W.push(E),ae=!0),_.indicesToOffset&&(W.push(P),ae=!0),_.broadcastedIndicesToOffset&&(Object.values(ie).forEach(fe=>W.push(fe)),ae=!0),_.set&&(W.push(ne),ae=!0),_.setByIndices&&(W.push(ee),ae=!0),_.get&&(W.push(R),ae=!0),_.getByIndices&&(W.push(M),ae=!0),!i&&ae&&W.unshift(`const ${v} = ${m.indices}(${r.join(",")});`,`const ${S} = ${m.indices}(${K.computeStrides(r).join(",")});`),W.join(`
`)},type:m,offsetToIndices:T,indicesToOffset:B,broadcastedIndicesToOffset:te,indices:D,indicesGet:q,indicesSet:H,set:(...W)=>{if(W.length!==s+1)throw new Error(`indices length must be ${s}`);let ae=W[s];if(typeof ae!="string")throw new Error("value must be string");let fe=W.slice(0,s).map(d).join(",");return s===0?de("0u",ae):s===1?de(fe[0],ae):(_.set=!0,_.setByIndices=!0,_.indicesToOffset=!0,`set_${t}(${fe}, ${ae})`)},setByOffset:de,setByIndices:(W,ae)=>s<2?de(W,ae):(_.setByIndices=!0,_.indicesToOffset=!0,`set_${t}ByIndices(${W}, ${ae});`),get:G,getByOffset:se,getByIndices:re,usage:n,name:t,strides:S,shape:v,rank:s}},X=(t,e,r,n=1)=>$i(t,e,r,"input",n),ge=(t,e,r,n=1)=>$i(t,e,r,"output",n),Jo=(t,e,r,n=1)=>$i(t,e,r,"internal",n),Eu=class{constructor(t,e){this.normalizedDispatchGroup=t,this.limits=e,this.internalVariables=[],this.variables=[],this.uniforms=[],this.variableIndex=0}guardAgainstOutOfBoundsWorkgroupSizes(t){return`if (global_idx >= ${typeof t=="number"?`${t}u`:t}) { return; }`}mainStart(t=zn){let e=typeof t=="number"?t:t[0],r=typeof t=="number"?1:t[1],n=typeof t=="number"?1:t[2];if(e>this.limits.maxComputeWorkgroupSizeX||r>this.limits.maxComputeWorkgroupSizeY||n>this.limits.maxComputeWorkgroupSizeZ)throw new Error(`workgroup size [${e}, ${r}, ${n}] exceeds the maximum workgroup size [${this.limits.maxComputeWorkgroupSizeX}, ${this.limits.maxComputeWorkgroupSizeY}, ${this.limits.maxComputeWorkgroupSizeZ}].`);if(e*r*n>this.limits.maxComputeInvocationsPerWorkgroup)throw new Error(`workgroup size [${e}, ${r}, ${n}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let a=this.normalizedDispatchGroup[1]===1&&this.normalizedDispatchGroup[2]===1,i=a?`@builtin(global_invocation_id) global_id : vec3<u32>,
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    @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=a?"let global_idx = global_id.x; let local_idx = local_id.x;":`let global_idx = (workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
          workgroup_id.y * num_workgroups[0] + workgroup_id.x) * ${e*r*n}u + local_idx;`;return`@compute @workgroup_size(${e}, ${r}, ${n})
  fn main(${i}) {
    ${s}
  `}appendVariableUniforms(t){t.rank!==0&&(t.shape.startsWith("uniforms.")&&this.uniforms.push({name:t.shape.replace("uniforms.",""),type:"u32",length:t.rank}),t.strides.startsWith("uniforms.")&&this.uniforms.push({name:t.strides.replace("uniforms.",""),type:"u32",length:t.rank}))}declareVariable(t,e){if(t.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(t),this.appendVariableUniforms(t);let r=t.usage==="input"?"read":"read_write",n=t.type.storage;return`@group(0) @binding(${e}) var<storage, ${r}> ${t.name}: array<${n}>;`}declareVariables(...t){return t.map(e=>this.declareVariable(e,this.variableIndex++)).join(`
`)}registerInternalVariable(t){if(t.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() instead.");this.internalVariables.push(t),this.appendVariableUniforms(t)}registerInternalVariables(...t){return t.forEach(e=>this.registerInternalVariable(e)),this}registerUniform(t,e,r=1){return this.uniforms.push({name:t,type:e,length:r}),this}registerUniforms(t){return this.uniforms=this.uniforms.concat(t),this}uniformDeclaration(){if(this.uniforms.length===0)return"";let t=[];for(let{name:e,type:r,length:n}of this.uniforms)if(n&&n>4)r==="f16"?t.push(`@align(16) ${e}:array<mat2x4<${r}>, ${Math.ceil(n/8)}>`):t.push(`${e}:array<vec4<${r}>, ${Math.ceil(n/4)}>`);else{let a=n==null||n===1?r:`vec${n}<${r}>`;t.push(`${e}:${a}`)}return`
      struct Uniforms { ${t.join(", ")} };
      @group(0) @binding(${this.variableIndex}) var<uniform> uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(t=>t.impl()).join(`
`)+this.internalVariables.map(t=>t.impl()).join(`
`)}get variablesInfo(){if(this.uniforms.length===0)return;let t=e=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(e)];return this.uniforms.map(e=>[t(e.type),e.length??1])}},vh=(t,e)=>new Eu(t,e),xa=(t,e)=>{let r=t.length,n=[];for(let a=0;a<r;a++){let i=r-1-a,s=t[i]||1;(e[e.length-1-a]||1)>1&&s===1&&n.unshift(i)}return n}}),Cu,Fs,Tu,Au,Er,$h,xh,Ia=J(()=>{$e(),Me(),ct(),Te(),Cu=t=>{if(!t||t.length!==1)throw new Error("Transpose requires 1 input.")},Fs=(t,e)=>e&&e.length!==t?[...new Array(t).keys()].reverse():e,Tu=(t,e)=>K.sortBasedOnPerm(t,Fs(t.length,e)),Au=(t,e,r,n)=>{let a=[];a.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
    var a: ${r.type.indices};`);for(let i=0;i<e;++i)a.push(r.indicesSet("a",t[i],`i[${i}]`));return a.push("return a;}"),a.join(`
`)},Er=(t,e)=>{let r=t.dataType,n=t.dims.length,a=Fs(n,e),i=Tu(t.dims,a),s=ge("output",r,i.length),o=X("a",r,n),l=u=>`
  ${u.registerUniform("output_size","u32").declareVariables(o,s)}

  ${Au(a,n,o,s)}

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

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

    ${s.setByOffset("global_idx",o.getByIndices("aIndices"))}
  }`;return{name:"Transpose",shaderCache:{hint:`${e}`,inputDependencies:["rank"]},getRunData:u=>{let p=K.size(i);return{outputs:[{dims:i,dataType:u[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...ye(u[0].dims,i)]}},getShaderSource:l}},$h=(t,e)=>{Cu(t.inputs),t.compute(Er(t.inputs[0],e.perm))},xh=t=>Ke({perm:t.perm})}),Iu,Mu,Ou,zu,Pu,Ru,Bu,Du,Nu,Fu,tr,Sh,kh,Eh,Ch,Th,Ah,Ih,Mh,Oh,zh,vy=J(()=>{$e(),Me(),Te(),el(),Ia(),Iu={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"},Mu={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"},Ou={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},zu={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},Pu=(t,e)=>{let r=[];for(let n=e-t;n<e;++n)r.push(n);return r},Ru=(t,e)=>{let r=[],n=t.length;for(let i=0;i<n;i++)e.indexOf(i)===-1&&r.push(t[i]);let a=e.map(i=>t[i]);return[r,a]},Bu=(t,e)=>{let r=t.length+e.length,n=[],a=0;for(let i=0;i<r;i++)e.indexOf(i)===-1?n.push(t[a++]):n.push(1);return n},Du=(t,e)=>{for(let r=0;r<t.length;++r)if(t[t.length-r-1]!==e-1-r)return!1;return!0},Nu=(t,e)=>{let r=[];if(!Du(t,e)){for(let n=0;n<e;++n)t.indexOf(n)===-1&&r.push(n);t.forEach(n=>r.push(n))}return r},Fu=(t,e,r,n,a,i,s)=>{let o=r[0].dims,l=K.size(i),u=K.size(s),p=X("_A",r[0].dataType,o),h=ge("output",a,i),m=32,d=`
          var<workgroup> aBestValues : array<f32, ${m}>;
       `;return{name:t,shaderCache:e,getShaderSource:_=>`
        ${_.registerUniform("reduceSize","u32").declareVariables(p,h)}
        ${d}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${_.mainStart(m)}

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

          var bestValue = f32(${Ou[n]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${m}) {
           let candidate = f32(${p.getByOffset("offset + k")});
           bestValue = ${Iu[n]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

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

         if (local_idx == 0u) {
          ${h.setByOffset("outputIndex",`${n==="mean"?`${h.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${h.type.storage}(${zu[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:l},programUniforms:[{type:12,data:u}]})}},tr=(t,e,r,n)=>{let a=t.inputs.length===1?r:xo(t.inputs,r),i=a.axes;i.length===0&&!a.noopWithEmptyAxes&&(i=t.inputs[0].dims.map((d,_)=>_));let s=K.normalizeAxes(i,t.inputs[0].dims.length),o=s,l=t.inputs[0],u=Nu(o,t.inputs[0].dims.length);u.length>0&&(l=t.compute(Er(t.inputs[0],u),{inputs:[0],outputs:[-1]})[0],o=Pu(o.length,l.dims.length));let[p,h]=Ru(l.dims,o),m=p;a.keepDims&&(m=Bu(p,s)),t.compute(Fu(e,{hint:a.cacheKey,inputDependencies:["type"]},[l],n,t.inputs[0].dataType,m,h),{inputs:[l]})},Sh=(t,e)=>{tr(t,"ReduceMeanShared",e,"mean")},kh=(t,e)=>{tr(t,"ReduceL1Shared",e,"l1")},Eh=(t,e)=>{tr(t,"ReduceL2Shared",e,"l2")},Ch=(t,e)=>{tr(t,"ReduceLogSumExpShared",e,"logSumExp")},Th=(t,e)=>{tr(t,"ReduceMaxShared",e,"max")},Ah=(t,e)=>{tr(t,"ReduceMinShared",e,"min")},Ih=(t,e)=>{tr(t,"ReduceProdShared",e,"prod")},Mh=(t,e)=>{tr(t,"ReduceSumShared",e,"sum")},Oh=(t,e)=>{tr(t,"ReduceSumSquareShared",e,"sumSquare")},zh=(t,e)=>{tr(t,"ReduceLogSumShared",e,"logSum")}}),rr,Lu,ji,xo,nr,Uu,Wu,Vu,Gu,Hu,ju,qu,Ku,Yu,Xu,ar,Ph,Rh,Bh,Dh,Nh,Fh,Lh,Uh,Wh,Vh,el=J(()=>{$e(),Me(),ct(),Te(),vy(),rr=t=>{if(!t||t.length===0||t.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(t.length===2&&t[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Lu=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],ji=(t,e,r,n,a,i,s=!1,o=!1)=>{let l=[],u=r[0].dims,p=u.length,h=K.normalizeAxes(a,p),m=!o&&h.length===0;u.forEach((w,v)=>{m||h.indexOf(v)>=0?s&&l.push(1):l.push(w)});let d=l.length,_=K.size(l);return{name:t,shaderCache:e,getShaderSource:w=>{let v=[],S=X("_A",r[0].dataType,p),$=ge("output",i,d),E=n(S,$,h),T=E[2];for(let A=0,P=0;A<p;A++)m||h.indexOf(A)>=0?(s&&P++,T=`for(var j${A}: u32 = 0; j${A} < ${u[A]}; j${A}++) {
                  ${E[2].includes("last_index")?`let last_index = j${A};`:""}
                  ${S.indicesSet("input_indices",A,`j${A}`)}
                  ${T}
                }`):(v.push(`${S.indicesSet("input_indices",A,$.indicesGet("output_indices",P))};`),P++);return`

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

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

          ${v.join(`
`)}
          ${E[0]}       // init ops for reduce max/min
          ${E[1]}
          ${T}
          ${E[3]}
          ${E.length===4?$.setByOffset("global_idx","value"):E.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:l,dataType:i}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...ye(u,l)]})}},xo=(t,e)=>{let r=[];return t[1].dims[0]>0&&t[1].getBigInt64Array().forEach(n=>r.push(Number(n))),Ke({axes:r,keepDims:e.keepDims,noopWithEmptyAxes:e.noopWithEmptyAxes})},nr=(t,e,r,n)=>{let a=t.inputs,i=a.length===1?r:xo(a,r);t.compute(ji(e,{hint:i.cacheKey,inputDependencies:["rank"]},[a[0]],i.noopWithEmptyAxes&&i.axes.length===0?Lu:n,i.axes,a[0].dataType,i.keepDims,i.noopWithEmptyAxes),{inputs:[0]})},Uu=(t,e)=>{rr(t.inputs),nr(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},Wu=(t,e)=>{rr(t.inputs),nr(t,"ReduceL1",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},Vu=(t,e)=>{rr(t.inputs),nr(t,"ReduceL2",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},Gu=(t,e)=>{rr(t.inputs),nr(t,"ReduceLogSumExp",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},Hu=(t,e)=>{rr(t.inputs),nr(t,"ReduceMax",e,(r,n,a)=>{let i=[];for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&i.push(r.indicesSet("input_indices",s,0));return[`${i.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = max(value, ${r.getByIndices("input_indices")});`,""]})},ju=(t,e)=>{rr(t.inputs),nr(t,"ReduceMean",e,(r,n,a)=>{let i=1;for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&(i*=t.inputs[0].dims[s]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${i});`]})},qu=(t,e)=>{rr(t.inputs),nr(t,"ReduceMin",e,(r,n,a)=>{let i=[];for(let s=0;s<r.rank;s++)(a.indexOf(s)>=0||a.length===0)&&i.push(`input_indices[${s}] = 0;`);return[`${i.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},Ku=(t,e)=>{rr(t.inputs),nr(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},Yu=(t,e)=>{rr(t.inputs),nr(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},Xu=(t,e)=>{rr(t.inputs),nr(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},ar=(t,e,r)=>{if(e.length===0)return r;let n=1,a=1;for(let i=0;i<e.length;i++)e.indexOf(i)===-1?n*=t[i]:a*=t[i];return a<32&&n>1024},Ph=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?ju(t,e):Sh(t,e)},Rh=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Wu(t,e):kh(t,e)},Bh=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Vu(t,e):Eh(t,e)},Dh=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Gu(t,e):Ch(t,e)},Nh=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Hu(t,e):Th(t,e)},Fh=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?qu(t,e):Ah(t,e)},Lh=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Ku(t,e):Ih(t,e)},Uh=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Yu(t,e):Mh(t,e)},Wh=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Xu(t,e):Oh(t,e)},Vh=(t,e)=>{ar(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Uu(t,e):zh(t,e)}}),Ls,Gh,Hh,So,$y=J(()=>{$e(),ct(),el(),Ls=t=>{if(!t||t.length===0||t.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(t[0].dataType!==1)throw new Error("Invalid input type.")},Gh=(t,e)=>{Ls(t.inputs);let r=(n,a,i)=>{let s=[];for(let o=0;o<n.rank;o++)(i.indexOf(o)>=0||i.length===0)&&s.push(`input_indices[${o}] = 0;`);return[`${s.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?"<=":"<"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(ji("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Hh=(t,e)=>{Ls(t.inputs);let r=(n,a,i)=>{let s=[];for(let o=0;o<n.rank;o++)(i.indexOf(o)>=0||i.length===0)&&s.push(`input_indices[${o}] = 0;`);return[`${s.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?">=":">"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(ji("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},So=t=>Ke(t)}),Qu,Zu,Ju,qi,jh,qh,Kh=J(()=>{$e(),Me(),ct(),Te(),Qu=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");let r=0,n=t[r],a=n.dataType,i=n.dims.length;t.forEach((s,o)=>{if(o!==r){if(s.dataType!==a)throw new Error("input tensors should be one type");if(s.dims.length!==i)throw new Error("input tensors should have the same shape");s.dims.forEach((l,u)=>{if(u!==e&&l!==n.dims[u])throw new Error("non concat dimensions must match")})}})},Zu=(t,e)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${t}u>(${e});
    for (var i: u32 = 0u; i < ${t}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${t}u;
  }`,Ju=(t,e)=>{let r=t.length,n=[];for(let a=0;a<r;++a){let i=e.setByOffset("global_idx",t[a].getByIndices("indices"));r===1?n.push(i):a===0?n.push(`if (inputIndex == ${a}u) { ${i} }`):a===r-1?n.push(`else { ${i} }`):n.push(`else if (inputIndex == ${a}) { ${i} }`)}return n.join(`
`)},qi=(t,e,r,n)=>{let a=K.size(r),i=new Array(t.length),s=new Array(t.length),o=0,l=[],u=[],p=[{type:12,data:a}];for(let w=0;w<t.length;++w)o+=t[w].dims[e],i[w]=o,u.push(t[w].dims.length),s[w]=X(`input${w}`,n,u[w]),l.push("rank"),p.push({type:12,data:i[w]});for(let w=0;w<t.length;++w)p.push(...ye(t[w].dims));p.push(...ye(r));let h=ge("output",n,r.length),m=h.indicesGet("indices",e),d=Array.from(Array(i.length).keys()).map(w=>`uniforms.sizeInConcatAxis${w}`).join(","),_=w=>`

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

  ${Zu(i.length,d)}

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

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

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

    ${Ju(s,h)}
  }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:_}},jh=(t,e)=>{let r=t.inputs,n=r[0].dims,a=K.normalizeAxis(e.axis,n.length);Qu(r,a);let i=n.slice();i[a]=r.reduce((o,l)=>o+(l.dims.length>a?l.dims[a]:0),0);let s=r.filter(o=>K.size(o.dims)>0);t.compute(qi(s,a,i,r[0].dataType),{inputs:s})},qh=t=>Ke({axis:t.axis})}),ed,td,rd,nd,Ki,ad,Yh,Xh=J(()=>{$e(),Xo(),Te(),Kh(),ed=(t,e)=>{let r=t[0],n=t[1],a=t[2],i=t[3],s=t[4],o=t[5];if(s&&o)throw new Error("Attention cannot have both past and relative_position_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let l=r.dims[0],u=r.dims[1],p=r.dims[2];if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(a.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let h=a.dims[0]/3,m=h,d=m;if(e.qkvHiddenSizes.length>0){if(e.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let E of e.qkvHiddenSizes)if(E%e.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");h=e.qkvHiddenSizes[0],m=e.qkvHiddenSizes[1],d=e.qkvHiddenSizes[2]}let _=u;if(h!==m)throw new Error("qkv_hidden_sizes first element should be same as the second");if(a.dims[0]!==h+m+d)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let w=0;if(s){if(m!==d)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(s.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(s.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(s.dims[1]!==l)throw new Error('Input "past" second dimension must be batch_size');if(s.dims[2]!==e.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(s.dims[4]!==m/e.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');e.pastPresentShareBuffer||(w=s.dims[3])}let v=_+w,S=-1,$=0;if(i)throw new Error("Mask not supported");if(s)throw new Error("past is not supported");return{batchSize:l,sequenceLength:u,pastSequenceLength:w,kvSequenceLength:_,totalSequenceLength:v,maxSequenceLength:S,inputHiddenSize:p,hiddenSize:h,vHiddenSize:d,headSize:Math.floor(h/e.numHeads),vHeadSize:Math.floor(d/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:$,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},td=(t,e,r,n)=>{let a=ot(n),i=64,s=n/a;s<i?i=1:s/8<64&&(i=Math.ceil(s/8));let o=Math.ceil(n/a/i),l=[{type:e.dataType,data:1/n},{type:12,data:s},{type:12,data:o}],u=mt(e.dataType,a),p=Pt(1,a),h=m=>{let d=ge("x",e.dataType,e.dims,a),_=[{name:"d_inv",type:Pt(e.dataType)},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${i}>;
  var<workgroup> thread_sum: array<f32, ${i}>;
  ${m.registerUniforms(_).declareVariables(d)}
  ${m.mainStart([i,1,1])}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = workgroup_id.x * uniforms.d_comp + local_offset;

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

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

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

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

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        x[offset + i] = ${d.type.value}(uniforms.d_inv);
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        var f32input = ${p}(x[offset + i]);
        x[offset + i] = ${d.type.value}(exp(f32input - max_value) / sum);
      }
    }
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${i};${u};${a}`},getShaderSource:h,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:l})}},rd=(t,e,r,n,a,i,s)=>{let o=s+a.kvSequenceLength,l=[a.batchSize,a.numHeads,a.sequenceLength,o],u=i.scale===0?1/Math.sqrt(a.headSize):i.scale,p=ot(a.headSize),h=a.headSize/p,m=12,d={x:Math.ceil(o/m),y:Math.ceil(a.sequenceLength/m),z:a.batchSize*a.numHeads},_=[{type:12,data:a.sequenceLength},{type:12,data:h},{type:12,data:o},{type:12,data:a.numHeads},{type:1,data:u}],w=n?["type","type","type"]:["type","type"],v=S=>{let $=X("q",e.dataType,e.dims,p),E=X("key",r.dataType,r.dims,p),T=[$,E];n&&T.push(X("relative_position_bias",n.dataType,n.dims));let A=ge("output",e.dataType,l),P=Pt(1,p),B=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return`
  const TILE_SIZE = ${m}u;

  var<workgroup> tileQ: array<${$.type.storage}, ${m*m}>;
  var<workgroup> tileK: array<${$.type.storage}, ${m*m}>;
  ${S.registerUniforms(B).declareVariables(...T,A)}
  ${S.mainStart([m,m,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;
    let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;

    var value = ${P}(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) {
        tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      workgroupBarrier();

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

      workgroupBarrier();
    }

    let headOffset = headIdx * uniforms.M * uniforms.N;
    if (global_id.y < uniforms.M && global_id.x < uniforms.N) {
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(p){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: ${p}`)}})()};
        output[outputIdx] = ${A.type.value} (sum * uniforms.alpha) + ${n?"relative_position_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${p}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:l,dataType:e.dataType,gpuDataType:0}],dispatchGroup:d,programUniforms:_}),getShaderSource:v}},nd=(t,e,r,n,a)=>{let i=a+n.kvSequenceLength,s=[n.batchSize,n.sequenceLength,n.vHiddenSize],o=12,l={x:Math.ceil(n.vHeadSize/o),y:Math.ceil(n.sequenceLength/o),z:n.batchSize*n.numHeads},u=[{type:12,data:n.sequenceLength},{type:12,data:i},{type:12,data:n.vHeadSize},{type:12,data:n.numHeads},{type:12,data:n.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:u}),getShaderSource:p=>{let h=X("probs",e.dataType,e.dims),m=X("v",r.dataType,r.dims),d=ge("output",e.dataType,s),_=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`
  const TILE_SIZE = ${o}u;
  var<workgroup> tileQ: array<${h.type.value}, ${o*o}>;
  var<workgroup> tileK: array<${h.type.value}, ${o*o}>;
  ${p.registerUniforms(_).declareVariables(h,m,d)}
  ${p.mainStart([o,o,1])}
   let headIdx = workgroup_id.z;
   let m = global_id.y;
   let n = global_id.x;

   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;
   let offsetB = headIdx * (uniforms.N * uniforms.K) + n;

   var value = ${h.type.storage}(0);
   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
     if (m < uniforms.M && w + local_id.x < uniforms.K) {
       tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
     }
     if (n < uniforms.N && w + local_id.y < uniforms.K) {
       tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];
     }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + currentBatchHeadNumber * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`}}},Ki=(t,e,r,n,a,i,s,o,l,u,p)=>{let h=t.outputCount>1,m=t.outputCount>2,d=h&&m?u.pastSequenceLength:0,_=d+u.kvSequenceLength,w=[u.batchSize,u.numHeads,_,u.headSize],v=s?[s,r]:[r],S=h?t.compute(qi(v,2,w,r.dataType),{inputs:v,outputs:[1]})[0]:r,$=[u.batchSize,u.numHeads,_,u.headSize],E=o?[o,n]:[n],T=m?t.compute(qi(E,2,$,n.dataType),{inputs:E,outputs:[2]})[0]:n,A=[e,S];l&&A.push(l);let P=t.compute(rd(t,e,S,l,u,p,d),{inputs:A,outputs:[-1]})[0];t.compute(td(t,P,u.batchSize*u.numHeads*u.sequenceLength,_),{inputs:[P],outputs:[]});let B=[P,T];t.compute(nd(t,P,T,u,d),{inputs:B,outputs:[0]})},ad=(t,e)=>{let r=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],n=e.sequenceLength,a=e.inputHiddenSize,i=e.headSize,s=12,o={x:Math.ceil(e.headSize/s),y:Math.ceil(e.sequenceLength/s),z:e.batchSize*e.numHeads},l=[t.inputs[0],t.inputs[1],t.inputs[2]],u=[{type:12,data:n},{type:12,data:a},{type:12,data:i},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],p=h=>{let m=ge("output_q",l[0].dataType,r),d=ge("output_k",l[0].dataType,r),_=ge("output_v",l[0].dataType,r),w=X("input",l[0].dataType,l[0].dims),v=X("weight",l[1].dataType,l[1].dims),S=X("bias",l[2].dataType,l[2].dims),$=w.type.storage,E=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
  const TILE_SIZE = ${s}u;
  var<workgroup> tileInput: array<${$}, ${s*s}>;
  var<workgroup> tileWeightQ: array<${$}, ${s*s}>;
  var<workgroup> tileWeightK: array<${$}, ${s*s}>;
  var<workgroup> tileWeightV: array<${$}, ${s*s}>;
  ${h.registerUniforms(E).declareVariables(w,v,S,m,d,_)}
  ${h.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 = ${$}(0);
    var valueK = ${$}(0);
    var valueV = ${$}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return t.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:u}),getShaderSource:p},{inputs:l,outputs:[-1,-1,-1]})},Yh=(t,e)=>{let r=ed(t.inputs,e),[n,a,i]=ad(t,r);return Ki(t,n,a,i,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),id,sd,od,Qh,xy=J(()=>{dr(),$e(),Me(),ct(),Te(),id=(t,e)=>{if(!t||t.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,a,i)=>{let s=a.length;if(s!==n.length)throw new Error(`${i}: num dimensions != ${s}`);a.forEach((o,l)=>{if(o!==n[l])throw new Error(`${i}: dim[${l}] do not match`)})};if(t[0].dims.length>1){let n=e.format==="NHWC"?e.spatial?t[0].dims.slice(-1):t[0].dims.slice(-1).concat(t[0].dims.slice(1,t[0].dims.length-1)):t[0].dims.slice(1,e.spatial?2:void 0);r(t[1].dims,n,"Invalid input scale"),r(t[2].dims,n,"Invalid input B"),r(t[3].dims,n,"Invalid input mean"),r(t[4].dims,n,"Invalid input var")}else r(t[1].dims,[1],"Invalid input scale"),r(t[2].dims,[1],"Invalid input B"),r(t[3].dims,[1],"Invalid input mean"),r(t[4].dims,[1],"Invalid input var")},sd=(t,e)=>{let{epsilon:r,spatial:n,format:a}=e,i=t[0].dims,s=n?ot(i[i.length-1]):1,o=a==="NHWC"&&i.length>1?s:1,l=K.size(i)/s,u=n,p=u?i.length:i,h=X("x",t[0].dataType,t[0].dims,s),m=X("scale",t[1].dataType,t[1].dims,o),d=X("bias",t[2].dataType,t[2].dims,o),_=X("inputMean",t[3].dataType,t[3].dims,o),w=X("inputVar",t[4].dataType,t[4].dims,o),v=ge("y",t[0].dataType,p,s),S=()=>{let E="";if(n)E=`let cOffset = ${i.length===1?"0u":a==="NHWC"?`outputIndices[${i.length-1}] / ${s}`:"outputIndices[1]"};`;else if(a==="NCHW")E=`
            ${v.indicesSet("outputIndices","0","0")}
            let cOffset = ${v.indicesToOffset("outputIndices")};`;else{E=`var cIndices = ${m.type.indices}(0);
                       cIndices[0] = outputIndices[${i.length-1}];`;for(let T=1;T<m.rank;T++)E+=`cIndices[${T}] = outputIndices[${T}];`;E+=`let cOffset = ${m.indicesToOffset("cIndices")};`}return E},$=E=>`
  const epsilon = ${r};
  ${E.registerUniform("outputSize","u32").declareVariables(h,m,d,_,w,v)}
  ${E.mainStart()}
  ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${v.offsetToIndices(`global_idx * ${s}`)};
    ${S()}
    let scale = ${m.getByOffset("cOffset")};
    let bias = ${d.getByOffset("cOffset")};
    let inputMean = ${_.getByOffset("cOffset")};
    let inputVar = ${w.getByOffset("cOffset")};
    let x = ${h.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${v.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${e.epsilon}_${e.format}_${n}_${s}`,inputDependencies:u?["rank","type","type","type","type"]:void 0},getShaderSource:$,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u?[{type:12,data:l},...ye(i)]:[{type:12,data:l}]})}},od=t=>Ke(t),Qh=(t,e)=>{let{inputs:r,outputCount:n}=t,a=od({...e,outputCount:n});if(Ue.webgpu.validateInputContent&&id(r,a),e.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");t.compute(sd(r,a))}}),ld,ud,Zh,Sy=J(()=>{Me(),Te(),ld=t=>{if(t[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(t[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(t[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw new Error("last dimension of input and bias are not the same")},ud=t=>{let e=t[0].dims,r=t[0].dims[2],n=K.size(e)/4,a=t[0].dataType,i=X("input",a,e,4),s=X("bias",a,[r],4),o=X("residual",a,e,4),l=ge("output",a,e,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:u=>`
  const channels = ${r}u / 4;
  ${u.declareVariables(i,s,o,l)}

  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${i.getByOffset("global_idx")}
      + ${s.getByOffset("global_idx % channels")} + ${o.getByOffset("global_idx")};
    ${l.setByOffset("global_idx","value")}
  }`}},Zh=t=>{ld(t.inputs),t.compute(ud(t.inputs))}}),dd,Re,Jh,ef,tf,rf,nf,af,sf,of,lf,cd,uf,df,cf,pf,zi,hf,Pi,ff,mf,gf,_f,yf,wf,bf,vf,$f,xf,Sf,kf,Ef,Cf,Tf,Af,Us,If,ko,Eo,Mf,Of,zf,tl=J(()=>{$e(),Me(),ct(),Te(),dd=(t,e,r,n,a,i)=>{let s=Math.ceil(e/4),o="";typeof a=="string"?o=`${a}(a)`:o=a("a");let l=X("inputData",r,[s],4),u=ge("outputData",n,[s],4);return`
      ${t.registerUniform("vec_size","u32").declareVariables(l,u)}

  ${i??""}

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

    let a = ${l.getByOffset("global_idx")};
    ${u.setByOffset("global_idx",o)}
  }`},Re=(t,e,r,n,a,i=t.dataType)=>({name:e,shaderCache:{hint:a,inputDependencies:["type"]},getShaderSource:s=>dd(s,K.size(t.dims),t.dataType,i,r,n),getRunData:s=>({outputs:[{dims:t.dims,dataType:i}],dispatchGroup:{x:Math.ceil(K.size(s[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(K.size(t.dims)/4)}]})}),Jh=t=>{t.compute(Re(t.inputs[0],"Abs","abs"))},ef=t=>{t.compute(Re(t.inputs[0],"Acos","acos"))},tf=t=>{t.compute(Re(t.inputs[0],"Acosh","acosh"))},rf=t=>{t.compute(Re(t.inputs[0],"Asin","asin"))},nf=t=>{t.compute(Re(t.inputs[0],"Asinh","asinh"))},af=t=>{t.compute(Re(t.inputs[0],"Atan","atan"))},sf=t=>{t.compute(Re(t.inputs[0],"Atanh","atanh"))},of=t=>Ke(t),lf=(t,e)=>{let r;switch(e.to){case 10:r="vec4<f16>";break;case 1:r="vec4<f32>";break;case 12:r="vec4<u32>";break;case 6:r="vec4<i32>";break;case 9:r="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${e.to}`)}t.compute(Re(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},cd=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:Qo,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:Zo;return Ke({min:e,max:r})},uf=(t,e)=>{let r=t.inputs.length===1?e:cd(t.inputs),n=Pt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"Clip",a=>`clamp(${a}, clip_min_, clip_max_)`,`
    const clip_min_: vec4<${n}> = vec4(${n}(${r.min}));
    const clip_max_: vec4<${n}> = vec4(${n}(${r.max}));
`,r.cacheKey),{inputs:[0]})},df=t=>{t.compute(Re(t.inputs[0],"Ceil","ceil"))},cf=t=>{t.compute(Re(t.inputs[0],"Cos","cos"))},pf=t=>{t.compute(Re(t.inputs[0],"Cosh","cosh"))},zi=t=>Ke(t),hf=(t,e)=>{let r=Pt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"Elu",n=>`elu_vf32(${n})`,`
  const elu_alpha_ = ${r}(${e.alpha});

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

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

fn erf_vf32(v: vec4<${t}>) -> vec4<${t}> {
  let absv = abs(v);
  let x = 1.0 / (1.0 + r0 * absv);
  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
}`,ff=t=>{let e=Pt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,Pi(e)))},mf=t=>{t.compute(Re(t.inputs[0],"Exp","exp"))},gf=t=>{t.compute(Re(t.inputs[0],"Floor","floor"))},_f=t=>{let e=Pt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,Pi(e)))},yf=(t,e)=>{let r=Pt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${e.alpha});`,e.cacheKey))},wf=t=>{t.compute(Re(t.inputs[0],"Not",e=>`!${e}`))},bf=t=>{t.compute(Re(t.inputs[0],"Neg",e=>`-${e}`))},vf=t=>{t.compute(Re(t.inputs[0],"Reciprocal",e=>`1.0/${e}`))},$f=t=>{let e=Pt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"Relu",r=>`select(vec4<${e}>(0.0), ${r}, ${r} > vec4<${e}>(0.0))`))},xf=t=>{t.compute(Re(t.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},Sf=t=>Ke(t),kf=(t,e)=>{let r=Pt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${e.alpha} * ${n} + vec4<${r}>(${e.beta})))`,void 0,e.cacheKey))},Ef=t=>{t.compute(Re(t.inputs[0],"Sin","sin"))},Cf=t=>{t.compute(Re(t.inputs[0],"Sinh","sinh"))},Tf=t=>{t.compute(Re(t.inputs[0],"Sqrt","sqrt"))},Af=t=>{t.compute(Re(t.inputs[0],"Tan","tan"))},Us=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,If=t=>{t.compute(Re(t.inputs[0],"Tanh",Us))},ko=(t="f32")=>`
const fast_gelu_a: ${t} = 0.5;
const fast_gelu_b: ${t} = 0.7978845608028654;
const fast_gelu_c: ${t} = 0.035677408136300125;

fn tanh_v(v: vec4<${t}>) -> vec4<${t}> {
  return ${Us("v")};
}
`,Eo=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,Mf=t=>{let e=Pt(t.inputs[0].dataType);t.compute(Re(t.inputs[0],"FastGelu",Eo,ko(e),void 0,t.inputs[0].dataType))},Of=(t,e)=>{let r=Pt(t.inputs[0].dataType);return t.compute(Re(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},zf=t=>{t.compute(Re(t.inputs[0],"Log","log"))}}),pd,hd,Pf,ky=J(()=>{Me(),Te(),tl(),pd=t=>{if(t[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(t[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(t[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw new Error("last dimension of input and bias are not the same")},hd=t=>{let e=t[0].dims.slice();e[2]=e[2]/2;let r=X("input",t[0].dataType,t[0].dims,4),n=X("bias",t[0].dataType,[t[0].dims[2]],4),a=ge("output",t[0].dataType,e,4),i=K.size(e)/4,s=mt(t[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)}}),getShaderSource:o=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${t[0].dims[2]/4/2}u;

  ${o.declareVariables(r,n,a)}

  ${Pi(s)}

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

    ${a.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Pf=t=>{pd(t.inputs),t.compute(hd(t.inputs))}}),fd,md,ir,Rf,Bf,Df,Nf,Ff,Lf,Uf,Wf,Vf,Gf,Ey=J(()=>{$e(),Me(),Te(),fd=(t,e,r,n,a,i,s,o,l,u,p,h)=>{let m,d;typeof o=="string"?m=d=($,E)=>`${o}((${$}),(${E}))`:typeof o=="function"?m=d=o:(m=o.scalar,d=o.vector);let _=ge("outputData",p,n.length,4),w=X("aData",l,e.length,4),v=X("bData",u,r.length,4),S;if(a)if(i){let $=K.size(e)===1,E=K.size(r)===1,T=e.length>0&&e[e.length-1]%4===0,A=r.length>0&&r[r.length-1]%4===0;$||E?S=_.setByOffset("global_idx",d($?`${w.type.value}(${w.getByOffset("0")}.x)`:w.getByOffset("global_idx"),E?`${v.type.value}(${v.getByOffset("0")}.x)`:v.getByOffset("global_idx"))):S=`
            let outputIndices = ${_.offsetToIndices("global_idx * 4u")};
            let offsetA = ${w.broadcastedIndicesToOffset("outputIndices",_)};
            let offsetB = ${v.broadcastedIndicesToOffset("outputIndices",_)};
            ${_.setByOffset("global_idx",d(s||T?w.getByOffset("offsetA / 4u"):`${w.type.value}(${w.getByOffset("offsetA / 4u")}[offsetA % 4u])`,s||A?v.getByOffset("offsetB / 4u"):`${v.type.value}(${v.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else S=_.setByOffset("global_idx",d(w.getByOffset("global_idx"),v.getByOffset("global_idx")));else{if(!i)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let $=(E,T,A="")=>{let P=`aData[indexA${T}][componentA${T}]`,B=`bData[indexB${T}][componentB${T}]`;return`
            let outputIndices${T} = ${_.offsetToIndices(`global_idx * 4u + ${T}u`)};
            let offsetA${T} = ${w.broadcastedIndicesToOffset(`outputIndices${T}`,_)};
            let offsetB${T} = ${v.broadcastedIndicesToOffset(`outputIndices${T}`,_)};
            let indexA${T} = offsetA${T} / 4u;
            let indexB${T} = offsetB${T} / 4u;
            let componentA${T} = offsetA${T} % 4u;
            let componentB${T} = offsetB${T} % 4u;
            ${E}[${T}] = ${A}(${m(P,B)});
          `};p===9?S=`
            var data = vec4<u32>(0);
            ${$("data",0,"u32")}
            ${$("data",1,"u32")}
            ${$("data",2,"u32")}
            ${$("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:S=`
            ${$("outputData[global_idx]",0)}
            ${$("outputData[global_idx]",1)}
            ${$("outputData[global_idx]",2)}
            ${$("outputData[global_idx]",3)}
          `}return`
        ${t.registerUniform("vec_size","u32").declareVariables(w,v,_)}

        ${h??""}

        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${S}
      }`},md=(t,e,r,n,a,i,s=r.dataType)=>{let o=!K.areEqual(r.dims,n.dims),l=r.dims,u=K.size(r.dims),p=!1,h=!1,m=[o];if(o){let d=On.calcShape(r.dims,n.dims,!1);if(!d)throw new Error("Can't perform binary op on the given tensors");l=d,u=K.size(l);let _=K.size(r.dims)===1,w=K.size(n.dims)===1,v=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,S=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;m.push(_),m.push(w),m.push(v),m.push(S);let $=1;for(let E=1;E<l.length;E++){let T=r.dims[r.dims.length-E]??1,A=n.dims[n.dims.length-E]??1;if(T===A)$*=T;else break}$%4===0?(h=!0,p=!0):(_||w||v||S)&&(p=!0)}else p=!0;return m.push(p),{name:t,shaderCache:{hint:e+m.map(d=>d.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:d=>fd(d,r.dims,n.dims,l,p,o,h,a,r.dataType,n.dataType,s,i),getRunData:()=>({outputs:[{dims:l,dataType:s}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:Math.ceil(K.size(l)/4)},...ye(r.dims,n.dims,l)]})}},ir=(t,e,r,n,a,i)=>{t.compute(md(e,a??"",t.inputs[0],t.inputs[1],r,n,i))},Rf=t=>{ir(t,"Add",(e,r)=>`${e}+${r}`)},Bf=t=>{ir(t,"Div",(e,r)=>`${e}/${r}`)},Df=t=>{ir(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4<u32>(${e}==${r})`},void 0,void 0,9)},Nf=t=>{ir(t,"Mul",(e,r)=>`${e}*${r}`)},Ff=t=>{let e=X("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;ir(t,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},`
    fn pow_custom(a : ${e}, b : ${e}) -> ${e} {
      if (b == ${e}(0.0)) {
        return ${e}(1.0);
      } else if (a < ${e}(0.0) && f32(b) != floor(f32(b))) {
        return ${e}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${e}(1.0), round(f32(abs(b) % ${e}(2.0))) != 1.0) * ${e}(${e==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${e}>, b : vec4<${e}>) -> vec4<${e}> {
      // TODO: implement vectorized pow
      return vec4<${e}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},Lf=t=>{ir(t,"Sub",(e,r)=>`${e}-${r}`)},Uf=t=>{ir(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4<u32>(${e}>${r})`},void 0,void 0,9)},Wf=t=>{ir(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4<u32>(${e}<${r})`},void 0,void 0,9)},Vf=t=>{ir(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4<u32>(${e}>=${r})`},void 0,void 0,9)},Gf=t=>{ir(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4<u32>(${e}<=${r})`},void 0,void 0,9)}}),sn,on,ln,rl,dn=J(()=>{$e(),Me(),sn=(t,e,r="f32")=>{switch(t.activation){case"Relu":return`value = max(value, ${e}(0.0));`;case"Sigmoid":return`value = (${e}(1.0) / (${e}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${e}(${r}(uniforms.clip_min)), ${e}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${e}(0.0), min(${e}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${e}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${t.activation}`)}},on=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},ln=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},rl=t=>{let e=t?.activation||"";if(e==="HardSigmoid"){let[r,n]=t?.activation_params||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=t?.activation_params||[Qo,Zo];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=t?.activation_params||[.01];return{activation:e,alpha:r}}return{activation:e}}}),kt,nl,al=J(()=>{kt=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},nl=t=>`
      ${t?"value = value + getBiasByOutputCoords(coords);":""}
      `}),il,Hf=J(()=>{il=t=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${t}.x), i32(${t}.y), i32(${t}.z), 1));
}
`}),gd,_d,Yi,Ws,yd,Xi,wd,sl,as=J(()=>{$e(),Me(),Te(),dn(),al(),gd=(t,e)=>t?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${e?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${e?", batchIndices":""});
        `,_d=(t,e)=>t?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${e===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
        for (var i = 0; i < rowPerThread; i = i + 1) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${e===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
        }`:`
        for (var i = 0; i < rowPerThread; i = i + 1) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${e===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,Yi=(t,e,r="f32",n,a=!1,i=32,s=!1,o=32)=>{let l=e[1]*t[1],u=e[0]*t[0],p=a?l:i,h=a?i:l,m=p/e[0],d=i/e[1];if(!((a&&m===4&&t[1]===4||!a&&(m===3||m===4))&&p%e[0]===0&&i%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${m} and workPerThread[1] ${t[1]} must be 4.
      Otherwise, innerElementSize ${m} must be 3 or 4.
  tileAWidth ${p} must be divisible by workgroupSize[0]${e[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${m}<${r}>, ${p/m}>, ${h}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${u/t[0]}>, ${i}>;

const rowPerThread = ${t[1]};
const colPerThread = ${t[0]};
const innerElementSize = ${m};
const tileInner = ${i};

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

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

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

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

  // Loop over shared dimension.
  let tileRowB = localRow * ${d};
  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;
          ${gd(a,n)}
      }

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

          ${_d(a,m)}
      }

      workgroupBarrier();
  }

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

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

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

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

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

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${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) {
      ${yd(a)}
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
      }
    }
  }

  workgroupBarrier();
}

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

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

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

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

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(t,w)}) {
      let col = colIn * ${t};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${e?`value = value + ${i?"bias[colIn]":`${kt(t,w)}(bias[row])`};`:""}
        ${r}
        ${m.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},sl=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i.slice(0,-2),l=s.slice(0,-2),u=n?n.slice(0,-2):r.slice(0,-2),p=K.size(u),h=i[i.length-2],m=i[i.length-1],d=s[s.length-1],_=m%4===0&&d%4===0,w=h<=8?[4,1,1]:[4,4,1],v=[8,8,1],S=[Math.ceil(d/v[0]/w[0]),Math.ceil(h/v[1]/w[1]),Math.ceil(p/v[2]/w[2])],$=_?4:1,E=[...o,h,m/$],T=E.length,A=[...l,m,d/$],P=A.length,B=[p,h,d/$],D=[{type:6,data:h},{type:6,data:d},{type:6,data:m}];on(e,D),D.push(...ye(u,E,A));let q=["rank","rank"],H=t.length>2;H&&(D.push(...ye(t[2].dims)),q.push("rank")),D.push(...ye(B));let ie=te=>{let de=u.length,se=Jo("batchDims",t[0].dataType,de,1),M=mt(t[0].dataType),R=X("a",t[0].dataType,T,$),G=X("b",t[1].dataType,P,$),re=ge("result",t[0].dataType,B.length,$),ee=[R,G];if(H){let Ce=a?$:1;ee.push(X("bias",t[2].dataType,t[2].dims.length,Ce))}let ne=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];ln(e,ne);let W=mt(re.type.tensor),ae=sn(e,re.type.value,W),fe=wd($,H,ae,[se,R,G,re],[o,l,u],a);return`
  ${te.registerUniforms(ne).registerInternalVariables(se).declareVariables(...ee,re)}
  ${fe}
  ${_?Yi(w,v,M,se):Xi(w,v,M,se)}
                   `};return{name:"MatMul",shaderCache:{hint:`${w};${e.activation};${_};${a}`,inputDependencies:q},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:S[0],y:S[1],z:S[2]},programUniforms:D}),getShaderSource:ie}}}),bd,jf,Cy=J(()=>{$e(),un(),Te(),dn(),al(),Hf(),as(),bd=(t,e,r,n,a=!1,i,s=4,o=4,l=4,u="f32")=>{let p=q=>{switch(q){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${u}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${q} is not supported.`)}},h=q=>{switch(q){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 ${q} is not supported.`)}},m=t?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,d=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,_=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",v=t?"row":"col",S=t?"col":"row",$=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${v} / outWidth;
    let outCol = ${v} % outWidth;

    let WRow = ${S} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${S} / 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 = ${S} % inChannels;
    var resData = ${kt(s,u)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${_} && xCol >= 0 && xCol < ${w}) {
      ${m}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${p(s)}
    }
    return resData;`,E=t?e&&n?`
    let col = colIn * ${s};
    ${$}`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${$}
    }
    return ${kt(s,u)}(0.0);`:n&&r?`
    let col = colIn * ${s};
    ${$}`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${$}
    }
    return ${kt(s,u)}(0.0);`,T=`${h(o)}`,A=kt(l,u),P=kt(t?s:o,u),B=kt(t?o:s,u),D=sn(i,A,u);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${P} {
      ${t?E:T}
    }

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${A}) {
      let col = colIn * ${l};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${d}
      ${nl(a)}
      ${D}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},jf=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",u=l?t[0].dims[3]:t[0].dims[1],p=r[0],h=l?r[2]:r[3],m=l?r[1]:r[2],d=l?r[3]:r[1],_=l&&(u%4===0||u%3===0)&&d%4===0,w=l?d:h*m,v=l?h*m:d,S=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(w/S[0]/$[0]),Math.ceil(v/S[1]/$[1]),Math.ceil(p/S[2]/$[2])];at("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let T=_?l&&u%4!==0?3:4:1,A=S[1]*$[1],P=S[0]*$[0],B=Math.max(S[0]*T,S[1]),D=n%A===0,q=a%P===0,H=i%B===0,ie=_?[T,4,4]:[1,1,1],te=[{type:6,data:n},{type:6,data:a},{type:6,data:i},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];on(e,te),te.push(...ye(t[0].dims,t[1].dims));let de=["rank","rank"];s&&(te.push(...ye(t[2].dims)),de.push("rank")),te.push(...ye(r));let se=M=>{let R=[{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}];ln(e,R);let G=_?4:1,re=mt(t[0].dataType),ee=`
      fn setOutputAtIndex(flatIndex : i32, value : ${_?`vec4<${re}>`:re}) {
        result[flatIndex] = ${_?`vec4<${re}>`:re}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${_?`vec4<${re}>`:re}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${_?"/ 4":""}, value);
      }`,ne=X("x",t[0].dataType,t[0].dims.length,T===3?1:T),W=X("w",t[1].dataType,t[1].dims.length,G),ae=[ne,W],fe=ge("result",t[0].dataType,r.length,G);if(s){let Ce=X("bias",t[2].dataType,t[2].dims.length,G);ae.push(Ce),ee+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${_?`vec4<${re}>`:re} {
          return bias[coords.${l?"w":"y"}${_?"/ 4":""}];
        }`}return`
        ${il("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 };
        ${M.registerUniforms(R).declareVariables(...ae,fe)}
        ${ee}
        ${bd(l,D,q,H,s,e,ie[0],ie[1],ie[2],re)}
        ${_?Yi($,S,re,void 0,!l,B):Xi($,S,re,void 0,!l,B,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${T};${_};${D};${q};${H};${A};${P};${B}`,inputDependencies:de},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:te}),getShaderSource:se}}}),Co,qf,Ty=J(()=>{$e(),Me(),Te(),Xf(),dn(),Co=(t,e,r)=>{let n=t.length>2,a=n?"value += b[output_channel];":"",i=t[0].dims,s=t[1].dims,o=s[0]/e.group,l=e.format==="NHWC",u=Ri(i,s,e.dilations,e.pads,e.strides,l),p=K.size(u),h=[{type:12,data:p},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];on(e,h),h.push(...ye(i,s));let m=["rank","rank"];n&&(h.push(...ye(t[2].dims)),m.push("rank")),h.push(...ye(u));let d=_=>{let w=ge("output",t[0].dataType,u.length),v=mt(w.type.tensor),S=sn(e,w.type.value,v),$=X("x",t[0].dataType,i.length),E=X("w",t[1].dataType,s.length),T=[$,E];n&&T.push(X("b",t[2].dataType,t[2].dims.length));let A=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:e.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return ln(e,A),`
  ${_.registerUniforms(A).declareVariables(...T,w)}

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

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

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

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

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

          let xVal = ${l?$.get("batch","xHeight","xWidth","input_channel"):$.get("batch","input_channel","xHeight","xWidth")};
          let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${a}
    ${S}
    ${w.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:m},getRunData:()=>({outputs:[{dims:r?r(u):u,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:d}},qf=(t,e,r)=>{let n=t.length>2,a=ot(r[3]),i=ot(r[2]),s=K.size(r)/a/i,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],l=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],u=[r[0],r[1],r[2],r[3]/a],p=[{type:12,data:s},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];on(e,p),p.push(...ye(o,l,u));let h=(i-1)*e.strides[1]+l[1],m=d=>{let _=ge("output",t[0].dataType,u.length,a),w=mt(_.type.tensor),v=sn(e,_.type.value,w),S=X("x",t[0].dataType,o.length,a),$=X("w",t[1].dataType,l.length,a),E=[S,$];n&&E.push(X("b",t[2].dataType,t[2].dims,a));let T=n?"value += b[output_channel];":"",A=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return ln(e,A),`
  ${d.registerUniforms(A).declareVariables(...E,_)}
  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let width0 = uniforms.output_shape[3];
    let output_channel = global_idx % width0;
    var index1 = global_idx / width0;
    let width1 = uniforms.output_shape[2] / ${i}u;
    let col = (index1 % width1) * ${i}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

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

    var x_vals: array<${S.type.value}, ${h}>;
    var values: array<${_.type.value}, ${i}>;
    let input_channel = output_channel;
    // Use constant instead of uniform can give better performance for w's height/width.
    for (var w_height: u32 = 0u; w_height < ${l[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${h}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${S.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${S.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${l[1]}; w_width++) {
          let w_val = ${$.get("w_height","w_width","0","output_channel")};
          for (var i = 0u; i < ${i}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${i}u; i++) {
      var value = values[i];
      ${T}
      ${v}
      ${_.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${i};${h};${l[0]};${l[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:p}),getShaderSource:m}}}),To,vd,Kf,Yf=J(()=>{$e(),Me(),as(),Te(),dn(),To=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i[i.length-2],l=s[s.length-1],u=i[i.length-1],p=ot(l),h=ot(u),m=ot(o),d=K.size(r)/p/m,_=t.length>2,w=n?n.slice(0,-2):r.slice(0,-2),v=[K.size(w),o,l],S=[{type:12,data:d},{type:12,data:o},{type:12,data:l},{type:12,data:u}];on(e,S),S.push(...ye(w,i,s)),_&&S.push(...ye(t[2].dims)),S.push(...ye(v));let $=E=>{let T=Jo("batch_dims",t[0].dataType,w.length),A=X("a",t[0].dataType,i.length,h),P=X("b",t[1].dataType,s.length,p),B=ge("output",t[0].dataType,v.length,p),D=mt(B.type.tensor),q=sn(e,B.type.value,D),H=[A,P],ie="";if(_){let ee=a?p:1;H.push(X("bias",t[2].dataType,t[2].dims.length,ee)),ie=`${a?`value += bias[col / ${ee}];`:`value += ${B.type.value}(bias[row + i]);`}`}let te=i.slice(0,-2),de=s.slice(0,-2),se=xa(te,w),M=xa(de,w),R=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];ln(e,R);let G=(ee,ne)=>{let W=ee.rank,ae=ee.name;if(W===2)return`var ${ae}_indices = ${ee.type.indices}(0u, 0u);`;let fe=T.rank,Ce=`var ${ae}_indices: ${ee.type.indices};`;for(let Be=W-2-1,Ye=fe-1;Be>=0;Be--,Ye--)Ce+=`
${ae}_indices[${Be}] = ${fe>1?`batch_indices[${Ye}]`:"batch_indices"};`;return ne.forEach(Be=>{Ce+=`
${ae}_indices[${Be}] = 0;`}),Ce+=`${ae}_indices[${W-2}] = 0u;
                     ${ae}_indices[${W-1}] = 0u;`,Ce},re=()=>{let ee=`var a_data: ${A.type.value};`;for(let ne=0;ne<h;ne++)ee+=`
              let b_data${ne} = b[(b_offset + (k + ${ne}) * uniforms.N + col) / ${p}];`;for(let ne=0;ne<m;ne++){ee+=`a_data = a[(a_offset + (row + ${ne}) * uniforms.K + k) / ${h}];`;for(let W=0;W<h;W++)ee+=`
            values[${ne}] = fma(${P.type.value}(a_data${h===1?"":`[${W}]`}), b_data${W}, values[${ne}]);
`}return ee};return`
  ${E.registerUniforms(R).registerInternalVariables(T).declareVariables(...H,B)}
  ${E.mainStart()}
    ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${p})) * ${p};
    var index1 = global_idx / (uniforms.N / ${p});
    let stride1 = uniforms.M / ${m};
    let row = (index1 % stride1) * ${m};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${T.offsetToIndices("batch")};`}
    ${G(A,se)}
    let a_offset = ${A.indicesToOffset("a_indices")};
    ${G(P,M)}
    let b_offset = ${P.indicesToOffset("b_indices")};
    var values: array<${B.type.value}, ${m}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${h}) {
      ${re()}
    }
    for (var i = 0u; i < ${m}u; i++) {
      var value = values[i];
      ${ie}
      ${q}
      let cur_indices = ${B.type.indices}(batch, row + i, col);
      let offset = ${B.indicesToOffset("cur_indices")};
      ${B.setByOffset(`offset / ${p}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${p};${h};${m};${a}`,inputDependencies:_?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:S}),getShaderSource:$}},vd=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.")},Kf=t=>{vd(t.inputs);let e=On.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!e)throw new Error("Can't use matmul on the given tensors");let r=e[e.length-1],n=t.inputs[0].dims[t.inputs[0].dims.length-1];r<8&&n<8?t.compute(To(t.inputs,{activation:""},e)):t.compute(sl(t.inputs,{activation:""},e))}}),Ri,xi,$d,Vs,Ao,xd,Sd,Io,Xf=J(()=>{Me(),Cy(),as(),Ty(),dn(),Yf(),Ia(),Ri=(t,e,r,n,a,i)=>{let s=t[0],o=t.slice(i?1:2,i?3:4),l=o.length,u=e[0],p=e.slice(2).map((m,d)=>m+(m-1)*(r[d]-1)),h=o.map((m,d)=>m+n[d]+n[d+l]).map((m,d)=>Math.floor((m-p[d]+a[d])/a[d]));return h.splice(0,0,s),h.splice(i?3:1,0,u),h},xi=[2,3,1,0],$d=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[1]*e.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let a=t[0].dims.length-2;if(e.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(e.strides.length!==a)throw new Error(`strides should be ${a}D`);if(e.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},Vs=(t,e)=>{let r=t.kernelShape.slice();for(let i=2;i<e[1].dims.length;++i)r[i-2]===0&&(r[i-2]=e[1].dims[i]);let n=t.pads.slice();Hi.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,r,n,t.format==="NHWC",t.autoPad);let a=Object.assign({},t);return Object.assign(a,{kernelShape:r,pads:n}),a},Ao=t=>{let e=rl(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,i=t.group,s=t.kernel_shape,o=t.pads,l=t.strides,u=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:i,kernelShape:s,pads:o,strides:l,wIsConst:u,...e,cacheKey:`${t.format};${e.activation};`}},xd=(t,e,r)=>{let n=Vs(r,e),a=r.format==="NHWC";if(r.group!==1){if(!t.adapterInfo.isArchitecture("ampere")&&a&&e[1].dims[0]===r.group&&e[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let P=Ri(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),B=t.kernelCustomData.wT??t.compute(Er(e[1],xi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=B);let D=[e[0],B];e.length===3&&D.push(e[2]),t.compute(qf(D,n,P),{inputs:D})}else t.compute(Co(e,n));return}let i=e.length===3,s=e[0].dims[a?1:2],o=e[0].dims[a?2:3],l=e[0].dims[a?3:1],u=e[1].dims[2],p=e[1].dims[3],h=Ri(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),m=h[a?1:2],d=h[a?2:3],_=h[a?3:1],w=a&&u===s&&p===o&&r.pads[0]===0&&r.pads[1]===0;if(w||u===1&&p===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let P=h[0],B,D,q,H=[];if(a){let de=t.kernelCustomData.wT??t.compute(Er(e[1],xi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=de),w){let se=s*o*l;B=e[0].reshape([1,P,se]),D=de.reshape([1,se,_]),q=[1,P,_]}else B=e[0].reshape([P,s*o,l]),D=de.reshape([1,l,_]),q=[P,m*d,_];H.push(B),H.push(D)}else B=e[0].reshape([P,l,s*o]),D=e[1].reshape([1,_,l]),q=[P,_,m*d],H.push(D),H.push(B);i&&H.push(e[2]);let ie=q[2],te=H[0].dims[H[0].dims.length-1];ie<8&&te<8?t.compute(To(H,n,h,q,a),{inputs:H}):t.compute(sl(H,n,h,q,a),{inputs:H});return}let v=!0,S=t.kernelCustomData.wT??t.compute(Er(e[1],xi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=S);let $=[e[0],S];i&&$.push(e[2]);let E=a?m*d:_,T=a?_:m*d,A=u*p*l;t.compute(jf($,n,h,E,T,A,i,v),{inputs:$})},Sd=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&n.push(t.inputs[2]);let a=[0,e.pads[0],0,e.pads[1]],i=[1].concat(e.strides),s=[1].concat(e.dilations),o=[1].concat(e.kernelShape),l=Vs({...e,pads:a,strides:i,dilations:s,kernelShape:o},n);t.compute(Co(n,l,u=>r?[u[0],u[2],u[3]]:[]))},Io=(t,e)=>{$d(t.inputs,e),t.inputs[0].dims.length===3?Sd(t,e):xd(t,t.inputs,e)}}),kd,Qf,Ay=J(()=>{$e(),un(),Te(),dn(),al(),Hf(),as(),kd=(t,e=!1,r,n,a=4)=>{let i=v=>{switch(v){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`
            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];
            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];
            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];
            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];
            return ${n}(v0, v1, v2, v3);
            `;default:throw new Error(`innerElementSize ${v} is not supported.`)}},s=t?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,o=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,l=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",u=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",p=t?"row":"col",h=t?"col":"row",m=`
      let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${p} / outWidth;
      let outCol = ${p} % outWidth;

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

  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} {
    ${t?_:d}
  }

  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) {
    let col = colIn * ${a};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
      var value = valueInput;
      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${o}
      ${nl(e)}
      ${w}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${a}] = value;
    }
  }`},Qf=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",u=l?t[0].dims[3]:t[0].dims[1],p=r[0],h=l?r[2]:r[3],m=l?r[1]:r[2],d=l?r[3]:r[1],_=l&&u%4===0&&u%3&&d%4===0,w=l?d:h*m,v=l?h*m:d,S=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(w/S[0]/$[0]),Math.ceil(v/S[1]/$[1]),Math.ceil(p/S[2]/$[2])];at("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let T=_?4:1,A=Math.max(S[0]*T,S[1]),P=_?4:1,B=[e.kernelShape[l?1:2],e.kernelShape[l?2:3]],D=[B[0]+(e.dilations[0]<=1?0:(B[0]-1)*(e.dilations[0]-1)),B[1]+(e.dilations[1]<=1?0:(B[1]-1)*(e.dilations[1]-1))],q=[D[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),D[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],H=[{type:6,data:n},{type:6,data:a},{type:6,data:i},{type:6,data:e.strides},{type:6,data:e.dilations},{type:6,data:B},{type:6,data:q}];on(e,H),H.push(...ye(t[0].dims,t[1].dims));let ie=["rank","rank"];s&&(H.push(...ye(t[2].dims)),ie.push("rank")),H.push(...ye(r));let te=de=>{let se=X("x",t[0].dataType,t[0].dims.length,P),M=X("w",t[1].dataType,t[1].dims.length,1),R=ge("result",t[0].dataType,r.length,P),G=[se,M],re="";if(s){let W=X("bias",t[2].dataType,t[2].dims.length,P);G.push(W),re+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${W.type.value} {
            return bias[coords.${l?"w":"y"}${_?"/ 4":""}];
          }`}let ee=[{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:B.length},{name:"pads",type:"i32",length:q.length}];ln(e,ee);let ne=mt(t[0].dataType,1);if(ne!=="f16"&&ne!=="f32")throw new Error(`elemType ${ne} is not supported.`);return`
        ${il("uniforms.result_strides")}
        ${de.registerUniforms(ee).declareVariables(...G,R)};
        ${re}
        ${kd(l,s,e,se.type.value,T)}
        ${_?Yi($,S,ne,void 0,!l,A):Xi($,S,ne,void 0,!l,A,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${$};${S};${_}`,inputDependencies:ie},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:H}),getShaderSource:te}}}),Ed,Mo,Iy=J(()=>{$e(),un(),Me(),Te(),Ed=(t,e,r,n,a,i=!1,s,o,l=!1)=>{let u=l?1:2,p=l?2:3,h=l?3:1,m=i?2:1,d=`
  fn setOutputAtIndex(flatIndex : u32, value : ${i?`vec4<${s}>`:s}) {
    result[flatIndex] = ${i?`vec4<${s}>`:s}(value);
  }`;n&&(d+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${i?`vec4<${s}>`:s} {
      return bias[coords.${l?"w":"y"}${i?"/ 4":""}];
    }`);let _=i?4:1,w=X("W",e[1].dataType,e[1].dims.length,_),v=X("Dy",e[0].dataType,e[0].dims.length,_),S=[v,w];n&&S.push(X("bias",e[2].dataType,[r[h]].length,_));let $=ge("result",e[0].dataType,r.length,_),E=`{
        let batch: u32 = ${a?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];
        let r = ${a?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];
        let c = ${a?"global_id.y":"workgroup_id.y"} * ${m};
        let d1: u32 = ${a?"global_id.x":"workgroup_id.x"} * 4;

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

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        var dotProd: array<vec4<${s}>, ${m}>;
        for (var i = 0; i < ${m}; i++) {
          dotProd[i] = vec4<${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 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

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

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

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

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

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

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

            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
              if (wC % uniforms.dilations.y != 0) {
                continue;
              }
              let dyC = (${s}(dyCCorner) + ${s}(wC)) / ${s}(uniforms.strides.y);
              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
              if (dyC < 0.0 || dyC >= ${s}(uniforms.Dy_shape[${p}]) ||
                  fract(dyC) > 0.0 || wCPerm < 0) {
                continue;
              }
              let idyC: u32 = u32(dyC);
              var inputChannel = groupId * uniforms.input_channels_per_group;
              for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {
                let xValue = ${l?v.get("batch","idyR","idyC","inputChannel"):v.get("batch","inputChannel","idyR","idyC")};
                let wValue = ${w.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};
                dotProd = dotProd + xValue * wValue;
                inputChannel = inputChannel + 1;
              }
            }
          }
          let value = dotProd + ${n?"bias[d1]":`${s}(0.0)`};
          ${$.setByOffset("global_idx","value")};
        `;return`
  ${t.registerUniforms(o).declareVariables(...S,$)}
  ${d}

    ${t.mainStart()}
    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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      let x_in = x + bias;
      ${u.setByOffset("global_idx",Eo("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getShaderSource:i,getRunData:s=>({outputs:[{dims:s[0].dims,dataType:s[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/zn/4)}})}},om=t=>{t.inputs.length<2||K.size(t.inputs[1].dims)===0?Mf(t):t.compute(jd(t.inputs))}}),qd,Kd,lm,um,Dy=J(()=>{$e(),Me(),ct(),Te(),qd=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},Kd=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,i=K.normalizeAxis(e.axis,a),s=r.slice(0);s.splice(i,1,...n);let o=r[i],l=t[0].dataType===9?4:1,u=Math.ceil(K.size(s)/l),p=[{type:12,data:u},{type:6,data:o},{type:12,data:i},...ye(t[0].dims,t[1].dims,s)],h=m=>{let d=X("data",t[0].dataType,t[0].dims.length,l),_=X("inputIndices",t[1].dataType,t[1].dims.length),w=ge("output",t[0].dataType,s.length,l),v=$=>{let E=n.length,T=`var indicesIndices${$}  = ${_.type.indices}(0);`;for(let A=0;A<E;A++)T+=`${E>1?`indicesIndices${$}[${A}]`:`indicesIndices${$}`} = ${s.length>1?`outputIndices${$}[uniforms.axis + ${A}]`:`outputIndices${$}`};`;T+=`
          var idx${$} = ${_.getByIndices(`indicesIndices${$}`)};
          if (idx${$} < 0) {
            idx${$} = idx${$} + uniforms.axisDimLimit;
          }
          var dataIndices${$} : ${d.type.indices};
        `;for(let A=0,P=0;A<a;A++)A===i?(T+=`${a>1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = u32(idx${$});`,P+=E):(T+=`${a>1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = ${s.length>1?`outputIndices${$}[${P}]`:`outputIndices${$}`};`,P++);return T},S;if(t[0].dataType===9){let $=(E,T,A="")=>`
          let outputIndices${T} = ${w.offsetToIndices(`outputOffset + ${T}u`)};
          ${v(T)};
          let offset${T} = ${d.indicesToOffset(`dataIndices${T}`)};
          let index${T} = offset${T} / 4u;
          let component${T} = offset${T} % 4u;
          ${E}[${T}] = ${A}(${d.getByOffset(`index${T}`)}[component${T}]);
        `;S=`
        let outputOffset = global_idx * ${l};
        var value = vec4<u32>(0);
        ${$("value",0,"u32")}
        ${$("value",1,"u32")}
        ${$("value",2,"u32")}
        ${$("value",3,"u32")}
        ${w.setByOffset("global_idx","value")}
      `}else S=`
      let outputIndices = ${w.offsetToIndices("global_idx")};
      ${v("")};
      let value = ${d.getByIndices("dataIndices")};
      ${w.setByOffset("global_idx","value")};
      `;return`
      ${m.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(d,_,w)}
      ${m.mainStart()}
        ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${S}
      }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:p}),getShaderSource:h}},lm=t=>Ke({axis:t.axis}),um=(t,e)=>{let r=t.inputs;qd(r),t.compute(Kd(t.inputs,e))}}),Yd,Xd,dm,cm,Ny=J(()=>{$e(),Me(),ct(),Te(),Yd=t=>{if(!t||t.length!==2)throw new Error("GatherElements requires 2 inputs.");if(t[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(t[0].dims.length!==t[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},Xd=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,i=t[1].dims,s=t[1].dataType,o=K.normalizeAxis(e.axis,a),l=r[o],u=i.slice(0),p=K.size(u),h=X("input",n,a),m=X("indicesInput",s,i.length),d=ge("output",n,u.length),_=[{type:12,data:p},{type:6,data:l},{type:12,data:o}];return _.push(...ye(r,i,u)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:u,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:_}),getShaderSource:w=>`
      ${w.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(h,m,d)}
      ${w.mainStart()}
      ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${d.setByOffset("global_idx","value")};
  }`}},dm=t=>Ke({axis:t.axis}),cm=(t,e)=>{let r=t.inputs;Yd(r),t.compute(Xd(t.inputs,e))}}),Qd,Zd,pm,hm,Fy=J(()=>{$e(),Me(),Te(),Qd=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},Zd=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,i,s]=bh.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[a,i];if(!o)throw new Error("Can't use gemm on the given tensors");let l=K.size(o),u=[{type:12,data:l},{type:12,data:a},{type:12,data:i},{type:12,data:s},{type:1,data:e.alpha},{type:1,data:e.beta}],p=["type","type"];t.length===3&&(u.push(...ye(t[2].dims)),p.push("rank")),u.push(...ye(o));let h=m=>{let d="";e.transA&&e.transB?d="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?d="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?d="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(d="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let _=e.alpha===1?"":"value *= uniforms.alpha;",w=X("a",t[0].dataType,t[0].dims),v=X("b",t[1].dataType,t[1].dims),S=w.type.value,$=null,E=[w,v];t.length===3&&($=X("c",t[2].dataType,t[2].dims.length),E.push($));let T=ge("output",t[0].dataType,o.length);E.push(T);let A=[{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`
  ${m.registerUniforms(A).declareVariables(...E)}

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

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

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

    ${_}
    ${$!=null?`let cOffset = ${$.broadcastedIndicesToOffset("vec2(m, n)",T)}; value += ${S}(uniforms.beta) * ${$.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:h}},pm=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},hm=(t,e)=>{Qd(t.inputs),t.compute(Zd(t.inputs,e))}}),Jd,ec,tc,fm,Ly=J(()=>{$e(),Me(),Te(),Jd=(t,e)=>{let r=t[0].dims,n=r,a=2,i=K.sizeToDimension(r,a),s=K.sizeFromDimension(r,a),o=ot(s),l=s/o,u=[r[0],r[1],l],p=["rank","type","type"],h=[{type:12,data:s},{type:12,data:l}];h.push(...ye(u,u));let m=d=>{let _=X("x",t[0].dataType,u.length,o),w=X("scale",t[1].dataType,t[1].dims),v=X("bias",t[2].dataType,t[2].dims),S=ge("output",t[0].dataType,u.length,o),$=[_,w,v,S],E=_.type.value,T=o===1?"f32":`vec${o}<f32>`,A=64,P=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`
  var<workgroup> meanShared : f32;
  var<workgroup> squaredNormShared : f32;
  var<workgroup> workgroupShared : array<${T}, ${A}>;
  const workgroupSize = ${A}u;
  ${d.registerUniforms(P).declareVariables(...$)}
  ${d.mainStart(A)}
    let norm = global_idx / workgroupSize;
    let batch = norm / uniforms.x_shape[1];
    let channel = norm % uniforms.x_shape[1];
    let localIndex = local_id.x;

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

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

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

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

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

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

    let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;
    var sum = ${Sr("f32",l)};
    var squaredSum = ${Sr("f32",l)};
    for (var i: u32 = wgOffset; i < wgMax; i++) {
        let value = ${h}(input[offset + i * uniforms.C]);
        sum += value;
        squaredSum += value * value;
    }
    output[global_idx] = ${m("sum","squaredSum")};
  }`},$=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:[a,s,u,2],dataType:1}],dispatchGroup:{x:a*s/l},programUniforms:v}),getShaderSource:S},{inputs:[e],outputs:[-1]})[0],E=[{type:12,data:d},{type:12,data:i},{type:12,data:Math.floor(s/l)},{type:12,data:Math.floor(u*s/l)}],T=["type","type","type"],A=P=>{let B=X("scale",r.dataType,r.dims,l),D=X("bias",n.dataType,n.dims,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${p}>;
  @group(0) @binding(1) var<storage, read> scale : array<${B.type.storage}>;
  @group(0) @binding(2) var<storage, read> bias : array<${D.type.storage}>;
  @group(0) @binding(3) var<storage, read_write> output : array<${p}>;
  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};
  @group(0) @binding(4) var<uniform> uniforms: Uniforms;

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

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

    output[global_idx] = ${m("channelScale","channelShift")};
  }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${o}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:[a,s,2],dataType:1}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:E}),getShaderSource:A},{inputs:[$,r,n],outputs:[-1]})[0]},tc=(t,e,r)=>{let n=e[0].dims,a=n,i=n[0],s=n[n.length-1],o=K.sizeFromDimension(n,1)/s,l=ot(s),u=K.size(a)/l,p=[{type:12,data:o},{type:12,data:Math.floor(s/l)}],h=["type","type"],m=ec(t,e[0],e[1],e[2],i,o,s,r.epsilon),d=_=>{let w=mt(e[0].dataType),v=l===1?"vec2f":`mat2x${l}f`,S=l===1?w:`vec${l}<${w}>`,$=X("input",e[0].dataType,e[0].dims,l),E=ge("output",e[0].dataType,a,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${$.type.storage}>;
  @group(0) @binding(1) var<storage, read> scaleInput : array<${v}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${E.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

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

    let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;
    let scale = scaleInput[scaleOffset];
    output[global_idx] = fma(input[global_idx], ${S}(scale[0]), ${S}(scale[1]));
  }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:p}),getShaderSource:d},{inputs:[e[0],m]})},fm=(t,e)=>{e.format==="NHWC"?tc(t,t.inputs,e):t.compute(Jd(t.inputs,e))}}),rc,nc,mm,Uy=J(()=>{$e(),Me(),Te(),rc=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},nc=(t,e,r)=>{let n=e.simplified,a=t[0].dims,i=t[1],s=!n&&t[2],o=a,l=K.normalizeAxis(e.axis,a.length),u=K.sizeToDimension(a,l),p=K.sizeFromDimension(a,l),h=K.size(i.dims),m=s?K.size(s.dims):0;if(h!==p||s&&m!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${h} and bias size of ${m}`);let d=[];for(let A=0;A<a.length;++A)A<l?d.push(a[A]):d.push(1);let _=ot(p),w=["type","type"],v=[{type:12,data:u},{type:1,data:p},{type:12,data:Math.floor(p/_)},{type:1,data:e.epsilon}];s&&w.push("type");let S=r>1,$=r>2,E=A=>{let P=mt(t[0].dataType),B=[X("x",t[0].dataType,t[0].dims,_),X("scale",i.dataType,i.dims,_)];s&&B.push(X("bias",s.dataType,s.dims,_)),B.push(ge("output",t[0].dataType,o,_)),S&&B.push(ge("mean_data_output",1,d)),$&&B.push(ge("inv_std_output",1,d));let D=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${A.registerUniforms(D).declareVariables(...B)}
  ${A.mainStart()}
    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${Sr("f32",_)};
    var mean_square_vector = ${Sr("f32",_)};

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

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

    ${S?"mean_data_output[global_idx] = mean":""};
    ${$?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},T=[{dims:o,dataType:t[0].dataType}];return S&&T.push({dims:d,dataType:1}),$&&T.push({dims:d,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${_};${r};${n}`,inputDependencies:w},getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:v}),getShaderSource:E}},mm=(t,e)=>{rc(t.inputs),t.compute(nc(t.inputs,e,t.outputCount))}}),ac,ic,gm,_m,Wy=J(()=>{$e(),Me(),ct(),Te(),ac=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((e.k+e.blockSize-1)/e.blockSize),i=e.blockSize/8*e.bits,s=t[1];if(!K.areEqual(s.dims,[e.n,a,i]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(K.size(o)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let l=t[3].dims,u=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(K.size(l)!==u)throw new Error("zeroPoints input size error.")}},ic=(t,e,r,n)=>{let a=t[0].dims,i=a.length,s=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=a[i-2],l=e.k,u=e.n,p=a.slice(0,i-2),h=K.size(p),m=e.blockSize/8*e.bits/4,d=t[0].dataType,_=ot(o),w=ot(e.k),v=ot(m),S=$a(d),$=o*s*S,E=Math.floor(n/$),T=s<=r[0]&&E>0,A=!T||E>=4?ot(u):E>=2&&ot(u)>=2?2:1,P=p.concat([o,u]),B=K.size(P)/A/_,D=T?[]:[{type:12,data:B},{type:12,data:e.blockSize}],q=[h,o,l/w],H=K.convertShape(t[1].dims).slice();H.splice(-1,1,m/v),D.push(...ye(q)),D.push(...ye(H)),D.push(...ye(t[2].dims)),t.length===4&&D.push(...ye(K.convertShape(t[3].dims)));let ie=[h,o,u/A];D.push(...ye(ie));let te=de=>{let se=q.length,M=X("a",t[0].dataType,se,w),R=X("b",12,H.length,v),G=X("scales",t[2].dataType,t[2].dims.length),re=[M,R,G],ee=t.length===4?X("zero_points",12,t[3].dims.length):void 0;ee&&re.push(ee);let ne=ie.length,W=ge("output",t[0].dataType,ne,A),ae=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],fe=mt(t[0].dataType),Ce=(()=>{switch(w){case 1:return`array<${fe}, 8>`;case 2:return`mat4x2<${fe}>`;case 4:return`mat2x4<${fe}>`;default:throw new Error(`${w}-component is not supported.`)}})(),Be=`
        for (var word: u32 = 0; word < ${m}; word += ${v}) {
          ${R.indicesSet("b_indices","2","word")};
          let b_data = ${R.getByIndices("b_indices")};
          for (var i: u32 = 0; i < ${v}; i++) {
            let b_value: u32 = ${v===1?"b_data":"b_data[word + i]"};
            let b_mask: u32 = 0x0F0F0F0Fu;
            let b_value_lower: vec4<u32> = unpack4xU8(b_value & b_mask);
            let b_value_upper: vec4<u32> = unpack4xU8((b_value >> 4) & b_mask);
            let b_quantized_values = ${Ce}(${Array.from({length:4},(Qe,We)=>`${fe}(b_value_lower[${We}]), ${fe}(b_value_upper[${We}])`).join(", ")});
            let b_dequantized_values = ${w===1?`${Ce}(${Array.from({length:8},(Qe,We)=>`(b_quantized_values[${We}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Ce}(${Array(8).fill("zero_point").join(",")})) * scale;`};
            // Number of B elements per 32-bit word is 32/bits = 32/4 = 8
            for (var m: u32 = 0; m < ${T?o:_}u; m++) {
              ${M.indicesSet("a_indices",se-2,T?"m":`row * ${_} + m`)};
              ${M.indicesSet("a_indices",se-1,"word_offset")};
              var input_offset = ${M.indicesToOffset("a_indices")};
              var a_data: ${Ce};
              for (var j: u32 = 0; j < ${8/w}; j++) {
                a_data[j] = ${M.getByOffset("input_offset")};
                input_offset++;
              }
              ${T?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${A>1?"[c]":""} += ${Array.from({length:8/w},(Qe,We)=>`${w===1?`a_data[${We}] * b_dequantized_values[${We}]`:`dot(a_data[${We}], b_dequantized_values[${We}])`}`).join(" + ")};
            }
            word_offset += ${8/w};
          }
        }`,Ye=ee?`
          zero_point_offset += 4;
          if (zero_point_offset == 32) {
            zero_point_offset = 0;
            zero_point_index++;
            zero_point_word = ${ee.getByOffset("zero_point_index")};
          }`:"";return T?`
        var<workgroup> workgroup_shared: array<${W.type.value}, ${o*s}>;
        ${de.declareVariables(...re,W)}
        ${de.mainStart([s,1,1])}
          var a_indices: ${M.type.indices};
          var block = local_id.x;
          var col = workgroup_id.y;
          var batch = workgroup_id.z;
          ${M.indicesSet("a_indices","0","batch")};
          // Two zero points are packed into one byte when uniforms.bits is 4.
          for (var c: u32 = 0; c < ${A}; c++) {
            let col_times_components_plus_c = col * ${A} + c;
              ${ee?`
            var zero_point_bytes_per_col: u32 = (${s} + 1) / 2;
            var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u);
            var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u;
            var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u;
            var zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            var zero_point_word: u32 = ${ee.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}
            var b_indices: ${R.type.indices};
            ${R.indicesSet("b_indices","0","col_times_components_plus_c")};
            // The scale and zero points are computed per block.
            var scales_index = col_times_components_plus_c * ${s} + block;
            let scale = ${G.getByOffset("scales_index")};
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${fe}(${ee?"(zero_point_word) & 0xFu":8});
            ${R.indicesSet("b_indices","1","block")};
            var word_offset: u32 = block * ${e.blockSize/w};
            var workgroup_shared_offset: u32 = block * ${o};
            ${Be}
          }
          workgroupBarrier();
          if (local_id.x == 0u) {
            var output_indices: ${W.type.indices};
            ${W.indicesSet("output_indices","0","batch")};
            ${W.indicesSet("output_indices",ne-1,"col")};
            ${W.indicesSet("output_indices",ne-2,"0")};
            var output_offset = ${W.indicesToOffset("output_indices")};
            for (var m: u32 = 0u; m < ${o}u; m++) {
              var output_value: ${W.type.value} = ${W.type.value}(0);
              var workgroup_shared_offset: u32 = m;
              for (var b: u32 = 0u; b < ${s}u; b++) {
                output_value += workgroup_shared[workgroup_shared_offset];
                workgroup_shared_offset += ${o};
              }
              ${W.setByOffset("output_offset","output_value")};
              output_offset += ${u/A};
            }
          }
        }`:`
        ${de.registerUniforms(ae).declareVariables(...re,W)}
        ${de.mainStart()}
          ${de.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var output_values: array<${W.type.value}, ${_}>;
          var output_indices = ${W.offsetToIndices("global_idx")};
          var col = ${W.indicesGet("output_indices",ne-1)};
          var row = ${W.indicesGet("output_indices",ne-2)};
          var a_indices: ${M.type.indices} = output_indices;
          // Two zero points are packed into one byte because uniforms.bits <= 4.
          // zero_point_offset is either 0 or 4. It is bit offset within one byte.
          // TODO support zero_point_offset for bits > 4
          ${ee?`
          var zero_point_abs_offset = col * ${A} * ((${s} + 1) / 2);
          var zero_point_index: u32 = zero_point_abs_offset / 4;
          var zero_point_word: u32 = ${ee.getByOffset("zero_point_index")};
          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}
          var scale_index = col * ${s*A};
          var b_indices: ${R.type.indices};
          for (var c: u32 = 0; c < ${A}; c++) {
            ${R.indicesSet("b_indices","0",`col * ${A} + c`)};
            var block_offset: u32 = 0;
            for (var block: u32 = 0; block < ${s}; block++) {
              // The scale and zero points are computed per block.
              let scale = ${G.getByOffset("scale_index")};
              // The default zero point is 8 for unsigned 4-bit quantization.
              let zero_point = ${fe}(${ee?"extractBits(zero_point_word, zero_point_offset, 4)":8});
              ${R.indicesSet("b_indices","1","block")};
              var word_offset: u32 = block_offset;
              ${Be}
              scale_index++;
              ${Ye}
              block_offset += uniforms.block_size / ${w};
            }
            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.
            ${ee?`if (zero_point_offset % 8 > 0) {
                ${Ye}
              }`:""}
            }
            for (var k: u32 = 0u; k < ${_}u; k++) {
              ${W.indicesSet("output_indices",ne-2,`${_} * row + k`)};
              ${W.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:T?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${d};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:P,dataType:d}],name:T?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:T?{x:1,y:Math.ceil(u/A),z:h}:{x:Math.ceil(B/64)},programUniforms:D}),getShaderSource:te}},gm=(t,e)=>{ac(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(ic(t.inputs,e,r,n))},_m=t=>Ke(t)}),St,sc,ym,Ks,oc,ki,wm,Vy=J(()=>{$e(),Me(),ct(),Xo(),Xh(),Te(),Ia(),St=(t,e)=>t.length>e&&t[e].dims.length>0&&K.size(t[e].dims)>0?t[e]:void 0,sc=(t,e)=>{let r=t[0],n=St(t,1),a=St(t,2),i=St(t,3),s=St(t,4),o=St(t,5),l=St(t,6),u=St(t,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let p=!1,h=r.dims[0],m=r.dims[1],d=r.dims.length===3?p?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],_=m,w=0,v=0,S=Math.floor(d/e.numHeads);if(l&&u){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==h||l.dims[1]!==e.numHeads||l.dims[3]!==S)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[0]!==h||u.dims[1]!==e.numHeads||u.dims[3]!==S)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==u.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(u.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');w=l.dims[2],v=l.dims[2]}else if(l||u)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let $;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');$=2,_=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==S)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(a)throw new Error('Expect "value" be none when "key" has packed kv format.');$=5,_=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==S)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');$=0,_=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==e.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');$=3}if(i){if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(a&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(s){E=8;let D=s.dims;throw D.length===1?D[0]===h?E=1:D[0]===3*h+2&&(E=3):D.length===2&&D[0]===h&&D[1]===_&&(E=5),E===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let T=!1,A=d;if(a){if(a.dims.length!==3&&a.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==a.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(a.dims.length===3){if(_!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');A=a.dims[2]}else{if(_!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');A=a.dims[1]*a.dims[3],T=!0}}let P=w+_,B=!1;if(s)throw new Error("Key padding mask is not supported");if(o){if(o.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(o.dims[0]!==h&&o.dims[0]!==1||o.dims[1]!==e.numHeads||o.dims[2]!==m||o.dims[3]!==P)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:h,sequenceLength:m,pastSequenceLength:w,kvSequenceLength:_,totalSequenceLength:P,maxSequenceLength:v,inputHiddenSize:0,hiddenSize:d,vHiddenSize:A,headSize:S,vHeadSize:Math.floor(A/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:E,scale:e.scale,broadcastResPosBias:B,passPastInKv:T,qkvFormat:$}},ym=t=>Ke({...t}),Ks=Ke({perm:[0,2,1,3]}),oc=(t,e,r,n,a,i,s)=>{let o=[n,a,i],l=K.size(o),u=[{type:12,data:l},{type:12,data:s},{type:12,data:i}],p=h=>{let m=ge("qkv_with_bias",e.dataType,o),d=X("qkv",e.dataType,o),_=X("bias",r.dataType,o),w=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${h.registerUniforms(w).declareVariables(d,_,m)}
  ${h.mainStart()}
    ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:p},{inputs:[e,r],outputs:[-1]})[0]},ki=(t,e,r,n,a,i,s,o)=>{let l=i;if(s){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=oc(t,i,s,e,n,r*a,o),l=l.reshape([e,n,r,a]),t.compute(Er(l,Ks.perm),{inputs:[l],outputs:[-1]})[0]}else return i.dims.length===3&&(l=i.reshape([e,n,r,a])),t.compute(Er(l,Ks.perm),{inputs:[l],outputs:[-1]})[0]},wm=(t,e)=>{let r=sc(t.inputs,e),n=t.inputs[0],a=St(t.inputs,1),i=St(t.inputs,2),s=St(t.inputs,3),o=St(t.inputs,4),l=St(t.inputs,5),u=St(t.inputs,6),p=St(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if(a?.dims.length===5)throw new Error("Packed KV is not implemented");let h=a&&i&&a.dims.length===4&&i.dims.length===4,m=ki(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,s,0);if(h)return Ki(t,m,a,i,o,void 0,u,p,l,r,e);if(!a||!i)throw new Error("key and value must be provided");let d=ki(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,s,r.hiddenSize),_=ki(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,i,s,2*r.hiddenSize);Ki(t,m,d,_,o,void 0,u,p,l,r,e)}}),lc,uc,dc,cc,pc,hc,fc,mc,bm,Gy=J(()=>{$e(),Me(),Te(),lc=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1&&t[0].dataType!==10)throw new Error("Input type must be float or float16.");if(t.length>=2){let e=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(e=t[3].dims[0]*2===t[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},uc=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
            k = i32(${t.indicesGet("indices",a)}) - ${Se("uniforms.pads",a,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${Se("uniforms.x_shape",a,e)})) {
              break;
            }
            offset += k * i32(${Se("uniforms.x_strides",a,e)});
        `;return`
          value = ${t.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${n}
            value = x[offset];
          }
      `},dc=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${Se("uniforms.pads",a,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${Se("uniforms.x_shape",a,e)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${Se("uniforms.x_shape",a,e)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${Se("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},cc=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${Se("uniforms.pads",a,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${Se("uniforms.x_shape",a,e)})) {
                  k = i32(${Se("uniforms.x_shape",a,e)}) - 1;
                }
                offset += k * i32(${Se("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},pc=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${Se("uniforms.pads",a,r)};
                if (k < 0)  {
                  k += i32(${Se("uniforms.x_shape",a,e)}]);
                }
                if (k >= i32(${Se("uniforms.x_shape",a,e)})) {
                  k -= i32(${Se("uniforms.x_shape",a,e)});
                }
                offset += k * i32(${Se("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},hc=(t,e,r)=>{switch(r.mode){case 0:return uc(t,e,r.pads.length);case 1:return dc(t,e,r.pads.length);case 2:return cc(t,e,r.pads.length);case 3:return pc(t,e,r.pads.length);default:throw new Error("Invalid mode")}},fc=(t,e)=>{let r=K.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=K.size(r),i=[{type:12,data:a},{type:6,data:e.pads}];e.mode===0&&i.push({type:t[0].dataType,data:e.value}),i.push(...ye(t[0].dims,r));let s=["rank"],o=l=>{let u=ge("output",t[0].dataType,r.length),p=X("x",t[0].dataType,n.length),h=p.type.value,m=hc(u,n.length,e),d=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&d.push({name:"constant_value",type:h}),`
            ${l.registerUniforms(d).declareVariables(p,u)}
            ${l.mainStart()}
            ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${h}(0);
            ${m}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:s},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(K.size(r)/64)},programUniforms:i}),getShaderSource:o}},mc=(t,e)=>{if(t.length>1){let r=t[1].getBigInt64Array(),n=t.length>=3&&t[2].data?t[2].getFloat32Array()[0]:0,a=t[0].dims.length,i=new Int32Array(2*a).fill(0);if(t.length>=4){let o=t[3].getBigInt64Array();for(let l=0;l<o.length;l++)i[Number(o[l])]=Number(r[l]),i[Number(o[l])+a]=Number(r[l+o.length])}else r.forEach((o,l)=>i[Number(l)]=Number(o));let s=[];return i.forEach(o=>s.push(o)),{mode:e.mode,value:n,pads:s}}else return e},bm=(t,e)=>{lc(t.inputs);let r=mc(t.inputs,e);t.compute(fc(t.inputs,r),{inputs:[0]})}}),sa,Ys,Xs,Qs,Zs,gc,_c,Js,eo,vm,$m,to,xm,Sm,ro,km,Em,Cm,Tm,Hy=J(()=>{dr(),$e(),Me(),Te(),sa=t=>{if(Ue.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},Ys=(t,e,r)=>{let n=e.format==="NHWC",a=t.dims.slice();n&&a.splice(1,0,a.pop());let i=Object.hasOwnProperty.call(e,"dilations"),s=e.kernelShape.slice(),o=e.strides.slice(),l=i?e.dilations.slice():[],u=e.pads.slice();Hi.adjustPoolAttributes(r,a,s,o,l,u);let p=Hi.computePoolOutputShape(r,a,o,l,s,u,e.autoPad),h=Object.assign({},e);i?Object.assign(h,{kernelShape:s,strides:o,pads:u,dilations:l,cacheKey:e.cacheKey}):Object.assign(h,{kernelShape:s,strides:o,pads:u,cacheKey:e.cacheKey});let m=p.slice();return m.push(m.splice(1,1)[0]),[h,n?m:p]},Xs=(t,e)=>{let r=e.format==="NHWC",n=K.size(t),a=K.size(e.kernelShape),i=[{type:12,data:n},{type:12,data:a}],s=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let o=e.kernelShape[e.kernelShape.length-1],l=e.strides[e.strides.length-1],u=e.pads[e.pads.length/2-1],p=e.pads[e.pads.length-1],h=!!(u+p);i.push({type:12,data:o},{type:12,data:l},{type:12,data:u},{type:12,data:p}),s.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let m=!1;if(e.kernelShape.length===2){let d=e.kernelShape[e.kernelShape.length-2],_=e.strides[e.strides.length-2],w=e.pads[e.pads.length/2-2],v=e.pads[e.pads.length-2];m=!!(w+v),i.push({type:12,data:d},{type:12,data:_},{type:12,data:w},{type:12,data:v}),s.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,s,!0,h,m]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=K.computeStrides(e.kernelShape);i.push({type:12,data:o},{type:12,data:e.pads},{type:12,data:e.strides}),s.push({name:"kernelStrides",type:"u32",length:o.length},{name:"pads",type:"u32",length:e.pads.length},{name:"strides",type:"u32",length:e.strides.length});let l=e.pads.reduce((u,p)=>u+p);return[i,s,!!l,!1,!1]}},Qs=(t,e,r,n,a,i,s,o,l,u,p,h)=>{let m=a.format==="NHWC",d=e.type.value,_=ge("output",e.type.tensor,n);if(a.kernelShape.length<=2){let w="",v="",S="",$=r-(m?2:1);if(p?w=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${$}] < 0 || xIndices[${$}]
                      >= uniforms.x_shape[${$}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`:w=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`,a.kernelShape.length===2){let E=r-(m?3:2);h?v=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${E}] = indices[${E}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${E}] < 0 || xIndices[${E}] >= uniforms.x_shape[${E}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:v=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${E}] = indices[${E}] * uniforms.sh - uniforms.phStart + j;
                `,S=`
              }
            `}return`
            ${t.registerUniforms(l).declareVariables(e,_)}

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

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

              var value = ${d}(${o});
              var pad = 0;
              ${v}
              ${w}
              ${S}
              ${s}

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

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

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

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

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

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

              output[global_idx] = value;
            }`}},Zs=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,gc=t=>`${Zs(t)};${t.countIncludePad}`,_c=t=>`${Zs(t)};${t.storageOrder};${t.dilations}`,Js=t=>({format:t.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],ceilMode:t.ceil_mode,kernelShape:t.kernel_shape,strides:t.strides,pads:t.pads}),eo=(t,e,r,n)=>{let[a,i]=Ys(e,n,r),s=X("x",e.dataType,e.dims.length),o=s.type.value,l="value += x_val;",u="";a.countIncludePad?u+=`value /= ${o}(uniforms.kernelSize);`:u+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[p,h,m,d,_]=Xs(i,a);p.push(...ye(e.dims,i));let w=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${m};${d};${_}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(K.size(i)/64)},programUniforms:p}),getShaderSource:v=>Qs(v,s,e.dims.length,i.length,a,l,u,0,h,m,d,_)}},vm=t=>{let e=t.count_include_pad!==0,r=Js(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:gc(n)}},$m=(t,e)=>{sa(t.inputs),t.compute(eo("AveragePool",t.inputs[0],!1,e))},to={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},xm=t=>{let e=t.format;return{format:e,...to,cacheKey:e}},Sm=(t,e)=>{sa(t.inputs),t.compute(eo("GlobalAveragePool",t.inputs[0],!0,e))},ro=(t,e,r,n)=>{let[a,i]=Ys(e,n,r),s=`
      value = max(x_val, value);
    `,o="",l=X("x",e.dataType,e.dims.length),u=["rank"],[p,h,m,d,_]=Xs(i,a);return p.push(...ye(e.dims,i)),{name:t,shaderCache:{hint:`${n.cacheKey};${m};${d};${_}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(K.size(i)/64)},programUniforms:p}),getShaderSource:w=>Qs(w,l,e.dims.length,i.length,a,s,o,e.dataType===10?-65504:-1e5,h,m,d,_)}},km=(t,e)=>{sa(t.inputs),t.compute(ro("MaxPool",t.inputs[0],!1,e))},Em=t=>{let e=t.storage_order,r=t.dilations,n=Js(t);if(e!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let a={storageOrder:e,dilations:r,...n,cacheKey:""};return{...a,cacheKey:_c(a)}},Cm=t=>{let e=t.format;return{format:e,...to,cacheKey:e}},Tm=(t,e)=>{sa(t.inputs),t.compute(ro("GlobalMaxPool",t.inputs[0],!0,e))}}),yc,wc,Am,jy=J(()=>{dr(),$e(),Te(),yc=(t,e,r)=>{let n=t===e,a=t<e&&r<0,i=t>e&&r>0;if(n||a||i)throw new Error("Range these inputs' contents are invalid.")},wc=(t,e,r,n)=>{let a=Math.abs(Math.ceil((e-t)/r)),i=[a],s=a,o=[{type:12,data:s},{type:n,data:t},{type:n,data:r},...ye(i)],l=u=>{let p=ge("output",n,i.length),h=p.type.value,m=[{name:"outputSize",type:"u32"},{name:"start",type:h},{name:"delta",type:h}];return`
        ${u.registerUniforms(m).declareVariables(p)}
        ${u.mainStart()}
        ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${h}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:o})}},Am=t=>{let e=0,r=0,n=0;t.inputs[0].dataType===6?(e=t.inputs[0].getInt32Array()[0],r=t.inputs[1].getInt32Array()[0],n=t.inputs[2].getInt32Array()[0]):t.inputs[0].dataType===1&&(e=t.inputs[0].getFloat32Array()[0],r=t.inputs[1].getFloat32Array()[0],n=t.inputs[2].getFloat32Array()[0]),Ue.webgpu.validateInputContent&&yc(e,r,n),t.compute(wc(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),bc,vc,$c,xc,Sc,kc,Ec,Cc,Tc,Ac,Ic,no,Mc,Oc,zc,Pc,Rc,Im,Mm,qy=J(()=>{$e(),Me(),ct(),Te(),bc=(t,e)=>{if(t.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),t.length>0){if(e.mode==="linear"){if(!(t.length===2||t.length===3||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1||t.length===5&&t[0]===1&&t[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(e.mode==="cubic"&&!(t.length===2||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},vc=(t,e,r)=>{e.every(a=>a>=0&&a<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return e.forEach((a,i)=>n[a]=t[i]),n},$c=(t,e,r,n,a,i)=>{let[s,o,l]=r>10?[1,2,3]:[-1,t.length>1?1:-1,-1],u=t[0].dims.length;if(s>0&&t.length>s&&t[s].dims.length>0)t[s].getFloat32Array().forEach(p=>i.push(p));else if(e.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&t.length>o&&t[o].dims.length>0){if(t[o].getFloat32Array().forEach(p=>n.push(p)),n.length!==0&&n.length!==u&&r>=18&&n.length!==e.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");bc(n,e),e.axes.length>0&&vc(n,e.axes,u).forEach((p,h)=>n[h]=p)}if(l>0&&t.length>l&&(t[l].getBigInt64Array().forEach(p=>a.push(Number(p))),a.length!==u||r>=18&&a.length===e.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(e.axes.length>0){if(n.length!==e.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(a.length!==e.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof a<"u"&&n.length>0&&a.length>u)throw new Error("Resize requires only of scales or sizes to be specified")},xc=(t,e)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${e} { `+(()=>{switch(t){case"asymmetric":return`return ${e}(xResized) / ${e}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${e}(xResized) + 0.5) / ${e}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${e}(xResized) + 0.5) / ${e}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating
                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
                    // offset-by-one error later in floor().
                    let whole = ${e}(xResized * (lengthOriginal - 1) / (lengthResized - 1));
                    let fract =
                        ${e}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${e}(lengthResized - 1);
                    return whole + fract;
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${e}(roiStart) * ${e}(lengthOriginal - 1) +
                        (${e}(xResized) * ${e}(roiEnd - roiStart) * ${e}(lengthOriginal - 1)) /
                        ${e}(lengthResized - 1);
                  } else {
                    return 0.5 * ${e}(roiStart + roiEnd) * ${e}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${e}xScale * ${e}(lengthResized);
                  const adjustment = ${e}(lengthResized) / outputWidth;
                  const center = ${e}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;case"half_pixel":return`return ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${t} is not supported`)}})()+"}",Sc=(t,e,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(t){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(e<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${t} is not supported`)}})()+"}",kc=(t,e,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),a=t.length===0?n:t.slice();return e.length>0?(e.forEach((i,s)=>{n[i]=a[s],n[s+r]=a[e.length+s]}),n):a},Ec=(t,e,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(t.forEach(i=>a.push(i)),Math.max(...n)>t.length)throw new Error("axes is out of bound");n.forEach((i,s)=>a[i]=r[s])}else r.forEach(i=>a.push(i));else{if(e.length===0)throw new Error("Resize requires either scales or sizes.");a=t.map((i,s)=>Math.round(i*e[s]))}return a},Cc=(t,e,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(i=>e[i]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(i=>e[i]),Number.MIN_VALUE):Math.max(...e,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();e.fill(1,0,e.length);let a=t.slice();return r.axes.length>0?(r.axes.forEach(i=>e[i]=n),r.axes.forEach(i=>a[i]=Math.round(t[i]*e[i]))):(e.fill(n,0,e.length),a.forEach((i,s)=>a[s]=Math.round(i*e[s]))),a},Tc=(t,e,r,n,a)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> array<${t.type.value}, ${r.length}> {
      var original_indices: array<${t.type.value}, ${r.length}>;
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var output_index = ${t.indicesGet("output_indices","i")};
        var scale = ${Se("uniforms.scales","i",n)};
        var roi_low = ${Se("uniforms.roi","i",a)};
        var roi_hi = ${Se("uniforms.roi",`i + ${e.length}`,a)};
        if (scale == 1.0) {
          original_indices[i] = ${t.type.value}(output_index);
        } else {
          var input_shape_i = ${Se("uniforms.input_shape","i",e.length)};
          var output_shape_i = ${Se("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;
    }`,Ac=(t,e,r,n,a,i,s)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} {
      var input_indices: ${t.type.indices};
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${Se("uniforms.scales","i",a)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${Se("uniforms.roi","i",i)};
          var roi_hi = ${Se("uniforms.roi",`i + ${r.length}`,i)};
          var input_shape_i = ${Se("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${Se("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${s} || (original_idx >= 0 && original_idx < ${e.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${e.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${t.indicesSet("input_indices","i"," input_index")}
      }
      return input_indices;
    }`,Ic=(t,e)=>`
    fn checkInputIndices(input_indices: ${t.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${e.length}; i++) {
        var input_index = ${t.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${Se("uniforms.input_shape","i",e.length)}) {
          return false;
        }
      }
      return true;
    }`,no=(t,e,r,n)=>t.rank>n?`
    ${t.indicesSet("input_indices",e,"channel")};
    ${t.indicesSet("input_indices",r,"batch")};
`:"",Mc=(t,e,r,n,a)=>{let[i,s,o,l]=r.length===2?[-1,0,1,-1]:[0,2,3,1],u=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${u} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",s,`max(0, min(row, ${r[s]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)};
      ${no(t,l,i,2)}
      return ${t.getByIndices("input_indices")};
    }

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

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

  fn bicubicInterpolation(output_indices: ${e.type.indices}) -> ${d} {
    var input_indices: ${t.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},zc=(t,e,r,n,a)=>{let[i,s,o,l,u]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],p=t.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${p} {
      var input_indices: ${t.type.indices};
      ${t.indicesSet("input_indices",s,`max(0, min(depth, ${r[s]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(height, ${r[o]} - 1))`)};
      ${t.indicesSet("input_indices",l,`max(0, min(width, ${r[l]} - 1))`)};
      ${no(t,u,i,3)}
      return ${t.getByIndices("input_indices")};
    }

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

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

      var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${p} = abs(depth - ${p}(depth1));
      var dx2: ${p} = abs(${p}(depth2) - depth);
      var dy1: ${p} = abs(height - ${p}(height1));
      var dy2: ${p} = abs(${p}(height2) - height);
      var dz1: ${p} = abs(width - ${p}(width1));
      var dz2: ${p} = abs(${p}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},Pc=(t,e,r,n,a,i)=>{let s=t.dims,o=kc(i,e.axes,s.length),l=Ec(s,n,a,e.axes),u=n.slice();n.length===0&&(u=s.map(($,E)=>$===0?1:l[E]/$),e.keepAspectRatioPolicy!=="stretch"&&(l=Cc(s,u,e)));let p=ge("output",t.dataType,l.length),h=X("input",t.dataType,s.length),m=K.size(l),d=s.length===l.length&&s.every(($,E)=>$===l[E]),_=e.coordinateTransformMode==="tf_crop_and_resize",w=e.extrapolationValue,v=h.type.value,S=$=>`
      ${d?"":`
      ${xc(e.coordinateTransformMode,v)};
      ${(()=>{switch(e.mode){case"nearest":return`
              ${Ic(h,s)};
              ${Sc(e.nearestMode,r,v)};
              ${Ac(h,p,s,l,u.length,o.length,_)};
              `;case"linear":return`
              ${Tc(p,s,l,u.length,o.length)};
              ${(()=>{if(s.length===2||s.length===4)return`${Mc(h,p,s,_,w)}`;if(s.length===3||s.length===5)return`${zc(h,p,s,_,w)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(s.length===2||s.length===4)return`${Oc(h,p,s,l,u,o,e.cubicCoeffA,_,e.extrapolationValue,e.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${$.registerUniform("output_size","u32").registerUniform("scales","f32",u.length).registerUniform("roi","f32",o.length).declareVariables(h,p)}
      ${$.mainStart()}
        ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${d?"output[global_idx] = input[global_idx];":`
        let output_indices = ${p.offsetToIndices("global_idx")};
        var input_indices: ${h.type.indices};
        ${(()=>{switch(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${h.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${e.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${s.length===2||s.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${e.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${e.cacheKey}|${r}|${u.length>0?u:""}|${a.length>0?a:""}|${o.length>0?o:""}|${d}|${s}`,inputDependencies:["rank"]},getShaderSource:S,getRunData:()=>({outputs:[{dims:l,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:[{type:12,data:m},{type:1,data:u},{type:1,data:o},...ye(s,l)]})}},Rc=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},Im=(t,e)=>{let r=[],n=[],a=[],i=Rc(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");$c(t.inputs,e,i,r,n,a),t.compute(Pc(t.inputs[0],e,i,r,n,a),{inputs:[0]})},Mm=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,a=t.cubicCoeffA,i=t.excludeOutside!==0,s=t.extrapolationValue,o=t.keepAspectRatioPolicy,l=t.mode,u=t.nearestMode===""?"simple":t.nearestMode;return Ke({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:i,extrapolationValue:s,keepAspectRatioPolicy:o,mode:l,nearestMode:u})}}),Bc,Dc,Om,Ky=J(()=>{$e(),Me(),ct(),Te(),Bc=(t,e)=>{let[r,n,a,i]=t,{numHeads:s,rotaryEmbeddingDim:o}=e;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(!K.areEqual(n.dims,[])&&!K.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(a.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(!K.areEqual(a.dims,i.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(o>0&&s===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let l=r.dims[0],u=r.dims[r.dims.length-2],p=a.dims[0],h=K.sizeFromDimension(r.dims,1)/u,m=o===0?a.dims[1]*2:h/s;if(o>m)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(l!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(u!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(m/2!==a.dims[1]&&o/2!==a.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${a.dims[1]}`);if(u>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Dc=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:i}=e,s=t[0].dims[0],o=K.sizeFromDimension(t[0].dims,1),l=t[0].dims[t[0].dims.length-2],u=o/l,p=t[2].dims[1],h=a===0?p*2:u/n,m=new Array(s,l,u/h,h-p),d=K.computeStrides(m),_=[{type:1,data:i},{type:12,data:m},{type:12,data:d},...t[0].dims.length===3?new Array({type:12,data:[o,u,h,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,h,l*h,1]}):[],...ye(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],w=v=>{let S=X("input",t[0].dataType,t[0].dims.length),$=X("position_ids",t[1].dataType,t[1].dims.length),E=X("cos_cache",t[2].dataType,t[2].dims.length),T=X("sin_cache",t[3].dataType,t[3].dims.length),A=ge("output",t[0].dataType,t[0].dims.length);return v.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:m.length},{name:"global_strides",type:"u32",length:d.length},{name:"input_output_strides",type:"u32",length:d.length}]),`
        ${v.declareVariables(S,$,E,T,A)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${$.broadcastedIndicesToOffset("bsnh.xy",ge("",$.type.tensor,2))};
            let position_id =
                u32(${$.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 = ${S.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} -
                ${S.getByOffset("j")} * ${T.get("position_id","bsnh[3]")};
            ${A.setByOffset("i","re")}
            let im = ${S.getByOffset("i")} * ${T.get("position_id","bsnh[3]")} +
                ${S.getByOffset("j")} * ${E.get("position_id","bsnh[3]")};
            ${A.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${A.setByOffset("k",S.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:Ke({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(K.size(m)/zn)},programUniforms:_})}},Om=(t,e)=>{Bc(t.inputs,e),t.compute(Dc(t.inputs,e))}}),Nc,Fc,zm,Yy=J(()=>{$e(),Me(),Te(),Nc=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let a=e.dims[e.dims.length-1],i=e.dims[e.dims.length-2];if(r.dims[r.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==i)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==a)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let s=t[3];if(s.dims.length!==1)throw new Error("Beta must be 1D");if(s.dims[s.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let s=t[4];if(s.dims.length!==1)throw new Error("Bias must be 1D");if(s.dims[s.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},Fc=(t,e,r,n)=>{let a=e.simplified,i=t[0].dims,s=K.size(i),o=i,l=s,u=i.slice(-1)[0],p=n?i.slice(0,-1).concat(1):[],h=!a&&t.length>3,m=t.length>4,d=n&&r>1,_=n&&r>2,w=r>3,v=ot(u),S=[{type:12,data:l},{type:12,data:v},{type:12,data:u},{type:1,data:e.epsilon}],$=T=>{let A=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],P=[X("x",t[0].dataType,t[0].dims,v),X("skip",t[1].dataType,t[1].dims,v),X("gamma",t[2].dataType,t[2].dims,v)];h&&P.push(X("beta",t[3].dataType,t[3].dims,v)),m&&P.push(X("bias",t[4].dataType,t[4].dims,v)),P.push(ge("output",t[0].dataType,o,v)),d&&P.push(ge("mean_output",1,p)),_&&P.push(ge("inv_std_output",1,p)),w&&P.push(ge("input_skip_bias_sum",t[0].dataType,o,v));let B=mt(t[0].dataType);return`

      ${T.registerUniforms(A).declareVariables(...P)}

      ${T.mainStart()}
        ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}
        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
        let offset = global_idx * hidden_size_vectorized;
        var sum = ${Sr("f32",v)};
        var squareSum = ${Sr("f32",v)};
        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {
          let skip_value = skip[offset + i];
          let bias_value = ${m?"bias[i]":B+"(0.0)"};
          let input_value = x[offset + i];
          let value = input_value + skip_value + bias_value;
          ${w?"input_skip_bias_sum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32_value = ${In(B,v,"value")};
          sum += f32_value;
          squareSum += f32_value * f32_value;
        }
        let mean = ${Nr("sum",v)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Nr("squareSum",v)} / f32(uniforms.hidden_size) ${a?"":"- mean * mean"} + uniforms.epsilon);
        ${d?"mean_output[global_idx] = mean;":""}
        ${_?"inv_std_output[global_idx] = inv_std_dev;":""}
        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {
          output[offset + i] = (output[offset + i] ${a?"":`- ${B}(mean)`}) * ${B}(inv_std_dev) * gamma[i] ${h?"+ beta[i]":""};
        }
      }`},E=[{dims:o,dataType:t[0].dataType}];return r>1&&E.push({dims:p,dataType:1}),r>2&&E.push({dims:p,dataType:1}),r>3&&E.push({dims:i,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${v};${d};${_};${w}`,inputDependencies:t.map((T,A)=>"type")},getShaderSource:$,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/u/64)},programUniforms:S})}},zm=(t,e)=>{Nc(t.inputs);let r=[0];t.outputCount>1&&r.push(-3),t.outputCount>2&&r.push(-3),t.outputCount>3&&r.push(3),t.compute(Fc(t.inputs,e,t.outputCount,!1),{outputs:r})}}),Lc,oa,Uc,ao,Wc,Vc,Pm,Rm,Xy=J(()=>{$e(),Me(),ct(),Te(),Lc=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");if(e.axes.length!==0){if(e.axes.length!==e.starts.length||e.axes.length!==e.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(e.starts.length!==e.ends.length)throw new Error("starts and ends must have the same length");t.slice(1).forEach((r,n)=>{if(t[n+1].dataType!==6&&t[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},oa=(t,e)=>{let r=[];if(t.length>e)if(t[e].dataType===7)t[e].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(t[e].dataType===6)t[e].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${e} must be an array of int32 or int64`);return r},Uc=(t,e)=>{if(t.length>1){let r=oa(t,1),n=oa(t,2),a=oa(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),Ke({starts:r,ends:n,axes:a})}else return e},ao=(t,e,r,n,a)=>{let i=t;return t<0&&(i+=r[n[e]]),a[e]<0?Math.max(0,Math.min(i,r[n[e]]-1)):Math.max(0,Math.min(i,r[n[e]]))},Wc=(t,e,r)=>`fn calculateInputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} {
          var input_indices: ${t.type.indices};
          var carry = 0u;
          for (var i = ${r.length}; i >= 0; i--) {
            let input_shape_i = ${Se("uniforms.input_shape","i",r.length)};
            let steps_i = ${Se("uniforms.steps","i",r.length)};
            let signs_i = ${Se("uniforms.signs","i",r.length)};
            let starts_i = ${Se("uniforms.starts","i",r.length)};
            var output_index = ${e.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${t.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,Vc=(t,e)=>{let r=t[0].dims,n=K.size(r),a=e.axes.length>0?K.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],i=oa(t,4);i.forEach(S=>S!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(a.length).fill(1));let s=e.starts.map((S,$)=>ao(S,$,r,a,i)),o=e.ends.map((S,$)=>ao(S,$,r,a,i));if(a.length!==s.length||a.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(a.length!==r.length)for(let S=0;S<r.length;++S)a.includes(S)||(s.splice(S,0,0),o.splice(S,0,r[S]),i.splice(S,0,1));let l=i.map(S=>Math.sign(S));i.forEach((S,$,E)=>{if(S<0){let T=(o[$]-s[$])/S,A=s[$],P=A+T*i[$];s[$]=P,o[$]=A,E[$]=-S}});let u=r.slice(0);a.forEach((S,$)=>{u[S]=Math.ceil((o[S]-s[S])/i[S])});let p={dims:u,dataType:t[0].dataType},h=ge("output",t[0].dataType,u.length),m=X("input",t[0].dataType,t[0].dims.length),d=K.size(u),_=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:s.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:i.length}],w=[{type:12,data:d},{type:12,data:s},{type:6,data:l},{type:12,data:i},...ye(t[0].dims,u)],v=S=>`
      ${S.registerUniforms(_).declareVariables(m,h)}
        ${Wc(m,h,r)}
        ${S.mainStart()}
          ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${h.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${h.setByOffset("global_idx",m.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${s.length}_${i.length}`,inputDependencies:["rank"]},getShaderSource:v,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:w})}},Pm=(t,e)=>{Lc(t.inputs,e);let r=Uc(t.inputs,e);t.compute(Vc(t.inputs,r),{inputs:[0]})},Rm=t=>{let e=t.starts,r=t.ends,n=t.axes;return Ke({starts:e,ends:r,axes:n})}}),Gc,Hc,Bm,Dm,Qy=J(()=>{$e(),Me(),ct(),Te(),Gc=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},Hc=(t,e)=>{let r=t.dims,n=K.size(r),a=64,i=e.axis;if(i<0&&(i=r.length+i),i<r.length-1)throw new Error("softmax only supports last axis for now.");let s=r[i],o=n/s,l=ot(s),u=s/l,p=(v,S)=>S===4?`max(max(${v}.x, ${v}.y), max(${v}.z, ${v}.w))`:S===2?`max(${v}.x, ${v}.y)`:S===3?`max(max(${v}.x, ${v}.y), ${v}.z)`:v,h=X("x",t.dataType,t.dims,l),m=ge("result",t.dataType,t.dims,l),d=h.type.value,_=mt(t.dataType)==="f32"?`var threadMax = ${d}(-3.402823e+38f);`:`var threadMax = ${d}(-65504.0h);`,w=v=>`
      var<workgroup> rowMaxShared : ${d};
      var<workgroup> rowSumShared : ${d};
      var<workgroup> threadShared : array<${d}, ${a}>;

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

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

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

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

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

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

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          setValue(row, col, row_stride, value);
        }
      }`;return{name:"Softmax",shaderCache:{hint:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:6,data:u}]}),getShaderSource:w}},Bm=(t,e)=>{Gc(t.inputs),t.compute(Hc(t.inputs[0],e))},Dm=t=>Ke({axis:t.axis})}),jc,qc,Kc,Yc,Xc,Nm,Fm,Zy=J(()=>{$e(),Me(),ct(),Te(),jc=t=>{if(!t||t.length<1)throw new Error("too few inputs")},qc=(t,e)=>{let r=[],n=e.numOutputs;return t[1].dims[0]>0&&(t[1].getBigInt64Array().forEach(a=>r.push(Number(a))),n=r.length),Ke({numOutputs:n,axis:e.axis,splitSizes:r})},Kc=t=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${t}u; i += 1u ) {
    if (index < ${Se("uniforms.size_in_split_axis","i",t)}) {
        return i;
    }
    }
    return ${t}u;
}`,Yc=t=>{let e=t.length,r=[];for(let n=0;n<e;++n){let a=t[n].setByIndices("indices","input[global_idx]");e===1?r.push(a):n===0?r.push(`if (output_number == ${n}u) { ${a} }`):n===e-1?r.push(`else { ${a} }`):r.push(`else if (output_number == ${n}) { ${a} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${t[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},Xc=(t,e)=>{let r=t[0].dims,n=K.size(r),a=t[0].dataType,i=K.normalizeAxis(e.axis,r.length),s=new Array(e.numOutputs),o=X("input",a,r.length),l=new Array(e.numOutputs),u=[],p=[],h=0,m=[{type:12,data:n}];for(let _=0;_<e.numOutputs;_++){h+=e.splitSizes[_],l[_]=h;let w=r.slice();w[e.axis]=e.splitSizes[_],p.push(w),s[_]=ge(`output${_}`,a,w.length),u.push({dims:p[_],dataType:t[0].dataType})}m.push({type:12,data:l},...ye(r,...p));let d=_=>`
  ${_.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...s)}
  ${Kc(l.length)}
  ${Yc(s)}

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

    var indices = ${o.offsetToIndices("global_idx")};
    var index = ${o.indicesGet("indices",i)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${Se("uniforms.size_in_split_axis","output_number - 1u",l.length)};
      ${o.indicesSet("indices",i,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:d,getRunData:()=>({outputs:u,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:m})}},Nm=(t,e)=>{jc(t.inputs);let r=t.inputs.length===1?e:qc(t.inputs,e);t.compute(Xc(t.inputs,r),{inputs:[0]})},Fm=t=>{let e=t.axis,r=t.splitSizes,n=t.numOutputs<0?r.length:t.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Ke({axis:e,numOutputs:n,splitSizes:r})}}),io,Qc,Zc,Jc,Lm,Jy=J(()=>{$e(),Me(),Te(),io=t=>Array.from(t.getBigInt64Array(),Number),Qc=t=>{if(!t||t.length!==2)throw new Error("Tile requires 2 inputs.");if(t[0].dataType!==1&&t[0].dataType!==6&&t[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(t[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(t[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(io(t[1]).length!==t[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Zc=(t,e)=>{let r=[];for(let n=0;n<t.length;++n)r.push(t[n]*e[n]);return r},Jc=t=>{let e=t[0].dims,r=io(t[1]),n=Zc(e,r),a=K.size(n),i=t[0].dataType,s=X("input",i,e.length),o=ge("output",i,n.length),l=u=>`
      const inputShape = ${s.indices(...e)};
      ${u.registerUniform("output_size","u32").declareVariables(s,o)}
      ${u.mainStart()}
      ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${o.offsetToIndices("global_idx")};
      var input_indices: ${s.type.indices};
      for (var i = 0; i < ${e.length}; i++) {
        let input_dim_i = ${s.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${o.indicesGet("output_indices","i")}  % input_dim_i;

        ${s.indicesSet("input_indices","i","input_dim_value")}
      }
      ${o.setByOffset("global_idx",s.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...ye(t[0].dims,n)]}),getShaderSource:l}},Lm=t=>{Qc(t.inputs),t.compute(Jc(t.inputs),{inputs:[0]})}}),ep,tp,Um,ew=J(()=>{$e(),Me(),Te(),ep=(t,e,r,n,a)=>{let i=ge("output_data",a,r.length,4),s=X("a_data",e[1].dataType,e[1].dims.length,4),o=X("b_data",e[2].dataType,e[2].dims.length,4),l=X("c_data",e[0].dataType,e[0].dims.length,4),u,p=(h,m,d)=>`select(${m}, ${h}, ${d})`;if(!n)u=i.setByOffset("global_idx",p(s.getByOffset("global_idx"),o.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let h=(m,d,_="")=>{let w=`a_data[index_a${d}][component_a${d}]`,v=`b_data[index_b${d}][component_b${d}]`,S=`bool(c_data[index_c${d}] & (0xffu << (component_c${d} * 8)))`;return`
            let output_indices${d} = ${i.offsetToIndices(`global_idx * 4u + ${d}u`)};
            let offset_a${d} = ${s.broadcastedIndicesToOffset(`output_indices${d}`,i)};
            let offset_b${d} = ${o.broadcastedIndicesToOffset(`output_indices${d}`,i)};
            let offset_c${d} = ${l.broadcastedIndicesToOffset(`output_indices${d}`,i)};
            let index_a${d} = offset_a${d} / 4u;
            let index_b${d} = offset_b${d} / 4u;
            let index_c${d} = offset_c${d} / 4u;
            let component_a${d} = offset_a${d} % 4u;
            let component_b${d} = offset_b${d} % 4u;
            let component_c${d} = offset_c${d} % 4u;
            ${m}[${d}] = ${_}(${p(w,v,S)});
          `};a===9?u=`
            var data = vec4<u32>(0);
            ${h("data",0,"u32")}
            ${h("data",1,"u32")}
            ${h("data",2,"u32")}
            ${h("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:u=`
            ${h("output_data[global_idx]",0)}
            ${h("output_data[global_idx]",1)}
            ${h("output_data[global_idx]",2)}
            ${h("output_data[global_idx]",3)}
          `}return`
        ${t.registerUniform("vec_size","u32").declareVariables(l,s,o,i)}
        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${u}
      }`},tp=t=>{let e=t[1].dims,r=t[2].dims,n=t[0].dims,a=t[1].dataType,i=!(K.areEqual(e,r)&&K.areEqual(r,n)),s=e,o=K.size(e);if(i){let u=On.calcShape(On.calcShape(e,r,!1),n,!1);if(!u)throw new Error("Can't perform where op on the given tensors");s=u,o=K.size(s)}let l=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:u=>ep(u,t,s,i,a),getRunData:()=>({outputs:[{dims:s,dataType:a}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:l},...ye(n,e,r,s)]})}},Um=t=>{t.compute(tp(t.inputs))}}),Wm,tw=J(()=>{$y(),Xh(),xy(),Sy(),ky(),Ey(),Kh(),Xf(),My(),Oy(),zy(),Py(),Ry(),By(),Dy(),Ny(),Fy(),Ly(),Uy(),Yf(),Wy(),Vy(),Gy(),Hy(),jy(),el(),qy(),Ky(),Yy(),Xy(),Qy(),Zy(),Jy(),Ia(),tl(),ew(),Wm=new Map([["Abs",[Jh]],["Acos",[ef]],["Acosh",[tf]],["Add",[Rf]],["ArgMax",[Hh,So]],["ArgMin",[Gh,So]],["Asin",[rf]],["Asinh",[nf]],["Atan",[af]],["Atanh",[sf]],["Attention",[Yh]],["AveragePool",[$m,vm]],["BatchNormalization",[Qh]],["BiasAdd",[Zh]],["BiasSplitGelu",[Pf]],["Cast",[lf,of]],["Ceil",[df]],["Clip",[uf]],["Concat",[jh,qh]],["Conv",[Io,Ao]],["ConvTranspose",[Jf,Zf]],["Cos",[cf]],["Cosh",[pf]],["CumSum",[em,tm]],["DepthToSpace",[rm,nm]],["Div",[Bf]],["Einsum",[am,im]],["Elu",[hf,zi]],["Equal",[Df]],["Erf",[ff]],["Exp",[mf]],["Expand",[sm]],["FastGelu",[om]],["Floor",[gf]],["FusedConv",[Io,Ao]],["Gather",[um,lm]],["GatherElements",[cm,dm]],["Gelu",[_f]],["Gemm",[hm,pm]],["GlobalAveragePool",[Sm,xm]],["GlobalMaxPool",[Tm,Cm]],["Greater",[Uf]],["GreaterOrEqual",[Vf]],["HardSigmoid",[kf,Sf]],["InstanceNormalization",[fm]],["LayerNormalization",[mm]],["LeakyRelu",[yf,zi]],["Less",[Wf]],["LessOrEqual",[Gf]],["Log",[zf]],["MatMul",[Kf]],["MatMulNBits",[gm,_m]],["MaxPool",[km,Em]],["Mul",[Nf]],["MultiHeadAttention",[wm,ym]],["Neg",[bf]],["Not",[wf]],["Pad",[bm]],["Pow",[Ff]],["Range",[Am]],["Reciprocal",[vf]],["ReduceMin",[Fh]],["ReduceMean",[Ph]],["ReduceMax",[Nh]],["ReduceSum",[Uh]],["ReduceProd",[Lh]],["ReduceL1",[Rh]],["ReduceL2",[Bh]],["ReduceLogSum",[Vh]],["ReduceLogSumExp",[Dh]],["ReduceSumSquare",[Wh]],["Relu",[$f]],["Resize",[Im,Mm]],["RotaryEmbedding",[Om]],["Sigmoid",[xf]],["Sin",[Ef]],["Sinh",[Cf]],["Slice",[Pm,Rm]],["SkipLayerNormalization",[zm]],["Split",[Nm,Fm]],["Sqrt",[Tf]],["Softmax",[Bm,Dm]],["Sub",[Lf]],["Tan",[Af]],["Tanh",[If]],["ThresholdedRelu",[Of,zi]],["Tile",[Lm]],["Transpose",[$h,xh]],["Where",[Um]]])}),Vm,rw=J(()=>{dr(),un(),Te(),Vm=class{constructor(t){this.backend=t,this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,e){this.repo.set(t,e)}run(t,e,r,n,a){ur(t.programInfo.name);let i=this.backend.device,s=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let o=[];for(let u of e)o.push({binding:o.length,resource:{buffer:u.buffer}});for(let u of r)o.push({binding:o.length,resource:{buffer:u.buffer}});a&&o.push({binding:o.length,resource:a});let l=i.createBindGroup({layout:t.computePipeline.getBindGroupLayout(0),entries:o,label:t.programInfo.name});if(this.backend.sessionStatus==="capturing"){let u={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:l,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(u)}s.setPipeline(t.computePipeline),s.setBindGroup(0,l),s.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),jt(t.programInfo.name)}dispose(){}build(t,e){ur(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=vh(e,this.backend.device.limits),i=t.getShaderSource(a),s=`${n.join(`
`)}
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(should not happen)");let t=this.kernelCustomData.get(this.currentKernelId);return t||(t={},this.kernelCustomData.set(this.currentKernelId,t)),t}async initialize(t,e){this.env=t;let r=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:e.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:e.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:e.limits.maxStorageBufferBindingSize,maxBufferSize:e.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:e.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:e.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:e.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:e.limits.maxComputeWorkgroupSizeZ},requiredFeatures:r};e.features.has("chromium-experimental-timestamp-query-inside-passes")?r.push("chromium-experimental-timestamp-query-inside-passes"):e.features.has("timestamp-query")&&r.push("timestamp-query"),e.features.has("shader-f16")&&r.push("shader-f16"),this.device=await e.requestDevice(n),this.adapterInfo=new ap(await e.requestAdapterInfo()),this.gpuDataManager=wh(this),this.programManager=new Vm(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,_h(t.logLevel,!!t.debug),this.device.onuncapturederror=a=>{a.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${a.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:e,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let t=this.getCommandEncoder(),e={};this.queryType==="at-passes"&&(e.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=t.beginComputePass(e)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;ur(),this.endComputePass();let t;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&t.mapAsync(GPUMapMode.READ).then(()=>{let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let n=0;n<e.length/2;n++){let a=r[n],i=a.kernelId,s=this.kernels.get(i),o=s.kernelType,l=s.kernelName,u=a.programName,p=a.inputTensorViews,h=a.outputTensorViews,m=e[n*2],d=e[n*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=m);let _=Number(m-this.queryTimeBase),w=Number(d-this.queryTimeBase);if(!Number.isSafeInteger(_)||!Number.isSafeInteger(w))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:p.map(v=>({dims:v.dims,dataType:en(v.dataType)})),outputsMetadata:h.map(v=>({dims:v.dims,dataType:en(v.dataType)})),kernelId:i,kernelType:o,kernelName:l,programName:u,startTime:_,endTime:w});else{let v="";p.forEach(($,E)=>{v+=`input[${E}]: [${$.dims}] | ${en($.dataType)}, `});let S="";h.forEach(($,E)=>{S+=`output[${E}]: [${$.dims}] | ${en($.dataType)}, `}),console.log(`[profiling] kernel "${i}|${o}|${l}|${u}" ${v}${S}execution time: ${w-_} ns`)}va("GPU",`${u}::${m}::${d}`)}t.unmap(),this.pendingQueries.delete(t)}),jt()}run(t,e,r,n,a,i){ur(t.name);let s=[];for(let $=0;$<e.length;++$){let E=e[$].data;if(E===0)continue;let T=this.gpuDataManager.get(E);if(!T)throw new Error(`no GPU data for input: ${E}`);s.push(T)}let{outputs:o,dispatchGroup:l,programUniforms:u}=t.getRunData(e),p=r.length===0?o.map(($,E)=>E):r;if(p.length!==o.length)throw new Error(`Output size ${p.length} must be equal to ${o.length}.`);let h=[],m=[];for(let $=0;$<o.length;++$){if(!Number.isInteger(p[$])||p[$]<-3||p[$]>=i)throw new Error(`Invalid output index: ${p[$]}`);if(p[$]===-3)continue;let E=p[$]===-1,T=p[$]===-2,A=E||T?a(o[$].dataType,o[$].dims):n(p[$],o[$].dataType,o[$].dims);if(h.push(A),A.data===0)continue;let P=this.gpuDataManager.get(A.data);if(!P)throw new Error(`no GPU data for output: ${A.data}`);if(E&&this.temporaryData.push(P),T){let B=this.kernelPersistentData.get(this.currentKernelId);B||(B=[],this.kernelPersistentData.set(this.currentKernelId,B)),B.push(P)}m.push(P)}if(s.length!==e.length||m.length!==h.length){if(m.length===0)return jt(t.name),h;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let d;if(u){let $=0,E=[];u.forEach(B=>{let D=typeof B.data=="number"?[B.data]:B.data;if(D.length===0)return;let q=B.type===10?2:4,H,ie;B.type===10?(ie=D.length>4?16:D.length>2?8:D.length*q,H=D.length>4?16:q*D.length):(ie=D.length<=2?D.length*q:16,H=16),$=Math.ceil($/ie)*ie,E.push($);let te=B.type===10?8:4;$+=D.length>4?Math.ceil(D.length/te)*H:D.length*q});let T=16;$=Math.ceil($/T)*T;let A=new ArrayBuffer($);u.forEach((B,D)=>{let q=E[D],H=typeof B.data=="number"?[B.data]:B.data;if(B.type===6)new Int32Array(A,q,H.length).set(H);else if(B.type===12)new Uint32Array(A,q,H.length).set(H);else if(B.type===10)new Uint16Array(A,q,H.length).set(H);else if(B.type===1)new Float32Array(A,q,H.length).set(H);else throw new Error(`Unsupported uniform type: ${en(B.type)}`)});let P=this.gpuDataManager.create($,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(P.buffer,0,A,0,$),this.gpuDataManager.release(P.id),d={offset:0,size:$,buffer:P.buffer}}let _=this.programManager.normalizeDispatchGroupSize(l),w=_[1]===1&&_[2]===1,v=np(t,e,w),S=this.programManager.getArtifact(v);if(S||(S=this.programManager.build(t,_),this.programManager.setArtifact(v,S),at("info",()=>`[artifact] key: ${v}, programName: ${t.name}`)),u&&S.uniformVariablesInfo){if(u.length!==S.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${S.uniformVariablesInfo.length}, got ${u.length} in program "${S.programInfo.name}".`);for(let $=0;$<u.length;$++){let E=u[$],T=E.type,A=typeof E.data=="number"?1:E.data.length,[P,B]=S.uniformVariablesInfo[$];if(T!==P||A!==B)throw new Error(`Uniform variable ${$} mismatch: expect type ${P} with size ${B}, got type ${T} with size ${A} in program "${S.programInfo.name}".`)}}if(at("info",()=>`[ProgramManager] run "${t.name}" (key=${v}) with ${_[0]}x${_[1]}x${_[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let $={kernelId:this.currentKernelId,programName:S.programInfo.name,inputTensorViews:e,outputTensorViews:h};this.pendingKernels.push($),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push($)}return this.programManager.run(S,s,m,_,d),jt(t.name),h}upload(t,e){this.gpuDataManager.upload(t,e)}memcpy(t,e){this.gpuDataManager.memcpy(t,e)}async download(t,e){await this.gpuDataManager.download(t,e)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,e,r,n){let a=Wm.get(t);if(!a)throw new Error(`kernel not implemented: ${t}`);let i={kernelType:t,kernelName:n,kernelEntry:a[0],attributes:[a[1],r]};this.kernels.set(e,i)}releaseKernel(t){let e=this.kernelPersistentData.get(t);if(e){for(let r of e)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,e,r){let n=this.kernels.get(t);if(!n)throw new Error(`kernel not created: ${t}`);let a=n.kernelType,i=n.kernelName,s=n.kernelEntry,o=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${a}] ${i}" is not allowed to be called recursively`);this.currentKernelId=t,o[0]&&(o[1]=o[0](o[1]),o[0]=void 0),at("info",()=>`[WebGPU] Start to run kernel "[${a}] ${i}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),s(e,o[1]),0}catch(u){return r.push(Promise.resolve(`[WebGPU] Kernel "[${a}] ${i}" failed. ${u}`)),1}finally{l&&r.push(this.device.popErrorScope().then(u=>u?`GPU validation error for kernel "[${a}] ${i}": ${u.message}`:null));for(let u of this.temporaryData)this.gpuDataManager.release(u.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,e,r,n){let a=this.sessionExternalDataMapping.get(t);a||(a=new Map,this.sessionExternalDataMapping.set(t,a));let i=a.get(e),s=this.gpuDataManager.registerExternalBuffer(r,n,i?.[1]);return a.set(e,[s,r]),s}unregisterBuffers(t){let e=this.sessionExternalDataMapping.get(t);e&&(e.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[1])),this.sessionExternalDataMapping.delete(t))}getBuffer(t){let e=this.gpuDataManager.get(t);if(!e)throw new Error(`no GPU data for buffer: ${t}`);return e.buffer}createDownloader(t,e,r){return async()=>{let n=await $o(this,t,e);return yh(n.buffer,r)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){this.queryType="none",(this.env.webgpu.profiling?.mode==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){at("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(){at("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){at("info","replay"),this.sessionStatus="replaying";let t=this.capturedCommandList.get(this.currentSessionId),e=this.capturedPendingKernels.get(this.currentSessionId),r=t.length;this.pendingKernels=[];for(let n=0;n<r;n++){let a=this.getComputePassEncoder(),i=t[n];this.writeTimestamp(this.pendingDispatchNumber*2),a.setPipeline(i.computePipeline),a.setBindGroup(0,i.bindGroup),a.dispatchWorkgroups(...i.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(e[n]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onReleaseSession(t){this.unregisterBuffers(t),this.capturedCommandList.has(t)&&this.capturedCommandList.delete(t),this.capturedPendingKernels.has(t)&&this.capturedPendingKernels.delete(t),this.gpuDataManager.onReleaseSession(t)}onRunStart(t){this.currentSessionId=t,this.setQueryType()}}}),Hm={};Bn(Hm,{init:()=>jm});var Ei,ip,jm,aw=J(()=>{$e(),nw(),un(),Me(),Ei=class qm{constructor(e,r,n,a){this.module=e,this.dataType=r,this.data=n,this.dims=a}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let e=K.size(this.dims);return e===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,e)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let e=K.size(this.dims);return e===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,e)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let e=K.size(this.dims);return e===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,e)}reshape(e){if(K.size(e)!==K.size(this.dims))throw new Error("Invalid new shape");return new qm(this.module,this.dataType,this.data,e)}},ip=class{constructor(t,e,r){this.module=t,this.backend=e,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=e.adapterInfo;let n=t.HEAPU32,a=r>>>2;this.opKernelContext=n[a++];let i=n[a++];this.outputCount=n[a++],this.customDataOffset=n[a++],this.customDataSize=n[a++];let s=[];for(let o=0;o<i;o++){let l=n[a++],u=n[a++],p=n[a++],h=[];for(let m=0;m<p;m++)h.push(n[a++]);s.push(new Ei(t,l,u,h))}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(t,e){let r=e?.inputs?.map(s=>typeof s=="number"?this.inputs[s]:s)??this.inputs,n=e?.outputs??[],a=(s,o,l)=>new Ei(this.module,o,this.output(s,l),l),i=(s,o)=>{let l=$a(s);if(!l)throw new Error(`Unsupported data type: ${s}`);let u=l*K.size(o),p=u>0?this.backend.gpuDataManager.create(u).id:0;return new Ei(this.module,s,p,o)};return this.backend.run(t,r,n,a,i,this.outputCount)}output(t,e){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+e.length)*4),a=n>>2;this.module.HEAPU32[a++]=e.length;for(let i=0;i<e.length;i++)this.module.HEAPU32[a++]=e[i];return this.module._JsepOutput(this.opKernelContext,t,n)}catch(n){throw new Error(`Failed to generate kernel's output[${t}] with dims [${e}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(r)}}},jm=async(t,e,r,n)=>{let a=e.jsepInit;if(!a)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(t==="webgpu"){let i=new Gm;await i.initialize(r,n),a("webgpu",[i,s=>i.alloc(s),s=>i.free(s),(s,o,l,u=!1)=>{if(u)at("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${s}, dst=${o}, size=${l}`),i.memcpy(s,o);else{at("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${s}, gpuDataId=${o}, size=${l}`);let p=e.HEAPU8.subarray(s>>>0,(s>>>0)+l);i.upload(o,p)}},async(s,o,l)=>{at("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${s}, dataOffset=${o}, size=${l}`),await i.download(s,()=>e.HEAPU8.subarray(o>>>0,(o>>>0)+l))},(s,o,l)=>i.createKernel(s,o,l,e.UTF8ToString(e._JsepGetNodeName(o))),s=>i.releaseKernel(s),(s,o,l,u)=>{at("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${l}, kernel=${s}, contextDataOffset=${o}`);let p=new ip(e,i,o);return i.computeKernel(s,p,u)},()=>i.captureBegin(),()=>i.captureEnd(),()=>i.replay()])}else a("webnn")}}),sp,Km,Ym,zr,op,Oo,Xm,Qm,so,Zm,Jm,eg,iw=J(()=>{_y(),yy(),$e(),Aa(),qo(),gh(),sp=(t,e)=>{st()._OrtInit(t,e)!==0&&qe("Can't initialize onnxruntime.")},Km=async t=>{sp(t.wasm.numThreads,Vi(t.logLevel))},Ym=async(t,e)=>{{let r=(aw(),nn(Hm)).init;if(e==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let n=t.webgpu.adapter;if(n){if(typeof n.limits!="object"||typeof n.features!="object"||typeof n.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let a=t.webgpu.powerPreference;if(a!==void 0&&a!=="low-power"&&a!=="high-performance")throw new Error(`Invalid powerPreference setting: "${a}"`);let i=t.webgpu.forceFallbackAdapter;if(i!==void 0&&typeof i!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${i}"`);if(n=await navigator.gpu.requestAdapter({powerPreference:a,forceFallbackAdapter:i}),!n)throw new Error('Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.')}if(!t.wasm.simd)throw new Error("Not supported for WebGPU=ON and SIMD=OFF. Please set `env.wasm.simd` to true when using `webgpu` EP");await r("webgpu",st(),t,n)}if(e==="webnn"){if(typeof navigator>"u"||!navigator.ml)throw new Error("WebNN is not supported in current environment");await r("webnn",st(),t)}}},zr=new Map,op=t=>{let e=st(),r=e.stackSave();try{let n=e.stackAlloc(8);return e._OrtGetInputOutputCount(t,n,n+4)!==0&&qe("Can't get session input/output count."),[e.HEAP32[n/4],e.HEAP32[n/4+1]]}finally{e.stackRestore(r)}},Oo=t=>{let e=st(),r=e._malloc(t.byteLength);if(r===0)throw new Error(`Can't create a session. failed to allocate a buffer of size ${t.byteLength}.`);return e.HEAPU8.set(t,r),[r,t.byteLength]},Xm=async(t,e)=>{let r,n,a=st();Array.isArray(t)?[r,n]=t:t.buffer===a.HEAPU8.buffer?[r,n]=[t.byteOffset,t.byteLength]:[r,n]=Oo(t);let i=0,s=0,o=0,l=[],u=[],p=[];try{if([s,l]=mh(e),e?.externalData&&a.mountExternalData){let $=[];for(let E of e.externalData){let T=typeof E=="string"?E:E.path;$.push(Gi(typeof E=="string"?E:E.data).then(A=>{a.mountExternalData(T,A)}))}await Promise.all($)}i=await a._OrtCreateSession(r,n,s),i===0&&qe("Can't create a session.");let[h,m]=op(i),d=!!e?.enableGraphCapture,_=[],w=[],v=[];for(let $=0;$<h;$++){let E=a._OrtGetInputName(i,$);E===0&&qe("Can't get an input name."),u.push(E),_.push(a.UTF8ToString(E))}for(let $=0;$<m;$++){let E=a._OrtGetOutputName(i,$);E===0&&qe("Can't get an output name."),p.push(E);let T=a.UTF8ToString(E);w.push(T);{if(d&&e?.preferredOutputLocation===void 0){v.push("gpu-buffer");continue}let A=typeof e?.preferredOutputLocation=="string"?e.preferredOutputLocation:e?.preferredOutputLocation?.[T]??"cpu";if(A!=="cpu"&&A!=="cpu-pinned"&&A!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${A}.`);if(d&&A!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${A}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);v.push(A)}}let S=null;return v.some($=>$==="gpu-buffer")&&(o=a._OrtCreateBinding(i),o===0&&qe("Can't create IO binding."),S={handle:o,outputPreferredLocations:v,outputPreferredLocationsEncoded:v.map($=>vo($))}),zr.set(i,[i,u,p,S,d,!1]),[i,_,w]}catch(h){throw u.forEach(m=>a._OrtFree(m)),p.forEach(m=>a._OrtFree(m)),o!==0&&a._OrtReleaseBinding(o),i!==0&&a._OrtReleaseSession(i),h}finally{a._free(r),s!==0&&a._OrtReleaseSessionOptions(s),l.forEach(h=>a._free(h)),a.unmountExternalData?.()}},Qm=t=>{let e=st(),r=zr.get(t);if(!r)throw new Error(`cannot release session. invalid session id: ${t}`);let[n,a,i,s,o]=r;s&&(o&&e._OrtClearBoundOutputs(s.handle),e._OrtReleaseBinding(s.handle)),e.jsepOnReleaseSession?.(t),a.forEach(l=>e._OrtFree(l)),i.forEach(l=>e._OrtFree(l)),e._OrtReleaseSession(n),zr.delete(t)},so=(t,e,r,n,a,i=!1)=>{if(!t){e.push(0);return}let 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${R}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = headIdx * uniforms.M * uniforms.N;\n    if (global_id.y < uniforms.M && global_id.x < uniforms.N) {\n      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;\n      var sum: f32 = ${(()=>{switch(d){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: ${d}`)}})()};\n        output[outputIdx] = ${O.type.value} (sum * uniforms.alpha) + ${o?"relative_position_bias[outputIdx]":"0.0"};\n    }\n  }`};return{name:"AttentionProbs",shaderCache:{hint:`${d}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:p,dataType:t.dataType,gpuDataType:0}],dispatchGroup:_,programUniforms:v}),getShaderSource:A}},nc=(e,t,r,o,i)=>{let u=i+o.kvSequenceLength,a=[o.batchSize,o.sequenceLength,o.vHiddenSize],c=12,p={x:Math.ceil(o.vHeadSize/c),y:Math.ceil(o.sequenceLength/c),z:o.batchSize*o.numHeads},h=[{type:12,data:o.sequenceLength},{type:12,data:u},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType,gpuDataType:0}],dispatchGroup:p,programUniforms:h}),getShaderSource:w=>{let _=U("probs",t.dataType,t.dims),v=U("v",r.dataType,r.dims),S=j("output",t.dataType,a),A=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`\n  const TILE_SIZE = ${c}u;\n  var<workgroup> tileQ: array<${_.type.value}, ${c*c}>;\n  var<workgroup> tileK: array<${_.type.value}, ${c*c}>;\n  ${w.registerUniforms(A).declareVariables(_,v,S)}\n  ${w.mainStart([c,c,1])}\n   let headIdx = workgroup_id.z;\n   let m = global_id.y;\n   let n = global_id.x;\n\n   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n   let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n   var value = ${_.type.storage}(0);\n   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n     if (m < uniforms.M && w + local_id.x < uniforms.K) {\n       tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n     }\n     if (n < uniforms.N && w + local_id.y < uniforms.K) {\n       tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n     }\n     workgroupBarrier();\n     for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {\n       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];\n     }\n     workgroupBarrier();\n   }\n\n   // we need to transpose output from BNSH_v to BSND_v\n   let batchIdx = workgroup_id.z / uniforms.num_heads;\n   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;\n   if (m < uniforms.M && n < uniforms.N) {\n     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size\n       + currentBatchHeadNumber * uniforms.N + n;\n     output[outputIdx] = value;\n   }\n  }`}}},Pn=(e,t,r,o,i,u,a,c,p,h,d)=>{let y=e.outputCount>1,w=e.outputCount>2,_=y&&w?h.pastSequenceLength:0,v=_+h.kvSequenceLength,S=[h.batchSize,h.numHeads,v,h.headSize],A=a?[a,r]:[r],I=y?e.compute(En(A,2,S,r.dataType),{inputs:A,outputs:[1]})[0]:r,x=[h.batchSize,h.numHeads,v,h.headSize],E=c?[c,o]:[o],P=w?e.compute(En(E,2,x,o.dataType),{inputs:E,outputs:[2]})[0]:o,O=[t,I];p&&O.push(p);let R=e.compute(rc(e,t,I,p,h,d,_),{inputs:O,outputs:[-1]})[0];e.compute(tc(e,R,h.batchSize*h.numHeads*h.sequenceLength,v),{inputs:[R],outputs:[]});let L=[R,P];e.compute(nc(e,R,P,h,_),{inputs:L,outputs:[0]})},oc=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,i=t.inputHiddenSize,u=t.headSize,a=12,c={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:o},{type:12,data:i},{type:12,data:u},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],d=y=>{let w=j("output_q",p[0].dataType,r),_=j("output_k",p[0].dataType,r),v=j("output_v",p[0].dataType,r),S=U("input",p[0].dataType,p[0].dims),A=U("weight",p[1].dataType,p[1].dims),I=U("bias",p[2].dataType,p[2].dims),x=S.type.storage,E=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`\n  const TILE_SIZE = ${a}u;\n  var<workgroup> tileInput: array<${x}, ${a*a}>;\n  var<workgroup> tileWeightQ: array<${x}, ${a*a}>;\n  var<workgroup> tileWeightK: array<${x}, ${a*a}>;\n  var<workgroup> tileWeightV: array<${x}, ${a*a}>;\n  ${y.registerUniforms(E).declareVariables(S,A,I,w,_,v)}\n  ${y.mainStart([a,a,1])}\n    let batchIndex = workgroup_id.z / uniforms.num_heads;\n    let headNumber = workgroup_id.z % uniforms.num_heads;\n    let m = global_id.y;\n    let n = global_id.x;\n\n    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;\n    let biasOffsetQ = headNumber * uniforms.head_size;\n    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;\n    let biasOffsetV = uniforms.hidden_size + biasOffsetK;\n\n    var valueQ = ${x}(0);\n    var valueK = ${x}(0);\n    var valueV = ${x}(0);\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (m < uniforms.M && w + local_id.x < uniforms.K) {\n        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n      }\n      if (n < uniforms.N && w + local_id.y < uniforms.K) {\n        let offset = n + (w + local_id.y) * uniforms.ldb;\n        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n      }\n      workgroupBarrier();\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        let inputTileOffset = TILE_SIZE * local_id.y + k;\n        let weightTileOffset = TILE_SIZE * k + local_id.x;\n        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];\n        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];\n        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = (m * uniforms.N + n) % uniforms.head_size;\n    valueQ += bias[headOffset + biasOffsetQ];\n    valueK += bias[headOffset + biasOffsetK];\n    valueV += bias[headOffset + biasOffsetV];\n\n    let offset = workgroup_id.z * uniforms.M * uniforms.N;\n    if (m < uniforms.M && n < uniforms.N) {\n      let outputIdx = offset + m * uniforms.N + n;\n      output_q[outputIdx] = valueQ;\n      output_k[outputIdx] = valueK;\n      output_v[outputIdx] = valueV;\n    }\n  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:c,programUniforms:h}),getShaderSource:d},{inputs:p,outputs:[-1,-1,-1]})},Xa=(e,t)=>{let r=ec(e.inputs,t),[o,i,u]=oc(e,r);return Pn(e,o,i,u,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var ic,ac,sc,Qa,Ja=Y(()=>{"use strict";$r();ye();Se();Ze();_e();ic=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(o,i,u)=>{let 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t=e[0].dims,r=e[0].dims[2],o=M.size(t)/4,i=e[0].dataType,u=U("input",i,t,4),a=U("bias",i,[r],4),c=U("residual",i,t,4),p=j("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:d=>`\n  const channels = ${r}u / 4;\n  ${d.declareVariables(u,a,c,p)}\n\n  ${d.mainStart()}\n    ${d.guardAgainstOutOfBoundsWorkgroupSizes(o)}\n    let value = ${u.getByOffset("global_idx")}\n      + ${a.getByOffset("global_idx % channels")} + ${c.getByOffset("global_idx")};\n    ${p.setByOffset("global_idx","value")}\n  }`}},es=e=>{uc(e.inputs),e.compute(dc(e.inputs))}});var lc,ke,rs,ns,os,is,as,ss,us,ds,ls,cc,cs,ps,ms,fs,kn,hs,On,gs,ys,bs,ws,vs,$s,_s,Ss,xs,Cs,As,Is,Ts,Es,Ps,ks,Os,Rs,Bo,Do,Bs,Ds,zs,Rn=Y(()=>{"use strict";ye();Se();Ze();_e();lc=(e,t,r,o,i,u)=>{let a=Math.ceil(t/4),c="";typeof i=="string"?c=`${i}(a)`:c=i("a");let p=U("inputData",r,[a],4),h=j("outputData",o,[a],4);return`\n      ${e.registerUniform("vec_size","u32").declareVariables(p,h)}\n\n  ${u??""}\n\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n\n    let a = ${p.getByOffset("global_idx")};\n    ${h.setByOffset("global_idx",c)}\n  }`},ke=(e,t,r,o,i,u=e.dataType)=>({name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:a=>lc(a,M.size(e.dims),e.dataType,u,r,o),getRunData:a=>({outputs:[{dims:e.dims,dataType:u}],dispatchGroup:{x:Math.ceil(M.size(a[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(e.dims)/4)}]})}),rs=e=>{e.compute(ke(e.inputs[0],"Abs","abs"))},ns=e=>{e.compute(ke(e.inputs[0],"Acos","acos"))},os=e=>{e.compute(ke(e.inputs[0],"Acosh","acosh"))},is=e=>{e.compute(ke(e.inputs[0],"Asin","asin"))},as=e=>{e.compute(ke(e.inputs[0],"Asinh","asinh"))},ss=e=>{e.compute(ke(e.inputs[0],"Atan","atan"))},us=e=>{e.compute(ke(e.inputs[0],"Atanh","atanh"))},ds=e=>ve(e),ls=(e,t)=>{let r;switch(t.to){case 10:r="vec4<f16>";break;case 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= ${r}(${t.alpha});\n\n  fn elu_f32(a: ${r}) -> ${r} {\n  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);\n  }\n\n  fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {\n  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));\n  }`,t.cacheKey))},On=(e="f32")=>`\nconst r0: ${e} = 0.3275911;\nconst r1: ${e} = 0.254829592;\nconst r2: ${e} = -0.284496736;\nconst r3: ${e} = 1.421413741;\nconst r4: ${e} = -1.453152027;\nconst r5: ${e} = 1.061405429;\n\nfn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {\n  let absv = abs(v);\n  let x = 1.0 / (1.0 + r0 * absv);\n  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));\n}`,gs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,On(t)))},ys=e=>{e.compute(ke(e.inputs[0],"Exp","exp"))},bs=e=>{e.compute(ke(e.inputs[0],"Floor","floor"))},ws=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,On(t)))},vs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"LeakyRelu",o=>`select(leaky_relu_alpha_ * ${o}, ${o}, ${o} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},$s=e=>{e.compute(ke(e.inputs[0],"Not",t=>`!${t}`))},_s=e=>{e.compute(ke(e.inputs[0],"Neg",t=>`-${t}`))},Ss=e=>{e.compute(ke(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},xs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Cs=e=>{e.compute(ke(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},As=e=>ve(e),Is=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"HardSigmoid",o=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${o} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Ts=e=>{e.compute(ke(e.inputs[0],"Sin","sin"))},Es=e=>{e.compute(ke(e.inputs[0],"Sinh","sinh"))},Ps=e=>{e.compute(ke(e.inputs[0],"Sqrt","sqrt"))},ks=e=>{e.compute(ke(e.inputs[0],"Tan","tan"))},Os=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Rs=e=>{e.compute(ke(e.inputs[0],"Tanh",Os))},Bo=(e="f32")=>`\nconst fast_gelu_a: ${e} = 0.5;\nconst fast_gelu_b: ${e} = 0.7978845608028654;\nconst fast_gelu_c: ${e} = 0.035677408136300125;\n\nfn tanh_v(v: vec4<${e}>) -> vec4<${e}> {\n  return ${Os("v")};\n}\n`,Do=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Bs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"FastGelu",Do,Bo(t),void 0,e.inputs[0].dataType))},Ds=(e,t)=>{let r=et(e.inputs[0].dataType);return e.compute(ke(e.inputs[0],"ThresholdedRelu",o=>`select(vec4<${r}>(0.0), ${o}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},zs=e=>{e.compute(ke(e.inputs[0],"Log","log"))}});var pc,mc,Us,Vs=Y(()=>{"use strict";Se();_e();Rn();pc=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")},mc=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=U("input",e[0].dataType,e[0].dims,4),o=U("bias",e[0].dataType,[e[0].dims[2]],4),i=j("output",e[0].dataType,t,4),u=M.size(t)/4,a=De(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)}}),getShaderSource:p=>`\n  const M_SQRT2 = sqrt(2.0);\n  const halfChannels = ${e[0].dims[2]/4/2}u;\n\n  ${p.declareVariables(r,o,i)}\n\n  ${On(a)}\n\n  ${p.mainStart()}\n    ${p.guardAgainstOutOfBoundsWorkgroupSizes(u)}\n    let biasIdx = global_idx % halfChannels;\n    let batchIndex = global_idx / halfChannels;\n    let inputOffset = biasIdx + batchIndex * halfChannels * 2;\n    let valueLeft = input[inputOffset] + bias[biasIdx];\n    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];\n    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);\n\n    ${i.setByOffset("global_idx","valueLeft * geluRight")}\n  }`}},Us=e=>{pc(e.inputs),e.compute(mc(e.inputs))}});var fc,hc,Ot,Ws,Ns,Gs,Hs,Ls,Fs,qs,js,Ks,Ys,Zs=Y(()=>{"use strict";ye();Se();_e();fc=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w,_;typeof c=="string"?w=_=(x,E)=>`${c}((${x}),(${E}))`:typeof c=="function"?w=_=c:(w=c.scalar,_=c.vector);let v=j("outputData",d,o.length,4),S=U("aData",p,t.length,4),A=U("bData",h,r.length,4),I;if(i)if(u){let x=M.size(t)===1,E=M.size(r)===1,P=t.length>0&&t[t.length-1]%4===0,O=r.length>0&&r[r.length-1]%4===0;x||E?I=v.setByOffset("global_idx",_(x?`${S.type.value}(${S.getByOffset("0")}.x)`:S.getByOffset("global_idx"),E?`${A.type.value}(${A.getByOffset("0")}.x)`:A.getByOffset("global_idx"))):I=`\n            let outputIndices = ${v.offsetToIndices("global_idx * 4u")};\n            let offsetA = ${S.broadcastedIndicesToOffset("outputIndices",v)};\n            let offsetB = ${A.broadcastedIndicesToOffset("outputIndices",v)};\n            ${v.setByOffset("global_idx",_(a||P?S.getByOffset("offsetA / 4u"):`${S.type.value}(${S.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||O?A.getByOffset("offsetB / 4u"):`${A.type.value}(${A.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n          `}else I=v.setByOffset("global_idx",_(S.getByOffset("global_idx"),A.getByOffset("global_idx")));else{if(!u)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let x=(E,P,O="")=>{let R=`aData[indexA${P}][componentA${P}]`,L=`bData[indexB${P}][componentB${P}]`;return`\n            let outputIndices${P} = ${v.offsetToIndices(`global_idx * 4u + ${P}u`)};\n            let offsetA${P} = ${S.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n            let offsetB${P} = ${A.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n            let indexA${P} = offsetA${P} / 4u;\n            let indexB${P} = offsetB${P} / 4u;\n            let componentA${P} = offsetA${P} % 4u;\n            let componentB${P} = offsetB${P} % 4u;\n            ${E}[${P}] = ${O}(${w(R,L)});\n          `};d===9?I=`\n            var data = vec4<u32>(0);\n            ${x("data",0,"u32")}\n            ${x("data",1,"u32")}\n            ${x("data",2,"u32")}\n            ${x("data",3,"u32")}\n            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:I=`\n            ${x("outputData[global_idx]",0)}\n            ${x("outputData[global_idx]",1)}\n            ${x("outputData[global_idx]",2)}\n            ${x("outputData[global_idx]",3)}\n          `}return`\n        ${e.registerUniform("vec_size","u32").declareVariables(S,A,v)}\n\n        ${y??""}\n\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n        ${I}\n      }`},hc=(e,t,r,o,i,u,a=r.dataType)=>{let c=!M.areEqual(r.dims,o.dims),p=r.dims,h=M.size(r.dims),d=!1,y=!1,w=[c];if(c){let _=It.calcShape(r.dims,o.dims,!1);if(!_)throw new Error("Can\'t perform binary op on the given tensors");p=_,h=M.size(p);let v=M.size(r.dims)===1,S=M.size(o.dims)===1,A=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,I=o.dims.length>0&&o.dims[o.dims.length-1]%4===0;w.push(v),w.push(S),w.push(A),w.push(I);let x=1;for(let E=1;E<p.length;E++){let P=r.dims[r.dims.length-E]??1,O=o.dims[o.dims.length-E]??1;if(P===O)x*=P;else break}x%4===0?(y=!0,d=!0):(v||S||A||I)&&(d=!0)}else d=!0;return w.push(d),{name:e,shaderCache:{hint:t+w.map(_=>_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>fc(_,r.dims,o.dims,p,d,c,y,i,r.dataType,o.dataType,a,u),getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(h/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(p)/4)},...Z(r.dims,o.dims,p)]})}},Ot=(e,t,r,o,i,u)=>{e.compute(hc(t,i??"",e.inputs[0],e.inputs[1],r,o,u))},Ws=e=>{Ot(e,"Add",(t,r)=>`${t}+${r}`)},Ns=e=>{Ot(e,"Div",(t,r)=>`${t}/${r}`)},Gs=e=>{Ot(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},Hs=e=>{Ot(e,"Mul",(t,r)=>`${t}*${r}`)},Ls=e=>{let t=U("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Ot(e,"Pow",{scalar:(o,i)=>`pow_custom(${o},${i})`,vector:(o,i)=>`pow_vector_custom(${o},${i})`},`\n    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {\n      if (b == ${t}(0.0)) {\n        return ${t}(1.0);\n      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {\n        return ${t}(pow(f32(a), f32(b))); // NaN\n      }\n      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));\n    }\n    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {\n      // TODO: implement vectorized pow\n      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));\n    }\n      `)},Fs=e=>{Ot(e,"Sub",(t,r)=>`${t}-${r}`)},qs=e=>{Ot(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},js=e=>{Ot(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},Ks=e=>{Ot(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},Ys=e=>{Ot(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}});var St,xt,Ct,Bn,Ft=Y(()=>{"use strict";ye();Se();St=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},xt=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Ct=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Bn=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[r,o]=e?.activation_params||[.2,.5];return{activation:t,alpha:r,beta:o}}else if(t==="Clip"){let[r,o]=e?.activation_params||[xn,Cn];return{activation:t,clipMax:o,clipMin:r}}else if(t==="LeakyRelu"){let[r]=e?.activation_params||[.01];return{activation:t,alpha:r}}return{activation:t}}});var tt,Dn,zn=Y(()=>{"use strict";tt=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw 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innerElementSize][localRow];\n        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n        }`:`\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n        }`,Hr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32)=>{let p=t[1]*e[1],h=t[0]*e[0],d=i?p:u,y=i?u:p,w=d/t[0],_=u/t[1];if(!((i&&w===4&&e[1]===4||!i&&(w===3||w===4))&&d%t[0]===0&&u%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${w} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${w} must be 3 or 4.\n  tileAWidth ${d} must be divisible by workgroupSize[0]${t[0]}. tileInner ${u} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${w}<${r}>, ${d/w}>, ${y}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${r}>, ${h/e[0]}>, ${u}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${w};\nconst tileInner = ${u};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n  let localRow = i32(localId.y);\n  let tileRow = localRow * rowPerThread;\n  let tileCol = i32(localId.x);\n\n  let globalRow =i32(globalId.y) * rowPerThread;\n  let globalCol = i32(globalId.x);\n  let batch = ${a?"0":"i32(globalId.z)"};\n  ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n  let globalRowStart = i32(workgroupId.y) * ${p};\n\n  let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n  var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};\n\n  var acc: array<vec4<${r}>, rowPerThread>;\n\n  // Loop over shared dimension.\n  let tileRowB = localRow * ${_};\n  for (var t = 0; t < num_tiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let inputRow = tileRow + innerRow;\n          let inputCol = tileCol;\n          ${yc(i,o)}\n      }\n\n      // Load one tile of B into local memory.\n      for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n          let inputRow = tileRowB + innerRow;\n          let inputCol = tileCol;\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${o?", batchIndices":""});\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n          ${w===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n          ${bc(i,w)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},Xs=(e,t)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${t?", batchIndices":""});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${t?", batchIndices":""});\n            `,wc=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Lr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32,p=!1)=>{let h=e[1]*t[1],d=e[0]*t[0],y=i?h:u,w=i?u:h;if(!(w%t[1]===0&&y%t[0]===0&&u%t[1]===0))throw new Error(`tileAHight ${w} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${y} must be divisible by workgroupSize[0]${t[0]}, tileInner ${u} must be divisible by workgroupSize[1]${t[1]}`);let _=w/t[1],v=y/t[0],S=u/t[1],A=p?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${h};\n    let globalColStart = i32(workgroupId.x) * ${d};\n\n    // Loop over shared dimension.\n    for (var t = 0; t < num_tiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var inputRow = localRow; inputRow < ${w}; inputRow = inputRow + ${t[1]}) {\n        for (var inputCol = localCol; inputCol < ${y}; inputCol = inputCol + ${t[0]}) {\n          ${Xs(i,o)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${u}; inputRow = inputRow + ${t[1]}) {\n            for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${t[0]}) {\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n            kStart + inputRow,\n            globalColStart + inputCol${o?", batchIndices":""});\n        }\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      var BCached : array<${r}, colPerThread>;\n      for (var k = 0; k < tileInner; k = k + 1) {\n        for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n        }\n        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n            acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n                ACached * BCached[innerCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        let gCol = globalColStart + localCol + innerCol * ${t[0]};\n        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n      }\n    }\n    `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${h};\n\nlet tileRowA = i32(localId.y) * ${_};\nlet tileColA = i32(localId.x) * ${v};\nlet tileRowB = i32(localId.y) * ${S};\n// Loop over shared dimension.\nfor (var t = 0; t < num_tiles; t = t + 1) {\n  // Load one tile of A into local memory.\n  for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < ${v}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${Xs(i,o)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${S}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n      let inputRow = tileRowB + innerRow;\n      let inputCol = tileCol + innerCol;\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n        kStart + inputRow,\n        globalCol + innerCol${o?", batchIndices":""});\n    }\n  }\n  kStart = kStart + tileInner;\n  workgroupBarrier();\n\n  // Compute acc values for a single thread.\n  var BCached : array<${r}, colPerThread>;\n  for (var k = 0; k < tileInner; k = k + 1) {\n    for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n      BCached[inner] = mm_Bsub[k][tileCol + inner];\n    }\n\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      ${wc(i)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${r}, ${y}>, ${w}>;\n  var<workgroup> mm_Bsub : array<array<${r}, ${d}>, ${u}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${u};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n    let batch = ${a?"0":"i32(globalId.z)"};\n    ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n    let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n    var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};\n\n    var acc : array<array<${r}, colPerThread>, rowPerThread>;\n\n    // Without this initialization strange values show up in acc.\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = 0.0;\n      }\n    }\n    ${A}\n  }\n`},vc=(e,t,r,o,i,u=!1)=>{let[a,c,p]=i,[h,d,y,w]=o,_=_r(a,p),v=_r(c,p),S=De(o[0].type.tensor),A=()=>{let E=d.rank,P=h.rank,O=`var aIndices: ${d.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\naIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return _.forEach(R=>{O+=`\naIndices[${R}] = 0;`}),O+=`\naIndices[${E-2}] = u32(row);\n                   aIndices[${E-1}] = u32(colIn);`,O},I=()=>{let E=y.rank,P=h.rank,O=`var bIndices: ${y.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\nbIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return v.forEach(R=>{O+=`\nbIndices[${R}] = 0;`}),O+=`\nbIndices[${E-2}] = u32(row);\n                   bIndices[${E-1}] = u32(colIn);`,O};return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n      var value = ${tt(e,S)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n      {\n        ${A()}\n        value = ${d.getByIndices("aIndices")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n      var value = ${tt(e,S)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n      {\n        ${I()}\n        value = ${y.getByIndices("bIndices")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${tt(e,S)}) {\n      let col = colIn * ${e};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n        var value = valueIn;\n        let coords = vec3<i32>(batch, row, colIn);\n        ${t?`value = value + ${u?"bias[colIn]":`${tt(e,S)}(bias[row])`};`:""}\n        ${r}\n        ${w.setByIndices("vec3<u32>(coords)","value")}\n      }\n    }\n    `},Un=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u.slice(0,-2),p=a.slice(0,-2),h=o?o.slice(0,-2):r.slice(0,-2),d=M.size(h),y=u[u.length-2],w=u[u.length-1],_=a[a.length-1],v=w%4===0&&_%4===0,S=y<=8?[4,1,1]:[4,4,1],A=[8,8,1],I=[Math.ceil(_/A[0]/S[0]),Math.ceil(y/A[1]/S[1]),Math.ceil(d/A[2]/S[2])],x=v?4:1,E=[...c,y,w/x],P=E.length,O=[...p,w,_/x],R=O.length,L=[d,y,_/x],N=[{type:6,data:y},{type:6,data:_},{type:6,data:w}];xt(t,N),N.push(...Z(h,E,O));let K=["rank","rank"],Q=e.length>2;Q&&(N.push(...Z(e[2].dims)),K.push("rank")),N.push(...Z(L));let he=W=>{let se=h.length,Ce=An("batchDims",e[0].dataType,se,1),We=De(e[0].dataType),ee=U("a",e[0].dataType,P,x),ae=U("b",e[1].dataType,R,x),Ae=j("result",e[0].dataType,L.length,x),me=[ee,ae];if(Q){let G=i?x:1;me.push(U("bias",e[2].dataType,e[2].dims.length,G))}let ie=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ct(t,ie);let ue=De(Ae.type.tensor),le=St(t,Ae.type.value,ue),qe=vc(x,Q,le,[Ce,ee,ae,Ae],[c,p,h],i);return`\n  ${W.registerUniforms(ie).registerInternalVariables(Ce).declareVariables(...me,Ae)}\n  ${qe}\n  ${v?Hr(S,A,We,Ce):Lr(S,A,We,Ce)}\n                   `};return{name:"MatMul",shaderCache:{hint:`${S};${t.activation};${v};${i}`,inputDependencies:K},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:I[0],y:I[1],z:I[2]},programUniforms:N}),getShaderSource:he}}});var $c,Qs,Js=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();$c=(e,t,r,o,i=!1,u,a=4,c=4,p=4,h="f32")=>{let d=Q=>{switch(Q){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Q} is not supported.`)}},y=Q=>{switch(Q){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 ${Q} is not supported.`)}},w=e?`\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    `:`\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    `,_=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,v=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",S=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",A=e?"row":"col",I=e?"col":"row",x=`\n    let inChannels = i32(uniforms.w_shape[2]);\n    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n    let outRow = ${A} / outWidth;\n    let outCol = ${A} % outWidth;\n\n    let WRow = ${I} / (i32(uniforms.w_shape[1]) * inChannels);\n    let WCol = ${I} / inChannels % i32(uniforms.w_shape[1]);\n    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n    let xCh = ${I} % inChannels;\n    var resData = ${tt(a,h)}(0.0);\n    // The bounds checking is always needed since we use it to pad zero for\n    // the \'same\' padding type.\n    if (xRow >= 0 && xRow < ${v} && xCol >= 0 && xCol < ${S}) {\n      ${w}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${d(a)}\n    }\n    return resData;`,E=e?t&&o?`\n    let col = colIn * ${a};\n    ${x}`:`\n    let col = colIn * ${a};\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n      ${x}\n    }\n    return ${tt(a,h)}(0.0);`:o&&r?`\n    let col = colIn * ${a};\n    ${x}`:`\n    let col = colIn * ${a};\n    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n      ${x}\n    }\n    return ${tt(a,h)}(0.0);`,P=`${y(c)}`,O=tt(p,h),R=e?tt(a,h):tt(c,h),L=e?tt(c,h):tt(a,h),N=St(u,O,h);return`\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {\n      ${e?E:P}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${L} {\n      ${e?P:E}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${O}) {\n      let col = colIn * ${p};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)\n      {\n      var value = valueIn;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${_}\n      ${Dn(i)}\n      ${N}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},Qs=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&(h%4===0||h%3===0)&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let P=v?p&&h%4!==0?3:4:1,O=I[1]*x[1],R=I[0]*x[0],L=Math.max(I[0]*P,I[1]),N=o%O===0,K=i%R===0,Q=u%L===0,he=v?[P,4,4]:[1,1,1],W=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];xt(t,W),W.push(...Z(e[0].dims,e[1].dims));let se=["rank","rank"];a&&(W.push(...Z(e[2].dims)),se.push("rank")),W.push(...Z(r));let Ce=We=>{let ee=[{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}];Ct(t,ee);let ae=v?4:1,Ae=De(e[0].dataType),me=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n        result[flatIndex] = ${v?`vec4<${Ae}>`:Ae}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${v?"/ 4":""}, value);\n      }`,ie=U("x",e[0].dataType,e[0].dims.length,P===3?1:P),ue=U("w",e[1].dataType,e[1].dims.length,ae),le=[ie,ue],qe=j("result",e[0].dataType,r.length,ae);if(a){let G=U("bias",e[2].dataType,e[2].dims.length,ae);le.push(G),me+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${v?`vec4<${Ae}>`:Ae} {\n          return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n        }`}return`\n        ${Mn("uniforms.result_strides")}\n        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,\n        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,\n        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n        ${We.registerUniforms(ee).declareVariables(...le,qe)}\n        ${me}\n        ${$c(p,N,K,Q,a,t,he[0],he[1],he[2],Ae)}\n        ${v?Hr(x,I,Ae,void 0,!p,L):Lr(x,I,Ae,void 0,!p,L,!1,void 0,c)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${P};${v};${N};${K};${Q};${O};${R};${L}`,inputDependencies:se},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:W}),getShaderSource:Ce}}});var Mo,eu,tu=Y(()=>{"use strict";ye();Se();_e();Uo();Ft();Mo=(e,t,r)=>{let o=e.length>2,i=o?"value += b[output_channel];":"",u=e[0].dims,a=e[1].dims,c=a[0]/t.group,p=t.format==="NHWC",h=Vn(u,a,t.dilations,t.pads,t.strides,p),d=M.size(h),y=[{type:12,data:d},{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:c}];xt(t,y),y.push(...Z(u,a));let w=["rank","rank"];o&&(y.push(...Z(e[2].dims)),w.push("rank")),y.push(...Z(h));let _=v=>{let S=j("output",e[0].dataType,h.length),A=De(S.type.tensor),I=St(t,S.type.value,A),x=U("x",e[0].dataType,u.length),E=U("w",e[1].dataType,a.length),P=[x,E];o&&P.push(U("b",e[2].dataType,e[2].dims.length));let O=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Ct(t,O),`\n  ${v.registerUniforms(O).declareVariables(...P,S)}\n\n  ${v.mainStart()}\n    ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n    let outputIndices = ${S.offsetToIndices("global_idx")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${p?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${p?1:2}], outputIndices[${p?2:3}]) * uniforms.strides - uniforms.pads;\n    let group_id: u32 = output_channel / uniforms.output_channels_per_group;\n\n    var value: ${S.type.value} = ${S.type.value}(0);\n    for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\n      let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\n      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\n        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\n\n        if (xHeight < 0u || xHeight >= uniforms.x_shape[${p?1:2}]) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];\n          if (xWidth < 0u || xWidth >= uniforms.x_shape[${p?2:3}]) {\n            continue;\n          }\n\n          let xVal = ${p?x.get("batch","xHeight","xWidth","input_channel"):x.get("batch","input_channel","xHeight","xWidth")};\n          let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${i}\n    ${I}\n    ${S.setByOffset("global_idx","value")}\n  }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:w},getRunData:()=>({outputs:[{dims:r?r(h):h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:y}),getShaderSource:_}},eu=(e,t,r)=>{let o=e.length>2,i=Me(r[3]),u=Me(r[2]),a=M.size(r)/i/u,c=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/i],p=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/i],h=[r[0],r[1],r[2],r[3]/i],d=[{type:12,data:a},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];xt(t,d),d.push(...Z(c,p,h));let y=(u-1)*t.strides[1]+p[1],w=_=>{let v=j("output",e[0].dataType,h.length,i),S=De(v.type.tensor),A=St(t,v.type.value,S),I=U("x",e[0].dataType,c.length,i),x=U("w",e[1].dataType,p.length,i),E=[I,x];o&&E.push(U("b",e[2].dataType,e[2].dims,i));let P=o?"value += b[output_channel];":"",O=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Ct(t,O),`\n  ${_.registerUniforms(O).declareVariables(...E,v)}\n  ${_.mainStart()}\n    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let width0 = uniforms.output_shape[3];\n    let output_channel = global_idx % width0;\n    var index1 = global_idx / width0;\n    let width1 = uniforms.output_shape[2] / ${u}u;\n    let col = (index1 % width1) * ${u}u;\n    index1 = index1 / width1;\n    let row = index1 % uniforms.output_shape[1];\n    let batch = index1 / uniforms.output_shape[1];\n\n    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;\n\n    var x_vals: array<${I.type.value}, ${y}>;\n    var values: array<${v.type.value}, ${u}>;\n    let input_channel = output_channel;\n    // Use constant instead of uniform can give better performance for w\'s height/width.\n    for (var w_height: u32 = 0u; w_height < ${p[0]}; w_height++) {\n      let x_height = x_corner.x + i32(w_height);\n      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {\n        for (var i = 0; i < ${y}; i++) {\n          let x_width = x_corner.y + i;\n          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {\n            x_vals[i] = ${I.get("batch","u32(x_height)","u32(x_width)","input_channel")};\n          } else {\n            x_vals[i] = ${I.type.value}(0);\n          }\n        }\n        for (var w_width: u32 = 0u; w_width < ${p[1]}; w_width++) {\n          let w_val = ${x.get("w_height","w_width","0","output_channel")};\n          for (var i = 0u; i < ${u}u; i++) {\n            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);\n          }\n        }\n      }\n    }\n\n    for (var i = 0u; i < ${u}u; i++) {\n      var value = values[i];\n      ${P}\n      ${A}\n      ${v.set("batch","row","col + i","output_channel","value")};\n    }\n  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${i};${u};${y};${p[0]};${p[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:w}}});var Vo,_c,ru,Wo=Y(()=>{"use strict";ye();Se();Fr();_e();Ft();Vo=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u[u.length-2],p=a[a.length-1],h=u[u.length-1],d=Me(p),y=Me(h),w=Me(c),_=M.size(r)/d/w,v=e.length>2,S=o?o.slice(0,-2):r.slice(0,-2),I=[M.size(S),c,p],x=[{type:12,data:_},{type:12,data:c},{type:12,data:p},{type:12,data:h}];xt(t,x),x.push(...Z(S,u,a)),v&&x.push(...Z(e[2].dims)),x.push(...Z(I));let E=P=>{let O=An("batch_dims",e[0].dataType,S.length),R=U("a",e[0].dataType,u.length,y),L=U("b",e[1].dataType,a.length,d),N=j("output",e[0].dataType,I.length,d),K=De(N.type.tensor),Q=St(t,N.type.value,K),he=[R,L],W="";if(v){let ie=i?d:1;he.push(U("bias",e[2].dataType,e[2].dims.length,ie)),W=`${i?`value += bias[col / ${ie}];`:`value += ${N.type.value}(bias[row + i]);`}`}let se=u.slice(0,-2),Ce=a.slice(0,-2),We=_r(se,S),ee=_r(Ce,S),ae=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Ct(t,ae);let Ae=(ie,ue)=>{let le=ie.rank,qe=ie.name;if(le===2)return`var ${qe}_indices = ${ie.type.indices}(0u, 0u);`;let G=O.rank,ne=`var ${qe}_indices: ${ie.type.indices};`;for(let xe=le-2-1,Ke=G-1;xe>=0;xe--,Ke--)ne+=`\n${qe}_indices[${xe}] = ${G>1?`batch_indices[${Ke}]`:"batch_indices"};`;return ue.forEach(xe=>{ne+=`\n${qe}_indices[${xe}] = 0;`}),ne+=`${qe}_indices[${le-2}] = 0u;\n                     ${qe}_indices[${le-1}] = 0u;`,ne},me=()=>{let ie=`var a_data: ${R.type.value};`;for(let ue=0;ue<y;ue++)ie+=`\n              let b_data${ue} = b[(b_offset + (k + ${ue}) * uniforms.N + col) / ${d}];`;for(let ue=0;ue<w;ue++){ie+=`a_data = a[(a_offset + (row + ${ue}) * uniforms.K + k) / ${y}];`;for(let le=0;le<y;le++)ie+=`\n            values[${ue}] = fma(${L.type.value}(a_data${y===1?"":`[${le}]`}), b_data${le}, values[${ue}]);\n`}return ie};return`\n  ${P.registerUniforms(ae).registerInternalVariables(O).declareVariables(...he,N)}\n  ${P.mainStart()}\n    ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let col = (global_idx % (uniforms.N / ${d})) * ${d};\n    var index1 = global_idx / (uniforms.N / ${d});\n    let stride1 = uniforms.M / ${w};\n    let row = (index1 % stride1) * ${w};\n    let batch = index1 / stride1;\n\n    ${r.length===2?"":`let batch_indices = ${O.offsetToIndices("batch")};`}\n    ${Ae(R,We)}\n    let a_offset = ${R.indicesToOffset("a_indices")};\n    ${Ae(L,ee)}\n    let b_offset = ${L.indicesToOffset("b_indices")};\n    var values: array<${N.type.value}, ${w}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${y}) {\n      ${me()}\n    }\n    for (var i = 0u; i < ${w}u; i++) {\n      var value = values[i];\n      ${W}\n      ${Q}\n      let cur_indices = ${N.type.indices}(batch, row + i, col);\n      let offset = ${N.indicesToOffset("cur_indices")};\n      ${N.setByOffset(`offset / ${d}`,"value")};\n    }\n  }\n  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${d};${y};${w};${i}`,inputDependencies:v?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:x}),getShaderSource:E}},_c=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.")},ru=e=>{_c(e.inputs);let t=It.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can\'t use matmul on the given tensors");let r=t[t.length-1],o=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&o<8?e.compute(Vo(e.inputs,{activation:""},t)):e.compute(Un(e.inputs,{activation:""},t))}});var Vn,No,Sc,nu,Go,xc,Cc,Ho,Uo=Y(()=>{"use strict";Se();Js();Fr();tu();Ft();Wo();Sr();Vn=(e,t,r,o,i,u)=>{let a=e[0],c=e.slice(u?1:2,u?3:4),p=c.length,h=t[0],y=t.slice(2).map((v,S)=>v+(v-1)*(r[S]-1)),_=c.map((v,S)=>v+o[S]+o[S+p]).map((v,S)=>Math.floor((v-y[S]+i[S])/i[S]));return _.splice(0,0,a),_.splice(u?3:1,0,h),_},No=[2,3,1,0],Sc=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[1]*t.group;if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let 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 ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},nu=(e,t)=>{let r=e.kernelShape.slice();for(let u=2;u<t[1].dims.length;++u)r[u-2]===0&&(r[u-2]=t[1].dims[u]);let o=e.pads.slice();nr.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,o,e.format==="NHWC",e.autoPad);let i=Object.assign({},e);return Object.assign(i,{kernelShape:r,pads:o}),i},Go=e=>{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,u=e.group,a=e.kernel_shape,c=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},xc=(e,t,r)=>{let o=nu(r,t),i=r.format==="NHWC";if(r.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&i&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let L=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),N=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=N);let K=[t[0],N];t.length===3&&K.push(t[2]),e.compute(eu(K,o,L),{inputs:K})}else e.compute(Mo(t,o));return}let u=t.length===3,a=t[0].dims[i?1:2],c=t[0].dims[i?2:3],p=t[0].dims[i?3:1],h=t[1].dims[2],d=t[1].dims[3],y=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),w=y[i?1:2],_=y[i?2:3],v=y[i?3:1],S=i&&h===a&&d===c&&r.pads[0]===0&&r.pads[1]===0;if(S||h===1&&d===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 R=y[0],L,N,K,Q=[];if(i){let se=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=se),S){let Ce=a*c*p;L=t[0].reshape([1,R,Ce]),N=se.reshape([1,Ce,v]),K=[1,R,v]}else L=t[0].reshape([R,a*c,p]),N=se.reshape([1,p,v]),K=[R,w*_,v];Q.push(L),Q.push(N)}else L=t[0].reshape([R,p,a*c]),N=t[1].reshape([1,v,p]),K=[R,v,w*_],Q.push(N),Q.push(L);u&&Q.push(t[2]);let he=K[2],W=Q[0].dims[Q[0].dims.length-1];he<8&&W<8?e.compute(Vo(Q,o,y,K,i),{inputs:Q}):e.compute(Un(Q,o,y,K,i),{inputs:Q});return}let A=!0,I=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=I);let x=[t[0],I];u&&x.push(t[2]);let E=i?w*_:v,P=i?v:w*_,O=h*d*p;e.compute(Qs(x,o,y,E,P,O,u,A),{inputs:x})},Cc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=[0,t.pads[0],0,t.pads[1]],u=[1].concat(t.strides),a=[1].concat(t.dilations),c=[1].concat(t.kernelShape),p=nu({...t,pads:i,strides:u,dilations:a,kernelShape:c},o);e.compute(Mo(o,p,h=>r?[h[0],h[2],h[3]]:[]))},Ho=(e,t)=>{Sc(e.inputs,t),e.inputs[0].dims.length===3?Cc(e,t):xc(e,e.inputs,t)}});var Ac,ou,iu=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();Ac=(e,t=!1,r,o,i=4)=>{let u=I=>{switch(I){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];\n            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];\n            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];\n            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];\n            return ${o}(v0, v1, v2, v3);\n            `;default:throw new Error(`innerElementSize ${I} is not supported.`)}},a=e?`\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      `,c=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,p=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",h=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",d=e?"row":"col",y=e?"col":"row",w=`\n      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      let outRow = ${d} / outWidth;\n      let outCol = ${d} % outWidth;\n\n      let WRow = ${y} / (uniforms.filter_dims[1] * inChannels);\n      let WCol = ${y} / inChannels % uniforms.filter_dims[1];\n      let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n      let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n      if (xR < 0.0 || xR >= f32(${p}) || fract(xR) > 0.0) {\n        return ${o}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${h}) || fract(xC) > 0.0) {\n        return ${o}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${y} % inChannels;\n      ${a}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${i}];`,_=e?`\n      let col = colIn * ${i};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n        ${w}\n      }\n      return ${o}(0.0);`:`\n      let col = colIn * ${i};\n      if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n        ${w}\n      }\n      return ${o}(0.0);`,v=`\n      let col = colIn * ${i};\n      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n      let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);\n      let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];\n      if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"}  && coordX >= 0 && coordY >= 0) {\n        let rowInner = row % inChannels;\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\n        ${u(i)}\n      }\n      return ${o}(0.0);\n      `,S=St(r,o);return`\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${o} {\n    ${e?_:v}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${o} {\n    ${e?v:_}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${o}) {\n    let col = colIn * ${i};\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n      var value = valueInput;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${c}\n      ${Dn(t)}\n      ${S}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${i}] = value;\n    }\n  }`},ou=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&h%4===0&&h%3&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let P=v?4:1,O=Math.max(I[0]*P,I[1]),R=v?4:1,L=[t.kernelShape[p?1:2],t.kernelShape[p?2:3]],N=[L[0]+(t.dilations[0]<=1?0:(L[0]-1)*(t.dilations[0]-1)),L[1]+(t.dilations[1]<=1?0:(L[1]-1)*(t.dilations[1]-1))],K=[N[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),N[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Q=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:L},{type:6,data:K}];xt(t,Q),Q.push(...Z(e[0].dims,e[1].dims));let he=["rank","rank"];a&&(Q.push(...Z(e[2].dims)),he.push("rank")),Q.push(...Z(r));let W=se=>{let Ce=U("x",e[0].dataType,e[0].dims.length,R),We=U("w",e[1].dataType,e[1].dims.length,1),ee=j("result",e[0].dataType,r.length,R),ae=[Ce,We],Ae="";if(a){let ue=U("bias",e[2].dataType,e[2].dims.length,R);ae.push(ue),Ae+=`\n          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${ue.type.value} {\n            return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n          }`}let me=[{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:L.length},{name:"pads",type:"i32",length:K.length}];Ct(t,me);let ie=De(e[0].dataType,1);if(ie!=="f16"&&ie!=="f32")throw new Error(`elemType ${ie} is not supported.`);return`\n        ${Mn("uniforms.result_strides")}\n        ${se.registerUniforms(me).declareVariables(...ae,ee)};\n        ${Ae}\n        ${Ac(p,a,t,Ce.type.value,P)}\n        ${v?Hr(x,I,ie,void 0,!p,O):Lr(x,I,ie,void 0,!p,O,!1,void 0,c)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${x};${I};${v}`,inputDependencies:he},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Q}),getShaderSource:W}}});var Ic,Lo,au=Y(()=>{"use strict";ye();Lt();Se();_e();Ic=(e,t,r,o,i,u=!1,a,c,p=!1)=>{let h=p?1:2,d=p?2:3,y=p?3:1,w=u?2:1,_=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${u?`vec4<${a}>`:a}) {\n    result[flatIndex] = ${u?`vec4<${a}>`:a}(value);\n  }`;o&&(_+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${u?`vec4<${a}>`:a} {\n      return bias[coords.${p?"w":"y"}${u?"/ 4":""}];\n    }`);let v=u?4:1,S=U("W",t[1].dataType,t[1].dims.length,v),A=U("Dy",t[0].dataType,t[0].dims.length,v),I=[A,S];o&&I.push(U("bias",t[2].dataType,[r[y]].length,v));let x=j("result",t[0].dataType,r.length,v),E=`{\n        let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];\n        let r = ${i?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];\n        let c = ${i?"global_id.y":"workgroup_id.y"} * ${w};\n        let d1: u32 = ${i?"global_id.x":"workgroup_id.x"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(uniforms.pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${a}>, ${w}>;\n        for (var i = 0; i < ${w}; i++) {\n          dotProd[i] = vec4<${a}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n          var dyR = (${a}(dyCorner.x) + ${a}(wR)) / ${a}(uniforms.strides.x);\n          let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n            let dyC = (${a}(dyCorner.y) + ${a}(wC)) / ${a}(uniforms.strides.y);\n            let dyC2 = (${a}(dyCorner.y) + 1.0 + ${a}(wC)) / ${a}(uniforms.strides.y);\n            let wCPerm = uniforms.filter_dims[1] - 1 - wC;\n            if (wCPerm < 0) {\n              continue;\n            }\n            var bDyCVal = true;\n            var bDyCVal2 = true;\n            if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${a}(uniforms.Dy_shape[2]) ||\n                fract(dyC2) > 0.0) {\n              bDyCVal2 = false;\n            }\n\n            let idyC: u32 = u32(dyC);\n            let idyC2: u32 = u32(dyC2);\n            if (bDyCVal && bDyCVal2) {\n              let d2Length = uniforms.Dy_shape[3];\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${A.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${a}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${A.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${a}>(dot(xValue, wValue0),\n                                                    dot(xValue, wValue1),\n                                                    dot(xValue, wValue2),\n                                                    dot(xValue, wValue3));\n              }\n            } else if (bDyCVal) {\n              let d2Length = uniforms.Dy_shape[${y}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${A.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${a}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n              }\n            } else if (bDyCVal2) {\n              let d2Length = uniforms.Dy_shape[3];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${A.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${a}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[1] = dotProd[1] + tmpval;\n              }\n            }\n          }\n        }\n\n        for (var i: u32 = 0; i < ${w}; i = i + 1) {\n          let value = dotProd[i] + ${o?"bias[c+i]":`vec4<${a}>(0.0)`};\n          ${x.set("batch","r","c + i","d1","value")};\n        }\n      }`,P=`\n          let outputIndices = ${x.offsetToIndices("global_idx")};\n          let batch = ${x.indicesGet("outputIndices",0)};\n          let d1 = ${x.indicesGet("outputIndices",y)};\n          let r = ${x.indicesGet("outputIndices",h)};\n          let c = ${x.indicesGet("outputIndices",d)};\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;\n          let dyRCorner = dyCorner.x;\n          let dyCCorner = dyCorner.y;\n          let groupId = d1 / uniforms.output_channels_per_group;\n          let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;\n          // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n          // ? = to be determined. : = across all values in that axis.\n          var dotProd = ${a}(0.0);\n          for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n            if (wR % uniforms.dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${a}(dyRCorner) + ${a}(wR)) / ${a}(uniforms.strides[0]);\n            let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n            if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[${h}]) || fract(dyR) > 0.0 ||\n                wRPerm < 0) {\n              continue;\n            }\n            let idyR: u32 = u32(dyR);\n\n            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {\n              if (wC % uniforms.dilations.y != 0) {\n                continue;\n              }\n              let dyC = (${a}(dyCCorner) + ${a}(wC)) / ${a}(uniforms.strides.y);\n              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n              if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[${d}]) ||\n                  fract(dyC) > 0.0 || wCPerm < 0) {\n                continue;\n              }\n              let idyC: u32 = u32(dyC);\n              var inputChannel = groupId * uniforms.input_channels_per_group;\n              for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {\n                let xValue = ${p?A.get("batch","idyR","idyC","inputChannel"):A.get("batch","inputChannel","idyR","idyC")};\n                let wValue = ${S.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${o?"bias[d1]":`${a}(0.0)`};\n          ${x.setByOffset("global_idx","value")};\n        `;return`\n  ${e.registerUniforms(c).declareVariables(...I,x)}\n  ${_}\n\n    ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};\n  ${u?E:P}}`},Lo=(e,t,r)=>{let o=e.length>2,i=t.outputShape,u=M.size(i),a=[Math.ceil(u/64),1,1];Ve("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${a}`);let c=t.format==="NHWC",p=["rank","rank"],h=[t.strides[0],t.strides[1]],d=[t.kernelShape[c?1:2],t.kernelShape[c?2:3]],y=[t.dilations[0],t.dilations[1]],w=[d[0]+(t.dilations[0]<=1?0:(t.kernelShape[c?1:2]-1)*(t.dilations[0]-1)),d[1]+(t.dilations[1]<=1?0:(t.kernelShape[c?2:3]-1)*(t.dilations[1]-1))],_=[w[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),w[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],v=!1,S=t.group,A=e[1].dims,I=A[0]/S,x=A[1],E=[{type:12,data:u},{type:12,data:h},{type:12,data:d},{type:12,data:y},{type:12,data:w},{type:6,data:_},{type:12,data:I},{type:12,data:x},...Z(e[0].dims,e[1].dims)];o&&(E.push(...Z(e[2].dims)),p.push("rank")),E.push(...Z(i));let P=a[1]===1&&a[2]===1,O=R=>{let L=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:h.length},{name:"filter_dims",type:"u32",length:d.length},{name:"dilations",type:"u32",length:d.length},{name:"effective_filter_dims",type:"u32",length:w.length},{name:"pads",type:"i32",length:_.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],N=De(e[0].dataType);return`${Ic(R,e,i,o,P,v,N,L,c)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:p},getRunData:()=>({dispatchGroup:{x:a[0],y:a[1],z:a[2]},outputs:[{dims:r?r(i):i,dataType:e[0].dataType}],programUniforms:E}),getShaderSource:O}}});var Tc,Ec,Pc,su,uu,kc,Oc,Rc,Bc,du,lu=Y(()=>{"use strict";iu();au();Ft();Sr();Tc=(e,t,r,o,i,u)=>(e-1)*t+r+(o-1)*i+1-u,Ec=(e,t,r,o,i)=>{let u=Math.floor(e/2);t==="SAME_UPPER"?(r[o]=u,r[i]=e-u):t==="SAME_LOWER"&&(r[o]=e-u,r[i]=u)},Pc=(e,t,r,o,i,u,a,c,p,h)=>{let d=e.length-2,y=h.length===0;if(p.length===0)for(let v=0;v<d;++v)p.push(0);let w=e[0],_=t[c?3:1]*i;for(let v=0,S=e.length-d-(c?1:0);v<d;++v,++S){let A=e[S],I=y?A*a[v]:h[v],x=Tc(A,a[v],u[v],t[S],r[v],I);Ec(x,o,u,v,v+d),y&&h.push(a[v]*(A-1)+p[v]+(t[S]-1)*r[v]+1-u[v]-u[v+d])}h.splice(0,0,w),h.splice(c?3:1,0,_)},su=(e,t)=>{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((y,w)=>y*w,1)===0){r.length=0;for(let y=2;y<t[1].dims.length;++y)r.push(t[1].dims[y])}let o=e.format==="NHWC";r.splice(0,0,t[1].dims[0]),r.splice(o?3:1,0,t[1].dims[1]);let i=e.pads.slice(),u=e.outputShape.slice(),a=e.outputPadding.slice(),c=t[0].dims,p=e.dilations.slice();if(p.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;p=new Array(y).fill(1)}let h=e.strides.slice();if(h.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;h=new Array(y).fill(1)}Pc(c,r,p,e.autoPad,e.group,i,h,o,a,u);let d=Object.assign({},e);return Object.assign(d,{kernelShape:r,pads:i,outputPadding:a,outputShape:u,dilations:p,strides:h}),d},uu=e=>{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,u=e.group,a=e.kernelShape,c=e.pads,p=e.strides,h=e.wIsConst(),d=e.outputPadding,y=e.outputShape;return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,outputPadding:d,outputShape:y,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},kc=(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],o=e[1].dims[0];if(r!==o)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 u=e[0].dims.length-2;if(t.dilations.reduce((d,y)=>d+y,0)>0&&t.dilations.length!==u)throw new Error(`dilations should be ${u}D`);if(t.strides.reduce((d,y)=>d+y,0)>0&&t.strides.length!==u)throw new Error(`strides should be ${u}D`);if(t.pads.reduce((d,y)=>d+y,0)>0&&t.pads.length!==u*2)throw new Error(`pads should be ${u*2}D`);if(t.outputPadding.length!==u&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${u}D`);if(t.kernelShape.reduce((d,y)=>d+y,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Oc=[2,3,1,0],Rc=(e,t,r)=>{let o=su(r,t),i=r.format==="NHWC",u=o.outputShape,a=u[i?3:1],c=t[0].dims[i?3:1];if(o.group!==1||a===1&&c===1){e.compute(Lo(t,o));return}let p=u[i?1:2],h=u[i?2:3],d=t[1].dims[2],y=t[1].dims[3],w=i?p*h:a,_=i?a:p*h,v=d*y*c,S=!0,A=e.kernelCustomData.wT??e.compute(yt(t[1],Oc),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=A);let I=[t[0],A],x=t.length===3;x&&(!i&&t[2].dims.length===1?I.push(t[2].reshape([t[2].dims[0],1,1])):I.push(t[2])),e.compute(ou(I,o,u,w,_,v,x,S),{inputs:I})},Bc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let u=t.dilations;(u.length===0||u[0]===0)&&(u=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let c=t.pads;c.length===0&&(c=[0,0]),c=[0,c[0],0,c[1]],a=[1].concat(a),u=[1].concat(u),i=[1].concat(i);let p=su({...t,pads:c,strides:a,dilations:u,kernelShape:i},o);e.compute(Lo(o,p,h=>r?[h[0],h[2],h[3]]:[h[0],h[1],h[3]]))},du=(e,t)=>{kc(e.inputs,t),e.inputs[0].dims.length===3?Bc(e,t):Rc(e,e.inputs,t)}});var Dc,cu,pu,mu=Y(()=>{"use strict";ye();Se();Ze();_e();Dc=(e,t,r,o)=>{let i=M.size(t),u=t.length,a=U("input",e,u),c=j("output",e,u),p=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),h=M.normalizeAxis(p,u),d=y=>{let w=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,_=fe("uniforms.input_shape","uniforms.axis",u),v=o.reverse?w+(o.exclusive?" + 1":""):"0",S=o.reverse?_:w+(o.exclusive?"":" + 1");return`\n                ${y.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(a,c)}\n                ${y.mainStart()}\n                  ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n                  var inputIndices = ${c.offsetToIndices("global_idx")};\n                  var sum = ${c.type.value}(0);\n                 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r=e[0].dims,o=r,i=2,u=M.sizeToDimension(r,i),a=M.sizeFromDimension(r,i),c=Me(a),p=a/c,h=[r[0],r[1],p],d=["rank","type","type"],y=[{type:12,data:a},{type:12,data:p}];y.push(...Z(h,h));let w=_=>{let v=U("x",e[0].dataType,h.length,c),S=U("scale",e[1].dataType,e[1].dims),A=U("bias",e[2].dataType,e[2].dims),I=j("output",e[0].dataType,h.length,c),x=[v,S,A,I],E=v.type.value,P=c===1?"f32":`vec${c}<f32>`,O=64,R=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n  var<workgroup> meanShared : f32;\n  var<workgroup> squaredNormShared : f32;\n  var<workgroup> workgroupShared : array<${P}, ${O}>;\n  const workgroupSize = ${O}u;\n  ${_.registerUniforms(R).declareVariables(...x)}\n  ${_.mainStart(O)}\n    let norm = global_idx / workgroupSize;\n    let batch = norm / uniforms.x_shape[1];\n    let channel = norm % uniforms.x_shape[1];\n    let localIndex = local_id.x;\n\n    // initialize workgroup memory\n    var initial = ${P}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      initial = initial + ${P}(${v.get("batch","channel","h")});\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the mean of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      meanShared = ${_t("workgroupShared[0]",c)} / f32(uniforms.normSize);\n    }\n    workgroupBarrier();\n\n    // reinitialize workgroup memory.\n    initial = ${P}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let deviation =  ${P}(${v.get("batch","channel","h")}) - ${P}(meanShared);\n      initial = initial + deviation * deviation;\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the sum of square of deviation of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      squaredNormShared = ${_t("workgroupShared[0]",c)};\n    }\n    workgroupBarrier();\n\n    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n    let channelScale = invStdDev * f32(${S.getByOffset("channel")});\n    let channelShift = f32(${A.getByOffset("channel")}) - meanShared * channelScale;\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let value = ${v.get("batch","channel","h")} * ${E}(${P}(channelScale)) + ${E}(${P}(channelShift));\n      ${I.set("batch","channel","h","value")};\n    }\n  }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${c}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:u},programUniforms:y}),getShaderSource:w}},Jc=(e,t,r,o,i,u,a,c)=>{let p=Me(a),h=64,d=p===1?"vec2f":`mat2x${p}f`,y=p===1?"f32":`vec${p}f`,w=(R,L)=>`${d}(${R}, ${L})`,_=i*a/p,v=Math.ceil(u/h),S=["type"],A=[{type:12,data:v},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(u*a/p)}],I=R=>{let L=U("input",t.dataType,t.dims,p);return`\n  ${R.declareVariables(L)}\n  @group(0) @binding(1) var<storage, read_write> output : array<${d}>;\n  struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n  @group(0) @binding(2) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart(h)}\n    let currentImageNumber = global_idx / ${h} / uniforms.C;\n    let currentChannelNumber = (global_idx / ${h}) % uniforms.C;\n    let wgOffset = local_id.x * uniforms.wg_size;\n    if (wgOffset >= uniforms.H) {\n        return;\n    }\n    let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);\n\n    let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;\n    var sum = ${$t("f32",p)};\n    var squaredSum = ${$t("f32",p)};\n    for (var i: u32 = wgOffset; i < wgMax; i++) {\n        let value = ${y}(input[offset + i * uniforms.C]);\n        sum += value;\n        squaredSum += value * value;\n    }\n    output[global_idx] = ${w("sum","squaredSum")};\n  }`},x=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${p}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:[i,a,h,2],dataType:1}],dispatchGroup:{x:i*a/p},programUniforms:A}),getShaderSource:I},{inputs:[t],outputs:[-1]})[0],E=[{type:12,data:_},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(h*a/p)}],P=["type","type","type"],O=R=>{let L=U("scale",r.dataType,r.dims,p),N=U("bias",o.dataType,o.dims,p);return`\n  @group(0) @binding(0) var<storage, read> input : array<${d}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${L.type.storage}>;\n  @group(0) @binding(2) var<storage, read> bias : array<${N.type.storage}>;\n  @group(0) @binding(3) var<storage, read_write> output : array<${d}>;\n  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n  @group(0) @binding(4) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart()}\n    ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n    let currentImageNumber = global_idx / uniforms.C;\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let offset = currentImageNumber * uniforms.image_size;\n    var sum = ${$t("f32",p)};\n    var squaredSum = ${$t("f32",p)};\n    for (var i: u32 = 0; i < min(${h}, uniforms.H); i++) {\n        let value = input[offset + i + currentChannelNumber * ${h}];\n        sum += value[0];\n        squaredSum += value[1];\n    }\n    sum = sum / f32(uniforms.H);\n    squaredSum = squaredSum / f32(uniforms.H);\n    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${c}));\n    let channelScale = invStdDev * ${y}(scale[currentChannelNumber]);\n    let channelShift = ${y}(bias[currentChannelNumber]) - sum * channelScale;\n\n    output[global_idx] = ${w("channelScale","channelShift")};\n  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${p};${c}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:[i,a,2],dataType:1}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:E}),getShaderSource:O},{inputs:[x,r,o],outputs:[-1]})[0]},ep=(e,t,r)=>{let o=t[0].dims,i=o,u=o[0],a=o[o.length-1],c=M.sizeFromDimension(o,1)/a,p=Me(a),h=M.size(i)/p,d=[{type:12,data:c},{type:12,data:Math.floor(a/p)}],y=["type","type"],w=Jc(e,t[0],t[1],t[2],u,c,a,r.epsilon),_=v=>{let S=De(t[0].dataType),A=p===1?"vec2f":`mat2x${p}f`,I=p===1?S:`vec${p}<${S}>`,x=U("input",t[0].dataType,t[0].dims,p),E=j("output",t[0].dataType,i,p);return`\n  @group(0) @binding(0) var<storage, read> input : array<${x.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${A}>;\n  @group(0) @binding(2) var<storage, read_write> output : array<${E.type.storage}>;\n  struct Uniforms {H: u32, C : u32};\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\n\n  ${v.mainStart()}\n    let currentImageNumber = global_idx / (uniforms.C * uniforms.H);\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;\n    let scale = scaleInput[scaleOffset];\n    output[global_idx] = fma(input[global_idx], ${I}(scale[0]), ${I}(scale[1]));\n  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:d}),getShaderSource:_},{inputs:[t[0],w]})},zu=(e,t)=>{t.format==="NHWC"?ep(e,e.inputs,t):e.compute(Qc(e.inputs,t))}});var tp,rp,Uu,Vu=Y(()=>{"use strict";ye();Se();_e();tp=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},rp=(e,t,r)=>{let o=t.simplified,i=e[0].dims,u=e[1],a=!o&&e[2],c=i,p=M.normalizeAxis(t.axis,i.length),h=M.sizeToDimension(i,p),d=M.sizeFromDimension(i,p),y=M.size(u.dims),w=a?M.size(a.dims):0;if(y!==d||a&&w!==d)throw new Error(`Size of X.shape()[axis:] == ${d}.\n       Size of scale and bias (if provided) must match this.\n       Got scale size of ${y} and bias size of ${w}`);let _=[];for(let O=0;O<i.length;++O)O<p?_.push(i[O]):_.push(1);let v=Me(d),S=["type","type"],A=[{type:12,data:h},{type:1,data:d},{type:12,data:Math.floor(d/v)},{type:1,data:t.epsilon}];a&&S.push("type");let I=r>1,x=r>2,E=O=>{let R=De(e[0].dataType),L=[U("x",e[0].dataType,e[0].dims,v),U("scale",u.dataType,u.dims,v)];a&&L.push(U("bias",a.dataType,a.dims,v)),L.push(j("output",e[0].dataType,c,v)),I&&L.push(j("mean_data_output",1,_)),x&&L.push(j("inv_std_output",1,_));let N=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n  ${O.registerUniforms(N).declareVariables(...L)}\n  ${O.mainStart()}\n    ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var mean_vector = ${$t("f32",v)};\n    var mean_square_vector = ${$t("f32",v)};\n\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n      let value = ${ir(R,v,"x[h + offset]")};\n      mean_vector += value;\n      mean_square_vector += value * value;\n    }\n    let mean = ${_t("mean_vector",v)} / uniforms.norm_size;\n    let inv_std_dev = inverseSqrt(${_t("mean_square_vector",v)} / uniforms.norm_size ${o?"":"- mean * mean"} + uniforms.epsilon);\n\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n      let f32input = ${ir(R,v,"x[j + offset]")};\n      let f32scale = ${ir(R,v,"scale[j]")};\n      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i=Math.floor((t.k+t.blockSize-1)/t.blockSize),u=t.blockSize/8*t.bits,a=e[1];if(!M.areEqual(a.dims,[t.n,i,u]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let p=e[2].dims;if(M.size(p)!==t.n*i)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,y=t.bits>4?t.n*i:t.n*Math.floor((i+1)/2);if(M.size(d)!==y)throw new Error("zeroPoints input size error.")}},op=(e,t,r,o)=>{let i=e[0].dims,u=i.length,a=Math.floor((t.k+t.blockSize-1)/t.blockSize),c=i[u-2],p=t.k,h=t.n,d=i.slice(0,u-2),y=M.size(d),_=t.blockSize/8*t.bits/4,v=e[0].dataType,S=Me(c),A=Me(t.k),I=Me(_),x=tr(v),E=c*a*x,P=Math.floor(o/E),O=a<=r[0]&&P>0,R=!O||P>=4?Me(h):P>=2&&Me(h)>=2?2:1,L=d.concat([c,h]),N=M.size(L)/R/S,K=O?[]:[{type:12,data:N},{type:12,data:t.blockSize}],Q=[y,c,p/A],he=M.convertShape(e[1].dims).slice();he.splice(-1,1,_/I),K.push(...Z(Q)),K.push(...Z(he)),K.push(...Z(e[2].dims)),e.length===4&&K.push(...Z(M.convertShape(e[3].dims)));let W=[y,c,h/R];K.push(...Z(W));let se=Ce=>{let We=Q.length,ee=U("a",e[0].dataType,We,A),ae=U("b",12,he.length,I),Ae=U("scales",e[2].dataType,e[2].dims.length),me=[ee,ae,Ae],ie=e.length===4?U("zero_points",12,e[3].dims.length):void 0;ie&&me.push(ie);let ue=W.length,le=j("output",e[0].dataType,ue,R),qe=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],G=De(e[0].dataType),ne=(()=>{switch(A){case 1:return`array<${G}, 8>`;case 2:return`mat4x2<${G}>`;case 4:return`mat2x4<${G}>`;default:throw new Error(`${A}-component is not supported.`)}})(),xe=`\n        for (var word: u32 = 0; word < ${_}; word += ${I}) {\n          ${ae.indicesSet("b_indices","2","word")};\n          let b_data = ${ae.getByIndices("b_indices")};\n          for (var i: u32 = 0; i < ${I}; i++) {\n            let b_value: u32 = ${I===1?"b_data":"b_data[word + i]"};\n            let b_mask: u32 = 0x0F0F0F0Fu;\n            let b_value_lower: vec4<u32> = unpack4xU8(b_value & b_mask);\n            let b_value_upper: vec4<u32> = unpack4xU8((b_value >> 4) & b_mask);\n            let b_quantized_values = ${ne}(${Array.from({length:4},(Be,Ge)=>`${G}(b_value_lower[${Ge}]), ${G}(b_value_upper[${Ge}])`).join(", ")});\n            let b_dequantized_values = ${(()=>A===1?`${ne}(${Array.from({length:8},(Be,Ge)=>`(b_quantized_values[${Ge}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${ne}(${Array(8).fill("zero_point").join(",")})) * scale;`)()};\n            // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\n            for (var m: u32 = 0; m < ${O?c:S}u; m++) {\n              ${ee.indicesSet("a_indices",We-2,O?"m":`row * ${S} + m`)};\n              ${ee.indicesSet("a_indices",We-1,"word_offset")};\n              var input_offset = ${ee.indicesToOffset("a_indices")};\n              var a_data: ${ne};\n              for (var j: u32 = 0; j < ${8/A}; j++) {\n                a_data[j] = ${ee.getByOffset("input_offset")};\n                input_offset++;\n              }\n              ${O?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${R>1?"[c]":""} += ${Array.from({length:8/A},(Be,Ge)=>`${A===1?`a_data[${Ge}] * b_dequantized_values[${Ge}]`:`dot(a_data[${Ge}], b_dequantized_values[${Ge}])`}`).join(" + ")};\n            }\n            word_offset += ${8/A};\n          }\n        }`,Ke=ie?`\n          zero_point_offset += 4;\n          if (zero_point_offset == 32) {\n            zero_point_offset = 0;\n            zero_point_index++;\n            zero_point_word = ${ie.getByOffset("zero_point_index")};\n          }`:"";return O?`\n        var<workgroup> workgroup_shared: array<${le.type.value}, ${c*a}>;\n        ${Ce.declareVariables(...me,le)}\n        ${Ce.mainStart([a,1,1])}\n          var a_indices: ${ee.type.indices};\n          var block = local_id.x;\n          var col = workgroup_id.y;\n          var batch = workgroup_id.z;\n          ${ee.indicesSet("a_indices","0","batch")};\n          // Two zero points are packed into one byte when uniforms.bits is 4.\n          for (var c: u32 = 0; c < ${R}; c++) {\n            let col_times_components_plus_c = col * ${R} + c;\n              ${ie?`\n            var zero_point_bytes_per_col: u32 = (${a} + 1) / 2;\n            var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u);\n            var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u;\n            var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u;\n            var zero_point_nibble_offset: u32 = block & 0x1u;\n            var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);\n            var zero_point_word: u32 = ${ie.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}\n            var b_indices: ${ae.type.indices};\n            ${ae.indicesSet("b_indices","0","col_times_components_plus_c")};\n            // The scale and zero points are computed per block.\n            var scales_index = col_times_components_plus_c * ${a} + block;\n            let scale = ${Ae.getByOffset("scales_index")};\n            // The default zero point is 8 for unsigned 4-bit quantization.\n            let zero_point = ${G}(${ie?"(zero_point_word) & 0xFu":8});\n            ${ae.indicesSet("b_indices","1","block")};\n            var word_offset: u32 = block * ${t.blockSize/A};\n            var workgroup_shared_offset: u32 = block * ${c};\n            ${xe}\n          }\n          workgroupBarrier();\n          if (local_id.x == 0u) {\n            var output_indices: ${le.type.indices};\n            ${le.indicesSet("output_indices","0","batch")};\n            ${le.indicesSet("output_indices",ue-1,"col")};\n            ${le.indicesSet("output_indices",ue-2,"0")};\n            var output_offset = ${le.indicesToOffset("output_indices")};\n            for (var m: u32 = 0u; m < ${c}u; m++) {\n              var output_value: ${le.type.value} = ${le.type.value}(0);\n              var workgroup_shared_offset: u32 = m;\n              for (var b: u32 = 0u; b < ${a}u; b++) {\n                output_value += workgroup_shared[workgroup_shared_offset];\n                workgroup_shared_offset += ${c};\n              }\n              ${le.setByOffset("output_offset","output_value")};\n              output_offset += ${h/R};\n            }\n          }\n        }`:`\n        ${Ce.registerUniforms(qe).declareVariables(...me,le)}\n        ${Ce.mainStart()}\n          ${Ce.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n          var output_values: array<${le.type.value}, ${S}>;\n          var output_indices = ${le.offsetToIndices("global_idx")};\n          var col = ${le.indicesGet("output_indices",ue-1)};\n          var row = ${le.indicesGet("output_indices",ue-2)};\n          var a_indices: ${ee.type.indices} = output_indices;\n          // Two zero points are packed into one byte because uniforms.bits <= 4.\n          // zero_point_offset is either 0 or 4. It is bit offset within one byte.\n          // TODO support zero_point_offset for bits > 4\n          ${ie?`\n          var zero_point_abs_offset = col * ${R} * ((${a} + 1) / 2);\n          var zero_point_index: u32 = zero_point_abs_offset / 4;\n          var zero_point_word: u32 = ${ie.getByOffset("zero_point_index")};\n          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}\n          var scale_index = col * ${a*R};\n          var b_indices: ${ae.type.indices};\n          for (var c: u32 = 0; c < ${R}; c++) {\n            ${ae.indicesSet("b_indices","0",`col * ${R} + c`)};\n            var block_offset: u32 = 0;\n            for (var block: u32 = 0; block < ${a}; block++) {\n              // The scale and zero points are computed per block.\n              let scale = ${Ae.getByOffset("scale_index")};\n              // The default zero point is 8 for unsigned 4-bit quantization.\n              let zero_point = ${G}(${ie?"extractBits(zero_point_word, zero_point_offset, 4)":8});\n              ${ae.indicesSet("b_indices","1","block")};\n              var word_offset: u32 = block_offset;\n              ${xe}\n              scale_index++;\n              ${Ke}\n              block_offset += uniforms.block_size / ${A};\n            }\n            // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.\n            ${ie?`if (zero_point_offset % 8 > 0) {\n                ${Ke}\n              }`:""}\n            }\n            for (var k: u32 = 0u; k < ${S}u; k++) {\n              ${le.indicesSet("output_indices",ue-2,`${S} * row + k`)};\n              ${le.setByIndices("output_indices","output_values[k]")}\n            }\n        }`};return{name:O?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${c};${v};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:L,dataType:v}],name:O?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:O?{x:1,y:Math.ceil(h/R),z:y}:{x:Math.ceil(N/64)},programUniforms:K}),getShaderSource:se}},Wu=(e,t)=>{np(e.inputs,t);let r=e.getMaxComputeWorkgroupSizes(),o=e.getMaxComputeWorkgroupStoragesize();e.compute(op(e.inputs,t,r,o))},Nu=e=>ve(e)});var it,ip,Lu,Hu,ap,Ko,Fu,qu=Y(()=>{"use strict";ye();Se();Ze();_n();Ro();_e();Sr();it=(e,t)=>e.length>t&&e[t].dims.length>0&&M.size(e[t].dims)>0?e[t]:void 0,ip=(e,t)=>{let r=e[0],o=it(e,1),i=it(e,2),u=it(e,3),a=it(e,4),c=it(e,5),p=it(e,6),h=it(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 d=!1,y=r.dims[0],w=r.dims[1],_=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],v=w,S=0,A=0,I=Math.floor(_/t.numHeads);if(p&&h){if(p.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims[0]!==y||p.dims[1]!==t.numHeads||p.dims[3]!==I)throw new Error(\'Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(h.dims[0]!==y||h.dims[1]!==t.numHeads||h.dims[3]!==I)throw new Error(\'Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(p.dims[2]!==h.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)\');if(h.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');S=p.dims[2],A=p.dims[2]}else if(p||h)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let x;if(o){if(r.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(o.dims.length<3||o.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(r.dims[0]!==o.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(o.dims.length===3){if(o.dims[2]!==r.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');x=2,v=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==I)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.\');x=5,v=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==I)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');x=0,v=o.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error(\'Input "query" is expected to have 3 or 5 dimensions when key is empty\');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error(\'Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\');x=3}if(u){if(u.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(i&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(a){E=8;let N=a.dims;throw N.length===1?N[0]===y?E=1:N[0]===3*y+2&&(E=3):N.length===2&&N[0]===y&&N[1]===v&&(E=5),E===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let P=!1,O=_;if(i){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(v!==i.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');O=i.dims[2]}else{if(v!==i.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');O=i.dims[1]*i.dims[3],P=!0}}let R=S+v,L=!1;if(a)throw new Error("Key padding mask is not supported");if(c){if(c.dims.length!==4)throw new Error(\'Input "relative_position_bias" is expected to have 4 dimensions\');if(c.dims[0]!==y&&c.dims[0]!==1||c.dims[1]!==t.numHeads||c.dims[2]!==w||c.dims[3]!==R)throw new Error(\'Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)\')}return{batchSize:y,sequenceLength:w,pastSequenceLength:S,kvSequenceLength:v,totalSequenceLength:R,maxSequenceLength:A,inputHiddenSize:0,hiddenSize:_,vHiddenSize:O,headSize:I,vHeadSize:Math.floor(O/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:L,passPastInKv:P,qkvFormat:x}},Lu=e=>ve({...e}),Hu=ve({perm:[0,2,1,3]}),ap=(e,t,r,o,i,u,a)=>{let c=[o,i,u],p=M.size(c),h=[{type:12,data:p},{type:12,data:a},{type:12,data:u}],d=y=>{let w=j("qkv_with_bias",t.dataType,c),_=U("qkv",t.dataType,c),v=U("bias",r.dataType,c),S=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n  ${y.registerUniforms(S).declareVariables(_,v,w)}\n  ${y.mainStart()}\n    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:c,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:d},{inputs:[t,r],outputs:[-1]})[0]},Ko=(e,t,r,o,i,u,a,c)=>{let p=u;if(a){if(o===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return p=ap(e,u,a,t,o,r*i,c),p=p.reshape([t,o,r,i]),e.compute(yt(p,Hu.perm),{inputs:[p],outputs:[-1]})[0]}else return u.dims.length===3&&(p=u.reshape([t,o,r,i])),e.compute(yt(p,Hu.perm),{inputs:[p],outputs:[-1]})[0]},Fu=(e,t)=>{let r=ip(e.inputs,t),o=e.inputs[0],i=it(e.inputs,1),u=it(e.inputs,2),a=it(e.inputs,3),c=it(e.inputs,4),p=it(e.inputs,5),h=it(e.inputs,6),d=it(e.inputs,7);if(o.dims.length===5)throw new Error("Packed QKV is not implemented");if(i?.dims.length===5)throw new Error("Packed KV is not implemented");let y=i&&u&&i.dims.length===4&&u.dims.length===4,w=Ko(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,o,a,0);if(y)return Pn(e,w,i,u,c,void 0,h,d,p,r,t);if(!i||!u)throw new Error("key and value must be provided");let _=Ko(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,i,a,r.hiddenSize),v=Ko(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,u,a,2*r.hiddenSize);Pn(e,w,_,v,c,void 0,h,d,p,r,t)}});var sp,up,dp,lp,cp,pp,mp,fp,ju,Ku=Y(()=>{"use strict";ye();Se();_e();sp=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].")}},up=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n            k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n            if (k < 0) {\n              break;\n            }\n            if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n              break;\n            }\n            offset += k * i32(${fe("uniforms.x_strides",i,t)});\n        `;return`\n          value = ${e.type.value}(uniforms.constant_value);\n          for (var i = 0; i < 1; i++) {\n            var offset = 0;\n            var k = 0;\n            ${o}\n            value = x[offset];\n          }\n      `},dp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n                if (k < 0) {\n                  k = -k;\n                }\n                {\n                  let _2n_1 = 2 * (i32(${fe("uniforms.x_shape",i,t)}) - 1);\n                  k = k % _2n_1;\n                  if(k >= i32(${fe("uniforms.x_shape",i,t)})) {\n                    k = _2n_1 - k;\n                  }\n                }\n                offset += k * i32(${fe("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},lp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n                if (k < 0) {\n                  k = 0;\n                }\n                if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n                  k = i32(${fe("uniforms.x_shape",i,t)}) - 1;\n                }\n                offset += k * i32(${fe("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},cp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n                k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n                if (k < 0)  {\n                  k += i32(${fe("uniforms.x_shape",i,t)}]);\n                }\n                if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n                  k -= i32(${fe("uniforms.x_shape",i,t)});\n                }\n                offset += k * i32(${fe("uniforms.x_strides",i,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},pp=(e,t,r)=>{switch(r.mode){case 0:return up(e,t,r.pads.length);case 1:return dp(e,t,r.pads.length);case 2:return lp(e,t,r.pads.length);case 3:return cp(e,t,r.pads.length);default:throw new Error("Invalid mode")}},mp=(e,t)=>{let r=M.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,i=M.size(r),u=[{type:12,data:i},{type:6,data:t.pads}];t.mode===0&&u.push({type:e[0].dataType,data:t.value}),u.push(...Z(e[0].dims,r));let a=["rank"],c=p=>{let h=j("output",e[0].dataType,r.length),d=U("x",e[0].dataType,o.length),y=d.type.value,w=pp(h,o.length,t),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&_.push({name:"constant_value",type:y}),`\n            ${p.registerUniforms(_).declareVariables(d,h)}\n            ${p.mainStart()}\n            ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n            let indices = ${h.offsetToIndices("global_idx")};\n\n            var value = ${y}(0);\n            ${w}\n            output[global_idx] = value;\n        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(r)/64)},programUniforms:u}),getShaderSource:c}},fp=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,i=e[0].dims.length,u=new Int32Array(2*i).fill(0);if(e.length>=4){let c=e[3].getBigInt64Array();for(let p=0;p<c.length;p++)u[Number(c[p])]=Number(r[p]),u[Number(c[p])+i]=Number(r[p+c.length])}else r.forEach((c,p)=>u[Number(p)]=Number(c));let a=[];return u.forEach(c=>a.push(c)),{mode:t.mode,value:o,pads:a}}else return t},ju=(e,t)=>{sp(e.inputs);let r=fp(e.inputs,t);e.compute(mp(e.inputs,r),{inputs:[0]})}});var Nn,Yu,Zu,Xu,Qu,hp,gp,Ju,ed,td,rd,nd,od,id,ad,sd,ud,dd,ld,cd=Y(()=>{"use strict";$r();ye();Se();_e();Nn=e=>{if(vr.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Yu=(e,t,r)=>{let o=t.format==="NHWC",i=e.dims.slice();o&&i.splice(1,0,i.pop());let u=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),c=t.strides.slice(),p=u?t.dilations.slice():[],h=t.pads.slice();nr.adjustPoolAttributes(r,i,a,c,p,h);let d=nr.computePoolOutputShape(r,i,c,p,a,h,t.autoPad),y=Object.assign({},t);u?Object.assign(y,{kernelShape:a,strides:c,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(y,{kernelShape:a,strides:c,pads:h,cacheKey:t.cacheKey});let w=d.slice();return w.push(w.splice(1,1)[0]),[y,o?w:d]},Zu=(e,t)=>{let r=t.format==="NHWC",o=M.size(e),i=M.size(t.kernelShape),u=[{type:12,data:o},{type:12,data:i}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let c=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],d=t.pads[t.pads.length-1],y=!!(h+d);u.push({type:12,data:c},{type:12,data:p},{type:12,data:h},{type:12,data:d}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let w=!1;if(t.kernelShape.length===2){let _=t.kernelShape[t.kernelShape.length-2],v=t.strides[t.strides.length-2],S=t.pads[t.pads.length/2-2],A=t.pads[t.pads.length-2];w=!!(S+A),u.push({type:12,data:_},{type:12,data:v},{type:12,data:S},{type:12,data:A}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[u,a,!0,y,w]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let c=M.computeStrides(t.kernelShape);u.push({type:12,data:c},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:c.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,d)=>h+d);return[u,a,!!p,!1,!1]}},Xu=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w=i.format==="NHWC",_=t.type.value,v=j("output",t.type.tensor,o);if(i.kernelShape.length<=2){let S="",A="",I="",x=r-(w?2:1);if(d?S=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  if (xIndices[${x}] < 0 || xIndices[${x}]\n                      >= uniforms.x_shape[${x}]) {\n                    pad++;\n                    continue;\n                  }\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${u}\n                }`:S=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${u}\n                }`,i.kernelShape.length===2){let P=r-(w?3:2);y?A=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${P}] = indices[${P}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${P}] < 0 || xIndices[${P}] >= uniforms.x_shape[${P}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:A=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${P}] = indices[${P}] * uniforms.sh - uniforms.phStart + j;\n                `,I=`\n              }\n            `}return`\n            ${e.registerUniforms(p).declareVariables(t,v)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n              let indices = ${v.offsetToIndices("global_idx")};\n              var xIndices = ${v.offsetToIndices("global_idx")};\n\n              var value = ${_}(${c});\n              var pad = 0;\n              ${A}\n              ${S}\n              ${I}\n              ${a}\n\n              output[global_idx] = value;\n            }`}else{if(w)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let S=i.kernelShape.length,A=i.pads.length,I="";return h?I=`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset("xIndices")}];\n                ${u}\n              }`:I=`\n              }\n              let x_val = x[${t.indicesToOffset("xIndices")}];\n              ${u}\n            `,`\n            ${e.registerUniforms(p).declareVariables(t,v)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n              let indices = ${v.offsetToIndices("global_idx")};\n              var xIndices = ${v.offsetToIndices("global_idx")};\n\n              var offsets: array<u32, ${S}>;\n\n              var value = ${_}(${c});\n              var pad = 0;\n              var isPad = false;\n\n              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n                var offset = i;\n                for (var j = 0u; j < ${S-1}u; j++) {\n                  offsets[j] = offset / ${fe("uniforms.kernelStrides","j",S)};\n                  offset -= offsets[j] * ${fe("uniforms.kernelStrides","j",S)};\n                }\n                offsets[${S-1}] = offset;\n\n                isPad = false;\n                for (var j = ${r-S}u; j < ${r}u; j++) {\n                  xIndices[j] = indices[j] * ${fe("uniforms.strides",`j - ${r-S}u`,S)}\n                    + offsets[j - ${r-S}u] - ${fe("uniforms.pads","j - 2u",A)};\n                  ${I}\n              }\n              ${a}\n\n              output[global_idx] = value;\n            }`}},Qu=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,hp=e=>`${Qu(e)};${e.countIncludePad}`,gp=e=>`${Qu(e)};${e.storageOrder};${e.dilations}`,Ju=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}),ed=(e,t,r,o)=>{let[i,u]=Yu(t,o,r),a=U("x",t.dataType,t.dims.length),c=a.type.value,p="value += x_val;",h="";i.countIncludePad?h+=`value /= ${c}(uniforms.kernelSize);`:h+=`value /= ${c}(i32(uniforms.kernelSize) - pad);`;let[d,y,w,_,v]=Zu(u,i);d.push(...Z(t.dims,u));let S=["rank"];return{name:e,shaderCache:{hint:`${o.cacheKey};${w};${_};${v}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(M.size(u)/64)},programUniforms:d}),getShaderSource:A=>Xu(A,a,t.dims.length,u.length,i,p,h,0,y,w,_,v)}},td=e=>{let t=e.count_include_pad!==0,r=Ju(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let o={countIncludePad:t,...r,cacheKey:""};return{...o,cacheKey:hp(o)}},rd=(e,t)=>{Nn(e.inputs),e.compute(ed("AveragePool",e.inputs[0],!1,t))},nd={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},od=e=>{let t=e.format;return{format:t,...nd,cacheKey:t}},id=(e,t)=>{Nn(e.inputs),e.compute(ed("GlobalAveragePool",e.inputs[0],!0,t))},ad=(e,t,r,o)=>{let[i,u]=Yu(t,o,r),a=`\n      value = max(x_val, value);\n    `,c="",p=U("x",t.dataType,t.dims.length),h=["rank"],[d,y,w,_,v]=Zu(u,i);return d.push(...Z(t.dims,u)),{name:e,shaderCache:{hint:`${o.cacheKey};${w};${_};${v}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(M.size(u)/64)},programUniforms:d}),getShaderSource:S=>Xu(S,p,t.dims.length,u.length,i,a,c,t.dataType===10?-65504:-1e5,y,w,_,v)}},sd=(e,t)=>{Nn(e.inputs),e.compute(ad("MaxPool",e.inputs[0],!1,t))},ud=e=>{let t=e.storage_order,r=e.dilations,o=Ju(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(o.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:t,dilations:r,...o,cacheKey:""};return{...i,cacheKey:gp(i)}},dd=e=>{let t=e.format;return{format:t,...nd,cacheKey:t}},ld=(e,t)=>{Nn(e.inputs),e.compute(ad("GlobalMaxPool",e.inputs[0],!0,t))}});var bp,wp,pd,md=Y(()=>{"use strict";$r();ye();_e();bp=(e,t,r)=>{let o=e===t,i=e<t&&r<0,u=e>t&&r>0;if(o||i||u)throw new Error("Range these inputs\' contents are invalid.")},wp=(e,t,r,o)=>{let i=Math.abs(Math.ceil((t-e)/r)),u=[i],a=i,c=[{type:12,data:a},{type:o,data:e},{type:o,data:r},...Z(u)],p=h=>{let d=j("output",o,u.length),y=d.type.value,w=[{name:"outputSize",type:"u32"},{name:"start",type:y},{name:"delta",type:y}];return`\n        ${h.registerUniforms(w).declareVariables(d)}\n        ${h.mainStart()}\n        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        output[global_idx] = uniforms.start + ${y}(global_idx) * uniforms.delta;\n      }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:u,dataType:o}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c})}},pd=e=>{let t=0,r=0,o=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],o=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],o=e.inputs[2].getFloat32Array()[0]),vr.webgpu.validateInputContent&&bp(t,r,o),e.compute(wp(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var vp,$p,_p,Sp,xp,Cp,Ap,Ip,Tp,Ep,Pp,fd,kp,Op,Rp,Bp,Dp,hd,gd,yd=Y(()=>{"use strict";ye();Se();Ze();_e();vp=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},$p=(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 o=new Array(r).fill(1);return t.forEach((i,u)=>o[i]=e[u]),o},_p=(e,t,r,o,i,u)=>{let[a,c,p]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(d=>u.push(d));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(c>0&&e.length>c&&e[c].dims.length>0){if(e[c].getFloat32Array().forEach(d=>o.push(d)),o.length!==0&&o.length!==h&&r>=18&&o.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");vp(o,t),t.axes.length>0&&$p(o,t.axes,h).forEach((d,y)=>o[y]=d)}if(p>0&&e.length>p&&(e[p].getBigInt64Array().forEach(d=>i.push(Number(d))),i.length!==h||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(o.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!==t.axes.length)throw new Error(\'Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified\')}if(typeof o<"u"&&typeof i<"u"&&o.length>0&&i.length>h)throw new Error("Resize requires only of scales or sizes to be specified")},Sp=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;\n                  } else {\n                    return 0.0;\n                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n                    return 0.0;\n                  } else {\n                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n                    // offset-by-one error later in floor().\n                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n                    let fract =\n                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);\n                    return whole + fract;\n                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +\n                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /\n                        ${t}(lengthResized - 1);\n                  } else {\n                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);\n                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);\n                  const adjustment = ${t}(lengthResized) / outputWidth;\n                  const center = ${t}(lengthOriginal) / 2;\n                  const offset = center * (1 - adjustment);\n                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",xp=(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`)}})()+"}",Cp=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),i=e.length===0?o:e.slice();return t.length>0?(t.forEach((u,a)=>{o[u]=i[a],o[a+r]=i[t.length+a]}),o):i},Ap=(e,t,r,o)=>{let i=[];if(r.length>0)if(o.length>0){if(e.forEach(u=>i.push(u)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((u,a)=>i[u]=r[a])}else r.forEach(u=>i.push(u));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((u,a)=>Math.round(u*t[a]))}return i},Ip=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(u=>t[u]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(u=>t[u]),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(u=>t[u]=o),r.axes.forEach(u=>i[u]=Math.round(e[u]*t[u]))):(t.fill(o,0,t.length),i.forEach((u,a)=>i[a]=Math.round(u*t[a]))),i},Tp=(e,t,r,o,i)=>`\n    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {\n      var original_indices: array<${e.type.value}, ${r.length}>;\n      for (var i:u32 = 0; i < ${r.length}; i++) {\n        var output_index = ${e.indicesGet("output_indices","i")};\n        var scale = ${fe("uniforms.scales","i",o)};\n        var roi_low = ${fe("uniforms.roi","i",i)};\n        var roi_hi = ${fe("uniforms.roi",`i + ${t.length}`,i)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${fe("uniforms.input_shape","i",t.length)};\n          var output_shape_i = ${fe("uniforms.output_shape","i",r.length)};\n          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                           input_shape_i, roi_low, roi_hi);\n        }\n      }\n      return original_indices;\n    }`,Ep=(e,t,r,o,i,u,a)=>`\n    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n      var input_indices: ${e.type.indices};\n      for (var i:u32 = 0; i < ${o.length}; i++) {\n        var output_index = ${t.indicesGet("output_indices","i")};\n        var input_index: u32;\n        var scale = ${fe("uniforms.scales","i",i)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${fe("uniforms.roi","i",u)};\n          var roi_hi = ${fe("uniforms.roi",`i + ${r.length}`,u)};\n          var input_shape_i = ${fe("uniforms.input_shape","i",r.length)};\n          var output_shape_i = ${fe("uniforms.output_shape","i",o.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${a} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {\n            if (original_idx < 0) {\n              input_index = 0;\n            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {\n              input_index = input_shape_i - 1;\n            } else {\n              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n            }\n          } else {\n            input_index = u32(original_idx);\n          }\n        }\n        ${e.indicesSet("input_indices","i"," input_index")}\n      }\n      return input_indices;\n    }`,Pp=(e,t)=>`\n    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n      for (var i:u32 = 0; i < ${t.length}; i++) {\n        var input_index = ${e.indicesGet("input_indices","i")};\n        if (input_index < 0 || input_index >= ${fe("uniforms.input_shape","i",t.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,fd=(e,t,r,o)=>e.rank>o?`\n    ${e.indicesSet("input_indices",t,"channel")};\n    ${e.indicesSet("input_indices",r,"batch")};\n`:"",kp=(e,t,r,o,i)=>{let[a,c,p,h]=r.length===2?[-1,0,1,-1]:[0,2,3,1],d=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",c,`max(0, min(row, ${r[c]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(col, ${r[p]} - 1))`)};\n      ${fd(e,h,a,2)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${d} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${d} = originalIndices[${c}];\n      var col:${d} = originalIndices[${p}];\n      ${o?`if (row < 0 || row > (${r[c]} - 1) || col < 0 || col > (${r[p]} - 1)) {\n        return ${i};\n      }`:""};\n      row = max(0, min(row, ${r[c]} - 1));\n      col = max(0, min(col, ${r[p]} - 1));\n      var row1: u32 = u32(row);\n      var col1: u32 = u32(col);\n      var row2: u32 = u32(row + 1);\n      var col2: u32 = u32(col + 1);\n      var channel: u32 = ${r.length>2?`u32(originalIndices[${h}])`:"0"};\n      var batch: u32 =  ${r.length>2?`u32(originalIndices[${a}])`:"0"};\n      var x11: ${d} = getInputValue(batch, channel, row1, col1);\n      var x12: ${d} = getInputValue(batch, channel, row1, col2);\n      var x21: ${d} = getInputValue(batch, channel, row2, col1);\n      var x22: ${d} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${d} = abs(row - ${d}(row1));\n      var dx2: ${d} = abs(${d}(row2) - row);\n      var dy1: ${d} = abs(col - ${d}(col1));\n      var dy2: ${d} = abs(${d}(col2) - col);\n      if (row1 == row2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (col1 == col2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n    }`},Op=(e,t,r,o,i,u,a,c,p,h)=>{let d=r.length===2,y=!0,[w,_]=d?[0,1]:y?[2,3]:[1,2],v=e.type.value,S=A=>{let I=A===w?"row":"col";return`\n      fn ${I}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${v} {\n        var output_index = ${t.indicesGet("output_indices",A)};\n        var originalIdx: ${v} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[A]},\n        ${o[A]}, ${r[A]}, ${u[A]}, ${u[A]} + ${r.length});\n        var fractOriginalIdx: ${v} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${c} && (originalIdx < 0 || originalIdx > (${r[A]} - 1))) {\n          return ${p};\n        }\n        var data: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${I}: ${v} = originalIdx + ${v}(i);\n          if (${I} < 0 || ${I} >= ${r[A]}) {\n            ${(()=>h?`coefs[i + 1] = 0.0;\n                        continue;`:c?`return ${p};`:`${I} = max(0, min(${I}, ${r[A]} - 1));`)()};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",A,`u32(${I})`)};\n          data[i + 1] = ${A===w?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${S(w)};\n    ${S(_)};\n  fn getCubicInterpolationCoefs(s: ${v}) -> array<${v}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${v} = 1.0 - absS;\n    var twoMinusAbsS: ${v} = 2.0 - absS;\n    var onePlusAbsS: ${v} = 1.0 + absS;\n    coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a};\n    coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${v}, 4>, coefs: array<${v}, 4>) -> ${v} {\n    var coefsSum: ${v} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n  }\n\n  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${v} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},Rp=(e,t,r,o,i)=>{let[a,c,p,h,d]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],y=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${y} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",c,`max(0, min(depth, ${r[c]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(height, ${r[p]} - 1))`)};\n      ${e.indicesSet("input_indices",h,`max(0, min(width, ${r[h]} - 1))`)};\n      ${fd(e,d,a,3)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${y} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${y} = originalIndices[${c}];\n      var height:${y} = originalIndices[${p}];\n      var width:${y} = originalIndices[${h}];\n      ${o?`if (depth < 0 || depth > (${r[c]} - 1) || height < 0 || height > (${r[p]} - 1) || width < 0 || (width > ${r[h]} - 1)) {\n      return ${i};\n        }`:""};\n\n    depth = max(0, min(depth, ${r[c]} - 1));\n      height = max(0, min(height, ${r[p]} - 1));\n      width = max(0, min(width, ${r[h]} - 1));\n      var depth1: u32 = u32(depth);\n      var height1: u32 = u32(height);\n      var width1: u32 = u32(width);\n      var depth2: u32 = u32(depth + 1);\n      var height2: u32 = u32(height + 1);\n      var width2: u32 = u32(width + 1);\n      var channel: u32 = ${r.length>3?`u32(originalIndices[${d}])`:"0"};\n      var batch: u32 =  ${r.length>3?`u32(originalIndices[${a}])`:"0"};\n\n      var x111: ${y} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${y} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${y} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${y} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${y} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${y} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${y} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${y} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${y} = abs(depth - ${y}(depth1));\n      var dx2: ${y} = abs(${y}(depth2) - depth);\n      var dy1: ${y} = abs(height - ${y}(height1));\n      var dy2: ${y} = abs(${y}(height2) - height);\n      var dz1: ${y} = abs(width - ${y}(width1));\n      var dz2: ${y} = abs(${y}(width2) - width);\n      if (depth1 == depth2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (height1 == height2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      if (width1 == width2) {\n        dz1 = 0.5;\n        dz2 = 0.5;\n      }\n      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n    }`},Bp=(e,t,r,o,i,u)=>{let a=e.dims,c=Cp(u,t.axes,a.length),p=Ap(a,o,i,t.axes),h=o.slice();o.length===0&&(h=a.map((x,E)=>x===0?1:p[E]/x),t.keepAspectRatioPolicy!=="stretch"&&(p=Ip(a,h,t)));let d=j("output",e.dataType,p.length),y=U("input",e.dataType,a.length),w=M.size(p),_=a.length===p.length&&a.every((x,E)=>x===p[E]),v=t.coordinateTransformMode==="tf_crop_and_resize",S=t.extrapolationValue,A=y.type.value,I=x=>`\n      ${_?"":`\n      ${Sp(t.coordinateTransformMode,A)};\n      ${(()=>{switch(t.mode){case"nearest":return`\n              ${Pp(y,a)};\n              ${xp(t.nearestMode,r,A)};\n              ${Ep(y,d,a,p,h.length,c.length,v)};\n              `;case"linear":return`\n              ${Tp(d,a,p,h.length,c.length)};\n              ${(()=>{if(a.length===2||a.length===4)return`${kp(y,d,a,v,S)}`;if(a.length===3||a.length===5)return`${Rp(y,d,a,v,S)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n            `;case"cubic":return`\n            ${(()=>{if(a.length===2||a.length===4)return`${Op(y,d,a,p,h,c,t.cubicCoeffA,v,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n            `;default:throw Error("Invalid resize mode")}})()};\n      `}\n      ${x.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",c.length).declareVariables(y,d)}\n      ${x.mainStart()}\n        ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n        ${_?"output[global_idx] = input[global_idx];":`\n        let output_indices = ${d.offsetToIndices("global_idx")};\n        var input_indices: ${y.type.indices};\n        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n                if (checkInputIndices(input_indices)) {\n                  output[global_idx] = ${y.getByIndices("input_indices")};\n                } else {\n                  output[global_idx] = ${t.extrapolationValue};\n                }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};\n`}\n      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${h.length>0?h:""}|${i.length>0?i:""}|${c.length>0?c:""}|${_}|${a}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:[{type:12,data:w},{type:1,data:h},{type:1,data:c},...Z(a,p)]})}},Dp=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},hd=(e,t)=>{let r=[],o=[],i=[],u=Dp(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");_p(e.inputs,t,u,r,o,i),e.compute(Bp(e.inputs[0],t,u,r,o,i),{inputs:[0]})},gd=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,i=e.cubicCoeffA,u=e.excludeOutside!==0,a=e.extrapolationValue,c=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return ve({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:i,excludeOutside:u,extrapolationValue:a,keepAspectRatioPolicy:c,mode:p,nearestMode:h})}});var zp,Mp,bd,wd=Y(()=>{"use strict";ye();Se();Ze();_e();zp=(e,t)=>{let[r,o,i,u]=e,{numHeads:a,rotaryEmbeddingDim:c}=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(!M.areEqual(o.dims,[])&&!M.areEqual(o.dims,[1])&&o.dims.length!==2)throw new Error(`Input \'position_ids\' is expected to have 0, 1, or 2 dimensions, got ${o.dims.length}`);if(i.dims.length!==2)throw new Error(`Input \'cos_cache\' is expected to have 2 dimensions, got ${i.dims.length}`);if(u.dims.length!==2)throw new Error(`Input \'sin_cache\' is expected to have 2 dimensions, got ${u.dims.length}`);if(!M.areEqual(i.dims,u.dims))throw new Error("Inputs \'cos_cache\' and \'sin_cache\' are expected to have the same shape");if(c>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=r.dims[0],h=r.dims[r.dims.length-2],d=i.dims[0],y=M.sizeFromDimension(r.dims,1)/h,w=c===0?i.dims[1]*2:y/a;if(c>w)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(o.dims.length===2){if(p!==o.dims[0])throw new Error(`Input \'position_ids\' dimension 0 should be of size batch_size, got ${o.dims[0]}`);if(h!==o.dims[1])throw new Error(`Input \'position_ids\' dimension 1 should be of size sequence_length, got ${o.dims[1]}`)}if(w/2!==i.dims[1]&&c/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(h>d)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Mp=(e,t)=>{let{interleaved:r,numHeads:o,rotaryEmbeddingDim:i,scale:u}=t,a=e[0].dims[0],c=M.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=c/p,d=e[2].dims[1],y=i===0?d*2:h/o,w=new Array(a,p,h/y,y-d),_=M.computeStrides(w),v=[{type:1,data:u},{type:12,data:w},{type:12,data:_},...e[0].dims.length===3?new Array({type:12,data:[c,h,y,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[c,y,p*y,1]}):[],...Z(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],S=A=>{let I=U("input",e[0].dataType,e[0].dims.length),x=U("position_ids",e[1].dataType,e[1].dims.length),E=U("cos_cache",e[2].dataType,e[2].dims.length),P=U("sin_cache",e[3].dataType,e[3].dims.length),O=j("output",e[0].dataType,e[0].dims.length);return A.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:w.length},{name:"global_strides",type:"u32",length:_.length},{name:"input_output_strides",type:"u32",length:_.length}]),`\n        ${A.declareVariables(I,x,E,P,O)}\n\n        ${A.mainStart(or)}\n          let half_rotary_emb_dim = uniforms.${E.name}_shape[1];\n          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;\n          let size = uniforms.global_shape[0] * uniforms.global_strides[0];\n          ${A.guardAgainstOutOfBoundsWorkgroupSizes("size")}\n\n          if (bsnh[3] < half_rotary_emb_dim) {\n            let position_ids_idx =\n                ${x.broadcastedIndicesToOffset("bsnh.xy",j("",x.type.tensor,2))};\n            let position_id =\n                u32(${x.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);\n            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r});\n            let j = i + select(half_rotary_emb_dim, 1, ${r});\n            let re = ${I.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} -\n                ${I.getByOffset("j")} * ${P.get("position_id","bsnh[3]")};\n            ${O.setByOffset("i","re")}\n            let im = ${I.getByOffset("i")} * ${P.get("position_id","bsnh[3]")} +\n                ${I.getByOffset("j")} * ${E.get("position_id","bsnh[3]")};\n            ${O.setByOffset("j","im")}\n          } else {\n            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;\n            ${O.setByOffset("k",I.getByOffset("k"))}\n          }\n        }`};return{name:"RotaryEmbedding",shaderCache:{hint:ve({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:S,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(w)/or)},programUniforms:v})}},bd=(e,t)=>{zp(e.inputs,t),e.compute(Mp(e.inputs,t))}});var Up,Vp,vd,$d=Y(()=>{"use strict";ye();Se();_e();Up=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],o=e[2];if(t.dataType!==r.dataType||t.dataType!==o.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let i=t.dims[t.dims.length-1],u=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]!==u)throw new Error("Skip must have the same sequence length as input");if(o.dims.length!==1)throw new Error("Gamma must be 1D");if(o.dims[o.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},Vp=(e,t,r,o)=>{let i=t.simplified,u=e[0].dims,a=M.size(u),c=u,p=a,h=u.slice(-1)[0],d=o?u.slice(0,-1).concat(1):[],y=!i&&e.length>3,w=e.length>4,_=o&&r>1,v=o&&r>2,S=r>3,A=Me(h),I=[{type:12,data:p},{type:12,data:A},{type:12,data:h},{type:1,data:t.epsilon}],x=P=>{let O=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],R=[U("x",e[0].dataType,e[0].dims,A),U("skip",e[1].dataType,e[1].dims,A),U("gamma",e[2].dataType,e[2].dims,A)];y&&R.push(U("beta",e[3].dataType,e[3].dims,A)),w&&R.push(U("bias",e[4].dataType,e[4].dims,A)),R.push(j("output",e[0].dataType,c,A)),_&&R.push(j("mean_output",1,d)),v&&R.push(j("inv_std_output",1,d)),S&&R.push(j("input_skip_bias_sum",e[0].dataType,c,A));let L=De(e[0].dataType);return`\n\n      ${P.registerUniforms(O).declareVariables(...R)}\n\n      ${P.mainStart()}\n        ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}\n        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;\n        let offset = global_idx * hidden_size_vectorized;\n        var sum = ${$t("f32",A)};\n        var squareSum = ${$t("f32",A)};\n        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n          let skip_value = skip[offset + i];\n          let bias_value = ${w?"bias[i]":L+"(0.0)"};\n          let input_value = x[offset + i];\n          let value = input_value + skip_value + bias_value;\n          ${S?"input_skip_bias_sum[offset + i] = value;":""}\n          output[offset + i] = value;\n          let f32_value = ${ir(L,A,"value")};\n          sum += f32_value;\n          squareSum += f32_value * f32_value;\n        }\n        let mean = ${_t("sum",A)} / f32(uniforms.hidden_size);\n        let inv_std_dev = inverseSqrt(${_t("squareSum",A)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon);\n        ${_?"mean_output[global_idx] = mean;":""}\n        ${v?"inv_std_output[global_idx] = inv_std_dev;":""}\n        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n          output[offset + i] = (output[offset + i] ${i?"":`- ${L}(mean)`}) * ${L}(inv_std_dev) * gamma[i] ${y?"+ beta[i]":""};\n        }\n      }`},E=[{dims:c,dataType:e[0].dataType}];return r>1&&E.push({dims:d,dataType:1}),r>2&&E.push({dims:d,dataType:1}),r>3&&E.push({dims:u,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${A};${_};${v};${S}`,inputDependencies:e.map((P,O)=>"type")},getShaderSource:x,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(p/h/64)},programUniforms:I})}},vd=(e,t)=>{Up(e.inputs);let o=[0];e.outputCount>1&&o.push(-3),e.outputCount>2&&o.push(-3),e.outputCount>3&&o.push(3),e.compute(Vp(e.inputs,t,e.outputCount,!1),{outputs:o})}});var Wp,Gn,Np,_d,Gp,Hp,Sd,xd,Cd=Y(()=>{"use strict";ye();Se();Ze();_e();Wp=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,o)=>{if(e[o+1].dataType!==6&&e[o+1].dataType!==7)throw new Error(`Input ${o} must be an array of int32 or int64`)})},Gn=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(o=>r.push(Number(o)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(o=>r.push(Number(o)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Np=(e,t)=>{if(e.length>1){let r=Gn(e,1),o=Gn(e,2),i=Gn(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),ve({starts:r,ends:o,axes:i})}else return t},_d=(e,t,r,o,i)=>{let u=e;return e<0&&(u+=r[o[t]]),i[t]<0?Math.max(0,Math.min(u,r[o[t]]-1)):Math.max(0,Math.min(u,r[o[t]]))},Gp=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n          var input_indices: ${e.type.indices};\n          var carry = 0u;\n          for (var i = ${r.length}; i >= 0; i--) {\n            let input_shape_i = ${fe("uniforms.input_shape","i",r.length)};\n            let steps_i = ${fe("uniforms.steps","i",r.length)};\n            let signs_i = ${fe("uniforms.signs","i",r.length)};\n            let starts_i = ${fe("uniforms.starts","i",r.length)};\n            var output_index = ${t.indicesGet("output_indices","i")};\n            var input_index = output_index * steps_i + starts_i + carry;\n            carry = input_index / input_shape_i;\n            input_index = input_index % input_shape_i;\n            if (signs_i < 0) {\n              input_index = input_shape_i - input_index - 1u + starts_i;\n            }\n            ${e.indicesSet("input_indices","i","input_index")};\n          }\n          return input_indices;\n      }`,Hp=(e,t)=>{let r=e[0].dims,o=M.size(r),i=t.axes.length>0?M.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],u=Gn(e,4);u.forEach(I=>I!==0||(()=>{throw new Error("step cannot be 0")})),u.length===0&&(u=Array(i.length).fill(1));let a=t.starts.map((I,x)=>_d(I,x,r,i,u)),c=t.ends.map((I,x)=>_d(I,x,r,i,u));if(i.length!==a.length||i.length!==c.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==r.length)for(let I=0;I<r.length;++I)i.includes(I)||(a.splice(I,0,0),c.splice(I,0,r[I]),u.splice(I,0,1));let p=u.map(I=>Math.sign(I));u.forEach((I,x,E)=>{if(I<0){let P=(c[x]-a[x])/I,O=a[x],R=O+P*u[x];a[x]=R,c[x]=O,E[x]=-I}});let h=r.slice(0);i.forEach((I,x)=>{h[I]=Math.ceil((c[I]-a[I])/u[I])});let 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n'}),Pr,Vt,En,la,ua,oo,Ci,qr,Kr,lp,da,tg,rg,ng,ag,ig,sg,og,lg=J(()=>{dr(),iw(),Aa(),Pr=()=>!!Ue.wasm.proxy&&typeof document<"u",En=!1,la=!1,ua=!1,Ci=new Map,qr=(t,e)=>{let r=Ci.get(t);r?r.push(e):Ci.set(t,[e])},Kr=()=>{if(En||!la||ua||!Vt)throw new Error("worker not 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fetchModelAndCopyToWasmMemory(t){return ng(await Gi(t))}async loadModel(t,e){ur();let r;typeof t=="string"?typeof process<"u"&&process.versions&&process.versions.node?r=await Gi(t):r=await this.fetchModelAndCopyToWasmMemory(t):r=t,[this.sessionId,this.inputNames,this.outputNames]=await ag(r,e),jt()}async dispose(){return ig(this.sessionId)}async run(t,e,r){ur();let n=[],a=[];Object.entries(t).forEach(h=>{let m=h[0],d=h[1],_=this.inputNames.indexOf(m);if(_===-1)throw new Error(`invalid input '${m}'`);n.push(d),a.push(_)});let i=[],s=[];Object.entries(e).forEach(h=>{let m=h[0],d=h[1],_=this.outputNames.indexOf(m);if(_===-1)throw new Error(`invalid output '${m}'`);i.push(d),s.push(_)});let o=n.map((h,m)=>lo(h,()=>`input "${this.inputNames[a[m]]}"`)),l=i.map((h,m)=>h?lo(h,()=>`output "${this.outputNames[s[m]]}"`):null),u=await sg(this.sessionId,a,o,s,l,r),p={};for(let h=0;h<u.length;h++)p[this.outputNames[s[h]]]=i[h]??up(u[h]);return jt(),p}startProfiling(){}endProfiling(){og(this.sessionId)}}}),dp,dg,lw=J(()=>{dr(),lg(),ow(),dp=()=>{if((typeof Ue.wasm.initTimeout!="number"||Ue.wasm.initTimeout<0)&&(Ue.wasm.initTimeout=0),typeof Ue.wasm.simd!="boolean"&&(Ue.wasm.simd=!0),typeof Ue.wasm.proxy!="boolean"&&(Ue.wasm.proxy=!1),typeof Ue.wasm.trace!="boolean"&&(Ue.wasm.trace=!1),typeof Ue.wasm.numThreads!="number"||!Number.isInteger(Ue.wasm.numThreads)||Ue.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(Ue.wasm.numThreads=1);let t=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;Ue.wasm.numThreads=Math.min(4,Math.ceil((t||1)/2))}},dg=class{async init(t){dp(),await tg(),await rg(t)}async createInferenceSessionHandler(t,e){let r=new ug;return await r.loadModel(t,e),Promise.resolve(r)}}}),cg={};Bn(cg,{wasmBackend:()=>pg});var pg,uw=J(()=>{lw(),pg=new dg});dr();dr();dr();var dw="1.18.0",cw=ah;{let t=(uw(),nn(cg)).wasmBackend;rn("webgpu",t,5),rn("webnn",t,5),rn("cpu",t,10),rn("wasm",t,10)}Object.defineProperty(Ue.versions,"web",{value:dw,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.
 * =============================================================================
 */const pw=Object.freeze(Object.defineProperty({__proto__:null,get InferenceSession(){return Vo},get TRACE(){return va},get TRACE_FUNC_BEGIN(){return ur},get TRACE_FUNC_END(){return jt},get Tensor(){return Et},get TrainingSession(){return Go},default:cw,get env(){return Ue},get registerBackend(){return rn}},Symbol.toStringTag,{value:"Module"})),hw=(t,e)=>{const r=typeof document<"u"?document.createElement("canvas"):new OffscreenCanvas(1,1);r.width=t.dims[3],r.height=t.dims[2];const n=r.getContext("2d");if(n!=null){let a,i;e?.tensorLayout!==void 0&&e.tensorLayout==="NHWC"?(a=t.dims[2],i=t.dims[3]):(a=t.dims[3],i=t.dims[2]);const s=e?.format!==void 0?e.format:"RGB",o=e?.norm;let l,u;o===void 0||o.mean===void 0?l=[255,255,255,255]:typeof o.mean=="number"?l=[o.mean,o.mean,o.mean,o.mean]:(l=[o.mean[0],o.mean[1],o.mean[2],0],o.mean[3]!==void 0&&(l[3]=o.mean[3])),o===void 0||o.bias===void 0?u=[0,0,0,0]:typeof o.bias=="number"?u=[o.bias,o.bias,o.bias,o.bias]:(u=[o.bias[0],o.bias[1],o.bias[2],0],o.bias[3]!==void 0&&(u[3]=o.bias[3]));const p=i*a;let h=0,m=p,d=p*2,_=-1;s==="RGBA"?(h=0,m=p,d=p*2,_=p*3):s==="RGB"?(h=0,m=p,d=p*2):s==="RBG"&&(h=0,d=p,m=p*2);for(let w=0;w<i;w++)for(let v=0;v<a;v++){const S=(t.data[h++]-u[0])*l[0],$=(t.data[m++]-u[1])*l[1],E=(t.data[d++]-u[2])*l[2],T=_===-1?255:(t.data[_++]-u[3])*l[3];n.fillStyle="rgba("+S+","+$+","+E+","+T+")",n.fillRect(v,w,1,1)}if("toDataURL"in r)return r.toDataURL();throw new Error("toDataURL is not supported")}else throw new Error("Can not access image data")},fw=(t,e)=>{const r=typeof document<"u"?document.createElement("canvas").getContext("2d"):new OffscreenCanvas(1,1).getContext("2d");let n;if(r!=null){let a,i,s;e?.tensorLayout!==void 0&&e.tensorLayout==="NHWC"?(a=t.dims[2],i=t.dims[1],s=t.dims[3]):(a=t.dims[3],i=t.dims[2],s=t.dims[1]);const o=e!==void 0&&e.format!==void 0?e.format:"RGB",l=e?.norm;let u,p;l===void 0||l.mean===void 0?u=[255,255,255,255]:typeof l.mean=="number"?u=[l.mean,l.mean,l.mean,l.mean]:(u=[l.mean[0],l.mean[1],l.mean[2],255],l.mean[3]!==void 0&&(u[3]=l.mean[3])),l===void 0||l.bias===void 0?p=[0,0,0,0]:typeof l.bias=="number"?p=[l.bias,l.bias,l.bias,l.bias]:(p=[l.bias[0],l.bias[1],l.bias[2],0],l.bias[3]!==void 0&&(p[3]=l.bias[3]));const h=i*a;if(e!==void 0&&(e.format!==void 0&&s===4&&e.format!=="RGBA"||s===3&&e.format!=="RGB"&&e.format!=="BGR"))throw new Error("Tensor format doesn't match input tensor dims");const m=4;let d=0,_=1,w=2,v=3,S=0,$=h,E=h*2,T=-1;o==="RGBA"?(S=0,$=h,E=h*2,T=h*3):o==="RGB"?(S=0,$=h,E=h*2):o==="RBG"&&(S=0,E=h,$=h*2),n=r.createImageData(a,i);for(let A=0;A<i*a;d+=m,_+=m,w+=m,v+=m,A++)n.data[d]=(t.data[S++]-p[0])*u[0],n.data[_]=(t.data[$++]-p[1])*u[1],n.data[w]=(t.data[E++]-p[2])*u[2],n.data[v]=T===-1?255:(t.data[T++]-p[3])*u[3]}else throw new Error("Can not access image data");return n},uo=(t,e)=>{if(t===void 0)throw new Error("Image buffer must be defined");if(e.height===void 0||e.width===void 0)throw new Error("Image height and width must be defined");if(e.tensorLayout==="NHWC")throw new Error("NHWC Tensor layout is not supported yet");const{height:r,width:n}=e,a=e.norm??{mean:255,bias:0};let i,s;typeof a.mean=="number"?i=[a.mean,a.mean,a.mean,a.mean]:i=[a.mean[0],a.mean[1],a.mean[2],a.mean[3]??255],typeof a.bias=="number"?s=[a.bias,a.bias,a.bias,a.bias]:s=[a.bias[0],a.bias[1],a.bias[2],a.bias[3]??0];const o=e.format!==void 0?e.format:"RGBA",l=e.tensorFormat!==void 0&&e.tensorFormat!==void 0?e.tensorFormat:"RGB",u=r*n,p=l==="RGBA"?new Float32Array(u*4):new Float32Array(u*3);let h=4,m=0,d=1,_=2,w=3,v=0,S=u,$=u*2,E=-1;o==="RGB"&&(h=3,m=0,d=1,_=2,w=-1),l==="RGBA"?E=u*3:l==="RBG"?(v=0,$=u,S=u*2):l==="BGR"&&($=0,S=u,v=u*2);for(let A=0;A<u;A++,m+=h,_+=h,d+=h,w+=h)p[v++]=(t[m]+s[0])/i[0],p[S++]=(t[d]+s[1])/i[1],p[$++]=(t[_]+s[2])/i[2],E!==-1&&w!==-1&&(p[E++]=(t[w]+s[3])/i[3]);return l==="RGBA"?new mr("float32",p,[1,4,r,n]):new mr("float32",p,[1,3,r,n])},mw=async(t,e)=>{const r=typeof HTMLImageElement<"u"&&t instanceof HTMLImageElement,n=typeof ImageData<"u"&&t instanceof ImageData,a=typeof ImageBitmap<"u"&&t instanceof ImageBitmap,i=typeof t=="string";let s,o=e??{};const l=()=>{if(typeof document<"u")return document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},u=p=>p instanceof HTMLCanvasElement||p instanceof OffscreenCanvas?p.getContext("2d"):null;if(r){const p=l();p.width=t.width,p.height=t.height;const h=u(p);if(h!=null){let m=t.height,d=t.width;if(e!==void 0&&e.resizedHeight!==void 0&&e.resizedWidth!==void 0&&(m=e.resizedHeight,d=e.resizedWidth),e!==void 0){if(o=e,e.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");o.tensorFormat="RGBA",o.height=m,o.width=d}else o.tensorFormat="RGBA",o.height=m,o.width=d;h.drawImage(t,0,0),s=h.getImageData(0,0,d,m).data}else throw new Error("Can not access image data")}else if(n){let p,h;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(p=e.resizedHeight,h=e.resizedWidth):(p=t.height,h=t.width),e!==void 0&&(o=e),o.format="RGBA",o.height=p,o.width=h,e!==void 0){const m=l();m.width=h,m.height=p;const d=u(m);if(d!=null)d.putImageData(t,0,0),s=d.getImageData(0,0,h,p).data;else throw new Error("Can not access image data")}else s=t.data}else if(a){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const p=l();p.width=t.width,p.height=t.height;const h=u(p);if(h!=null){const m=t.height,d=t.width;return h.drawImage(t,0,0,d,m),s=h.getImageData(0,0,d,m).data,o.height=m,o.width=d,uo(s,o)}else throw new Error("Can not access image data")}else{if(i)return new Promise((p,h)=>{const m=l(),d=u(m);if(!t||!d)return h();const _=new Image;_.crossOrigin="Anonymous",_.src=t,_.onload=()=>{m.width=_.width,m.height=_.height,d.drawImage(_,0,0,m.width,m.height);const w=d.getImageData(0,0,m.width,m.height);o.height=m.height,o.width=m.width,p(uo(w.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return uo(s,o);throw new Error("Input data provided is not supported - aborted tensor creation")},gw=(t,e)=>{const{width:r,height:n,download:a,dispose:i}=e,s=[1,n,r,4];return new mr({location:"texture",type:"float32",texture:t,dims:s,download:a,dispose:i})},_w=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:i}=e;return new mr({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:i})},yw=(t,e,r)=>new mr({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),Mn=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),Bi=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let cp=!1;const ww=()=>{if(!cp){cp=!0;const t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=typeof Float16Array<"u"&&Float16Array.from;t&&(Mn.set("int64",BigInt64Array),Bi.set(BigInt64Array,"int64")),e&&(Mn.set("uint64",BigUint64Array),Bi.set(BigUint64Array,"uint64")),r?(Mn.set("float16",Float16Array),Bi.set(Float16Array,"float16")):Mn.set("float16",Uint16Array)}},bw=t=>{let e=1;for(let r=0;r<t.length;r++){const n=t[r];if(typeof n!="number"||!Number.isSafeInteger(n))throw new TypeError(`dims[${r}] must be an integer, got: ${n}`);if(n<0)throw new RangeError(`dims[${r}] must be a non-negative integer, got: ${n}`);e*=n}return e},vw=(t,e)=>{switch(t.location){case"cpu":return new mr(t.type,t.data,e);case"cpu-pinned":return new mr({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new mr({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new mr({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let mr=class{constructor(e,r,n){ww();let a,i;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,a=e.type,i=e.dims,e.location){case"cpu-pinned":{const o=Mn.get(a);if(!o)throw new TypeError(`unsupported type "${a}" to create tensor from pinned buffer`);if(!(e.data instanceof o))throw new TypeError(`buffer should be of type ${o.name}`);this.cpuData=e.data;break}case"texture":{if(a!=="float32")throw new TypeError(`unsupported type "${a}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(a!=="float32"&&a!=="float16"&&a!=="int32"&&a!=="int64"&&a!=="uint32"&&a!=="uint8"&&a!=="bool")throw new TypeError(`unsupported type "${a}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let o,l;if(typeof e=="string")if(a=e,l=n,e==="string"){if(!Array.isArray(r))throw new TypeError("A string tensor's data must be a string array.");o=r}else{const u=Mn.get(e);if(u===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(r)){if(e==="float16"&&u===Uint16Array)throw new TypeError("Creating a float16 tensor from number array is not supported. Please use Uint16Array as data.");e==="uint64"||e==="int64"?o=u.from(r,BigInt):o=u.from(r)}else if(r instanceof u)o=r;else throw new TypeError(`A ${a} tensor's data must be type of ${u}`)}else if(l=r,Array.isArray(e)){if(e.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");const u=typeof e[0];if(u==="string")a="string",o=e;else if(u==="boolean")a="bool",o=Uint8Array.from(e);else throw new TypeError(`Invalid element type of data array: ${u}.`)}else{const u=Bi.get(e.constructor);if(u===void 0)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);a=u,o=e}if(l===void 0)l=[o.length];else if(!Array.isArray(l))throw new TypeError("A tensor's dims must be a number array");i=l,this.cpuData=o,this.dataLocation="cpu"}const s=bw(i);if(this.cpuData&&s!==this.cpuData.length)throw new Error(`Tensor's size(${s}) does not match data length(${this.cpuData.length}).`);this.type=a,this.dims=i,this.size=s}static async fromImage(e,r){return mw(e,r)}static fromTexture(e,r){return gw(e,r)}static fromGpuBuffer(e,r){return _w(e,r)}static fromPinnedBuffer(e,r,n){return yw(e,r,n)}toDataURL(e){return hw(this,e)}toImageData(e){return fw(this,e)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":{if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;const r=await this.downloader();return this.downloader=void 0,this.dataLocation="cpu",this.cpuData=r,e&&this.disposer&&(this.disposer(),this.disposer=void 0),r}finally{this.isDownloading=!1}}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return vw(this,e)}};const $w=mr,_a=[];let zo,Sa;an.IS_NODE_ENV?(Sa=Je??k0,_a.push("cpu"),zo=["cpu"]):(Sa=pw,an.IS_WEBGPU_AVAILABLE&&_a.push("webgpu"),_a.push("wasm"),zo=["wasm"]);const xw=Sa.InferenceSession;function Sw(t){let e=zo;if(t){if(!_a.includes(t))throw new Error(`Unsupported device: "${t}". Should be one of: ${_a.join(", ")}.`);e=[t]}return e}async function hg(t,e){return await xw.create(t,e)}function fg(t){return t instanceof Sa.Tensor}const tn=Sa?.env;tn?.wasm&&(tn.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.18.0/dist/",tn.wasm.proxy=!an.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(tn.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(tn.wasm.simd=!1));function kw(){return tn?.wasm?.proxy}zt.backends.onnx=tn;const Cn=async(t,e,r)=>{const n=await hg(new Uint8Array(t),e);return async a=>{const i=Object.fromEntries(Object.entries(a).map(([o,l])=>[o,l.ort_tensor])),s=await n.run(i);return Array.isArray(r)?r.map(o=>new ue(s[o])):new ue(s[r])}};class Qi{static session_options={};static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=Cn([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=Cn([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=Cn([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=Cn([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=Cn([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=Cn([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}}const pp=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 ue{get dims(){return this.ort_tensor.dims}set dims(e){this.ort_tensor.dims=e}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}ort_tensor;constructor(...e){return fg(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new $w(e[0],e[1],e[2]),new Proxy(this,{get:(r,n)=>{if(typeof n=="string"){let a=Number(n);if(Number.isInteger(a))return r._getitem(a)}return r[n]},set:(r,n,a)=>r[n]=a})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...r]=this.dims;if(r.length>0){const n=r.reduce((a,i)=>a*i);for(let a=0;a<e;++a)yield this._subarray(a,n,r)}else yield*this.data}_getitem(e){const[r,...n]=this.dims;if(e=fr(e,r),n.length>0){const a=n.reduce((i,s)=>i*s);return this._subarray(e,a,n)}else return new ue(this.type,[this.data[e]],n)}indexOf(e){const r=this.data;for(let n=0;n<r.length;++n)if(r[n]==e)return n;return-1}_subarray(e,r,n){const a=e*r,i=(e+1)*r,s="subarray"in this.data?this.data.subarray(a,i):this.data.slice(a,i);return new ue(this.type,s,n)}item(){const e=this.data;if(e.length!==1)throw new Error(`a Tensor with ${e.length} elements cannot be converted to Scalar`);return e[0]}tolist(){return Ew(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=1/(1+Math.exp(-e[r]));return this}mul(e){return this.clone().mul_(e)}mul_(e){const r=this.data;for(let n=0;n<r.length;++n)r[n]*=e;return this}div(e){return this.clone().div_(e)}div_(e){const r=this.data;for(let n=0;n<r.length;++n)r[n]/=e;return this}add(e){return this.clone().add_(e)}add_(e){const r=this.data;for(let n=0;n<r.length;++n)r[n]+=e;return this}clone(){return new ue(this.type,this.data.slice(),this.dims.slice())}slice(...e){let r=[],n=[];for(let u=0;u<this.dims.length;++u){let p=e[u];if(p==null)n.push([0,this.dims[u]]),r.push(this.dims[u]);else if(typeof p=="number")p=fr(p,this.dims[u],u),n.push([p,p+1]);else if(Array.isArray(p)&&p.length===2){let[h,m]=p;if(h=h===null?0:fr(h,this.dims[u],u,!1),m=m===null?this.dims[u]:fr(m,this.dims[u],u,!1),h>m)throw new Error(`Invalid slice: ${p}`);let d=[Math.max(h,0),Math.min(m,this.dims[u])];n.push(d),r.push(d[1]-d[0])}else throw new Error(`Invalid slice: ${p}`)}let a=n.map(([u,p])=>p-u),i=a.reduce((u,p)=>u*p);const s=this.data;let o=new s.constructor(i);const l=this.stride();for(let u=0;u<i;++u){let p=0;for(let h=a.length-1,m=u;h>=0;--h){const d=a[h];p+=(m%d+n[h][0])*l[h],m=Math.floor(m/d)}o[u]=s[p]}return new ue(this.type,o,r)}permute(...e){return Cw(this,e)}transpose(...e){return this.permute(...e)}sum(e=null,r=!1){return this.norm(1,e,r)}norm(e="fro",r=null,n=!1){if(e==="fro")e=2;else if(typeof e=="string")throw Error(`Unsupported norm: ${e}`);const a=this.data;if(r===null){let o=a.reduce((l,u)=>l+u**e,0)**(1/e);return new ue(this.type,[o],[])}r=fr(r,this.dims.length);const i=this.dims.slice();i[r]=1;const s=new a.constructor(a.length/this.dims[r]);for(let o=0;o<a.length;++o){let l=0;for(let u=this.dims.length-1,p=o,h=1;u>=0;--u){const m=this.dims[u];if(u!==r){const d=p%m;l+=d*h,h*=i[u]}p=Math.floor(p/m)}s[l]+=a[o]**e}if(e!==1)for(let o=0;o<s.length;++o)s[o]=s[o]**(1/e);return n||i.splice(r,1),new ue(this.type,s,i)}normalize_(e=2,r=1){r=fr(r,this.dims.length);const n=this.norm(e,r,!0),a=this.data;for(let i=0;i<a.length;++i){let s=0;for(let o=this.dims.length-1,l=i,u=1;o>=0;--o){const p=this.dims[o];if(o!==r){const h=l%p;s+=h*u,u*=this.dims[o]}l=Math.floor(l/p)}a[i]/=n.data[s]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return Ow(this.dims)}squeeze(e=null){return new ue(this.type,this.data,fp(this.dims,e))}squeeze_(e=null){return this.dims=fp(this.dims,e),this}unsqueeze(e=null){return new ue(this.type,this.data,mp(this.dims,e))}unsqueeze_(e=null){return this.dims=mp(this.dims,e),this}flatten_(e=0,r=-1){r=(r+this.dims.length)%this.dims.length;let n=this.dims.slice(0,e),a=this.dims.slice(e,r+1),i=this.dims.slice(r+1);return this.dims=[...n,a.reduce((s,o)=>s*o,1),...i],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let n=0;n<e.length;++n)if(e[n]===-1){if(r!==-1)throw new Error("Only one dimension can be inferred");r=n}if(r!==-1){const n=e.reduce((a,i,s)=>s!==r?a*i:a,1);e[r]=this.data.length/n}return new ue(this.type,this.data,e)}neg_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=-e[r];return this}neg(){return this.clone().neg_()}clamp_(e,r){const n=this.data;for(let a=0;a<n.length;++a)n[a]=Math.min(Math.max(n[a],e),r);return this}clamp(e,r){return this.clone().clamp_(e,r)}round_(){const e=this.data;for(let r=0;r<e.length;++r)e[r]=Math.round(e[r]);return this}round(){return this.clone().round_()}mean(e=null,r=!1){return ll(this,e,r)}to(e){if(this.type===e)return this;if(!pp.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);return new ue(e,pp[e].from(this.data),this.dims)}}function Ew(t,e){const r=t.length,n=e.reduce((i,s)=>i*s);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let a=t;for(let i=e.length-1;i>=0;i--)a=a.reduce((s,o)=>{let l=s[s.length-1];return l.length<e[i]?l.push(o):s.push([o]),s},[[]]);return a[0]}function Cw(t,e){const[r,n]=L0(t.data,t.dims,e);return new ue(t.type,r,n)}function ol(t,[e,r],n="bilinear",a=!1){const i=t.dims.at(-3)??1,s=t.dims.at(-2),o=t.dims.at(-1);let l=F0(t.data,[i,s,o],[e,r],n,a);return new ue(t.type,l,[i,e,r])}async function hp(t,{size:e=null,mode:r="bilinear"}={}){if(t.dims.length!==4)throw new Error("`interpolate_4d` currently only supports 4D input.");if(!e)throw new Error("`interpolate_4d` requires a `size` argument.");let n;if(e.length===2)n=[...t.dims.slice(0,2),...e];else if(e.length===3)n=[t.dims[0],...e];else if(e.length===4)n=e;else throw new Error("`size` must be of length 2, 3, or 4.");let a;if(r==="bilinear")a=await Qi.bilinear_interpolate_4d;else if(r==="bicubic")a=await Qi.bicubic_interpolate_4d;else throw new Error(`Unsupported mode: ${r}`);const i=new ue("int64",new BigInt64Array(n.map(BigInt)),[n.length]);return await a({x:t,s:i})}async function Tw(t,e){return await(await Qi.matmul)({a:t,b:e})}async function Dn(t,e){const r=await Qi.top_k;return e===null?e=t.dims.at(-1):e=Math.min(e,t.dims.at(-1)),await r({x:t,k:new ue("int64",[BigInt(e)],[1])})}function Aw(t,e){let r=[t.dims[0],t.dims[2]],n=new t.data.constructor(r[0]*r[1]),[a,i,s]=t.dims,o=0;for(let l=0;l<a;++l){let u=l*s*i;for(let p=0;p<s;++p){let h=0,m=0,d=l*i,_=u+p;for(let v=0;v<i;++v){let S=Number(e.data[d+v]);m+=S,h+=t.data[_+v*s]*S}let w=h/m;n[o++]=w}}return new ue(t.type,n,r)}function fp(t,e){return t=t.slice(),e===null?t=t.filter(r=>r!==1):typeof e=="number"?t[e]===1&&t.splice(e,1):Array.isArray(e)&&(t=t.filter((r,n)=>r!==1||!e.includes(n))),t}function mp(t,e){return e=fr(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function fr(t,e,r=null,n=!0){if(n&&(t<-e||t>=e))throw new Error(`IndexError: index ${t} is out of bounds for dimension${r===null?"":" "+r} with size ${e}`);return t<0&&(t=(t%e+e)%e),t}function gr(t,e=0){e=fr(e,t[0].dims.length);const r=t[0].dims.slice();r[e]=t.reduce((s,o)=>s+o.dims[e],0);const n=r.reduce((s,o)=>s*o,1),a=new t[0].data.constructor(n),i=t[0].type;if(e===0){let s=0;for(let o of t)a.set(o.data,s),s+=o.data.length}else{let s=0;for(let o=0;o<t.length;++o){let l=t[o];for(let u=0;u<l.data.length;++u){let p=0;for(let h=l.dims.length-1,m=u,d=1;h>=0;--h){const _=l.dims[h];let w=m%_;h===e&&(w+=s),p+=w*d,d*=r[h],m=Math.floor(m/_)}a[p]=l.data[u]}s+=l.dims[e]}}return new ue(i,a,r)}function ka(t,e=0){return gr(t.map(r=>r.unsqueeze(e)),e)}function Iw(t,e=null,r=1,n=!1){if(e===null){const u=t.data.reduce((d,_)=>d+_,0)/t.data.length,p=Math.sqrt(t.data.reduce((d,_)=>d+(_-u)**2,0)/(t.data.length-r)),h=new ue(t.type,[u],[]);return[new ue(t.type,[p],[]),h]}e=fr(e,t.dims.length);const a=ll(t,e,n),i=t.dims.slice();i[e]=1;const s=new t.data.constructor(t.data.length/t.dims[e]);for(let l=0;l<t.data.length;++l){let u=0;for(let p=t.dims.length-1,h=l,m=1;p>=0;--p){const d=t.dims[p];if(p!==e){const _=h%d;u+=_*m,m*=i[p]}h=Math.floor(h/d)}s[u]+=(t.data[l]-a.data[u])**2}for(let l=0;l<s.length;++l)s[l]=Math.sqrt(s[l]/(t.dims[e]-r));return n||i.splice(e,1),[new ue(t.type,s,i),a]}function ll(t,e=null,r=!1){if(e===null){let i=t.data.reduce((s,o)=>s+o,0);return new ue(t.type,[i/t.data.length],[])}e=fr(e,t.dims.length);const n=t.dims.slice();n[e]=1;const a=new t.data.constructor(t.data.length/t.dims[e]);for(let i=0;i<t.data.length;++i){let s=0;for(let o=t.dims.length-1,l=i,u=1;o>=0;--o){const p=t.dims[o];if(o!==e){const h=l%p;s+=h*u,u*=n[o]}l=Math.floor(l/p)}a[s]+=t.data[i]}if(t.dims[e]!==1)for(let i=0;i<a.length;++i)a[i]=a[i]/t.dims[e];return r||n.splice(e,1),new ue(t.type,a,n)}function Mw(t){const[e,r]=t.dims,n=[e+1,r+1],a=new ue("float32",new Float32Array(n[0]*n[1]).fill(1/0),n),i=new ue("float32",new Float32Array(n[0]*n[1]).fill(-1),n);a[0].data[0]=0;for(let p=1;p<r+1;++p)for(let h=1;h<e+1;++h){const m=a[h-1][p-1].item(),d=a[h-1][p].item(),_=a[h][p-1].item();let w,v;m<d&&m<_?(w=m,v=0):d<m&&d<_?(w=d,v=1):(w=_,v=2),a[h].data[p]=t[h-1][p-1].item()+w,i[h].data[p]=v}let s=e,o=r;i.data.fill(2,0,n[1]);for(let p=0;p<n[0];++p)i[p].data[0]=1;let l=[],u=[];for(;s>0||o>0;)switch(l.push(s-1),u.push(o-1),i[s][o].item()){case 0:--s,--o;break;case 1:--s;break;case 2:--o;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${o}]. Please file a bug report.`)}return l.reverse(),u.reverse(),[l,u]}function Ow(t){const e=new Array(t.length);for(let r=t.length-1,n=1;r>=0;--r)e[r]=n,n*=t[r];return e}function ul(t,e,r,n){const a=t.reduce((i,s)=>i*s,1);return new ue(r,new n(a).fill(e),t)}function zw(t,e){let r,n;return r="float32",n=Float32Array,ul(t,e,r,n)}function Pw(t,e){return zw(t.dims,e)}function Ma(t){return ul(t,1n,"int64",BigInt64Array)}function Rw(t){return Ma(t.dims)}function Bw(t){return ul(t,0n,"int64",BigInt64Array)}function Dw(t){return Bw(t.dims)}function Nw(t,e){if(t.dims.length!==2)throw new Error("The tensor must have 2 dimensions");if(t.dims.at(-1)%8!==0)throw new Error("The last dimension of the tensor must be a multiple of 8");if(!["binary","ubinary"].includes(e))throw new Error("The precision must be either 'binary' or 'ubinary'");const r=e==="binary",n=r?"int8":"uint8",a=r?Int8Array:Uint8Array,i=t.data,s=new a(i.length/8);for(let o=0;o<i.length;++o){const l=i[o]>0?1:0,u=Math.floor(o/8),p=o%8;s[u]|=l<<7-p,r&&p===0&&(s[u]-=128)}return new ue(n,s,[t.dims[0],t.dims[1]/8])}class Fw{constructor(e=(n,a)=>n>a,r=1/0){this._heap=[],this._comparator=e,this._maxSize=r}get size(){return this._heap.length}isEmpty(){return this.size===0}peek(){return this._heap[0]}push(...e){return this.extend(e)}extend(e){for(const r of e)if(this.size<this._maxSize)this._heap.push(r),this._siftUp();else{const n=this._smallest();this._comparator(r,this._heap[n])&&(this._heap[n]=r,this._siftUpFrom(n))}return this.size}pop(){const e=this.peek(),r=this.size-1;return r>0&&this._swap(0,r),this._heap.pop(),this._siftDown(),e}replace(e){const r=this.peek();return this._heap[0]=e,this._siftDown(),r}_parent(e){return(e+1>>>1)-1}_left(e){return(e<<1)+1}_right(e){return e+1<<1}_greater(e,r){return this._comparator(this._heap[e],this._heap[r])}_swap(e,r){const 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n=this.evaluate(t.right,e);switch(t.operator.value){case"==":return new rt(r.value==n.value);case"!=":return new rt(r.value!=n.value)}if(r instanceof sr||n instanceof sr)throw new Error("Cannot perform operation on undefined values");if(r instanceof wa||n instanceof wa)throw new Error("Cannot perform operation on null values");if(r instanceof je&&n instanceof je)switch(t.operator.value){case"+":return new je(r.value+n.value);case"-":return new je(r.value-n.value);case"*":return new je(r.value*n.value);case"/":return new je(r.value/n.value);case"%":return new je(r.value%n.value);case"<":return new rt(r.value<n.value);case">":return new rt(r.value>n.value);case">=":return new rt(r.value>=n.value);case"<=":return new rt(r.value<=n.value)}else if(r instanceof Ze&&n instanceof Ze)switch(t.operator.value){case"+":return new Ze(r.value.concat(n.value))}else if(n instanceof Ze){const a=n.value.find(i=>i.value===r.value)!==void 0;switch(t.operator.value){case"in":return new rt(a);case"not in":return new rt(!a)}}if(r instanceof Pe||n instanceof Pe)switch(t.operator.value){case"+":return new Pe(r.value.toString()+n.value.toString())}if(r instanceof Pe&&n instanceof Pe)switch(t.operator.value){case"in":return new rt(n.value.includes(r.value));case"not in":return new rt(!n.value.includes(r.value))}if(r instanceof Pe&&n instanceof or)switch(t.operator.value){case"in":return new rt(n.value.has(r.value));case"not in":return new rt(!n.value.has(r.value))}throw new SyntaxError(`Unknown operator "${t.operator.value}" between ${r.type} and ${n.type}`)}evaluateFilterExpression(t,e){const r=this.evaluate(t.operand,e);if(t.filter.type==="Identifier"){const n=t.filter;if(r instanceof Ze)switch(n.value){case"list":return r;case"first":return r.value[0];case"last":return r.value[r.value.length-1];case"length":return new je(r.value.length);case"reverse":return new Ze(r.value.reverse());case"sort":return new Ze(r.value.sort((a,i)=>{if(a.type!==i.type)throw new Error(`Cannot compare different types: ${a.type} and ${i.type}`);switch(a.type){case"NumericValue":return a.value-i.value;case"StringValue":return a.value.localeCompare(i.value);default:throw new Error(`Cannot compare type: ${a.type}`)}}));default:throw new Error(`Unknown ArrayValue filter: ${n.value}`)}else if(r instanceof Pe)switch(n.value){case"length":return new je(r.value.length);case"upper":return new Pe(r.value.toUpperCase());case"lower":return new Pe(r.value.toLowerCase());case"title":return new Pe(mg(r.value));case"capitalize":return new Pe(r.value.charAt(0).toUpperCase()+r.value.slice(1));case"trim":return new Pe(r.value.trim());default:throw new Error(`Unknown StringValue filter: ${n.value}`)}else if(r instanceof je)switch(n.value){case"abs":return new je(Math.abs(r.value));default:throw new Error(`Unknown NumericValue filter: ${n.value}`)}else if(r instanceof or)switch(n.value){case"items":return new Ze(Array.from(r.value.entries()).map(([a,i])=>new Ze([new Pe(a),i])));case"length":return new je(r.value.size);default:throw new Error(`Unknown ObjectValue filter: ${n.value}`)}throw new Error(`Cannot apply filter "${n.value}" to type: ${r.type}`)}else if(t.filter.type==="CallExpression"){const n=t.filter;if(n.callee.type!=="Identifier")throw new Error(`Unknown filter: ${n.callee.type}`);const a=n.callee.value;if(r instanceof Ze){switch(a){case"selectattr":{if(r.value.some(p=>!(p instanceof or)))throw new Error("`selectattr` can only be applied to array of objects");if(n.args.some(p=>p.type!=="StringLiteral"))throw new Error("arguments of `selectattr` must be strings");const[i,s,o]=n.args.map(p=>this.evaluate(p,e));let l;if(s){const p=e.tests.get(s.value);if(!p)throw new Error(`Unknown test: ${s.value}`);l=p}else l=(...p)=>p[0].__bool__().value;const u=r.value.filter(p=>{const h=p.value.get(i.value);return h?l(h,o):!1});return new Ze(u)}}throw new Error(`Unknown ArrayValue filter: ${a}`)}else throw new Error(`Cannot apply filter "${a}" to type: ${r.type}`)}throw new Error(`Unknown filter: ${t.filter.type}`)}evaluateTestExpression(t,e){const r=this.evaluate(t.operand,e),n=e.tests.get(t.test.value);if(!n)throw new Error(`Unknown test: ${t.test.value}`);const a=n(r);return new rt(t.negate?!a:a)}evaluateUnaryExpression(t,e){const r=this.evaluate(t.argument,e);switch(t.operator.value){case"not":return new rt(!r.value);default:throw new SyntaxError(`Unknown operator: ${t.operator.value}`)}}evalProgram(t,e){return this.evaluateBlock(t.body,e)}evaluateBlock(t,e){let r="";for(const n of t){const a=this.evaluate(n,e);a.type!=="NullValue"&&a.type!=="UndefinedValue"&&(r+=a.value)}return new Pe(r)}evaluateIdentifier(t,e){return e.lookupVariable(t.value)}evaluateCallExpression(t,e){const r=[],n=new Map;for(const i of t.args)if(i.type==="KeywordArgumentExpression"){const s=i;n.set(s.key.value,this.evaluate(s.value,e))}else r.push(this.evaluate(i,e));n.size>0&&r.push(new or(n));const a=this.evaluate(t.callee,e);if(a.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${a.type}`);return a.value(r,e)}evaluateSliceExpression(t,e,r){if(!(t instanceof Ze||t instanceof Pe))throw new Error("Slice object must be an array or string");const n=this.evaluate(e.start,r),a=this.evaluate(e.stop,r),i=this.evaluate(e.step,r);if(!(n instanceof je||n instanceof sr))throw new Error("Slice start must be numeric or undefined");if(!(a instanceof je||a instanceof sr))throw new Error("Slice stop must be numeric or undefined");if(!(i instanceof je||i instanceof sr))throw new Error("Slice step must be numeric or undefined");return t instanceof Ze?new Ze($p(t.value,n.value,a.value,i.value)):new Pe($p(Array.from(t.value),n.value,a.value,i.value).join(""))}evaluateMemberExpression(t,e){const r=this.evaluate(t.object,e);let n;if(t.computed){if(t.property.type==="SliceExpression")return this.evaluateSliceExpression(r,t.property,e);n=this.evaluate(t.property,e)}else n=new Pe(t.property.value);let a;if(r instanceof or){if(!(n instanceof Pe))throw new Error(`Cannot access property with non-string: got ${n.type}`);a=r.value.get(n.value)??r.builtins.get(n.value)}else if(r instanceof Ze||r instanceof Pe)if(n instanceof je)a=r.value.at(n.value),r instanceof Pe&&(a=new Pe(r.value.at(n.value)));else if(n instanceof Pe)a=r.builtins.get(n.value);else throw new Error(`Cannot access property with non-string/non-number: got ${n.type}`);else{if(!(n instanceof Pe))throw new Error(`Cannot access property with non-string: got ${n.type}`);a=r.builtins.get(n.value)}return a instanceof Cr?a:new sr}evaluateSet(t,e){const r=this.evaluate(t.value,e);if(t.assignee.type==="Identifier"){const n=t.assignee.value;e.setVariable(n,r)}else if(t.assignee.type==="MemberExpression"){const n=t.assignee,a=this.evaluate(n.object,e);if(!(a instanceof or))throw new Error("Cannot assign to member of non-object");if(n.property.type!=="Identifier")throw new Error("Cannot assign to member with non-identifier property");a.value.set(n.property.value,r)}else throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(t.assignee)}`);return new wa}evaluateIf(t,e){const r=this.evaluate(t.test,e);return this.evaluateBlock(r.__bool__().value?t.body:t.alternate,e)}evaluateFor(t,e){const r=new Po(e),n=this.evaluate(t.iterable,r);if(!(n instanceof Ze))throw new Error(`Expected iterable type in for loop: got ${n.type}`);let a="";for(let i=0;i<n.value.length;++i){const s=new Map([["index",new je(i+1)],["index0",new je(i)],["revindex",new je(n.value.length-i)],["revindex0",new je(n.value.length-i-1)],["first",new rt(i===0)],["last",new rt(i===n.value.length-1)],["length",new je(n.value.length)],["previtem",i>0?n.value[i-1]:new sr],["nextitem",i<n.value.length-1?n.value[i+1]:new sr]]);r.setVariable("loop",new or(s));const o=n.value[i];if(t.loopvar.type==="Identifier")r.setVariable(t.loopvar.value,o);else if(t.loopvar.type==="TupleLiteral"){const u=t.loopvar;if(o.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${o.type}`);const p=o;if(u.value.length!==p.value.length)throw new Error(`Too ${u.value.length>p.value.length?"few":"many"} items to unpack`);for(let h=0;h<u.value.length;++h){if(u.value[h].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${u.value[h].type}`);r.setVariable(u.value[h].value,p.value[h])}}const l=this.evaluateBlock(t.body,r);a+=l.value}return new Pe(a)}evaluate(t,e){if(t===void 0)return new sr;switch(t.type){case"Program":return this.evalProgram(t,e);case"Set":return this.evaluateSet(t,e);case"If":return this.evaluateIf(t,e);case"For":return this.evaluateFor(t,e);case"NumericLiteral":return new je(Number(t.value));case"StringLiteral":return new Pe(t.value);case"BooleanLiteral":return new rt(t.value);case"ArrayLiteral":return new Ze(t.value.map(r=>this.evaluate(r,e)));case"TupleLiteral":return new ob(t.value.map(r=>this.evaluate(r,e)));case"ObjectLiteral":{const r=new Map;for(const[n,a]of t.value){const i=this.evaluate(n,e);if(!(i instanceof Pe))throw new Error(`Object keys must be strings: got ${i.type}`);r.set(i.value,this.evaluate(a,e))}return new or(r)}case"Identifier":return this.evaluateIdentifier(t,e);case"CallExpression":return this.evaluateCallExpression(t,e);case"MemberExpression":return this.evaluateMemberExpression(t,e);case"UnaryExpression":return this.evaluateUnaryExpression(t,e);case"BinaryExpression":return this.evaluateBinaryExpression(t,e);case"FilterExpression":return this.evaluateFilterExpression(t,e);case"TestExpression":return this.evaluateTestExpression(t,e);default:throw new SyntaxError(`Unknown node type: ${t.type}`)}}};function Di(t){switch(typeof t){case"number":return new je(t);case"string":return new Pe(t);case"boolean":return new rt(t);case"object":return t===null?new wa:Array.isArray(t)?new Ze(t.map(Di)):new or(new Map(Object.entries(t).map(([e,r])=>[e,Di(r)])));case"function":return new kr((e,r)=>{const n=t(...e.map(a=>a.value))??null;return Di(n)});default:throw new Error(`Cannot convert to runtime value: ${t}`)}}var ub=class{parsed;constructor(t){const e=Hw(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=ib(e)}render(t){const e=new Po;e.set("false",!1),e.set("true",!0),e.set("raise_exception",a=>{throw new Error(a)}),e.set("range",sb);for(const[a,i]of Object.entries(t))e.set(a,i);return new lb(e).run(this.parsed).value}};const gg=[["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"]],Ni=new Map(gg),db=new Map([...gg.map(([t,e])=>[e,t]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function _g(t){t=t.toLowerCase();let e=db.get(t);if(e===void 0)if(Ni.has(t))e=t;else{const n=t.length===2?Ni.keys():Ni.values();throw new Error(`Language "${t}" is not supported. Must be one of: ${JSON.stringify(n)}`)}return e}const po="https://github.com/xenova/transformers.js/issues/new/choose";async function yg(t,e){const r=await Promise.all([Br(t,"tokenizer.json",!0,e),Br(t,"tokenizer_config.json",!0,e)]);return e.legacy!==null&&(r[1].legacy=e.legacy),r}function cb(t,e){const r=[];let n=0;for(const a of t.matchAll(e)){const i=a[0];n<a.index&&r.push(t.slice(n,a.index)),i.length>0&&r.push(i),n=a.index+i.length}return n<t.length&&r.push(t.slice(n)),r}function is(t,e=!0){if(t.Regex!==void 0){let r=t.Regex.replace(/\\([#&~])/g,"$1");for(const[n,a]of gb)r=r.replaceAll(n,a);return new RegExp(r,"gu")}else if(t.String!==void 0){const r=Np(t.String);return new RegExp(e?r:`(${r})`,"gu")}else return console.warn("Unknown pattern type:",t),null}function dl(t){return new Map(Object.entries(t))}function wg(t){const e=t.dims;switch(e.length){case 1:return t.tolist();case 2:if(e[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return t.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${e.length}.`)}}function cl(t){return t.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 bg(t){return t.replace(/[\u0300-\u036f]/g,"")}function pb(t){return bg(t.toLowerCase())}function hb(t){return t>=19968&&t<=40959||t>=13312&&t<=19903||t>=131072&&t<=173791||t>=173824&&t<=177983||t>=177984&&t<=178207||t>=178208&&t<=183983||t>=63744&&t<=64255||t>=194560&&t<=195103}function fb(t,e,r){const n=[];let a=0;for(;a<t.length;){if(n.push(t[a]),(r.get(t[a])??e)!==e){++a;continue}for(;a<t.length&&(r.get(t[a])??e)===e;)++a}return n}function mb(t){return t.match(/\S+/g)||[]}const Ea="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",gb=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class _b{constructor(e){this.content=e.content,this.id=e.id,this.single_word=e.single_word??!1,this.lstrip=e.lstrip??!1,this.rstrip=e.rstrip??!1,this.special=e.special??!1,this.normalized=e.normalized??null}}class za extends bt{constructor(e){super(),this.config=e,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(e,...r){switch(e.type){case"WordPiece":return new yb(e);case"Unigram":return new wb(e,...r);case"BPE":return new vb(e);default:if(e.vocab)return new $b(e,...r);throw new Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){let r=this.encode(e);return this.fuse_unk&&(r=fb(r,this.unk_token_id,this.tokens_to_ids)),r}encode(e){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(e){return e.map(r=>this.tokens_to_ids.get(r)??this.unk_token_id)}convert_ids_to_tokens(e){return e.map(r=>this.vocab[r]??this.unk_token)}}class yb extends za{constructor(e){super(e),this.tokens_to_ids=dl(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.max_input_chars_per_word=e.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r}encode(e){const r=[];for(const n of e){const a=[...n];if(a.length>this.max_input_chars_per_word){r.push(this.unk_token);continue}let i=!1,s=0;const o=[];for(;s<a.length;){let l=a.length,u=null;for(;s<l;){let p=a.slice(s,l).join("");if(s>0&&(p=this.config.continuing_subword_prefix+p),this.tokens_to_ids.has(p)){u=p;break}--l}if(u===null){i=!0;break}o.push(u),s=l}i?r.push(this.unk_token):r.push(...o)}return r}}class wb extends za{constructor(e,r){super(e);const n=e.vocab.length;this.vocab=new Array(n),this.scores=new Array(n);for(let a=0;a<n;++a){const i=e.vocab[a];this.vocab[a]=i[0],this.scores[a]=i[1]}this.unk_token_id=e.unk_id,this.unk_token=this.vocab[e.unk_id],this.tokens_to_ids=new Map(this.vocab.map((a,i)=>[a,i])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=r.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=Fp(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new Lw,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(e){const r=e.sentence,n=r.length;let a=0;for(;a<n;){let s=!1;for(let o of this.trie.commonPrefixSearch(r.slice(a))){const l=this.tokens_to_ids.get(o),u=this.scores[l],p=o.length;e.insert(a,p,u,l),!s&&p===1&&(s=!0)}s||e.insert(a,1,this.unkScore,this.unk_token_id),a+=1}}tokenize(e){const r=new Uw(e,this.bosTokenId,this.eosTokenId);return this.populateNodes(r),r.tokens()}encode(e){const r=[];for(const n of e){const a=this.tokenize(n);r.push(...a)}return r}}const vg=(()=>{const t=[...Array.from({length:94},(a,i)=>i+33),...Array.from({length:12},(a,i)=>i+161),...Array.from({length:82},(a,i)=>i+174)],e=t.slice();let r=0;for(let a=0;a<256;++a)t.includes(a)||(t.push(a),e.push(256+r),r+=1);const n=e.map(a=>String.fromCharCode(a));return Object.fromEntries(t.map((a,i)=>[a,n[i]]))})(),bb=O0(vg);class vb extends za{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=dl(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r;this.bpe_ranks=new Map(e.merges.map((r,n)=>[r,n])),this.merges=e.merges.map(r=>r.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.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(e){if(e.length===0)return[];const r=this.cache.get(e);if(r!==void 0)return r;const n=Array.from(e);this.end_of_word_suffix&&(n[n.length-1]+=this.end_of_word_suffix);let a=[];if(n.length>1){const i=new Fw((l,u)=>l.score<u.score);let s={token:n[0],bias:0,prev:null,next:null},o=s;for(let l=1;l<n.length;++l){const u={bias:l/n.length,token:n[l],prev:o,next:null};o.next=u,this._add_node(i,o),o=u}for(;!i.isEmpty();){const l=i.pop();if(l.deleted||!l.next||l.next.deleted)continue;if(l.deleted=!0,l.next.deleted=!0,l.prev){const p={...l.prev};l.prev.deleted=!0,l.prev=p,p.prev?p.prev.next=p:s=p}const u={token:l.token+l.next.token,bias:l.bias,prev:l.prev,next:l.next.next};u.prev?(u.prev.next=u,this._add_node(i,u.prev)):s=u,u.next&&(u.next.prev=u,this._add_node(i,u))}for(let l=s;l!==null;l=l.next)a.push(l.token)}else a=n;if(this.continuing_subword_suffix)for(let i=0;i<a.length-1;++i)a[i]+=this.continuing_subword_suffix;return this.cache.set(e,a),a}_add_node(e,r){const n=this.bpe_ranks.get(r.token+this.BPE_SPLIT_TOKEN+r.next.token);n!==void 0&&(r.score=n+r.bias,e.push(r))}encode(e){const r=[];for(const n of e){if(this.ignore_merges&&this.tokens_to_ids.has(n)){r.push(n);continue}const a=this.bpe(n);for(const i of a)this.tokens_to_ids.has(i)?r.push(i):this.byte_fallback?r.push(...Array.from(this.text_encoder.encode(i)).map(s=>`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class $b extends za{constructor(e,r){super(e),this.tokens_to_ids=dl(r.target_lang?e.vocab[r.target_lang]:e.vocab),this.bos_token=r.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=r.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=r.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=r.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[n,a]of this.tokens_to_ids)this.vocab[a]=n}encode(e){return e}}class Lt extends bt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new Ob(e);case"Precompiled":return new Qb(e);case"Sequence":return new Mb(e);case"Replace":return new xb(e);case"NFC":return new Sb(e);case"NFKC":return new kb(e);case"NFKD":return new Eb(e);case"Strip":return new Cb(e);case"StripAccents":return new Tb(e);case"Lowercase":return new Ab(e);case"Prepend":return new Ib(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class xb extends Lt{normalize(e){const r=is(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class Sb extends Lt{normalize(e){return e=e.normalize("NFC"),e}}class kb extends Lt{normalize(e){return e=e.normalize("NFKC"),e}}class Eb extends Lt{normalize(e){return e=e.normalize("NFKD"),e}}class Cb extends Lt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class Tb extends Lt{normalize(e){return e=bg(e),e}}class Ab extends Lt{normalize(e){return e=e.toLowerCase(),e}}class Ib extends Lt{normalize(e){return e=this.config.prepend+e,e}}class Mb extends Lt{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>Lt.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class Ob extends Lt{_tokenize_chinese_chars(e){const r=[];for(let n=0;n<e.length;++n){const a=e[n],i=a.charCodeAt(0);hb(i)?(r.push(" "),r.push(a),r.push(" ")):r.push(a)}return r.join("")}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(e){switch(e){case"	":case`
`:case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(e)}}_clean_text(e){const r=[];for(const n of e){const a=n.charCodeAt(0);a===0||a===65533||this._is_control(n)||(/^\s$/.test(n)?r.push(" "):r.push(n))}return r.join("")}normalize(e){return this.config.clean_text&&(e=this._clean_text(e)),this.config.handle_chinese_chars&&(e=this._tokenize_chinese_chars(e)),this.config.lowercase?(e=e.toLowerCase(),this.config.strip_accents!==!1&&(e=this.stripAccents(e))):this.config.strip_accents&&(e=this.stripAccents(e)),e}}class qt extends bt{static fromConfig(e){if(e===null)return null;switch(e.type){case"BertPreTokenizer":return new zb(e);case"Sequence":return new Zb(e);case"Whitespace":return new Jb(e);case"WhitespaceSplit":return new ev(e);case"Metaspace":return new kg(e);case"ByteLevel":return new Pb(e);case"Split":return new Rb(e);case"Punctuation":return new Bb(e);case"Digits":return new Db(e);case"Replace":return new tv(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,r){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,r){return(Array.isArray(e)?e.map(n=>this.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class zb extends qt{constructor(e){super(),this.pattern=new RegExp(`[^\\s${Ea}]+|[${Ea}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class Pb extends qt{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=vg,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),i=>this.byte_encoder[i]).join(""))}}class Rb extends qt{constructor(e){super(),this.config=e,this.pattern=is(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:cb(e,this.pattern)}}class Bb extends qt{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${Ea}]+|[${Ea}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Db extends qt{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Pn extends bt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new Nb(e);case"ByteLevel":return new Sg(e);case"RobertaProcessing":return new xg(e);case"BertProcessing":return new $g(e);case"Sequence":return new Fb(e);default:throw new Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...r){throw Error("post_process should be implemented in subclass.")}_call(e,...r){return this.post_process(e,...r)}}class $g extends Pn{constructor(e){super(e),this.cls=e.cls[0],this.sep=e.sep[0]}post_process(e,r=null,{add_special_tokens:n=!0}={}){n&&(e=ft([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const i=n&&this instanceof xg?[this.sep]:[],s=n?[this.sep]:[];e=ft(e,i,r,s),a=ft(a,new Array(r.length+i.length+s.length).fill(1))}return{tokens:e,token_type_ids:a}}}class xg extends $g{}class Nb extends Pn{constructor(e){super(e),this.single=e.single,this.pair=e.pair}post_process(e,r=null,{add_special_tokens:n=!0}={}){const a=r===null?this.single:this.pair;let i=[],s=[];for(const o of a)"SpecialToken"in o?n&&(i.push(o.SpecialToken.id),s.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(i=ft(i,e),s=ft(s,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(i=ft(i,r),s=ft(s,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:i,token_type_ids:s}}}class Sg extends Pn{post_process(e,r=null){return r&&(e=ft(e,r)),{tokens:e}}}class Fb extends Pn{constructor(e){super(e),this.processors=e.processors.map(r=>Pn.fromConfig(r))}post_process(e,r=null,n={}){let a;for(const i of this.processors)if(i instanceof Sg)e=i.post_process(e).tokens,r&&(r=i.post_process(r).tokens);else{const s=i.post_process(e,r,n);e=s.tokens,a=s.token_type_ids}return{tokens:e,token_type_ids:a}}}class Ut extends bt{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=e.trim_offsets}static fromConfig(e){if(e===null)return null;switch(e.type){case"WordPiece":return new Gb(e);case"Metaspace":return new Xb(e);case"ByteLevel":return new Hb(e);case"Replace":return new Lb(e);case"ByteFallback":return new Ub(e);case"Fuse":return new Wb(e);case"Strip":return new Vb(e);case"Sequence":return new qb(e);case"CTC":return new jb(e);case"BPEDecoder":return new Kb(e);default:throw new Error(`Unknown Decoder type: ${e.type}`)}}_call(e){return this.decode(e)}decode(e){return this.decode_chain(e).join("")}decode_chain(e){throw Error("`decode_chain` should be implemented in subclass.")}}class Lb extends Ut{decode_chain(e){const r=is(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class Ub extends Ut{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const r=[];let n=[];for(const a of e){let i=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const s=parseInt(a.slice(3,5),16);isNaN(s)||(i=s)}if(i!==null)n.push(i);else{if(n.length>0){const s=this.text_decoder.decode(Uint8Array.from(n));r.push(s),n=[]}r.push(a)}}if(n.length>0){const a=this.text_decoder.decode(Uint8Array.from(n));r.push(a),n=[]}return r}}class Wb extends Ut{decode_chain(e){return[e.join("")]}}class Vb extends Ut{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map(r=>{let n=0;for(let i=0;i<this.start&&r[i]===this.content;++i){n=i+1;continue}let a=r.length;for(let i=0;i<this.stop;++i){const s=r.length-i-1;if(r[s]===this.content){a=s;continue}else break}return r.slice(n,a)})}}class Gb extends Ut{constructor(e){super(e),this.cleanup=e.cleanup}decode_chain(e){return e.map((r,n)=>(n!==0&&(r.startsWith(this.config.prefix)?r=r.replace(this.config.prefix,""):r=" "+r),this.cleanup&&(r=cl(r)),r))}}class Hb extends Ut{constructor(e){super(e),this.byte_decoder=bb,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(e){const r=e.join(""),n=new Uint8Array([...r].map(i=>this.byte_decoder[i]));return this.text_decoder.decode(n)}decode_chain(e){const r=[];let n=[];for(const a of e)this.added_tokens.find(i=>i.content===a)!==void 0?(n.length>0&&(r.push(this.convert_tokens_to_string(n)),n=[]),r.push(a)):n.push(a);return n.length>0&&r.push(this.convert_tokens_to_string(n)),r}}class jb extends Ut{constructor(e){super(e),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(e){if(e.length===0)return"";const r=[e[0]];for(let i=1;i<e.length;++i)e[i]!==r.at(-1)&&r.push(e[i]);let a=r.filter(i=>i!==this.pad_token).join("");return this.cleanup&&(a=cl(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class qb extends Ut{constructor(e){super(e),this.decoders=e.decoders.map(r=>Ut.fromConfig(r))}decode_chain(e){return this.decoders.reduce((r,n)=>n.decode_chain(r),e)}}class Kb extends Ut{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map((r,n)=>r.replaceAll(this.suffix,n===e.length-1?"":" "))}}class Yb extends Ut{decode_chain(e){let r="";for(let n=1;n<e.length;n+=2)r+=e[n];return[r]}}class kg extends qt{constructor(e){super(),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement,this.strRep=e.str_rep||this.replacement,this.prepend_scheme=e.prepend_scheme??"always"}pre_tokenize_text(e,{section_index:r=void 0}={}){let n=e.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!n.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&r===0)&&(n=this.strRep+n),[n]}}class Xb extends Ut{constructor(e){super(e),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement}decode_chain(e){const r=[];for(let n=0;n<e.length;++n){let a=e[n].replaceAll(this.replacement," ");this.addPrefixSpace&&n==0&&a.startsWith(" ")&&(a=a.substring(1)),r.push(a)}return r}}class Qb extends Lt{constructor(e){super(e),this.charsmap=e.precompiled_charsmap}normalize(e){return e=e.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),e=e.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),e.includes("～")?e=e.split("～").map(n=>n.normalize("NFKC")).join("～"):e=e.normalize("NFKC"),e}}class Zb extends qt{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>qt.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class Jb extends qt{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class ev extends qt{constructor(e){super()}pre_tokenize_text(e,r){return mb(e)}}class tv extends qt{constructor(e){super(),this.config=e,this.pattern=is(this.config.pattern),this.content=this.config.content}pre_tokenize_text(e,r){return this.pattern===null?[e]:[e.replaceAll(this.pattern,this.config.content)]}}const rv=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function nv(t,e,r,n){for(const a of Object.keys(t)){const i=e-t[a].length,s=r(a),o=new Array(i).fill(s);t[a]=n==="right"?ft(t[a],o):ft(o,t[a])}}function av(t,e){for(const r of Object.keys(t))t[r].length=e}class Ee extends bt{return_token_type_ids=!1;_default_chat_template=`{% for message in messages %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}`;padding_side="right";constructor(e,r){super(),this._tokenizer_config=r,this.normalizer=Lt.fromConfig(e.normalizer),this.pre_tokenizer=qt.fromConfig(e.pre_tokenizer),this.model=za.fromConfig(e.model,r),this.post_processor=Pn.fromConfig(e.post_processor),this.decoder=Ut.fromConfig(e.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const n of e.added_tokens){const a=new _b(n);this.added_tokens.push(a),this.model.tokens_to_ids.set(a.content,a.id),this.model.vocab[a.id]=a.content,a.special&&(this.special_tokens.push(a.content),this.all_special_ids.push(a.id))}if(this.additional_special_tokens=r.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.map(n=>`${n.lstrip?"\\s*":""}(${Np(n.content)})${n.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=r.model_max_length,this.remove_space=r.remove_space,this.clean_up_tokenization_spaces=r.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=r.do_lowercase_and_remove_accent??!1,r.padding_side&&(this.padding_side=r.padding_side),this.legacy=!1,this.chat_template=r.chat_template??null,Array.isArray(this.chat_template)){const n=Object.create(null);for(const{name:a,template:i}of this.chat_template){if(typeof a!="string"||typeof i!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');n[a]=i}this.chat_template=n}this._compiled_template_cache=new Map}getToken(...e){for(const r of e){const n=this._tokenizer_config[r];if(n)if(typeof n=="object"){if(n.__type==="AddedToken")return n.content;throw Error(`Unknown token: ${n}`)}else return n}return null}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",legacy:o=null}={}){const l=await yg(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,legacy:o});return new this(...l)}_call(e,{text_pair:r=null,add_special_tokens:n=!0,padding:a=!1,truncation:i=null,max_length:s=null,return_tensor:o=!0,return_token_type_ids:l=null}={}){const u=Array.isArray(e);let p;if(u){if(e.length===0)throw Error("text array must be non-empty");if(r!==null){if(Array.isArray(r)){if(e.length!==r.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");p=e.map((m,d)=>this._encode_plus(m,{text_pair:r[d],add_special_tokens:n,return_token_type_ids:l}))}else p=e.map(m=>this._encode_plus(m,{add_special_tokens:n,return_token_type_ids:l}))}else{if(e==null)throw Error("text may not be null or undefined");if(Array.isArray(r))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");p=[this._encode_plus(e,{text_pair:r,add_special_tokens:n,return_token_type_ids:l})]}if(s===null?a==="max_length"?s=this.model_max_length:s=Kt(p.map(m=>m.input_ids.length))[0]:i||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."),s=Math.min(s,this.model_max_length??1/0),a||i)for(let m=0;m<p.length;++m)p[m].input_ids.length!==s&&(p[m].input_ids.length>s?i&&av(p[m],s):a&&nv(p[m],s,d=>d==="input_ids"?this.pad_token_id:0,this.padding_side));const h={};if(o){if(!(a&&i)&&p.some(d=>{for(const _ of Object.keys(d))if(d[_].length!==p[0][_]?.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 m=[p.length,p[0].input_ids.length];for(const d of Object.keys(p[0]))h[d]=new ue("int64",BigInt64Array.from(p.flatMap(_=>_[d]).map(BigInt)),m)}else{for(const m of Object.keys(p[0]))h[m]=p.map(d=>d[m]);if(!u)for(const m of Object.keys(h))h[m]=h[m][0]}return h}_encode_text(e){return e===null?null:(this.added_tokens_regex?e.split(this.added_tokens_regex).filter(a=>a):[e]).map((a,i)=>{if(this.added_tokens.find(o=>o.content===a)!==void 0)return a;{if(this.remove_space===!0&&(a=a.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(a=pb(a)),this.normalizer!==null&&(a=this.normalizer(a)),a.length===0)return[];const o=this.pre_tokenizer!==null?this.pre_tokenizer(a,{section_index:i}):[a];return this.model(o)}}).flat()}_encode_plus(e,{text_pair:r=null,add_special_tokens:n=!0,return_token_type_ids:a=null}={}){const{tokens:i,token_type_ids:s}=this._tokenize_helper(e,{pair:r,add_special_tokens:n}),o=this.model.convert_tokens_to_ids(i),l={input_ids:o,attention_mask:new Array(o.length).fill(1)};return(a??this.return_token_type_ids)&&s&&(l.token_type_ids=s),l}_tokenize_helper(e,{pair:r=null,add_special_tokens:n=!1}={}){const a=this._encode_text(e),i=this._encode_text(r);return this.post_processor?this.post_processor(a,i,{add_special_tokens:n}):{tokens:ft(a??[],i??[])}}tokenize(e,{pair:r=null,add_special_tokens:n=!1}={}){return this._tokenize_helper(e,{pair:r,add_special_tokens:n}).tokens}encode(e,{text_pair:r=null,add_special_tokens:n=!0,return_token_type_ids:a=null}={}){return this._encode_plus(e,{text_pair:r,add_special_tokens:n,return_token_type_ids:a}).input_ids}batch_decode(e,r={}){return e instanceof ue&&(e=e.tolist()),e.map(n=>this.decode(n,r))}decode(e,r={}){if(e instanceof ue&&(e=wg(e)),!Array.isArray(e)||e.length===0||!z0(e[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(e,r)}decode_single(e,{skip_special_tokens:r=!1,clean_up_tokenization_spaces:n=null}){let a=this.model.convert_ids_to_tokens(e);r&&(a=a.filter(s=>!this.special_tokens.includes(s)));let i=this.decoder?this.decoder(a):a.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(i=i.replaceAll(this.decoder.end_of_word_suffix," "),r&&(i=i.trim())),(n??this.clean_up_tokenization_spaces)&&(i=cl(i)),i}get default_chat_template(){return this._warned_about_chat_template||(console.warn("No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format. If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information."),this._warned_about_chat_template=!0),this._default_chat_template}apply_chat_template(e,{chat_template:r=null,add_generation_prompt:n=!1,tokenize:a=!0,padding:i=!1,truncation:s=!1,max_length:o=null,return_tensor:l=!0,return_dict:u=!1,tokenizer_kwargs:p={},...h}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const w=this.chat_template??this.default_chat_template;if(r!==null&&Object.hasOwn(w,r))r=w[r];else if(r===null&&"default"in w)r=w.default;else if(r===null)throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(w).sort()}.`)}else r??=this.chat_template??this.default_chat_template;if(typeof r!="string")throw Error(`chat_template must be a string, but got ${typeof r}`);let m=this._compiled_template_cache.get(r);m===void 0&&(m=new ub(r),this._compiled_template_cache.set(r,m));const d=Object.create(null);for(const w of rv){const v=this.getToken(w);v&&(d[w]=v)}const _=m.render({messages:e,add_generation_prompt:n,...d,...h});if(a){const w=this._call(_,{add_special_tokens:!1,padding:i,truncation:s,max_length:o,return_tensor:l,...p});return u?w:w.input_ids}return _}}class iv extends Ee{return_token_type_ids=!0}class sv extends Ee{return_token_type_ids=!0}class ov extends Ee{return_token_type_ids=!0}class lv extends Ee{return_token_type_ids=!0}class uv extends Ee{return_token_type_ids=!0}class dv extends Ee{return_token_type_ids=!0}class cv extends Ee{return_token_type_ids=!0}class pv extends Ee{return_token_type_ids=!0}class hv extends Ee{return_token_type_ids=!0}class fv extends Ee{}class mv extends Ee{}class gv extends Ee{return_token_type_ids=!0;constructor(e,r){super(e,r),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class _v extends Ee{return_token_type_ids=!0}class yv extends Ee{}class Eg extends Ee{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}'}class wv extends Ee{}class Cg extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return pl(this,e,r,n)}}class bv extends Cg{}class vv extends Ee{}class $v extends Eg{constructor(e,r){const n=".,!?…。，、।۔،",a=e.pre_tokenizer?.pretokenizers[0]?.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const Ti="▁";class Tg extends Ee{_default_chat_template=`{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>
' + system_message + '
<</SYS>>

' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>
' + content.strip() + '
<</SYS>>

' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}`;DEFAULT_SYSTEM_PROMPT=`You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.`;padding_side="left";constructor(e,r){super(e,r),this.use_default_system_prompt=r.use_default_system_prompt??!1,this.legacy=r.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new kg({replacement:Ti,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(e){if(e===null)return null;if(this.legacy||e.length===0)return super._encode_text(e);let r=super._encode_text(Ti+e.replaceAll(Ti," "));return r.length>1&&r[0]===Ti&&this.special_tokens.includes(r[1])&&(r=r.slice(1)),r}get default_chat_template(){return super.default_chat_template.replaceAll("USE_DEFAULT_PROMPT",this.use_default_system_prompt?"true":"false").replaceAll("DEFAULT_SYSTEM_MESSAGE",this.DEFAULT_SYSTEM_PROMPT.replaceAll(`
`,"\\n").replaceAll("'","\\'"))}}class xv extends Tg{}class Sv extends Ee{}class kv extends Ee{}class Ev extends Ee{}class Cv extends Ee{}class Tv extends Ee{}class Av extends Ee{}class Iv extends Ee{_default_chat_template=`{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
' + message['content'] | trim + '<end_of_turn>
' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
'}}{% endif %}`}class Mv extends Ee{}function pl(t,e,r,n){if(!("language_codes"in t)||!Array.isArray(t.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in t)||!(t.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in t)||typeof t.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const a=n.src_lang,i=n.tgt_lang;if(!t.language_codes.includes(i))throw new Error(`Target language code "${i}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);if(a!==void 0){if(!t.language_codes.includes(a))throw new Error(`Source language code "${a}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);for(const s of t.post_processor.config.single)if("SpecialToken"in s&&t.languageRegex.test(s.SpecialToken.id)){s.SpecialToken.id=t.lang_to_token(a);break}}return n.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(i)])[0],t._call(e,r)}class Ov extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return pl(this,e,r,n)}}class zv extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)).map(n=>n.slice(2,-2)),this.lang_to_token=n=>`__${n}__`}_build_translation_inputs(e,r,n){return pl(this,e,r,n)}}class Pv extends Ee{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}';get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(e,{return_timestamps:r=!1,return_language:n=!1,time_precision:a=null,force_full_sequences:i=!0}={}){if(a===null)throw Error("Must specify time_precision");let s=null;const o=r==="word";function l(){return{language:s,timestamp:[null,null],text:""}}const u=[];let p=l(),h=0;const m=this.timestamp_begin;let d=[],_=[],w=!1,v=null;const S=new Set(this.all_special_ids);for(const T of e){const A=T.tokens,P=o?T.token_timestamps:null;let B=null,D=m;if("stride"in T){const[ie,te,de]=T.stride;if(h-=te,v=ie-de,te&&(D=te/a+m),de)for(let se=A.length-1;se>=0;--se){const M=Number(A[se]);if(M>=m){if(B!==null&&(M-m)*a<v)break;B=M}}}let q=[],H=[];for(let ie=0;ie<A.length;++ie){const te=Number(A[ie]);if(S.has(te)){const de=this.decode([te]),se=Ni.get(de.slice(2,-2));if(se!==void 0){if(s!==null&&se!==s&&!r){d.push(q);const M=this.findLongestCommonSequence(d)[0],R=this.decode(M);p.text=R,u.push(p),d=[],q=[],p=l()}s=p.language=se}}else if(te>=m){const de=(te-m)*a+h,se=Mi(de,2);if(B!==null&&te>=B)w=!0;else if(w||d.length>0&&te<D)w=!1;else if(p.timestamp[0]===null)p.timestamp[0]=se;else if(se!==p.timestamp[0]){p.timestamp[1]=se,d.push(q),o&&_.push(H);const[M,R]=this.findLongestCommonSequence(d,_),G=this.decode(M);p.text=G,o&&(p.words=this.collateWordTimestamps(M,R,s)),u.push(p),d=[],q=[],_=[],H=[],p=l()}}else if(q.push(te),o){let de=Mi(P[ie]+h,2),se;ie+1<P.length?se=Mi(P[ie+1]+h,2):se=null,H.push([de,se])}}if("stride"in T){const[ie,te,de]=T.stride;h+=ie-de}q.length>0?(d.push(q),o&&_.push(H)):d.every(ie=>ie.length===0)&&(p=l(),d=[],q=[],_=[],H=[])}if(d.length>0){if(i&&r)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[T,A]=this.findLongestCommonSequence(d,_),P=this.decode(T);p.text=P,o&&(p.words=this.collateWordTimestamps(T,A,s)),u.push(p)}let $=Object.create(null);const E=u.map(T=>T.text).join("");if(r||n){for(let T=0;T<u.length;++T){const A=u[T];r||delete A.timestamp,n||delete A.language}if(o){const T=[];for(const A of u)for(const P of A.words)T.push(P);$={chunks:T}}else $={chunks:u}}return[E,$]}findLongestCommonSequence(e,r=null){let n=e[0],a=n.length,i=[];const s=Array.isArray(r)&&r.length>0;let o=s?[]:null,l=s?r[0]:null;for(let u=1;u<e.length;++u){const p=e[u];let h=0,m=[a,a,0,0];const d=p.length;for(let T=1;T<a+d;++T){const A=T/1e4,P=Math.max(0,a-T),B=Math.min(a,a+d-T),D=n.slice(P,B),q=Math.max(0,T-a),H=Math.min(d,T),ie=p.slice(q,H);if(D.length!==ie.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const te=D.filter((se,M)=>se===ie[M]).length,de=te/T+A;te>1&&de>h&&(h=de,m=[P,B,q,H])}const[_,w,v,S]=m,$=Math.floor((w+_)/2),E=Math.floor((S+v)/2);i.push(...n.slice(0,$)),n=p.slice(E),a=n.length,s&&(o.push(...l.slice(0,$)),l=r[u].slice(E))}return i.push(...n),s?(o.push(...l),[i,o]):[i,[]]}collateWordTimestamps(e,r,n){const[a,i,s]=this.combineTokensIntoWords(e,n),o=[];for(let l=0;l<a.length;++l){const u=s[l];o.push({text:a[l],timestamp:[r[u.at(0)][0],r[u.at(-1)][1]]})}return o}combineTokensIntoWords(e,r,n=`"'“¡¿([{-`,a=`"'.。,，!！?？:：”)]}、`){r=r??"english";let i,s,o;return["chinese","japanese","thai","lao","myanmar"].includes(r)?[i,s,o]=this.splitTokensOnUnicode(e):[i,s,o]=this.splitTokensOnSpaces(e),this.mergePunctuations(i,s,o,n,a)}decode(e,r){let n;return r?.decode_with_timestamps?(e instanceof ue&&(e=wg(e)),n=this.decodeWithTimestamps(e,r)):n=super.decode(e,r),n}decodeWithTimestamps(e,r){const n=r?.time_precision??.02,a=Array.from(this.all_special_ids).at(-1)+1;let i=[[]];for(let s of e)if(s=Number(s),s>=a){const o=((s-a)*n).toFixed(2);i.push(`<|${o}|>`),i.push([])}else i[i.length-1].push(s);return i=i.map(s=>typeof s=="string"?s:super.decode(s,r)),i.join("")}splitTokensOnUnicode(e){const r=this.decode(e,{decode_with_timestamps:!0}),n="�",a=[],i=[],s=[];let o=[],l=[],u=0;for(let p=0;p<e.length;++p){const h=e[p];o.push(h),l.push(p);const m=this.decode(o,{decode_with_timestamps:!0});(!m.includes(n)||r[u+m.indexOf(n)]===n)&&(a.push(m),i.push(o),s.push(l),o=[],l=[],u+=m.length)}return[a,i,s]}splitTokensOnSpaces(e){const[r,n,a]=this.splitTokensOnUnicode(e),i=[],s=[],o=[],l=new RegExp(`^[${Ea}]$`,"gu");for(let u=0;u<r.length;++u){const p=r[u],h=n[u],m=a[u],d=h[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),_=p.startsWith(" "),w=p.trim(),v=l.test(w);if(d||_||v||i.length===0)i.push(p),s.push(h),o.push(m);else{const S=i.length-1;i[S]+=p,s[S].push(...h),o[S].push(...m)}}return[i,s,o]}mergePunctuations(e,r,n,a,i){const s=structuredClone(e),o=structuredClone(r),l=structuredClone(n);let u=s.length-2,p=s.length-1;for(;u>=0;)s[u].startsWith(" ")&&a.includes(s[u].trim())?(s[p]=s[u]+s[p],o[p]=ft(o[u],o[p]),l[p]=ft(l[u],l[p]),s[u]="",o[u]=[],l[u]=[]):p=u,--u;for(u=0,p=1;p<s.length;)!s[u].endsWith(" ")&&i.includes(s[p])?(s[u]+=s[p],o[u]=ft(o[u],o[p]),l[u]=ft(l[u],l[p]),s[p]="",o[p]=[],l[p]=[]):u=p,++p;return[s.filter(h=>h),o.filter(h=>h.length>0),l.filter(h=>h.length>0)]}get_decoder_prompt_ids({language:e=null,task:r=null,no_timestamps:n=!0}={}){const a=[];if(e){const i=_g(e),s=this.model.tokens_to_ids.get(`<|${i}|>`);if(s===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at ${po}.`);a.push(s)}else a.push(null);if(r){if(r=r.toLowerCase(),r!=="transcribe"&&r!=="translate")throw new Error(`Task "${r}" is not supported. Must be one of: ["transcribe", "translate"]`);const i=this.model.tokens_to_ids.get(`<|${r}|>`);if(i===void 0)throw new Error(`Unable to find task "${r}" in model vocabulary. Please report this issue at ${po}.`);a.push(i)}else a.push(null);if(n){const i=this.model.tokens_to_ids.get("<|notimestamps|>");if(i===void 0)throw new Error(`Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at ${po}.`);a.push(i)}return a.map((i,s)=>[s+1,i]).filter(i=>i[1]!==null)}}class Rv extends Ee{}class Bv extends Ee{}class Dv extends Ee{}class Nv extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(n=>this.languageRegex.test(n)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[r,...n]=e.trim().split(this.languageRegex);if(n.length===0)return super._encode_text(r);if(n.length===2){const[a,i]=n;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),ft([a],super._encode_text(i))}}}class Fv extends Ee{}class Ag extends Ee{_default_chat_template="{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}"}class Lv extends Ag{}class Uv extends Ee{}class Wv extends Ee{}class Vv extends Ee{constructor(e,r){super(e,r),this.decoder=new Yb({})}}class Gv extends Ee{}class ht{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:yv,DistilBertTokenizer:fv,CamembertTokenizer:mv,DebertaTokenizer:uv,DebertaV2Tokenizer:dv,BertTokenizer:iv,HerbertTokenizer:cv,ConvBertTokenizer:pv,RoFormerTokenizer:hv,XLMTokenizer:gv,ElectraTokenizer:_v,MobileBertTokenizer:ov,SqueezeBertTokenizer:lv,AlbertTokenizer:sv,GPT2Tokenizer:Eg,BartTokenizer:wv,MBartTokenizer:Cg,MBart50Tokenizer:bv,RobertaTokenizer:vv,WhisperTokenizer:Pv,CodeGenTokenizer:Rv,CLIPTokenizer:Bv,SiglipTokenizer:Dv,MarianTokenizer:Nv,BloomTokenizer:$v,NllbTokenizer:Ov,M2M100Tokenizer:zv,LlamaTokenizer:Tg,CodeLlamaTokenizer:xv,XLMRobertaTokenizer:Sv,MPNetTokenizer:kv,FalconTokenizer:Ev,GPTNeoXTokenizer:Cv,EsmTokenizer:Tv,Wav2Vec2CTCTokenizer:Fv,BlenderbotTokenizer:Ag,BlenderbotSmallTokenizer:Lv,SpeechT5Tokenizer:Uv,NougatTokenizer:Wv,VitsTokenizer:Vv,Qwen2Tokenizer:Av,GemmaTokenizer:Iv,Grok1Tokenizer:Mv,CohereTokenizer:Gv,PreTrainedTokenizer:Ee};static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",legacy:o=null}={}){const[l,u]=await yg(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,legacy:o}),p=u.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let h=this.TOKENIZER_CLASS_MAPPING[p];return h||(console.warn(`Unknown tokenizer class "${p}", attempting to construct from base class.`),h=Ee),new h(l,u)}}async function Hv(t,e){return await Br(t,"config.json",!0,e)}function fa(t){const e={};let r={};switch(t.model_type){case"llava":case"paligemma":r=fa(t.text_config);break;case"moondream1":r=fa(t.phi_config);break;case"musicgen":r=fa(t.decoder);break;case"gpt2":case"gptj":case"codegen":case"gpt_bigcode":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"phi":case"phi3":case"falcon":e.num_heads="num_attention_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size";break;case"llama":case"cohere":case"mistral":case"starcoder2":case"qwen2":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size",e.num_attention_heads="num_attention_heads";break;case"gemma":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="head_dim";break;case"openelm":e.num_heads="num_kv_heads",e.num_layers="num_transformer_layers",e.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":e.num_heads="num_heads",e.num_layers="num_layers",e.hidden_size="hidden_size";break;case"bloom":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="hidden_size";break;case"mpt":e.num_heads="n_heads",e.num_layers="n_layers",e.hidden_size="d_model";break;case"t5":case"mt5":case"longt5":e.num_decoder_layers="num_decoder_layers",e.num_decoder_heads="num_heads",e.decoder_dim_kv="d_kv",e.num_encoder_layers="num_layers",e.num_encoder_heads="num_heads",e.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="d_model",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="d_model";break;case"speecht5":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="hidden_size",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="hidden_size";break;case"trocr":e.num_encoder_layers=e.num_decoder_layers="decoder_layers",e.num_encoder_heads=e.num_decoder_heads="decoder_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="d_model";break;case"musicgen_decoder":e.num_encoder_layers=e.num_decoder_layers="num_hidden_layers",e.num_encoder_heads=e.num_decoder_heads="num_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const a=fa(t.decoder),i="num_decoder_layers"in a,s=Dr(t,["model_type","is_encoder_decoder"]);return i?(s.num_decoder_layers=a.num_decoder_layers,s.num_decoder_heads=a.num_decoder_heads,s.decoder_hidden_size=a.decoder_hidden_size,s.num_encoder_layers=a.num_encoder_layers,s.num_encoder_heads=a.num_encoder_heads,s.encoder_hidden_size=a.encoder_hidden_size):(s.num_layers=a.num_layers,s.num_heads=a.num_heads,s.hidden_size=a.hidden_size),s}const n={...r,...Dr(t,["model_type","multi_query","is_encoder_decoder"])};for(const a in e)n[a]=t[e[a]];return n}function Ig(t,{prefix:e="past_key_values",encoder_add_pkv:r=!0}={}){const n={},a=t.normalized_config,i=1;if(a.is_encoder_decoder&&r){const s=a.encoder_dim_kv??a.encoder_hidden_size/a.num_encoder_heads,o=a.decoder_dim_kv??a.decoder_hidden_size/a.num_decoder_heads,l=[i,a.num_encoder_heads,0,s],u=[i,a.num_decoder_heads,0,o];for(let p=0;p<a.num_decoder_layers;++p)n[`${e}.${p}.encoder.key`]=l,n[`${e}.${p}.encoder.value`]=l,n[`${e}.${p}.decoder.key`]=u,n[`${e}.${p}.decoder.value`]=u}else{const s=a.num_heads,o=a.num_layers,l=a.dim_kv??a.hidden_size/(a.num_attention_heads??s);if(a.model_type==="falcon"){const u=[i*s,0,l];for(let p=0;p<o;++p)n[`${e}.${p}.key`]=u,n[`${e}.${p}.value`]=u}else if(a.multi_query){const u=[i*s,0,2*l];for(let p=0;p<o;++p)n[`${e}.${p}.key_value`]=u}else if(a.model_type==="bloom"){const u=[i*s,l,0],p=[i*s,0,l];for(let h=0;h<o;++h)n[`${e}.${h}.key`]=u,n[`${e}.${h}.value`]=p}else if(a.model_type==="openelm")for(let u=0;u<o;++u){const p=[i,s[u],0,l];n[`${e}.${u}.key`]=p,n[`${e}.${u}.value`]=p}else{const u=[i,s,0,l];for(let p=0;p<o;++p)n[`${e}.${p}.key`]=u,n[`${e}.${p}.value`]=u}}return n}class Ji{max_position_embeddings;constructor(e){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e),this.normalized_config=fa(this)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main"}={}){n&&!(n instanceof Ji)&&(n=new Ji(n));const o=n??await Hv(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s});return new this(o)}}class Mg{static async from_pretrained(...e){return Ji.from_pretrained(...e)}}const Ai=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),jv=function(){let t;return async function(){if(t===void 0)if(an.IS_NODE_ENV)t=!0;else if(!an.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}}(),Rt=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),qv=Object.freeze({[Ai.cpu]:Rt.q8,[Ai.gpu]:Rt.fp32,[Ai.wasm]:Rt.q8,[Ai.webgpu]:Rt.fp32}),xp=Object.freeze({[Rt.fp32]:"",[Rt.fp16]:"_fp16",[Rt.int8]:"_int8",[Rt.uint8]:"_uint8",[Rt.q8]:"_quantized",[Rt.q4]:"_q4",[Rt.q4f16]:"_q4f16",[Rt.bnb4]:"_bnb4"});class yr extends bt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class hl extends bt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class Ro extends bt{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,r){let n=r;for(const a of this.processors)n=a(e,n);return n}[Symbol.iterator](){return this.processors.values()}}class Kv extends yr{constructor(e){super(),this.bos_token_id=e}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===1){const a=r[n];a.data.fill(-1/0),a.data[this.bos_token_id]=0}return r}}class Yv extends yr{constructor(e,r){super(),this.max_length=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===this.max_length-1){const a=r[n];a.data.fill(-1/0);for(const i of this.eos_token_id)a.data[i]=0}return r}}class Xv extends yr{constructor(e,r){super(),this.begin_suppress_tokens=e,this.begin_index=r}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length===this.begin_index){const a=r[n];for(const i of this.begin_suppress_tokens)a.data[i]=-1/0}return r}}class Qv extends yr{constructor(e,r){super(),this.eos_token_id=Array.isArray(e.eos_token_id)?e.eos_token_id[0]:e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=r.length,r.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,r){for(let n=0;n<e.length;++n){const i=r[n].data;if(i[this.no_timestamps_token_id]=-1/0,e[n].length===this.begin_index-1){i.fill(-1/0),i[this.timestamp_begin]=0;continue}const s=e[n].slice(this.begin_index),o=s.length>=1&&s[s.length-1]>=this.timestamp_begin,l=s.length<2||s[s.length-2]>=this.timestamp_begin;if(o&&(l?i.subarray(this.timestamp_begin).fill(-1/0):i.subarray(0,this.eos_token_id).fill(-1/0)),e[n].length===this.begin_index&&this.max_initial_timestamp_index!==null){const m=this.timestamp_begin+this.max_initial_timestamp_index;i.subarray(m+1).fill(-1/0)}const u=U0(i),p=Math.log(u.subarray(this.timestamp_begin).map(Math.exp).reduce((m,d)=>m+d)),h=Kt(u.subarray(0,this.timestamp_begin))[0];p>h&&i.subarray(0,this.timestamp_begin).fill(-1/0)}return r}}class Zv extends yr{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const r=e.length,n=[];for(let i=0;i<r+1-this.no_repeat_ngram_size;++i){const s=[];for(let o=0;o<this.no_repeat_ngram_size;++o)s.push(e[i+o]);n.push(s)}const a=new Map;for(const i of n){const s=i.slice(0,i.length-1),o=JSON.stringify(s),l=a.get(o)??[];l.push(i[i.length-1]),a.set(o,l)}return a}getGeneratedNgrams(e,r){const n=r.slice(r.length+1-this.no_repeat_ngram_size,r.length);return e.get(JSON.stringify(n))??[]}calcBannedNgramTokens(e){const r=[];if(e.length+1<this.no_repeat_ngram_size)return r;{const n=this.getNgrams(e);return this.getGeneratedNgrams(n,e)}}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n],i=this.calcBannedNgramTokens(e[n]);for(const s of i)a.data[s]=-1/0}return r}}class Jv extends yr{constructor(e){super(),this.penalty=e}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n];for(const i of e[n])a.data[i]<0?a.data[i]*=this.penalty:a.data[i]/=this.penalty}return r}}class e2 extends yr{constructor(e,r){super(),this.min_length=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length<this.min_length){const a=r[n];for(const i of this.eos_token_id)a.data[i]=-1/0}return r}}class t2 extends yr{constructor(e,r,n){super(),this.prompt_length_to_skip=e,this.min_new_tokens=r,this.eos_token_id=Array.isArray(n)?n:[n]}_call(e,r){for(let n=0;n<e.length;++n)if(e[n].length-this.prompt_length_to_skip<this.min_new_tokens){const i=r[n];for(const s of this.eos_token_id)i[s]=-1/0}return r}}class r2 extends yr{constructor(e,r){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(r)?r:[r]}_call(e,r){for(let n=0;n<e.length;++n){const a=r[n];for(const i of this.bad_words_ids){let s=!0;for(let o=1;o<=i.length-1&&i.length<e[o].length;++o)if(i.at(-o-1)!==e[o].at(-o)){s=!1;break}s&&(a[i.at(-1)]=-1/0)}}return r}}class n2 extends yr{constructor(e){if(super(),e<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,r){if(r.dims[0]!==2*e.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${r.dims[0]} for the logits and ${e.length} for the input ids.`);const n=e.length,a=r.slice([0,n],null),i=r.slice([n,r.dims[0]],null);for(let s=0;s<i.data.length;++s)i.data[s]+=(a.data[s]-i.data[s])*this.guidance_scale;return i}}class a2 extends hl{constructor(e){super(),this.temperature=e}_call(e,r){const n=r.data;for(let a=0;a<n.length;++a)n[a]/=this.temperature;return r}}class i2 extends hl{constructor(e,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),e<0||e>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${e}`);if(!Number.isInteger(n)||n<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${n}`);this.top_p=e,this.filter_value=r,this.min_tokens_to_keep=n}}class s2 extends hl{constructor(e,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),!Number.isInteger(e)||e<0)throw new Error(`\`top_k\` must be a positive integer, but is ${e}`);this.top_k=Math.max(e,n),this.filter_value=r}}class Og{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(e){Object.assign(this,Dr(e,Object.getOwnPropertyNames(this)))}}class fl extends bt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class ml extends bt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof ml?e=e.criteria:e instanceof fl&&(e=[e]),this.criteria.push(...e)}_call(e,r){const n=new Array(e.length).fill(!1);for(const a of this.criteria){const i=a(e,r);for(let s=0;s<n.length;++s)n[s]||=i[s]}return n}[Symbol.iterator](){return this.criteria.values()}}class o2 extends fl{constructor(e,r=null){super(),this.max_length=e,this.max_position_embeddings=r}_call(e){return e.map(r=>r.length>=this.max_length)}}class l2 extends fl{constructor(e){super(),Array.isArray(e)||(e=[e]),this.eos_token_id=e}_call(e,r){return e.map(n=>{const a=n.at(-1);return this.eos_token_id.some(i=>a==i)})}}class ss extends bt{constructor(e){super(),this.generation_config=e}async _call(e){return this.sample(e)}async sample(e){throw Error("sample should be implemented in subclasses.")}getLogits(e,r){let n=e.dims.at(-1),a=e.data;if(r===-1)a=a.slice(-n);else{let i=r*n;a=a.slice(i,i+n)}return a}randomSelect(e){let r=0;for(let a=0;a<e.length;++a)r+=e[a];let n=Math.random()*r;for(let a=0;a<e.length;++a)if(n-=e[a],n<=0)return a;return 0}static getSampler(e){if(e.do_sample)return new d2(e);if(e.num_beams>1)return new c2(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new u2(e)}}class u2 extends ss{async sample(e){const r=Kt(e.data)[1];return[[BigInt(r),0]]}}class d2 extends ss{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await Dn(e,r),i=wt(n.data);return Array.from({length:this.generation_config.num_beams},()=>{const s=this.randomSelect(i);return[a.data[s],Math.log(i[s])]})}}class c2 extends ss{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await Dn(e,r),i=wt(n.data);return Array.from({length:this.generation_config.num_beams},(s,o)=>[a.data[o],Math.log(i[o])])}}class p2 extends Og{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1}const ve={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},es=new Map,zg=new Map,ba=new Map;async function h2(t,e,r){let n=r.device;n&&typeof n!="string"&&(n.hasOwnProperty(e)?n=n[e]:(console.warn(`Device not specified for ${e}. Using the default device.`),n=null));const a=Sw(n);let i=r.dtype;if(typeof i!="string"&&(i&&i.hasOwnProperty(e)?i=i[e]:(i=qv[a[0]],console.warn(`Dtype not specified for ${e}. Using the default dtype: ${i}.`))),xp.hasOwnProperty(i)){if(i===Rt.fp16&&!await jv())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${i}. Should be one of: ${Object.keys(Rt).join(", ")}`);const s=xp[i],o=`${r.subfolder??""}/${e}${s}.onnx`,l={...r.session_options};l.executionProviders??=a;const u=Ii(t,o,!0,r);let p=[];if(r.use_external_data_format){if(an.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const m=`${e}${s}.onnx_data`,d=`${r.subfolder??""}/${m}`;p.push(new Promise(async(_,w)=>{const v=await Ii(t,d,!0,r);_({path:m,data:v})}))}else l.externalData!==void 0&&(p=l.externalData.map(async m=>{if(typeof m.data=="string"){const d=await Ii(t,m.data,!0,r);return{...m,data:d}}return m}));if(p.length>0&&(l.externalData=await Promise.all(p)),n==="webgpu"){const m=Ig(r.config,{prefix:"present"});if(Object.keys(m).length>0){const d={};for(const _ in m)d[_]="gpu-buffer";l.preferredOutputLocation=d}}return{buffer:await u,session_options:l}}async function Xr(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async s=>h2(t,e[s],r))),i={};for(let s=0;s<n.length;++s){const{buffer:o,session_options:l}=a[s],u=await hg(o,l);i[n[s]]=u}return i}function f2(t,e){const r=Object.create(null),n=[];for(const s of t.inputNames){const o=e[s];if(!(o instanceof ue)){n.push(s);continue}r[s]=kw()?o.clone():o}if(n.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${n.join(", ")}.`);const a=Object.keys(e).length,i=t.inputNames.length;if(a>i){let s=Object.keys(e).filter(o=>!t.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${i}). The following inputs will be ignored: "${s.join(", ")}".`)}return r}async function Fr(t,e){const r=f2(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([i,s])=>[i,s.ort_tensor]));let a=await t.run(n);return a=Pg(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Pg(t){for(let e in t)fg(t[e])?t[e]=new ue(t[e]):typeof t[e]=="object"&&Pg(t[e]);return t}function Rg(t){if(t instanceof ue)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new ue("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new ue("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function Bg(t){return new ue("bool",[t],[1])}async function Sp(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const l=Dr(e,t.sessions.model.inputNames);r=(await Ca(t,l)).last_hidden_state}const{input_ids:a,decoder_input_ids:i,...s}=e;return s.input_ids=i,s.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(s.encoder_attention_mask=e.attention_mask),await gl(t,s,!0)}async function Ca(t,e){const r=t.sessions.model,n=Object.create(null);for(const a of r.inputNames)n[a]=e[a];return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new ue("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await Fr(r,n)}async function gl(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...i}=e;n.inputNames.includes("use_cache_branch")&&(i.use_cache_branch=Bg(!!a)),n.inputNames.includes("position_ids")&&i.attention_mask&&!i.position_ids&&(i.position_ids=g2(i,a)),t.addPastKeyValues(i,a);const s=Dr(i,n.inputNames);return await Fr(n,s)}async function m2(t,{input_ids:e=null,attention_mask:r=null,pixel_values:n=null,position_ids:a=null,inputs_embeds:i=null,past_key_values:s=null,generation_config:o=null,logits_processor:l=null,...u}){if(!i){if(i=await t.encode_text({input_ids:e}),n&&e.dims[1]!==1){const h=await t.encode_image({pixel_values:n});({inputs_embeds:i,attention_mask:r}=t._merge_input_ids_with_image_features({image_features:h,inputs_embeds:i,input_ids:e,attention_mask:r}))}else if(s&&n&&e.dims[1]===1){const h=e.dims[1],m=Object.values(s)[0].dims.at(-2);r=gr([Ma([e.dims[0],m]),r.slice(null,[r.dims[1]-h,r.dims[1]])],1)}}return await gl(t,{inputs_embeds:i,past_key_values:s,attention_mask:r,position_ids:a,generation_config:o,logits_processor:l},!0)}function g2(t,e=null){const{input_ids:r,inputs_embeds:n,attention_mask:a}=t,[i,s]=a.dims,o=new BigInt64Array(a.data.length);for(let u=0;u<i;++u){const p=u*s;let h=BigInt(0);for(let m=0;m<s;++m){const d=p+m;a.data[d]===0n?o[d]=BigInt(1):(o[d]=h,h+=a.data[d])}}let l=new ue("int64",o,a.dims);if(e){const u=-(r??n).dims.at(1);l=l.slice(null,[u,null])}return l}function kp(t,e,r,n){if(r.past_key_values){const a=Object.values(r.past_key_values)[0].dims.at(-2),{input_ids:i,attention_mask:s}=r;if(!(s&&s.dims[1]>i.dims[1])){if(a<i.dims[1])r.input_ids=i.slice(null,[a,null]);else if(t.config.image_token_index!=null&&i.data.some(o=>o==t.config.image_token_index)){const o=t.config.num_image_tokens;if(!o)throw new Error("`num_image_tokens` is missing in the model configuration.");const l=i.dims[1]-(a-o);r.input_ids=i.slice(null,[-l,null]),r.attention_mask=Ma([1,a+l])}}}return r}function _2(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(s=>[s.at(-1)])),a.decoder_input_ids=Rg(e),a}class Q extends bt{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(e,r){super(),this.config=e,this.sessions=r;const n=ba.get(this.constructor),a=es.get(n);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,a===ve.DecoderOnly?(this.can_generate=!0,this._forward=gl,this._prepare_inputs_for_generation=kp):a===ve.Seq2Seq||a===ve.Vision2Seq||a===ve.Musicgen?(this.can_generate=!0,this._forward=Sp,this._prepare_inputs_for_generation=_2):a===ve.EncoderDecoder?this._forward=Sp:a===ve.ImageTextToText?(this.can_generate=!0,this._forward=m2,this._prepare_inputs_for_generation=kp):this._forward=Ca,this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const e=[];for(const r of Object.values(this.sessions))r?.handler?.dispose&&e.push(r.handler.dispose());return await Promise.all(e)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",model_file_name:o=null,subfolder:l="onnx",device:u=null,dtype:p=null,use_external_data_format:h=null,session_options:m={}}={}){let d={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,model_file_name:o,subfolder:l,device:u,dtype:p,use_external_data_format:h,session_options:m};const _=ba.get(this),w=es.get(_);d.config=await Mg.from_pretrained(e,d);let v;return w===ve.DecoderOnly?v=await Promise.all([Xr(e,{model:d.model_file_name??"model"},d),Br(e,"generation_config.json",!1,d)]):w===ve.Seq2Seq||w===ve.Vision2Seq?v=await Promise.all([Xr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},d),Br(e,"generation_config.json",!1,d)]):w===ve.MaskGeneration?v=await Promise.all([Xr(e,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},d)]):w===ve.EncoderDecoder?v=await Promise.all([Xr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},d)]):w===ve.ImageTextToText?v=await Promise.all([Xr(e,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},d),Br(e,"generation_config.json",!1,d)]):w===ve.Musicgen?v=await Promise.all([Xr(e,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},d),Br(e,"generation_config.json",!1,d)]):(w!==ve.EncoderOnly&&console.warn(`Model type for '${_??n?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),v=await Promise.all([Xr(e,{model:d.model_file_name??"model"},d)])),new this(d.config,...v)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_warper(e){const r=new Ro;return e.temperature!==null&&e.temperature!==1&&r.push(new a2(e.temperature)),e.top_k!==null&&e.top_k!==0&&r.push(new s2(e.top_k)),e.top_p!==null&&e.top_p<1&&r.push(new i2(e.top_p)),r}_get_logits_processor(e,r,n=null){const a=new Ro;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&a.push(new Jv(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&a.push(new Zv(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&a.push(new r2(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&a.push(new e2(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&a.push(new t2(r,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&a.push(new Kv(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&a.push(new Yv(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){const i=r>1||e.forced_bos_token_id===null?r:r+1;a.push(new Xv(e.begin_suppress_tokens,i))}return e.guidance_scale!==null&&e.guidance_scale>1&&a.push(new n2(e.guidance_scale)),n!==null&&a.extend(n),a}_prepare_generation_config(e,r,n=Og){const a={...this.config};for(const s of["decoder","generator","text_config"])s in a&&Object.assign(a,a[s]);const i=new n(a);return"generation_config"in this&&Object.assign(i,this.generation_config),e&&Object.assign(i,e),r&&Object.assign(i,Dr(r,Object.getOwnPropertyNames(i))),i}_get_stopping_criteria(e,r=null){const n=new ml;return e.max_length!==null&&n.push(new o2(e.max_length,this.config.max_position_embeddings??null)),e.eos_token_id!==null&&n.push(new l2(e.eos_token_id)),r&&n.extend(r),n}_validate_model_class(){if(!this.can_generate){const e=[Sl,kl,xl,$l],r=ba.get(this.constructor),n=new Set,a=this.config.model_type;for(const s of e){const o=s.get(a);o&&n.add(o[0])}let i=`The current model class (${r}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw n.size>0&&(i+=` Please use the following class instead: ${[...n].join(", ")}`),Error(i)}}prepare_inputs_for_generation(...e){return this._prepare_inputs_for_generation(this,...e)}_update_model_kwargs_for_generation({generated_input_ids:e,outputs:r,model_inputs:n,is_encoder_decoder:a}){return n.past_key_values=this.getPastKeyValues(r,n.past_key_values),n.input_ids=new ue("int64",e.flat(),[e.length,1]),a||(n.attention_mask=gr([n.attention_mask,Ma([n.attention_mask.dims[0],1])],1)),n.position_ids=null,n}_prepare_model_inputs({inputs:e,bos_token_id:r,model_kwargs:n}){const a=Dr(n,this.forward_params),i=this.main_input_name;if(i in a){if(e)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else a[i]=e;return{inputs_tensor:a[i],model_inputs:a,model_input_name:i}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:e,model_inputs:r,model_input_name:n,generation_config:a}){const i=Dr(r,this.sessions.model.inputNames);let{last_hidden_state:s}=await Ca(this,i);return a.guidance_scale!==null&&a.guidance_scale>1&&(s=gr([s,Pw(s,0)],0),"attention_mask"in r&&(r.attention_mask=gr([r.attention_mask,Dw(r.attention_mask)],0))),r.encoder_outputs=s,r}_prepare_decoder_input_ids_for_generation({batch_size:e,model_input_name:r,model_kwargs:n,decoder_start_token_id:a,bos_token_id:i,generation_config:s}){let{decoder_input_ids:o,...l}=n;if(!o)if(a??=i,this.config.model_type==="musicgen")o=Array.from({length:e*this.config.decoder.num_codebooks},()=>[a]);else if(Array.isArray(a)){if(a.length!==e)throw new Error(`\`decoder_start_token_id\` expcted to have length ${e} but got ${a.length}`);o=a}else o=Array.from({length:e},()=>[a]);return o=Rg(o),n.decoder_attention_mask=Rw(o),{input_ids:o,model_inputs:l}}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,streamer:i=null,...s}){this._validate_model_class(),r=this._prepare_generation_config(r,s);let{inputs_tensor:o,model_inputs:l,model_input_name:u}=this._prepare_model_inputs({inputs:e,model_kwargs:s});const p=this.config.is_encoder_decoder;p&&("encoder_outputs"in l||(l=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:o,model_inputs:l,model_input_name:u,generation_config:r})));let h;p?{input_ids:h,model_inputs:l}=this._prepare_decoder_input_ids_for_generation({batch_size:l[u].dims.at(0),model_input_name:u,model_kwargs:l,decoder_start_token_id:r.decoder_start_token_id,bos_token_id:r.bos_token_id,generation_config:r}):h=l[u];let m=h.dims.at(-1);r.max_new_tokens!==null&&(r.max_length=m+r.max_new_tokens);const d=this._get_logits_processor(r,m,n),_=this._get_stopping_criteria(r,a),w=l[u].dims.at(0),v=ss.getSampler(r),S=new Array(w).fill(0),$=h.tolist();i&&i.put($);let E=null;for(;;){l=this.prepare_inputs_for_generation($,l,r);const A=await this.forward(l),P=A.logits.slice(null,-1,null),B=d($,P),D=[];for(let H=0;H<B.dims.at(0);++H){const ie=B[H],te=await v(ie);for(const[de,se]of te){const M=BigInt(de);S[H]+=se,$[H].push(M),D.push([M]);break}}if(i&&i.put(D),_($).every(H=>H)){r.return_dict_in_generate&&(E=this.getPastKeyValues(A,l.past_key_values,!1));break}l=this._update_model_kwargs_for_generation({generated_input_ids:D,outputs:A,model_inputs:l,is_encoder_decoder:p})}i&&i.end();const T=new ue("int64",$.flat(),[$.length,$[0].length]);return r.return_dict_in_generate?{sequences:T,past_key_values:E}:T}addAttentionsToBeam(e,r){if(this.config.is_encoder_decoder){if(!r.cross_attentions||r.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(r.cross_attentions)}if(!r.decoder_attentions||r.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(r.decoder_attentions)}groupBeams(e){const r=Object.create(null);for(const n of e)r[n.id]===void 0?r[n.id]=[n]:r[n.id].push(n);return Object.values(r)}getPastKeyValues(e,r,n=!0){const a=Object.create(null);for(const i in e)if(i.startsWith("present")){let s=i.replace("present","past_key_values");if(r&&i.includes("encoder"))a[s]=r[s];else{if(n&&r){const o=r[s];o.location==="gpu-buffer"&&o.dispose()}a[s]=e[i]}}return a}getAttentions(e){const r=Object.create(null);for(const n of["cross_attentions","decoder_attentions"]){const a=[];for(const i in e)if(i.startsWith(n)){const s=i.split(".").pop();a[s]=e[i]}r[n]=a}return r}addPastKeyValues(e,r){if(r)Object.assign(e,r);else{const n=this.custom_config.kv_cache_dtype??"float32",a=n==="float16"?new Uint16Array:[],i=Ig(this.config);for(const s in i)e[s]=new ue(n,a,i[s])}}}class Yt{}class Pa extends Q{}class y2 extends Pa{}class w2 extends Pa{async _call(e){return new $t(await super._call(e))}}class b2 extends Pa{async _call(e){return new Ae(await super._call(e))}}class v2 extends Pa{async _call(e){return new vt(await super._call(e))}}class $2 extends Pa{async _call(e){return new Ct(await super._call(e))}}class x2 extends Q{}class S2 extends x2{}class Ra extends Q{}class k2 extends Ra{}class E2 extends Ra{async _call(e){return new $t(await super._call(e))}}class C2 extends Ra{async _call(e){return new Ae(await super._call(e))}}class T2 extends Ra{async _call(e){return new vt(await super._call(e))}}class A2 extends Ra{async _call(e){return new Ct(await super._call(e))}}class Ba extends Q{}class I2 extends Ba{}class M2 extends Ba{async _call(e){return new $t(await super._call(e))}}class O2 extends Ba{async _call(e){return new Ae(await super._call(e))}}class z2 extends Ba{async _call(e){return new vt(await super._call(e))}}class P2 extends Ba{async _call(e){return new Ct(await super._call(e))}}class Da extends Q{}class R2 extends Da{}class B2 extends Da{async _call(e){return new $t(await super._call(e))}}class D2 extends Da{async _call(e){return new Ae(await super._call(e))}}class N2 extends Da{async _call(e){return new vt(await super._call(e))}}class F2 extends Da{async _call(e){return new Ct(await super._call(e))}}class Na extends Q{}class L2 extends Na{}class U2 extends Na{async _call(e){return new $t(await super._call(e))}}class W2 extends Na{async _call(e){return new Ae(await super._call(e))}}class V2 extends Na{async _call(e){return new vt(await super._call(e))}}class G2 extends Na{async _call(e){return new Ct(await super._call(e))}}class Fa extends Q{}class H2 extends Fa{}class j2 extends Fa{async _call(e){return new $t(await super._call(e))}}class q2 extends Fa{async _call(e){return new Ae(await super._call(e))}}class K2 extends Fa{async _call(e){return new vt(await super._call(e))}}class Y2 extends Fa{async _call(e){return new Ct(await super._call(e))}}class La extends Q{}class X2 extends La{}class Q2 extends La{async _call(e){return new $t(await super._call(e))}}class Z2 extends La{async _call(e){return new Ae(await super._call(e))}}class J2 extends La{async _call(e){return new vt(await super._call(e))}}class e1 extends La{async _call(e){return new Ct(await super._call(e))}}class Ua extends Q{}class t1 extends Ua{}class r1 extends Ua{async _call(e){return new Ae(await super._call(e))}}class n1 extends Ua{async _call(e){return new vt(await super._call(e))}}class a1 extends Ua{async _call(e){return new Ct(await super._call(e))}}class i1 extends Ua{async _call(e){return new $t(await super._call(e))}}class os extends Q{}class s1 extends os{}class o1 extends os{async _call(e){return new $t(await super._call(e))}}class l1 extends os{async _call(e){return new Ae(await super._call(e))}}class u1 extends os{async _call(e){return new vt(await super._call(e))}}class ls extends Q{}class d1 extends ls{}class c1 extends ls{async _call(e){return new $t(await super._call(e))}}class p1 extends ls{async _call(e){return new Ae(await super._call(e))}}class h1 extends ls{async _call(e){return new Ct(await super._call(e))}}class Wa extends Q{}class f1 extends Wa{}class m1 extends Wa{async _call(e){return new $t(await super._call(e))}}class g1 extends Wa{async _call(e){return new Ae(await super._call(e))}}class _1 extends Wa{async _call(e){return new vt(await super._call(e))}}class y1 extends Wa{async _call(e){return new Ct(await super._call(e))}}class us extends Q{}class w1 extends us{}class b1 extends us{async _call(e){return new $t(await super._call(e))}}class v1 extends us{async _call(e){return new Ae(await super._call(e))}}class $1 extends us{async _call(e){return new Ct(await super._call(e))}}class ds extends Q{}class x1 extends ds{}class S1 extends ds{async _call(e){return new Ae(await super._call(e))}}class k1 extends ds{async _call(e){return new Ct(await super._call(e))}}class E1 extends ds{async _call(e){return new $t(await super._call(e))}}class Dg extends Q{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class C1 extends Dg{}class T1 extends Dg{}class Ng extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class A1 extends Ng{}class I1 extends Ng{}class Fg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class M1 extends Fg{}class O1 extends Fg{}class _l extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class z1 extends _l{}class P1 extends _l{}class R1 extends _l{async _call(e){return new Ae(await super._call(e))}}class cs extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class B1 extends cs{}class D1 extends cs{}class N1 extends cs{async _call(e){return new Ae(await super._call(e))}}class F1 extends cs{}class Lg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class L1 extends Lg{}class U1 extends Lg{}class Ug extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class W1 extends Ug{}class V1 extends Ug{}class Va extends Q{}class G1 extends Va{}class H1 extends Va{async _call(e){return new $t(await super._call(e))}}class j1 extends Va{async _call(e){return new Ae(await super._call(e))}}class q1 extends Va{async _call(e){return new vt(await super._call(e))}}class K1 extends Va{async _call(e){return new Ct(await super._call(e))}}class Ga extends Q{}class Y1 extends Ga{}class X1 extends Ga{async _call(e){return new $t(await super._call(e))}}class Q1 extends Ga{async _call(e){return new Ae(await super._call(e))}}class Z1 extends Ga{async _call(e){return new vt(await super._call(e))}}class J1 extends Ga{async _call(e){return new Ct(await super._call(e))}}class Ha extends Q{}class e$ extends Ha{}class t$ extends Ha{async _call(e){return new $t(await super._call(e))}}class r$ extends Ha{async _call(e){return new Ae(await super._call(e))}}class n$ extends Ha{async _call(e){return new vt(await super._call(e))}}class a$ extends Ha{async _call(e){return new Ct(await super._call(e))}}class Wg extends Q{}class i$ extends Wg{}class s$ extends Wg{}class Vg extends Q{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class o$ extends Vg{}class l$ extends Vg{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,p2)}_retrieve_init_tokens(e){const r=[e.decoder_start_token_id];let n=e.language;const a=e.task;if(e.is_multilingual){n||(console.warn("No language specified - defaulting to English (en)."),n="en");const s=`<|${_g(n)}|>`;r.push(e.lang_to_id[s]),r.push(e.task_to_id[a??"transcribe"])}else if(n||a)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!e.return_timestamps&&e.no_timestamps_token_id&&r.at(-1)!==e.no_timestamps_token_id?r.push(e.no_timestamps_token_id):e.return_timestamps&&r.at(-1)===e.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),r.pop()),r.filter(i=>i!=null)}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,...i}){r=this._prepare_generation_config(r,i);const s=this._retrieve_init_tokens(r);return r.return_timestamps&&(n??=new Ro,n.push(new Qv(r,s))),await super.generate({inputs:e,generation_config:r,logits_processor:n,decoder_input_ids:s,...i})}_extract_token_timestamps(e,r,n=null,a=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let i=this.config.median_filter_width;i===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),i=7);const s=e.cross_attentions.map(u=>{let p=Array.from({length:this.config.decoder_layers},(v,S)=>gr(u.map($=>$[S]),2)),h=ka(r.map(([v,S])=>n?p[v].slice(null,S,null,[0,n]):p[v].slice(null,S)));h=h.transpose(1,0,2,3);let[m,d]=Iw(h,-2,0,!0),_=h.clone();for(let v=0;v<_.dims[0];++v){let S=_[v];for(let $=0;$<S.dims[0];++$){let E=S[$];const T=m[v][$][0],A=d[v][$][0];for(let P=0;P<E.dims[0];++P){let B=E[P];for(let D=0;D<B.data.length;++D)B.data[D]=(B.data[D]-A.data[D])/T.data[D];B.data.set(G0(B.data,i))}}}return ll(_,1)}),o=[e.sequences.length,e.sequences[0].length],l=new ue("float32",new Float32Array(o[0]*o[1]),o);for(let u=0;u<o[0];++u){const p=s[u].neg().squeeze_(0);let[h,m]=Mw(p),d=Array.from({length:h.length-1},(v,S)=>h[S+1]-h[S]),_=ft([1],d).map(v=>!!v),w=[];for(let v=0;v<_.length;++v)_[v]&&w.push(m[v]*a);l[u].data.set(w,1)}return l}}class Gg extends Q{main_input_name="pixel_values";forward_params=["pixel_values","input_ids","encoder_hidden_states","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class u$ extends Q{forward_params=["input_ids","pixel_values","attention_mask","position_ids","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class Hg extends u${async encode_image({pixel_values:e}){const r=(await Fr(this.sessions.vision_encoder,{pixel_values:e})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${r.dims[1]}).`),this.config.num_image_tokens=r.dims[1]),r}async encode_text({input_ids:e}){return(await Fr(this.sessions.embed_tokens,{input_ids:e})).inputs_embeds}_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){const i=this.config.image_token_index,o=n.tolist().map(m=>m.findIndex(d=>d==i)),l=o.every(m=>m===-1),u=o.every(m=>m!==-1);if(!l&&!u)throw new Error("Every input should contain either 0 or 1 image token.");if(l)return{inputs_embeds:e,attention_mask:a};const p=[],h=[];for(let m=0;m<o.length;++m){const d=o[m],_=e[m],w=r[m],v=a[m];p.push(gr([_.slice([0,d]),w,_.slice([d+1,_.dims[0]])],0)),h.push(gr([v.slice([0,d]),Ma([w.dims[0]]),v.slice([d+1,v.dims[0]])],0))}return{inputs_embeds:ka(p,0),attention_mask:ka(h,0)}}}class d$ extends Hg{}class ps extends Q{}class c$ extends ps{}class p$ extends ps{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class h$ extends ps{static async from_pretrained(e,r={}){return r.model_file_name??="vision_model",super.from_pretrained(e,r)}}class jg extends Q{}class f$ extends jg{}class m$ extends jg{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class g$ extends ps{static async from_pretrained(e,r={}){return r.model_file_name??="vision_model",super.from_pretrained(e,r)}}class _$ extends Q{}class y$ extends _${}class qg extends Q{}class w$ extends qg{}class b$ extends qg{}class Kg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class v$ extends Kg{}class $$ extends Kg{}class Yg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class x$ extends Yg{}class S$ extends Yg{}class Xg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class k$ extends Xg{}class E$ extends Xg{}class Qg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class C$ extends Qg{}class T$ extends Qg{}class Zg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class A$ extends Zg{}class I$ extends Zg{}class Jg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class M$ extends Jg{}class O$ extends Jg{}class e_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class z$ extends e_{}class P$ extends e_{}class t_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class R$ extends t_{}class B$ extends t_{}class r_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class D$ extends r_{}class N$ extends r_{}class n_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class F$ extends n_{}class L$ extends n_{}class a_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class U$ extends a_{}class W$ extends a_{}class i_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class V$ extends i_{}class G$ extends i_{}class s_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class H$ extends s_{}class j$ extends s_{}class o_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class q$ extends o_{}class K$ extends o_{}class l_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class Y$ extends l_{}class X$ extends l_{}class u_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class Q$ extends u_{}class Z$ extends u_{}class d_ extends Q{}class J$ extends d_{}class ex extends d_{async _call(e){return new Ae(await super._call(e))}}class c_ extends Q{}class tx extends c_{}class rx extends c_{async _call(e){return new Ae(await super._call(e))}}class nx extends Q{}class ax extends nx{async _call(e){return new C3(await super._call(e))}}class p_ extends Q{}class ix extends p_{}class sx extends p_{async _call(e){return new Ae(await super._call(e))}}class h_ extends Q{}class ox extends h_{}class lx extends h_{async _call(e){return new Ae(await super._call(e))}}class f_ extends Q{}class ux extends f_{}class dx extends f_{}class m_ extends Q{}class cx extends m_{}class px extends m_{}class g_ extends Q{}class hx extends g_{}class fx extends g_{async _call(e){return new Ae(await super._call(e))}}class yl extends Q{}class mx extends yl{}class gx extends yl{async _call(e){return new __(await super._call(e))}}class _x extends yl{async _call(e){return new yx(await super._call(e))}}class __ extends Yt{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class yx extends Yt{constructor({logits:e,pred_boxes:r,pred_masks:n}){super(),this.logits=e,this.pred_boxes=r,this.pred_masks=n}}class y_ extends Q{}class wx extends y_{}class bx extends y_{async _call(e){return new vx(await super._call(e))}}class vx extends __{}class w_ extends Q{}class $x extends w_{}class xx extends w_{async _call(e){return new Ae(await super._call(e))}}class b_ extends Q{}class Sx extends b_{}class kx extends b_{async _call(e){return new Ae(await super._call(e))}}class v_ extends Q{}class Ex extends v_{}class Cx extends v_{async _call(e){return new Ae(await super._call(e))}}class $_ extends Q{}class Tx extends $_{}class Ax extends $_{}class x_ extends Q{}class Ix extends x_{}class Mx extends x_{}class Ox extends Q{}class zx extends Ox{}class S_ extends Q{}class Px extends S_{}class Rx extends S_{}class Bx extends Q{}class Dx extends Bx{}class k_ extends Q{}class Nx extends k_{}class Fx extends k_{async _call(e){return new Ae(await super._call(e))}}class E_ extends Q{}class Lx extends E_{}class Ux extends E_{async _call(e){return new Ae(await super._call(e))}}class C_ extends Q{}class Wx extends C_{}class Vx extends C_{async _call(e){return new Ae(await super._call(e))}}class T_ extends Q{}class Gx extends T_{}class Hx extends T_{async _call(e){return new jx(await super._call(e))}}class jx extends Yt{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class qx extends Q{}class Kx extends qx{async get_image_embeddings({pixel_values:e}){return await Ca(this,{pixel_values:e})}async forward(e){if((!e.image_embeddings||!e.image_positional_embeddings)&&(e={...e,...await this.get_image_embeddings(e)}),!e.input_labels&&e.input_points){const n=e.input_points.dims.slice(0,-1),a=n.reduce((i,s)=>i*s,1);e.input_labels=new ue("int64",new BigInt64Array(a).fill(1n),n)}const r={image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings};return e.input_points&&(r.input_points=e.input_points),e.input_labels&&(r.input_labels=e.input_labels),e.input_boxes&&(r.input_boxes=e.input_boxes),await Fr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new Yx(await super._call(e))}}class Yx extends Yt{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class A_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class Xx extends A_{}class Qx extends A_{}class I_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class Zx extends I_{}class Jx extends I_{}class cn extends Q{}class eS extends cn{}class tS extends cn{async _call(e){return new Fn(await super._call(e))}}class rS extends cn{async _call(e){return new Ae(await super._call(e))}}class nS extends cn{async _call(e){return new vt(await super._call(e))}}class wl extends Q{}class aS extends wl{}class iS extends wl{async _call(e){return new Fn(await super._call(e))}}class sS extends wl{async _call(e){return new Ae(await super._call(e))}}class hs extends Q{}class oS extends hs{}class lS extends hs{async _call(e){return new Fn(await super._call(e))}}class uS extends hs{async _call(e){return new Ae(await super._call(e))}}class dS extends hs{async _call(e){return new vt(await super._call(e))}}class bl extends Q{}class cS extends bl{}class pS extends bl{async _call(e){return new Fn(await super._call(e))}}class hS extends bl{async _call(e){return new Ae(await super._call(e))}}class fS extends cn{}class mS extends cn{async _call(e){return new Fn(await super._call(e))}}class gS extends cn{async _call(e){return new Ae(await super._call(e))}}class ja extends Q{}class _S extends ja{}class yS extends ja{async _call(e){return new Fn(await super._call(e))}}class wS extends ja{async _call(e){return new Ae(await super._call(e))}}class bS extends ja{async _call(e){return new E3(await super._call(e))}}class vS extends ja{async _call(e){return new vt(await super._call(e))}}class M_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class $S extends M_{}class xS extends M_{async generate_speech(e,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:i=20,vocoder:s=null}={}){const o={input_ids:e},{encoder_outputs:l,encoder_attention_mask:u}=await Ca(this,o),p=l.dims[1]/this.config.reduction_factor,h=Math.floor(p*i),m=Math.floor(p*a),d=this.config.num_mel_bins;let _=[],w=null,v=null,S=0;for(;;){++S;const T=Bg(!!v);let A;v?A=v.output_sequence_out:A=new ue("float32",new Float32Array(d),[1,1,d]);let P={use_cache_branch:T,output_sequence:A,encoder_attention_mask:u,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(P,w),v=await Fr(this.sessions.decoder_model_merged,P),w=this.getPastKeyValues(v,w);const{prob:B,spectrum:D}=v;if(_.push(D),S>=m&&(Array.from(B.data).filter(q=>q>=n).length>0||S>=h))break}const $=gr(_),{waveform:E}=await Fr(s.sessions.model,{spectrogram:$});return{spectrogram:$,waveform:E}}}class SS extends Q{main_input_name="spectrogram"}class kS extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class ES extends kS{}class O_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class CS extends O_{}class TS extends O_{}class z_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class AS extends z_{}class IS extends z_{}class P_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class MS extends P_{}class OS extends P_{}class vl extends Q{}class zS extends vl{}class PS extends vl{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class RS extends vl{static async from_pretrained(e,r={}){return r.model_file_name??="audio_model",super.from_pretrained(e,r)}}class BS extends Q{}class R_ extends BS{async _call(e){return new T3(await super._call(e))}}class B_ extends Q{}class DS extends B_{}class NS extends B_{}class D_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class FS extends D_{}class LS extends D_{}class N_ extends Q{}class US extends N_{}class WS extends N_{async _call(e){return new Ae(await super._call(e))}}class F_ extends Q{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}_apply_and_filter_by_delay_pattern_mask(e){const[r,n]=e.dims,a=this.config.decoder.num_codebooks,i=n-a;let s=0;for(let u=0;u<e.size;++u){if(e.data[u]===this.config.decoder.pad_token_id)continue;const p=u%n,h=Math.floor(u/n)%a,m=p-h;m>0&&m<=i&&(e.data[s++]=e.data[u])}const o=Math.floor(r/a),l=s/(o*a);return new ue(e.type,e.data.slice(0,s),[o,a,l])}prepare_inputs_for_generation(e,r,n){let a=structuredClone(e);for(let s=0;s<a.length;++s)for(let o=0;o<a[s].length;++o)s%this.config.decoder.num_codebooks>=o&&(a[s][o]=BigInt(this.config.decoder.pad_token_id));return n.guidance_scale!==null&&n.guidance_scale>1&&(a=a.concat(a)),super.prepare_inputs_for_generation(a,r,n)}async generate(e){const r=await super.generate(e),n=this._apply_and_filter_by_delay_pattern_mask(r).unsqueeze_(0),{audio_values:a}=await Fr(this.sessions.encodec_decode,{audio_codes:n});return a}}class L_ extends Q{}class VS extends L_{}class GS extends L_{async _call(e){return new Ae(await super._call(e))}}class U_ extends Q{}class HS extends U_{}class jS extends U_{async _call(e){return new Ae(await super._call(e))}}class W_ extends Q{}class qS extends W_{}class KS extends W_{async _call(e){return new Ae(await super._call(e))}}class V_ extends Q{}class YS extends V_{}class XS extends V_{async _call(e){return new Ae(await super._call(e))}}class et{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",model_file_name:o=null,subfolder:l="onnx",device:u=null,dtype:p=null,use_external_data_format:h=null,session_options:m={}}={}){let d={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,model_file_name:o,subfolder:l,device:u,dtype:p,use_external_data_format:h,session_options:m};if(d.config=await Mg.from_pretrained(e,d),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let _ of this.MODEL_CLASS_MAPPINGS){const w=_.get(d.config.model_type);if(w)return await w[1].from_pretrained(e,d)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${d.config.model_type}", attempting to construct from base class.`),await Q.from_pretrained(e,d);throw Error(`Unsupported model type: ${d.config.model_type}`)}}const QS=new Map([["bert",["BertModel",y2]],["nomic_bert",["NomicBertModel",S2]],["roformer",["RoFormerModel",k2]],["electra",["ElectraModel",R2]],["esm",["EsmModel",s1]],["convbert",["ConvBertModel",I2]],["camembert",["CamembertModel",L2]],["deberta",["DebertaModel",H2]],["deberta-v2",["DebertaV2Model",X2]],["mpnet",["MPNetModel",f1]],["albert",["AlbertModel",x1]],["distilbert",["DistilBertModel",t1]],["roberta",["RobertaModel",G1]],["xlm",["XLMModel",Y1]],["xlm-roberta",["XLMRobertaModel",e$]],["clap",["ClapModel",zS]],["clip",["CLIPModel",c$]],["clipseg",["CLIPSegModel",w$]],["chinese_clip",["ChineseCLIPModel",y$]],["siglip",["SiglipModel",f$]],["mobilebert",["MobileBertModel",d1]],["squeezebert",["SqueezeBertModel",w1]],["wav2vec2",["Wav2Vec2Model",eS]],["wav2vec2-bert",["Wav2Vec2BertModel",cS]],["unispeech",["UniSpeechModel",aS]],["unispeech-sat",["UniSpeechSatModel",oS]],["hubert",["HubertModel",fS]],["wavlm",["WavLMModel",_S]],["audio-spectrogram-transformer",["ASTModel",i$]],["vits",["VitsModel",R_]],["detr",["DetrModel",mx]],["table-transformer",["TableTransformerModel",wx]],["vit",["ViTModel",J$]],["fastvit",["FastViTModel",tx]],["mobilevit",["MobileViTModel",ix]],["mobilevitv2",["MobileViTV2Model",ox]],["owlvit",["OwlViTModel",ux]],["owlv2",["Owlv2Model",cx]],["beit",["BeitModel",hx]],["deit",["DeiTModel",$x]],["convnext",["ConvNextModel",Nx]],["convnextv2",["ConvNextV2Model",Lx]],["dinov2",["Dinov2Model",Wx]],["resnet",["ResNetModel",Sx]],["swin",["SwinModel",Ex]],["swin2sr",["Swin2SRModel",Tx]],["donut-swin",["DonutSwinModel",Dx]],["yolos",["YolosModel",Gx]],["dpt",["DPTModel",Ix]],["glpn",["GLPNModel",Px]],["hifigan",["SpeechT5HifiGan",SS]],["efficientnet",["EfficientNetModel",US]],["mobilenet_v1",["MobileNetV1Model",VS]],["mobilenet_v2",["MobileNetV2Model",HS]],["mobilenet_v3",["MobileNetV3Model",qS]],["mobilenet_v4",["MobileNetV4Model",YS]]]),ZS=new Map([["t5",["T5Model",C1]],["longt5",["LongT5Model",A1]],["mt5",["MT5Model",M1]],["bart",["BartModel",z1]],["mbart",["MBartModel",B1]],["marian",["MarianModel",Xx]],["whisper",["WhisperModel",o$]],["m2m_100",["M2M100Model",Zx]],["blenderbot",["BlenderbotModel",L1]],["blenderbot-small",["BlenderbotSmallModel",W1]]]),JS=new Map([["bloom",["BloomModel",q$]],["gpt2",["GPT2Model",v$]],["gptj",["GPTJModel",C$]],["gpt_bigcode",["GPTBigCodeModel",A$]],["gpt_neo",["GPTNeoModel",x$]],["gpt_neox",["GPTNeoXModel",k$]],["codegen",["CodeGenModel",M$]],["llama",["LlamaModel",z$]],["cohere",["CohereModel",R$]],["gemma",["GemmaModel",D$]],["openelm",["OpenELMModel",F$]],["qwen2",["Qwen2Model",U$]],["phi",["PhiModel",V$]],["phi3",["Phi3Model",H$]],["mpt",["MptModel",Y$]],["opt",["OPTModel",Q$]],["mistral",["MistralModel",CS]],["starcoder2",["Starcoder2Model",AS]],["falcon",["FalconModel",MS]],["stablelm",["StableLmModel",FS]]]),$l=new Map([["speecht5",["SpeechT5ForSpeechToText",$S]],["whisper",["WhisperForConditionalGeneration",l$]]]),G_=new Map([["speecht5",["SpeechT5ForTextToSpeech",xS]]]),H_=new Map([["vits",["VitsModel",R_]],["musicgen",["MusicgenForConditionalGeneration",F_]]]),j_=new Map([["bert",["BertForSequenceClassification",b2]],["roformer",["RoFormerForSequenceClassification",C2]],["electra",["ElectraForSequenceClassification",D2]],["esm",["EsmForSequenceClassification",l1]],["convbert",["ConvBertForSequenceClassification",O2]],["camembert",["CamembertForSequenceClassification",W2]],["deberta",["DebertaForSequenceClassification",q2]],["deberta-v2",["DebertaV2ForSequenceClassification",Z2]],["mpnet",["MPNetForSequenceClassification",g1]],["albert",["AlbertForSequenceClassification",S1]],["distilbert",["DistilBertForSequenceClassification",r1]],["roberta",["RobertaForSequenceClassification",j1]],["xlm",["XLMForSequenceClassification",Q1]],["xlm-roberta",["XLMRobertaForSequenceClassification",r$]],["bart",["BartForSequenceClassification",R1]],["mbart",["MBartForSequenceClassification",N1]],["mobilebert",["MobileBertForSequenceClassification",p1]],["squeezebert",["SqueezeBertForSequenceClassification",v1]]]),q_=new Map([["bert",["BertForTokenClassification",v2]],["roformer",["RoFormerForTokenClassification",T2]],["electra",["ElectraForTokenClassification",N2]],["esm",["EsmForTokenClassification",u1]],["convbert",["ConvBertForTokenClassification",z2]],["camembert",["CamembertForTokenClassification",V2]],["deberta",["DebertaForTokenClassification",K2]],["deberta-v2",["DebertaV2ForTokenClassification",J2]],["mpnet",["MPNetForTokenClassification",_1]],["distilbert",["DistilBertForTokenClassification",n1]],["roberta",["RobertaForTokenClassification",q1]],["xlm",["XLMForTokenClassification",Z1]],["xlm-roberta",["XLMRobertaForTokenClassification",n$]]]),xl=new Map([["t5",["T5ForConditionalGeneration",T1]],["longt5",["LongT5ForConditionalGeneration",I1]],["mt5",["MT5ForConditionalGeneration",O1]],["bart",["BartForConditionalGeneration",P1]],["mbart",["MBartForConditionalGeneration",D1]],["marian",["MarianMTModel",Qx]],["m2m_100",["M2M100ForConditionalGeneration",Jx]],["blenderbot",["BlenderbotForConditionalGeneration",U1]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",V1]]]),Sl=new Map([["bloom",["BloomForCausalLM",K$]],["gpt2",["GPT2LMHeadModel",$$]],["gptj",["GPTJForCausalLM",T$]],["gpt_bigcode",["GPTBigCodeForCausalLM",I$]],["gpt_neo",["GPTNeoForCausalLM",S$]],["gpt_neox",["GPTNeoXForCausalLM",E$]],["codegen",["CodeGenForCausalLM",O$]],["llama",["LlamaForCausalLM",P$]],["cohere",["CohereForCausalLM",B$]],["gemma",["GemmaForCausalLM",N$]],["openelm",["OpenELMForCausalLM",L$]],["qwen2",["Qwen2ForCausalLM",W$]],["phi",["PhiForCausalLM",G$]],["phi3",["Phi3ForCausalLM",j$]],["mpt",["MptForCausalLM",X$]],["opt",["OPTForCausalLM",Z$]],["mbart",["MBartForCausalLM",F1]],["mistral",["MistralForCausalLM",TS]],["starcoder2",["Starcoder2ForCausalLM",IS]],["falcon",["FalconForCausalLM",OS]],["trocr",["TrOCRForCausalLM",ES]],["stablelm",["StableLmForCausalLM",LS]]]),K_=new Map([["bert",["BertForMaskedLM",w2]],["roformer",["RoFormerForMaskedLM",E2]],["electra",["ElectraForMaskedLM",B2]],["esm",["EsmForMaskedLM",o1]],["convbert",["ConvBertForMaskedLM",M2]],["camembert",["CamembertForMaskedLM",U2]],["deberta",["DebertaForMaskedLM",j2]],["deberta-v2",["DebertaV2ForMaskedLM",Q2]],["mpnet",["MPNetForMaskedLM",m1]],["albert",["AlbertForMaskedLM",E1]],["distilbert",["DistilBertForMaskedLM",i1]],["roberta",["RobertaForMaskedLM",H1]],["xlm",["XLMWithLMHeadModel",X1]],["xlm-roberta",["XLMRobertaForMaskedLM",t$]],["mobilebert",["MobileBertForMaskedLM",c1]],["squeezebert",["SqueezeBertForMaskedLM",b1]]]),Y_=new Map([["bert",["BertForQuestionAnswering",$2]],["roformer",["RoFormerForQuestionAnswering",A2]],["electra",["ElectraForQuestionAnswering",F2]],["convbert",["ConvBertForQuestionAnswering",P2]],["camembert",["CamembertForQuestionAnswering",G2]],["deberta",["DebertaForQuestionAnswering",Y2]],["deberta-v2",["DebertaV2ForQuestionAnswering",e1]],["mpnet",["MPNetForQuestionAnswering",y1]],["albert",["AlbertForQuestionAnswering",k1]],["distilbert",["DistilBertForQuestionAnswering",a1]],["roberta",["RobertaForQuestionAnswering",K1]],["xlm",["XLMForQuestionAnswering",J1]],["xlm-roberta",["XLMRobertaForQuestionAnswering",a$]],["mobilebert",["MobileBertForQuestionAnswering",h1]],["squeezebert",["SqueezeBertForQuestionAnswering",$1]]]),kl=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Gg]]]),e3=new Map([["llava",["LlavaForConditionalGeneration",Hg]],["moondream1",["Moondream1ForConditionalGeneration",d$]]]),t3=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Gg]]]),X_=new Map([["vit",["ViTForImageClassification",ex]],["fastvit",["FastViTForImageClassification",rx]],["mobilevit",["MobileViTForImageClassification",sx]],["mobilevitv2",["MobileViTV2ForImageClassification",lx]],["beit",["BeitForImageClassification",fx]],["deit",["DeiTForImageClassification",xx]],["convnext",["ConvNextForImageClassification",Fx]],["convnextv2",["ConvNextV2ForImageClassification",Ux]],["dinov2",["Dinov2ForImageClassification",Vx]],["resnet",["ResNetForImageClassification",kx]],["swin",["SwinForImageClassification",Cx]],["segformer",["SegformerForImageClassification",DS]],["efficientnet",["EfficientNetForImageClassification",WS]],["mobilenet_v1",["MobileNetV1ForImageClassification",GS]],["mobilenet_v2",["MobileNetV2ForImageClassification",jS]],["mobilenet_v3",["MobileNetV3ForImageClassification",KS]],["mobilenet_v4",["MobileNetV4ForImageClassification",XS]]]),Q_=new Map([["detr",["DetrForObjectDetection",gx]],["table-transformer",["TableTransformerForObjectDetection",bx]],["yolos",["YolosForObjectDetection",Hx]]]),Z_=new Map([["owlvit",["OwlViTForObjectDetection",dx]],["owlv2",["Owlv2ForObjectDetection",px]]]),J_=new Map([["detr",["DetrForSegmentation",_x]],["clipseg",["CLIPSegForImageSegmentation",b$]]]),e0=new Map([["segformer",["SegformerForSemanticSegmentation",NS]]]),r3=new Map([["sam",["SamModel",Kx]]]),t0=new Map([["wav2vec2",["Wav2Vec2ForCTC",tS]],["wav2vec2-bert",["Wav2Vec2BertForCTC",pS]],["unispeech",["UniSpeechForCTC",iS]],["unispeech-sat",["UniSpeechSatForCTC",lS]],["wavlm",["WavLMForCTC",yS]],["hubert",["HubertForCTC",mS]]]),r0=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",rS]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",hS]],["unispeech",["UniSpeechForSequenceClassification",sS]],["unispeech-sat",["UniSpeechSatForSequenceClassification",uS]],["wavlm",["WavLMForSequenceClassification",wS]],["hubert",["HubertForSequenceClassification",gS]],["audio-spectrogram-transformer",["ASTForAudioClassification",s$]]]),n3=new Map([["wavlm",["WavLMForXVector",bS]]]),a3=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",dS]],["wavlm",["WavLMForAudioFrameClassification",vS]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",nS]]]),i3=new Map([["vitmatte",["VitMatteForImageMatting",ax]]]),n0=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Ax]]]),a0=new Map([["dpt",["DPTForDepthEstimation",Mx]],["depth_anything",["DepthAnythingForDepthEstimation",zx]],["glpn",["GLPNForDepthEstimation",Rx]]]),i0=new Map([["clip",["CLIPVisionModelWithProjection",h$]],["siglip",["SiglipVisionModel",g$]]]),s0=[[QS,ve.EncoderOnly],[ZS,ve.EncoderDecoder],[JS,ve.DecoderOnly],[j_,ve.EncoderOnly],[q_,ve.EncoderOnly],[xl,ve.Seq2Seq],[$l,ve.Seq2Seq],[Sl,ve.DecoderOnly],[K_,ve.EncoderOnly],[Y_,ve.EncoderOnly],[kl,ve.Vision2Seq],[e3,ve.ImageTextToText],[X_,ve.EncoderOnly],[J_,ve.EncoderOnly],[e0,ve.EncoderOnly],[i3,ve.EncoderOnly],[n0,ve.EncoderOnly],[a0,ve.EncoderOnly],[Q_,ve.EncoderOnly],[Z_,ve.EncoderOnly],[r3,ve.MaskGeneration],[t0,ve.EncoderOnly],[r0,ve.EncoderOnly],[G_,ve.Seq2Seq],[H_,ve.EncoderOnly],[n3,ve.EncoderOnly],[a3,ve.EncoderOnly],[i0,ve.EncoderOnly]];for(const[t,e]of s0)for(const[r,n]of t.values())es.set(r,e),ba.set(n,r),zg.set(r,n);const s3=[["MusicgenForConditionalGeneration",F_,ve.Musicgen],["CLIPTextModelWithProjection",p$,ve.EncoderOnly],["SiglipTextModel",m$,ve.EncoderOnly],["ClapTextModelWithProjection",PS,ve.EncoderOnly],["ClapAudioModelWithProjection",RS,ve.EncoderOnly]];for(const[t,e,r]of s3)es.set(t,r),ba.set(e,t),zg.set(t,e);class ma extends et{static MODEL_CLASS_MAPPINGS=s0.map(e=>e[0]);static BASE_IF_FAIL=!0}class Ep extends et{static MODEL_CLASS_MAPPINGS=[j_]}class o3 extends et{static MODEL_CLASS_MAPPINGS=[q_]}class ho extends et{static MODEL_CLASS_MAPPINGS=[xl]}class l3 extends et{static MODEL_CLASS_MAPPINGS=[$l]}class u3 extends et{static MODEL_CLASS_MAPPINGS=[G_]}class d3 extends et{static MODEL_CLASS_MAPPINGS=[H_]}class c3 extends et{static MODEL_CLASS_MAPPINGS=[Sl]}class p3 extends et{static MODEL_CLASS_MAPPINGS=[K_]}class h3 extends et{static MODEL_CLASS_MAPPINGS=[Y_]}class f3 extends et{static MODEL_CLASS_MAPPINGS=[kl]}class m3 extends et{static MODEL_CLASS_MAPPINGS=[X_]}class g3 extends et{static MODEL_CLASS_MAPPINGS=[J_]}class _3 extends et{static MODEL_CLASS_MAPPINGS=[e0]}class y3 extends et{static MODEL_CLASS_MAPPINGS=[Q_]}class w3 extends et{static MODEL_CLASS_MAPPINGS=[Z_]}class b3 extends et{static MODEL_CLASS_MAPPINGS=[t0]}class v3 extends et{static MODEL_CLASS_MAPPINGS=[r0]}class $3 extends et{static MODEL_CLASS_MAPPINGS=[t3]}class x3 extends et{static MODEL_CLASS_MAPPINGS=[n0]}class S3 extends et{static MODEL_CLASS_MAPPINGS=[a0]}class k3 extends et{static MODEL_CLASS_MAPPINGS=[i0]}class Ae extends Yt{constructor({logits:e}){super(),this.logits=e}}class E3 extends Yt{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class vt extends Yt{constructor({logits:e}){super(),this.logits=e}}class $t extends Yt{constructor({logits:e}){super(),this.logits=e}}class Ct extends Yt{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class Fn extends Yt{constructor({logits:e}){super(),this.logits=e}}class C3 extends Yt{constructor({alphas:e}){super(),this.alphas=e}}class T3 extends Yt{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const Gt=typeof self<"u",A3=Gt&&self.constructor.name==="DedicatedWorkerGlobalScope";let Qr,o0,Rr;if(Gt)Qr=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},Rr=self.createImageBitmap,o0=self.ImageData;else if(Je)Rr=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),i=new yt(new Uint8ClampedArray(n),a.width,a.height,a.channels);return r!==void 0&&r!==a.channels&&i.convert(r),i};else throw new Error("Unable to load image processing library.");const I3={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},M3=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class yt{constructor(e,r,n,a){this.data=e,this.width=r,this.height=n,this.channels=a}get size(){return[this.width,this.height]}static async read(e){if(e instanceof yt)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!Gt)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new yt(n,e.width,e.height,4)}static async fromURL(e){const r=await Ui(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(Gt){const r=await Rr(e),n=Qr(r.width,r.height).getContext("2d");return n.drawImage(r,0,0),new this(n.getImageData(0,0,r.width,r.height).data,r.width,r.height,4)}else{const r=Je(await e.arrayBuffer());return await Rr(r)}}static fromTensor(e,r="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(r==="CHW")e=e.transpose(1,2,0);else if(r!=="HWC")throw new Error(`Unsupported channel format: ${r}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new yt(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let r=0,n=0;r<this.data.length;r+=this.channels){const a=this.data[r],i=this.data[r+1],s=this.data[r+2];e[n++]=Math.round(.2989*a+.587*i+.114*s)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let r=0,n=0;r<this.data.length;++r)e[n++]=this.data[r],e[n++]=this.data[r],e[n++]=this.data[r];break;case 4:for(let r=0,n=0;r<this.data.length;r+=4)e[n++]=this.data[r],e[n++]=this.data[r+1],e[n++]=this.data[r+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let r=0,n=0;r<this.data.length;++r)e[n++]=this.data[r],e[n++]=this.data[r],e[n++]=this.data[r],e[n++]=255;break;case 3:for(let r=0,n=0;r<this.data.length;r+=3)e[n++]=this.data[r],e[n++]=this.data[r+1],e[n++]=this.data[r+2],e[n++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}async resize(e,r,{resample:n=2}={}){let a=I3[n]??n;if(Gt){const i=this.channels,s=this.toCanvas(),o=Qr(e,r).getContext("2d");return o.drawImage(s,0,0,e,r),new yt(o.getImageData(0,0,e,r).data,e,r,4).convert(i)}else{let i=this.toSharp();switch(a){case"box":case"hamming":(a==="box"||a==="hamming")&&(console.warn(`Resampling method ${a} is not yet supported. Using bilinear instead.`),a="bilinear");case"nearest":case"bilinear":case"bicubic":i=i.affine([e/this.width,0,0,r/this.height],{interpolator:a});break;case"lanczos":i=i.resize({width:e,height:r,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${a} is not supported.`)}return await Rr(i)}}async pad([e,r,n,a]){if(e=Math.max(e,0),r=Math.max(r,0),n=Math.max(n,0),a=Math.max(a,0),e===0&&r===0&&n===0&&a===0)return this;if(Gt){const i=this.channels,s=this.toCanvas(),o=this.width+e+r,l=this.height+n+a,u=Qr(o,l).getContext("2d");return u.drawImage(s,0,0,this.width,this.height,e,n,o,l),new yt(u.getImageData(0,0,o,l).data,o,l,4).convert(i)}else{const i=this.toSharp().extend({left:e,right:r,top:n,bottom:a});return await Rr(i)}}async crop([e,r,n,a]){if(e=Math.max(e,0),r=Math.max(r,0),n=Math.min(n,this.width-1),a=Math.min(a,this.height-1),e===0&&r===0&&n===this.width-1&&a===this.height-1)return this;const i=n-e+1,s=a-r+1;if(Gt){const o=this.channels,l=this.toCanvas(),u=Qr(i,s).getContext("2d");return u.drawImage(l,e,r,i,s,0,0,i,s),new yt(u.getImageData(0,0,i,s).data,i,s,4).convert(o)}else{const o=this.toSharp().extract({left:e,top:r,width:i,height:s});return await Rr(o)}}async center_crop(e,r){if(this.width===e&&this.height===r)return this;const n=(this.width-e)/2,a=(this.height-r)/2;if(Gt){const i=this.channels,s=this.toCanvas(),o=Qr(e,r).getContext("2d");let l=0,u=0,p=0,h=0;return n>=0?l=n:p=-n,a>=0?u=a:h=-a,o.drawImage(s,l,u,e,r,p,h,e,r),new yt(o.getImageData(0,0,e,r).data,e,r,4).convert(i)}else{let i=this.toSharp();if(n>=0&&a>=0)i=i.extract({left:Math.floor(n),top:Math.floor(a),width:e,height:r});else if(n<=0&&a<=0){const s=Math.floor(-a),o=Math.floor(-n);i=i.extend({top:s,left:o,right:e-this.width-o,bottom:r-this.height-s})}else{let s=[0,0],o=0;a<0?(s[0]=Math.floor(-a),s[1]=r-this.height-s[0]):o=Math.floor(a);let l=[0,0],u=0;n<0?(l[0]=Math.floor(-n),l[1]=e-this.width-l[0]):u=Math.floor(n),i=i.extend({top:s[0],bottom:s[1],left:l[0],right:l[1]}).extract({left:u,top:o,width:e,height:r})}return await Rr(i)}}async toBlob(e="image/png",r=1){if(!Gt)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:r})}toTensor(e="CHW"){let r=new ue("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(e!=="HWC")if(e==="CHW")r=r.permute(2,0,1);else throw new Error(`Unsupported channel format: ${e}`);return r}toCanvas(){if(!Gt)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=Qr(e.width,e.height),n=new o0(e.data,e.width,e.height);return r.getContext("2d").putImageData(n,0,0),r}_update(e,r,n,a=null){return this.data=e,this.width=r,this.height=n,a!==null&&(this.channels=a),this}clone(){return new yt(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(Gt){if(A3)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=M3.get(r)??"image/png",a=await this.toBlob(n),i=URL.createObjectURL(a),s=document.createElement("a");s.href=i,s.download=e,s.click(),s.remove()}else{if(zt.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Gt)throw new Error("toSharp() is only supported in server-side environments.");return Je(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}async function O3(t,e){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const r=await(await Ui(t)).arrayBuffer(),n=new AudioContext({sampleRate:e});typeof e>"u"&&console.warn(`No sampling rate provided, using default of ${n.sampleRate}Hz.`);const a=await n.decodeAudioData(r);let i;if(a.numberOfChannels===2){const s=Math.sqrt(2),o=a.getChannelData(0),l=a.getChannelData(1);i=new Float32Array(o.length);for(let u=0;u<a.length;++u)i[u]=s*(o[u]+l[u])/2}else i=a.getChannelData(0);return i}function Cp(t){if(t<1)return new Float64Array;if(t===1)return new Float64Array([1]);const e=t-1,r=Math.PI/e,n=new Float64Array(t);for(let a=0;a<t;++a){const i=2*a-e;n[a]=.5+.5*Math.cos(r*i)}return n}const z3={htk:t=>2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,r=15,n=27/Math.log(6.4))=>t>=e?r+Math.log(t/e)*n:3*t/200};function fo(t,e="htk"){const r=z3[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}const P3={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,r=15,n=Math.log(6.4)/27)=>t>=r?e*Math.exp(n*(t-r)):200*t/3};function R3(t,e="htk"){const r=P3[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}function B3(t,e){const r=Float64Array.from({length:e.length-1},(s,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let s=0;s<t.length;++s){const o=n[s];for(let l=0;l<e.length;++l)o[l]=e[l]-t[s]}const a=e.length-2,i=Array.from({length:a},()=>new Array(t.length));for(let s=0;s<t.length;++s){const o=n[s];for(let l=0;l<a;++l){const u=-o[l]/r[l],p=o[l+2]/r[l+1];i[l][s]=Math.max(0,Math.min(u,p))}}return i}function Tp(t,e,r){const n=(e-t)/(r-1);return Float64Array.from({length:r},(a,i)=>t+n*i)}function Ta(t,e,r,n,a,i=null,s="htk",o=!1){if(i!==null&&i!=="slaney")throw new Error('norm must be one of null or "slaney"');const l=fo(r,s),u=fo(n,s),p=Tp(l,u,e+2);let h=R3(p,s),m;if(o){const _=a/(t*2);m=fo(Float64Array.from({length:t},(w,v)=>v*_),s),h=p}else m=Tp(0,Math.floor(a/2),t);const d=B3(m,h);if(i!==null&&i==="slaney")for(let _=0;_<e;++_){const w=d[_],v=2/(h[_+2]-h[_]);for(let S=0;S<t;++S)w[S]*=v}return d}function D3(t,e,r){const n=new t.constructor(t.length+e+r),a=t.length-1;for(let i=0;i<t.length;++i)n[e+i]=t[i];for(let i=1;i<=e;++i)n[e-i]=t[Li(i,a)];for(let i=1;i<=r;++i)n[a+e+i]=t[Li(a-i,a)];return n}function l0(t,e,r,n,a){if(r<=0)throw new Error("reference must be greater than zero");if(n<=0)throw new Error("min_value must be greater than zero");r=Math.max(n,r);const i=Math.log10(r);for(let s=0;s<t.length;++s)t[s]=e*Math.log10(Math.max(n,t[s])-i);if(a!==null){if(a<=0)throw new Error("db_range must be greater than zero");const s=Kt(t)[0]-a;for(let o=0;o<t.length;++o)t[o]=Math.max(t[o],s)}return t}function N3(t,e=1,r=1e-5,n=null){return l0(t,20,e,r,n)}function F3(t,e=1,r=1e-10,n=null){return l0(t,10,e,r,n)}async function fs(t,e,r,n,{fft_length:a=null,power:i=1,center:s=!0,pad_mode:o="reflect",onesided:l=!0,preemphasis:u=null,mel_filters:p=null,mel_floor:h=1e-10,log_mel:m=null,reference:d=1,min_value:_=1e-10,db_range:w=null,remove_dc_offset:v=null,max_num_frames:S=null,do_pad:$=!0,transpose:E=!1}={}){const T=e.length;if(a===null&&(a=r),r>a)throw Error(`frame_length (${r}) may not be larger than fft_length (${a})`);if(T!==r)throw new Error(`Length of the window (${T}) must equal frame_length (${r})`);if(n<=0)throw new Error("hop_length must be greater than zero");if(i===null&&p!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(s){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const R=Math.floor((a-1)/2)+1;t=D3(t,R,R)}const A=Math.floor(1+Math.floor((t.length-r)/n)),P=l?Math.floor(a/2)+1:a;let B=A,D=A;S!==null&&(S>A?$&&(D=S):D=B=S);const q=new V0(a),H=new Float64Array(a),ie=new Float64Array(q.outputBufferSize),te=new Float32Array(P*D);for(let R=0;R<B;++R){const G=R*n;for(let re=0;re<r;++re)H[re]=t[G+re];if(v){let re=0;for(let ne=0;ne<r;++ne)re+=H[ne];const ee=re/r;for(let ne=0;ne<r;++ne)H[ne]-=ee}if(u!==null){for(let re=r-1;re>=1;--re)H[re]-=u*H[re-1];H[0]*=1-u}for(let re=0;re<e.length;++re)H[re]*=e[re];q.realTransform(ie,H);for(let re=0;re<P;++re){const ee=re<<1;te[re*D+R]=ie[ee]**2+ie[ee+1]**2}}if(i!==null&&i!==2){const R=2/i;for(let G=0;G<te.length;++G)te[G]**=R}const de=p.length;let se=await Tw(new ue("float32",p.flat(),[de,P]),new ue("float32",te,[P,D]));E&&(se=se.transpose(1,0));const M=se.data;for(let R=0;R<M.length;++R)M[R]=Math.max(h,M[R]);if(i!==null&&m!==null){const R=Math.min(M.length,B*de);switch(m){case"log":for(let G=0;G<R;++G)M[G]=Math.log(M[G]);break;case"log10":for(let G=0;G<R;++G)M[G]=Math.log10(M[G]);break;case"dB":if(i===1)N3(M,d,_,w);else if(i===2)F3(M,d,_,w);else throw new Error(`Cannot use log_mel option '${m}' with power ${i}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${m}'`)}}return se}function ms(t,e,{periodic:r=!0,frame_length:n=null,center:a=!0}={}){const i=r?t+1:t;let s;switch(e){case"boxcar":s=new Float64Array(i).fill(1);break;case"hann":case"hann_window":s=Cp(i);break;case"povey":s=Cp(i).map(o=>Math.pow(o,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(s=s.subarray(0,t)),n===null)return s;if(t>n)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${n})`);return s}function L3([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function El(t,e=.5,r=null,n=!1){const a=t.logits,i=t.pred_boxes,[s,o,l]=a.dims;if(r!==null&&r.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let u=[];for(let p=0;p<s;++p){let h=r!==null?r[p]:null,m={boxes:[],classes:[],scores:[]},d=a[p],_=i[p];for(let w=0;w<o;++w){let v=d[w],S=[],$;if(n){$=v.sigmoid().data;for(let E=0;E<$.length;++E)$[E]>e&&S.push(E)}else{let E=Kt(v.data)[1];if(E===l-1||($=wt(v.data),$[E]<e))continue;S.push(E)}for(const E of S){let T=_[w].data;T=L3(T),h!==null&&(T=T.map((A,P)=>A*h[(P+1)%2])),m.boxes.push(T),m.classes.push(E),m.scores.push($[E])}}u.push(m)}return u}function qa(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${t?.constructor?.name??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function Ap(t,e,r=0,n=null){const a=t/e;let i=H0(a)*e;return n!==null&&i>n&&(i=Math.floor(a)*e),i<r&&(i=Math.ceil(a)*e),i}function mo([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}class pn extends bt{constructor(e){super(),this.config=e}}class He extends pn{constructor(e){super(e),this.image_mean=this.config.image_mean??this.config.mean,this.image_std=this.config.image_std??this.config.std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.size_divisibility=this.config.size_divisibility??this.config.size_divisor,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=this.config.do_flip_channel_order??!1}async thumbnail(e,r,n=2){const a=e.height,i=e.width,s=r.height,o=r.width;let l=Math.min(a,s),u=Math.min(i,o);return l===a&&u===i?e:(a>i?u=Math.floor(i*l/a):i>a&&(l=Math.floor(a*u/i)),await e.resize(u,l,{resample:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),a=Fp(n.data)[0],s=Kt(n.data)[0]-a;if(s===0)return e;const o=r/255;let l=n.width,u=n.height,p=0,h=0;const m=n.data;for(let d=0;d<n.height;++d){const _=d*n.width;for(let w=0;w<n.width;++w)(m[_+w]-a)/s<o&&(l=Math.min(l,w),u=Math.min(u,d),p=Math.max(p,w),h=Math.max(h,d))}return e=await e.crop([l,u,p,h]),e}pad_image(e,r,n,{mode:a="constant",center:i=!1,constant_values:s=0}={}){const[o,l,u]=r;let p,h;if(typeof n=="number"?(p=n,h=n):(p=n.width,h=n.height),p!==l||h!==o){const m=new Float32Array(p*h*u);if(Array.isArray(s))for(let w=0;w<m.length;++w)m[w]=s[w%u];else s!==0&&m.fill(s);const[d,_]=i?[Math.floor((p-l)/2),Math.floor((h-o)/2)]:[0,0];for(let w=0;w<o;++w){const v=(w+_)*p,S=w*l;for(let $=0;$<l;++$){const E=(v+$+d)*u,T=(S+$)*u;for(let A=0;A<u;++A)m[E+A]=e[T+A]}}if(a==="symmetric"){if(i)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const w=o-1,v=l-1;for(let S=0;S<h;++S){const $=S*p,E=Li(S,w)*l;for(let T=0;T<p;++T){if(S<o&&T<l)continue;const A=($+T)*u,P=(E+Li(T,v))*u;for(let B=0;B<u;++B)m[A+B]=e[P+B]}}}e=m,r=[h,p,u]}return[e,r]}rescale(e){for(let r=0;r<e.length;++r)e[r]=this.rescale_factor*e[r]}get_resize_output_image_size(e,r){const[n,a]=e.size;let i,s;if(this.do_thumbnail){const{height:o,width:l}=r;i=Math.min(o,l)}else Number.isInteger(r)?(i=r,s=this.config.max_size??i):r!==void 0&&(i=r.shortest_edge,s=r.longest_edge);if(i!==void 0||s!==void 0){const o=i===void 0?1:Math.max(i/n,i/a),l=n*o,u=a*o,p=s===void 0?1:Math.min(s/l,s/u);let h=Math.floor(Number((l*p).toFixed(2))),m=Math.floor(Number((u*p).toFixed(2)));return this.size_divisibility!==void 0&&([h,m]=mo([h,m],this.size_divisibility)),[h,m]}else if(r!==void 0&&r.width!==void 0&&r.height!==void 0){let o=r.width,l=r.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let u=l/a,p=o/n;Math.abs(1-p)<Math.abs(1-u)?u=p:p=u,l=Ap(u*a,this.config.ensure_multiple_of),o=Ap(p*n,this.config.ensure_multiple_of)}return[o,l]}else{if(this.size_divisibility!==void 0)return mo([n,a],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(r)}`)}}async resize(e){const[r,n]=this.get_resize_output_image_size(e,this.size);return await e.resize(r,n,{resample:this.resample})}async preprocess(e,{do_normalize:r=null,do_pad:n=null,do_convert_rgb:a=null,do_convert_grayscale:i=null,do_flip_channel_order:s=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[o,l]=e.size;if(a??this.do_convert_rgb?e=e.rgb():i&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let d,_;Number.isInteger(this.crop_size)?(d=this.crop_size,_=this.crop_size):(d=this.crop_size.width,_=this.crop_size.height),e=await e.center_crop(d,_)}const u=[e.height,e.width];let p=Float32Array.from(e.data),h=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(p),r??this.do_normalize){let d=this.image_mean;Array.isArray(this.image_mean)||(d=new Array(e.channels).fill(d));let _=this.image_std;if(Array.isArray(this.image_std)||(_=new Array(e.channels).fill(d)),d.length!==e.channels||_.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${d.length}) and \`image_std\` (${_.length}) must match the number of channels in the image (${e.channels}).`);for(let w=0;w<p.length;w+=e.channels)for(let v=0;v<e.channels;++v)p[w+v]=(p[w+v]-d[v])/_[v]}if(n??this.do_pad){if(this.pad_size)[p,h]=this.pad_image(p,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){const[d,_]=mo([h[1],h[0]],this.size_divisibility);[p,h]=this.pad_image(p,h,{width:d,height:_})}}if(s??this.do_flip_channel_order){if(h[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let d=0;d<p.length;d+=3){const _=p[d];p[d]=p[d+2],p[d+2]=_}}const m=new ue("float32",p,h).permute(2,0,1);return{original_size:[l,o],reshaped_input_size:u,pixel_values:m}}async _call(e,...r){Array.isArray(e)||(e=[e]);const n=await Promise.all(e.map(i=>this.preprocess(i)));return{pixel_values:ka(n.map(i=>i.pixel_values),0),original_sizes:n.map(i=>i.original_size),reshaped_input_sizes:n.map(i=>i.reshaped_input_size)}}}class U3 extends He{post_process_semantic_segmentation(e,r=null){const n=e.logits,a=n.dims[0];if(r!==null&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const i=[];for(let s=0;s<a;++s){const o=r!==null?r[s]:null;let l=n[s];o!==null&&(l=ol(l,o,"bilinear",!1));const[u,p]=o??l.dims.slice(-2),h=new ue("int32",new Int32Array(u*p),[u,p]),m=l[0].data,d=h.data;for(let S=1;S<l.dims[0];++S){const $=l[S].data;for(let E=0;E<$.length;++E)$[E]>m[E]&&(m[E]=$[E],d[E]=S)}const _=new Array(l.dims[0]),w=h.data;for(let S=0;S<w.length;++S){const $=w[S];_[$]=$}const v=_.filter(S=>S!==void 0);i.push({segmentation:h,labels:v})}return i}}class u0 extends He{}class W3 extends u0{}class V3 extends He{}class G3 extends He{}class d0 extends He{}class H3 extends d0{}class j3 extends He{}class q3 extends He{}class c0 extends He{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){const r=this.size?.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){const n=Math.floor(r/this.crop_pct),[a,i]=this.get_resize_output_image_size(e,{shortest_edge:n});e=await e.resize(a,i,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}}class K3 extends c0{}class Y3 extends He{}class X3 extends He{}class Q3 extends He{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(r=>r*r))}}class Z3 extends He{}class J3 extends He{}class ek extends He{}class tk extends He{}class p0 extends He{}class rk extends p0{}class h0 extends He{post_process_object_detection(...e){return El(...e)}}class nk extends h0{}class ak extends He{}class ik extends He{}class f0 extends He{pad_image(e,r,n,a={}){const[i,s,o]=r;let l=this.image_mean;Array.isArray(this.image_mean)||(l=new Array(o).fill(l));let u=this.image_std;Array.isArray(u)||(u=new Array(o).fill(l));const p=l.map((h,m)=>-h/u[m]);return super.pad_image(e,r,n,{center:!0,constant_values:p,...a})}}class sk extends f0{}class ok extends He{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new ue("int64",new BigInt64Array(n.reduce((i,s)=>i*s)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return El(...e)}remove_low_and_no_objects(e,r,n,a){let i=[],s=[],o=[];for(let l=0;l<e.dims[0];++l){let u=e[l],p=r[l],h=Kt(u.data)[1];if(h===a)continue;let d=wt(u.data)[h];d>n&&(i.push(p),s.push(d),o.push(h))}return[i,s,o]}check_segment_validity(e,r,n,a=.5,i=.8){let s=[],o=0,l=0;const u=r[n].data;for(let h=0;h<e.length;++h)e[h]===n&&(s.push(h),++o),u[h]>=a&&++l;let p=o>0&&l>0;return p&&(p=o/l>i),[p,s]}compute_segments(e,r,n,a,i,s=null,o=null){let[l,u]=o??e[0].dims,p=new ue("int32",new Int32Array(l*u),[l,u]),h=[];if(o!==null)for(let v=0;v<e.length;++v)e[v]=ol(e[v],o,"bilinear",!1);let m=new Int32Array(e[0].data.length),d=new Float32Array(e[0].data.length);for(let v=0;v<e.length;++v){let S=r[v];const $=e[v].data;for(let E=0;E<$.length;++E)$[E]*=S,$[E]>d[E]&&(m[E]=v,d[E]=$[E])}let _=0;const w=p.data;for(let v=0;v<n.length;++v){let S=n[v],[$,E]=this.check_segment_validity(m,e,v,a,i);if($){++_;for(let T of E)w[T]=_;h.push({id:_,label_id:S,score:r[v]})}}return[p,h]}post_process_panoptic_segmentation(e,r=.5,n=.5,a=.8,i=null,s=null){i===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),i=new Set);const o=e.logits,u=e.pred_masks.sigmoid();let[p,h,m]=o.dims;if(m-=1,s!==null&&s.length!==p)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let d=[];for(let _=0;_<p;++_){let w=s!==null?s[_]:null,v=o[_],S=u[_],[$,E,T]=this.remove_low_and_no_objects(v,S,r,m);if(T.length===0){let[B,D]=w??S.dims.slice(-2),q=new ue("int32",new Int32Array(B*D).fill(-1),[B,D]);d.push({segmentation:q,segments_info:[]});continue}let[A,P]=this.compute_segments($,E,T,n,a,i,w);d.push({segmentation:A,segments_info:P})}return d}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class lk extends He{post_process_object_detection(...e){return El(...e)}}class uk extends He{reshape_input_points(e,r,n,a=!1){e=structuredClone(e);let i=nu(e);if(i.length===3)a||(i=[1,...i]),e=[e];else if(i.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 s=0;s<e.length;++s){let o=r[s],l=n[s],u=[l[0]/o[0],l[1]/o[1]];for(let p=0;p<e[s].length;++p)for(let h=0;h<e[s][p].length;++h)for(let m=0;m<e[s][p][h].length;++m)e[s][p][h][m]*=u[m%2]}return new ue("float32",Float32Array.from(e.flat(1/0)),i)}add_input_labels(e,r){let n=nu(e);if(n.length===2)n=[1,...n],e=[e];else if(n.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(n.some((a,i)=>a!==r.dims[i]))throw Error(`The first ${n.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new ue("int64",e.flat(1/0).map(BigInt),n)}async _call(e,{input_points:r=null,input_labels:n=null,input_boxes:a=null}={}){const i=await super._call(e);if(r&&(i.input_points=this.reshape_input_points(r,i.original_sizes,i.reshaped_input_sizes)),n){if(!i.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");i.input_labels=this.add_input_labels(n,i.input_points)}return a&&(i.input_boxes=this.reshape_input_points(a,i.original_sizes,i.reshaped_input_sizes,!0)),i}async post_process_masks(e,r,n,{mask_threshold:a=0,binarize:i=!0,pad_size:s=null}={}){const o=[];s=s??this.pad_size;const l=[s.height,s.width];for(let u=0;u<r.length;++u){const p=r[u],h=n[u];let m=await hp(e[u],{mode:"bilinear",size:l});if(m=m.slice(null,null,[0,h[0]],[0,h[1]]),m=await hp(m,{mode:"bilinear",size:p}),i){const d=m.data,_=new Uint8Array(d.length);for(let w=0;w<d.length;++w)d[w]>a&&(_[w]=1);m=new ue("bool",_,m.dims)}o.push(m)}return o}generate_crop_boxes(e,r,{crop_n_layers:n=0,overlap_ratio:a=512/1500,points_per_crop:i=32,crop_n_points_downscale_factor:s=1}={}){}}class dk extends He{pad_image(e,r,n,a={}){const[i,s,o]=r;return super.pad_image(e,r,{width:s+(n-s%n)%n,height:i+(n-i%n)%n},{mode:"symmetric",center:!1,constant_values:-1,...a})}}class ck extends He{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);const n=await Promise.all(e.map(s=>this.preprocess(s))),a=await Promise.all(r.map(s=>this.preprocess(s,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:ka(n.map((s,o)=>gr([s.pixel_values,a[o].pixel_values],0)),0),original_sizes:n.map(s=>s.original_size),reshaped_input_sizes:n.map(s=>s.reshaped_input_size)}}}class pk extends pn{constructor(e){super(e),this.config.mel_filters??=Ta(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=ms(this.config.n_fft,"hann")}async _extract_fbank_features(e){const r=await fs(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),n=r.data,a=Kt(n)[0];for(let i=0;i<n.length;++i)n[i]=(Math.max(n[i],a-8)+4)/4;return r}async _call(e){qa(e,"WhisperFeatureExtractor");let r;return e.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),r=e.slice(0,this.config.n_samples)):(r=new Float32Array(this.config.n_samples),r.set(e)),{input_features:(await this._extract_fbank_features(r)).unsqueeze_(0)}}}class hk extends pn{_zero_mean_unit_var_norm(e){const n=e.reduce((i,s)=>i+s,0)/e.length,a=e.reduce((i,s)=>i+(s-n)**2,0)/e.length;return e.map(i=>(i-n)/Math.sqrt(a+1e-7))}async _call(e){qa(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=e;this.config.do_normalize&&(r=this._zero_mean_unit_var_norm(r));const n=[1,r.length];return{input_values:new ue("float32",r,n),attention_mask:new ue("int64",new BigInt64Array(r.length).fill(1n),n)}}}class fk extends pn{constructor(e){super(e);const r=this.config.sampling_rate,n=Ta(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;a<n.length;++a)n[a].push(0);this.mel_filters=n,this.window=ms(400,"povey",{periodic:!1})}async _extract_fbank_features(e,r){return e=e.map(n=>n*32768),fs(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e,{padding:r=!0,pad_to_multiple_of:n=2,do_normalize_per_mel_bins:a=!0,return_attention_mask:i=!0}={}){qa(e,"SeamlessM4TFeatureExtractor");let s=await this._extract_fbank_features(e,this.config.max_length);if(a){const[_,w]=s.dims,v=s.data;for(let S=0;S<w;++S){let $=0;for(let P=0;P<_;++P)$+=v[P*w+S];const E=$/_;let T=0;for(let P=0;P<_;++P)T+=(v[P*w+S]-E)**2;T/=_-1;const A=Math.sqrt(T+1e-7);for(let P=0;P<_;++P){const B=P*w+S;v[B]=(v[B]-E)/A}}}let o;if(r){const[_,w]=s.dims,v=s.data,S=_%n;if(S>0){const $=new Float32Array(w*(_+S));$.set(v),$.fill(this.config.padding_value,v.length);const E=_+S;s=new ue(s.type,$,[E,w]),i&&(o=new ue("int64",new BigInt64Array(E),[1,E]),o.data.fill(1n,0,_))}}const[l,u]=s.dims,p=this.config.stride;if(l%p!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${p}).`);const m=s.view(1,Math.floor(l/p),u*p),d={input_features:m};if(i){const _=m.dims[1],w=new BigInt64Array(_);if(o){const v=o.data;for(let S=1,$=0;S<l;S+=p,++$)w[$]=v[S]}else w.fill(1n);d.attention_mask=new ue("int64",w,[1,_])}return d}}class mk extends pn{constructor(e){super(e);const r=this.config.sampling_rate,n=Ta(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;a<n.length;++a)n[a].push(0);this.mel_filters=n,this.window=ms(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(e,r){return fs(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e){qa(e,"ASTFeatureExtractor");const r=await this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){const n=this.std*2,a=r.data;for(let i=0;i<a.length;++i)a[i]=(a[i]-this.mean)/n}return{input_values:r.unsqueeze_(0)}}}class gk extends pn{constructor(e){super(e),this.mel_filters=Ta(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=Ta(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=ms(this.config.fft_window_size,"hann")}async _get_input_mel(e,r,n,a){let i;const s=e.length-r;if(s>0)if(n==="rand_trunc"){const o=Math.floor(Math.random()*(s+1));e=e.subarray(o,o+r),i=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(s<0){let o=new Float64Array(r);if(o.set(e),a==="repeat")for(let l=e.length;l<r;l+=e.length)o.set(e.subarray(0,Math.min(e.length,r-l)),l);else if(a==="repeatpad")for(let l=e.length;l<-s;l+=e.length)o.set(e,l);e=o}if(n==="fusion")throw new Error(`Truncation strategy "${n}" not implemented`);i=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}return i.unsqueeze_(0)}async _extract_fbank_features(e,r,n=null){return fs(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:r,log_mel:"dB",max_num_frames:n,do_pad:!1,transpose:!0})}async _call(e,{max_length:r=null}={}){return qa(e,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(e,r??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}class _k extends pn{}class Ln extends bt{constructor(e){super(),this.feature_extractor=e}async _call(e,...r){return await this.feature_extractor(e,...r)}}class yk extends Ln{async _call(...e){return await this.feature_extractor(...e)}post_process_masks(...e){return this.feature_extractor.post_process_masks(...e)}reshape_input_points(...e){return this.feature_extractor.reshape_input_points(...e)}}class wk extends Ln{async _call(e){return await this.feature_extractor(e)}}class bk extends Ln{async _call(e){return await this.feature_extractor(e)}}class vk extends Ln{async _call(e){return await this.feature_extractor(e)}}class $k extends Ln{}class Ot{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor:He,WhisperFeatureExtractor:pk,ViTFeatureExtractor:Y3,MobileViTFeatureExtractor:p0,MobileViTImageProcessor:rk,MobileNetV1FeatureExtractor:Z3,MobileNetV2FeatureExtractor:J3,MobileNetV3FeatureExtractor:ek,MobileNetV4FeatureExtractor:tk,OwlViTFeatureExtractor:h0,Owlv2ImageProcessor:nk,CLIPFeatureExtractor:d0,CLIPImageProcessor:H3,ChineseCLIPFeatureExtractor:j3,SiglipImageProcessor:q3,ConvNextFeatureExtractor:c0,ConvNextImageProcessor:K3,SegformerFeatureExtractor:U3,BitImageProcessor:V3,DPTImageProcessor:W3,DPTFeatureExtractor:u0,GLPNFeatureExtractor:G3,BeitFeatureExtractor:ik,DeiTFeatureExtractor:ak,DetrFeatureExtractor:ok,YolosFeatureExtractor:lk,DonutFeatureExtractor:f0,NougatImageProcessor:sk,EfficientNetImageProcessor:Q3,ViTImageProcessor:X3,VitMatteImageProcessor:ck,SamImageProcessor:uk,Swin2SRImageProcessor:dk,Wav2Vec2FeatureExtractor:hk,SeamlessM4TFeatureExtractor:fk,SpeechT5FeatureExtractor:_k,ASTFeatureExtractor:mk,ClapFeatureExtractor:gk};static PROCESSOR_CLASS_MAPPING={WhisperProcessor:wk,Wav2Vec2ProcessorWithLM:bk,SamProcessor:yk,SpeechT5Processor:vk,OwlViTProcessor:$k};static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main"}={}){let o=n??await Br(e,"preprocessor_config.json",!0,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s}),l=o.feature_extractor_type??o.image_processor_type,u=this.FEATURE_EXTRACTOR_CLASS_MAPPING[l];if(!u)if(o.size!==void 0)console.warn(`Feature extractor type "${l}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),u=He;else throw new Error(`Unknown Feature Extractor type: ${l}`);let p=this.PROCESSOR_CLASS_MAPPING[o.processor_class]??Ln,h=new u(o);return new p(h)}}async function Tr(t){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(e=>yt.read(e)))}async function ts(t,e){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(r=>typeof r=="string"||r instanceof URL?O3(r,e):r instanceof Float64Array?new Float32Array(r):r))}function m0(t,e){e&&(t=t.map(s=>s|0));const[r,n,a,i]=t;return{xmin:r,ymin:n,xmax:a,ymax:i}}class tt extends bt{constructor({task:e,model:r,tokenizer:n=null,processor:a=null}){super(),this.task=e,this.model=r,this.tokenizer=n,this.processor=a}async dispose(){await this.model.dispose()}}class xk extends tt{constructor(e){super(e)}async _call(e,{top_k:r=1}={}){const n=this.tokenizer(e,{padding:!0,truncation:!0}),a=await this.model(n),i=this.model.config.problem_type==="multi_label_classification"?l=>l.sigmoid():l=>new ue("float32",wt(l.data),l.dims),s=this.model.config.id2label,o=[];for(const l of a.logits){const u=i(l),p=await Dn(u,r),h=p[0].tolist(),d=p[1].tolist().map((_,w)=>({label:s?s[_]:`LABEL_${_}`,score:h[w]}));r===1?o.push(...d):o.push(d)}return Array.isArray(e)||r===1?o:o[0]}}class Sk extends tt{constructor(e){super(e)}async _call(e,{ignore_labels:r=["O"]}={}){const n=Array.isArray(e),a=this.tokenizer(n?e:[e],{padding:!0,truncation:!0}),s=(await this.model(a)).logits,o=this.model.config.id2label,l=[];for(let u=0;u<s.dims[0];++u){const p=a.input_ids[u],h=s[u],m=[];for(let d=0;d<h.dims[0];++d){const _=h[d],w=Kt(_.data)[1],v=o?o[w]:`LABEL_${w}`;if(r.includes(v))continue;const S=this.tokenizer.decode([p[d].item()],{skip_special_tokens:!0});if(S==="")continue;const $=wt(_.data);m.push({entity:v,score:$[w],index:d,word:S})}l.push(m)}return n?l:l[0]}}class kk extends tt{constructor(e){super(e)}async _call(e,r,{top_k:n=1}={}){const a=this.tokenizer(e,{text_pair:r,padding:!0,truncation:!0}),{start_logits:i,end_logits:s}=await this.model(a),o=a.input_ids.tolist(),l=a.attention_mask.tolist(),u=this.tokenizer.all_special_ids,p=[];for(let h=0;h<i.dims[0];++h){const m=o[h],d=m.findIndex(E=>E==this.tokenizer.sep_token_id);l[h].map((E,T)=>E==1&&(T===0||T>d&&u.findIndex(A=>A==m[T])===-1));const _=i[h].tolist(),w=s[h].tolist();for(let E=1;E<_.length;++E)(l[h]==0||E<=d||u.findIndex(T=>T==m[E])!==-1)&&(_[E]=-1/0,w[E]=-1/0);const v=wt(_).map((E,T)=>[E,T]),S=wt(w).map((E,T)=>[E,T]);v[0][0]=0,S[0][0]=0;const $=P0(v,S).filter(E=>E[0][1]<=E[1][1]).map(E=>[E[0][1],E[1][1],E[0][0]*E[1][0]]).sort((E,T)=>T[2]-E[2]);for(let E=0;E<Math.min($.length,n);++E){const[T,A,P]=$[E],B=m.slice(T,A+1),D=this.tokenizer.decode(B,{skip_special_tokens:!0});p.push({answer:D,score:P})}}return n===1?p[0]:p}}class Ek extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=this.tokenizer(e,{padding:!0,truncation:!0}),{logits:a}=await this.model(n),i=[],s=n.input_ids.tolist();for(let o=0;o<s.length;++o){const l=s[o],u=l.findIndex(_=>_==this.tokenizer.mask_token_id);if(u===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const p=a[o][u],h=await Dn(new ue("float32",wt(p.data),p.dims),r),m=h[0].tolist(),d=h[1].tolist();i.push(d.map((_,w)=>{const v=l.slice();return v[u]=_,{score:m[w],token:Number(_),token_str:this.tokenizer.model.vocab[_],sequence:this.tokenizer.decode(v,{skip_special_tokens:!0})}}))}return Array.isArray(e)?i:i[0]}}class Cl extends tt{_key="generated_text";constructor(e){super(e)}async _call(e,r={}){Array.isArray(e)||(e=[e]),this.model.config.prefix&&(e=e.map(l=>this.model.config.prefix+l));const n=this.model.config.task_specific_params;n&&n[this.task]&&n[this.task].prefix&&(e=e.map(l=>n[this.task].prefix+l));const a=this.tokenizer,i={padding:!0,truncation:!0};let s;this instanceof g0&&"_build_translation_inputs"in a?s=a._build_translation_inputs(e,i,r):s=a(e,i);const o=await this.model.generate({...s,...r});return a.batch_decode(o,{skip_special_tokens:!0}).map(l=>({[this._key]:l}))}}class Ck extends Cl{_key="summary_text";constructor(e){super(e)}}class g0 extends Cl{_key="translation_text";constructor(e){super(e)}}class Tk extends tt{constructor(e){super(e)}async _call(e,r={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class Ak extends tt{constructor(e){super(e),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([r,n])=>[r.toLowerCase(),n])),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(e,r,{hypothesis_template:n="This example is {}.",multi_label:a=!1}={}){const i=Array.isArray(e);i||(e=[e]),Array.isArray(r)||(r=[r]);const s=r.map(u=>n.replace("{}",u)),o=a||r.length===1,l=[];for(const u of e){const p=[];for(const d of s){const _=this.tokenizer(u,{text_pair:d,padding:!0,truncation:!0}),w=await this.model(_);o?p.push([w.logits.data[this.contradiction_id],w.logits.data[this.entailment_id]]):p.push(w.logits.data[this.entailment_id])}const m=(o?p.map(d=>wt(d)[1]):wt(p)).map((d,_)=>[d,_]).sort((d,_)=>_[0]-d[0]);l.push({sequence:u,labels:m.map(d=>r[d[1]]),scores:m.map(d=>d[0])})}return i?l:l[0]}}class Ik extends tt{constructor(e){super(e)}async _call(e,{pooling:r="none",normalize:n=!1,quantize:a=!1,precision:i="binary"}={}){const s=this.tokenizer(e,{padding:!0,truncation:!0}),o=await this.model(s);let l=o.last_hidden_state??o.logits??o.token_embeddings;if(r!=="none")if(r==="mean")l=Aw(l,s.attention_mask);else if(r==="cls")l=l.slice(null,0);else throw Error(`Pooling method '${r}' not supported.`);return n&&(l=l.normalize(2,-1)),a&&(l=Nw(l,i)),l}}class Mk extends tt{constructor(e){super(e)}async _call(e,{pool:r=null}={}){const n=await Tr(e),{pixel_values:a}=await this.processor(n),i=await this.model({pixel_values:a});let s;if(r){if(!("pooler_output"in i))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");s=i.pooler_output}else s=i.last_hidden_state??i.logits??i.image_embeds;return s}}class Ok extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=this.processor.feature_extractor.config.sampling_rate,a=await ts(e,n),i=this.model.config.id2label,s=[];for(const o of a){const l=await this.processor(o),p=(await this.model(l)).logits[0],h=await Dn(new ue("float32",wt(p.data),p.dims),r),m=h[0].tolist(),_=h[1].tolist().map((w,v)=>({label:i?i[w]:`LABEL_${w}`,score:m[v]}));s.push(_)}return Array.isArray(e)?s:s[0]}}class zk extends tt{constructor(e){super(e)}async _call(e,r,{hypothesis_template:n="This is a sound of {}."}={}){const a=!Array.isArray(e);a&&(e=[e]);const i=r.map(p=>n.replace("{}",p)),s=this.tokenizer(i,{padding:!0,truncation:!0}),o=this.processor.feature_extractor.config.sampling_rate,l=await ts(e,o),u=[];for(const p of l){const h=await this.processor(p),m=await this.model({...s,...h}),d=wt(m.logits_per_audio.data);u.push([...d].map((_,w)=>({score:_,label:r[w]})))}return a?u[0]:u}}class Pk extends tt{constructor(e){super(e)}async _call(e,r={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(e,r);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(e,r);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,r){r.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),r.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const n=!Array.isArray(e);n&&(e=[e]);const a=this.processor.feature_extractor.config.sampling_rate,i=await ts(e,a),s=[];for(const o of i){const l=await this.processor(o),p=(await this.model(l)).logits[0],h=[];for(const d of p)h.push(Kt(d.data)[1]);const m=this.tokenizer.decode(h);s.push({text:m})}return n?s[0]:s}async _call_whisper(e,r){const n=r.return_timestamps??!1,a=r.chunk_length_s??0,i=r.force_full_sequences??!1;let s=r.stride_length_s??null;n==="word"&&(r.return_token_timestamps=!0);const o=!Array.isArray(e);o&&(e=[e]);const l=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,u=this.processor.feature_extractor.config.hop_length,p=this.processor.feature_extractor.config.sampling_rate,h=await ts(e,p),m=[];for(const d of h){let _=[];if(a>0){if(s===null)s=a/6;else if(a<=s)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const S=p*a,$=p*s,E=S-2*$;let T=0;for(;T<d.length;){const A=d.subarray(T,T+S),P=await this.processor(A),B=T===0,D=T+E>=d.length;_.push({stride:[A.length,B?0:$,D?0:$],input_features:P.input_features,is_last:D}),T+=E}}else _=[{stride:[d.length,0,0],input_features:(await this.processor(d)).input_features,is_last:!0}];for(const S of _){r.num_frames=Math.floor(S.stride[0]/u);const $=await this.model.generate({inputs:S.input_features,...r});n==="word"?(S.tokens=$.sequences[0].tolist(),S.token_timestamps=$.token_timestamps.tolist()[0].map(E=>Mi(E,2))):S.tokens=$[0].tolist(),S.stride=S.stride.map(E=>E/p)}const[w,v]=this.tokenizer._decode_asr(_,{time_precision:l,return_timestamps:n,force_full_sequences:i});m.push({text:w,...v})}return o?m[0]:m}}class Rk extends tt{constructor(e){super(e)}async _call(e,r={}){const n=Array.isArray(e),a=await Tr(e),{pixel_values:i}=await this.processor(a),s=[];for(const o of i){o.dims=[1,...o.dims];const l=await this.model.generate({inputs:o,...r}),u=this.tokenizer.batch_decode(l,{skip_special_tokens:!0}).map(p=>({generated_text:p.trim()}));s.push(u)}return n?s:s[0]}}class Bk extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=await Tr(e),{pixel_values:a}=await this.processor(n),i=await this.model({pixel_values:a}),s=this.model.config.id2label,o=[];for(const l of i.logits){const u=await Dn(new ue("float32",wt(l.data),l.dims),r),p=u[0].tolist(),m=u[1].tolist().map((d,_)=>({label:s?s[d]:`LABEL_${d}`,score:p[_]}));o.push(m)}return Array.isArray(e)?o:o[0]}}class Dk extends tt{constructor(e){super(e),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(e,{threshold:r=.5,mask_threshold:n=.5,overlap_mask_area_threshold:a=.8,label_ids_to_fuse:i=null,target_sizes:s=null,subtask:o=null}={}){if(Array.isArray(e)&&e.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const u=await Tr(e),p=u.map(S=>[S.height,S.width]),{pixel_values:h,pixel_mask:m}=await this.processor(u),d=await this.model({pixel_values:h,pixel_mask:m});let _=null;if(o!==null)_=this.subtasks_mapping[o];else for(let[S,$]of Object.entries(this.subtasks_mapping))if($ in this.processor.feature_extractor){_=this.processor.feature_extractor[$].bind(this.processor.feature_extractor),o=S;break}const w=this.model.config.id2label,v=[];if(o==="panoptic"||o==="instance"){const S=_(d,r,n,a,i,s??p)[0],$=S.segmentation;for(const E of S.segments_info){const T=new Uint8ClampedArray($.data.length);for(let P=0;P<$.data.length;++P)$.data[P]===E.id&&(T[P]=255);const A=new yt(T,$.dims[1],$.dims[0],1);v.push({score:E.score,label:w[E.label_id],mask:A})}}else if(o==="semantic"){const{segmentation:S,labels:$}=_(d,s??p)[0];for(const E of $){const T=new Uint8ClampedArray(S.data.length);for(let P=0;P<S.data.length;++P)S.data[P]===E&&(T[P]=255);const A=new yt(T,S.dims[1],S.dims[0],1);v.push({score:null,label:w[E],mask:A})}}else throw Error(`Subtask ${o} not supported.`);return v}}class Nk extends tt{constructor(e){super(e)}async _call(e,r,{hypothesis_template:n="This is a photo of {}"}={}){const a=Array.isArray(e),i=await Tr(e),s=r.map(m=>n.replace("{}",m)),o=this.tokenizer(s,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:l}=await this.processor(i),u=await this.model({...o,pixel_values:l}),p=this.model.config.model_type==="siglip"?m=>m.sigmoid().data:m=>wt(m.data),h=[];for(const m of u.logits_per_image){const _=[...p(m)].map((w,v)=>({score:w,label:r[v]}));_.sort((w,v)=>v.score-w.score),h.push(_)}return a?h:h[0]}}class Fk extends tt{constructor(e){super(e)}async _call(e,{threshold:r=.9,percentage:n=!1}={}){const a=Array.isArray(e);if(a&&e.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const i=await Tr(e),s=n?null:i.map(d=>[d.height,d.width]),{pixel_values:o,pixel_mask:l}=await this.processor(i),u=await this.model({pixel_values:o,pixel_mask:l}),p=this.processor.feature_extractor.post_process_object_detection(u,r,s),h=this.model.config.id2label,m=p.map(d=>d.boxes.map((_,w)=>({score:d.scores[w],label:h[d.classes[w]],box:m0(_,!n)})));return a?m:m[0]}}class Lk extends tt{constructor(e){super(e)}async _call(e,r,{threshold:n=.1,top_k:a=null,percentage:i=!1}={}){const s=Array.isArray(e),o=await Tr(e),l=this.tokenizer(r,{padding:!0,truncation:!0}),u=await this.processor(o),p=[];for(let h=0;h<o.length;++h){const m=o[h],d=i?null:[[m.height,m.width]],_=u.pixel_values[h].unsqueeze_(0),w=await this.model({...l,pixel_values:_}),v=this.processor.feature_extractor.post_process_object_detection(w,n,d,!0)[0];let S=v.boxes.map(($,E)=>({score:v.scores[E],label:r[v.classes[E]],box:m0($,!i)})).sort(($,E)=>E.score-$.score);a!==null&&(S=S.slice(0,a)),p.push(S)}return s?p:p[0]}}class Uk extends tt{constructor(e){super(e)}async _call(e,r,n={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class Wk extends tt{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(e){super(e),this.vocoder=e.vocoder??null}async _call(e,{speaker_embeddings:r=null}={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}async _call_text_to_waveform(e){const r=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:n}=await this.model(r),a=this.model.config.sampling_rate;return{audio:n.data,sampling_rate:a}}async _call_text_to_spectrogram(e,{speaker_embeddings:r}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await ma.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof r=="string"||r instanceof URL)&&(r=new Float32Array(await(await fetch(r)).arrayBuffer())),r instanceof Float32Array)r=new ue("float32",r,[1,r.length]);else if(!(r instanceof ue))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:n}=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:a}=await this.model.generate_speech(n,r,{vocoder:this.vocoder}),i=this.processor.feature_extractor.config.sampling_rate;return{audio:a.data,sampling_rate:i}}}class Vk extends tt{constructor(e){super(e)}async _call(e){const r=await Tr(e),n=await this.processor(r),a=await this.model(n),i=[];for(const s of a.reconstruction){const o=s.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");i.push(yt.fromTensor(o))}return i.length>1?i:i[0]}}class Gk extends tt{constructor(e){super(e)}async _call(e){const r=await Tr(e),n=await this.processor(r),{predicted_depth:a}=await this.model(n),i=[];for(let s=0;s<r.length;++s){const o=ol(a[s],r[s].size.reverse(),"bilinear",!1),l=o.mul_(255/Kt(o.data)[0]).to("uint8");i.push({predicted_depth:a[s],depth:yt.fromTensor(l)})}return i.length>1?i:i[0]}}const Ip=Object.freeze({"text-classification":{tokenizer:ht,pipeline:xk,model:Ep,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:ht,pipeline:Sk,model:o3,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:ht,pipeline:kk,model:h3,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:ht,pipeline:Ek,model:p3,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:ht,pipeline:Ck,model:ho,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:ht,pipeline:g0,model:ho,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:ht,pipeline:Cl,model:ho,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:ht,pipeline:Tk,model:c3,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:ht,pipeline:Ak,model:Ep,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:Ok,model:v3,processor:Ot,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:ht,pipeline:zk,model:ma,processor:Ot,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:ht,pipeline:Pk,model:[l3,b3],processor:Ot,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:ht,pipeline:Wk,model:[d3,u3],processor:[Ot,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:ht,pipeline:Rk,model:f3,processor:Ot,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Bk,model:m3,processor:Ot,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Dk,model:[g3,_3],processor:Ot,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:ht,pipeline:Nk,model:ma,processor:Ot,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:Fk,model:y3,processor:Ot,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:ht,pipeline:Lk,model:w3,processor:Ot,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:ht,pipeline:Uk,model:$3,processor:Ot,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:Vk,model:x3,processor:Ot,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Gk,model:S3,processor:Ot,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:ht,pipeline:Ik,model:ma,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:Ot,pipeline:Mk,model:[k3,ma],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Hk=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function jk(t,e=null,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",device:o=null,dtype:l=null,model_file_name:u=null,session_options:p={}}={}){t=Hk[t]??t;const h=Ip[t.split("_",1)[0]];if(!h)throw Error(`Unsupported pipeline: ${t}. Must be one of [${Object.keys(Ip)}]`);e||(e=h.default.model,console.log(`No model specified. Using default model: "${e}".`));const m={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,device:o,dtype:l,model_file_name:u,session_options:p},d=new Map([["tokenizer",h.tokenizer],["model",h.model],["processor",h.processor]]),_=await qk(d,e,m);_.task=t,An(r,{status:"ready",task:t,model:e});const w=h.pipeline;return new w(_)}async function qk(t,e,r){const n=Object.create(null),a=[];for(let[i,s]of t.entries()){if(!s)continue;let o;Array.isArray(s)?o=new Promise(async(l,u)=>{let p;for(let h of s){if(h===null){l(null);return}try{l(await h.from_pretrained(e,r));return}catch(m){if(m.message?.includes("Unsupported model type"))p=m;else{u(m);return}}}u(p)}):o=s.from_pretrained(e,r),n[i]=o,a.push(o)}await Promise.all(a);for(let[i,s]of Object.entries(n))n[i]=await s;return n}an.IS_PROCESS_AVAILABLE;const gs=document.getElementById("status"),Mp=document.getElementById("container"),ga=document.getElementById("video"),go=document.getElementById("overlay");gs.textContent="Loading model (16 MB)...";const Kk="onnx-community/mobilenetv4_conv_small.e2400_r224_in1k";let _0;try{_0=await jk("image-classification",Kk,{dtype:"fp32"})}catch(t){throw gs.textContent=t.message,alert(t.message),t}gs.textContent="Ready";const Yk=.1,Zr=256,Bo=document.createElement("canvas");Bo.width=Bo.height=Zr;const Op=Bo.getContext("2d",{willReadFrequently:!0});let _o=!1,yo;function y0(){_o||(_o=!0,async function(){Op.drawImage(ga,0,0,Zr,Zr);const t=Op.getImageData(0,0,Zr,Zr).data,e=new yt(t,Zr,Zr,4),r=await _0(e,{top_k:null});go.innerHTML="";for(const{label:n,score:a}of r){if(a<Yk)break;go.appendChild(document.createTextNode(`${n}: ${a.toFixed(2)}`)),go.appendChild(document.createElement("br"))}if(yo!==void 0){const n=1e3/(performance.now()-yo);gs.textContent=`FPS: ${n.toFixed(2)}`}yo=performance.now(),_o=!1}()),window.requestAnimationFrame(y0)}navigator.mediaDevices.getUserMedia({video:!0}).then(t=>{ga.srcObject=t,ga.play();const e=t.getVideoTracks()[0],{width:r,height:n}=e.getSettings();ga.width=r,ga.height=n;const a=r/n,[i,s]=a>720/405?[720,720/a]:[405*a,405];Mp.style.width=`${i}px`,Mp.style.height=`${s}px`,window.requestAnimationFrame(y0)}).catch(t=>{alert(t)});