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i=e[a],o=e[a+1],l=e[a+s],u=e[a+s+1];r[n]=i+l,r[n+1]=o+u,r[n+2]=i-l,r[n+3]=o-u}_singleTransform4(e,r,n,a,s,i){const o=s*2,l=s*3,u=e[a],d=e[a+1],h=e[a+s],m=e[a+s+1],g=e[a+o],p=e[a+o+1],w=e[a+l],v=e[a+l+1],S=u+g,$=d+p,E=u-g,T=d-p,A=h+w,z=m+v,R=i*(h-w),V=i*(m-v);r[n]=S+A,r[n+1]=$+z,r[n+2]=E+V,r[n+3]=T-R,r[n+4]=S-A,r[n+5]=$-z,r[n+6]=E-V,r[n+7]=T+R}_realTransform4(e,r,n){const a=this._csize;let i=1<<this._width,o=a/i<<1,l,u;const d=this._bitrev;if(o===4)for(l=0,u=0;l<a;l+=o,++u){const g=d[u];this._singleRealTransform2(r,e,l,g>>>1,i>>>1)}else for(l=0,u=0;l<a;l+=o,++u){const g=d[u];this._singleRealTransform4(r,e,l,g>>>1,i>>>1,n)}const h=this.table;for(i>>=2;i>=2;i>>=2){o=a/i<<1;const g=o>>>1,p=g>>>1,w=p>>>1;for(l=0;l<a;l+=o)for(let v=0,S=0;v<=w;v+=2,S+=i){const 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 * ONNX Runtime Web v1.18.0
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
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computePoolOutputShape(e,r,n,a,s,i,o){if(r.length<=0)throw new Error("input shape must be of size greater than 0");let l=[r[0],r[1]];return Za.computeShapeHelper(e,r,l,n,a,s,i,o),l}static computeConvOutputShape(e,r,n,a,s,i,o){if(e.length<=0||r.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let l=[e[0],r[0]];return Za.computeShapeHelper(!1,e,l,n,a,s,i,o),l}static computeShapeHelper(e,r,n,a,s,i,o,l){if(e)for(let u=0;u<r.length-2;u++)n.push(1);else for(let u=0;u<r.length-2;u++)n.push(Za.adjustPadAndReturnShape(r[u+2],a[u],s[u],i[u],o,u,u+r.length-2,l))}static adjustPadAndReturnShape(e,r,n,a,s,i,o,l){let u=n*(a-1)+1;if(l&&l!=="NOTSET")switch(l){case"VALID":return s[i]=0,s[o]=0,Math.floor((e-u)/r+1);case"SAME_LOWER":case"SAME_UPPER":if(n!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let d=((e+r-1)/r-1)*r+a-e;return s[i]=Math.floor(l==="SAME_LOWER"?(d+1)/2:d/2),s[o]=d-s[i],Math.floor((e+d-a)/r+1)}default:throw new 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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: ${t}`)}},_t=(t,e=1)=>{let r=fi(t,e);return typeof r=="string"?r:r[0]},Ot=(t,e=1)=>{let r=fi(t,e);return typeof r=="string"?r:r[1]},we=(...t)=>{let e=[];return t.forEach(r=>{r.length!==0&&e.push({type:12,data:r},{type:12,data:X.computeStrides(r)})}),e},st=t=>t%4===0?4:t%2===0?2:1,Sr=(t="f32",e,r="0")=>!e||e===1?`${t}(${r})`:`vec${e}<${t}>(${r})`,kn=(t,e,r)=>t==="f32"?r:e===1?`f32(${r})`:`vec${e}<f32>(${r})`,Dr=(t,e)=>e===4?`(${t}.x + ${t}.y + ${t}.z + ${t}.w)`:e===2?`(${t}.x + ${t}.y)`:e===3?`(${t}.x + ${t}.y + ${t}.z)`:t,ke=(t,e,r,n)=>t.startsWith("uniforms.")&&r>4?typeof e=="string"?n==="f16"?`${t}[(${e}) / 8][(${e}) % 8 / 4][(${e}) % 8 % 4]`:`${t}[(${e}) / 4][(${e}) % 4]`:n==="f16"?`${t}[${Math.floor(e/8)}][${Math.floor(e%8/4)}][${e%8%4}]`:`${t}[${Math.floor(e/4)}][${e%4}]`:r>1?`${t}[${e}]`:t,mi=(t,e,r,n,a)=>{let s=typeof r=="number",i=s?r:r.length,o=[...new Array(i).keys()],l=i<2?"u32":i<=4?`vec${i}<u32>`:`array<u32, ${i}>`,u=fi(e,a),d=typeof u=="string"?u:u[1],h=typeof u=="string"?u:u[0],m={indices:l,value:d,storage:h,tensor:e},g=N=>typeof N=="string"?N:`${N}u`,p={offsetToIndices:!1,indicesToOffset:!1,broadcastedIndicesToOffset:!1,set:!1,setByIndices:!1,get:!1,getByIndices:!1},w=s?"uniforms.":"",v=`${w}${t}_shape`,S=`${w}${t}_strides`,$="";for(let N=0;N<i-1;N++)$+=`
    let dim${N} = current / ${ke(S,N,i)};
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    indices[${N}] = dim${N};
    current = rest${N};
    `;$+=`indices[${i-1}] = current;`;let E=i<2?"":`
  fn o2i_${t}(offset: u32) -> ${m.indices} {
    var indices: ${m.indices};
    var current = offset;
    ${$}
    return indices;
  }`,T=N=>(p.offsetToIndices=!0,i<2?N:`o2i_${t}(${N})`),A=[];if(i>=2)for(let N=i-1;N>=0;N--)A.push(`${ke(S,N,i)} * (indices[${N}])`);let z=i<2?"":`
  fn i2o_${t}(indices: ${m.indices}) -> u32 {
    return ${A.join("+")};
  }`,R=N=>(p.indicesToOffset=!0,i<2?N:`i2o_${t}(${N})`),V=(...N)=>i===0?"0u":`${m.indices}(${N.map(g).join(",")})`,j=(N,ae)=>i<2?`${N}`:`${ke(N,ae,i)}`,q=(N,ae,de)=>i<2?`${N}=${de};`:`${ke(N,ae,i)}=${de};`,fe={},ee=(N,ae)=>{p.broadcastedIndicesToOffset=!0;let de=`${ae.name}broadcastedIndicesTo${t}Offset`;if(de in fe)return`${de}(${N})`;let Se=[];for(let Te=i-1;Te>=0;Te--){let Xe=ae.indicesGet("outputIndices",Te+ae.rank-i);Se.push(`${j(S,Te)} * (${Xe} % ${j(v,Te)})`)}return fe[de]=`fn ${de}(outputIndices: ${ae.type.indices}) -> u32 {
             return ${Se.length>0?Se.join("+"):"0u"};
           }`,`${de}(${N})`},ne=(N,ae)=>(()=>{if(m.storage===m.value)return`${t}[${N}]=${ae};`;if(m.storage==="vec2<u32>"&&m.value==="i32")return`${t}[${N}]=vec2<u32>(u32(${ae}), select(0u, 0xFFFFFFFFu, ${ae} < 0));`;if(m.storage==="vec2<u32>"&&m.value==="u32")return`${t}[${N}]=vec2<u32>(u32(${ae}), 0u);`;if(m.storage==="u32"&&m.value==="vec4<bool>")return`${t}[${N}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${ae}));`;throw new Error(`not supported combination of storage type ${m.storage} and value type ${m.value} yet`)})(),se=N=>(()=>{if(m.storage===m.value)return`${t}[${N}]`;if(m.storage==="vec2<u32>"&&m.value==="i32")return`i32(${t}[${N}].x)`;if(m.storage==="vec2<u32>"&&m.value==="u32")return`u32(${t}[${N}].x)`;if(m.storage==="u32"&&m.value==="vec4<bool>")return`vec4<bool>(bool(${t}[${N}] & 0xFFu), bool(${t}[${N}] & 0xFF00u), bool(${t}[${N}] & 0xFF0000u), bool(${t}[${N}] & 0xFF000000u))`;throw new Error(`not supported combination of storage type ${m.storage} and value type ${m.value} yet`)})(),D=i<2?"":`
  fn get_${t}ByIndices(indices: ${m.indices}) -> ${d} {
    return ${se(`i2o_${t}(indices)`)};
  }`,M=i<2?"":(()=>{let N=o.map(de=>`d${de}: u32`).join(", "),ae=o.map(de=>`d${de}`).join(", ");return`
  fn get_${t}(${N}) -> ${d} {
    return get_${t}ByIndices(${V(ae)});
  }`})(),G=(...N)=>{if(N.length!==i)throw new Error(`indices length must be ${i}`);let ae=N.map(g).join(",");return i===0?se("0u"):i===1?se(ae[0]):(p.get=!0,p.getByIndices=!0,p.indicesToOffset=!0,`get_${t}(${ae})`)},K=N=>i<2?se(N):(p.getByIndices=!0,p.indicesToOffset=!0,`get_${t}ByIndices(${N})`),pe=i<2?"":`
  fn set_${t}ByIndices(indices: ${m.indices}, value: ${d}) {
    ${ne(`i2o_${t}(indices)`,"value")}
  }`,le=i<2?"":(()=>{let N=o.map(de=>`d${de}: u32`).join(", "),ae=o.map(de=>`d${de}`).join(", ");return`
  fn set_${t}(${N}, value: ${d}) {
    set_${t}ByIndices(${V(ae)}, value);
  }`})();return{impl:()=>{let N=[],ae=!1;return p.offsetToIndices&&(N.push(E),ae=!0),p.indicesToOffset&&(N.push(z),ae=!0),p.broadcastedIndicesToOffset&&(Object.values(fe).forEach(de=>N.push(de)),ae=!0),p.set&&(N.push(le),ae=!0),p.setByIndices&&(N.push(pe),ae=!0),p.get&&(N.push(M),ae=!0),p.getByIndices&&(N.push(D),ae=!0),!s&&ae&&N.unshift(`const ${v} = ${m.indices}(${r.join(",")});`,`const ${S} = ${m.indices}(${X.computeStrides(r).join(",")});`),N.join(`
`)},type:m,offsetToIndices:T,indicesToOffset:R,broadcastedIndicesToOffset:ee,indices:V,indicesGet:j,indicesSet:q,set:(...N)=>{if(N.length!==i+1)throw new Error(`indices length must be ${i}`);let ae=N[i];if(typeof ae!="string")throw new Error("value must be string");let de=N.slice(0,i).map(g).join(",");return i===0?ne("0u",ae):i===1?ne(de[0],ae):(p.set=!0,p.setByIndices=!0,p.indicesToOffset=!0,`set_${t}(${de}, ${ae})`)},setByOffset:ne,setByIndices:(N,ae)=>i<2?ne(N,ae):(p.setByIndices=!0,p.indicesToOffset=!0,`set_${t}ByIndices(${N}, ${ae});`),get:G,getByOffset:se,getByIndices:K,usage:n,name:t,strides:S,shape:v,rank:i}},Q=(t,e,r,n=1)=>mi(t,e,r,"input",n),_e=(t,e,r,n=1)=>mi(t,e,r,"output",n),Qs=(t,e,r,n=1)=>mi(t,e,r,"internal",n),Du=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=Sn){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,s=a?`@builtin(global_invocation_id) global_id : vec3<u32>,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(local_invocation_id) local_id : vec3<u32>`:`@builtin(global_invocation_id) global_id : vec3<u32>,
                                             @builtin(local_invocation_id) local_id : vec3<u32>,
    @builtin(local_invocation_index) local_idx : u32,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(num_workgroups) num_workgroups : vec3<u32>`,i=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}, ${n})
  fn main(${s}) {
    ${i}
  `}appendVariableUniforms(t){t.rank!==0&&(t.shape.startsWith("uniforms.")&&this.uniforms.push({name:t.shape.replace("uniforms.",""),type:"u32",length:t.rank}),t.strides.startsWith("uniforms.")&&this.uniforms.push({name:t.strides.replace("uniforms.",""),type:"u32",length:t.rank}))}declareVariable(t,e){if(t.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(t),this.appendVariableUniforms(t);let r=t.usage==="input"?"read":"read_write",n=t.type.storage;return`@group(0) @binding(${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])}},Nu=(t,e)=>new Du(t,e),na=(t,e)=>{let r=t.length,n=[];for(let a=0;a<r;a++){let s=r-1-a,i=t[s]||1;(e[e.length-1-a]||1)>1&&i===1&&n.unshift(s)}return n}}),Fu,Zs,Lu,Uu,kr,Wu,Vu,aa=te(()=>{xe(),Oe(),ct(),Ie(),Fu=t=>{if(!t||t.length!==1)throw new Error("Transpose requires 1 input.")},Zs=(t,e)=>e&&e.length!==t?[...new Array(t).keys()].reverse():e,Lu=(t,e)=>X.sortBasedOnPerm(t,Zs(t.length,e)),Uu=(t,e,r,n)=>{let a=[];a.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
    var a: ${r.type.indices};`);for(let s=0;s<e;++s)a.push(r.indicesSet("a",t[s],`i[${s}]`));return a.push("return a;}"),a.join(`
`)},kr=(t,e)=>{let r=t.dataType,n=t.dims.length,a=Zs(n,e),s=Lu(t.dims,a),i=_e("output",r,s.length),o=Q("a",r,n),l=u=>`
  ${u.registerUniform("output_size","u32").declareVariables(o,i)}

  ${Uu(a,n,o,i)}

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

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

    ${i.setByOffset("global_idx",o.getByIndices("aIndices"))}
  }`;return{name:"Transpose",shaderCache:{hint:`${e}`,inputDependencies:["rank"]},getRunData:u=>{let d=X.size(s);return{outputs:[{dims:s,dataType:u[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:[{type:12,data:d},...we(u[0].dims,s)]}},getShaderSource:l}},Wu=(t,e)=>{Fu(t.inputs),t.compute(kr(t.inputs[0],e.perm))},Vu=t=>Ke({perm:t.perm})}),Gu,Hu,ju,qu,Ku,Yu,Xu,Qu,Zu,Ju,nr,ed,td,rd,nd,ad,id,sd,od,ld,ud,zy=te(()=>{xe(),Oe(),Ie(),eo(),aa(),Gu={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"},Hu={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"},ju={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},qu={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},Ku=(t,e)=>{let r=[];for(let n=e-t;n<e;++n)r.push(n);return r},Yu=(t,e)=>{let r=[],n=t.length;for(let s=0;s<n;s++)e.indexOf(s)===-1&&r.push(t[s]);let a=e.map(s=>t[s]);return[r,a]},Xu=(t,e)=>{let r=t.length+e.length,n=[],a=0;for(let s=0;s<r;s++)e.indexOf(s)===-1?n.push(t[a++]):n.push(1);return n},Qu=(t,e)=>{for(let r=0;r<t.length;++r)if(t[t.length-r-1]!==e-1-r)return!1;return!0},Zu=(t,e)=>{let r=[];if(!Qu(t,e)){for(let n=0;n<e;++n)t.indexOf(n)===-1&&r.push(n);t.forEach(n=>r.push(n))}return r},Ju=(t,e,r,n,a,s,i)=>{let o=r[0].dims,l=X.size(s),u=X.size(i),d=Q("_A",r[0].dataType,o),h=_e("output",a,s),m=32,g=`
          var<workgroup> aBestValues : array<f32, ${m}>;
       `;return{name:t,shaderCache:e,getShaderSource:p=>`
        ${p.registerUniform("reduceSize","u32").declareVariables(d,h)}
        ${g}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${p.mainStart(m)}

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

          var bestValue = f32(${ju[n]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${m}) {
           let candidate = f32(${d.getByOffset("offset + k")});
           bestValue = ${Gu[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 = ${Hu[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}(${qu[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:s,dataType:a}],dispatchGroup:{x:l},programUniforms:[{type:12,data:u}]})}},nr=(t,e,r,n)=>{let a=t.inputs.length===1?r:Js(t.inputs,r),s=a.axes;s.length===0&&!a.noopWithEmptyAxes&&(s=t.inputs[0].dims.map((g,p)=>p));let i=X.normalizeAxes(s,t.inputs[0].dims.length),o=i,l=t.inputs[0],u=Zu(o,t.inputs[0].dims.length);u.length>0&&(l=t.compute(kr(t.inputs[0],u),{inputs:[0],outputs:[-1]})[0],o=Ku(o.length,l.dims.length));let[d,h]=Yu(l.dims,o),m=d;a.keepDims&&(m=Xu(d,i)),t.compute(Ju(e,{hint:a.cacheKey,inputDependencies:["type"]},[l],n,t.inputs[0].dataType,m,h),{inputs:[l]})},ed=(t,e)=>{nr(t,"ReduceMeanShared",e,"mean")},td=(t,e)=>{nr(t,"ReduceL1Shared",e,"l1")},rd=(t,e)=>{nr(t,"ReduceL2Shared",e,"l2")},nd=(t,e)=>{nr(t,"ReduceLogSumExpShared",e,"logSumExp")},ad=(t,e)=>{nr(t,"ReduceMaxShared",e,"max")},id=(t,e)=>{nr(t,"ReduceMinShared",e,"min")},sd=(t,e)=>{nr(t,"ReduceProdShared",e,"prod")},od=(t,e)=>{nr(t,"ReduceSumShared",e,"sum")},ld=(t,e)=>{nr(t,"ReduceSumSquareShared",e,"sumSquare")},ud=(t,e)=>{nr(t,"ReduceLogSumShared",e,"logSum")}}),ar,dd,gi,Js,ir,cd,pd,hd,fd,md,gd,_d,yd,wd,bd,sr,vd,$d,xd,Sd,kd,Ed,Cd,Td,Ad,Id,eo=te(()=>{xe(),Oe(),ct(),Ie(),zy(),ar=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.")},dd=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],gi=(t,e,r,n,a,s,i=!1,o=!1)=>{let l=[],u=r[0].dims,d=u.length,h=X.normalizeAxes(a,d),m=!o&&h.length===0;u.forEach((w,v)=>{m||h.indexOf(v)>=0?i&&l.push(1):l.push(w)});let g=l.length,p=X.size(l);return{name:t,shaderCache:e,getShaderSource:w=>{let v=[],S=Q("_A",r[0].dataType,d),$=_e("output",s,g),E=n(S,$,h),T=E[2];for(let A=0,z=0;A<d;A++)m||h.indexOf(A)>=0?(i&&z++,T=`for(var j${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",z))};`),z++);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:s}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...we(u,l)]})}},Js=(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})},ir=(t,e,r,n)=>{let a=t.inputs,s=a.length===1?r:Js(a,r);t.compute(gi(e,{hint:s.cacheKey,inputDependencies:["rank"]},[a[0]],s.noopWithEmptyAxes&&s.axes.length===0?dd:n,s.axes,a[0].dataType,s.keepDims,s.noopWithEmptyAxes),{inputs:[0]})},cd=(t,e)=>{ar(t.inputs),ir(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},pd=(t,e)=>{ar(t.inputs),ir(t,"ReduceL1",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},hd=(t,e)=>{ar(t.inputs),ir(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);"])},fd=(t,e)=>{ar(t.inputs),ir(t,"ReduceLogSumExp",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},md=(t,e)=>{ar(t.inputs),ir(t,"ReduceMax",e,(r,n,a)=>{let s=[];for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&s.push(r.indicesSet("input_indices",i,0));return[`${s.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = max(value, ${r.getByIndices("input_indices")});`,""]})},gd=(t,e)=>{ar(t.inputs),ir(t,"ReduceMean",e,(r,n,a)=>{let s=1;for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&(s*=t.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${s});`]})},_d=(t,e)=>{ar(t.inputs),ir(t,"ReduceMin",e,(r,n,a)=>{let s=[];for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||a.length===0)&&s.push(`input_indices[${i}] = 0;`);return[`${s.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},yd=(t,e)=>{ar(t.inputs),ir(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},wd=(t,e)=>{ar(t.inputs),ir(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},bd=(t,e)=>{ar(t.inputs),ir(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},sr=(t,e,r)=>{if(e.length===0)return r;let n=1,a=1;for(let s=0;s<e.length;s++)e.indexOf(s)===-1?n*=t[s]:a*=t[s];return a<32&&n>1024},vd=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?gd(t,e):ed(t,e)},$d=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?pd(t,e):td(t,e)},xd=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?hd(t,e):rd(t,e)},Sd=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?fd(t,e):nd(t,e)},kd=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?md(t,e):ad(t,e)},Ed=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?_d(t,e):id(t,e)},Cd=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?yd(t,e):sd(t,e)},Td=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?wd(t,e):od(t,e)},Ad=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?bd(t,e):ld(t,e)},Id=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?cd(t,e):ud(t,e)}}),to,Md,Od,ro,Py=te(()=>{xe(),ct(),eo(),to=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.")},Md=(t,e)=>{to(t.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o<n.rank;o++)(s.indexOf(o)>=0||s.length===0)&&i.push(`input_indices[${o}] = 0;`);return[`${i.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?"<=":"<"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",a.setByOffset("global_idx","best_index")]};t.compute(gi("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Od=(t,e)=>{to(t.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o<n.rank;o++)(s.indexOf(o)>=0||s.length===0)&&i.push(`input_indices[${o}] = 0;`);return[`${i.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(gi("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},ro=t=>Ke(t)}),zd,Pd,Rd,_i,Bd,Dd,Nd=te(()=>{xe(),Oe(),ct(),Ie(),zd=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");let r=0,n=t[r],a=n.dataType,s=n.dims.length;t.forEach((i,o)=>{if(o!==r){if(i.dataType!==a)throw new Error("input tensors should be one type");if(i.dims.length!==s)throw new Error("input tensors should have the same shape");i.dims.forEach((l,u)=>{if(u!==e&&l!==n.dims[u])throw new Error("non concat dimensions must match")})}})},Pd=(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;
  }`,Rd=(t,e)=>{let r=t.length,n=[];for(let a=0;a<r;++a){let s=e.setByOffset("global_idx",t[a].getByIndices("indices"));r===1?n.push(s):a===0?n.push(`if (inputIndex == ${a}u) { ${s} }`):a===r-1?n.push(`else { ${s} }`):n.push(`else if (inputIndex == ${a}) { ${s} }`)}return n.join(`
`)},_i=(t,e,r,n)=>{let a=X.size(r),s=new Array(t.length),i=new Array(t.length),o=0,l=[],u=[],d=[{type:12,data:a}];for(let w=0;w<t.length;++w)o+=t[w].dims[e],s[w]=o,u.push(t[w].dims.length),i[w]=Q(`input${w}`,n,u[w]),l.push("rank"),d.push({type:12,data:s[w]});for(let w=0;w<t.length;++w)d.push(...we(t[w].dims));d.push(...we(r));let h=_e("output",n,r.length),m=h.indicesGet("indices",e),g=Array.from(Array(s.length).keys()).map(w=>`uniforms.sizeInConcatAxis${w}`).join(","),p=w=>`

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

  ${Pd(s.length,g)}

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

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

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

    ${Rd(i,h)}
  }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:p}},Bd=(t,e)=>{let r=t.inputs,n=r[0].dims,a=X.normalizeAxis(e.axis,n.length);zd(r,a);let s=n.slice();s[a]=r.reduce((o,l)=>o+(l.dims.length>a?l.dims[a]:0),0);let i=r.filter(o=>X.size(o.dims)>0);t.compute(_i(i,a,s,r[0].dataType),{inputs:i})},Dd=t=>Ke({axis:t.axis})}),Fd,Ld,Ud,Wd,yi,Vd,Gd,Hd=te(()=>{xe(),Hs(),Ie(),Nd(),Fd=(t,e)=>{let r=t[0],n=t[1],a=t[2],s=t[3],i=t[4],o=t[5];if(i&&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],d=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]!==d)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,g=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],g=e.qkvHiddenSizes[2]}let p=u;if(h!==m)throw new Error("qkv_hidden_sizes first element should be same as the second");if(a.dims[0]!==h+m+g)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let w=0;if(i){if(m!==g)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==l)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==e.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==m/e.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');e.pastPresentShareBuffer||(w=i.dims[3])}let v=p+w,S=-1,$=0;if(s)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");return{batchSize:l,sequenceLength:u,pastSequenceLength:w,kvSequenceLength:p,totalSequenceLength:v,maxSequenceLength:S,inputHiddenSize:d,hiddenSize:h,vHiddenSize:g,headSize:Math.floor(h/e.numHeads),vHeadSize:Math.floor(g/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:$,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Ld=(t,e,r,n)=>{let a=st(n),s=64,i=n/a;i<s?s=1:i/8<64&&(s=Math.ceil(i/8));let o=Math.ceil(n/a/s),l=[{type:e.dataType,data:1/n},{type:12,data:i},{type:12,data:o}],u=_t(e.dataType,a),d=Ot(1,a),h=m=>{let g=_e("x",e.dataType,e.dims,a),p=[{name:"d_inv",type:Ot(e.dataType)},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${s}>;
  var<workgroup> thread_sum: array<f32, ${s}>;
  ${m.registerUniforms(p).declareVariables(g)}
  ${m.mainStart([s,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 = ${d}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
      thread_max_vector = max(${d}(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 < ${s}; i++) {
      max_value = max(thread_max[i], max_value);
    }

    var sum_vector = ${d}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
      sum_vector += exp(${d}(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 < ${s}; i++) {
      sum += thread_sum[i];
    }

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        x[offset + i] = ${g.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 = ${d}(x[offset + i]);
        x[offset + i] = ${g.type.value}(exp(f32input - max_value) / sum);
      }
    }
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${u};${a}`},getShaderSource:h,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:l})}},Ud=(t,e,r,n,a,s,i)=>{let o=i+a.kvSequenceLength,l=[a.batchSize,a.numHeads,a.sequenceLength,o],u=s.scale===0?1/Math.sqrt(a.headSize):s.scale,d=st(a.headSize),h=a.headSize/d,m=12,g={x:Math.ceil(o/m),y:Math.ceil(a.sequenceLength/m),z:a.batchSize*a.numHeads},p=[{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 $=Q("q",e.dataType,e.dims,d),E=Q("key",r.dataType,r.dims,d),T=[$,E];n&&T.push(Q("relative_position_bias",n.dataType,n.dims));let A=_e("output",e.dataType,l),z=Ot(1,d),R=[{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(R).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 = ${z}(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 += ${z}(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(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}`)}})()};
        output[outputIdx] = ${A.type.value} (sum * uniforms.alpha) + ${n?"relative_position_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${d}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:l,dataType:e.dataType,gpuDataType:0}],dispatchGroup:g,programUniforms:p}),getShaderSource:v}},Wd=(t,e,r,n,a)=>{let s=a+n.kvSequenceLength,i=[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:s},{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:i,dataType:e.dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:u}),getShaderSource:d=>{let h=Q("probs",e.dataType,e.dims),m=Q("v",r.dataType,r.dims),g=_e("output",e.dataType,i),p=[{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}>;
  ${d.registerUniforms(p).declareVariables(h,m,g)}
  ${d.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;
   }
  }`}}},yi=(t,e,r,n,a,s,i,o,l,u,d)=>{let h=t.outputCount>1,m=t.outputCount>2,g=h&&m?u.pastSequenceLength:0,p=g+u.kvSequenceLength,w=[u.batchSize,u.numHeads,p,u.headSize],v=i?[i,r]:[r],S=h?t.compute(_i(v,2,w,r.dataType),{inputs:v,outputs:[1]})[0]:r,$=[u.batchSize,u.numHeads,p,u.headSize],E=o?[o,n]:[n],T=m?t.compute(_i(E,2,$,n.dataType),{inputs:E,outputs:[2]})[0]:n,A=[e,S];l&&A.push(l);let z=t.compute(Ud(t,e,S,l,u,d,g),{inputs:A,outputs:[-1]})[0];t.compute(Ld(t,z,u.batchSize*u.numHeads*u.sequenceLength,p),{inputs:[z],outputs:[]});let R=[z,T];t.compute(Wd(t,z,T,u,g),{inputs:R,outputs:[0]})},Vd=(t,e)=>{let r=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],n=e.sequenceLength,a=e.inputHiddenSize,s=e.headSize,i=12,o={x:Math.ceil(e.headSize/i),y:Math.ceil(e.sequenceLength/i),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:s},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],d=h=>{let m=_e("output_q",l[0].dataType,r),g=_e("output_k",l[0].dataType,r),p=_e("output_v",l[0].dataType,r),w=Q("input",l[0].dataType,l[0].dims),v=Q("weight",l[1].dataType,l[1].dims),S=Q("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 = ${i}u;
  var<workgroup> tileInput: array<${$}, ${i*i}>;
  var<workgroup> tileWeightQ: array<${$}, ${i*i}>;
  var<workgroup> tileWeightK: array<${$}, ${i*i}>;
  var<workgroup> tileWeightV: array<${$}, ${i*i}>;
  ${h.registerUniforms(E).declareVariables(w,v,S,m,g,p)}
  ${h.mainStart([i,i,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_id.x;

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

    var valueQ = ${$}(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:d},{inputs:l,outputs:[-1,-1,-1]})},Gd=(t,e)=>{let r=Fd(t.inputs,e),[n,a,s]=Vd(t,r);return yi(t,n,a,s,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),jd,qd,Kd,Yd,Ry=te(()=>{rr(),xe(),Oe(),ct(),Ie(),jd=(t,e)=>{if(!t||t.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,a,s)=>{let i=a.length;if(i!==n.length)throw new Error(`${s}: num dimensions != ${i}`);a.forEach((o,l)=>{if(o!==n[l])throw new Error(`${s}: 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")},qd=(t,e)=>{let{epsilon:r,spatial:n,format:a}=e,s=t[0].dims,i=n?st(s[s.length-1]):1,o=a==="NHWC"&&s.length>1?i:1,l=X.size(s)/i,u=n,d=u?s.length:s,h=Q("x",t[0].dataType,t[0].dims,i),m=Q("scale",t[1].dataType,t[1].dims,o),g=Q("bias",t[2].dataType,t[2].dims,o),p=Q("inputMean",t[3].dataType,t[3].dims,o),w=Q("inputVar",t[4].dataType,t[4].dims,o),v=_e("y",t[0].dataType,d,i),S=()=>{let E="";if(n)E=`let cOffset = ${s.length===1?"0u":a==="NHWC"?`outputIndices[${s.length-1}] / ${i}`:"outputIndices[1]"};`;else if(a==="NCHW")E=`
            ${v.indicesSet("outputIndices","0","0")}
            let cOffset = ${v.indicesToOffset("outputIndices")};`;else{E=`var cIndices = ${m.type.indices}(0);
                       cIndices[0] = outputIndices[${s.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,g,p,w,v)}
  ${E.mainStart()}
  ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${v.offsetToIndices(`global_idx * ${i}`)};
    ${S()}
    let scale = ${m.getByOffset("cOffset")};
    let bias = ${g.getByOffset("cOffset")};
    let inputMean = ${p.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}_${i}`,inputDependencies:u?["rank","type","type","type","type"]:void 0},getShaderSource:$,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u?[{type:12,data:l},...we(s)]:[{type:12,data:l}]})}},Kd=t=>Ke(t),Yd=(t,e)=>{let{inputs:r,outputCount:n}=t,a=Kd({...e,outputCount:n});if(Ue.webgpu.validateInputContent&&jd(r,a),e.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");t.compute(qd(r,a))}}),Xd,Qd,Zd,By=te(()=>{Oe(),Ie(),Xd=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")},Qd=t=>{let e=t[0].dims,r=t[0].dims[2],n=X.size(e)/4,a=t[0].dataType,s=Q("input",a,e,4),i=Q("bias",a,[r],4),o=Q("residual",a,e,4),l=_e("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(s,i,o,l)}

  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${s.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${o.getByOffset("global_idx")};
    ${l.setByOffset("global_idx","value")}
  }`}},Zd=t=>{Xd(t.inputs),t.compute(Qd(t.inputs))}}),Jd,De,ec,tc,rc,nc,ac,ic,sc,oc,lc,uc,dc,cc,pc,hc,wi,fc,bi,mc,gc,_c,yc,wc,bc,vc,$c,xc,Sc,kc,Ec,Cc,Tc,Ac,Ic,no,Mc,ao,io,Oc,zc,Pc,so=te(()=>{xe(),Oe(),ct(),Ie(),Jd=(t,e,r,n,a,s)=>{let i=Math.ceil(e/4),o="";typeof a=="string"?o=`${a}(a)`:o=a("a");let l=Q("inputData",r,[i],4),u=_e("outputData",n,[i],4);return`
      ${t.registerUniform("vec_size","u32").declareVariables(l,u)}

  ${s??""}

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

    let a = ${l.getByOffset("global_idx")};
    ${u.setByOffset("global_idx",o)}
  }`},De=(t,e,r,n,a,s=t.dataType)=>({name:e,shaderCache:{hint:a,inputDependencies:["type"]},getShaderSource:i=>Jd(i,X.size(t.dims),t.dataType,s,r,n),getRunData:i=>({outputs:[{dims:t.dims,dataType:s}],dispatchGroup:{x:Math.ceil(X.size(i[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(X.size(t.dims)/4)}]})}),ec=t=>{t.compute(De(t.inputs[0],"Abs","abs"))},tc=t=>{t.compute(De(t.inputs[0],"Acos","acos"))},rc=t=>{t.compute(De(t.inputs[0],"Acosh","acosh"))},nc=t=>{t.compute(De(t.inputs[0],"Asin","asin"))},ac=t=>{t.compute(De(t.inputs[0],"Asinh","asinh"))},ic=t=>{t.compute(De(t.inputs[0],"Atan","atan"))},sc=t=>{t.compute(De(t.inputs[0],"Atanh","atanh"))},oc=t=>Ke(t),lc=(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): ${e.to}`)}t.compute(De(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},uc=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:Ys,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:Xs;return Ke({min:e,max:r})},dc=(t,e)=>{let r=t.inputs.length===1?e:uc(t.inputs),n=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Clip",a=>`clamp(${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]})},cc=t=>{t.compute(De(t.inputs[0],"Ceil","ceil"))},pc=t=>{t.compute(De(t.inputs[0],"Cos","cos"))},hc=t=>{t.compute(De(t.inputs[0],"Cosh","cosh"))},wi=t=>Ke(t),fc=(t,e)=>{let r=Ot(t.inputs[0].dataType);t.compute(De(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))},bi=(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));
}`,mc=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,bi(e)))},gc=t=>{t.compute(De(t.inputs[0],"Exp","exp"))},_c=t=>{t.compute(De(t.inputs[0],"Floor","floor"))},yc=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,bi(e)))},wc=(t,e)=>{let r=Ot(t.inputs[0].dataType);t.compute(De(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))},bc=t=>{t.compute(De(t.inputs[0],"Not",e=>`!${e}`))},vc=t=>{t.compute(De(t.inputs[0],"Neg",e=>`-${e}`))},$c=t=>{t.compute(De(t.inputs[0],"Reciprocal",e=>`1.0/${e}`))},xc=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Relu",r=>`select(vec4<${e}>(0.0), ${r}, ${r} > vec4<${e}>(0.0))`))},Sc=t=>{t.compute(De(t.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},kc=t=>Ke(t),Ec=(t,e)=>{let r=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${e.alpha} * ${n} + vec4<${r}>(${e.beta})))`,void 0,e.cacheKey))},Cc=t=>{t.compute(De(t.inputs[0],"Sin","sin"))},Tc=t=>{t.compute(De(t.inputs[0],"Sinh","sinh"))},Ac=t=>{t.compute(De(t.inputs[0],"Sqrt","sqrt"))},Ic=t=>{t.compute(De(t.inputs[0],"Tan","tan"))},no=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,Mc=t=>{t.compute(De(t.inputs[0],"Tanh",no))},ao=(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 ${no("v")};
}
`,io=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,Oc=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"FastGelu",io,ao(e),void 0,t.inputs[0].dataType))},zc=(t,e)=>{let r=Ot(t.inputs[0].dataType);return t.compute(De(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},Pc=t=>{t.compute(De(t.inputs[0],"Log","log"))}}),Rc,Bc,Dc,Dy=te(()=>{Oe(),Ie(),so(),Rc=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")},Bc=t=>{let e=t[0].dims.slice();e[2]=e[2]/2;let r=Q("input",t[0].dataType,t[0].dims,4),n=Q("bias",t[0].dataType,[t[0].dims[2]],4),a=_e("output",t[0].dataType,e,4),s=X.size(e)/4,i=_t(t[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:o=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${t[0].dims[2]/4/2}u;

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

  ${bi(i)}

  ${o.mainStart()}
    ${o.guardAgainstOutOfBoundsWorkgroupSizes(s)}
    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);

    ${a.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},Dc=t=>{Rc(t.inputs),t.compute(Bc(t.inputs))}}),Nc,Fc,or,Lc,Uc,Wc,Vc,Gc,Hc,jc,qc,Kc,Yc,Ny=te(()=>{xe(),Oe(),Ie(),Nc=(t,e,r,n,a,s,i,o,l,u,d,h)=>{let m,g;typeof o=="string"?m=g=($,E)=>`${o}((${$}),(${E}))`:typeof o=="function"?m=g=o:(m=o.scalar,g=o.vector);let p=_e("outputData",d,n.length,4),w=Q("aData",l,e.length,4),v=Q("bData",u,r.length,4),S;if(a)if(s){let $=X.size(e)===1,E=X.size(r)===1,T=e.length>0&&e[e.length-1]%4===0,A=r.length>0&&r[r.length-1]%4===0;$||E?S=p.setByOffset("global_idx",g($?`${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 = ${p.offsetToIndices("global_idx * 4u")};
            let offsetA = ${w.broadcastedIndicesToOffset("outputIndices",p)};
            let offsetB = ${v.broadcastedIndicesToOffset("outputIndices",p)};
            ${p.setByOffset("global_idx",g(i||T?w.getByOffset("offsetA / 4u"):`${w.type.value}(${w.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||A?v.getByOffset("offsetB / 4u"):`${v.type.value}(${v.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else S=p.setByOffset("global_idx",g(w.getByOffset("global_idx"),v.getByOffset("global_idx")));else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let $=(E,T,A="")=>{let z=`aData[indexA${T}][componentA${T}]`,R=`bData[indexB${T}][componentB${T}]`;return`
            let outputIndices${T} = ${p.offsetToIndices(`global_idx * 4u + ${T}u`)};
            let offsetA${T} = ${w.broadcastedIndicesToOffset(`outputIndices${T}`,p)};
            let offsetB${T} = ${v.broadcastedIndicesToOffset(`outputIndices${T}`,p)};
            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(z,R)});
          `};d===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,p)}

        ${h??""}

        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${S}
      }`},Fc=(t,e,r,n,a,s,i=r.dataType)=>{let o=!X.areEqual(r.dims,n.dims),l=r.dims,u=X.size(r.dims),d=!1,h=!1,m=[o];if(o){let g=xn.calcShape(r.dims,n.dims,!1);if(!g)throw new Error("Can't perform binary op on the given tensors");l=g,u=X.size(l);let p=X.size(r.dims)===1,w=X.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(p),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}$%4===0?(h=!0,d=!0):(p||w||v||S)&&(d=!0)}else d=!0;return m.push(d),{name:t,shaderCache:{hint:e+m.map(g=>g.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:g=>Nc(g,r.dims,n.dims,l,d,o,h,a,r.dataType,n.dataType,i,s),getRunData:()=>({outputs:[{dims:l,dataType:i}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:Math.ceil(X.size(l)/4)},...we(r.dims,n.dims,l)]})}},or=(t,e,r,n,a,s)=>{t.compute(Fc(e,a??"",t.inputs[0],t.inputs[1],r,n,s))},Lc=t=>{or(t,"Add",(e,r)=>`${e}+${r}`)},Uc=t=>{or(t,"Div",(e,r)=>`${e}/${r}`)},Wc=t=>{or(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4<u32>(${e}==${r})`},void 0,void 0,9)},Vc=t=>{or(t,"Mul",(e,r)=>`${e}*${r}`)},Gc=t=>{let e=Q("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;or(t,"Pow",{scalar:(r,n)=>`pow_custom(${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));
    }
      `)},Hc=t=>{or(t,"Sub",(e,r)=>`${e}-${r}`)},jc=t=>{or(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4<u32>(${e}>${r})`},void 0,void 0,9)},qc=t=>{or(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4<u32>(${e}<${r})`},void 0,void 0,9)},Kc=t=>{or(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4<u32>(${e}>=${r})`},void 0,void 0,9)},Yc=t=>{or(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4<u32>(${e}<=${r})`},void 0,void 0,9)}}),Qr,Zr,Jr,oo,en=te(()=>{xe(),Oe(),Qr=(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 ${t.activation}`)}},Zr=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},Jr=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},oo=t=>{let e=(t==null?void 0:t.activation)||"";if(e==="HardSigmoid"){let[r,n]=(t==null?void 0:t.activation_params)||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=(t==null?void 0:t.activation_params)||[Ys,Xs];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=(t==null?void 0:t.activation_params)||[.01];return{activation:e,alpha:r}}return{activation:e}}}),Et,lo,uo=te(()=>{Et=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},lo=t=>`
      ${t?"value = value + getBiasByOutputCoords(coords);":""}
      `}),co,Xc=te(()=>{co=t=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${t}.x), i32(${t}.y), i32(${t}.z), 1));
}
`}),Qc,Zc,vi,po,Jc,$i,ep,ho,xi=te(()=>{xe(),Oe(),Ie(),en(),uo(),Qc=(t,e)=>t?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${e?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${e?", batchIndices":""});
        `,Zc=(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];"}
        }`,vi=(t,e,r="f32",n,a=!1,s=32,i=!1,o=32)=>{let l=e[1]*t[1],u=e[0]*t[0],d=a?l:s,h=a?s:l,m=d/e[0],g=s/e[1];if(!((a&&m===4&&t[1]===4||!a&&(m===3||m===4))&&d%e[0]===0&&s%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 ${d} must be divisible by workgroupSize[0]${e[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${m}<${r}>, ${d/m}>, ${h}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${u/t[0]}>, ${s}>;

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

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

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

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

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

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

          ${Zc(a,m)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},po=(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":""});
            `,Jc=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",$i=(t,e,r="f32",n,a=!1,s=32,i=!1,o=32,l=!1)=>{let u=t[1]*e[1],d=t[0]*e[0],h=a?u:s,m=a?s:u;if(!(m%e[1]===0&&h%e[0]===0&&s%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 ${s} must be divisible by workgroupSize[1]${e[1]}`);let g=m/e[1],p=h/e[0],w=s/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) * ${d};

    // 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]}) {
          ${po(a,n)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${e[1]}) {
            for (var inputCol = localCol; inputCol < ${d}; 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) * ${g};
let tileColA = i32(localId.x) * ${p};
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 < ${g}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${p}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${po(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) {
      ${Jc(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}, ${d}>, ${s}>;
  const rowPerThread = ${t[1]};
  const colPerThread = ${t[0]};
  const tileInner = ${s};

@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 = ${i?"0":"i32(globalId.z)"};
    ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}
    let num_tiles = ${i?`${Math.ceil(o/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
    var kStart = ${i?`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}
  }
`},ep=(t,e,r,n,a,s=!1)=>{let[i,o,l]=a,[u,d,h,m]=n,g=na(i,l),p=na(o,l),w=_t(n[0].type.tensor),v=()=>{let $=d.rank,E=u.rank,T=`var aIndices: ${d.type.indices};`;for(let A=$-2-1,z=E-1;A>=0;A--,z--)T+=`
aIndices[${A}] = ${E>1?`batchIndices[${z}]`:"batchIndices"};`;return g.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,z=E-1;A>=0;A--,z--)T+=`
bIndices[${A}] = ${E>1?`batchIndices[${z}]`:"batchIndices"};`;return p.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}) -> ${Et(t,w)} {
      var value = ${Et(t,w)}(0.0);
      let col = colIn * ${t};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${v()}
        value = ${d.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${u.type.indices}) -> ${Et(t,w)} {
      var value = ${Et(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: ${Et(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 + ${s?"bias[colIn]":`${Et(t,w)}(bias[row])`};`:""}
        ${r}
        ${m.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},ho=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s.slice(0,-2),l=i.slice(0,-2),u=n?n.slice(0,-2):r.slice(0,-2),d=X.size(u),h=s[s.length-2],m=s[s.length-1],g=i[i.length-1],p=m%4===0&&g%4===0,w=h<=8?[4,1,1]:[4,4,1],v=[8,8,1],S=[Math.ceil(g/v[0]/w[0]),Math.ceil(h/v[1]/w[1]),Math.ceil(d/v[2]/w[2])],$=p?4:1,E=[...o,h,m/$],T=E.length,A=[...l,m,g/$],z=A.length,R=[d,h,g/$],V=[{type:6,data:h},{type:6,data:g},{type:6,data:m}];Zr(e,V),V.push(...we(u,E,A));let j=["rank","rank"],q=t.length>2;q&&(V.push(...we(t[2].dims)),j.push("rank")),V.push(...we(R));let fe=ee=>{let ne=u.length,se=Qs("batchDims",t[0].dataType,ne,1),D=_t(t[0].dataType),M=Q("a",t[0].dataType,T,$),G=Q("b",t[1].dataType,z,$),K=_e("result",t[0].dataType,R.length,$),pe=[M,G];if(q){let Se=a?$:1;pe.push(Q("bias",t[2].dataType,t[2].dims.length,Se))}let le=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Jr(e,le);let N=_t(K.type.tensor),ae=Qr(e,K.type.value,N),de=ep($,q,ae,[se,M,G,K],[o,l,u],a);return`
  ${ee.registerUniforms(le).registerInternalVariables(se).declareVariables(...pe,K)}
  ${de}
  ${p?vi(w,v,D,se):$i(w,v,D,se)}
                   `};return{name:"MatMul",shaderCache:{hint:`${w};${e.activation};${p};${a}`,inputDependencies:j},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:S[0],y:S[1],z:S[2]},programUniforms:V}),getShaderSource:fe}}}),tp,rp,Fy=te(()=>{xe(),Xr(),Ie(),en(),uo(),Xc(),xi(),tp=(t,e,r,n,a=!1,s,i=4,o=4,l=4,u="f32")=>{let d=j=>{switch(j){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 ${j} is not supported.`)}},h=j=>{switch(j){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 ${j} is not supported.`)}},m=t?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,g=t?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,p=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",v=t?"row":"col",S=t?"col":"row",$=`
    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 = ${Et(i,u)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${p} && xCol >= 0 && xCol < ${w}) {
      ${m}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${d(i)}
    }
    return resData;`,E=t?e&&n?`
    let col = colIn * ${i};
    ${$}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${$}
    }
    return ${Et(i,u)}(0.0);`:n&&r?`
    let col = colIn * ${i};
    ${$}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${$}
    }
    return ${Et(i,u)}(0.0);`,T=`${h(o)}`,A=Et(l,u),z=Et(t?i:o,u),R=Et(t?o:i,u),V=Qr(s,A,u);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${z} {
      ${t?E:T}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${R} {
      ${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])"};
      ${g}
      ${lo(a)}
      ${V}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},rp=(t,e,r,n,a,s,i,o)=>{let l=e.format==="NHWC",u=l?t[0].dims[3]:t[0].dims[1],d=r[0],h=l?r[2]:r[3],m=l?r[1]:r[2],g=l?r[3]:r[1],p=l&&(u%4===0||u%3===0)&&g%4===0,w=l?g:h*m,v=l?h*m:g,S=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(w/S[0]/$[0]),Math.ceil(v/S[1]/$[1]),Math.ceil(d/S[2]/$[2])];nt("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let T=p?l&&u%4!==0?3:4:1,A=S[1]*$[1],z=S[0]*$[0],R=Math.max(S[0]*T,S[1]),V=n%A===0,j=a%z===0,q=s%R===0,fe=p?[T,4,4]:[1,1,1],ee=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];Zr(e,ee),ee.push(...we(t[0].dims,t[1].dims));let ne=["rank","rank"];i&&(ee.push(...we(t[2].dims)),ne.push("rank")),ee.push(...we(r));let se=D=>{let M=[{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}];Jr(e,M);let G=p?4:1,K=_t(t[0].dataType),pe=`
      fn setOutputAtIndex(flatIndex : i32, value : ${p?`vec4<${K}>`:K}) {
        result[flatIndex] = ${p?`vec4<${K}>`:K}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${p?`vec4<${K}>`:K}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${p?"/ 4":""}, value);
      }`,le=Q("x",t[0].dataType,t[0].dims.length,T===3?1:T),N=Q("w",t[1].dataType,t[1].dims.length,G),ae=[le,N],de=_e("result",t[0].dataType,r.length,G);if(i){let Se=Q("bias",t[2].dataType,t[2].dims.length,G);ae.push(Se),pe+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${p?`vec4<${K}>`:K} {
          return bias[coords.${l?"w":"y"}${p?"/ 4":""}];
        }`}return`
        ${co("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 };
        ${D.registerUniforms(M).declareVariables(...ae,de)}
        ${pe}
        ${tp(l,V,j,q,i,e,fe[0],fe[1],fe[2],K)}
        ${p?vi($,S,K,void 0,!l,R):$i($,S,K,void 0,!l,R,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${T};${p};${V};${j};${q};${A};${z};${R}`,inputDependencies:ne},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:ee}),getShaderSource:se}}}),fo,np,Ly=te(()=>{xe(),Oe(),Ie(),dp(),en(),fo=(t,e,r)=>{let n=t.length>2,a=n?"value += b[output_channel];":"",s=t[0].dims,i=t[1].dims,o=i[0]/e.group,l=e.format==="NHWC",u=Si(s,i,e.dilations,e.pads,e.strides,l),d=X.size(u),h=[{type:12,data:d},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];Zr(e,h),h.push(...we(s,i));let m=["rank","rank"];n&&(h.push(...we(t[2].dims)),m.push("rank")),h.push(...we(u));let g=p=>{let w=_e("output",t[0].dataType,u.length),v=_t(w.type.tensor),S=Qr(e,w.type.value,v),$=Q("x",t[0].dataType,s.length),E=Q("w",t[1].dataType,i.length),T=[$,E];n&&T.push(Q("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 Jr(e,A),`
  ${p.registerUniforms(A).declareVariables(...T,w)}

  ${p.mainStart()}
    ${p.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}
    ${w.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:m},getRunData:()=>({outputs:[{dims:r?r(u):u,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:h}),getShaderSource:g}},np=(t,e,r)=>{let n=t.length>2,a=st(r[3]),s=st(r[2]),i=X.size(r)/a/s,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],l=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],u=[r[0],r[1],r[2],r[3]/a],d=[{type:12,data:i},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];Zr(e,d),d.push(...we(o,l,u));let h=(s-1)*e.strides[1]+l[1],m=g=>{let p=_e("output",t[0].dataType,u.length,a),w=_t(p.type.tensor),v=Qr(e,p.type.value,w),S=Q("x",t[0].dataType,o.length,a),$=Q("w",t[1].dataType,l.length,a),E=[S,$];n&&E.push(Q("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 Jr(e,A),`
  ${g.registerUniforms(A).declareVariables(...E,p)}
  ${g.mainStart()}
    ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let width0 = uniforms.output_shape[3];
    let output_channel = global_idx % width0;
    var index1 = global_idx / width0;
    let width1 = uniforms.output_shape[2] / ${s}u;
    let col = (index1 % width1) * ${s}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

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

    var x_vals: array<${S.type.value}, ${h}>;
    var values: array<${p.type.value}, ${s}>;
    let input_channel = output_channel;
    // Use constant instead of uniform can give better performance for w's height/width.
    for (var w_height: u32 = 0u; w_height < ${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 < ${s}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${s}u; i++) {
      var value = values[i];
      ${T}
      ${v}
      ${p.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${s};${h};${l[0]};${l[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:d}),getShaderSource:m}}}),mo,ap,ip,sp=te(()=>{xe(),Oe(),xi(),Ie(),en(),mo=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s[s.length-2],l=i[i.length-1],u=s[s.length-1],d=st(l),h=st(u),m=st(o),g=X.size(r)/d/m,p=t.length>2,w=n?n.slice(0,-2):r.slice(0,-2),v=[X.size(w),o,l],S=[{type:12,data:g},{type:12,data:o},{type:12,data:l},{type:12,data:u}];Zr(e,S),S.push(...we(w,s,i)),p&&S.push(...we(t[2].dims)),S.push(...we(v));let $=E=>{let T=Qs("batch_dims",t[0].dataType,w.length),A=Q("a",t[0].dataType,s.length,h),z=Q("b",t[1].dataType,i.length,d),R=_e("output",t[0].dataType,v.length,d),V=_t(R.type.tensor),j=Qr(e,R.type.value,V),q=[A,z],fe="";if(p){let pe=a?d:1;q.push(Q("bias",t[2].dataType,t[2].dims.length,pe)),fe=`${a?`value += bias[col / ${pe}];`:`value += ${R.type.value}(bias[row + i]);`}`}let ee=s.slice(0,-2),ne=i.slice(0,-2),se=na(ee,w),D=na(ne,w),M=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Jr(e,M);let G=(pe,le)=>{let N=pe.rank,ae=pe.name;if(N===2)return`var ${ae}_indices = ${pe.type.indices}(0u, 0u);`;let de=T.rank,Se=`var ${ae}_indices: ${pe.type.indices};`;for(let Te=N-2-1,Xe=de-1;Te>=0;Te--,Xe--)Se+=`
${ae}_indices[${Te}] = ${de>1?`batch_indices[${Xe}]`:"batch_indices"};`;return le.forEach(Te=>{Se+=`
${ae}_indices[${Te}] = 0;`}),Se+=`${ae}_indices[${N-2}] = 0u;
                     ${ae}_indices[${N-1}] = 0u;`,Se},K=()=>{let pe=`var a_data: ${A.type.value};`;for(let le=0;le<h;le++)pe+=`
              let b_data${le} = b[(b_offset + (k + ${le}) * uniforms.N + col) / ${d}];`;for(let le=0;le<m;le++){pe+=`a_data = a[(a_offset + (row + ${le}) * uniforms.K + k) / ${h}];`;for(let N=0;N<h;N++)pe+=`
            values[${le}] = fma(${z.type.value}(a_data${h===1?"":`[${N}]`}), b_data${N}, values[${le}]);
`}return pe};return`
  ${E.registerUniforms(M).registerInternalVariables(T).declareVariables(...q,R)}
  ${E.mainStart()}
    ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${d})) * ${d};
    var index1 = global_idx / (uniforms.N / ${d});
    let stride1 = uniforms.M / ${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(z,D)}
    let b_offset = ${z.indicesToOffset("b_indices")};
    var values: array<${R.type.value}, ${m}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${h}) {
      ${K()}
    }
    for (var i = 0u; i < ${m}u; i++) {
      var value = values[i];
      ${fe}
      ${j}
      let cur_indices = ${R.type.indices}(batch, row + i, col);
      let offset = ${R.indicesToOffset("cur_indices")};
      ${R.setByOffset(`offset / ${d}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${d};${h};${m};${a}`,inputDependencies:p?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:S}),getShaderSource:$}},ap=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.")},ip=t=>{ap(t.inputs);let e=xn.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(mo(t.inputs,{activation:""},e)):t.compute(ho(t.inputs,{activation:""},e))}}),Si,ki,op,go,_o,lp,up,yo,dp=te(()=>{Oe(),Fy(),xi(),Ly(),en(),sp(),aa(),Si=(t,e,r,n,a,s)=>{let i=t[0],o=t.slice(s?1:2,s?3:4),l=o.length,u=e[0],d=e.slice(2).map((m,g)=>m+(m-1)*(r[g]-1)),h=o.map((m,g)=>m+n[g]+n[g+l]).map((m,g)=>Math.floor((m-d[g]+a[g])/a[g]));return h.splice(0,0,i),h.splice(s?3:1,0,u),h},ki=[2,3,1,0],op=(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 ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},go=(t,e)=>{let r=t.kernelShape.slice();for(let s=2;s<e[1].dims.length;++s)r[s-2]===0&&(r[s-2]=e[1].dims[s]);let n=t.pads.slice();hi.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,r,n,t.format==="NHWC",t.autoPad);let a=Object.assign({},t);return Object.assign(a,{kernelShape:r,pads:n}),a},_o=t=>{let e=oo(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,s=t.group,i=t.kernel_shape,o=t.pads,l=t.strides,u=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:s,kernelShape:i,pads:o,strides:l,wIsConst:u,...e,cacheKey:`${t.format};${e.activation};`}},lp=(t,e,r)=>{let n=go(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 z=Si(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),R=t.kernelCustomData.wT??t.compute(kr(e[1],ki),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=R);let V=[e[0],R];e.length===3&&V.push(e[2]),t.compute(np(V,n,z),{inputs:V})}else t.compute(fo(e,n));return}let s=e.length===3,i=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],d=e[1].dims[3],h=Si(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),m=h[a?1:2],g=h[a?2:3],p=h[a?3:1],w=a&&u===i&&d===o&&r.pads[0]===0&&r.pads[1]===0;if(w||u===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 z=h[0],R,V,j,q=[];if(a){let ne=t.kernelCustomData.wT??t.compute(kr(e[1],ki),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=ne),w){let se=i*o*l;R=e[0].reshape([1,z,se]),V=ne.reshape([1,se,p]),j=[1,z,p]}else R=e[0].reshape([z,i*o,l]),V=ne.reshape([1,l,p]),j=[z,m*g,p];q.push(R),q.push(V)}else R=e[0].reshape([z,l,i*o]),V=e[1].reshape([1,p,l]),j=[z,p,m*g],q.push(V),q.push(R);s&&q.push(e[2]);let fe=j[2],ee=q[0].dims[q[0].dims.length-1];fe<8&&ee<8?t.compute(mo(q,n,h,j,a),{inputs:q}):t.compute(ho(q,n,h,j,a),{inputs:q});return}let v=!0,S=t.kernelCustomData.wT??t.compute(kr(e[1],ki),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=S);let $=[e[0],S];s&&$.push(e[2]);let E=a?m*g:p,T=a?p:m*g,A=u*d*l;t.compute(rp($,n,h,E,T,A,s,v),{inputs:$})},up=(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]],s=[1].concat(e.strides),i=[1].concat(e.dilations),o=[1].concat(e.kernelShape),l=go({...e,pads:a,strides:s,dilations:i,kernelShape:o},n);t.compute(fo(n,l,u=>r?[u[0],u[2],u[3]]:[]))},yo=(t,e)=>{op(t.inputs,e),t.inputs[0].dims.length===3?up(t,e):lp(t,t.inputs,e)}}),cp,pp,Uy=te(()=>{xe(),Xr(),Ie(),en(),uo(),Xc(),xi(),cp=(t,e=!1,r,n,a=4)=>{let s=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.`)}},i=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])",d=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 = ${d} / outWidth;
      let outCol = ${d} % 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;
      ${i}
      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${a}];`,g=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);`,p=`
      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);
        ${s(a)}
      }
      return ${n}(0.0);
      `,w=Qr(r,n);return`
  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} {
    ${t?g:p}
  }

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

  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}
      ${lo(e)}
      ${w}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${a}] = value;
    }
  }`},pp=(t,e,r,n,a,s,i,o)=>{let l=e.format==="NHWC",u=l?t[0].dims[3]:t[0].dims[1],d=r[0],h=l?r[2]:r[3],m=l?r[1]:r[2],g=l?r[3]:r[1],p=l&&u%4===0&&u%3&&g%4===0,w=l?g:h*m,v=l?h*m:g,S=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(w/S[0]/$[0]),Math.ceil(v/S[1]/$[1]),Math.ceil(d/S[2]/$[2])];nt("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let T=p?4:1,A=Math.max(S[0]*T,S[1]),z=p?4:1,R=[e.kernelShape[l?1:2],e.kernelShape[l?2:3]],V=[R[0]+(e.dilations[0]<=1?0:(R[0]-1)*(e.dilations[0]-1)),R[1]+(e.dilations[1]<=1?0:(R[1]-1)*(e.dilations[1]-1))],j=[V[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),V[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],q=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:e.strides},{type:6,data:e.dilations},{type:6,data:R},{type:6,data:j}];Zr(e,q),q.push(...we(t[0].dims,t[1].dims));let fe=["rank","rank"];i&&(q.push(...we(t[2].dims)),fe.push("rank")),q.push(...we(r));let ee=ne=>{let se=Q("x",t[0].dataType,t[0].dims.length,z),D=Q("w",t[1].dataType,t[1].dims.length,1),M=_e("result",t[0].dataType,r.length,z),G=[se,D],K="";if(i){let N=Q("bias",t[2].dataType,t[2].dims.length,z);G.push(N),K+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${N.type.value} {
            return bias[coords.${l?"w":"y"}${p?"/ 4":""}];
          }`}let pe=[{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:R.length},{name:"pads",type:"i32",length:j.length}];Jr(e,pe);let le=_t(t[0].dataType,1);if(le!=="f16"&&le!=="f32")throw new Error(`elemType ${le} is not supported.`);return`
        ${co("uniforms.result_strides")}
        ${ne.registerUniforms(pe).declareVariables(...G,M)};
        ${K}
        ${cp(l,i,e,se.type.value,T)}
        ${p?vi($,S,le,void 0,!l,A):$i($,S,le,void 0,!l,A,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${$};${S};${p}`,inputDependencies:fe},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:q}),getShaderSource:ee}}}),hp,wo,Wy=te(()=>{xe(),Xr(),Oe(),Ie(),hp=(t,e,r,n,a,s=!1,i,o,l=!1)=>{let u=l?1:2,d=l?2:3,h=l?3:1,m=s?2:1,g=`
  fn setOutputAtIndex(flatIndex : u32, value : ${s?`vec4<${i}>`:i}) {
    result[flatIndex] = ${s?`vec4<${i}>`:i}(value);
  }`;n&&(g+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${s?`vec4<${i}>`:i} {
      return bias[coords.${l?"w":"y"}${s?"/ 4":""}];
    }`);let p=s?4:1,w=Q("W",e[1].dataType,e[1].dims.length,p),v=Q("Dy",e[0].dataType,e[0].dims.length,p),S=[v,w];n&&S.push(Q("bias",e[2].dataType,[r[h]].length,p));let $=_e("result",e[0].dataType,r.length,p),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<${i}>, ${m}>;
        for (var i = 0; i < ${m}; i++) {
          dotProd[i] = vec4<${i}>(0.0);
        }
        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {
          var dyR = (${i}(dyCorner.x) + ${i}(wR)) / ${i}(uniforms.strides.x);
          let wRPerm = uniforms.filter_dims[0] - 1 - wR;
          if (dyR < 0.0 || dyR >= ${i}(uniforms.Dy_shape[1]) ||
              fract(dyR) > 0.0 || wRPerm < 0) {
            continue;
          }
          let idyR: u32 = u32(dyR);

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

            let idyC: u32 = u32(dyC);
            let idyC2: u32 = u32(dyC2);
            if (bDyCVal && bDyCVal2) {
              let d2Length = uniforms.Dy_shape[3];
              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {
                let wValue0 = ${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 = ${v.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${i}>(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<${i}>(dot(xValue, wValue0),
                                                    dot(xValue, wValue1),
                                                    dot(xValue, wValue2),
                                                    dot(xValue, wValue3));
              }
            } else if (bDyCVal) {
              let d2Length = uniforms.Dy_shape[${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 = ${v.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${i}>(dot(xValue, wValue0),
                                      dot(xValue, wValue1),
                                      dot(xValue, wValue2),
                                      dot(xValue, wValue3));
                dotProd[0] = dotProd[0] + tmpval;
              }
            } else if (bDyCVal2) {
              let d2Length = uniforms.Dy_shape[3];
              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {
                let wValue0 = ${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 = ${v.get("batch","idyR","idyC2","d2")};
                let tmpval = vec4<${i}>(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] + ${n?"bias[c+i]":`vec4<${i}>(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",d)};
          let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;
          let dyRCorner = dyCorner.x;
          let dyCCorner = dyCorner.y;
          let groupId = d1 / uniforms.output_channels_per_group;
          let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;
          // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
          // ? = to be determined. : = across all values in that axis.
          var dotProd = ${i}(0.0);
          for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
            if (wR % uniforms.dilations.x != 0) {
              continue;
            }
            let dyR = (${i}(dyRCorner) + ${i}(wR)) / ${i}(uniforms.strides[0]);
            let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
            if (dyR < 0.0 || dyR >= ${i}(uniforms.Dy_shape[${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 = (${i}(dyCCorner) + ${i}(wC)) / ${i}(uniforms.strides.y);
              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
              if (dyC < 0.0 || dyC >= ${i}(uniforms.Dy_shape[${d}]) ||
                  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 + ${n?"bias[d1]":`${i}(0.0)`};
          ${$.setByOffset("global_idx","value")};
        `;return`
  ${t.registerUniforms(o).declareVariables(...S,$)}
  ${g}

    ${t.mainStart()}
    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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      let x_in = x + bias;
      ${u.setByOffset("global_idx",io("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getShaderSource:s,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/Sn/4)}})}},Vp=t=>{t.inputs.length<2||X.size(t.inputs[1].dims)===0?Oc(t):t.compute(Wp(t.inputs))}}),Gp,Hp,jp,qp,Yy=te(()=>{xe(),Oe(),ct(),Ie(),Gp=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},Hp=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,s=X.normalizeAxis(e.axis,a),i=r.slice(0);i.splice(s,1,...n);let o=r[s],l=t[0].dataType===9?4:1,u=Math.ceil(X.size(i)/l),d=[{type:12,data:u},{type:6,data:o},{type:12,data:s},...we(t[0].dims,t[1].dims,i)],h=m=>{let g=Q("data",t[0].dataType,t[0].dims.length,l),p=Q("inputIndices",t[1].dataType,t[1].dims.length),w=_e("output",t[0].dataType,i.length,l),v=$=>{let E=n.length,T=`var indicesIndices${$}  = ${p.type.indices}(0);`;for(let A=0;A<E;A++)T+=`${E>1?`indicesIndices${$}[${A}]`:`indicesIndices${$}`} = ${i.length>1?`outputIndices${$}[uniforms.axis + ${A}]`:`outputIndices${$}`};`;T+=`
          var idx${$} = ${p.getByIndices(`indicesIndices${$}`)};
          if (idx${$} < 0) {
            idx${$} = idx${$} + uniforms.axisDimLimit;
          }
          var dataIndices${$} : ${g.type.indices};
        `;for(let A=0,z=0;A<a;A++)A===s?(T+=`${a>1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = u32(idx${$});`,z+=E):(T+=`${a>1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = ${i.length>1?`outputIndices${$}[${z}]`:`outputIndices${$}`};`,z++);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} = ${g.indicesToOffset(`dataIndices${T}`)};
          let index${T} = offset${T} / 4u;
          let component${T} = offset${T} % 4u;
          ${E}[${T}] = ${A}(${g.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 = ${g.getByIndices("dataIndices")};
      ${w.setByOffset("global_idx","value")};
      `;return`
      ${m.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(g,p,w)}
      ${m.mainStart()}
        ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${S}
      }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:d}),getShaderSource:h}},jp=t=>Ke({axis:t.axis}),qp=(t,e)=>{let r=t.inputs;Gp(r),t.compute(Hp(t.inputs,e))}}),Kp,Yp,Xp,Qp,Xy=te(()=>{xe(),Oe(),ct(),Ie(),Kp=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.`)},Yp=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,s=t[1].dims,i=t[1].dataType,o=X.normalizeAxis(e.axis,a),l=r[o],u=s.slice(0),d=X.size(u),h=Q("input",n,a),m=Q("indicesInput",i,s.length),g=_e("output",n,u.length),p=[{type:12,data:d},{type:6,data:l},{type:12,data:o}];return p.push(...we(r,s,u)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:u,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:w=>`
      ${w.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(h,m,g)}
      ${w.mainStart()}
      ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

      let outputIndices = ${g.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")};

      ${g.setByOffset("global_idx","value")};
  }`}},Xp=t=>Ke({axis:t.axis}),Qp=(t,e)=>{let r=t.inputs;Kp(r),t.compute(Yp(t.inputs,e))}}),Zp,Jp,eh,th,Qy=te(()=>{xe(),Oe(),Ie(),Zp=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},Jp=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,s,i]=Bu.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[a,s];if(!o)throw new Error("Can't use gemm on the given tensors");let l=X.size(o),u=[{type:12,data:l},{type:12,data:a},{type:12,data:s},{type:12,data:i},{type:1,data:e.alpha},{type:1,data:e.beta}],d=["type","type"];t.length===3&&(u.push(...we(t[2].dims)),d.push("rank")),u.push(...we(o));let h=m=>{let g="";e.transA&&e.transB?g="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?g="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?g="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(g="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let p=e.alpha===1?"":"value *= uniforms.alpha;",w=Q("a",t[0].dataType,t[0].dims),v=Q("b",t[1].dataType,t[1].dims),S=w.type.value,$=null,E=[w,v];t.length===3&&($=Q("c",t[2].dataType,t[2].dims.length),E.push($));let T=_e("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++) {
      ${g}
    }

    ${p}
    ${$!=null?`let cOffset = ${$.broadcastedIndicesToOffset("vec2(m, n)",T)}; value += ${S}(uniforms.beta) * ${$.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:h}},eh=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},th=(t,e)=>{Zp(t.inputs),t.compute(Jp(t.inputs,e))}}),rh,nh,ah,ih,Zy=te(()=>{xe(),Oe(),Ie(),rh=(t,e)=>{let r=t[0].dims,n=r,a=2,s=X.sizeToDimension(r,a),i=X.sizeFromDimension(r,a),o=st(i),l=i/o,u=[r[0],r[1],l],d=["rank","type","type"],h=[{type:12,data:i},{type:12,data:l}];h.push(...we(u,u));let m=g=>{let p=Q("x",t[0].dataType,u.length,o),w=Q("scale",t[1].dataType,t[1].dims),v=Q("bias",t[2].dataType,t[2].dims),S=_e("output",t[0].dataType,u.length,o),$=[p,w,v,S],E=p.type.value,T=o===1?"f32":`vec${o}<f32>`,A=64,z=[{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;
  ${g.registerUniforms(z).declareVariables(...$)}
  ${g.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 + ${T}(${p.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 = ${Dr("workgroupShared[0]",o)} / f32(uniforms.normSize);
    }
    workgroupBarrier();

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

  ${z.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:`${l}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:[a,i,u,2],dataType:1}],dispatchGroup:{x:a*i/l},programUniforms:v}),getShaderSource:S},{inputs:[e],outputs:[-1]})[0],E=[{type:12,data:g},{type:12,data:s},{type:12,data:Math.floor(i/l)},{type:12,data:Math.floor(u*i/l)}],T=["type","type","type"],A=z=>{let R=Q("scale",r.dataType,r.dims,l),V=Q("bias",n.dataType,n.dims,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${d}>;
  @group(0) @binding(1) var<storage, read> scale : array<${R.type.storage}>;
  @group(0) @binding(2) var<storage, read> bias : array<${V.type.storage}>;
  @group(0) @binding(3) var<storage, read_write> output : array<${d}>;
  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};
  @group(0) @binding(4) var<uniform> uniforms: Uniforms;

  ${z.mainStart()}
    ${z.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};${o}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:[a,i,2],dataType:1}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:E}),getShaderSource:A},{inputs:[$,r,n],outputs:[-1]})[0]},ah=(t,e,r)=>{let n=e[0].dims,a=n,s=n[0],i=n[n.length-1],o=X.sizeFromDimension(n,1)/i,l=st(i),u=X.size(a)/l,d=[{type:12,data:o},{type:12,data:Math.floor(i/l)}],h=["type","type"],m=nh(t,e[0],e[1],e[2],s,o,i,r.epsilon),g=p=>{let w=_t(e[0].dataType),v=l===1?"vec2f":`mat2x${l}f`,S=l===1?w:`vec${l}<${w}>`,$=Q("input",e[0].dataType,e[0].dims,l),E=_e("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;

  ${p.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:d}),getShaderSource:g},{inputs:[e[0],m]})},ih=(t,e)=>{e.format==="NHWC"?ah(t,t.inputs,e):t.compute(rh(t.inputs,e))}}),sh,oh,lh,Jy=te(()=>{xe(),Oe(),Ie(),sh=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},oh=(t,e,r)=>{let n=e.simplified,a=t[0].dims,s=t[1],i=!n&&t[2],o=a,l=X.normalizeAxis(e.axis,a.length),u=X.sizeToDimension(a,l),d=X.sizeFromDimension(a,l),h=X.size(s.dims),m=i?X.size(i.dims):0;if(h!==d||i&&m!==d)throw new Error(`Size of X.shape()[axis:] == ${d}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${h} and bias size of ${m}`);let g=[];for(let A=0;A<a.length;++A)A<l?g.push(a[A]):g.push(1);let p=st(d),w=["type","type"],v=[{type:12,data:u},{type:1,data:d},{type:12,data:Math.floor(d/p)},{type:1,data:e.epsilon}];i&&w.push("type");let S=r>1,$=r>2,E=A=>{let z=_t(t[0].dataType),R=[Q("x",t[0].dataType,t[0].dims,p),Q("scale",s.dataType,s.dims,p)];i&&R.push(Q("bias",i.dataType,i.dims,p)),R.push(_e("output",t[0].dataType,o,p)),S&&R.push(_e("mean_data_output",1,g)),$&&R.push(_e("inv_std_output",1,g));let V=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${A.registerUniforms(V).declareVariables(...R)}
  ${A.mainStart()}
    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${Sr("f32",p)};
    var mean_square_vector = ${Sr("f32",p)};

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

    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
      let f32input = ${kn(z,p,"x[j + offset]")};
      let f32scale = ${kn(z,p,"scale[j]")};
      output[j + offset] = ${R[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale
        ${i?`+ ${kn(z,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:g,dataType:1}),$&&T.push({dims:g,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${p};${r};${n}`,inputDependencies:w},getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:v}),getShaderSource:E}},lh=(t,e)=>{sh(t.inputs),t.compute(oh(t.inputs,e,t.outputCount))}}),uh,dh,ch,ph,ew=te(()=>{xe(),Oe(),ct(),Ie(),uh=(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),s=e.blockSize/8*e.bits,i=t[1];if(!X.areEqual(i.dims,[e.n,a,s]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(X.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(X.size(l)!==u)throw new Error("zeroPoints input size error.")}},dh=(t,e,r,n)=>{let a=t[0].dims,s=a.length,i=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=a[s-2],l=e.k,u=e.n,d=a.slice(0,s-2),h=X.size(d),m=e.blockSize/8*e.bits/4,g=t[0].dataType,p=st(o),w=st(e.k),v=st(m),S=ra(g),$=o*i*S,E=Math.floor(n/$),T=i<=r[0]&&E>0,A=!T||E>=4?st(u):E>=2&&st(u)>=2?2:1,z=d.concat([o,u]),R=X.size(z)/A/p,V=T?[]:[{type:12,data:R},{type:12,data:e.blockSize}],j=[h,o,l/w],q=X.convertShape(t[1].dims).slice();q.splice(-1,1,m/v),V.push(...we(j)),V.push(...we(q)),V.push(...we(t[2].dims)),t.length===4&&V.push(...we(X.convertShape(t[3].dims)));let fe=[h,o,u/A];V.push(...we(fe));let ee=ne=>{let se=j.length,D=Q("a",t[0].dataType,se,w),M=Q("b",12,q.length,v),G=Q("scales",t[2].dataType,t[2].dims.length),K=[D,M,G],pe=t.length===4?Q("zero_points",12,t[3].dims.length):void 0;pe&&K.push(pe);let le=fe.length,N=_e("output",t[0].dataType,le,A),ae=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],de=_t(t[0].dataType),Se=(()=>{switch(w){case 1:return`array<${de}, 8>`;case 2:return`mat4x2<${de}>`;case 4:return`mat2x4<${de}>`;default:throw new Error(`${w}-component is not supported.`)}})(),Te=`
        for (var word: u32 = 0; word < ${m}; word += ${v}) {
          ${M.indicesSet("b_indices","2","word")};
          let b_data = ${M.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 = ${Se}(${Array.from({length:4},(ot,je)=>`${de}(b_value_lower[${je}]), ${de}(b_value_upper[${je}])`).join(", ")});
            let b_dequantized_values = ${w===1?`${Se}(${Array.from({length:8},(ot,je)=>`(b_quantized_values[${je}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Se}(${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:p}u; m++) {
              ${D.indicesSet("a_indices",se-2,T?"m":`row * ${p} + m`)};
              ${D.indicesSet("a_indices",se-1,"word_offset")};
              var input_offset = ${D.indicesToOffset("a_indices")};
              var a_data: ${Se};
              for (var j: u32 = 0; j < ${8/w}; j++) {
                a_data[j] = ${D.getByOffset("input_offset")};
                input_offset++;
              }
              ${T?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${A>1?"[c]":""} += ${Array.from({length:8/w},(ot,je)=>`${w===1?`a_data[${je}] * b_dequantized_values[${je}]`:`dot(a_data[${je}], b_dequantized_values[${je}])`}`).join(" + ")};
            }
            word_offset += ${8/w};
          }
        }`,Xe=pe?`
          zero_point_offset += 4;
          if (zero_point_offset == 32) {
            zero_point_offset = 0;
            zero_point_index++;
            zero_point_word = ${pe.getByOffset("zero_point_index")};
          }`:"";return T?`
        var<workgroup> workgroup_shared: array<${N.type.value}, ${o*i}>;
        ${ne.declareVariables(...K,N)}
        ${ne.mainStart([i,1,1])}
          var a_indices: ${D.type.indices};
          var block = local_id.x;
          var col = workgroup_id.y;
          var batch = workgroup_id.z;
          ${D.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;
              ${pe?`
            var zero_point_bytes_per_col: u32 = (${i} + 1) / 2;
            var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u);
            var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u;
            var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u;
            var zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            var zero_point_word: u32 = ${pe.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}
            var b_indices: ${M.type.indices};
            ${M.indicesSet("b_indices","0","col_times_components_plus_c")};
            // The scale and zero points are computed per block.
            var scales_index = col_times_components_plus_c * ${i} + block;
            let scale = ${G.getByOffset("scales_index")};
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${de}(${pe?"(zero_point_word) & 0xFu":8});
            ${M.indicesSet("b_indices","1","block")};
            var word_offset: u32 = block * ${e.blockSize/w};
            var workgroup_shared_offset: u32 = block * ${o};
            ${Te}
          }
          workgroupBarrier();
          if (local_id.x == 0u) {
            var output_indices: ${N.type.indices};
            ${N.indicesSet("output_indices","0","batch")};
            ${N.indicesSet("output_indices",le-1,"col")};
            ${N.indicesSet("output_indices",le-2,"0")};
            var output_offset = ${N.indicesToOffset("output_indices")};
            for (var m: u32 = 0u; m < ${o}u; m++) {
              var output_value: ${N.type.value} = ${N.type.value}(0);
              var workgroup_shared_offset: u32 = m;
              for (var b: u32 = 0u; b < ${i}u; b++) {
                output_value += workgroup_shared[workgroup_shared_offset];
                workgroup_shared_offset += ${o};
              }
              ${N.setByOffset("output_offset","output_value")};
              output_offset += ${u/A};
            }
          }
        }`:`
        ${ne.registerUniforms(ae).declareVariables(...K,N)}
        ${ne.mainStart()}
          ${ne.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var output_values: array<${N.type.value}, ${p}>;
          var output_indices = ${N.offsetToIndices("global_idx")};
          var col = ${N.indicesGet("output_indices",le-1)};
          var row = ${N.indicesGet("output_indices",le-2)};
          var a_indices: ${D.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
          ${pe?`
          var zero_point_abs_offset = col * ${A} * ((${i} + 1) / 2);
          var zero_point_index: u32 = zero_point_abs_offset / 4;
          var zero_point_word: u32 = ${pe.getByOffset("zero_point_index")};
          var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}
          var scale_index = col * ${i*A};
          var b_indices: ${M.type.indices};
          for (var c: u32 = 0; c < ${A}; c++) {
            ${M.indicesSet("b_indices","0",`col * ${A} + c`)};
            var block_offset: u32 = 0;
            for (var block: u32 = 0; block < ${i}; block++) {
              // The scale and zero points are computed per block.
              let scale = ${G.getByOffset("scale_index")};
              // The default zero point is 8 for unsigned 4-bit quantization.
              let zero_point = ${de}(${pe?"extractBits(zero_point_word, zero_point_offset, 4)":8});
              ${M.indicesSet("b_indices","1","block")};
              var word_offset: u32 = block_offset;
              ${Te}
              scale_index++;
              ${Xe}
              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.
            ${pe?`if (zero_point_offset % 8 > 0) {
                ${Xe}
              }`:""}
            }
            for (var k: u32 = 0u; k < ${p}u; k++) {
              ${N.indicesSet("output_indices",le-2,`${p} * row + k`)};
              ${N.setByIndices("output_indices","output_values[k]")}
            }
        }`};return{name:T?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${g};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:z,dataType:g}],name:T?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:T?{x:1,y:Math.ceil(u/A),z:h}:{x:Math.ceil(R/64)},programUniforms:V}),getShaderSource:ee}},ch=(t,e)=>{uh(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(dh(t.inputs,e,r,n))},ph=t=>Ke(t)}),Ct,hh,fh,So,mh,Ci,gh,tw=te(()=>{xe(),Oe(),ct(),Hs(),Hd(),Ie(),aa(),Ct=(t,e)=>t.length>e&&t[e].dims.length>0&&X.size(t[e].dims)>0?t[e]:void 0,hh=(t,e)=>{let r=t[0],n=Ct(t,1),a=Ct(t,2),s=Ct(t,3),i=Ct(t,4),o=Ct(t,5),l=Ct(t,6),u=Ct(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 d=!1,h=r.dims[0],m=r.dims[1],g=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],p=m,w=0,v=0,S=Math.floor(g/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,p=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.');$=5,p=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==S)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');$=0,p=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');$=3}if(s){if(s.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(a&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(i){E=8;let V=i.dims;throw V.length===1?V[0]===h?E=1:V[0]===3*h+2&&(E=3):V.length===2&&V[0]===h&&V[1]===p&&(E=5),E===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let T=!1,A=g;if(a){if(a.dims.length!==3&&a.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==a.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(a.dims.length===3){if(p!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');A=a.dims[2]}else{if(p!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');A=a.dims[1]*a.dims[3],T=!0}}let z=w+p,R=!1;if(i)throw new Error("Key padding mask is not supported");if(o){if(o.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(o.dims[0]!==h&&o.dims[0]!==1||o.dims[1]!==e.numHeads||o.dims[2]!==m||o.dims[3]!==z)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:h,sequenceLength:m,pastSequenceLength:w,kvSequenceLength:p,totalSequenceLength:z,maxSequenceLength:v,inputHiddenSize:0,hiddenSize:g,vHiddenSize:A,headSize:S,vHeadSize:Math.floor(A/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:E,scale:e.scale,broadcastResPosBias:R,passPastInKv:T,qkvFormat:$}},fh=t=>Ke({...t}),So=Ke({perm:[0,2,1,3]}),mh=(t,e,r,n,a,s,i)=>{let o=[n,a,s],l=X.size(o),u=[{type:12,data:l},{type:12,data:i},{type:12,data:s}],d=h=>{let m=_e("qkv_with_bias",e.dataType,o),g=Q("qkv",e.dataType,o),p=Q("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(g,p,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:d},{inputs:[e,r],outputs:[-1]})[0]},Ci=(t,e,r,n,a,s,i,o)=>{let l=s;if(i){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=mh(t,s,i,e,n,r*a,o),l=l.reshape([e,n,r,a]),t.compute(kr(l,So.perm),{inputs:[l],outputs:[-1]})[0]}else return s.dims.length===3&&(l=s.reshape([e,n,r,a])),t.compute(kr(l,So.perm),{inputs:[l],outputs:[-1]})[0]},gh=(t,e)=>{let r=hh(t.inputs,e),n=t.inputs[0],a=Ct(t.inputs,1),s=Ct(t.inputs,2),i=Ct(t.inputs,3),o=Ct(t.inputs,4),l=Ct(t.inputs,5),u=Ct(t.inputs,6),d=Ct(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((a==null?void 0:a.dims.length)===5)throw new Error("Packed KV is not implemented");let h=a&&s&&a.dims.length===4&&s.dims.length===4,m=Ci(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(h)return yi(t,m,a,s,o,void 0,u,d,l,r,e);if(!a||!s)throw new Error("key and value must be provided");let g=Ci(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,i,r.hiddenSize),p=Ci(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,s,i,2*r.hiddenSize);yi(t,m,g,p,o,void 0,u,d,l,r,e)}}),_h,yh,wh,bh,vh,$h,xh,Sh,kh,rw=te(()=>{xe(),Oe(),Ie(),_h=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1&&t[0].dataType!==10)throw new Error("Input type must be float or float16.");if(t.length>=2){let e=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(e=t[3].dims[0]*2===t[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},yh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
            k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${ke("uniforms.x_shape",a,e)})) {
              break;
            }
            offset += k * i32(${ke("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;
            ${n}
            value = x[offset];
          }
      `},wh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${ke("uniforms.x_shape",a,e)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${ke("uniforms.x_shape",a,e)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${ke("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},bh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${ke("uniforms.x_shape",a,e)})) {
                  k = i32(${ke("uniforms.x_shape",a,e)}) - 1;
                }
                offset += k * i32(${ke("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},vh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=`
                k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)};
                if (k < 0)  {
                  k += i32(${ke("uniforms.x_shape",a,e)}]);
                }
                if (k >= i32(${ke("uniforms.x_shape",a,e)})) {
                  k -= i32(${ke("uniforms.x_shape",a,e)});
                }
                offset += k * i32(${ke("uniforms.x_strides",a,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},$h=(t,e,r)=>{switch(r.mode){case 0:return yh(t,e,r.pads.length);case 1:return wh(t,e,r.pads.length);case 2:return bh(t,e,r.pads.length);case 3:return vh(t,e,r.pads.length);default:throw new Error("Invalid mode")}},xh=(t,e)=>{let r=X.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=X.size(r),s=[{type:12,data:a},{type:6,data:e.pads}];e.mode===0&&s.push({type:t[0].dataType,data:e.value}),s.push(...we(t[0].dims,r));let i=["rank"],o=l=>{let u=_e("output",t[0].dataType,r.length),d=Q("x",t[0].dataType,n.length),h=d.type.value,m=$h(u,n.length,e),g=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&g.push({name:"constant_value",type:h}),`
            ${l.registerUniforms(g).declareVariables(d,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:i},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(X.size(r)/64)},programUniforms:s}),getShaderSource:o}},Sh=(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,s=new Int32Array(2*a).fill(0);if(t.length>=4){let o=t[3].getBigInt64Array();for(let l=0;l<o.length;l++)s[Number(o[l])]=Number(r[l]),s[Number(o[l])+a]=Number(r[l+o.length])}else r.forEach((o,l)=>s[Number(l)]=Number(o));let i=[];return s.forEach(o=>i.push(o)),{mode:e.mode,value:n,pads:i}}else return e},kh=(t,e)=>{_h(t.inputs);let r=Sh(t.inputs,e);t.compute(xh(t.inputs,r),{inputs:[0]})}}),sa,ko,Eo,Co,To,Eh,Ch,Ao,Io,Th,Ah,Mo,Ih,Mh,Oo,Oh,zh,Ph,Rh,nw=te(()=>{rr(),xe(),Oe(),Ie(),sa=t=>{if(Ue.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},ko=(t,e,r)=>{let n=e.format==="NHWC",a=t.dims.slice();n&&a.splice(1,0,a.pop());let s=Object.hasOwnProperty.call(e,"dilations"),i=e.kernelShape.slice(),o=e.strides.slice(),l=s?e.dilations.slice():[],u=e.pads.slice();hi.adjustPoolAttributes(r,a,i,o,l,u);let d=hi.computePoolOutputShape(r,a,o,l,i,u,e.autoPad),h=Object.assign({},e);s?Object.assign(h,{kernelShape:i,strides:o,pads:u,dilations:l,cacheKey:e.cacheKey}):Object.assign(h,{kernelShape:i,strides:o,pads:u,cacheKey:e.cacheKey});let m=d.slice();return m.push(m.splice(1,1)[0]),[h,n?m:d]},Eo=(t,e)=>{let r=e.format==="NHWC",n=X.size(t),a=X.size(e.kernelShape),s=[{type:12,data:n},{type:12,data:a}],i=[{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],d=e.pads[e.pads.length-1],h=!!(u+d);s.push({type:12,data:o},{type:12,data:l},{type:12,data:u},{type:12,data:d}),i.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 g=e.kernelShape[e.kernelShape.length-2],p=e.strides[e.strides.length-2],w=e.pads[e.pads.length/2-2],v=e.pads[e.pads.length-2];m=!!(w+v),s.push({type:12,data:g},{type:12,data:p},{type:12,data:w},{type:12,data:v}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,i,!0,h,m]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=X.computeStrides(e.kernelShape);s.push({type:12,data:o},{type:12,data:e.pads},{type:12,data:e.strides}),i.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,d)=>u+d);return[s,i,!!l,!1,!1]}},Co=(t,e,r,n,a,s,i,o,l,u,d,h)=>{let m=a.format==="NHWC",g=e.type.value,p=_e("output",e.type.tensor,n);if(a.kernelShape.length<=2){let w="",v="",S="",$=r-(m?2:1);if(d?w=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${$}] < 0 || xIndices[${$}]
                      >= uniforms.x_shape[${$}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${s}
                }`:w=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${s}
                }`,a.kernelShape.length===2){let E=r-(m?3:2);h?v=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${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,p)}

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

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

              var value = ${g}(${o});
              var pad = 0;
              ${v}
              ${w}
              ${S}
              ${i}

              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")}];
                ${s}
              }`:S=`
              }
              let x_val = x[${e.indicesToOffset("xIndices")}];
              ${s}
            `,`
            ${t.registerUniforms(l).declareVariables(e,p)}

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

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

              var value = ${g}(${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 / ${ke("uniforms.kernelStrides","j",w)};
                  offset -= offsets[j] * ${ke("uniforms.kernelStrides","j",w)};
                }
                offsets[${w-1}] = offset;

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

              output[global_idx] = value;
            }`}},To=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,Eh=t=>`${To(t)};${t.countIncludePad}`,Ch=t=>`${To(t)};${t.storageOrder};${t.dilations}`,Ao=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}),Io=(t,e,r,n)=>{let[a,s]=ko(e,n,r),i=Q("x",e.dataType,e.dims.length),o=i.type.value,l="value += x_val;",u="";a.countIncludePad?u+=`value /= ${o}(uniforms.kernelSize);`:u+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[d,h,m,g,p]=Eo(s,a);d.push(...we(e.dims,s));let w=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${m};${g};${p}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(X.size(s)/64)},programUniforms:d}),getShaderSource:v=>Co(v,i,e.dims.length,s.length,a,l,u,0,h,m,g,p)}},Th=t=>{let e=t.count_include_pad!==0,r=Ao(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:Eh(n)}},Ah=(t,e)=>{sa(t.inputs),t.compute(Io("AveragePool",t.inputs[0],!1,e))},Mo={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Ih=t=>{let e=t.format;return{format:e,...Mo,cacheKey:e}},Mh=(t,e)=>{sa(t.inputs),t.compute(Io("GlobalAveragePool",t.inputs[0],!0,e))},Oo=(t,e,r,n)=>{let[a,s]=ko(e,n,r),i=`
      value = max(x_val, value);
    `,o="",l=Q("x",e.dataType,e.dims.length),u=["rank"],[d,h,m,g,p]=Eo(s,a);return d.push(...we(e.dims,s)),{name:t,shaderCache:{hint:`${n.cacheKey};${m};${g};${p}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(X.size(s)/64)},programUniforms:d}),getShaderSource:w=>Co(w,l,e.dims.length,s.length,a,i,o,e.dataType===10?-65504:-1e5,h,m,g,p)}},Oh=(t,e)=>{sa(t.inputs),t.compute(Oo("MaxPool",t.inputs[0],!1,e))},zh=t=>{let e=t.storage_order,r=t.dilations,n=Ao(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:Ch(a)}},Ph=t=>{let e=t.format;return{format:e,...Mo,cacheKey:e}},Rh=(t,e)=>{sa(t.inputs),t.compute(Oo("GlobalMaxPool",t.inputs[0],!0,e))}}),Bh,Dh,Nh,aw=te(()=>{rr(),xe(),Ie(),Bh=(t,e,r)=>{let n=t===e,a=t<e&&r<0,s=t>e&&r>0;if(n||a||s)throw new Error("Range these inputs' contents are invalid.")},Dh=(t,e,r,n)=>{let a=Math.abs(Math.ceil((e-t)/r)),s=[a],i=a,o=[{type:12,data:i},{type:n,data:t},{type:n,data:r},...we(s)],l=u=>{let d=_e("output",n,s.length),h=d.type.value,m=[{name:"outputSize",type:"u32"},{name:"start",type:h},{name:"delta",type:h}];return`
        ${u.registerUniforms(m).declareVariables(d)}
        ${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:s,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:o})}},Nh=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&&Bh(e,r,n),t.compute(Dh(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),Fh,Lh,Uh,Wh,Vh,Gh,Hh,jh,qh,Kh,Yh,zo,Xh,Qh,Zh,Jh,ef,tf,rf,iw=te(()=>{xe(),Oe(),ct(),Ie(),Fh=(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")}},Lh=(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,s)=>n[a]=t[s]),n},Uh=(t,e,r,n,a,s)=>{let[i,o,l]=r>10?[1,2,3]:[-1,t.length>1?1:-1,-1],u=t[0].dims.length;if(i>0&&t.length>i&&t[i].dims.length>0)t[i].getFloat32Array().forEach(d=>s.push(d));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(d=>n.push(d)),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");Fh(n,e),e.axes.length>0&&Lh(n,e.axes,u).forEach((d,h)=>n[h]=d)}if(l>0&&t.length>l&&(t[l].getBigInt64Array().forEach(d=>a.push(Number(d))),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")},Wh=(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`)}})()+"}",Vh=(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`)}})()+"}",Gh=(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((s,i)=>{n[s]=a[i],n[i+r]=a[e.length+i]}),n):a},Hh=(t,e,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(t.forEach(s=>a.push(s)),Math.max(...n)>t.length)throw new Error("axes is out of bound");n.forEach((s,i)=>a[s]=r[i])}else r.forEach(s=>a.push(s));else{if(e.length===0)throw new Error("Resize requires either scales or sizes.");a=t.map((s,i)=>Math.round(s*e[i]))}return a},jh=(t,e,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>e[s]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>e[s]),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(s=>e[s]=n),r.axes.forEach(s=>a[s]=Math.round(t[s]*e[s]))):(e.fill(n,0,e.length),a.forEach((s,i)=>a[i]=Math.round(s*e[i]))),a},qh=(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 = ${ke("uniforms.scales","i",n)};
        var roi_low = ${ke("uniforms.roi","i",a)};
        var roi_hi = ${ke("uniforms.roi",`i + ${e.length}`,a)};
        if (scale == 1.0) {
          original_indices[i] = ${t.type.value}(output_index);
        } else {
          var input_shape_i = ${ke("uniforms.input_shape","i",e.length)};
          var output_shape_i = ${ke("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;
    }`,Kh=(t,e,r,n,a,s,i)=>`
    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 = ${ke("uniforms.scales","i",a)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${ke("uniforms.roi","i",s)};
          var roi_hi = ${ke("uniforms.roi",`i + ${r.length}`,s)};
          var input_shape_i = ${ke("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${ke("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${i} || (original_idx >= 0 && original_idx < ${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;
    }`,Yh=(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 >= ${ke("uniforms.input_shape","i",e.length)}) {
          return false;
        }
      }
      return true;
    }`,zo=(t,e,r,n)=>t.rank>n?`
    ${t.indicesSet("input_indices",e,"channel")};
    ${t.indicesSet("input_indices",r,"batch")};
`:"",Xh=(t,e,r,n,a)=>{let[s,i,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",i,`max(0, min(row, ${r[i]} - 1))`)};
      ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)};
      ${zo(t,l,s,2)}
      return ${t.getByIndices("input_indices")};
    }

    fn bilinearInterpolation(output_indices: ${e.type.indices}) -> ${u} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var row:${u} = originalIndices[${i}];
      var col:${u} = originalIndices[${o}];
      ${n?`if (row < 0 || row > (${r[i]} - 1) || col < 0 || col > (${r[o]} - 1)) {
        return ${a};
      }`:""};
      row = max(0, min(row, ${r[i]} - 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 =  ${r.length>2?`u32(originalIndices[${s}])`:"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);
    }`},Qh=(t,e,r,n,a,s,i,o,l,u)=>{let d=r.length===2,[h,m]=d?[0,1]:[2,3],g=t.type.value,p=w=>{let v=w===h?"row":"col";return`
      fn ${v}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${g} {
        var output_index = ${e.indicesGet("output_indices",w)};
        var originalIdx: ${g} = getOriginalCoordinateFromResizedCoordinate(output_index, ${a[w]},
        ${n[w]}, ${r[w]}, ${s[w]}, ${s[w]} + ${r.length});
        var fractOriginalIdx: ${g} = originalIdx - floor(originalIdx);
        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);

        if (${o} && (originalIdx < 0 || originalIdx > (${r[w]} - 1))) {
          return ${l};
        }
        var data: array<${g}, 4> = array<${g}, 4>(0.0, 0.0, 0.0, 0.0);
        for (var i: i32 = -1; i < 3; i++) {
          var ${v}: ${g} = originalIdx + ${g}(i);
          if (${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`
    ${p(h)};
    ${p(m)};
  fn getCubicInterpolationCoefs(s: ${g}) -> array<${g}, 4> {
    var absS = abs(s);
    var coeffs: array<${g}, 4> = array<${g}, 4>(0.0, 0.0, 0.0, 0.0);
    var oneMinusAbsS: ${g} = 1.0 - absS;
    var twoMinusAbsS: ${g} = 2.0 - absS;
    var onePlusAbsS: ${g} = 1.0 + absS;
    coeffs[0] = ((${i} * onePlusAbsS - 5 * ${i}) * onePlusAbsS + 8 * ${i}) * onePlusAbsS - 4 * ${i};
    coeffs[1] = ((${i} + 2) * absS - (${i} + 3)) * absS * absS + 1;
    coeffs[2] = ((${i} + 2) * oneMinusAbsS - (${i} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;
    coeffs[3] = ((${i} * twoMinusAbsS - 5 * ${i}) * twoMinusAbsS + 8 * ${i}) * twoMinusAbsS - 4 * ${i};
    return coeffs;
  }

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

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

    depth = max(0, min(depth, ${r[i]} - 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 =  ${r.length>3?`u32(originalIndices[${s}])`:"0"};

      var x111: ${d} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${d} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${d} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${d} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${d} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${d} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${d} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${d} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${d} = abs(depth - ${d}(depth1));
      var dx2: ${d} = abs(${d}(depth2) - depth);
      var dy1: ${d} = abs(height - ${d}(height1));
      var dy2: ${d} = abs(${d}(height2) - height);
      var dz1: ${d} = abs(width - ${d}(width1));
      var dz2: ${d} = abs(${d}(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);
    }`},Jh=(t,e,r,n,a,s)=>{let i=t.dims,o=Gh(s,e.axes,i.length),l=Hh(i,n,a,e.axes),u=n.slice();n.length===0&&(u=i.map(($,E)=>$===0?1:l[E]/$),e.keepAspectRatioPolicy!=="stretch"&&(l=jh(i,u,e)));let d=_e("output",t.dataType,l.length),h=Q("input",t.dataType,i.length),m=X.size(l),g=i.length===l.length&&i.every(($,E)=>$===l[E]),p=e.coordinateTransformMode==="tf_crop_and_resize",w=e.extrapolationValue,v=h.type.value,S=$=>`
      ${g?"":`
      ${Wh(e.coordinateTransformMode,v)};
      ${(()=>{switch(e.mode){case"nearest":return`
              ${Yh(h,i)};
              ${Vh(e.nearestMode,r,v)};
              ${Kh(h,d,i,l,u.length,o.length,p)};
              `;case"linear":return`
              ${qh(d,i,l,u.length,o.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${Xh(h,d,i,p,w)}`;if(i.length===3||i.length===5)return`${Zh(h,d,i,p,w)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${Qh(h,d,i,l,u,o,e.cubicCoeffA,p,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,d)}
      ${$.mainStart()}
        ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${g?"output[global_idx] = input[global_idx];":`
        let output_indices = ${d.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] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${e.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${e.cacheKey}|${r}|${u.length>0?u:""}|${a.length>0?a:""}|${o.length>0?o:""}|${g}|${i}`,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},...we(i,l)]})}},ef=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},tf=(t,e)=>{let r=[],n=[],a=[],s=ef(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Uh(t.inputs,e,s,r,n,a),t.compute(Jh(t.inputs[0],e,s,r,n,a),{inputs:[0]})},rf=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,a=t.cubicCoeffA,s=t.excludeOutside!==0,i=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:s,extrapolationValue:i,keepAspectRatioPolicy:o,mode:l,nearestMode:u})}}),nf,af,sf,sw=te(()=>{xe(),Oe(),ct(),Ie(),nf=(t,e)=>{let[r,n,a,s]=t,{numHeads:i,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(!X.areEqual(n.dims,[])&&!X.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(s.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${s.dims.length}`);if(!X.areEqual(a.dims,s.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(o>0&&i===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],d=a.dims[0],h=X.sizeFromDimension(r.dims,1)/u,m=o===0?a.dims[1]*2:h/i;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 ${a.dims[1]}`);if(u>d)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},af=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:s}=e,i=t[0].dims[0],o=X.sizeFromDimension(t[0].dims,1),l=t[0].dims[t[0].dims.length-2],u=o/l,d=t[2].dims[1],h=a===0?d*2:u/n,m=new Array(i,l,u/h,h-d),g=X.computeStrides(m),p=[{type:1,data:s},{type:12,data:m},{type:12,data:g},...t[0].dims.length===3?new Array({type:12,data:[o,u,h,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,h,l*h,1]}):[],...we(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],w=v=>{let S=Q("input",t[0].dataType,t[0].dims.length),$=Q("position_ids",t[1].dataType,t[1].dims.length),E=Q("cos_cache",t[2].dataType,t[2].dims.length),T=Q("sin_cache",t[3].dataType,t[3].dims.length),A=_e("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:g.length},{name:"input_output_strides",type:"u32",length:g.length}]),`
        ${v.declareVariables(S,$,E,T,A)}

        ${v.mainStart(Sn)}
          let half_rotary_emb_dim = uniforms.${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",_e("",$.type.tensor,2))};
            let position_id =
                u32(${$.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;
            ${A.setByOffset("k",S.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:Ke({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(X.size(m)/Sn)},programUniforms:p})}},sf=(t,e)=>{nf(t.inputs,e),t.compute(af(t.inputs,e))}}),of,lf,uf,ow=te(()=>{xe(),Oe(),Ie(),of=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let a=e.dims[e.dims.length-1],s=e.dims[e.dims.length-2];if(r.dims[r.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==a)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let i=t[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let i=t[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},lf=(t,e,r,n)=>{let a=e.simplified,s=t[0].dims,i=X.size(s),o=s,l=i,u=s.slice(-1)[0],d=n?s.slice(0,-1).concat(1):[],h=!a&&t.length>3,m=t.length>4,g=n&&r>1,p=n&&r>2,w=r>3,v=st(u),S=[{type:12,data:l},{type:12,data:v},{type:12,data:u},{type:1,data:e.epsilon}],$=T=>{let A=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],z=[Q("x",t[0].dataType,t[0].dims,v),Q("skip",t[1].dataType,t[1].dims,v),Q("gamma",t[2].dataType,t[2].dims,v)];h&&z.push(Q("beta",t[3].dataType,t[3].dims,v)),m&&z.push(Q("bias",t[4].dataType,t[4].dims,v)),z.push(_e("output",t[0].dataType,o,v)),g&&z.push(_e("mean_output",1,d)),p&&z.push(_e("inv_std_output",1,d)),w&&z.push(_e("input_skip_bias_sum",t[0].dataType,o,v));let R=_t(t[0].dataType);return`

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

      ${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]":R+"(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 = ${kn(R,v,"value")};
          sum += f32_value;
          squareSum += f32_value * f32_value;
        }
        let mean = ${Dr("sum",v)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Dr("squareSum",v)} / f32(uniforms.hidden_size) ${a?"":"- mean * mean"} + uniforms.epsilon);
        ${g?"mean_output[global_idx] = mean;":""}
        ${p?"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?"":`- ${R}(mean)`}) * ${R}(inv_std_dev) * gamma[i] ${h?"+ beta[i]":""};
        }
      }`},E=[{dims:o,dataType:t[0].dataType}];return r>1&&E.push({dims:d,dataType:1}),r>2&&E.push({dims:d,dataType:1}),r>3&&E.push({dims:s,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${v};${g};${p};${w}`,inputDependencies:t.map((T,A)=>"type")},getShaderSource:$,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/u/64)},programUniforms:S})}},uf=(t,e)=>{of(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(lf(t.inputs,e,t.outputCount,!1),{outputs:r})}}),df,oa,cf,Po,pf,hf,ff,mf,lw=te(()=>{xe(),Oe(),ct(),Ie(),df=(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},cf=(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},Po=(t,e,r,n,a)=>{let s=t;return t<0&&(s+=r[n[e]]),a[e]<0?Math.max(0,Math.min(s,r[n[e]]-1)):Math.max(0,Math.min(s,r[n[e]]))},pf=(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 = ${ke("uniforms.input_shape","i",r.length)};
            let steps_i = ${ke("uniforms.steps","i",r.length)};
            let signs_i = ${ke("uniforms.signs","i",r.length)};
            let starts_i = ${ke("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;
            }
            ${t.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,hf=(t,e)=>{let r=t[0].dims,n=X.size(r),a=e.axes.length>0?X.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],s=oa(t,4);s.forEach(S=>S!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(a.length).fill(1));let i=e.starts.map((S,$)=>Po(S,$,r,a,s)),o=e.ends.map((S,$)=>Po(S,$,r,a,s));if(a.length!==i.length||a.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(a.length!==r.length)for(let S=0;S<r.length;++S)a.includes(S)||(i.splice(S,0,0),o.splice(S,0,r[S]),s.splice(S,0,1));let l=s.map(S=>Math.sign(S));s.forEach((S,$,E)=>{if(S<0){let T=(o[$]-i[$])/S,A=i[$],z=A+T*s[$];i[$]=z,o[$]=A,E[$]=-S}});let u=r.slice(0);a.forEach((S,$)=>{u[S]=Math.ceil((o[S]-i[S])/s[S])});let d={dims:u,dataType:t[0].dataType},h=_e("output",t[0].dataType,u.length),m=Q("input",t[0].dataType,t[0].dims.length),g=X.size(u),p=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:s.length}],w=[{type:12,data:g},{type:12,data:i},{type:6,data:l},{type:12,data:s},...we(t[0].dims,u)],v=S=>`
      ${S.registerUniforms(p).declareVariables(m,h)}
        ${pf(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}_${i.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:v,getRunData:()=>({outputs:[d],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:w})}},ff=(t,e)=>{df(t.inputs,e);let r=cf(t.inputs,e);t.compute(hf(t.inputs,r),{inputs:[0]})},mf=t=>{let e=t.starts,r=t.ends,n=t.axes;return Ke({starts:e,ends:r,axes:n})}}),gf,_f,yf,wf,uw=te(()=>{xe(),Oe(),ct(),Ie(),gf=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},_f=(t,e)=>{let r=t.dims,n=X.size(r),a=64,s=e.axis;if(s<0&&(s=r.length+s),s<r.length-1)throw new Error("softmax only supports last axis for now.");let i=r[s],o=n/i,l=st(i),u=i/l,d=(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=Q("x",t.dataType,t.dims,l),m=_e("result",t.dataType,t.dims,l),g=h.type.value,p=_t(t.dataType)==="f32"?`var threadMax = ${g}(-3.402823e+38f);`:`var threadMax = ${g}(-65504.0h);`,w=v=>`
      var<workgroup> rowMaxShared : ${g};
      var<workgroup> rowSumShared : ${g};
      var<workgroup> threadShared : array<${g}, ${a}>;

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

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${g}) {
        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
        ${p}
        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 = ${g}(${d("threadShared[0]",l)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${g}(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 = ${g}(${Dr("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:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:6,data:u}]}),getShaderSource:w}},yf=(t,e)=>{gf(t.inputs),t.compute(_f(t.inputs[0],e))},wf=t=>Ke({axis:t.axis})}),bf,vf,$f,xf,Sf,kf,Ef,dw=te(()=>{xe(),Oe(),ct(),Ie(),bf=t=>{if(!t||t.length<1)throw new Error("too few inputs")},vf=(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})},$f=t=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${t}u; i += 1u ) {
    if (index < ${ke("uniforms.size_in_split_axis","i",t)}) {
        return i;
    }
    }
    return ${t}u;
}`,xf=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) {
        ${r.join(`
`)}
      }`},Sf=(t,e)=>{let r=t[0].dims,n=X.size(r),a=t[0].dataType,s=X.normalizeAxis(e.axis,r.length),i=new Array(e.numOutputs),o=Q("input",a,r.length),l=new Array(e.numOutputs),u=[],d=[],h=0,m=[{type:12,data:n}];for(let p=0;p<e.numOutputs;p++){h+=e.splitSizes[p],l[p]=h;let w=r.slice();w[e.axis]=e.splitSizes[p],d.push(w),i[p]=_e(`output${p}`,a,w.length),u.push({dims:d[p],dataType:t[0].dataType})}m.push({type:12,data:l},...we(r,...d));let g=p=>`
  ${p.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...i)}
  ${$f(l.length)}
  ${xf(i)}

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

    var indices = ${o.offsetToIndices("global_idx")};
    var index = ${o.indicesGet("indices",s)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${ke("uniforms.size_in_split_axis","output_number - 1u",l.length)};
      ${o.indicesSet("indices",s,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:g,getRunData:()=>({outputs:u,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:m})}},kf=(t,e)=>{bf(t.inputs);let r=t.inputs.length===1?e:vf(t.inputs,e);t.compute(Sf(t.inputs,r),{inputs:[0]})},Ef=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})}}),Ro,Cf,Tf,Af,If,cw=te(()=>{xe(),Oe(),Ie(),Ro=t=>Array.from(t.getBigInt64Array(),Number),Cf=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(Ro(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")},Tf=(t,e)=>{let r=[];for(let n=0;n<t.length;++n)r.push(t[n]*e[n]);return r},Af=t=>{let e=t[0].dims,r=Ro(t[1]),n=Tf(e,r),a=X.size(n),s=t[0].dataType,i=Q("input",s,e.length),o=_e("output",s,n.length),l=u=>`
      const inputShape = ${i.indices(...e)};
      ${u.registerUniform("output_size","u32").declareVariables(i,o)}
      ${u.mainStart()}
      ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${o.offsetToIndices("global_idx")};
      var input_indices: ${i.type.indices};
      for (var i = 0; i < ${e.length}; i++) {
        let input_dim_i = ${i.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${o.indicesGet("output_indices","i")}  % input_dim_i;

        ${i.indicesSet("input_indices","i","input_dim_value")}
      }
      ${o.setByOffset("global_idx",i.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...we(t[0].dims,n)]}),getShaderSource:l}},If=t=>{Cf(t.inputs),t.compute(Af(t.inputs),{inputs:[0]})}}),Mf,Of,zf,pw=te(()=>{xe(),Oe(),Ie(),Mf=(t,e,r,n,a)=>{let s=_e("output_data",a,r.length,4),i=Q("a_data",e[1].dataType,e[1].dims.length,4),o=Q("b_data",e[2].dataType,e[2].dims.length,4),l=Q("c_data",e[0].dataType,e[0].dims.length,4),u,d=(h,m,g)=>`select(${m}, ${h}, ${g})`;if(!n)u=s.setByOffset("global_idx",d(i.getByOffset("global_idx"),o.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let h=(m,g,p="")=>{let w=`a_data[index_a${g}][component_a${g}]`,v=`b_data[index_b${g}][component_b${g}]`,S=`bool(c_data[index_c${g}] & (0xffu << (component_c${g} * 8)))`;return`
            let output_indices${g} = ${s.offsetToIndices(`global_idx * 4u + ${g}u`)};
            let offset_a${g} = ${i.broadcastedIndicesToOffset(`output_indices${g}`,s)};
            let offset_b${g} = ${o.broadcastedIndicesToOffset(`output_indices${g}`,s)};
            let offset_c${g} = ${l.broadcastedIndicesToOffset(`output_indices${g}`,s)};
            let index_a${g} = offset_a${g} / 4u;
            let index_b${g} = offset_b${g} / 4u;
            let index_c${g} = offset_c${g} / 4u;
            let component_a${g} = offset_a${g} % 4u;
            let component_b${g} = offset_b${g} % 4u;
            let component_c${g} = offset_c${g} % 4u;
            ${m}[${g}] = ${p}(${d(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,i,o,s)}
        ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${u}
      }`},Of=t=>{let e=t[1].dims,r=t[2].dims,n=t[0].dims,a=t[1].dataType,s=!(X.areEqual(e,r)&&X.areEqual(r,n)),i=e,o=X.size(e);if(s){let u=xn.calcShape(xn.calcShape(e,r,!1),n,!1);if(!u)throw new Error("Can't perform where op on the given tensors");i=u,o=X.size(i)}let l=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:u=>Mf(u,t,i,s,a),getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:l},...we(n,e,r,i)]})}},zf=t=>{t.compute(Of(t.inputs))}}),Pf,hw=te(()=>{Py(),Hd(),Ry(),By(),Dy(),Ny(),Nd(),dp(),Vy(),Gy(),Hy(),jy(),qy(),Ky(),Yy(),Xy(),Qy(),Zy(),Jy(),sp(),ew(),tw(),rw(),nw(),aw(),eo(),iw(),sw(),ow(),lw(),uw(),dw(),cw(),aa(),so(),pw(),Pf=new Map([["Abs",[ec]],["Acos",[tc]],["Acosh",[rc]],["Add",[Lc]],["ArgMax",[Od,ro]],["ArgMin",[Md,ro]],["Asin",[nc]],["Asinh",[ac]],["Atan",[ic]],["Atanh",[sc]],["Attention",[Gd]],["AveragePool",[Ah,Th]],["BatchNormalization",[Yd]],["BiasAdd",[Zd]],["BiasSplitGelu",[Dc]],["Cast",[lc,oc]],["Ceil",[cc]],["Clip",[dc]],["Concat",[Bd,Dd]],["Conv",[yo,_o]],["ConvTranspose",[$p,_p]],["Cos",[pc]],["Cosh",[hc]],["CumSum",[Sp,kp]],["DepthToSpace",[Ap,Ip]],["Div",[Uc]],["Einsum",[Bp,Dp]],["Elu",[fc,wi]],["Equal",[Wc]],["Erf",[mc]],["Exp",[gc]],["Expand",[Up]],["FastGelu",[Vp]],["Floor",[_c]],["FusedConv",[yo,_o]],["Gather",[qp,jp]],["GatherElements",[Qp,Xp]],["Gelu",[yc]],["Gemm",[th,eh]],["GlobalAveragePool",[Mh,Ih]],["GlobalMaxPool",[Rh,Ph]],["Greater",[jc]],["GreaterOrEqual",[Kc]],["HardSigmoid",[Ec,kc]],["InstanceNormalization",[ih]],["LayerNormalization",[lh]],["LeakyRelu",[wc,wi]],["Less",[qc]],["LessOrEqual",[Yc]],["Log",[Pc]],["MatMul",[ip]],["MatMulNBits",[ch,ph]],["MaxPool",[Oh,zh]],["Mul",[Vc]],["MultiHeadAttention",[gh,fh]],["Neg",[vc]],["Not",[bc]],["Pad",[kh]],["Pow",[Gc]],["Range",[Nh]],["Reciprocal",[$c]],["ReduceMin",[Ed]],["ReduceMean",[vd]],["ReduceMax",[kd]],["ReduceSum",[Td]],["ReduceProd",[Cd]],["ReduceL1",[$d]],["ReduceL2",[xd]],["ReduceLogSum",[Id]],["ReduceLogSumExp",[Sd]],["ReduceSumSquare",[Ad]],["Relu",[xc]],["Resize",[tf,rf]],["RotaryEmbedding",[sf]],["Sigmoid",[Sc]],["Sin",[Cc]],["Sinh",[Tc]],["Slice",[ff,mf]],["SkipLayerNormalization",[uf]],["Split",[kf,Ef]],["Sqrt",[Ac]],["Softmax",[yf,wf]],["Sub",[Hc]],["Tan",[Ic]],["Tanh",[Mc]],["ThresholdedRelu",[zc,wi]],["Tile",[If]],["Transpose",[Wu,Vu]],["Where",[zf]]])}),Rf,fw=te(()=>{rr(),Xr(),Ie(),Rf=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){tr(t.programInfo.name);let s=this.backend.device,i=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=s.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)}i.setPipeline(t.computePipeline),i.setBindGroup(0,l),i.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Yt(t.programInfo.name)}dispose(){}build(t,e){tr(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=Nu(e,this.backend.device.limits),s=t.getShaderSource(a),i=`${n.join(`
`)}
${a.additionalImplementations}
${s}`,o=r.createShaderModule({code:i,label:t.name});nt("verbose",()=>`[WebGPU] ${t.name} shader code: ${i}`);let l=r.createComputePipeline({compute:{module:o,entryPoint:"main"},layout:"auto",label:t.name});return Yt(t.name),{programInfo:t,computePipeline:l,uniformVariablesInfo:a.variablesInfo}}normalizeDispatchGroupSize(t){let e=typeof t=="number"?t:t.x,r=typeof t=="number"?1:t.y||1,n=typeof t=="number"?1:t.z||1,a=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(e<=a&&r<=a&&n<=a)return[e,r,n];let s=e*r*n,i=Math.ceil(Math.sqrt(s));if(i>a){if(i=Math.ceil(Math.cbrt(s)),i>a)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),Bf,Df,Nf,Ff,mw=te(()=>{rr(),xe(),Xr(),My(),Oy(),hw(),fw(),Bf=(t,e)=>{if(e.length!==t.length)throw new Error(`inputDependencies length ${e.length} is not equal to inputTensors length ${t.length}.`);let r=[];for(let n=0;n<t.length;++n){let a=t[n].dataType;switch(e[n]){case"none":{r.push("");break}case"type":{r.push(`${a}`);break}case"rank":{let s=t[n].dims.length;r.push(`${a};${s}`);break}case"dims":{let s=t[n].dims.join(",");r.push(`${a};${s}`);break}default:throw new Error(`unsupported input dependency: ${e[n]}`)}}return r.join("|")},Df=(t,e,r)=>{var a,s;let n=t.name;return(a=t.shaderCache)!=null&&a.hint&&(n+="["+t.shaderCache.hint+"]"),n+=":"+r+`:${Bf(e,((s=t.shaderCache)==null?void 0:s.inputDependencies)??new Array(e.length).fill("dims"))}`,n},Nf=class{constructor(t){t&&(this.architecture=t.architecture,this.vendor=t.vendor)}isArchitecture(t){return this.architecture===t}isVendor(t){return this.vendor===t}},Ff=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. (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 Nf(await e.requestAdapterInfo()),this.gpuDataManager=zu(this),this.programManager=new Rf(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Cu(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;tr(),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(()=>{var n;let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let a=0;a<e.length/2;a++){let s=r[a],i=s.kernelId,o=this.kernels.get(i),l=o.kernelType,u=o.kernelName,d=s.programName,h=s.inputTensorViews,m=s.outputTensorViews,g=e[a*2],p=e[a*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=g);let w=Number(g-this.queryTimeBase),v=Number(p-this.queryTimeBase);if(!Number.isSafeInteger(w)||!Number.isSafeInteger(v))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:h.map(S=>({dims:S.dims,dataType:Yr(S.dataType)})),outputsMetadata:m.map(S=>({dims:S.dims,dataType:Yr(S.dataType)})),kernelId:i,kernelType:l,kernelName:u,programName:d,startTime:w,endTime:v});else{let S="";h.forEach((E,T)=>{S+=`input[${T}]: [${E.dims}] | ${Yr(E.dataType)}, `});let $="";m.forEach((E,T)=>{$+=`output[${T}]: [${E.dims}] | ${Yr(E.dataType)}, `}),console.log(`[profiling] kernel "${i}|${l}|${u}|${d}" ${S}${$}execution time: ${v-w} ns`)}Jn("GPU",`${d}::${g}::${p}`)}t.unmap(),this.pendingQueries.delete(t)}),Yt()}run(t,e,r,n,a,s){tr(t.name);let i=[];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: ${E}`);i.push(T)}let{outputs:o,dispatchGroup:l,programUniforms:u}=t.getRunData(e),d=r.length===0?o.map(($,E)=>E):r;if(d.length!==o.length)throw new Error(`Output size ${d.length} must be equal to ${o.length}.`);let h=[],m=[];for(let $=0;$<o.length;++$){if(!Number.isInteger(d[$])||d[$]<-3||d[$]>=s)throw new Error(`Invalid output index: ${d[$]}`);if(d[$]===-3)continue;let E=d[$]===-1,T=d[$]===-2,A=E||T?a(o[$].dataType,o[$].dims):n(d[$],o[$].dataType,o[$].dims);if(h.push(A),A.data===0)continue;let z=this.gpuDataManager.get(A.data);if(!z)throw new Error(`no GPU data for output: ${A.data}`);if(E&&this.temporaryData.push(z),T){let R=this.kernelPersistentData.get(this.currentKernelId);R||(R=[],this.kernelPersistentData.set(this.currentKernelId,R)),R.push(z)}m.push(z)}if(i.length!==e.length||m.length!==h.length){if(m.length===0)return Yt(t.name),h;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let g;if(u){let $=0,E=[];u.forEach(R=>{let V=typeof R.data=="number"?[R.data]:R.data;if(V.length===0)return;let j=R.type===10?2:4,q,fe;R.type===10?(fe=V.length>4?16:V.length>2?8:V.length*j,q=V.length>4?16:j*V.length):(fe=V.length<=2?V.length*j:16,q=16),$=Math.ceil($/fe)*fe,E.push($);let ee=R.type===10?8:4;$+=V.length>4?Math.ceil(V.length/ee)*q:V.length*j});let T=16;$=Math.ceil($/T)*T;let A=new ArrayBuffer($);u.forEach((R,V)=>{let j=E[V],q=typeof R.data=="number"?[R.data]:R.data;if(R.type===6)new Int32Array(A,j,q.length).set(q);else if(R.type===12)new Uint32Array(A,j,q.length).set(q);else if(R.type===10)new Uint16Array(A,j,q.length).set(q);else if(R.type===1)new Float32Array(A,j,q.length).set(q);else throw new Error(`Unsupported uniform type: ${Yr(R.type)}`)});let z=this.gpuDataManager.create($,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(z.buffer,0,A,0,$),this.gpuDataManager.release(z.id),g={offset:0,size:$,buffer:z.buffer}}let p=this.programManager.normalizeDispatchGroupSize(l),w=p[1]===1&&p[2]===1,v=Df(t,e,w),S=this.programManager.getArtifact(v);if(S||(S=this.programManager.build(t,p),this.programManager.setArtifact(v,S),nt("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,[z,R]=S.uniformVariablesInfo[$];if(T!==z||A!==R)throw new Error(`Uniform variable ${$} mismatch: expect type ${z} with size ${R}, got type ${T} with size ${A} in program "${S.programInfo.name}".`)}}if(nt("info",()=>`[ProgramManager] run "${t.name}" (key=${v}) with ${p[0]}x${p[1]}x${p[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,i,m,p,g),Yt(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=Pf.get(t);if(!a)throw new Error(`kernel not implemented: ${t}`);let s={kernelType:t,kernelName:n,kernelEntry:a[0],attributes:[a[1],r]};this.kernels.set(e,s)}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: ${t}`);let a=n.kernelType,s=n.kernelName,i=n.kernelEntry,o=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${a}] ${s}" is not allowed to be called recursively`);this.currentKernelId=t,o[0]&&(o[1]=o[0](o[1]),o[0]=void 0),nt("info",()=>`[WebGPU] Start to run kernel "[${a}] ${s}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),i(e,o[1]),0}catch(u){return r.push(Promise.resolve(`[WebGPU] Kernel "[${a}] ${s}" failed. ${u}`)),1}finally{l&&r.push(this.device.popErrorScope().then(u=>u?`GPU validation error for kernel "[${a}] ${s}": ${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 s=a.get(e),i=this.gpuDataManager.registerExternalBuffer(r,n,s==null?void 0:s[1]);return a.set(e,[i,r]),i}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 Ks(this,t,e);return Au(n.buffer,r)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){var t;this.queryType="none",(((t=this.env.webgpu.profiling)==null?void 0:t.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(){nt("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(){nt("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){nt("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(),s=t[n];this.writeTimestamp(this.pendingDispatchNumber*2),a.setPipeline(s.computePipeline),a.setBindGroup(0,s.bindGroup),a.dispatchWorkgroups(...s.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()}}}),Lf={};$n(Lf,{init:()=>Wf});var Ti,Uf,Wf,gw=te(()=>{xe(),mw(),Xr(),Oe(),Ti=class D0{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=X.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=X.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=X.size(this.dims);return e===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,e)}reshape(e){if(X.size(e)!==X.size(this.dims))throw new Error("Invalid new shape");return new D0(this.module,this.dataType,this.data,e)}},Uf=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 s=n[a++];this.outputCount=n[a++],this.customDataOffset=n[a++],this.customDataSize=n[a++];let i=[];for(let o=0;o<s;o++){let l=n[a++],u=n[a++],d=n[a++],h=[];for(let m=0;m<d;m++)h.push(n[a++]);i.push(new Ti(t,l,u,h))}this.inputs=i}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}getMaxComputeWorkgroupSizes(){return[this.backend.device.limits.maxComputeWorkgroupSizeX,this.backend.device.limits.maxComputeWorkgroupSizeY,this.backend.device.limits.maxComputeWorkgroupSizeZ]}getMaxComputeWorkgroupStoragesize(){return this.backend.device.limits.maxComputeWorkgroupStorageSize}compute(t,e){var i;let r=((i=e==null?void 0:e.inputs)==null?void 0:i.map(o=>typeof o=="number"?this.inputs[o]:o))??this.inputs,n=(e==null?void 0:e.outputs)??[],a=(o,l,u)=>new Ti(this.module,l,this.output(o,u),u),s=(o,l)=>{let u=ra(o);if(!u)throw new Error(`Unsupported data type: ${o}`);let d=u*X.size(l),h=d>0?this.backend.gpuDataManager.create(d).id:0;return new Ti(this.module,o,h,l)};return this.backend.run(t,r,n,a,s,this.outputCount)}output(t,e){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+e.length)*4),a=n>>2;this.module.HEAPU32[a++]=e.length;for(let s=0;s<e.length;s++)this.module.HEAPU32[a++]=e[s];return this.module._JsepOutput(this.opKernelContext,t,n)}catch(n){throw new Error(`Failed to generate kernel's output[${t}] with dims [${e}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(r)}}},Wf=async(t,e,r,n)=>{let a=e.jsepInit;if(!a)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(t==="webgpu"){let s=new Ff;await s.initialize(r,n),a("webgpu",[s,i=>s.alloc(i),i=>s.free(i),(i,o,l,u=!1)=>{if(u)nt("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${i}, dst=${o}, size=${l}`),s.memcpy(i,o);else{nt("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${i}, gpuDataId=${o}, size=${l}`);let d=e.HEAPU8.subarray(i>>>0,(i>>>0)+l);s.upload(o,d)}},async(i,o,l)=>{nt("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${i}, dataOffset=${o}, size=${l}`),await s.download(i,()=>e.HEAPU8.subarray(o>>>0,(o>>>0)+l))},(i,o,l)=>s.createKernel(i,o,l,e.UTF8ToString(e._JsepGetNodeName(o))),i=>s.releaseKernel(i),(i,o,l,u)=>{nt("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${l}, kernel=${i}, contextDataOffset=${o}`);let d=new Uf(e,s,o);return s.computeKernel(i,d,u)},()=>s.captureBegin(),()=>s.captureEnd(),()=>s.replay()])}else a("webnn")}}),Vf,Gf,Hf,Nr,jf,Bo,qf,Kf,Do,Yf,Xf,Qf,_w=te(()=>{Ay(),Iy(),xe(),ta(),Ls(),$u(),Vf=(t,e)=>{it()._OrtInit(t,e)!==0&&qe("Can't initialize onnxruntime.")},Gf=async t=>{Vf(t.wasm.numThreads,ui(t.logLevel))},Hf=async(t,e)=>{{let r=(gw(),jr(Lf)).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: "${a}"`);let s=t.webgpu.forceFallbackAdapter;if(s!==void 0&&typeof s!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${s}"`);if(n=await navigator.gpu.requestAdapter({powerPreference:a,forceFallbackAdapter:s}),!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",it(),t,n)}if(e==="webnn"){if(typeof navigator>"u"||!navigator.ml)throw new Error("WebNN is not supported in current environment");await r("webnn",it(),t)}}},Nr=new Map,jf=t=>{let e=it(),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)}},Bo=t=>{let e=it(),r=e._malloc(t.byteLength);if(r===0)throw new Error(`Can't create a session. failed to allocate a buffer of size ${t.byteLength}.`);return e.HEAPU8.set(t,r),[r,t.byteLength]},qf=async(t,e)=>{var h,m;let r,n,a=it();Array.isArray(t)?[r,n]=t:t.buffer===a.HEAPU8.buffer?[r,n]=[t.byteOffset,t.byteLength]:[r,n]=Bo(t);let s=0,i=0,o=0,l=[],u=[],d=[];try{if([i,l]=vu(e),(e==null?void 0:e.externalData)&&a.mountExternalData){let T=[];for(let A of e.externalData){let z=typeof A=="string"?A:A.path;T.push(di(typeof A=="string"?A:A.data).then(R=>{a.mountExternalData(z,R)}))}await Promise.all(T)}s=await a._OrtCreateSession(r,n,i),s===0&&qe("Can't create a session.");let[g,p]=jf(s),w=!!(e!=null&&e.enableGraphCapture),v=[],S=[],$=[];for(let T=0;T<g;T++){let A=a._OrtGetInputName(s,T);A===0&&qe("Can't get an input name."),u.push(A),v.push(a.UTF8ToString(A))}for(let T=0;T<p;T++){let A=a._OrtGetOutputName(s,T);A===0&&qe("Can't get an output name."),d.push(A);let z=a.UTF8ToString(A);S.push(z);{if(w&&(e==null?void 0:e.preferredOutputLocation)===void 0){$.push("gpu-buffer");continue}let R=typeof(e==null?void 0:e.preferredOutputLocation)=="string"?e.preferredOutputLocation:((h=e==null?void 0:e.preferredOutputLocation)==null?void 0:h[z])??"cpu";if(R!=="cpu"&&R!=="cpu-pinned"&&R!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${R}.`);if(w&&R!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${R}. 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}`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${u};${h};${i}`},getShaderSource:y,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:p})}},rc=(e,t,r,o,i,u,a)=>{let c=a+i.kvSequenceLength,p=[i.batchSize,i.numHeads,i.sequenceLength,c],h=u.scale===0?1/Math.sqrt(i.headSize):u.scale,d=Me(i.headSize),y=i.headSize/d,w=12,_={x:Math.ceil(c/w),y:Math.ceil(i.sequenceLength/w),z:i.batchSize*i.numHeads},v=[{type:12,data:i.sequenceLength},{type:12,data:y},{type:12,data:c},{type:12,data:i.numHeads},{type:1,data:h}],S=o?["type","type","type"]:["type","type"],A=I=>{let x=U("q",t.dataType,t.dims,d),E=U("key",r.dataType,r.dims,d),P=[x,E];o&&P.push(U("relative_position_bias",o.dataType,o.dims));let O=j("output",t.dataType,p),R=et(1,d),L=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return`\n  const TILE_SIZE = ${w}u;\n\n  var<workgroup> tileQ: array<${x.type.storage}, ${w*w}>;\n  var<workgroup> tileK: array<${x.type.storage}, ${w*w}>;\n  ${I.registerUniforms(L).declareVariables(...P,O)}\n  ${I.mainStart([w,w,1])}\n    // x holds the N and y holds the M\n    let headIdx = workgroup_id.z;\n    let m = workgroup_id.y * TILE_SIZE;\n    let n = workgroup_id.x * TILE_SIZE;\n    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n    let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;\n\n    var value = ${R}(0);\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {\n        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n        tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      workgroupBarrier();\n\n      for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {\n        value += ${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 a=i.length;if(a!==o.length)throw new Error(`${u}: num dimensions != ${a}`);i.forEach((c,p)=>{if(c!==o[p])throw new Error(`${u}: dim[${p}] do not match`)})};if(e[0].dims.length>1){let o=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,o,"Invalid input scale"),r(e[2].dims,o,"Invalid input B"),r(e[3].dims,o,"Invalid input mean"),r(e[4].dims,o,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input 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${E.registerUniform("outputSize","u32").declareVariables(y,w,_,v,S,A)}\n  ${E.mainStart()}\n  ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    var outputIndices = ${A.offsetToIndices(`global_idx * ${a}`)};\n    ${I()}\n    let scale = ${w.getByOffset("cOffset")};\n    let bias = ${_.getByOffset("cOffset")};\n    let inputMean = ${v.getByOffset("cOffset")};\n    let inputVar = ${S.getByOffset("cOffset")};\n    let x = ${y.getByOffset("global_idx")};\n    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;\n    ${A.setByOffset("global_idx","value")}\n  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${o}_${a}`,inputDependencies:h?["rank","type","type","type","type"]:void 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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 1:r="vec4<f32>";break;case 12:r="vec4<u32>";break;case 6:r="vec4<i32>";break;case 9:r="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute \'to\' from \'Cast\' operator): ${t.to}`)}e.compute(ke(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},cc=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:xn,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:Cn;return ve({min:t,max:r})},cs=(e,t)=>{let r=e.inputs.length===1?t:cc(e.inputs),o=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Clip",i=>`clamp(${i}, clip_min_, clip_max_)`,`\n    const clip_min_: vec4<${o}> = vec4(${o}(${r.min}));\n    const clip_max_: vec4<${o}> = vec4(${o}(${r.max}));\n`,r.cacheKey),{inputs:[0]})},ps=e=>{e.compute(ke(e.inputs[0],"Ceil","ceil"))},ms=e=>{e.compute(ke(e.inputs[0],"Cos","cos"))},fs=e=>{e.compute(ke(e.inputs[0],"Cosh","cosh"))},kn=e=>ve(e),hs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Elu",o=>`elu_vf32(${o})`,`\n  const elu_alpha_ 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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}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = 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${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'}),Fr,zt,En,la,ua,No,Ai,tn,rn,Zf,da,Jf,em,tm,rm,nm,am,im,sm=te(()=>{var t;rr(),_w(),ta(),Fr=()=>!!Ue.wasm.proxy&&typeof document<"u",En=!1,la=!1,ua=!1,Ai=new Map,tn=(e,r)=>{let n=Ai.get(e);n?n.push(r):Ai.set(e,[r])},rn=()=>{if(En||!la||ua||!zt)throw new Error("worker not 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${t[3]}`)}},lm=class{async fetchModelAndCopyToWasmMemory(t){return tm(await di(t))}async loadModel(t,e){tr();let r;typeof t=="string"?typeof process<"u"&&process.versions&&process.versions.node?r=await di(t):r=await this.fetchModelAndCopyToWasmMemory(t):r=t,[this.sessionId,this.inputNames,this.outputNames]=await rm(r,e),Yt()}async dispose(){return nm(this.sessionId)}async run(t,e,r){tr();let n=[],a=[];Object.entries(t).forEach(h=>{let m=h[0],g=h[1],p=this.inputNames.indexOf(m);if(p===-1)throw new Error(`invalid input '${m}'`);n.push(g),a.push(p)});let s=[],i=[];Object.entries(e).forEach(h=>{let m=h[0],g=h[1],p=this.outputNames.indexOf(m);if(p===-1)throw new Error(`invalid output '${m}'`);s.push(g),i.push(p)});let o=n.map((h,m)=>Fo(h,()=>`input "${this.inputNames[a[m]]}"`)),l=s.map((h,m)=>h?Fo(h,()=>`output "${this.outputNames[i[m]]}"`):null),u=await am(this.sessionId,a,o,i,l,r),d={};for(let h=0;h<u.length;h++)d[this.outputNames[i[h]]]=s[h]??om(u[h]);return Yt(),d}startProfiling(){}endProfiling(){im(this.sessionId)}}}),um,dm,bw=te(()=>{rr(),sm(),ww(),um=()=>{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))}},dm=class{async init(t){um(),await Jf(),await em(t)}async createInferenceSessionHandler(t,e){let r=new lm;return await r.loadModel(t,e),Promise.resolve(r)}}}),cm={};$n(cm,{wasmBackend:()=>pm});var pm,vw=te(()=>{bw(),pm=new dm});rr(),rr(),rr();var $w="1.18.0",xw=tu;{let t=(vw(),jr(cm)).wasmBackend;qr("webgpu",t,5),qr("webnn",t,5),qr("cpu",t,10),qr("wasm",t,10)}Object.defineProperty(Ue.versions,"web",{value:$w,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.
 * =============================================================================
 */var Sw=Object.freeze({__proto__:null,get InferenceSession(){return Ps},get TRACE(){return Jn},get TRACE_FUNC_BEGIN(){return tr},get TRACE_FUNC_END(){return Yt},get Tensor(){return kt},get TrainingSession(){return Rs},default:xw,get env(){return Ue},get registerBackend(){return qr}});const kw=(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,s;(e==null?void 0:e.tensorLayout)!==void 0&&e.tensorLayout==="NHWC"?(a=t.dims[2],s=t.dims[3]):(a=t.dims[3],s=t.dims[2]);const i=(e==null?void 0:e.format)!==void 0?e.format:"RGB",o=e==null?void 0: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 d=s*a;let h=0,m=d,g=d*2,p=-1;i==="RGBA"?(h=0,m=d,g=d*2,p=d*3):i==="RGB"?(h=0,m=d,g=d*2):i==="RBG"&&(h=0,g=d,m=d*2);for(let w=0;w<s;w++)for(let v=0;v<a;v++){const S=(t.data[h++]-u[0])*l[0],$=(t.data[m++]-u[1])*l[1],E=(t.data[g++]-u[2])*l[2],T=p===-1?255:(t.data[p++]-u[3])*l[3];n.fillStyle="rgba("+S+","+$+","+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")},Ew=(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,s,i;(e==null?void 0:e.tensorLayout)!==void 0&&e.tensorLayout==="NHWC"?(a=t.dims[2],s=t.dims[1],i=t.dims[3]):(a=t.dims[3],s=t.dims[2],i=t.dims[1]);const o=e!==void 0&&e.format!==void 0?e.format:"RGB",l=e==null?void 0:e.norm;let u,d;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?d=[0,0,0,0]:typeof l.bias=="number"?d=[l.bias,l.bias,l.bias,l.bias]:(d=[l.bias[0],l.bias[1],l.bias[2],0],l.bias[3]!==void 0&&(d[3]=l.bias[3]));const h=s*a;if(e!==void 0&&(e.format!==void 0&&i===4&&e.format!=="RGBA"||i===3&&e.format!=="RGB"&&e.format!=="BGR"))throw new Error("Tensor format doesn't match input tensor dims");const m=4;let g=0,p=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,s);for(let A=0;A<s*a;g+=m,p+=m,w+=m,v+=m,A++)n.data[g]=(t.data[S++]-d[0])*u[0],n.data[p]=(t.data[$++]-d[1])*u[1],n.data[w]=(t.data[E++]-d[2])*u[2],n.data[v]=T===-1?255:(t.data[T++]-d[3])*u[3]}else throw new Error("Can not access image data");return n},Lo=(t,e)=>{if(t===void 0)throw new Error("Image buffer 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mr("float32",d,[1,3,r,n])},Cw=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,s=typeof t=="string";let i,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=d=>d instanceof HTMLCanvasElement||d instanceof OffscreenCanvas?d.getContext("2d"):null;if(r){const d=l();d.width=t.width,d.height=t.height;const h=u(d);if(h!=null){let m=t.height,g=t.width;if(e!==void 0&&e.resizedHeight!==void 0&&e.resizedWidth!==void 0&&(m=e.resizedHeight,g=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=g}else o.tensorFormat="RGBA",o.height=m,o.width=g;h.drawImage(t,0,0),i=h.getImageData(0,0,g,m).data}else throw new Error("Can not access image data")}else if(n){let d,h;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(d=e.resizedHeight,h=e.resizedWidth):(d=t.height,h=t.width),e!==void 0&&(o=e),o.format="RGBA",o.height=d,o.width=h,e!==void 0){const m=l();m.width=h,m.height=d;const g=u(m);if(g!=null)g.putImageData(t,0,0),i=g.getImageData(0,0,h,d).data;else throw new Error("Can not access image data")}else i=t.data}else if(a){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const d=l();d.width=t.width,d.height=t.height;const h=u(d);if(h!=null){const m=t.height,g=t.width;return h.drawImage(t,0,0,g,m),i=h.getImageData(0,0,g,m).data,o.height=m,o.width=g,Lo(i,o)}else throw new Error("Can not access image data")}else{if(s)return new Promise((d,h)=>{const m=l(),g=u(m);if(!t||!g)return h();const p=new Image;p.crossOrigin="Anonymous",p.src=t,p.onload=()=>{m.width=p.width,m.height=p.height,g.drawImage(p,0,0,m.width,m.height);const w=g.getImageData(0,0,m.width,m.height);o.height=m.height,o.width=m.width,d(Lo(w.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(i!==void 0)return Lo(i,o);throw new Error("Input data provided is not supported - aborted tensor creation")},Tw=(t,e)=>{const{width:r,height:n,download:a,dispose:s}=e,i=[1,n,r,4];return new mr({location:"texture",type:"float32",texture:t,dims:i,download:a,dispose:s})},Aw=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:s}=e;return new mr({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:s})},Iw=(t,e,r)=>new mr({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),Cn=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),Ii=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let hm=!1;const Mw=()=>{if(!hm){hm=!0;const t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=typeof Float16Array<"u"&&Float16Array.from;t&&(Cn.set("int64",BigInt64Array),Ii.set(BigInt64Array,"int64")),e&&(Cn.set("uint64",BigUint64Array),Ii.set(BigUint64Array,"uint64")),r?(Cn.set("float16",Float16Array),Ii.set(Float16Array,"float16")):Cn.set("float16",Uint16Array)}},Ow=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},zw=(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 ${t.location} is not supported`)}};let mr=class{constructor(e,r,n){Mw();let a,s;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,a=e.type,s=e.dims,e.location){case"cpu-pinned":{const o=Cn.get(a);if(!o)throw new TypeError(`unsupported type "${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=Cn.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=Ii.get(e.constructor);if(u===void 0)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);a=u,o=e}if(l===void 0)l=[o.length];else if(!Array.isArray(l))throw new TypeError("A tensor's dims must be a number array");s=l,this.cpuData=o,this.dataLocation="cpu"}const i=Ow(s);if(this.cpuData&&i!==this.cpuData.length)throw new Error(`Tensor's size(${i}) does not match data length(${this.cpuData.length}).`);this.type=a,this.dims=s,this.size=i}static async fromImage(e,r){return Cw(e,r)}static fromTexture(e,r){return Tw(e,r)}static fromGpuBuffer(e,r){return Aw(e,r)}static fromPinnedBuffer(e,r,n){return Iw(e,r,n)}toDataURL(e){return kw(this,e)}toImageData(e){return Ew(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: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return zw(this,e)}};const Pw=mr,ca=[];let Uo,nn;Hr.IS_NODE_ENV?(nn=Ve??vr,ca.push("cpu"),Uo=["cpu"]):(nn=Sw,Hr.IS_WEBGPU_AVAILABLE&&ca.push("webgpu"),ca.push("wasm"),Uo=["wasm"]);const Rw=nn.InferenceSession;function Bw(t){let e=Uo;if(t){if(!ca.includes(t))throw new Error(`Unsupported device: "${t}". Should be one of: ${ca.join(", ")}.`);e=[t]}return e}async function fm(t,e){return await Rw.create(t,e)}function mm(t){return t instanceof nn.Tensor}const lr=nn==null?void 0:nn.env;lr!=null&&lr.wasm&&(lr.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.18.0/dist/",lr.wasm.proxy=!Hr.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(lr.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(lr.wasm.simd=!1));function Dw(){var t;return(t=lr==null?void 0:lr.wasm)==null?void 0:t.proxy}Mt.backends.onnx=lr;const Tn=async(t,e,r)=>{const n=await fm(new Uint8Array(t),e);return async a=>{const s=Object.fromEntries(Object.entries(a).map(([o,l])=>[o,l.ort_tensor])),i=await n.run(s);return Array.isArray(r)?r.map(o=>new ce(i[o])):new ce(i[r])}};class pa{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=Tn([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=Tn([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=Tn([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=Tn([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=Tn([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=Tn([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}}B(pa,"session_options",{});const gm=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 ce{constructor(...e){B(this,"ort_tensor");return mm(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new Pw(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})}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}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...r]=this.dims;if(r.length>0){const n=r.reduce((a,s)=>a*s);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=gr(e,r),n.length>0){const a=n.reduce((s,i)=>s*i);return this._subarray(e,a,n)}else return new ce(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,s=(e+1)*r,i="subarray"in this.data?this.data.subarray(a,s):this.data.slice(a,s);return new ce(this.type,i,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 Nw(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 ce(this.type,this.data.slice(),this.dims.slice())}slice(...e){let r=[],n=[];for(let u=0;u<this.dims.length;++u){let d=e[u];if(d==null)n.push([0,this.dims[u]]),r.push(this.dims[u]);else if(typeof d=="number")d=gr(d,this.dims[u],u),n.push([d,d+1]);else if(Array.isArray(d)&&d.length===2){let[h,m]=d;if(h=h===null?0:gr(h,this.dims[u],u,!1),m=m===null?this.dims[u]:gr(m,this.dims[u],u,!1),h>m)throw new Error(`Invalid slice: ${d}`);let g=[Math.max(h,0),Math.min(m,this.dims[u])];n.push(g),r.push(g[1]-g[0])}else throw new Error(`Invalid slice: ${d}`)}let a=n.map(([u,d])=>d-u),s=a.reduce((u,d)=>u*d);const i=this.data;let o=new i.constructor(s);const l=this.stride();for(let u=0;u<s;++u){let d=0;for(let h=a.length-1,m=u;h>=0;--h){const g=a[h];d+=(m%g+n[h][0])*l[h],m=Math.floor(m/g)}o[u]=i[d]}return new ce(this.type,o,r)}permute(...e){return Fw(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 ce(this.type,[o],[])}r=gr(r,this.dims.length);const s=this.dims.slice();s[r]=1;const i=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,d=o,h=1;u>=0;--u){const m=this.dims[u];if(u!==r){const g=d%m;l+=g*h,h*=s[u]}d=Math.floor(d/m)}i[l]+=a[o]**e}if(e!==1)for(let o=0;o<i.length;++o)i[o]=i[o]**(1/e);return n||s.splice(r,1),new ce(this.type,i,s)}normalize_(e=2,r=1){r=gr(r,this.dims.length);const n=this.norm(e,r,!0),a=this.data,s=n.data;for(let i=0;i<a.length;++i){let o=0;for(let l=this.dims.length-1,u=i,d=1;l>=0;--l){const h=this.dims[l];if(l!==r){const m=u%h;o+=m*d,d*=this.dims[l]}u=Math.floor(u/h)}a[i]/=s[o]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return Vw(this.dims)}squeeze(e=null){return new ce(this.type,this.data,ym(this.dims,e))}squeeze_(e=null){return this.dims=ym(this.dims,e),this}unsqueeze(e=null){return new ce(this.type,this.data,wm(this.dims,e))}unsqueeze_(e=null){return this.dims=wm(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),s=this.dims.slice(r+1);return this.dims=[...n,a.reduce((i,o)=>i*o,1),...s],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let a=0;a<e.length;++a)if(e[a]===-1){if(r!==-1)throw new Error("Only one dimension can be inferred");r=a}const n=this.data;if(r!==-1){const a=e.reduce((s,i,o)=>o!==r?s*i:s,1);e[r]=n.length/a}return new ce(this.type,n,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 Vo(this,e,r)}to(e){if(this.type===e)return this;if(!gm.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);return new ce(e,gm[e].from(this.data),this.dims)}}function Nw(t,e){const r=t.length,n=e.reduce((s,i)=>s*i);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let a=t;for(let s=e.length-1;s>=0;s--)a=a.reduce((i,o)=>{let l=i[i.length-1];return l.length<e[s]?l.push(o):i.push([o]),i},[[]]);return a[0]}function Fw(t,e){const[r,n]=Q0(t.data,t.dims,e);return new ce(t.type,r,n)}function Wo(t,[e,r],n="bilinear",a=!1){const s=t.dims.at(-3)??1,i=t.dims.at(-2),o=t.dims.at(-1);let l=X0(t.data,[s,i,o],[e,r],n,a);return new ce(t.type,l,[s,e,r])}async function _m(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 pa.bilinear_interpolate_4d;else if(r==="bicubic")a=await pa.bicubic_interpolate_4d;else throw new Error(`Unsupported mode: ${r}`);const s=new ce("int64",new BigInt64Array(n.map(BigInt)),[n.length]);return await a({x:t,s})}async function Lw(t,e){return await(await pa.matmul)({a:t,b:e})}async function An(t,e){const r=await pa.top_k;return e===null?e=t.dims.at(-1):e=Math.min(e,t.dims.at(-1)),await r({x:t,k:new ce("int64",[BigInt(e)],[1])})}function Uw(t,e){const r=t.data,n=e.data,a=[t.dims[0],t.dims[2]],s=new r.constructor(a[0]*a[1]),[i,o,l]=t.dims;let u=0;for(let d=0;d<i;++d){const h=d*l*o;for(let m=0;m<l;++m){let g=0,p=0;const w=d*o,v=h+m;for(let $=0;$<o;++$){const E=Number(n[w+$]);p+=E,g+=r[v+$*l]*E}const S=g/p;s[u++]=S}}return new ce(t.type,s,a)}function ym(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 wm(t,e){return e=gr(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function gr(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 Xt(t,e=0){e=gr(e,t[0].dims.length);const r=t[0].dims.slice();r[e]=t.reduce((i,o)=>i+o.dims[e],0);const n=r.reduce((i,o)=>i*o,1),a=new t[0].data.constructor(n),s=t[0].type;if(e===0){let i=0;for(const o of t){const l=o.data;a.set(l,i),i+=l.length}}else{let i=0;for(let o=0;o<t.length;++o){const{data:l,dims:u}=t[o];for(let d=0;d<l.length;++d){let h=0;for(let m=u.length-1,g=d,p=1;m>=0;--m){const w=u[m];let v=g%w;m===e&&(v+=i),h+=v*p,p*=r[m],g=Math.floor(g/w)}a[h]=l[d]}i+=u[e]}}return new ce(s,a,r)}function ha(t,e=0){return Xt(t.map(r=>r.unsqueeze(e)),e)}function Ww(t,e=null,r=1,n=!1){const a=t.data,s=t.dims;if(e===null){const m=a.reduce((v,S)=>v+S,0)/a.length,g=Math.sqrt(a.reduce((v,S)=>v+(S-m)**2,0)/(a.length-r)),p=new ce(t.type,[m],[]);return[new ce(t.type,[g],[]),p]}e=gr(e,s.length);const i=Vo(t,e,n),o=i.data,l=s.slice();l[e]=1;const u=new a.constructor(a.length/s[e]);for(let h=0;h<a.length;++h){let m=0;for(let g=s.length-1,p=h,w=1;g>=0;--g){const v=s[g];if(g!==e){const S=p%v;m+=S*w,w*=l[g]}p=Math.floor(p/v)}u[m]+=(a[h]-o[m])**2}for(let h=0;h<u.length;++h)u[h]=Math.sqrt(u[h]/(s[e]-r));return n||l.splice(e,1),[new ce(t.type,u,l),i]}function Vo(t,e=null,r=!1){const n=t.data;if(e===null){const o=n.reduce((l,u)=>l+u,0);return new ce(t.type,[o/n.length],[])}const a=t.dims;e=gr(e,a.length);const s=a.slice();s[e]=1;const i=new n.constructor(n.length/a[e]);for(let o=0;o<n.length;++o){let l=0;for(let u=a.length-1,d=o,h=1;u>=0;--u){const m=a[u];if(u!==e){const g=d%m;l+=g*h,h*=s[u]}d=Math.floor(d/m)}i[l]+=n[o]}if(a[e]!==1)for(let o=0;o<i.length;++o)i[o]=i[o]/a[e];return r||s.splice(e,1),new ce(t.type,i,s)}function Vw(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 Go(t,e,r,n){const a=t.reduce((s,i)=>s*i,1);return new ce(r,new n(a).fill(e),t)}function Gw(t,e){let r,n;return r="float32",n=Float32Array,Go(t,e,r,n)}function Hw(t,e){return Gw(t.dims,e)}function In(t){return Go(t,1n,"int64",BigInt64Array)}function jw(t){return In(t.dims)}function qw(t){return Go(t,0n,"int64",BigInt64Array)}function Kw(t){return qw(t.dims)}function Yw(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 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Re(t.value);case"BooleanLiteral":return new at(t.value);case"ArrayLiteral":return new et(t.value.map(r=>this.evaluate(r,e)));case"TupleLiteral":return new yb(t.value.map(r=>this.evaluate(r,e)));case"ObjectLiteral":{const r=new Map;for(const[n,a]of t.value){const s=this.evaluate(n,e);if(!(s instanceof Re))throw new Error(`Object keys must be strings: got ${s.type}`);r.set(s.value,this.evaluate(a,e))}return new ur(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 Oi(t){switch(typeof t){case"number":return new Ye(t);case"string":return new Re(t);case"boolean":return new at(t);case"object":return t===null?new _a:Array.isArray(t)?new et(t.map(Oi)):new ur(new Map(Object.entries(t).map(([e,r])=>[e,Oi(r)])));case"function":return new Cr((e,r)=>{const n=t(...e.map(a=>a.value))??null;return Oi(n)});default:throw new Error(`Cannot convert to runtime value: ${t}`)}}var bb=class{constructor(t){B(this,"parsed");const e=rb(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=gb(e)}render(t){const e=new jo;e.set("false",!1),e.set("true",!0),e.set("raise_exception",a=>{throw new Error(a)}),e.set("range",_b);for(const[a,s]of Object.entries(t))e.set(a,s);return new wb(e).run(this.parsed).value}};const Tm=[["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"]],zi=new Map(Tm),vb=new Map([...Tm.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 Am(t){t=t.toLowerCase();let e=vb.get(t);if(e===void 0)if(zi.has(t))e=t;else{const n=t.length===2?zi.keys():zi.values();throw new Error(`Language "${t}" is not supported. Must be one of: ${JSON.stringify(n)}`)}return e}const qo="https://github.com/xenova/transformers.js/issues/new/choose";async function Im(t,e){const r=await Promise.all([Rr(t,"tokenizer.json",!0,e),Rr(t,"tokenizer_config.json",!0,e)]);return e.legacy!==null&&(r[1].legacy=e.legacy),r}function $b(t,e){const r=[];let n=0;for(const a of t.matchAll(e)){const s=a[0];n<a.index&&r.push(t.slice(n,a.index)),s.length>0&&r.push(s),n=a.index+s.length}return n<t.length&&r.push(t.slice(n)),r}function Pi(t,e=!0){if(t.Regex!==void 0){let r=t.Regex.replace(/\\([#&~])/g,"$1");for(const[n,a]of Tb)r=r.replaceAll(n,a);return new RegExp(r,"gu")}else if(t.String!==void 0){const r=zl(t.String);return new RegExp(e?r:`(${r})`,"gu")}else return console.warn("Unknown pattern type:",t),null}function Ko(t){return new Map(Object.entries(t))}function Mm(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 ${e.length}.`)}}function Yo(t){return t.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function Om(t){return t.replace(/[\u0300-\u036f]/g,"")}function xb(t){return Om(t.toLowerCase())}function Sb(t){return t>=19968&&t<=40959||t>=13312&&t<=19903||t>=131072&&t<=173791||t>=173824&&t<=177983||t>=177984&&t<=178207||t>=178208&&t<=183983||t>=63744&&t<=64255||t>=194560&&t<=195103}function kb(t,e,r){const n=[];let a=0;for(;a<t.length;){if(n.push(t[a]),(r.get(t[a])??e)!==e){++a;continue}for(;a<t.length&&(r.get(t[a])??e)===e;)++a}return n}function Eb(t){return t.match(/\S+/g)||[]}const zn="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",Cb=new RegExp(`^[${zn}]+$`,"gu"),Tb=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class Ab{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 ya extends wt{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 Ib(e);case"Unigram":return new Mb(e,...r);case"BPE":return new zb(e);default:if(e.vocab)return new Pb(e,...r);throw new Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){let r=this.encode(e);return this.fuse_unk&&(r=kb(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 Ib extends ya{constructor(e){super(e),this.tokens_to_ids=Ko(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 s=!1,i=0;const o=[];for(;i<a.length;){let l=a.length,u=null;for(;i<l;){let d=a.slice(i,l).join("");if(i>0&&(d=this.config.continuing_subword_prefix+d),this.tokens_to_ids.has(d)){u=d;break}--l}if(u===null){s=!0;break}o.push(u),i=l}s?r.push(this.unk_token):r.push(...o)}return r}}class Mb extends ya{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 s=e.vocab[a];this.vocab[a]=s[0],this.scores[a]=s[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,s)=>[a,s])),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=Bl(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new Qw,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 i=!1;for(let o of this.trie.commonPrefixSearch(r.slice(a))){const l=this.tokens_to_ids.get(o),u=this.scores[l],d=o.length;e.insert(a,d,u,l),!i&&d===1&&(i=!0)}i||e.insert(a,1,this.unkScore,this.unk_token_id),a+=1}}tokenize(e){const r=new Zw(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 zm=(()=>{const t=[...Array.from({length:94},(a,s)=>s+33),...Array.from({length:12},(a,s)=>s+161),...Array.from({length:82},(a,s)=>s+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,s)=>[a,n[s]]))})(),Ob=V0(zm);class zb extends ya{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=Ko(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 s=new Xw((l,u)=>l.score<u.score);let i={token:n[0],bias:0,prev:null,next:null},o=i;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(s,o),o=u}for(;!s.isEmpty();){const l=s.pop();if(l.deleted||!l.next||l.next.deleted)continue;if(l.deleted=!0,l.next.deleted=!0,l.prev){const d={...l.prev};l.prev.deleted=!0,l.prev=d,d.prev?d.prev.next=d:i=d}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(s,u.prev)):i=u,u.next&&(u.next.prev=u,this._add_node(s,u))}for(let l=i;l!==null;l=l.next)a.push(l.token)}else a=n;if(this.continuing_subword_suffix)for(let s=0;s<a.length-1;++s)a[s]+=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 s of a)this.tokens_to_ids.has(s)?r.push(s):this.byte_fallback?r.push(...Array.from(this.text_encoder.encode(s)).map(i=>`<0x${i.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class Pb extends ya{constructor(e,r){super(e),this.tokens_to_ids=Ko(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 wt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new Gb(e);case"Precompiled":return new uv(e);case"Sequence":return new Vb(e);case"Replace":return new Rb(e);case"NFC":return new Bb(e);case"NFKC":return new Db(e);case"NFKD":return new Nb(e);case"Strip":return new Fb(e);case"StripAccents":return new Lb(e);case"Lowercase":return new Ub(e);case"Prepend":return new Wb(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class Rb extends Lt{normalize(e){const r=Pi(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class Bb extends Lt{normalize(e){return e=e.normalize("NFC"),e}}class Db extends Lt{normalize(e){return e=e.normalize("NFKC"),e}}class Nb extends Lt{normalize(e){return e=e.normalize("NFKD"),e}}class Fb extends Lt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class Lb extends Lt{normalize(e){return e=Om(e),e}}class Ub extends Lt{normalize(e){return e=e.toLowerCase(),e}}class Wb extends Lt{normalize(e){return e=this.config.prepend+e,e}}class Vb extends Lt{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>Lt.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class Gb extends Lt{_tokenize_chinese_chars(e){const r=[];for(let n=0;n<e.length;++n){const a=e[n],s=a.charCodeAt(0);Sb(s)?(r.push(" "),r.push(a),r.push(" ")):r.push(a)}return r.join("")}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(e){switch(e){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","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 wt{static fromConfig(e){if(e===null)return null;switch(e.type){case"BertPreTokenizer":return new Hb(e);case"Sequence":return new dv(e);case"Whitespace":return new cv(e);case"WhitespaceSplit":return new pv(e);case"Metaspace":return new Dm(e);case"ByteLevel":return new jb(e);case"Split":return new qb(e);case"Punctuation":return new Kb(e);case"Digits":return new Yb(e);case"Replace":return new hv(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,r){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,r){return(Array.isArray(e)?e.map(n=>this.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class Hb extends Qt{constructor(e){super(),this.pattern=new RegExp(`[^\\s${zn}]+|[${zn}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class jb extends Qt{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=zm,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),s=>this.byte_encoder[s]).join(""))}}class qb extends Qt{constructor(e){super(),this.config=e,this.pattern=Pi(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:$b(e,this.pattern)}}class Kb extends Qt{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${zn}]+|[${zn}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Yb 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 wt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new Xb(e);case"ByteLevel":return new Bm(e);case"RobertaProcessing":return new Rm(e);case"BertProcessing":return new Pm(e);case"Sequence":return new Qb(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 Pm 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=gt([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const s=n&&this instanceof Rm?[this.sep]:[],i=n?[this.sep]:[];e=gt(e,s,r,i),a=gt(a,new Array(r.length+s.length+i.length).fill(1))}return{tokens:e,token_type_ids:a}}}class Rm extends Pm{}class Xb 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 s=[],i=[];for(const o of a)"SpecialToken"in o?n&&(s.push(o.SpecialToken.id),i.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(s=gt(s,e),i=gt(i,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(s=gt(s,r),i=gt(i,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:s,token_type_ids:i}}}class Bm extends Pn{post_process(e,r=null){return r&&(e=gt(e,r)),{tokens:e}}}class Qb 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 s of this.processors)if(s instanceof Bm)e=s.post_process(e).tokens,r&&(r=s.post_process(r).tokens);else{const i=s.post_process(e,r,n);e=i.tokens,a=i.token_type_ids}return{tokens:e,token_type_ids:a}}}class Ut extends wt{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 rv(e);case"Metaspace":return new lv(e);case"ByteLevel":return new nv(e);case"Replace":return new Zb(e);case"ByteFallback":return new Jb(e);case"Fuse":return new ev(e);case"Strip":return new tv(e);case"Sequence":return new iv(e);case"CTC":return new av(e);case"BPEDecoder":return new sv(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 Zb extends Ut{decode_chain(e){const r=Pi(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class Jb extends Ut{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const r=[];let n=[];for(const a of e){let s=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const i=parseInt(a.slice(3,5),16);isNaN(i)||(s=i)}if(s!==null)n.push(s);else{if(n.length>0){const i=this.text_decoder.decode(Uint8Array.from(n));r.push(i),n=[]}r.push(a)}}if(n.length>0){const a=this.text_decoder.decode(Uint8Array.from(n));r.push(a),n=[]}return r}}class ev extends Ut{decode_chain(e){return[e.join("")]}}class tv 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 s=0;s<this.start&&r[s]===this.content;++s){n=s+1;continue}let a=r.length;for(let s=0;s<this.stop;++s){const i=r.length-s-1;if(r[i]===this.content){a=i;continue}else break}return r.slice(n,a)})}}class rv 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=Yo(r)),r))}}class nv extends Ut{constructor(e){super(e),this.byte_decoder=Ob,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(s=>this.byte_decoder[s]));return this.text_decoder.decode(n)}decode_chain(e){const r=[];let n=[];for(const a of e)this.added_tokens.find(s=>s.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 av 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 s=1;s<e.length;++s)e[s]!==r.at(-1)&&r.push(e[s]);let a=r.filter(s=>s!==this.pad_token).join("");return this.cleanup&&(a=Yo(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class iv 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 sv 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 ov extends Ut{decode_chain(e){let r="";for(let n=1;n<e.length;n+=2)r+=e[n];return[r]}}class Dm 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 lv 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 uv 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 dv 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 cv extends Qt{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class pv extends Qt{constructor(e){super()}pre_tokenize_text(e,r){return Eb(e)}}class hv extends Qt{constructor(e){super(),this.config=e,this.pattern=Pi(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 fv=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function mv(t,e,r,n){for(const a of Object.keys(t)){const s=e-t[a].length,i=r(a),o=new Array(s).fill(i);t[a]=n==="right"?gt(t[a],o):gt(o,t[a])}}function gv(t,e){for(const r of Object.keys(t))t[r].length=e}class Ce extends wt{constructor(r,n){super();B(this,"return_token_type_ids",!1);B(this,"_default_chat_template",`{% for message in messages %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}`);B(this,"padding_side","right");this._tokenizer_config=n,this.normalizer=Lt.fromConfig(r.normalizer),this.pre_tokenizer=Qt.fromConfig(r.pre_tokenizer),this.model=ya.fromConfig(r.model,n),this.post_processor=Pn.fromConfig(r.post_processor),this.decoder=Ut.fromConfig(r.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const a of r.added_tokens){const s=new Ab(a);this.added_tokens.push(s),this.model.tokens_to_ids.set(s.content,s.id),this.model.vocab[s.id]=s.content,s.special&&(this.special_tokens.push(s.content),this.all_special_ids.push(s.id))}if(this.additional_special_tokens=n.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.toSorted((a,s)=>s.content.length-a.content.length).map(a=>`${a.lstrip?"\\s*":""}(${zl(a.content)})${a.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=n.model_max_length,this.remove_space=n.remove_space,this.clean_up_tokenization_spaces=n.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=n.do_lowercase_and_remove_accent??!1,n.padding_side&&(this.padding_side=n.padding_side),this.legacy=!1,this.chat_template=n.chat_template??null,Array.isArray(this.chat_template)){const a=Object.create(null);for(const{name:s,template:i}of this.chat_template){if(typeof s!="string"||typeof i!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');a[s]=i}this.chat_template=a}this._compiled_template_cache=new Map}getToken(...r){for(const n of r){const a=this._tokenizer_config[n];if(a)if(typeof a=="object"){if(a.__type==="AddedToken")return a.content;throw Error(`Unknown token: ${a}`)}else return a}return null}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:s=null,local_files_only:i=!1,revision:o="main",legacy:l=null}={}){const u=await Im(r,{progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,legacy:l});return new this(...u)}_call(r,{text_pair:n=null,add_special_tokens:a=!0,padding:s=!1,truncation:i=null,max_length:o=null,return_tensor:l=!0,return_token_type_ids:u=null}={}){const d=Array.isArray(r);let h;if(d){if(r.length===0)throw Error("text array must be non-empty");if(n!==null){if(Array.isArray(n)){if(r.length!==n.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");h=r.map((g,p)=>this._encode_plus(g,{text_pair:n[p],add_special_tokens:a,return_token_type_ids:u}))}else h=r.map(g=>this._encode_plus(g,{add_special_tokens:a,return_token_type_ids:u}))}else{if(r==null)throw Error("text may not be null or undefined");if(Array.isArray(n))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");h=[this._encode_plus(r,{text_pair:n,add_special_tokens:a,return_token_type_ids:u})]}if(o===null?s==="max_length"?o=this.model_max_length:o=qt(h.map(g=>g.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."),o=Math.min(o,this.model_max_length??1/0),s||i)for(let g=0;g<h.length;++g)h[g].input_ids.length!==o&&(h[g].input_ids.length>o?i&&gv(h[g],o):s&&mv(h[g],o,p=>p==="input_ids"?this.pad_token_id:0,this.padding_side));const m={};if(l){if(!(s&&i)&&h.some(p=>{var w;for(const v of Object.keys(p))if(p[v].length!==((w=h[0][v])==null?void 0:w.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 g=[h.length,h[0].input_ids.length];for(const p of Object.keys(h[0]))m[p]=new ce("int64",BigInt64Array.from(h.flatMap(w=>w[p]).map(BigInt)),g)}else{for(const g of Object.keys(h[0]))m[g]=h.map(p=>p[g]);if(!d)for(const g of Object.keys(m))m[g]=m[g][0]}return m}_encode_text(r){return r===null?null:(this.added_tokens_regex?r.split(this.added_tokens_regex).filter(s=>s):[r]).map((s,i)=>{if(this.added_tokens.find(l=>l.content===s)!==void 0)return s;{if(this.remove_space===!0&&(s=s.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(s=xb(s)),this.normalizer!==null&&(s=this.normalizer(s)),s.length===0)return[];const l=this.pre_tokenizer!==null?this.pre_tokenizer(s,{section_index:i}):[s];return this.model(l)}}).flat()}_encode_plus(r,{text_pair:n=null,add_special_tokens:a=!0,return_token_type_ids:s=null}={}){const{tokens:i,token_type_ids:o}=this._tokenize_helper(r,{pair:n,add_special_tokens:a}),l=this.model.convert_tokens_to_ids(i),u={input_ids:l,attention_mask:new Array(l.length).fill(1)};return(s??this.return_token_type_ids)&&o&&(u.token_type_ids=o),u}_tokenize_helper(r,{pair:n=null,add_special_tokens:a=!1}={}){const s=this._encode_text(r),i=this._encode_text(n);return this.post_processor?this.post_processor(s,i,{add_special_tokens:a}):{tokens:gt(s??[],i??[])}}tokenize(r,{pair:n=null,add_special_tokens:a=!1}={}){return this._tokenize_helper(r,{pair:n,add_special_tokens:a}).tokens}encode(r,{text_pair:n=null,add_special_tokens:a=!0,return_token_type_ids:s=null}={}){return this._encode_plus(r,{text_pair:n,add_special_tokens:a,return_token_type_ids:s}).input_ids}batch_decode(r,n={}){return r instanceof ce&&(r=r.tolist()),r.map(a=>this.decode(a,n))}decode(r,n={}){if(r instanceof ce&&(r=Mm(r)),!Array.isArray(r)||r.length===0||!G0(r[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(r,n)}decode_single(r,{skip_special_tokens:n=!1,clean_up_tokenization_spaces:a=null}){let s=this.model.convert_ids_to_tokens(r);n&&(s=s.filter(o=>!this.special_tokens.includes(o)));let i=this.decoder?this.decoder(s):s.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(i=i.replaceAll(this.decoder.end_of_word_suffix," "),n&&(i=i.trim())),(a??this.clean_up_tokenization_spaces)&&(i=Yo(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(r,{chat_template:n=null,add_generation_prompt:a=!1,tokenize:s=!0,padding:i=!1,truncation:o=!1,max_length:l=null,return_tensor:u=!0,return_dict:d=!1,tokenizer_kwargs:h={},...m}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const v=this.chat_template??this.default_chat_template;if(n!==null&&Object.hasOwn(v,n))n=v[n];else if(n===null&&"default"in v)n=v.default;else if(n===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(v).sort()}.`)}else n??(n=this.chat_template??this.default_chat_template);if(typeof n!="string")throw Error(`chat_template must be a string, but got ${typeof n}`);let g=this._compiled_template_cache.get(n);g===void 0&&(g=new bb(n),this._compiled_template_cache.set(n,g));const p=Object.create(null);for(const v of fv){const S=this.getToken(v);S&&(p[v]=S)}const w=g.render({messages:r,add_generation_prompt:a,...p,...m});if(s){const v=this._call(w,{add_special_tokens:!1,padding:i,truncation:o,max_length:l,return_tensor:u,...h});return d?v:v.input_ids}return w}}class _v extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class yv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class wv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class bv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class vv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class $v extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class xv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Sv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class kv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Ev extends Ce{}class Cv extends Ce{}class Tv extends Ce{constructor(r,n){super(r,n);B(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Av extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Iv extends Ce{}class Nm extends Ce{constructor(){super(...arguments);B(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}}class Mv extends Ce{}class Fm extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,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 Xo(this,e,r,n)}}class Ov extends Fm{}class zv extends Ce{}class Pv extends Nm{constructor(e,r){var s,i;const n=".,!?…。，、।۔،",a=(i=(s=e.pre_tokenizer)==null?void 0:s.pretokenizers[0])==null?void 0:i.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const Ri="▁";class Lm extends Ce{constructor(r,n){super(r,n);B(this,"_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 %}`);B(this,"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.`);B(this,"padding_side","left");this.use_default_system_prompt=n.use_default_system_prompt??!1,this.legacy=n.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new Dm({replacement:Ri,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(r){if(r===null)return null;if(this.legacy||r.length===0)return super._encode_text(r);let n=super._encode_text(Ri+r.replaceAll(Ri," "));return n.length>1&&n[0]===Ri&&this.special_tokens.includes(n[1])&&(n=n.slice(1)),n}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 Rv extends Lm{}class Bv extends Ce{}class Dv extends Ce{}class Nv extends Ce{}class Fv extends Ce{}class Lv extends Ce{}class Uv extends Ce{}class Wv extends Ce{constructor(){super(...arguments);B(this,"_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 Vv extends Ce{}function Xo(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,s=n.tgt_lang;if(!t.language_codes.includes(s))throw new Error(`Target language code "${s}" is not valid. Must be one of: {${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 i of t.post_processor.config.single)if("SpecialToken"in i&&t.languageRegex.test(i.SpecialToken.id)){i.SpecialToken.id=t.lang_to_token(a);break}}return n.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(s)])[0],t._call(e,r)}class Gv extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return Xo(this,e,r,n)}}class Hv extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,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 Xo(this,e,r,n)}}class jv extends Ce{constructor(){super(...arguments);B(this,"_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(r,{return_timestamps:n=!1,return_language:a=!1,time_precision:s=null,force_full_sequences:i=!0}={}){if(s===null)throw Error("Must specify time_precision");let o=null;const l=n==="word";function u(){return{language:o,timestamp:[null,null],text:""}}const d=[];let h=u(),m=0;const g=this.timestamp_begin;let p=[],w=[],v=!1,S=null;const $=new Set(this.all_special_ids);for(const A of r){const z=A.tokens,R=l?A.token_timestamps:null;let V=null,j=g;if("stride"in A){const[ee,ne,se]=A.stride;if(m-=ne,S=ee-se,ne&&(j=ne/s+g),se)for(let D=z.length-1;D>=0;--D){const M=Number(z[D]);if(M>=g){if(V!==null&&(M-g)*s<S)break;V=M}}}let q=[],fe=[];for(let ee=0;ee<z.length;++ee){const ne=Number(z[ee]);if($.has(ne)){const se=this.decode([ne]),D=zi.get(se.slice(2,-2));if(D!==void 0){if(o!==null&&D!==o&&!n){p.push(q);const M=this.findLongestCommonSequence(p)[0],G=this.decode(M);h.text=G,d.push(h),p=[],q=[],h=u()}o=h.language=D}}else if(ne>=g){const se=(ne-g)*s+m,D=Xn(se,2);if(V!==null&&ne>=V)v=!0;else if(v||p.length>0&&ne<j)v=!1;else if(h.timestamp[0]===null)h.timestamp[0]=D;else if(D!==h.timestamp[0]){h.timestamp[1]=D,p.push(q),l&&w.push(fe);const[M,G]=this.findLongestCommonSequence(p,w),K=this.decode(M);h.text=K,l&&(h.words=this.collateWordTimestamps(M,G,o)),d.push(h),p=[],q=[],w=[],fe=[],h=u()}}else if(q.push(ne),l){let se=Xn(R[ee]+m,2),D;if(ee+1<R.length){D=Xn(R[ee+1]+m,2);const M=this.decode([ne]);Cb.test(M)&&(D=Xn(Math.min(se+s,D),2))}else D=null;fe.push([se,D])}}if("stride"in A){const[ee,ne,se]=A.stride;m+=ee-se}q.length>0?(p.push(q),l&&w.push(fe)):p.every(ee=>ee.length===0)&&(h=u(),p=[],q=[],w=[],fe=[])}if(p.length>0){if(i&&n)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[A,z]=this.findLongestCommonSequence(p,w),R=this.decode(A);h.text=R,l&&(h.words=this.collateWordTimestamps(A,z,o)),d.push(h)}let E=Object.create(null);const T=d.map(A=>A.text).join("");if(n||a){for(let A=0;A<d.length;++A){const z=d[A];n||delete z.timestamp,a||delete z.language}if(l){const A=[];for(const z of d)for(const R of z.words)A.push(R);E={chunks:A}}else E={chunks:d}}return[T,E]}findLongestCommonSequence(r,n=null){let a=r[0],s=a.length,i=[];const o=Array.isArray(n)&&n.length>0;let l=o?[]:null,u=o?n[0]:null;for(let d=1;d<r.length;++d){const h=r[d];let m=0,g=[s,s,0,0];const p=h.length;for(let A=1;A<s+p;++A){const z=Math.max(0,s-A),R=Math.min(s,s+p-A),V=a.slice(z,R),j=Math.max(0,A-s),q=Math.min(p,A),fe=h.slice(j,q);if(V.length!==fe.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let ee;o?ee=V.filter((D,M)=>D===fe[M]&&u[z+M]<=n[d][j+M]).length:ee=V.filter((D,M)=>D===fe[M]).length;const ne=A/1e4,se=ee/A+ne;ee>1&&se>m&&(m=se,g=[z,R,j,q])}const[w,v,S,$]=g,E=Math.floor((v+w)/2),T=Math.floor(($+S)/2);i.push(...a.slice(0,E)),a=h.slice(T),s=a.length,o&&(l.push(...u.slice(0,E)),u=n[d].slice(T))}return i.push(...a),o?(l.push(...u),[i,l]):[i,[]]}collateWordTimestamps(r,n,a){const[s,i,o]=this.combineTokensIntoWords(r,a),l=[];for(let u=0;u<s.length;++u){const d=o[u];l.push({text:s[u],timestamp:[n[d.at(0)][0],n[d.at(-1)][1]]})}return l}combineTokensIntoWords(r,n,a=`"'“¡¿([{-`,s=`"'.。,，!！?？:：”)]}、`){n=n??"english";let i,o,l;return["chinese","japanese","thai","lao","myanmar"].includes(n)?[i,o,l]=this.splitTokensOnUnicode(r):[i,o,l]=this.splitTokensOnSpaces(r),this.mergePunctuations(i,o,l,a,s)}decode(r,n){let a;return n!=null&&n.decode_with_timestamps?(r instanceof ce&&(r=Mm(r)),a=this.decodeWithTimestamps(r,n)):a=super.decode(r,n),a}decodeWithTimestamps(r,n){const a=(n==null?void 0:n.time_precision)??.02,s=Array.from(this.all_special_ids).at(-1)+1;let i=[[]];for(let o of r)if(o=Number(o),o>=s){const l=((o-s)*a).toFixed(2);i.push(`<|${l}|>`),i.push([])}else i[i.length-1].push(o);return i=i.map(o=>typeof o=="string"?o:super.decode(o,n)),i.join("")}splitTokensOnUnicode(r){const n=this.decode(r,{decode_with_timestamps:!0}),a="�",s=[],i=[],o=[];let l=[],u=[],d=0;for(let h=0;h<r.length;++h){const m=r[h];l.push(m),u.push(h);const g=this.decode(l,{decode_with_timestamps:!0});(!g.includes(a)||n[d+g.indexOf(a)]===a)&&(s.push(g),i.push(l),o.push(u),l=[],u=[],d+=g.length)}return[s,i,o]}splitTokensOnSpaces(r){const[n,a,s]=this.splitTokensOnUnicode(r),i=[],o=[],l=[],u=new RegExp(`^[${zn}]$`,"gu");for(let d=0;d<n.length;++d){const h=n[d],m=a[d],g=s[d],p=m[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),w=h.startsWith(" "),v=h.trim(),S=u.test(v);if(p||w||S||i.length===0)i.push(h),o.push(m),l.push(g);else{const $=i.length-1;i[$]+=h,o[$].push(...m),l[$].push(...g)}}return[i,o,l]}mergePunctuations(r,n,a,s,i){const o=structuredClone(r),l=structuredClone(n),u=structuredClone(a);let d=o.length-2,h=o.length-1;for(;d>=0;)o[d].startsWith(" ")&&s.includes(o[d].trim())?(o[h]=o[d]+o[h],l[h]=gt(l[d],l[h]),u[h]=gt(u[d],u[h]),o[d]="",l[d]=[],u[d]=[]):h=d,--d;for(d=0,h=1;h<o.length;)!o[d].endsWith(" ")&&i.includes(o[h])?(o[d]+=o[h],l[d]=gt(l[d],l[h]),u[d]=gt(u[d],u[h]),o[h]="",l[h]=[],u[h]=[]):d=h,++h;return[o.filter(m=>m),l.filter(m=>m.length>0),u.filter(m=>m.length>0)]}get_decoder_prompt_ids({language:r=null,task:n=null,no_timestamps:a=!0}={}){const s=[];if(r){const i=Am(r),o=this.model.tokens_to_ids.get(`<|${i}|>`);if(o===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at ${qo}.`);s.push(o)}else s.push(null);if(n){if(n=n.toLowerCase(),n!=="transcribe"&&n!=="translate")throw new Error(`Task "${n}" is not supported. Must be one of: ["transcribe", "translate"]`);const i=this.model.tokens_to_ids.get(`<|${n}|>`);if(i===void 0)throw new Error(`Unable to find task "${n}" in model vocabulary. Please report this issue at ${qo}.`);s.push(i)}else s.push(null);if(a){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 ${qo}.`);s.push(i)}return s.map((i,o)=>[o+1,i]).filter(i=>i[1]!==null)}}class qv extends Ce{}class Kv extends Ce{}class Yv extends Ce{}class Xv extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(n=>this.languageRegex.test(n)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[r,...n]=e.trim().split(this.languageRegex);if(n.length===0)return super._encode_text(r);if(n.length===2){const[a,s]=n;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),gt([a],super._encode_text(s))}}}class Qv extends Ce{}class Um extends Ce{constructor(){super(...arguments);B(this,"_default_chat_template","{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}")}}class Zv extends Um{}class Jv extends Ce{}class e1 extends Ce{}class t1 extends Ce{constructor(e,r){super(e,r),this.decoder=new ov({})}}class r1 extends Ce{}class pt{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",legacy:o=null}={}){var m;const[l,u]=await Im(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,legacy:o}),d=((m=u.tokenizer_class)==null?void 0:m.replace(/Fast$/,""))??"PreTrainedTokenizer";let h=this.TOKENIZER_CLASS_MAPPING[d];return h||(console.warn(`Unknown tokenizer class "${d}", attempting to construct from base class.`),h=Ce),new h(l,u)}}B(pt,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:Iv,DistilBertTokenizer:Ev,CamembertTokenizer:Cv,DebertaTokenizer:vv,DebertaV2Tokenizer:$v,BertTokenizer:_v,HerbertTokenizer:xv,ConvBertTokenizer:Sv,RoFormerTokenizer:kv,XLMTokenizer:Tv,ElectraTokenizer:Av,MobileBertTokenizer:wv,SqueezeBertTokenizer:bv,AlbertTokenizer:yv,GPT2Tokenizer:Nm,BartTokenizer:Mv,MBartTokenizer:Fm,MBart50Tokenizer:Ov,RobertaTokenizer:zv,WhisperTokenizer:jv,CodeGenTokenizer:qv,CLIPTokenizer:Kv,SiglipTokenizer:Yv,MarianTokenizer:Xv,BloomTokenizer:Pv,NllbTokenizer:Gv,M2M100Tokenizer:Hv,LlamaTokenizer:Lm,CodeLlamaTokenizer:Rv,XLMRobertaTokenizer:Bv,MPNetTokenizer:Dv,FalconTokenizer:Nv,GPTNeoXTokenizer:Fv,EsmTokenizer:Lv,Wav2Vec2CTCTokenizer:Qv,BlenderbotTokenizer:Um,BlenderbotSmallTokenizer:Zv,SpeechT5Tokenizer:Jv,NougatTokenizer:e1,VitsTokenizer:t1,Qwen2Tokenizer:Uv,GemmaTokenizer:Wv,Grok1Tokenizer:Vv,CohereTokenizer:r1,PreTrainedTokenizer:Ce});async function n1(t,e){return await Rr(t,"config.json",!0,e)}function wa(t){const e={};let r={};switch(t.model_type){case"llava":case"paligemma":case"florence2":r=wa(t.text_config);break;case"moondream1":r=wa(t.phi_config);break;case"musicgen":r=wa(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":case"florence2_language":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=wa(t.decoder),s="num_decoder_layers"in a,i=$r(t,["model_type","is_encoder_decoder"]);return s?(i.num_decoder_layers=a.num_decoder_layers,i.num_decoder_heads=a.num_decoder_heads,i.decoder_hidden_size=a.decoder_hidden_size,i.num_encoder_layers=a.num_encoder_layers,i.num_encoder_heads=a.num_encoder_heads,i.encoder_hidden_size=a.encoder_hidden_size):(i.num_layers=a.num_layers,i.num_heads=a.num_heads,i.hidden_size=a.hidden_size),i}const n={...r,...$r(t,["model_type","multi_query","is_encoder_decoder"])};for(const a in e)n[a]=t[e[a]];return n}function Wm(t,{prefix:e="past_key_values"}={}){const r={},n=t.normalized_config,a=1;if(n.is_encoder_decoder&&"num_encoder_heads"in n&&"num_decoder_heads"in n){const s=n.encoder_dim_kv??n.encoder_hidden_size/n.num_encoder_heads,i=n.decoder_dim_kv??n.decoder_hidden_size/n.num_decoder_heads,o=[a,n.num_encoder_heads,0,s],l=[a,n.num_decoder_heads,0,i];for(let u=0;u<n.num_decoder_layers;++u)r[`${e}.${u}.encoder.key`]=o,r[`${e}.${u}.encoder.value`]=o,r[`${e}.${u}.decoder.key`]=l,r[`${e}.${u}.decoder.value`]=l}else{const s=n.num_heads,i=n.num_layers,o=n.dim_kv??n.hidden_size/(n.num_attention_heads??s);if(n.model_type==="falcon"){const l=[a*s,0,o];for(let u=0;u<i;++u)r[`${e}.${u}.key`]=l,r[`${e}.${u}.value`]=l}else if(n.multi_query){const l=[a*s,0,2*o];for(let u=0;u<i;++u)r[`${e}.${u}.key_value`]=l}else if(n.model_type==="bloom"){const l=[a*s,o,0],u=[a*s,0,o];for(let d=0;d<i;++d)r[`${e}.${d}.key`]=l,r[`${e}.${d}.value`]=u}else if(n.model_type==="openelm")for(let l=0;l<i;++l){const u=[a,s[l],0,o];r[`${e}.${l}.key`]=u,r[`${e}.${l}.value`]=u}else{const l=[a,s,0,o];for(let u=0;u<i;++u)r[`${e}.${u}.key`]=l,r[`${e}.${u}.value`]=l}}return r}class Bi{constructor(e){B(this,"max_position_embeddings");this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e),this.normalized_config=wa(this)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main"}={}){n&&!(n instanceof Bi)&&(n=new Bi(n));const o=n??await n1(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i});return new this(o)}}class Vm{static async from_pretrained(...e){return Bi.from_pretrained(...e)}}const Di=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),a1=function(){let t;return async function(){if(t===void 0)if(Hr.IS_NODE_ENV)t=!0;else if(!Hr.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}}(),Pt=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),i1=Object.freeze({[Di.cpu]:Pt.q8,[Di.gpu]:Pt.fp32,[Di.wasm]:Pt.q8,[Di.webgpu]:Pt.fp32}),Gm=Object.freeze({[Pt.fp32]:"",[Pt.fp16]:"_fp16",[Pt.int8]:"_int8",[Pt.uint8]:"_uint8",[Pt.q8]:"_quantized",[Pt.q4]:"_q4",[Pt.q4f16]:"_q4f16",[Pt.bnb4]:"_bnb4"});class yr extends wt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class Qo extends wt{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class Ni extends wt{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 s1 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 o1 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 s of this.eos_token_id)a.data[s]=0}return r}}class Hm 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 s of this.begin_suppress_tokens)a.data[s]=-1/0}return r}}class l1 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 s=r[n].data;if(s[this.no_timestamps_token_id]=-1/0,e[n].length===this.begin_index-1){s.fill(-1/0),s[this.timestamp_begin]=0;continue}const i=e[n].slice(this.begin_index),o=i.length>=1&&i[i.length-1]>=this.timestamp_begin,l=i.length<2||i[i.length-2]>=this.timestamp_begin;if(o&&(l?s.subarray(this.timestamp_begin).fill(-1/0):s.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;s.subarray(m+1).fill(-1/0)}const u=Z0(s),d=Math.log(u.subarray(this.timestamp_begin).map(Math.exp).reduce((m,g)=>m+g)),h=qt(u.subarray(0,this.timestamp_begin))[0];d>h&&s.subarray(0,this.timestamp_begin).fill(-1/0)}return r}}class u1 extends yr{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const r=e.length,n=[];for(let s=0;s<r+1-this.no_repeat_ngram_size;++s){const i=[];for(let o=0;o<this.no_repeat_ngram_size;++o)i.push(e[s+o]);n.push(i.map(Number))}const a=new Map;for(const s of n){const i=s.slice(0,s.length-1),o=JSON.stringify(i),l=a.get(o)??[];l.push(s[s.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.map(Number)))??[]}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],s=this.calcBannedNgramTokens(e[n]);for(const i of s)a.data[i]=-1/0}return r}}class d1 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 s of e[n])a.data[s]<0?a.data[s]*=this.penalty:a.data[s]/=this.penalty}return r}}class c1 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 s of this.eos_token_id)a.data[s]=-1/0}return r}}class p1 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 s=r[n];for(const i of this.eos_token_id)s[i]=-1/0}return r}}class h1 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 s of this.bad_words_ids){let i=!0;for(let o=1;o<=s.length-1&&s.length<e[o].length;++o)if(s.at(-o-1)!==Number(e[o].at(-o))){i=!1;break}i&&(a[s.at(-1)]=-1/0)}}return r}}class f1 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),s=r.slice([n,r.dims[0]],null);for(let i=0;i<s.data.length;++i)s.data[i]+=(a.data[i]-s.data[i])*this.guidance_scale;return s}}class m1 extends Qo{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 g1 extends Qo{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 _1 extends Qo{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 ${e}`);this.top_k=Math.max(e,n),this.filter_value=r}}class jm{constructor(e){B(this,"max_length",20);B(this,"max_new_tokens",null);B(this,"min_length",0);B(this,"min_new_tokens",null);B(this,"early_stopping",!1);B(this,"max_time",null);B(this,"do_sample",!1);B(this,"num_beams",1);B(this,"num_beam_groups",1);B(this,"penalty_alpha",null);B(this,"use_cache",!0);B(this,"temperature",1);B(this,"top_k",50);B(this,"top_p",1);B(this,"typical_p",1);B(this,"epsilon_cutoff",0);B(this,"eta_cutoff",0);B(this,"diversity_penalty",0);B(this,"repetition_penalty",1);B(this,"encoder_repetition_penalty",1);B(this,"length_penalty",1);B(this,"no_repeat_ngram_size",0);B(this,"bad_words_ids",null);B(this,"force_words_ids",null);B(this,"renormalize_logits",!1);B(this,"constraints",null);B(this,"forced_bos_token_id",null);B(this,"forced_eos_token_id",null);B(this,"remove_invalid_values",!1);B(this,"exponential_decay_length_penalty",null);B(this,"suppress_tokens",null);B(this,"begin_suppress_tokens",null);B(this,"forced_decoder_ids",null);B(this,"guidance_scale",null);B(this,"num_return_sequences",1);B(this,"output_attentions",!1);B(this,"output_hidden_states",!1);B(this,"output_scores",!1);B(this,"return_dict_in_generate",!1);B(this,"pad_token_id",null);B(this,"bos_token_id",null);B(this,"eos_token_id",null);B(this,"encoder_no_repeat_ngram_size",0);B(this,"decoder_start_token_id",null);B(this,"generation_kwargs",{});Object.assign(this,$r(e,Object.getOwnPropertyNames(this)))}}class Zo extends wt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class Jo extends wt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof Jo?e=e.criteria:e instanceof Zo&&(e=[e]),this.criteria.push(...e)}_call(e,r){const n=new Array(e.length).fill(!1);for(const a of this.criteria){const s=a(e,r);for(let i=0;i<n.length;++i)n[i]||(n[i]=s[i])}return n}[Symbol.iterator](){return this.criteria.values()}}class y1 extends Zo{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 w1 extends Zo{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(s=>a==s)})}}class Fi extends wt{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 s=r*n;a=a.slice(s,s+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 v1(e);if(e.num_beams>1)return new $1(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 b1(e)}}class b1 extends Fi{async sample(e){const r=qt(e.data)[1];return[[BigInt(r),0]]}}class v1 extends Fi{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 An(e,r),s=bt(n.data);return Array.from({length:this.generation_config.num_beams},()=>{const i=this.randomSelect(s);return[a.data[i],Math.log(s[i])]})}}class $1 extends Fi{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 An(e,r),s=bt(n.data);return Array.from({length:this.generation_config.num_beams},(i,o)=>[a.data[o],Math.log(s[o])])}}class x1 extends jm{constructor(){super(...arguments);B(this,"return_timestamps",null);B(this,"return_token_timestamps",null);B(this,"num_frames",null);B(this,"alignment_heads",null);B(this,"task",null);B(this,"language",null);B(this,"no_timestamps_token_id",null);B(this,"prompt_ids",null);B(this,"is_multilingual",null);B(this,"lang_to_id",null);B(this,"task_to_id",null);B(this,"max_initial_timestamp_index",1)}}const $e={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},Li=new Map,qm=new Map,ba=new Map;async function S1(t,e,r){let n=r.device;n&&typeof n!="string"&&(n.hasOwnProperty(e)?n=n[e]:(console.warn(`device not specified for "${e}". Using the default device.`),n=null));const a=Bw(n);let s=r.dtype;if(typeof s!="string"&&(s&&s.hasOwnProperty(e)?s=s[e]:(s=i1[a[0]],console.warn(`dtype not specified for "${e}". Using the default dtype for this device (${s}).`))),Gm.hasOwnProperty(s)){if(s===Pt.fp16&&!await a1())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${s}. Should be one of: ${Object.keys(Pt).join(", ")}`);const i=Gm[s],o=`${r.subfolder??""}/${e}${i}.onnx`,l={...r.session_options};l.executionProviders??(l.executionProviders=a);const u=ai(t,o,!0,r);let d=[];if(r.use_external_data_format&&(r.use_external_data_format===!0||typeof r.use_external_data_format=="object"&&r.use_external_data_format.hasOwnProperty(e)&&r.use_external_data_format[e]===!0)){if(Hr.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const m=`${e}${i}.onnx_data`,g=`${r.subfolder??""}/${m}`;d.push(new Promise(async(p,w)=>{const v=await ai(t,g,!0,r);p({path:m,data:v})}))}else l.externalData!==void 0&&(d=l.externalData.map(async m=>{if(typeof m.data=="string"){const g=await ai(t,m.data,!0,r);return{...m,data:g}}return m}));if(d.length>0&&(l.externalData=await Promise.all(d)),n==="webgpu"){const m=Wm(r.config,{prefix:"present"});if(Object.keys(m).length>0){const g={};for(const p in m)g[p]="gpu-buffer";l.preferredOutputLocation=g}}return{buffer:await u,session_options:l}}async function sn(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async i=>S1(t,e[i],r))),s={};for(let i=0;i<n.length;++i){const{buffer:o,session_options:l}=a[i],u=await fm(o,l);s[n[i]]=u}return s}function k1(t,e){const r=Object.create(null),n=[];for(const i of t.inputNames){const o=e[i];if(!(o instanceof ce)){n.push(i);continue}r[i]=Dw()?o.clone():o}if(n.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${n.join(", ")}.`);const a=Object.keys(e).length,s=t.inputNames.length;if(a>s){let i=Object.keys(e).filter(o=>!t.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${s}). The following inputs will be ignored: "${i.join(", ")}".`)}return r}async function Lr(t,e){const r=k1(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([s,i])=>[s,i.ort_tensor]));let a=await t.run(n);return a=Km(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Km(t){for(let e in t)mm(t[e])?t[e]=new ce(t[e]):typeof t[e]=="object"&&Km(t[e]);return t}function Ym(t){if(t instanceof ce)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new ce("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new ce("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function Xm(t){return new ce("bool",[t],[1])}async function Qm(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const l=$r(e,t.sessions.model.inputNames);r=(await Rn(t,l)).last_hidden_state}const{input_ids:a,decoder_input_ids:s,...i}=e;return i.input_ids=s,i.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(i.encoder_attention_mask=e.attention_mask),await Ui(t,i,!0)}async function Rn(t,e){const r=t.sessions.model,n=$r(e,r.inputNames);if(r.inputNames.includes("inputs_embeds")&&!n.inputs_embeds){if(!e.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");n.inputs_embeds=await t.encode_text({input_ids:e.input_ids})}return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new ce("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await Lr(r,n)}async function Ui(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...s}=e;n.inputNames.includes("use_cache_branch")&&(s.use_cache_branch=Xm(!!a)),n.inputNames.includes("position_ids")&&s.attention_mask&&!s.position_ids&&(s.position_ids=C1(s,a)),t.addPastKeyValues(s,a);const i=$r(s,n.inputNames);return await Lr(n,i)}async function E1(t,{input_ids:e=null,attention_mask:r=null,pixel_values:n=null,position_ids:a=null,inputs_embeds:s=null,past_key_values:i=null,generation_config:o=null,logits_processor:l=null,...u}){if(!s){if(s=await t.encode_text({input_ids:e}),n&&e.dims[1]!==1){const h=await t.encode_image({pixel_values:n});({inputs_embeds:s,attention_mask:r}=t._merge_input_ids_with_image_features({image_features:h,inputs_embeds:s,input_ids:e,attention_mask:r}))}else if(i&&n&&e.dims[1]===1){const h=e.dims[1],m=Object.values(i)[0].dims.at(-2);r=Xt([In([e.dims[0],m]),r.slice(null,[r.dims[1]-h,r.dims[1]])],1)}}return await Ui(t,{inputs_embeds:s,past_key_values:i,attention_mask:r,position_ids:a,generation_config:o,logits_processor:l},!0)}function C1(t,e=null){const{input_ids:r,inputs_embeds:n,attention_mask:a}=t,[s,i]=a.dims,o=new BigInt64Array(a.data.length);for(let u=0;u<s;++u){const d=u*i;let h=BigInt(0);for(let m=0;m<i;++m){const g=d+m;a.data[g]===0n?o[g]=BigInt(1):(o[g]=h,h+=a.data[g])}}let l=new ce("int64",o,a.dims);if(e){const u=-(r??n).dims.at(1);l=l.slice(null,[u,null])}return l}function Zm(t,e,r,n){if(r.past_key_values){const a=Object.values(r.past_key_values)[0].dims.at(-2),{input_ids:s,attention_mask:i}=r;if(!(i&&i.dims[1]>s.dims[1])){if(a<s.dims[1])r.input_ids=s.slice(null,[a,null]);else if(t.config.image_token_index!=null&&s.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=s.dims[1]-(a-o);r.input_ids=s.slice(null,[-l,null]),r.attention_mask=In([1,a+l])}}}return r}function Jm(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(i=>[i.at(-1)])),a.decoder_input_ids=Ym(e),a}function T1(t,...e){return t.config.is_encoder_decoder?Jm(t,...e):Zm(t,...e)}class Z extends wt{constructor(r,n){super();B(this,"main_input_name","input_ids");B(this,"forward_params",["input_ids","attention_mask"]);this.config=r,this.sessions=n;const a=ba.get(this.constructor),s=Li.get(a);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,s){case $e.DecoderOnly:this.can_generate=!0,this._forward=Ui,this._prepare_inputs_for_generation=Zm;break;case $e.Seq2Seq:case $e.Vision2Seq:case $e.Musicgen:this.can_generate=!0,this._forward=Qm,this._prepare_inputs_for_generation=Jm;break;case $e.EncoderDecoder:this._forward=Qm;break;case $e.ImageTextToText:this.can_generate=!0,this._forward=E1,this._prepare_inputs_for_generation=T1;break;default:this._forward=Rn;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var n;const r=[];for(const a of Object.values(this.sessions))(n=a==null?void 0:a.handler)!=null&&n.dispose&&r.push(a.handler.dispose());return await Promise.all(r)}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:s=null,local_files_only:i=!1,revision:o="main",model_file_name:l=null,subfolder:u="onnx",device:d=null,dtype:h=null,use_external_data_format:m=null,session_options:g={}}={}){let p={progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,model_file_name:l,subfolder:u,device:d,dtype:h,use_external_data_format:m,session_options:g};const w=ba.get(this),v=Li.get(w);a=p.config=await Vm.from_pretrained(r,p);let S;if(v===$e.DecoderOnly)S=await Promise.all([sn(r,{model:p.model_file_name??"model"},p),Rr(r,"generation_config.json",!1,p)]);else if(v===$e.Seq2Seq||v===$e.Vision2Seq)S=await Promise.all([sn(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p),Rr(r,"generation_config.json",!1,p)]);else if(v===$e.MaskGeneration)S=await Promise.all([sn(r,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},p)]);else if(v===$e.EncoderDecoder)S=await Promise.all([sn(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p)]);else if(v===$e.ImageTextToText){const $={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};a.is_encoder_decoder&&($.model="encoder_model"),S=await Promise.all([sn(r,$,p),Rr(r,"generation_config.json",!1,p)])}else v===$e.Musicgen?S=await Promise.all([sn(r,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},p),Rr(r,"generation_config.json",!1,p)]):(v!==$e.EncoderOnly&&console.warn(`Model type for '${w??(a==null?void 0:a.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),S=await Promise.all([sn(r,{model:p.model_file_name??"model"},p)]));return new this(a,...S)}async _call(r){return await this.forward(r)}async forward(r){return await this._forward(this,r)}_get_logits_warper(r){const n=new Ni;return r.temperature!==null&&r.temperature!==1&&n.push(new m1(r.temperature)),r.top_k!==null&&r.top_k!==0&&n.push(new _1(r.top_k)),r.top_p!==null&&r.top_p<1&&n.push(new g1(r.top_p)),n}_get_logits_processor(r,n,a=null){const s=new Ni;if(r.repetition_penalty!==null&&r.repetition_penalty!==1&&s.push(new d1(r.repetition_penalty)),r.no_repeat_ngram_size!==null&&r.no_repeat_ngram_size>0&&s.push(new u1(r.no_repeat_ngram_size)),r.bad_words_ids!==null&&s.push(new h1(r.bad_words_ids,r.eos_token_id)),r.min_length!==null&&r.eos_token_id!==null&&r.min_length>0&&s.push(new c1(r.min_length,r.eos_token_id)),r.min_new_tokens!==null&&r.eos_token_id!==null&&r.min_new_tokens>0&&s.push(new p1(n,r.min_new_tokens,r.eos_token_id)),r.forced_bos_token_id!==null&&s.push(new s1(r.forced_bos_token_id)),r.forced_eos_token_id!==null&&s.push(new o1(r.max_length,r.forced_eos_token_id)),r.begin_suppress_tokens!==null){const i=n>1||r.forced_bos_token_id===null?n:n+1;s.push(new Hm(r.begin_suppress_tokens,i))}return r.guidance_scale!==null&&r.guidance_scale>1&&s.push(new f1(r.guidance_scale)),a!==null&&s.extend(a),s}_prepare_generation_config(r,n,a=jm){const s={...this.config};for(const o of["decoder","generator","text_config"])o in s&&Object.assign(s,s[o]);const i=new a(s);return"generation_config"in this&&Object.assign(i,this.generation_config),r&&Object.assign(i,r),n&&Object.assign(i,$r(n,Object.getOwnPropertyNames(i))),i}_get_stopping_criteria(r,n=null){const a=new Jo;return r.max_length!==null&&a.push(new y1(r.max_length,this.config.max_position_embeddings??null)),r.eos_token_id!==null&&a.push(new w1(r.eos_token_id)),n&&a.extend(n),a}_validate_model_class(){if(!this.can_generate){const r=[ol,ll,sl,il],n=ba.get(this.constructor),a=new Set,s=this.config.model_type;for(const o of r){const l=o.get(s);l&&a.add(l[0])}let i=`The current model class (${n}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw a.size>0&&(i+=` Please use the following class instead: ${[...a].join(", ")}`),Error(i)}}prepare_inputs_for_generation(...r){return this._prepare_inputs_for_generation(this,...r)}_update_model_kwargs_for_generation({generated_input_ids:r,outputs:n,model_inputs:a,is_encoder_decoder:s}){return a.past_key_values=this.getPastKeyValues(n,a.past_key_values),a.input_ids=new ce("int64",r.flat(),[r.length,1]),s||(a.attention_mask=Xt([a.attention_mask,In([a.attention_mask.dims[0],1])],1)),a.position_ids=null,a}_prepare_model_inputs({inputs:r,bos_token_id:n,model_kwargs:a}){const s=$r(a,this.forward_params),i=this.main_input_name;if(i in s){if(r)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else s[i]=r;return{inputs_tensor:s[i],model_inputs:s,model_input_name:i}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:r,model_inputs:n,model_input_name:a,generation_config:s}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!n.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:o,pixel_values:l,attention_mask:u,...d}=n,h=await this._prepare_inputs_embeds(n);n={...d,...$r(h,["inputs_embeds","attention_mask"])}}let{last_hidden_state:i}=await Rn(this,n);return s.guidance_scale!==null&&s.guidance_scale>1&&(i=Xt([i,Hw(i,0)],0),"attention_mask"in n&&(n.attention_mask=Xt([n.attention_mask,Kw(n.attention_mask)],0))),n.encoder_outputs=i,n}_prepare_decoder_input_ids_for_generation({batch_size:r,model_input_name:n,model_kwargs:a,decoder_start_token_id:s,bos_token_id:i,generation_config:o}){let{decoder_input_ids:l,...u}=a;if(l)Array.isArray(l[0])||(l=Array.from({length:r},()=>l));else if(s??(s=i),this.config.model_type==="musicgen")l=Array.from({length:r*this.config.decoder.num_codebooks},()=>[s]);else if(Array.isArray(s)){if(s.length!==r)throw new Error(`\`decoder_start_token_id\` expcted to have length ${r} but got ${s.length}`);l=s}else l=Array.from({length:r},()=>[s]);return l=Ym(l),a.decoder_attention_mask=jw(l),{input_ids:l,model_inputs:u}}async generate({inputs:r=null,generation_config:n=null,logits_processor:a=null,stopping_criteria:s=null,streamer:i=null,...o}){this._validate_model_class(),n=this._prepare_generation_config(n,o);let{inputs_tensor:l,model_inputs:u,model_input_name:d}=this._prepare_model_inputs({inputs:r,model_kwargs:o});const h=this.config.is_encoder_decoder;h&&("encoder_outputs"in u||(u=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:l,model_inputs:u,model_input_name:d,generation_config:n})));let m;h?{input_ids:m,model_inputs:u}=this._prepare_decoder_input_ids_for_generation({batch_size:u[d].dims.at(0),model_input_name:d,model_kwargs:u,decoder_start_token_id:n.decoder_start_token_id,bos_token_id:n.bos_token_id,generation_config:n}):m=u[d];let g=m.dims.at(-1);n.max_new_tokens!==null&&(n.max_length=g+n.max_new_tokens);const p=this._get_logits_processor(n,g,a),w=this._get_stopping_criteria(n,s),v=u[d].dims.at(0),S=Fi.getSampler(n),$=new Array(v).fill(0),E=m.tolist();i&&i.put(E);let T=null,A={};for(;;){u=this.prepare_inputs_for_generation(E,u,n);const R=await this.forward(u);if(n.output_attentions&&n.return_dict_in_generate){const ee=this.getAttentions(R);for(const ne in ee)ne in A||(A[ne]=[]),A[ne].push(ee[ne])}const V=R.logits.slice(null,-1,null),j=p(E,V),q=[];for(let ee=0;ee<j.dims.at(0);++ee){const ne=j[ee],se=await S(ne);for(const[D,M]of se){const G=BigInt(D);$[ee]+=M,E[ee].push(G),q.push([G]);break}}if(i&&i.put(q),w(E).every(ee=>ee)){n.return_dict_in_generate&&(T=this.getPastKeyValues(R,u.past_key_values,!1));break}u=this._update_model_kwargs_for_generation({generated_input_ids:q,outputs:R,model_inputs:u,is_encoder_decoder:h})}i&&i.end();const z=new ce("int64",E.flat(),[E.length,E[0].length]);return n.return_dict_in_generate?{sequences:z,past_key_values:T,...A}:z}getPastKeyValues(r,n,a=!0){const s=Object.create(null);for(const i in r)if(i.startsWith("present")){const o=i.replace("present","past_key_values");if(n&&i.includes("encoder"))s[o]=n[o];else{if(a&&n){const l=n[o];l.location==="gpu-buffer"&&l.dispose()}s[o]=r[i]}}return s}getAttentions(r){const n={};for(const a of["cross_attentions","encoder_attentions","decoder_attentions"])for(const s in r)s.startsWith(a)&&(a in n||(n[a]=[]),n[a].push(r[s]));return n}addPastKeyValues(r,n){if(n)Object.assign(r,n);else{const a=this.custom_config.kv_cache_dtype??"float32",s=a==="float16"?new Uint16Array:[],i=Wm(this.config);for(const o in i)r[o]=new ce(a,s,i[o])}}async encode_image({pixel_values:r}){const n=(await Lr(this.sessions.vision_encoder,{pixel_values:r})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${n.dims[1]}).`),this.config.num_image_tokens=n.dims[1]),n}async encode_text({input_ids:r}){return(await Lr(this.sessions.embed_tokens,{input_ids:r})).inputs_embeds}}class Wt{}class va extends Z{}class A1 extends va{}class I1 extends va{async _call(e){return new $t(await super._call(e))}}class M1 extends va{async _call(e){return new Me(await super._call(e))}}class O1 extends va{async _call(e){return new vt(await super._call(e))}}class z1 extends va{async _call(e){return new Tt(await super._call(e))}}class P1 extends Z{}class R1 extends P1{}class $a extends Z{}class B1 extends $a{}class D1 extends $a{async _call(e){return new $t(await super._call(e))}}class N1 extends $a{async _call(e){return new Me(await super._call(e))}}class F1 extends $a{async _call(e){return new vt(await super._call(e))}}class L1 extends $a{async _call(e){return new Tt(await super._call(e))}}class xa extends Z{}class U1 extends xa{}class W1 extends xa{async _call(e){return new $t(await super._call(e))}}class V1 extends xa{async _call(e){return new Me(await super._call(e))}}class G1 extends xa{async _call(e){return new vt(await super._call(e))}}class H1 extends xa{async _call(e){return new Tt(await super._call(e))}}class Sa extends Z{}class j1 extends Sa{}class q1 extends Sa{async _call(e){return new $t(await super._call(e))}}class K1 extends Sa{async _call(e){return new Me(await super._call(e))}}class Y1 extends Sa{async _call(e){return new vt(await super._call(e))}}class X1 extends Sa{async _call(e){return new Tt(await super._call(e))}}class ka extends Z{}class Q1 extends ka{}class Z1 extends ka{async _call(e){return new $t(await super._call(e))}}class J1 extends ka{async _call(e){return new Me(await super._call(e))}}class e2 extends ka{async _call(e){return new vt(await super._call(e))}}class t2 extends ka{async _call(e){return new Tt(await super._call(e))}}class Ea extends Z{}class r2 extends Ea{}class n2 extends Ea{async _call(e){return new $t(await super._call(e))}}class a2 extends Ea{async _call(e){return new Me(await super._call(e))}}class i2 extends Ea{async _call(e){return new vt(await super._call(e))}}class s2 extends Ea{async _call(e){return new Tt(await super._call(e))}}class Ca extends Z{}class o2 extends Ca{}class l2 extends Ca{async _call(e){return new $t(await super._call(e))}}class u2 extends Ca{async _call(e){return new Me(await super._call(e))}}class d2 extends Ca{async _call(e){return new vt(await super._call(e))}}class c2 extends Ca{async _call(e){return new Tt(await super._call(e))}}class Ta extends Z{}class p2 extends Ta{}class h2 extends Ta{async _call(e){return new Me(await super._call(e))}}class f2 extends Ta{async _call(e){return new vt(await super._call(e))}}class m2 extends Ta{async _call(e){return new Tt(await super._call(e))}}class g2 extends Ta{async _call(e){return new $t(await super._call(e))}}class Wi extends Z{}class _2 extends Wi{}class y2 extends Wi{async _call(e){return new $t(await super._call(e))}}class w2 extends Wi{async _call(e){return new Me(await super._call(e))}}class b2 extends Wi{async _call(e){return new vt(await super._call(e))}}class Vi extends Z{}class v2 extends Vi{}class $2 extends Vi{async _call(e){return new $t(await super._call(e))}}class x2 extends Vi{async _call(e){return new Me(await super._call(e))}}class S2 extends Vi{async _call(e){return new Tt(await super._call(e))}}class Aa extends Z{}class k2 extends Aa{}class E2 extends Aa{async _call(e){return new $t(await super._call(e))}}class C2 extends Aa{async _call(e){return new Me(await super._call(e))}}class T2 extends Aa{async _call(e){return new vt(await super._call(e))}}class A2 extends Aa{async _call(e){return new Tt(await super._call(e))}}class Gi extends Z{}class I2 extends Gi{}class M2 extends Gi{async _call(e){return new $t(await super._call(e))}}class O2 extends Gi{async _call(e){return new Me(await super._call(e))}}class z2 extends Gi{async _call(e){return new Tt(await super._call(e))}}class Hi extends Z{}class P2 extends Hi{}class R2 extends Hi{async _call(e){return new Me(await super._call(e))}}class B2 extends Hi{async _call(e){return new Tt(await super._call(e))}}class D2 extends Hi{async _call(e){return new $t(await super._call(e))}}class eg extends Z{constructor(r,n,a){super(r,n);B(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class N2 extends eg{}class F2 extends eg{}class tg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class L2 extends tg{}class U2 extends tg{}class rg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class W2 extends rg{}class V2 extends rg{}class el extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class G2 extends el{}class H2 extends el{}class j2 extends el{async _call(e){return new Me(await super._call(e))}}class ji extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class q2 extends ji{}class K2 extends ji{}class Y2 extends ji{async _call(e){return new Me(await super._call(e))}}class X2 extends ji{}class ng extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class Q2 extends ng{}class Z2 extends ng{}class ag extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class J2 extends ag{}class e$ extends ag{}class Ia extends Z{}class t$ extends Ia{}class r$ extends Ia{async _call(e){return new $t(await super._call(e))}}class n$ extends Ia{async _call(e){return new Me(await super._call(e))}}class a$ extends Ia{async _call(e){return new vt(await super._call(e))}}class i$ extends Ia{async _call(e){return new Tt(await super._call(e))}}class Ma extends Z{}class s$ extends Ma{}class o$ extends Ma{async _call(e){return new $t(await super._call(e))}}class l$ extends Ma{async _call(e){return new Me(await super._call(e))}}class u$ extends Ma{async _call(e){return new vt(await super._call(e))}}class d$ extends Ma{async _call(e){return new Tt(await super._call(e))}}class Oa extends Z{}class c$ extends Oa{}class p$ extends Oa{async _call(e){return new $t(await super._call(e))}}class h$ extends Oa{async _call(e){return new Me(await super._call(e))}}class f$ extends Oa{async _call(e){return new vt(await super._call(e))}}class m$ extends Oa{async _call(e){return new Tt(await super._call(e))}}class ig extends Z{}class g$ extends ig{}class _$ extends ig{}class sg extends Z{constructor(r,n,a){super(r,n);B(this,"requires_attention_mask",!1);B(this,"main_input_name","input_features");B(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class y$ extends sg{}class w$ extends sg{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,x1)}_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 i=`<|${Am(n)}|>`;r.push(e.lang_to_id[i]),r.push(e.task_to_id[a??"transcribe"])}else if(n||a)throw new Error("Cannot specify `task` or `language` for an English-only model. 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See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");r.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),r.output_attentions=!0,r.return_dict_in_generate=!0}const o=await super.generate({inputs:e,generation_config:r,logits_processor:n,decoder_input_ids:i,...s});return r.return_token_timestamps&&(o.token_timestamps=this._extract_token_timestamps(o,r.alignment_heads,r.num_frames)),o}_extract_token_timestamps(e,r,n=null,a=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");n==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. 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v=m[w].neg().squeeze_(0),[S,$]=ny(v.tolist()),E=Array.from({length:S.length-1},(z,R)=>S[R+1]-S[R]),T=gt([1],E).map(z=>!!z),A=[];for(let z=0;z<T.length;++z)T[z]&&A.push($[z]*a);p[w].data.set(A,1)}return p}}class og extends Z{constructor(r,n,a){super(r,n);B(this,"main_input_name","pixel_values");B(this,"forward_params",["pixel_values","input_ids","encoder_hidden_states","past_key_values"]);this.generation_config=a}}class b$ extends Z{constructor(r,n,a){super(r,n);B(this,"forward_params",["input_ids","pixel_values","attention_mask","position_ids","past_key_values"]);this.generation_config=a}}class lg extends b${_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){const s=this.config.image_token_index,o=n.tolist().map(m=>m.findIndex(g=>g==s)),l=o.every(m=>m===-1),u=o.every(m=>m!==-1);if(!l&&!u)throw new Error("Every input should contain either 0 or 1 image token.");if(l)return{inputs_embeds:e,attention_mask:a};const d=[],h=[];for(let m=0;m<o.length;++m){const g=o[m],p=e[m],w=r[m],v=a[m];d.push(Xt([p.slice([0,g]),w,p.slice([g+1,p.dims[0]])],0)),h.push(Xt([v.slice([0,g]),In([w.dims[0]]),v.slice([g+1,v.dims[0]])],0))}return{inputs_embeds:ha(d,0),attention_mask:ha(h,0)}}}class v$ extends lg{}class $$ extends Z{constructor(r,n,a){super(r,n);B(this,"forward_params",["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"]);B(this,"main_input_name","inputs_embeds");this.generation_config=a}}class x$ extends $${_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){return{inputs_embeds:Xt([r,e],1),attention_mask:Xt([In(r.dims.slice(0,2)),a],1)}}async _prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:n,attention_mask:a}){if(!e&&!r)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let s,i;return e&&(s=await this.encode_text({input_ids:e})),r&&(i=await this.encode_image({pixel_values:r})),s&&i?{inputs_embeds:n,attention_mask:a}=this._merge_input_ids_with_image_features({inputs_embeds:s,image_features:i,input_ids:e,attention_mask:a}):n=s||i,{inputs_embeds:n,attention_mask:a}}async forward({input_ids:e,pixel_values:r,attention_mask:n,decoder_input_ids:a,decoder_attention_mask:s,encoder_outputs:i,past_key_values:o,inputs_embeds:l,decoder_inputs_embeds:u}){if(l||({inputs_embeds:l,attention_mask:n}=await this._prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:l,attention_mask:n})),!i){let{last_hidden_state:m}=await Rn(this,{inputs_embeds:l,attention_mask:n});i=m}if(!u){if(!a)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");u=await this.encode_text({input_ids:a})}return await Ui(this,{inputs_embeds:u,attention_mask:s,encoder_attention_mask:n,encoder_hidden_states:i,past_key_values:o},!0)}}class qi extends Z{}class S$ extends qi{}class k$ extends qi{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class E$ extends qi{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(e,r)}}class ug extends Z{}class C$ extends ug{}class T$ extends ug{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class A$ extends qi{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="vision_model"),super.from_pretrained(e,r)}}class I$ extends Z{}class M$ extends I${}class dg extends Z{}class O$ extends dg{}class z$ extends dg{}class cg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class P$ extends cg{}class R$ extends cg{}class pg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class B$ extends pg{}class D$ extends pg{}class hg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class N$ extends hg{}class F$ extends hg{}class fg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class L$ extends fg{}class U$ extends fg{}class mg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class W$ extends mg{}class V$ extends mg{}class gg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class G$ extends gg{}class H$ extends gg{}class _g extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class j$ extends _g{}class q$ extends _g{}class yg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class K$ extends yg{}class Y$ extends yg{}class wg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class X$ extends wg{}class Q$ extends wg{}class bg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class Z$ extends bg{}class J$ extends bg{}class vg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class ex extends vg{}class tx extends vg{}class $g extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class rx extends $g{}class nx extends $g{}class xg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class ax extends xg{}class ix extends xg{}class Sg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class sx extends Sg{}class ox extends Sg{}class kg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class lx extends kg{}class ux extends kg{}class Eg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class dx extends Eg{}class cx extends Eg{}class Cg extends Z{}class px extends Cg{}class hx extends Cg{async _call(e){return new Me(await super._call(e))}}class Tg extends Z{}class fx extends Tg{}class mx extends Tg{async _call(e){return new Me(await super._call(e))}}class gx extends Z{}class _x extends gx{async _call(e){return new Sk(await super._call(e))}}class Ag extends Z{}class yx extends Ag{}class wx extends Ag{async _call(e){return new Me(await super._call(e))}}class Ig extends Z{}class bx extends Ig{}class vx extends Ig{async _call(e){return new Me(await super._call(e))}}class Mg extends Z{}class $x extends Mg{}class xx extends Mg{}class Og extends Z{}class Sx extends Og{}class kx extends Og{}class zg extends Z{}class Ex extends zg{}class Cx extends zg{async _call(e){return new Me(await super._call(e))}}class tl extends Z{}class Tx extends tl{}class Ax extends tl{async _call(e){return new Pg(await super._call(e))}}class Ix extends tl{async _call(e){return new Mx(await super._call(e))}}class Pg extends Wt{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class Mx extends Wt{constructor({logits:e,pred_boxes:r,pred_masks:n}){super(),this.logits=e,this.pred_boxes=r,this.pred_masks=n}}class Rg extends Z{}class Ox extends Rg{}class zx extends Rg{async _call(e){return new Px(await super._call(e))}}class Px extends Wt{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class Bg extends Z{}class Rx extends Bg{}class Bx extends Bg{async _call(e){return new Dx(await super._call(e))}}class Dx extends Pg{}class Dg extends Z{}class Nx extends Dg{}class Fx extends Dg{async _call(e){return new Me(await super._call(e))}}class Ng extends Z{}class Lx extends Ng{}class Ux extends Ng{async _call(e){return new Me(await super._call(e))}}class Fg extends Z{}class Wx extends Fg{}class Vx extends Fg{async _call(e){return new Me(await super._call(e))}}class Lg extends Z{}class Gx extends Lg{}class Hx extends Lg{}class Ug extends Z{}class jx extends Ug{}class qx extends Ug{}class Kx extends Z{}class Yx extends Kx{}class Wg extends Z{}class Xx extends Wg{}class Qx extends Wg{}class Zx extends Z{}class Jx extends Zx{}class Vg extends Z{}class eS extends Vg{}class tS extends Vg{async _call(e){return new Me(await super._call(e))}}class Gg extends Z{}class rS extends Gg{}class nS extends Gg{async _call(e){return new Me(await super._call(e))}}class Hg extends Z{}class aS extends Hg{}class iS extends Hg{async _call(e){return new Me(await super._call(e))}}class jg extends Z{}class sS extends jg{}class oS extends jg{async _call(e){return new lS(await super._call(e))}}class lS extends Wt{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}}class uS extends Z{}class dS extends uS{async get_image_embeddings({pixel_values:e}){return await Rn(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((s,i)=>s*i,1);e.input_labels=new ce("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 Lr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new cS(await super._call(e))}}class cS extends Wt{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class qg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class pS extends qg{}class hS extends qg{}class Kg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class fS extends Kg{}class mS extends Kg{}class on extends Z{}class gS extends on{}class _S extends on{async _call(e){return new Bn(await super._call(e))}}class yS extends on{async _call(e){return new Me(await super._call(e))}}class wS extends on{async _call(e){return new vt(await super._call(e))}}class rl extends Z{}class bS extends rl{}class vS extends rl{async _call(e){return new Bn(await super._call(e))}}class $S extends rl{async _call(e){return new Me(await super._call(e))}}class Ki extends Z{}class xS extends Ki{}class SS extends Ki{async _call(e){return new Bn(await super._call(e))}}class kS extends Ki{async _call(e){return new Me(await super._call(e))}}class ES extends Ki{async _call(e){return new vt(await super._call(e))}}class nl extends Z{}class CS extends nl{}class TS extends nl{async _call(e){return new Bn(await super._call(e))}}class AS extends nl{async _call(e){return new Me(await super._call(e))}}class IS extends on{}class MS extends on{async _call(e){return new Bn(await super._call(e))}}class OS extends on{async _call(e){return new Me(await super._call(e))}}class za extends Z{}class zS extends za{}class PS extends za{async _call(e){return new Bn(await super._call(e))}}class RS extends za{async _call(e){return new Me(await super._call(e))}}class BS extends za{async _call(e){return new xk(await super._call(e))}}class DS extends za{async _call(e){return new vt(await super._call(e))}}class Yg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class NS extends Yg{}class FS extends Yg{async generate_speech(e,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:s=20,vocoder:i=null}={}){const o={input_ids:e},{encoder_outputs:l,encoder_attention_mask:u}=await Rn(this,o),d=l.dims[1]/this.config.reduction_factor,h=Math.floor(d*s),m=Math.floor(d*a),g=this.config.num_mel_bins;let p=[],w=null,v=null,S=0;for(;;){++S;const T=Xm(!!v);let A;v?A=v.output_sequence_out:A=new ce("float32",new Float32Array(g),[1,1,g]);let z={use_cache_branch:T,output_sequence:A,encoder_attention_mask:u,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(z,w),v=await Lr(this.sessions.decoder_model_merged,z),w=this.getPastKeyValues(v,w);const{prob:R,spectrum:V}=v;if(p.push(V),S>=m&&(Array.from(R.data).filter(j=>j>=n).length>0||S>=h))break}const $=Xt(p),{waveform:E}=await Lr(i.sessions.model,{spectrogram:$});return{spectrogram:$,waveform:E}}}class LS extends Z{constructor(){super(...arguments);B(this,"main_input_name","spectrogram")}}class US extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class WS extends US{}class Xg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class VS extends Xg{}class GS extends Xg{}class Qg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class HS extends Qg{}class jS extends Qg{}class Zg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class qS extends Zg{}class KS extends Zg{}class al extends Z{}class YS extends al{}class XS extends al{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class QS extends al{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="audio_model"),super.from_pretrained(e,r)}}class ZS extends Z{}class Jg extends ZS{async _call(e){return new kk(await super._call(e))}}class e_ extends Z{}class JS extends e_{}class ek extends e_{}class t_ extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class tk extends t_{}class rk extends t_{}class r_ extends Z{}class nk extends r_{}class ak extends r_{async _call(e){return new Me(await super._call(e))}}class n_ extends Z{constructor(r,n,a){super(r,n);B(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}_apply_and_filter_by_delay_pattern_mask(r){const[n,a]=r.dims,s=this.config.decoder.num_codebooks,i=a-s;let o=0;for(let d=0;d<r.size;++d){if(r.data[d]===this.config.decoder.pad_token_id)continue;const h=d%a,m=Math.floor(d/a)%s,g=h-m;g>0&&g<=i&&(r.data[o++]=r.data[d])}const l=Math.floor(n/s),u=o/(l*s);return new ce(r.type,r.data.slice(0,o),[l,s,u])}prepare_inputs_for_generation(r,n,a){let s=structuredClone(r);for(let o=0;o<s.length;++o)for(let l=0;l<s[o].length;++l)o%this.config.decoder.num_codebooks>=l&&(s[o][l]=BigInt(this.config.decoder.pad_token_id));return a.guidance_scale!==null&&a.guidance_scale>1&&(s=s.concat(s)),super.prepare_inputs_for_generation(s,n,a)}async generate(r){const n=await super.generate(r),a=this._apply_and_filter_by_delay_pattern_mask(n).unsqueeze_(0),{audio_values:s}=await Lr(this.sessions.encodec_decode,{audio_codes:a});return s}}class a_ extends Z{}class ik extends a_{}class sk extends a_{async _call(e){return new Me(await super._call(e))}}class i_ extends Z{}class ok extends i_{}class lk extends i_{async _call(e){return new Me(await super._call(e))}}class s_ extends Z{}class uk extends s_{}class dk extends s_{async _call(e){return new Me(await super._call(e))}}class o_ extends Z{}class ck extends o_{}class pk extends o_{async _call(e){return new Me(await super._call(e))}}class Qe{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",model_file_name:o=null,subfolder:l="onnx",device:u=null,dtype:d=null,use_external_data_format:h=null,session_options:m={}}={}){let g={progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,model_file_name:o,subfolder:l,device:u,dtype:d,use_external_data_format:h,session_options:m};if(g.config=await Vm.from_pretrained(e,g),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let p of this.MODEL_CLASS_MAPPINGS){const w=p.get(g.config.model_type);if(w)return await w[1].from_pretrained(e,g)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${g.config.model_type}", attempting to construct from base class.`),await Z.from_pretrained(e,g);throw Error(`Unsupported model type: ${g.config.model_type}`)}}B(Qe,"MODEL_CLASS_MAPPINGS",null),B(Qe,"BASE_IF_FAIL",!1);const hk=new 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Map([["t5",["T5Model",N2]],["longt5",["LongT5Model",L2]],["mt5",["MT5Model",W2]],["bart",["BartModel",G2]],["mbart",["MBartModel",q2]],["marian",["MarianModel",pS]],["whisper",["WhisperModel",y$]],["m2m_100",["M2M100Model",fS]],["blenderbot",["BlenderbotModel",Q2]],["blenderbot-small",["BlenderbotSmallModel",J2]]]),mk=new Map([["bloom",["BloomModel",sx]],["gpt2",["GPT2Model",P$]],["gptj",["GPTJModel",L$]],["gpt_bigcode",["GPTBigCodeModel",W$]],["gpt_neo",["GPTNeoModel",B$]],["gpt_neox",["GPTNeoXModel",N$]],["codegen",["CodeGenModel",G$]],["llama",["LlamaModel",j$]],["cohere",["CohereModel",K$]],["gemma",["GemmaModel",X$]],["openelm",["OpenELMModel",Z$]],["qwen2",["Qwen2Model",ex]],["phi",["PhiModel",rx]],["phi3",["Phi3Model",ax]],["mpt",["MptModel",lx]],["opt",["OPTModel",dx]],["mistral",["MistralModel",VS]],["starcoder2",["Starcoder2Model",HS]],["falcon",["FalconModel",qS]],["stablelm",["StableLmModel",tk]]]),il=new Map([["speecht5",["SpeechT5ForSpeechToText",NS]],["whisper",["WhisperForConditionalGeneration",w$]]]),l_=new Map([["speecht5",["SpeechT5ForTextToSpeech",FS]]]),u_=new Map([["vits",["VitsModel",Jg]],["musicgen",["MusicgenForConditionalGeneration",n_]]]),d_=new Map([["bert",["BertForSequenceClassification",M1]],["roformer",["RoFormerForSequenceClassification",N1]],["electra",["ElectraForSequenceClassification",K1]],["esm",["EsmForSequenceClassification",w2]],["convbert",["ConvBertForSequenceClassification",V1]],["camembert",["CamembertForSequenceClassification",J1]],["deberta",["DebertaForSequenceClassification",a2]],["deberta-v2",["DebertaV2ForSequenceClassification",u2]],["mpnet",["MPNetForSequenceClassification",C2]],["albert",["AlbertForSequenceClassification",R2]],["distilbert",["DistilBertForSequenceClassification",h2]],["roberta",["RobertaForSequenceClassification",n$]],["xlm",["XLMForSequenceClassification",l$]],["xlm-roberta",["XLMRobertaForSequenceClassification",h$]],["bart",["BartForSequenceClassification",j2]],["mbart",["MBartForSequenceClassification",Y2]],["mobilebert",["MobileBertForSequenceClassification",x2]],["squeezebert",["SqueezeBertForSequenceClassification",O2]]]),c_=new Map([["bert",["BertForTokenClassification",O1]],["roformer",["RoFormerForTokenClassification",F1]],["electra",["ElectraForTokenClassification",Y1]],["esm",["EsmForTokenClassification",b2]],["convbert",["ConvBertForTokenClassification",G1]],["camembert",["CamembertForTokenClassification",e2]],["deberta",["DebertaForTokenClassification",i2]],["deberta-v2",["DebertaV2ForTokenClassification",d2]],["mpnet",["MPNetForTokenClassification",T2]],["distilbert",["DistilBertForTokenClassification",f2]],["roberta",["RobertaForTokenClassification",a$]],["xlm",["XLMForTokenClassification",u$]],["xlm-roberta",["XLMRobertaForTokenClassification",f$]]]),sl=new Map([["t5",["T5ForConditionalGeneration",F2]],["longt5",["LongT5ForConditionalGeneration",U2]],["mt5",["MT5ForConditionalGeneration",V2]],["bart",["BartForConditionalGeneration",H2]],["mbart",["MBartForConditionalGeneration",K2]],["marian",["MarianMTModel",hS]],["m2m_100",["M2M100ForConditionalGeneration",mS]],["blenderbot",["BlenderbotForConditionalGeneration",Z2]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",e$]]]),ol=new Map([["bloom",["BloomForCausalLM",ox]],["gpt2",["GPT2LMHeadModel",R$]],["gptj",["GPTJForCausalLM",U$]],["gpt_bigcode",["GPTBigCodeForCausalLM",V$]],["gpt_neo",["GPTNeoForCausalLM",D$]],["gpt_neox",["GPTNeoXForCausalLM",F$]],["codegen",["CodeGenForCausalLM",H$]],["llama",["LlamaForCausalLM",q$]],["cohere",["CohereForCausalLM",Y$]],["gemma",["GemmaForCausalLM",Q$]],["openelm",["OpenELMForCausalLM",J$]],["qwen2",["Qwen2ForCausalLM",tx]],["phi",["PhiForCausalLM",nx]],["phi3",["Phi3ForCausalLM",ix]],["mpt",["MptForCausalLM",ux]],["opt",["OPTForCausalLM",cx]],["mbart",["MBartForCausalLM",X2]],["mistral",["MistralForCausalLM",GS]],["starcoder2",["Starcoder2ForCausalLM",jS]],["falcon",["FalconForCausalLM",KS]],["trocr",["TrOCRForCausalLM",WS]],["stablelm",["StableLmForCausalLM",rk]]]),p_=new Map([["bert",["BertForMaskedLM",I1]],["roformer",["RoFormerForMaskedLM",D1]],["electra",["ElectraForMaskedLM",q1]],["esm",["EsmForMaskedLM",y2]],["convbert",["ConvBertForMaskedLM",W1]],["camembert",["CamembertForMaskedLM",Z1]],["deberta",["DebertaForMaskedLM",n2]],["deberta-v2",["DebertaV2ForMaskedLM",l2]],["mpnet",["MPNetForMaskedLM",E2]],["albert",["AlbertForMaskedLM",D2]],["distilbert",["DistilBertForMaskedLM",g2]],["roberta",["RobertaForMaskedLM",r$]],["xlm",["XLMWithLMHeadModel",o$]],["xlm-roberta",["XLMRobertaForMaskedLM",p$]],["mobilebert",["MobileBertForMaskedLM",$2]],["squeezebert",["SqueezeBertForMaskedLM",M2]]]),h_=new Map([["bert",["BertForQuestionAnswering",z1]],["roformer",["RoFormerForQuestionAnswering",L1]],["electra",["ElectraForQuestionAnswering",X1]],["convbert",["ConvBertForQuestionAnswering",H1]],["camembert",["CamembertForQuestionAnswering",t2]],["deberta",["DebertaForQuestionAnswering",s2]],["deberta-v2",["DebertaV2ForQuestionAnswering",c2]],["mpnet",["MPNetForQuestionAnswering",A2]],["albert",["AlbertForQuestionAnswering",B2]],["distilbert",["DistilBertForQuestionAnswering",m2]],["roberta",["RobertaForQuestionAnswering",i$]],["xlm",["XLMForQuestionAnswering",d$]],["xlm-roberta",["XLMRobertaForQuestionAnswering",m$]],["mobilebert",["MobileBertForQuestionAnswering",S2]],["squeezebert",["SqueezeBertForQuestionAnswering",z2]]]),ll=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",og]]]),gk=new Map([["llava",["LlavaForConditionalGeneration",lg]],["moondream1",["Moondream1ForConditionalGeneration",v$]],["florence2",["Florence2ForConditionalGeneration",x$]]]),_k=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",og]]]),f_=new Map([["vit",["ViTForImageClassification",hx]],["fastvit",["FastViTForImageClassification",mx]],["mobilevit",["MobileViTForImageClassification",wx]],["mobilevitv2",["MobileViTV2ForImageClassification",vx]],["beit",["BeitForImageClassification",Cx]],["deit",["DeiTForImageClassification",Fx]],["convnext",["ConvNextForImageClassification",tS]],["convnextv2",["ConvNextV2ForImageClassification",nS]],["dinov2",["Dinov2ForImageClassification",iS]],["resnet",["ResNetForImageClassification",Ux]],["swin",["SwinForImageClassification",Vx]],["segformer",["SegformerForImageClassification",JS]],["efficientnet",["EfficientNetForImageClassification",ak]],["mobilenet_v1",["MobileNetV1ForImageClassification",sk]],["mobilenet_v2",["MobileNetV2ForImageClassification",lk]],["mobilenet_v3",["MobileNetV3ForImageClassification",dk]],["mobilenet_v4",["MobileNetV4ForImageClassification",pk]]]),m_=new Map([["detr",["DetrForObjectDetection",Ax]],["rt_detr",["RTDetrForObjectDetection",zx]],["table-transformer",["TableTransformerForObjectDetection",Bx]],["yolos",["YolosForObjectDetection",oS]]]),g_=new Map([["owlvit",["OwlViTForObjectDetection",xx]],["owlv2",["Owlv2ForObjectDetection",kx]]]),__=new Map([["detr",["DetrForSegmentation",Ix]],["clipseg",["CLIPSegForImageSegmentation",z$]]]),y_=new Map([["segformer",["SegformerForSemanticSegmentation",ek]]]),yk=new Map([["sam",["SamModel",dS]]]),w_=new Map([["wav2vec2",["Wav2Vec2ForCTC",_S]],["wav2vec2-bert",["Wav2Vec2BertForCTC",TS]],["unispeech",["UniSpeechForCTC",vS]],["unispeech-sat",["UniSpeechSatForCTC",SS]],["wavlm",["WavLMForCTC",PS]],["hubert",["HubertForCTC",MS]]]),b_=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",yS]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",AS]],["unispeech",["UniSpeechForSequenceClassification",$S]],["unispeech-sat",["UniSpeechSatForSequenceClassification",kS]],["wavlm",["WavLMForSequenceClassification",RS]],["hubert",["HubertForSequenceClassification",OS]],["audio-spectrogram-transformer",["ASTForAudioClassification",_$]]]),wk=new Map([["wavlm",["WavLMForXVector",BS]]]),bk=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",ES]],["wavlm",["WavLMForAudioFrameClassification",DS]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",wS]]]),vk=new Map([["vitmatte",["VitMatteForImageMatting",_x]]]),v_=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Hx]]]),$_=new Map([["dpt",["DPTForDepthEstimation",qx]],["depth_anything",["DepthAnythingForDepthEstimation",Yx]],["glpn",["GLPNForDepthEstimation",Qx]]]),x_=new Map([["clip",["CLIPVisionModelWithProjection",E$]],["siglip",["SiglipVisionModel",A$]]]),S_=[[hk,$e.EncoderOnly],[fk,$e.EncoderDecoder],[mk,$e.DecoderOnly],[d_,$e.EncoderOnly],[c_,$e.EncoderOnly],[sl,$e.Seq2Seq],[il,$e.Seq2Seq],[ol,$e.DecoderOnly],[p_,$e.EncoderOnly],[h_,$e.EncoderOnly],[ll,$e.Vision2Seq],[gk,$e.ImageTextToText],[f_,$e.EncoderOnly],[__,$e.EncoderOnly],[y_,$e.EncoderOnly],[vk,$e.EncoderOnly],[v_,$e.EncoderOnly],[$_,$e.EncoderOnly],[m_,$e.EncoderOnly],[g_,$e.EncoderOnly],[yk,$e.MaskGeneration],[w_,$e.EncoderOnly],[b_,$e.EncoderOnly],[l_,$e.Seq2Seq],[u_,$e.EncoderOnly],[wk,$e.EncoderOnly],[bk,$e.EncoderOnly],[x_,$e.EncoderOnly]];for(const[t,e]of S_)for(const[r,n]of t.values())Li.set(r,e),ba.set(n,r),qm.set(r,n);const $k=[["MusicgenForConditionalGeneration",n_,$e.Musicgen],["CLIPTextModelWithProjection",k$,$e.EncoderOnly],["SiglipTextModel",T$,$e.EncoderOnly],["ClapTextModelWithProjection",XS,$e.EncoderOnly],["ClapAudioModelWithProjection",QS,$e.EncoderOnly]];for(const[t,e,r]of $k)Li.set(t,r),ba.set(e,t),qm.set(t,e);class ln extends Qe{}B(ln,"MODEL_CLASS_MAPPINGS",S_.map(e=>e[0])),B(ln,"BASE_IF_FAIL",!0);class ul extends Qe{}B(ul,"MODEL_CLASS_MAPPINGS",[d_]);class k_ extends Qe{}B(k_,"MODEL_CLASS_MAPPINGS",[c_]);class Yi extends Qe{}B(Yi,"MODEL_CLASS_MAPPINGS",[sl]);class E_ extends Qe{}B(E_,"MODEL_CLASS_MAPPINGS",[il]);class C_ extends Qe{}B(C_,"MODEL_CLASS_MAPPINGS",[l_]);class T_ extends Qe{}B(T_,"MODEL_CLASS_MAPPINGS",[u_]);class A_ extends Qe{}B(A_,"MODEL_CLASS_MAPPINGS",[ol]);class I_ extends Qe{}B(I_,"MODEL_CLASS_MAPPINGS",[p_]);class M_ extends Qe{}B(M_,"MODEL_CLASS_MAPPINGS",[h_]);class O_ extends Qe{}B(O_,"MODEL_CLASS_MAPPINGS",[ll]);class z_ extends Qe{}B(z_,"MODEL_CLASS_MAPPINGS",[f_]);class P_ extends Qe{}B(P_,"MODEL_CLASS_MAPPINGS",[__]);class R_ extends Qe{}B(R_,"MODEL_CLASS_MAPPINGS",[y_]);class B_ extends Qe{}B(B_,"MODEL_CLASS_MAPPINGS",[m_]);class D_ extends Qe{}B(D_,"MODEL_CLASS_MAPPINGS",[g_]);class N_ extends Qe{}B(N_,"MODEL_CLASS_MAPPINGS",[w_]);class F_ extends Qe{}B(F_,"MODEL_CLASS_MAPPINGS",[b_]);class L_ extends Qe{}B(L_,"MODEL_CLASS_MAPPINGS",[_k]);class U_ extends Qe{}B(U_,"MODEL_CLASS_MAPPINGS",[v_]);class W_ extends Qe{}B(W_,"MODEL_CLASS_MAPPINGS",[$_]);class V_ extends Qe{}B(V_,"MODEL_CLASS_MAPPINGS",[x_]);class Me extends Wt{constructor({logits:e}){super(),this.logits=e}}class xk extends Wt{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class vt extends Wt{constructor({logits:e}){super(),this.logits=e}}class $t extends Wt{constructor({logits:e}){super(),this.logits=e}}class Tt extends Wt{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class Bn extends Wt{constructor({logits:e}){super(),this.logits=e}}class Sk extends Wt{constructor({alphas:e}){super(),this.alphas=e}}class kk extends Wt{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const Zt=typeof self<"u",Ek=Zt&&self.constructor.name==="DedicatedWorkerGlobalScope";let un,G_,Ur;if(Zt)un=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},Ur=self.createImageBitmap,G_=self.ImageData;else if(Ve)Ur=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),s=new At(new Uint8ClampedArray(n),a.width,a.height,a.channels);return r!==void 0&&r!==a.channels&&s.convert(r),s};else throw new Error("Unable to load image processing library.");const Ck={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},Tk=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class At{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 At)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!Zt)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new At(n,e.width,e.height,4)}static async fromURL(e){const r=await ni(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(Zt){const r=await Ur(e),n=un(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=Ve(await e.arrayBuffer());return await Ur(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 At(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],s=this.data[r+1],i=this.data[r+2];e[n++]=Math.round(.2989*a+.587*s+.114*i)}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=Ck[n]??n;if(Zt){const s=this.channels,i=this.toCanvas(),o=un(e,r).getContext("2d");return o.drawImage(i,0,0,e,r),new At(o.getImageData(0,0,e,r).data,e,r,4).convert(s)}else{let s=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":s=s.affine([e/this.width,0,0,r/this.height],{interpolator:a});break;case"lanczos":s=s.resize({width:e,height:r,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${a} is not supported.`)}return await Ur(s)}}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(Zt){const s=this.channels,i=this.toCanvas(),o=this.width+e+r,l=this.height+n+a,u=un(o,l).getContext("2d");return u.drawImage(i,0,0,this.width,this.height,e,n,o,l),new At(u.getImageData(0,0,o,l).data,o,l,4).convert(s)}else{const s=this.toSharp().extend({left:e,right:r,top:n,bottom:a});return await Ur(s)}}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 s=n-e+1,i=a-r+1;if(Zt){const o=this.channels,l=this.toCanvas(),u=un(s,i).getContext("2d");return u.drawImage(l,e,r,s,i,0,0,s,i),new At(u.getImageData(0,0,s,i).data,s,i,4).convert(o)}else{const o=this.toSharp().extract({left:e,top:r,width:s,height:i});return await Ur(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(Zt){const s=this.channels,i=this.toCanvas(),o=un(e,r).getContext("2d");let l=0,u=0,d=0,h=0;return n>=0?l=n:d=-n,a>=0?u=a:h=-a,o.drawImage(i,l,u,e,r,d,h,e,r),new At(o.getImageData(0,0,e,r).data,e,r,4).convert(s)}else{let s=this.toSharp();if(n>=0&&a>=0)s=s.extract({left:Math.floor(n),top:Math.floor(a),width:e,height:r});else if(n<=0&&a<=0){const i=Math.floor(-a),o=Math.floor(-n);s=s.extend({top:i,left:o,right:e-this.width-o,bottom:r-this.height-i})}else{let i=[0,0],o=0;a<0?(i[0]=Math.floor(-a),i[1]=r-this.height-i[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),s=s.extend({top:i[0],bottom:i[1],left:l[0],right:l[1]}).extract({left:u,top:o,width:e,height:r})}return await Ur(s)}}async toBlob(e="image/png",r=1){if(!Zt)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 ce("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(!Zt)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=un(e.width,e.height),n=new G_(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 At(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(Zt){if(Ek)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=Tk.get(r)??"image/png",a=await this.toBlob(n),s=URL.createObjectURL(a),i=document.createElement("a");i.href=s,i.download=e,i.click(),i.remove()}else{if(Mt.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Zt)throw new Error("toSharp() is only supported in server-side environments.");return Ve(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}async function Ak(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 ni(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 s;if(a.numberOfChannels===2){const i=Math.sqrt(2),o=a.getChannelData(0),l=a.getChannelData(1);s=new Float32Array(o.length);for(let u=0;u<a.length;++u)s[u]=i*(o[u]+l[u])/2}else s=a.getChannelData(0);return s}function H_(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 s=2*a-e;n[a]=.5+.5*Math.cos(r*s)}return n}const Ik={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 dl(t,e="htk"){const r=Ik[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 Mk={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 Ok(t,e="htk"){const r=Mk[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 zk(t,e){const r=Float64Array.from({length:e.length-1},(i,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let i=0;i<t.length;++i){const o=n[i];for(let l=0;l<e.length;++l)o[l]=e[l]-t[i]}const a=e.length-2,s=Array.from({length:a},()=>new Array(t.length));for(let i=0;i<t.length;++i){const o=n[i];for(let l=0;l<a;++l){const u=-o[l]/r[l],d=o[l+2]/r[l+1];s[l][i]=Math.max(0,Math.min(u,d))}}return s}function j_(t,e,r){const n=(e-t)/(r-1);return Float64Array.from({length:r},(a,s)=>t+n*s)}function Pa(t,e,r,n,a,s=null,i="htk",o=!1){if(s!==null&&s!=="slaney")throw new Error('norm must be one of null or "slaney"');const l=dl(r,i),u=dl(n,i),d=j_(l,u,e+2);let h=Ok(d,i),m;if(o){const p=a/(t*2);m=dl(Float64Array.from({length:t},(w,v)=>v*p),i),h=d}else m=j_(0,Math.floor(a/2),t);const g=zk(m,h);if(s!==null&&s==="slaney")for(let p=0;p<e;++p){const w=g[p],v=2/(h[p+2]-h[p]);for(let S=0;S<t;++S)w[S]*=v}return g}function Pk(t,e,r){const n=new t.constructor(t.length+e+r),a=t.length-1;for(let s=0;s<t.length;++s)n[e+s]=t[s];for(let s=1;s<=e;++s)n[e-s]=t[ti(s,a)];for(let s=1;s<=r;++s)n[a+e+s]=t[ti(a-s,a)];return n}function q_(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 s=Math.log10(r);for(let i=0;i<t.length;++i)t[i]=e*Math.log10(Math.max(n,t[i])-s);if(a!==null){if(a<=0)throw new Error("db_range must be greater than zero");const i=qt(t)[0]-a;for(let o=0;o<t.length;++o)t[o]=Math.max(t[o],i)}return t}function Rk(t,e=1,r=1e-5,n=null){return q_(t,20,e,r,n)}function Bk(t,e=1,r=1e-10,n=null){return q_(t,10,e,r,n)}async function Xi(t,e,r,n,{fft_length:a=null,power:s=1,center:i=!0,pad_mode:o="reflect",onesided:l=!0,preemphasis:u=null,mel_filters:d=null,mel_floor:h=1e-10,log_mel:m=null,reference:g=1,min_value:p=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(s===null&&d!==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(i){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const M=Math.floor((a-1)/2)+1;t=Pk(t,M,M)}const A=Math.floor(1+Math.floor((t.length-r)/n)),z=l?Math.floor(a/2)+1:a;let R=A,V=A;S!==null&&(S>A?$&&(V=S):V=R=S);const j=new ey(a),q=new Float64Array(a),fe=new Float64Array(j.outputBufferSize),ee=new Float32Array(z*V);for(let M=0;M<R;++M){const G=M*n;for(let K=0;K<r;++K)q[K]=t[G+K];if(v){let K=0;for(let le=0;le<r;++le)K+=q[le];const pe=K/r;for(let le=0;le<r;++le)q[le]-=pe}if(u!==null){for(let K=r-1;K>=1;--K)q[K]-=u*q[K-1];q[0]*=1-u}for(let K=0;K<e.length;++K)q[K]*=e[K];j.realTransform(fe,q);for(let K=0;K<z;++K){const pe=K<<1;ee[K*V+M]=fe[pe]**2+fe[pe+1]**2}}if(s!==null&&s!==2){const M=2/s;for(let G=0;G<ee.length;++G)ee[G]**=M}const ne=d.length;let se=await Lw(new ce("float32",d.flat(),[ne,z]),new ce("float32",ee,[z,V]));E&&(se=se.transpose(1,0));const D=se.data;for(let M=0;M<D.length;++M)D[M]=Math.max(h,D[M]);if(s!==null&&m!==null){const M=Math.min(D.length,R*ne);switch(m){case"log":for(let G=0;G<M;++G)D[G]=Math.log(D[G]);break;case"log10":for(let G=0;G<M;++G)D[G]=Math.log10(D[G]);break;case"dB":if(s===1)Rk(D,g,p,w);else if(s===2)Bk(D,g,p,w);else throw new Error(`Cannot use log_mel option '${m}' with power ${s}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${m}'`)}}return se}function Qi(t,e,{periodic:r=!0,frame_length:n=null,center:a=!0}={}){const s=r?t+1:t;let i;switch(e){case"boxcar":i=new Float64Array(s).fill(1);break;case"hann":case"hann_window":i=H_(s);break;case"povey":i=H_(s).map(o=>Math.pow(o,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(i=i.subarray(0,t)),n===null)return i;if(t>n)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${n})`);return i}function Dk([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function Zi(t,e=.5,r=null,n=!1){const a=t.logits,s=t.pred_boxes,[i,o,l]=a.dims;if(r!==null&&r.length!==i)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let u=[];for(let d=0;d<i;++d){let h=r!==null?r[d]:null,m={boxes:[],classes:[],scores:[]},g=a[d],p=s[d];for(let w=0;w<o;++w){let v=g[w],S=[],$;if(n){$=v.sigmoid().data;for(let E=0;E<$.length;++E)$[E]>e&&S.push(E)}else{let E=qt(v.data)[1];if(E===l-1||($=bt(v.data),$[E]<e))continue;S.push(E)}for(const E of S){let T=p[w].data;T=Dk(T),h!==null&&(T=T.map((A,z)=>A*h[(z+1)%2])),m.boxes.push(T),m.classes.push(E),m.scores.push($[E])}}u.push(m)}return u}function Ra(t,e){var r;if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${((r=t==null?void 0:t.constructor)==null?void 0:r.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 K_(t,e,r=0,n=null){const a=t/e;let s=ry(a)*e;return n!==null&&s>n&&(s=Math.floor(a)*e),s<r&&(s=Math.ceil(a)*e),s}function cl([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}class dn extends wt{constructor(e){super(),this.config=e}}class Ge extends dn{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,s=e.width,i=r.height,o=r.width;let l=Math.min(a,i),u=Math.min(s,o);return l===a&&u===s?e:(a>s?u=Math.floor(s*l/a):s>a&&(l=Math.floor(a*u/s)),await e.resize(u,l,{resample:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),a=Bl(n.data)[0],i=qt(n.data)[0]-a;if(i===0)return e;const o=r/255;let l=n.width,u=n.height,d=0,h=0;const m=n.data;for(let g=0;g<n.height;++g){const p=g*n.width;for(let w=0;w<n.width;++w)(m[p+w]-a)/i<o&&(l=Math.min(l,w),u=Math.min(u,g),d=Math.max(d,w),h=Math.max(h,g))}return e=await e.crop([l,u,d,h]),e}pad_image(e,r,n,{mode:a="constant",center:s=!1,constant_values:i=0}={}){const[o,l,u]=r;let d,h;if(typeof n=="number"?(d=n,h=n):(d=n.width,h=n.height),d!==l||h!==o){const m=new Float32Array(d*h*u);if(Array.isArray(i))for(let w=0;w<m.length;++w)m[w]=i[w%u];else i!==0&&m.fill(i);const[g,p]=s?[Math.floor((d-l)/2),Math.floor((h-o)/2)]:[0,0];for(let w=0;w<o;++w){const v=(w+p)*d,S=w*l;for(let $=0;$<l;++$){const E=(v+$+g)*u,T=(S+$)*u;for(let A=0;A<u;++A)m[E+A]=e[T+A]}}if(a==="symmetric"){if(s)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 $=S*d,E=ti(S,w)*l;for(let T=0;T<d;++T){if(S<o&&T<l)continue;const A=($+T)*u,z=(E+ti(T,v))*u;for(let R=0;R<u;++R)m[A+R]=e[z+R]}}}e=m,r=[h,d,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 s,i;if(this.do_thumbnail){const{height:o,width:l}=r;s=Math.min(o,l)}else Number.isInteger(r)?(s=r,i=this.config.max_size??s):r!==void 0&&(s=r.shortest_edge,i=r.longest_edge);if(s!==void 0||i!==void 0){const o=s===void 0?1:Math.max(s/n,s/a),l=n*o,u=a*o,d=i===void 0?1:Math.min(i/l,i/u);let h=Math.floor(Number((l*d).toFixed(2))),m=Math.floor(Number((u*d).toFixed(2)));return this.size_divisibility!==void 0&&([h,m]=cl([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,d=o/n;Math.abs(1-d)<Math.abs(1-u)?u=d:d=u,l=K_(u*a,this.config.ensure_multiple_of),o=K_(d*n,this.config.ensure_multiple_of)}return[o,l]}else{if(this.size_divisibility!==void 0)return cl([n,a],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(r)}`)}}async resize(e){const[r,n]=this.get_resize_output_image_size(e,this.size);return await e.resize(r,n,{resample:this.resample})}async preprocess(e,{do_normalize:r=null,do_pad:n=null,do_convert_rgb:a=null,do_convert_grayscale:s=null,do_flip_channel_order:i=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[o,l]=e.size;if(a??this.do_convert_rgb?e=e.rgb():s&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let g,p;Number.isInteger(this.crop_size)?(g=this.crop_size,p=this.crop_size):(g=this.crop_size.width,p=this.crop_size.height),e=await e.center_crop(g,p)}const u=[e.height,e.width];let d=Float32Array.from(e.data),h=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(d),r??this.do_normalize){let g=this.image_mean;Array.isArray(this.image_mean)||(g=new Array(e.channels).fill(g));let p=this.image_std;if(Array.isArray(this.image_std)||(p=new Array(e.channels).fill(g)),g.length!==e.channels||p.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${g.length}) and \`image_std\` (${p.length}) must match the number of channels in the image (${e.channels}).`);for(let w=0;w<d.length;w+=e.channels)for(let v=0;v<e.channels;++v)d[w+v]=(d[w+v]-g[v])/p[v]}if(n??this.do_pad){if(this.pad_size)[d,h]=this.pad_image(d,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){const[g,p]=cl([h[1],h[0]],this.size_divisibility);[d,h]=this.pad_image(d,h,{width:g,height:p})}}if(i??this.do_flip_channel_order){if(h[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let g=0;g<d.length;g+=3){const p=d[g];d[g]=d[g+2],d[g+2]=p}}const m=new ce("float32",d,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(s=>this.preprocess(s)));return{pixel_values:ha(n.map(s=>s.pixel_values),0),original_sizes:n.map(s=>s.original_size),reshaped_input_sizes:n.map(s=>s.reshaped_input_size)}}}class Nk extends Ge{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 s=[];for(let i=0;i<a;++i){const o=r!==null?r[i]:null;let l=n[i];o!==null&&(l=Wo(l,o,"bilinear",!1));const[u,d]=o??l.dims.slice(-2),h=new ce("int32",new Int32Array(u*d),[u,d]),m=l[0].data,g=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],g[E]=S)}const p=new Array(l.dims[0]),w=h.data;for(let S=0;S<w.length;++S){const $=w[S];p[$]=$}const v=p.filter(S=>S!==void 0);s.push({segmentation:h,labels:v})}return s}}class Y_ extends Ge{}class Fk extends Y_{}class Lk extends Ge{}class Uk extends Ge{}class X_ extends Ge{}class Wk extends X_{}class Vk extends Ge{}class Gk extends Ge{}class Q_ extends Ge{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){var n;const r=(n=this.size)==null?void 0:n.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){const a=Math.floor(r/this.crop_pct),[s,i]=this.get_resize_output_image_size(e,{shortest_edge:a});e=await e.resize(s,i,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}}class Hk extends Q_{}class jk extends Ge{}class qk extends Ge{}class Kk extends Ge{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 Yk extends Ge{}class Xk extends Ge{}class Qk extends Ge{}class Zk extends Ge{}class Z_ extends Ge{}class Jk extends Z_{}class J_ extends Ge{post_process_object_detection(...e){return Zi(...e)}}class e3 extends J_{}class t3 extends Ge{post_process_object_detection(...e){return Zi(...e)}}class r3 extends Ge{}class n3 extends Ge{}class e0 extends Ge{pad_image(e,r,n,a={}){const[s,i,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 d=l.map((h,m)=>-h/u[m]);return super.pad_image(e,r,n,{center:!0,constant_values:d,...a})}}class a3 extends e0{}class i3 extends Ge{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new ce("int64",new BigInt64Array(n.reduce((s,i)=>s*i)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return Zi(...e)}remove_low_and_no_objects(e,r,n,a){let s=[],i=[],o=[];for(let l=0;l<e.dims[0];++l){let u=e[l],d=r[l],h=qt(u.data)[1];if(h===a)continue;let g=bt(u.data)[h];g>n&&(s.push(d),i.push(g),o.push(h))}return[s,i,o]}check_segment_validity(e,r,n,a=.5,s=.8){let i=[],o=0,l=0;const u=r[n].data;for(let h=0;h<e.length;++h)e[h]===n&&(i.push(h),++o),u[h]>=a&&++l;let d=o>0&&l>0;return d&&(d=o/l>s),[d,i]}compute_segments(e,r,n,a,s,i=null,o=null){let[l,u]=o??e[0].dims,d=new ce("int32",new Int32Array(l*u),[l,u]),h=[];if(o!==null)for(let v=0;v<e.length;++v)e[v]=Wo(e[v],o,"bilinear",!1);let m=new Int32Array(e[0].data.length),g=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]>g[E]&&(m[E]=v,g[E]=$[E])}let p=0;const w=d.data;for(let v=0;v<n.length;++v){let S=n[v],[$,E]=this.check_segment_validity(m,e,v,a,s);if($){++p;for(let T of E)w[T]=p;h.push({id:p,label_id:S,score:r[v]})}}return[d,h]}post_process_panoptic_segmentation(e,r=.5,n=.5,a=.8,s=null,i=null){s===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),s=new Set);const o=e.logits,u=e.pred_masks.sigmoid();let[d,h,m]=o.dims;if(m-=1,i!==null&&i.length!==d)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let g=[];for(let p=0;p<d;++p){let w=i!==null?i[p]:null,v=o[p],S=u[p],[$,E,T]=this.remove_low_and_no_objects(v,S,r,m);if(T.length===0){let[R,V]=w??S.dims.slice(-2),j=new ce("int32",new Int32Array(R*V).fill(-1),[R,V]);g.push({segmentation:j,segments_info:[]});continue}let[A,z]=this.compute_segments($,E,T,n,a,s,w);g.push({segmentation:A,segments_info:z})}return g}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class s3 extends Ge{post_process_object_detection(...e){return Zi(...e)}}class o3 extends Ge{reshape_input_points(e,r,n,a=!1){e=structuredClone(e);let s=Pl(e);if(s.length===3)a||(s=[1,...s]),e=[e];else if(s.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 i=0;i<e.length;++i){let o=r[i],l=n[i],u=[l[0]/o[0],l[1]/o[1]];for(let d=0;d<e[i].length;++d)for(let h=0;h<e[i][d].length;++h)for(let m=0;m<e[i][d][h].length;++m)e[i][d][h][m]*=u[m%2]}return new ce("float32",Float32Array.from(e.flat(1/0)),s)}add_input_labels(e,r){let n=Pl(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,s)=>a!==r.dims[s]))throw Error(`The first ${n.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new ce("int64",e.flat(1/0).map(BigInt),n)}async _call(e,{input_points:r=null,input_labels:n=null,input_boxes:a=null}={}){const s=await super._call(e);if(r&&(s.input_points=this.reshape_input_points(r,s.original_sizes,s.reshaped_input_sizes)),n){if(!s.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");s.input_labels=this.add_input_labels(n,s.input_points)}return a&&(s.input_boxes=this.reshape_input_points(a,s.original_sizes,s.reshaped_input_sizes,!0)),s}async post_process_masks(e,r,n,{mask_threshold:a=0,binarize:s=!0,pad_size:i=null}={}){const o=[];i=i??this.pad_size;const l=[i.height,i.width];for(let u=0;u<r.length;++u){const d=r[u],h=n[u];let m=await _m(e[u],{mode:"bilinear",size:l});if(m=m.slice(null,null,[0,h[0]],[0,h[1]]),m=await _m(m,{mode:"bilinear",size:d}),s){const g=m.data,p=new Uint8Array(g.length);for(let w=0;w<g.length;++w)g[w]>a&&(p[w]=1);m=new ce("bool",p,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:s=32,crop_n_points_downscale_factor:i=1}={}){}}class l3 extends Ge{pad_image(e,r,n,a={}){const[s,i,o]=r;return super.pad_image(e,r,{width:i+(n-i%n)%n,height:s+(n-s%n)%n},{mode:"symmetric",center:!1,constant_values:-1,...a})}}class u3 extends Ge{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);const n=await Promise.all(e.map(i=>this.preprocess(i))),a=await Promise.all(r.map(i=>this.preprocess(i,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:ha(n.map((i,o)=>Xt([i.pixel_values,a[o].pixel_values],0)),0),original_sizes:n.map(i=>i.original_size),reshaped_input_sizes:n.map(i=>i.reshaped_input_size)}}}class d3 extends dn{constructor(e){var r;super(e),(r=this.config).mel_filters??(r.mel_filters=Pa(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney")),this.window=Qi(this.config.n_fft,"hann")}async _extract_fbank_features(e){const r=await Xi(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=qt(n)[0];for(let s=0;s<n.length;++s)n[s]=(Math.max(n[s],a-8)+4)/4;return r}async _call(e){Ra(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 c3 extends dn{_zero_mean_unit_var_norm(e){const n=e.reduce((s,i)=>s+i,0)/e.length,a=e.reduce((s,i)=>s+(i-n)**2,0)/e.length;return e.map(s=>(s-n)/Math.sqrt(a+1e-7))}async _call(e){Ra(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 ce("float32",r,n),attention_mask:new ce("int64",new BigInt64Array(r.length).fill(1n),n)}}}class p3 extends dn{constructor(e){super(e);const r=this.config.sampling_rate,n=Pa(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=Qi(400,"povey",{periodic:!1})}async _extract_fbank_features(e,r){return e=e.map(n=>n*32768),Xi(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:s=!0}={}){Ra(e,"SeamlessM4TFeatureExtractor");let i=await this._extract_fbank_features(e,this.config.max_length);if(a){const[p,w]=i.dims,v=i.data;for(let S=0;S<w;++S){let $=0;for(let z=0;z<p;++z)$+=v[z*w+S];const E=$/p;let T=0;for(let z=0;z<p;++z)T+=(v[z*w+S]-E)**2;T/=p-1;const A=Math.sqrt(T+1e-7);for(let z=0;z<p;++z){const R=z*w+S;v[R]=(v[R]-E)/A}}}let o;if(r){const[p,w]=i.dims,v=i.data,S=p%n;if(S>0){const $=new Float32Array(w*(p+S));$.set(v),$.fill(this.config.padding_value,v.length);const E=p+S;i=new ce(i.type,$,[E,w]),s&&(o=new ce("int64",new BigInt64Array(E),[1,E]),o.data.fill(1n,0,p))}}const[l,u]=i.dims,d=this.config.stride;if(l%d!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${d}).`);const m=i.view(1,Math.floor(l/d),u*d),g={input_features:m};if(s){const p=m.dims[1],w=new BigInt64Array(p);if(o){const v=o.data;for(let S=1,$=0;S<l;S+=d,++$)w[$]=v[S]}else w.fill(1n);g.attention_mask=new ce("int64",w,[1,p])}return g}}class h3 extends dn{constructor(e){super(e);const r=this.config.sampling_rate,n=Pa(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;a<n.length;++a)n[a].push(0);this.mel_filters=n,this.window=Qi(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(e,r){return Xi(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e){Ra(e,"ASTFeatureExtractor");const r=await this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){const n=this.std*2,a=r.data;for(let s=0;s<a.length;++s)a[s]=(a[s]-this.mean)/n}return{input_values:r.unsqueeze_(0)}}}class f3 extends dn{constructor(e){super(e),this.mel_filters=Pa(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=Pa(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=Qi(this.config.fft_window_size,"hann")}async _get_input_mel(e,r,n,a){let s;const i=e.length-r;if(i>0)if(n==="rand_trunc"){const o=Math.floor(Math.random()*(i+1));e=e.subarray(o,o+r),s=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(i<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<-i;l+=e.length)o.set(e,l);e=o}if(n==="fusion")throw new Error(`Truncation strategy "${n}" not implemented`);s=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}return s.unsqueeze_(0)}async _extract_fbank_features(e,r,n=null){return Xi(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 Ra(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 m3 extends dn{}class cn extends wt{constructor(e){super(),this.feature_extractor=e}async _call(e,...r){return await this.feature_extractor(e,...r)}}class g3 extends cn{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 _3 extends cn{async _call(e){return await this.feature_extractor(e)}}class y3 extends cn{async _call(e){return await this.feature_extractor(e)}}class w3 extends cn{async _call(e){return await this.feature_extractor(e)}}class b3 extends cn{}class t0 extends cn{constructor(e){super(e);const{tasks_answer_post_processing_type:r,task_prompts_without_inputs:n,task_prompts_with_input:a}=e.config;this.tasks_answer_post_processing_type=new Map(Object.entries(r??{})),this.task_prompts_without_inputs=new Map(Object.entries(n??{})),this.task_prompts_with_input=new Map(Object.entries(a??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(e){typeof e=="string"&&(e=[e]);const r=[];for(const n of e)if(this.task_prompts_without_inputs.has(n))r.push(this.task_prompts_without_inputs.get(n));else{for(const[a,s]of this.task_prompts_with_input)if(n.includes(a)){r.push(s.replaceAll("{input}",n).replaceAll(a,""));break}r.length!==e.length&&r.push(n)}return r}post_process_generation(e,r,n){const a=this.tasks_answer_post_processing_type.get(r)??"pure_text";e=e.replaceAll("<s>","").replaceAll("</s>","");let s;switch(a){case"pure_text":s=e;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const i=a==="ocr"?"quad_boxes":"bboxes",o=e.matchAll(this.regexes[i]),l=[],u=[];for(const[d,h,...m]of o)l.push(h?h.trim():l.at(-1)??""),u.push(m.map((g,p)=>(Number(g)+.5)/this.size_per_bin*n[p%2]));s={labels:l,[i]:u};break;default:throw new Error(`Task "${r}" (of type "${a}") not yet implemented.`)}return{[r]:s}}}class xt{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main"}={}){let o=n??await Rr(e,"preprocessor_config.json",!0,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i}),l=o.feature_extractor_type??o.image_processor_type,u=this.FEATURE_EXTRACTOR_CLASS_MAPPING[l];if(!u)if(o.size!==void 0)console.warn(`Feature extractor type "${l}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),u=Ge;else throw new Error(`Unknown Feature Extractor type: ${l}`);let d=this.PROCESSOR_CLASS_MAPPING[o.processor_class]??cn,h=new u(o);return new d(h)}}B(xt,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:Ge,WhisperFeatureExtractor:d3,ViTFeatureExtractor:jk,MobileViTFeatureExtractor:Z_,MobileViTImageProcessor:Jk,MobileNetV1FeatureExtractor:Yk,MobileNetV2FeatureExtractor:Xk,MobileNetV3FeatureExtractor:Qk,MobileNetV4FeatureExtractor:Zk,OwlViTFeatureExtractor:J_,Owlv2ImageProcessor:e3,CLIPFeatureExtractor:X_,CLIPImageProcessor:Wk,Florence2Processor:t0,ChineseCLIPFeatureExtractor:Vk,SiglipImageProcessor:Gk,ConvNextFeatureExtractor:Q_,ConvNextImageProcessor:Hk,SegformerFeatureExtractor:Nk,BitImageProcessor:Lk,DPTImageProcessor:Fk,DPTFeatureExtractor:Y_,GLPNFeatureExtractor:Uk,BeitFeatureExtractor:n3,DeiTFeatureExtractor:r3,DetrFeatureExtractor:i3,RTDetrImageProcessor:t3,YolosFeatureExtractor:s3,DonutFeatureExtractor:e0,NougatImageProcessor:a3,EfficientNetImageProcessor:Kk,ViTImageProcessor:qk,VitMatteImageProcessor:u3,SamImageProcessor:o3,Swin2SRImageProcessor:l3,Wav2Vec2FeatureExtractor:c3,SeamlessM4TFeatureExtractor:p3,SpeechT5FeatureExtractor:m3,ASTFeatureExtractor:h3,ClapFeatureExtractor:f3}),B(xt,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:_3,Wav2Vec2ProcessorWithLM:y3,SamProcessor:g3,SpeechT5Processor:w3,OwlViTProcessor:b3,Florence2Processor:t0});async function Tr(t){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(e=>At.read(e)))}async function Ji(t,e){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(r=>typeof r=="string"||r instanceof URL?Ak(r,e):r instanceof Float64Array?new Float32Array(r):r))}function r0(t,e){e&&(t=t.map(i=>i|0));const[r,n,a,s]=t;return{xmin:r,ymin:n,xmax:a,ymax:s}}class tt extends wt{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 v3 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),s=this.model.config.problem_type==="multi_label_classification"?l=>l.sigmoid():l=>new ce("float32",bt(l.data),l.dims),i=this.model.config.id2label,o=[];for(const l of a.logits){const u=s(l),d=await An(u,r),h=d[0].tolist(),g=d[1].tolist().map((p,w)=>({label:i?i[p]:`LABEL_${p}`,score:h[w]}));r===1?o.push(...g):o.push(g)}return Array.isArray(e)||r===1?o:o[0]}}class $3 extends tt{constructor(e){super(e)}async _call(e,{ignore_labels:r=["O"]}={}){const n=Array.isArray(e),a=this.tokenizer(n?e:[e],{padding:!0,truncation:!0}),i=(await this.model(a)).logits,o=this.model.config.id2label,l=[];for(let u=0;u<i.dims[0];++u){const d=a.input_ids[u],h=i[u],m=[];for(let g=0;g<h.dims[0];++g){const p=h[g],w=qt(p.data)[1],v=o?o[w]:`LABEL_${w}`;if(r.includes(v))continue;const S=this.tokenizer.decode([d[g].item()],{skip_special_tokens:!0});if(S==="")continue;const $=bt(p.data);m.push({entity:v,score:$[w],index:g,word:S})}l.push(m)}return n?l:l[0]}}class x3 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:s,end_logits:i}=await this.model(a),o=a.input_ids.tolist(),l=a.attention_mask.tolist(),u=this.tokenizer.all_special_ids,d=[];for(let h=0;h<s.dims[0];++h){const m=o[h],g=m.findIndex(E=>E==this.tokenizer.sep_token_id);l[h].map((E,T)=>E==1&&(T===0||T>g&&u.findIndex(A=>A==m[T])===-1));const p=s[h].tolist(),w=i[h].tolist();for(let E=1;E<p.length;++E)(l[h]==0||E<=g||u.findIndex(T=>T==m[E])!==-1)&&(p[E]=-1/0,w[E]=-1/0);const v=bt(p).map((E,T)=>[E,T]),S=bt(w).map((E,T)=>[E,T]);v[0][0]=0,S[0][0]=0;const $=H0(v,S).filter(E=>E[0][1]<=E[1][1]).map(E=>[E[0][1],E[1][1],E[0][0]*E[1][0]]).sort((E,T)=>T[2]-E[2]);for(let E=0;E<Math.min($.length,n);++E){const[T,A,z]=$[E],R=m.slice(T,A+1),V=this.tokenizer.decode(R,{skip_special_tokens:!0});d.push({answer:V,score:z})}}return n===1?d[0]:d}}class S3 extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=this.tokenizer(e,{padding:!0,truncation:!0}),{logits:a}=await this.model(n),s=[],i=n.input_ids.tolist();for(let o=0;o<i.length;++o){const l=i[o],u=l.findIndex(p=>p==this.tokenizer.mask_token_id);if(u===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const d=a[o][u],h=await An(new ce("float32",bt(d.data),d.dims),r),m=h[0].tolist(),g=h[1].tolist();s.push(g.map((p,w)=>{const v=l.slice();return v[u]=p,{score:m[w],token:Number(p),token_str:this.tokenizer.model.vocab[p],sequence:this.tokenizer.decode(v,{skip_special_tokens:!0})}}))}return Array.isArray(e)?s:s[0]}}class pl extends tt{constructor(r){super(r);B(this,"_key","generated_text")}async _call(r,n={}){Array.isArray(r)||(r=[r]),this.model.config.prefix&&(r=r.map(u=>this.model.config.prefix+u));const a=this.model.config.task_specific_params;a&&a[this.task]&&a[this.task].prefix&&(r=r.map(u=>a[this.task].prefix+u));const s=this.tokenizer,i={padding:!0,truncation:!0};let o;this instanceof n0&&"_build_translation_inputs"in s?o=s._build_translation_inputs(r,i,n):o=s(r,i);const l=await this.model.generate({...o,...n});return s.batch_decode(l,{skip_special_tokens:!0}).map(u=>({[this._key]:u}))}}class k3 extends pl{constructor(r){super(r);B(this,"_key","summary_text")}}class n0 extends pl{constructor(r){super(r);B(this,"_key","translation_text")}}function a0(t){return Array.isArray(t)&&t.every(e=>"role"in e&&"content"in e)}class E3 extends tt{constructor(e){super(e)}async _call(e,r={}){let n=!1,a=!1,s;if(typeof e=="string")s=e=[e];else if(Array.isArray(e)&&e.every(g=>typeof g=="string"))n=!0,s=e;else{if(a0(e))e=[e];else if(Array.isArray(e)&&e.every(a0))n=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");a=!0,s=e.map(g=>this.tokenizer.apply_chat_template(g,{tokenize:!1,add_generation_prompt:!0}))}const i=r.add_special_tokens??!1,o=a?!1:r.return_full_text??!0;this.tokenizer.padding_side="left";const l=this.tokenizer(s,{add_special_tokens:i,padding:!0,truncation:!0}),u=await this.model.generate({...l,...r}),d=this.tokenizer.batch_decode(u,{skip_special_tokens:!0});let h;!o&&l.input_ids.dims.at(-1)>0&&(h=this.tokenizer.batch_decode(l.input_ids,{skip_special_tokens:!0}).map(g=>g.length));const m=Array.from({length:e.length},g=>[]);for(let g=0;g<d.length;++g){const p=Math.floor(g/u.dims[0]*e.length);h&&(d[g]=d[g].slice(h[p])),m[p].push({generated_text:a?[...e[p],{role:"assistant",content:d[g]}]:d[g]})}return!n&&m.length===1?m[0]:m}}class C3 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 s=Array.isArray(e);s||(e=[e]),Array.isArray(r)||(r=[r]);const i=r.map(u=>n.replace("{}",u)),o=a||r.length===1,l=[];for(const u of e){const d=[];for(const g of i){const p=this.tokenizer(u,{text_pair:g,padding:!0,truncation:!0}),w=await this.model(p);o?d.push([w.logits.data[this.contradiction_id],w.logits.data[this.entailment_id]]):d.push(w.logits.data[this.entailment_id])}const m=(o?d.map(g=>bt(g)[1]):bt(d)).map((g,p)=>[g,p]).sort((g,p)=>p[0]-g[0]);l.push({sequence:u,labels:m.map(g=>r[g[1]]),scores:m.map(g=>g[0])})}return s?l:l[0]}}class T3 extends tt{constructor(e){super(e)}async _call(e,{pooling:r="none",normalize:n=!1,quantize:a=!1,precision:s="binary"}={}){const i=this.tokenizer(e,{padding:!0,truncation:!0}),o=await this.model(i);let l=o.last_hidden_state??o.logits??o.token_embeddings;if(r!=="none")if(r==="mean")l=Uw(l,i.attention_mask);else if(r==="cls")l=l.slice(null,0);else throw Error(`Pooling method '${r}' not supported.`);return n&&(l=l.normalize(2,-1)),a&&(l=Yw(l,s)),l}}class A3 extends tt{constructor(e){super(e)}async _call(e,{pool:r=null}={}){const n=await Tr(e),{pixel_values:a}=await this.processor(n),s=await this.model({pixel_values:a});let i;if(r){if(!("pooler_output"in s))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");i=s.pooler_output}else i=s.last_hidden_state??s.logits??s.image_embeds;return i}}class I3 extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=this.processor.feature_extractor.config.sampling_rate,a=await Ji(e,n),s=this.model.config.id2label,i=[];for(const o of a){const l=await this.processor(o),d=(await this.model(l)).logits[0],h=await An(new ce("float32",bt(d.data),d.dims),r),m=h[0].tolist(),p=h[1].tolist().map((w,v)=>({label:s?s[w]:`LABEL_${w}`,score:m[v]}));i.push(p)}return Array.isArray(e)?i:i[0]}}class M3 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 s=r.map(d=>n.replace("{}",d)),i=this.tokenizer(s,{padding:!0,truncation:!0}),o=this.processor.feature_extractor.config.sampling_rate,l=await Ji(e,o),u=[];for(const d of l){const h=await this.processor(d),m=await this.model({...i,...h}),g=bt(m.logits_per_audio.data);u.push([...g].map((p,w)=>({score:p,label:r[w]})))}return a?u[0]:u}}class O3 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,s=await Ji(e,a),i=[];for(const o of s){const l=await this.processor(o),d=(await this.model(l)).logits[0],h=[];for(const g of d)h.push(qt(g.data)[1]);const m=this.tokenizer.decode(h);i.push({text:m})}return n?i[0]:i}async _call_whisper(e,r){const n=r.return_timestamps??!1,a=r.chunk_length_s??0,s=r.force_full_sequences??!1;let i=r.stride_length_s??null;const o={...r};n==="word"&&(o.return_token_timestamps=!0,o.return_timestamps=!1);const l=!Array.isArray(e);l&&(e=[e]);const u=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,d=this.processor.feature_extractor.config.hop_length,h=this.processor.feature_extractor.config.sampling_rate,m=await Ji(e,h),g=[];for(const p of m){let w=[];if(a>0){if(i===null)i=a/6;else if(a<=i)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const $=h*a,E=h*i,T=$-2*E;let A=0;for(;;){const z=A+$,R=p.subarray(A,z),V=await this.processor(R),j=A===0,q=z>=p.length;if(w.push({stride:[R.length,j?0:E,q?0:E],input_features:V.input_features,is_last:q}),q)break;A+=T}}else w=[{stride:[p.length,0,0],input_features:(await this.processor(p)).input_features,is_last:!0}];for(const $ of w){o.num_frames=Math.floor($.stride[0]/d);const E=await this.model.generate({inputs:$.input_features,...o});n==="word"?($.tokens=E.sequences.tolist()[0],$.token_timestamps=E.token_timestamps.tolist()[0].map(T=>Xn(T,2))):$.tokens=E[0].tolist(),$.stride=$.stride.map(T=>T/h)}const[v,S]=this.tokenizer._decode_asr(w,{time_precision:u,return_timestamps:n,force_full_sequences:s});g.push({text:v,...S})}return l?g[0]:g}}class z3 extends tt{constructor(e){super(e)}async _call(e,r={}){const n=Array.isArray(e),a=await Tr(e),{pixel_values:s}=await this.processor(a),i=[];for(const o of s){o.dims=[1,...o.dims];const l=await this.model.generate({inputs:o,...r}),u=this.tokenizer.batch_decode(l,{skip_special_tokens:!0}).map(d=>({generated_text:d.trim()}));i.push(u)}return n?i:i[0]}}class P3 extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=await Tr(e),{pixel_values:a}=await this.processor(n),s=await this.model({pixel_values:a}),i=this.model.config.id2label,o=[];for(const l of s.logits){const u=await An(new ce("float32",bt(l.data),l.dims),r),d=u[0].tolist(),m=u[1].tolist().map((g,p)=>({label:i?i[g]:`LABEL_${g}`,score:d[p]}));o.push(m)}return Array.isArray(e)?o:o[0]}}class R3 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:s=null,target_sizes:i=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),d=u.map(S=>[S.height,S.width]),{pixel_values:h,pixel_mask:m}=await this.processor(u),g=await this.model({pixel_values:h,pixel_mask:m});let p=null;if(o!==null)p=this.subtasks_mapping[o];else for(let[S,$]of Object.entries(this.subtasks_mapping))if($ in this.processor.feature_extractor){p=this.processor.feature_extractor[$].bind(this.processor.feature_extractor),o=S;break}const w=this.model.config.id2label,v=[];if(o==="panoptic"||o==="instance"){const S=p(g,r,n,a,s,i??d)[0],$=S.segmentation;for(const E of S.segments_info){const T=new Uint8ClampedArray($.data.length);for(let z=0;z<$.data.length;++z)$.data[z]===E.id&&(T[z]=255);const A=new At(T,$.dims[1],$.dims[0],1);v.push({score:E.score,label:w[E.label_id],mask:A})}}else if(o==="semantic"){const{segmentation:S,labels:$}=p(g,i??d)[0];for(const E of $){const T=new Uint8ClampedArray(S.data.length);for(let z=0;z<S.data.length;++z)S.data[z]===E&&(T[z]=255);const A=new At(T,S.dims[1],S.dims[0],1);v.push({score:null,label:w[E],mask:A})}}else throw Error(`Subtask ${o} not supported.`);return v}}class B3 extends tt{constructor(e){super(e)}async _call(e,r,{hypothesis_template:n="This is a photo of {}"}={}){const a=Array.isArray(e),s=await Tr(e),i=r.map(m=>n.replace("{}",m)),o=this.tokenizer(i,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:l}=await this.processor(s),u=await this.model({...o,pixel_values:l}),d=this.model.config.model_type==="siglip"?m=>m.sigmoid().data:m=>bt(m.data),h=[];for(const m of u.logits_per_image){const p=[...d(m)].map((w,v)=>({score:w,label:r[v]}));p.sort((w,v)=>v.score-w.score),h.push(p)}return a?h:h[0]}}class D3 extends tt{constructor(e){super(e)}async _call(e,{threshold:r=.9,percentage:n=!1}={}){const a=Array.isArray(e);if(a&&e.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const s=await Tr(e),i=n?null:s.map(g=>[g.height,g.width]),{pixel_values:o,pixel_mask:l}=await this.processor(s),u=await this.model({pixel_values:o,pixel_mask:l}),d=this.processor.feature_extractor.post_process_object_detection(u,r,i),h=this.model.config.id2label,m=d.map(g=>g.boxes.map((p,w)=>({score:g.scores[w],label:h[g.classes[w]],box:r0(p,!n)})));return a?m:m[0]}}class N3 extends tt{constructor(e){super(e)}async _call(e,r,{threshold:n=.1,top_k:a=null,percentage:s=!1}={}){const i=Array.isArray(e),o=await Tr(e),l=this.tokenizer(r,{padding:!0,truncation:!0}),u=await this.processor(o),d=[];for(let h=0;h<o.length;++h){const m=o[h],g=s?null:[[m.height,m.width]],p=u.pixel_values[h].unsqueeze_(0),w=await this.model({...l,pixel_values:p}),v=this.processor.feature_extractor.post_process_object_detection(w,n,g,!0)[0];let S=v.boxes.map(($,E)=>({score:v.scores[E],label:r[v.classes[E]],box:r0($,!s)})).sort(($,E)=>E.score-$.score);a!==null&&(S=S.slice(0,a)),d.push(S)}return i?d:d[0]}}class F3 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 L3 extends tt{constructor(r){super(r);B(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=r.vocoder??null}async _call(r,{speaker_embeddings:n=null}={}){return this.processor?this._call_text_to_spectrogram(r,{speaker_embeddings:n}):this._call_text_to_waveform(r)}async _call_text_to_waveform(r){const n=this.tokenizer(r,{padding:!0,truncation:!0}),{waveform:a}=await this.model(n),s=this.model.config.sampling_rate;return{audio:a.data,sampling_rate:s}}async _call_text_to_spectrogram(r,{speaker_embeddings:n}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await ln.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof n=="string"||n instanceof URL)&&(n=new Float32Array(await(await fetch(n)).arrayBuffer())),n instanceof Float32Array)n=new ce("float32",n,[1,n.length]);else if(!(n instanceof ce))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:a}=this.tokenizer(r,{padding:!0,truncation:!0}),{waveform:s}=await this.model.generate_speech(a,n,{vocoder:this.vocoder}),i=this.processor.feature_extractor.config.sampling_rate;return{audio:s.data,sampling_rate:i}}}class U3 extends tt{constructor(e){super(e)}async _call(e){const r=await Tr(e),n=await this.processor(r),a=await this.model(n),s=[];for(const i of a.reconstruction){const o=i.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");s.push(At.fromTensor(o))}return s.length>1?s:s[0]}}class W3 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),s=[];for(let i=0;i<r.length;++i){const o=Wo(a[i],r[i].size.reverse(),"bilinear",!1),l=o.mul_(255/qt(o.data)[0]).to("uint8");s.push({predicted_depth:a[i],depth:At.fromTensor(l)})}return s.length>1?s:s[0]}}const i0=Object.freeze({"text-classification":{tokenizer:pt,pipeline:v3,model:ul,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:pt,pipeline:$3,model:k_,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:pt,pipeline:x3,model:M_,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:pt,pipeline:S3,model:I_,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:pt,pipeline:k3,model:Yi,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:pt,pipeline:n0,model:Yi,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:pt,pipeline:pl,model:Yi,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:pt,pipeline:E3,model:A_,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:pt,pipeline:C3,model:ul,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:I3,model:F_,processor:xt,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:pt,pipeline:M3,model:ln,processor:xt,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:pt,pipeline:O3,model:[E_,N_],processor:xt,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:pt,pipeline:L3,model:[T_,C_],processor:[xt,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:pt,pipeline:z3,model:O_,processor:xt,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:P3,model:z_,processor:xt,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:R3,model:[P_,R_],processor:xt,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:pt,pipeline:B3,model:ln,processor:xt,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:D3,model:B_,processor:xt,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:pt,pipeline:N3,model:D_,processor:xt,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:pt,pipeline:F3,model:L_,processor:xt,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:U3,model:U_,processor:xt,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:W3,model:W_,processor:xt,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:pt,pipeline:T3,model:ln,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:xt,pipeline:A3,model:[V_,ln],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),V3=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function G3(t,e=null,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",device:o=null,dtype:l=null,model_file_name:u=null,session_options:d={}}={}){t=V3[t]??t;const h=i0[t.split("_",1)[0]];if(!h)throw Error(`Unsupported pipeline: ${t}. Must be one of [${Object.keys(i0)}]`);e||(e=h.default.model,console.log(`No model specified. Using default model: "${e}".`));const m={progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,device:o,dtype:l,model_file_name:u,session_options:d},g=new Map([["tokenizer",h.tokenizer],["model",h.model],["processor",h.processor]]),p=await H3(g,e,m);p.task=t,bn(r,{status:"ready",task:t,model:e});const w=h.pipeline;return new w(p)}async function H3(t,e,r){const n=Object.create(null),a=[];for(let[s,i]of t.entries()){if(!i)continue;let o;Array.isArray(i)?o=new Promise(async(l,u)=>{var h,m;let d;for(let g of i){if(g===null){l(null);return}try{l(await g.from_pretrained(e,r));return}catch(p){if((h=p.message)!=null&&h.includes("Unsupported model type"))d=p;else if((m=p.message)!=null&&m.includes("Could not locate file"))d=p;else{u(p);return}}}u(d)}):o=i.from_pretrained(e,r),n[s]=o,a.push(o)}await Promise.all(a);for(let[s,i]of Object.entries(n))n[s]=await i;return n}Hr.IS_PROCESS_AVAILABLE;const j3={webgpu:{dtype:{encoder_model:"fp32",decoder_model_merged:"q4"},device:"webgpu"},wasm:{dtype:"q8",device:"wasm"}};class es{static async getInstance(e=null,r="webgpu"){return this.instance||(this.instance=G3("automatic-speech-recognition",this.model_id,{...j3[r],progress_callback:e})),this.instance}}B(es,"model_id","onnx-community/whisper-base_timestamped"),B(es,"instance",null);async function q3({device:t}){self.postMessage({status:"loading",data:`Loading model (${t})...`});const e=await es.getInstance(r=>{self.postMessage(r)},t);t==="webgpu"&&(self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."}),await e(new Float32Array(16e3),{language:"en"})),self.postMessage({status:"ready"})}async function K3({audio:t,language:e}){const r=await es.getInstance(),n=performance.now(),a=await r(t,{language:e,return_timestamps:"word",chunk_length_s:30}),s=performance.now();self.postMessage({status:"complete",result:a,time:s-n})}self.addEventListener("message",async t=>{const{type:e,data:r}=t.data;switch(e){case"load":q3(r);break;case"run":K3(r);break}})})();