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es({location:"gpu-buffer",type:t??"float32",gpuBuffer:e,dims:s,download:n,dispose:o})},jv=(e,r)=>{const{dataType:t,dims:s,download:n,dispose:o}=r;return new es({location:"ml-tensor",type:t??"float32",mlTensor:e,dims:s,download:n,dispose:o})},Nv=(e,r,t)=>new es({location:"cpu-pinned",type:e,data:r,dims:t??[r.length]}),An=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array],["int4",Uint8Array],["uint4",Uint8Array]]),Yo=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let Gu=!1;const Vv=()=>{if(!Gu){Gu=!0;const e=typeof BigInt64Array<"u"&&BigInt64Array.from,r=typeof BigUint64Array<"u"&&BigUint64Array.from,t=globalThis.Float16Array,s=typeof 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not supported`)}};let es=class{constructor(r,t,s){Vv();let n,o;if(typeof r=="object"&&"location"in r)switch(this.dataLocation=r.location,n=r.type,o=r.dims,r.location){case"cpu-pinned":{const a=An.get(n);if(!a)throw new TypeError(`unsupported type "${n}" to create tensor from pinned buffer`);if(!(r.data instanceof a))throw new TypeError(`buffer should be of type ${a.name}`);this.cpuData=r.data;break}case"texture":{if(n!=="float32")throw new TypeError(`unsupported type "${n}" to create tensor from texture`);this.gpuTextureData=r.texture,this.downloader=r.download,this.disposer=r.dispose;break}case"gpu-buffer":{if(n!=="float32"&&n!=="float16"&&n!=="int32"&&n!=="int64"&&n!=="uint32"&&n!=="uint8"&&n!=="bool"&&n!=="uint4"&&n!=="int4")throw new TypeError(`unsupported type "${n}" to create tensor from gpu buffer`);this.gpuBufferData=r.gpuBuffer,this.downloader=r.download,this.disposer=r.dispose;break}case"ml-tensor":{if(n!=="float32"&&n!=="float16"&&n!=="int32"&&n!=="int64"&&n!=="uint32"&&n!=="uint64"&&n!=="int8"&&n!=="uint8"&&n!=="bool"&&n!=="uint4"&&n!=="int4")throw new TypeError(`unsupported type "${n}" to create tensor from MLTensor`);this.mlTensorData=r.mlTensor,this.downloader=r.download,this.disposer=r.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let a,l;if(typeof r=="string")if(n=r,l=s,r==="string"){if(!Array.isArray(t))throw new TypeError("A string tensor's data must be a string array.");a=t}else{const c=An.get(r);if(c===void 0)throw new TypeError(`Unsupported tensor type: ${r}.`);if(Array.isArray(t)){if(r==="float16"&&c===Uint16Array||r==="uint4"||r==="int4")throw new TypeError(`Creating a ${r} tensor from number array is not supported. 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t=this.mlContextCache.findIndex(s=>s.gpuDevice===e);if(t!==-1)return this.mlContextCache[t].mlContext;{let s=await navigator.ml.createContext(e);return this.mlContextCache.push({gpuDevice:e,mlContext:s}),s}}else if(e===void 0){let t=this.mlContextCache.findIndex(s=>s.options===void 0&&s.gpuDevice===void 0);if(t!==-1)return this.mlContextCache[t].mlContext;{let s=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:s}),s}}let r=this.mlContextCache.findIndex(t=>Qd(t.options,e));if(r!==-1)return this.mlContextCache[r].mlContext;{let t=await navigator.ml.createContext(e);return this.mlContextCache.push({options:e,mlContext:t}),t}}registerMLContext(e,r){this.mlContextBySessionId.set(e,r);let t=this.sessionIdsByMLContext.get(r);t||(t=new Set,this.sessionIdsByMLContext.set(r,t)),t.add(e),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(e,this.temporaryGraphInputs),this.temporaryGraphInputs=[])}onReleaseSession(e){this.sessionGraphInputs.delete(e);let r=this.mlContextBySessionId.get(e);if(!r)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e);let t=this.sessionIdsByMLContext.get(r);if(t.delete(e),t.size===0){this.sessionIdsByMLContext.delete(r);let s=this.mlContextCache.findIndex(n=>n.mlContext===r);s!==-1&&this.mlContextCache.splice(s,1)}}getMLContext(e){return this.mlContextBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){Ot("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,r,t,s,n){let o=oa.get(t);if(!o)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e??this.currentSessionId,r,o,s,n)}async createTemporaryTensor(e,r,t){Ot("verbose",()=>`[WebNN] createTemporaryTensor {onnxDataType: ${r}, shape: ${t}}`);let s=oa.get(r);if(!s)throw new Error(`Unsupported ONNX data type: ${r}`);let n=this.tensorManager.reserveTensorId();await 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a=e;e.startsWith("./")&&(a=e.substring(2));let l=o.get(a);if(!l)throw new Error(`File with name ${a} not found in preloaded files.`);if(r+t>l.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let c=l.slice(r,r+t).buffer,p;switch(n.dataType){case"float32":p=new Float32Array(c);break;case"float16":p=typeof Float16Array<"u"&&Float16Array.from?new Float16Array(c):new Uint16Array(c);break;case"int32":p=new Int32Array(c);break;case"uint32":p=new Uint32Array(c);break;case"int64":i?(p=Qi(new Uint8Array(c),!1),n.dataType="int32"):p=new BigInt64Array(c);break;case"uint64":p=new BigUint64Array(c);break;case"int8":p=new Int8Array(c);break;case"int4":case"uint4":case"uint8":p=new Uint8Array(c);break;default:throw new Error(`Unsupported data type: ${n.dataType} in creating WebNN Constant from external data.`)}return Ot("verbose",()=>`[WebNN] registerMLConstant {dataType: ${n.dataType}, shape: ${n.shape}}} ${i?"(Note: it was int64 data type and 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`)}get variablesInfo(){if(this.uniforms.length===0)return;let e=r=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(r)];return this.uniforms.map(r=>[e(r.type),r.length??1])}},np=(e,r)=>new sp(e,r)}),op,il,ap,ip,lp,cp,Xr,up,dp,Ks=Ne(()=>{gt(),Tt(),dr(),St(),op=(e,r)=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(r.length!==0&&r.length!==e[0].dims.length)throw new Error(`perm size ${r.length} does not match input rank ${e[0].dims.length}`)},il=(e,r)=>r.length!==0?r:[...new Array(e).keys()].reverse(),ap=(e,r)=>we.sortBasedOnPerm(e,il(e.length,r)),ip=(e,r,t,s)=>{let n=`fn perm(i: ${s.type.indices}) -> ${t.type.indices} {
    var a: ${t.type.indices};`;for(let o=0;o<r;++o)n+=`a[${e[o]}]=i[${o}];`;return n+="return a;}"},lp=(e,r)=>{let t=[],s=[];for(let n=0;n<e.length;++n)e[n]!==1&&t.push(e[n]),e[r[n]]!==1&&s.push(r[n]);return{newShape:t,newPerm:s}},cp=(e,r)=>{let t=0;for(let s=0;s<e.length;++s)if(r[e[s]]!==1){if(e[s]<t)return!1;t=e[s]}return!0},Xr=(e,r)=>{let t=e.dataType,s=e.dims.length,n=il(s,r),o=ap(e.dims,n),i=e.dims,a=o,l=s<2||cp(n,e.dims),c;if(l)return c=_=>{let y=Ie("input",t,i,4),I=ot("output",t,a,4);return`
  ${_.registerUniform("output_size","u32").declareVariables(y,I)}
  ${_.mainStart()}
    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    output[global_idx] = input[global_idx];
  }`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let _=we.size(o);return{outputs:[{dims:o,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(_/64/4)},programUniforms:[{type:12,data:Math.ceil(_/4)}]}},getShaderSource:c};let{newShape:p,newPerm:d}=lp(e.dims,n),u=we.areEqual(d,[2,3,1]),f=we.areEqual(d,[3,1,2]);if(p.length===2||u||f){i=u?[p[0],p[1]*p[2]]:f?[p[0]*p[1],p[2]]:p,a=[i[1],i[0]];let _=16;return c=y=>{let I=Ie("a",t,i.length),w=ot("output",t,a.length);return`
  ${y.registerUniform("output_size","u32").declareVariables(I,w)}
  var<workgroup> tile : array<array<${w.type.value}, ${_+1}>, ${_}>;
  ${y.mainStart([_,_,1])}
    let stride = (uniforms.output_shape[1] - 1) / ${_} + 1;
    let workgroup_id_x = workgroup_index % stride;
    let workgroup_id_y = workgroup_index / stride;
    let input_col = workgroup_id_y * ${_}u + local_id.x;
    let input_row = workgroup_id_x * ${_}u + local_id.y;
    if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
      tile[local_id.y][local_id.x] = ${I.getByIndices(`${I.type.indices}(input_row, input_col)`)};
    }
    workgroupBarrier();

    let output_col = workgroup_id_x * ${_}u + local_id.x;
    let output_row = workgroup_id_y * ${_}u + local_id.y;
    if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) {
      ${w.setByIndices(`${w.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
    }
  }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let y=we.size(o);return{outputs:[{dims:o,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(a[1]/_),y:Math.ceil(a[0]/_)},programUniforms:[{type:12,data:y},...ct(i,a)]}},getShaderSource:c}}return c=_=>{let y=Ie("a",t,i.length),I=ot("output",t,a.length);return`
  ${_.registerUniform("output_size","u32").declareVariables(y,I)}

  ${ip(n,s,y,I)}

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

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

    ${I.setByOffset("global_idx",y.getByIndices("aIndices"))}
  }`},{name:"Transpose",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>{let _=we.size(o);return{outputs:[{dims:o,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...ct(i,a)]}},getShaderSource:c}},up=(e,r)=>{op(e.inputs,r.perm),e.compute(Xr(e.inputs[0],r.perm))},dp=e=>Rt({perm:e.perm})}),pp,mp,hp,_p,fp,gp,Mp,wp,bp,yp,hs,vp,xp,Tp,Pp,Ep,Cp,Sp,kp,$p,Ip,gx=Ne(()=>{gt(),Tt(),St(),cl(),Ks(),pp={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"},mp={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"},hp={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},_p={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},fp=(e,r)=>{let t=[];for(let s=r-e;s<r;++s)t.push(s);return t},gp=(e,r)=>{let t=[],s=e.length;for(let o=0;o<s;o++)r.indexOf(o)===-1&&t.push(e[o]);let n=r.map(o=>e[o]);return[t,n]},Mp=(e,r)=>{let t=e.length+r.length,s=[],n=0;for(let o=0;o<t;o++)r.indexOf(o)===-1?s.push(e[n++]):s.push(1);return s},wp=(e,r)=>{for(let t=0;t<e.length;++t)if(e[e.length-t-1]!==r-1-t)return!1;return!0},bp=(e,r)=>{let t=[];if(!wp(e,r)){for(let s=0;s<r;++s)e.indexOf(s)===-1&&t.push(s);e.forEach(s=>t.push(s))}return t},yp=(e,r,t,s,n,o,i)=>{let a=t[0].dims,l=we.size(o),c=we.size(i),p=Ie("_A",t[0].dataType,a),d=ot("output",n,o),u=64;l===1&&(u=256);let f=`
          var<workgroup> aBestValues : array<f32, ${u}>;
       `,_=y=>`
        ${y.registerUniform("reduceSize","u32").declareVariables(p,d)}
        ${f}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${y.mainStart(u)}

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

          var bestValue = f32(${hp[s]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${u}) {
           let candidate = f32(${p.getByOffset("offset + k")});
           bestValue = ${pp[s]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

         var reduceSize = min(Length, ${u}u);
         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
             currentSize = reduceSize / 2u) {
           let interval = DIV_CEIL(reduceSize, 2u);
           if (local_idx < currentSize) {
            let candidate = aBestValues[local_idx + interval];
            bestValue = ${mp[s]};
            aBestValues[local_idx] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (local_idx == 0u) {
          ${d.setByOffset("outputIndex",`${s==="mean"?`${d.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${d.type.storage}(${_p[s]})`}`)};
         }
        }`;return{name:e,shaderCache:{hint:`${r};${u}`,inputDependencies:["type"]},getShaderSource:_,getRunData:()=>({outputs:[{dims:o,dataType:n}],dispatchGroup:{x:l},programUniforms:[{type:12,data:c}]})}},hs=(e,r,t,s)=>{let n=e.inputs.length===1?t:ll(e.inputs,t),o=n.axes;o.length===0&&!n.noopWithEmptyAxes&&(o=e.inputs[0].dims.map((f,_)=>_));let i=we.normalizeAxes(o,e.inputs[0].dims.length),a=i,l=e.inputs[0],c=bp(a,e.inputs[0].dims.length);c.length>0&&(l=e.compute(Xr(e.inputs[0],c),{inputs:[0],outputs:[-1]})[0],a=fp(a.length,l.dims.length));let[p,d]=gp(l.dims,a),u=p;n.keepDims&&(u=Mp(p,i)),e.compute(yp(r,n.cacheKey,[l],s,e.inputs[0].dataType,u,d),{inputs:[l]})},vp=(e,r)=>{hs(e,"ReduceMeanShared",r,"mean")},xp=(e,r)=>{hs(e,"ReduceL1Shared",r,"l1")},Tp=(e,r)=>{hs(e,"ReduceL2Shared",r,"l2")},Pp=(e,r)=>{hs(e,"ReduceLogSumExpShared",r,"logSumExp")},Ep=(e,r)=>{hs(e,"ReduceMaxShared",r,"max")},Cp=(e,r)=>{hs(e,"ReduceMinShared",r,"min")},Sp=(e,r)=>{hs(e,"ReduceProdShared",r,"prod")},kp=(e,r)=>{hs(e,"ReduceSumShared",r,"sum")},$p=(e,r)=>{hs(e,"ReduceSumSquareShared",r,"sumSquare")},Ip=(e,r)=>{hs(e,"ReduceLogSumShared",r,"logSum")}}),_s,Ap,ca,ll,fs,Fp,Op,Dp,Lp,zp,Bp,Rp,jp,Np,Vp,gs,Up,Wp,Gp,Kp,Hp,qp,Qp,Xp,Jp,Yp,cl=Ne(()=>{gt(),Tt(),dr(),St(),gx(),_s=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Ap=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],ca=(e,r,t,s,n,o,i=!1,a=!1)=>{let l=[],c=t[0].dims,p=c.length,d=we.normalizeAxes(n,p),u=!a&&d.length===0;c.forEach((y,I)=>{u||d.indexOf(I)>=0?i&&l.push(1):l.push(y)});let f=l.length,_=we.size(l);return{name:e,shaderCache:r,getShaderSource:y=>{let I=[],w=Ie("_A",t[0].dataType,p),v=ot("output",o,f),k=s(w,v,d),T=k[2];for(let b=0,P=0;b<p;b++)u||d.indexOf(b)>=0?(i&&P++,T=`for(var j${b}: u32 = 0; j${b} < ${c[b]}; j${b}++) {
                  ${k[2].includes("last_index")?`let last_index = j${b};`:""}
                  ${w.indicesSet("input_indices",b,`j${b}`)}
                  ${T}
                }`):(I.push(`${w.indicesSet("input_indices",b,v.indicesGet("output_indices",P))};`),P++);return`

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

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

          ${I.join(`
`)}
          ${k[0]}       // init ops for reduce max/min
          ${k[1]}
          ${T}
          ${k[3]}
          ${k.length===4?v.setByOffset("global_idx","value"):k.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:l,dataType:o}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...ct(c,l)]})}},ll=(e,r)=>{let t=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(s=>t.push(Number(s))),Rt({axes:t,keepDims:r.keepDims,noopWithEmptyAxes:r.noopWithEmptyAxes})},fs=(e,r,t,s)=>{let n=e.inputs,o=n.length===1?t:ll(n,t);e.compute(ca(r,{hint:o.cacheKey,inputDependencies:["rank"]},[n[0]],o.noopWithEmptyAxes&&o.axes.length===0?Ap:s,o.axes,n[0].dataType,o.keepDims,o.noopWithEmptyAxes),{inputs:[0]})},Fp=(e,r)=>{_s(e.inputs),fs(e,"ReduceLogSum",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,"value = log(value);"])},Op=(e,r)=>{_s(e.inputs),fs(e,"ReduceL1",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += abs(${t.getByIndices("input_indices")});`,""])},Dp=(e,r)=>{_s(e.inputs),fs(e,"ReduceL2",r,(t,s)=>[`var t = ${s.type.value}(0); var value = ${s.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},Lp=(e,r)=>{_s(e.inputs),fs(e,"ReduceLogSumExp",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += exp(${t.getByIndices("input_indices")});`,"value = log(value);"])},zp=(e,r)=>{_s(e.inputs),fs(e,"ReduceMax",r,(t,s,n)=>{let o=[];for(let i=0;i<t.rank;i++)(n.indexOf(i)>=0||n.length===0)&&o.push(t.indicesSet("input_indices",i,0));return[`${o.join(`
`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = max(value, ${t.getByIndices("input_indices")});`,""]})},Bp=(e,r)=>{_s(e.inputs),fs(e,"ReduceMean",r,(t,s,n)=>{let o=1;for(let i=0;i<t.rank;i++)(n.indexOf(i)>=0||n.length===0)&&(o*=e.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${t.getByIndices("input_indices")});`,`let value = ${s.type.value}(sum / ${o});`]})},Rp=(e,r)=>{_s(e.inputs),fs(e,"ReduceMin",r,(t,s,n)=>{let o=[];for(let i=0;i<t.rank;i++)(n.indexOf(i)>=0||n.length===0)&&o.push(`input_indices[${i}] = 0;`);return[`${o.join(`
`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = min(value, ${t.getByIndices("input_indices")});`,""]})},jp=(e,r)=>{_s(e.inputs),fs(e,"ReduceProd",r,(t,s)=>[`var value = ${s.type.storage}(1);`,"",`value *= ${t.getByIndices("input_indices")};`,""])},Np=(e,r)=>{_s(e.inputs),fs(e,"ReduceSum",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,""])},Vp=(e,r)=>{_s(e.inputs),fs(e,"ReduceSumSquare",r,(t,s)=>[`var t = ${s.type.value}(0); var value = ${s.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += t * t;`,""])},gs=(e,r,t)=>{if(r.length===0)return t;let s=1,n=1;for(let o=0;o<r.length;o++)r.indexOf(o)===-1?s*=e[o]:n*=e[o];return n<32&&s>1024},Up=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Bp(e,r):vp(e,r)},Wp=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Op(e,r):xp(e,r)},Gp=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Dp(e,r):Tp(e,r)},Kp=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Lp(e,r):Pp(e,r)},Hp=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?zp(e,r):Ep(e,r)},qp=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Rp(e,r):Cp(e,r)},Qp=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?jp(e,r):Sp(e,r)},Xp=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Np(e,r):kp(e,r)},Jp=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Vp(e,r):$p(e,r)},Yp=(e,r)=>{gs(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Fp(e,r):Ip(e,r)}}),ul,Zp,em,dl,Mx=Ne(()=>{gt(),dr(),cl(),ul=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Zp=(e,r)=>{ul(e.inputs);let t=(s,n,o)=>{let i=[];for(let a=0;a<s.rank;a++)(o.indexOf(a)>=0||o.length===0)&&i.push(`input_indices[${a}] = 0;`);return[`${i.join(`
`)}`,`var value = ${s.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?"<=":"<"} value) {
         value = ${s.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",n.setByOffset("global_idx","best_index")]};e.compute(ca("ArgMin",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},em=(e,r)=>{ul(e.inputs);let t=(s,n,o)=>{let i=[];for(let a=0;a<s.rank;a++)(o.indexOf(a)>=0||o.length===0)&&i.push(`input_indices[${a}] = 0;`);return[`${i.join(`
`)}`,`var value = ${s.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?">=":">"} value) {
         value = ${s.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",n.setByOffset("global_idx","best_index")]};e.compute(ca("argMax",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},dl=e=>Rt(e)}),tm,ua,rm,sm,nm,fo,om,am,pl=Ne(()=>{gt(),Tt(),tl(),St(),tm=(e,r)=>{let t=e[0],s=e[1],n=e[2],o=e[3],i=e[4],a=e[5];if(i&&a)throw new Error("Attention cannot have both past and attention_bias");if(t.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let l=t.dims[0],c=t.dims[1],p=t.dims[2];if(n.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(s.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(s.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(n.dims[0]!==s.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let d=n.dims[0]/3,u=d,f=u;if(r.qkvHiddenSizes.length>0){if(r.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let k of r.qkvHiddenSizes)if(k%r.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");d=r.qkvHiddenSizes[0],u=r.qkvHiddenSizes[1],f=r.qkvHiddenSizes[2]}let _=c;if(d!==u)throw new Error("qkv_hidden_sizes first element should be same as the second");if(n.dims[0]!==d+u+f)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let y=0;if(i){if(u!==f)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]!==r.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==u/r.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');r.pastPresentShareBuffer||(y=i.dims[3])}let I=_+y,w=-1,v=0;if(o)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");if(a){if(a.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(a.dims[0]!==l||a.dims[1]!==r.numHeads||a.dims[2]!==c||a.dims[3]!==I)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:l,sequenceLength:c,pastSequenceLength:y,kvSequenceLength:_,totalSequenceLength:I,maxSequenceLength:w,inputHiddenSize:p,hiddenSize:d,vHiddenSize:f,headSize:Math.floor(d/r.numHeads),vHeadSize:Math.floor(f/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:v,scale:r.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},ua=(e,r,t)=>r&&e?`
      let total_sequence_length_input = u32(${r.getByOffset("0")});
      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
      total_sequence_length = u32(${e?.getByOffset("batchIdx")}) + 1;
      var past_sequence_length: u32 = 0;
      if (is_first_prompt == false) {
        past_sequence_length = total_sequence_length - sequence_length;
      }
       `:`
    ${t?"let past_sequence_length = uniforms.past_sequence_length":""};
    let present_sequence_length = total_sequence_length;
    `,rm=(e,r,t,s,n,o,i,a)=>{let l=or(i?1:o),c=64,p=o/l;p<c&&(c=32);let d=Math.ceil(o/l/c),u=[{type:12,data:r},{type:12,data:t},{type:12,data:s},{type:12,data:n},{type:12,data:p},{type:12,data:d}],f=Sr(e.dataType,l),_=Rr(1,l),y=["type"];i&&y.push("type"),a&&y.push("type");let I=w=>{let v=ot("x",e.dataType,e.dims,l),k=[v],T=i?Ie("seq_lens",i.dataType,i.dims):void 0;T&&k.push(T);let b=a?Ie("total_sequence_length_input",a.dataType,a.dims):void 0;b&&k.push(b);let P=Rr(e.dataType),x=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${c}>;
  var<workgroup> thread_sum: array<f32, ${c}>;
  ${w.registerUniforms(x).declareVariables(...k)}
  ${w.mainStart([c,1,1])}
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let sequence_length = uniforms.sequence_length;
    var total_sequence_length = uniforms.total_sequence_length;
    ${ua(T,b,!1)}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${c}) * uniforms.total_sequence_length + local_offset;
    let seq_causal_length = ${i?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
    var thread_max_vector = ${_}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${_}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(l){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: ${l}`)}})()};
    workgroupBarrier();

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

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

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

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        x[offset + i] = ${v.type.value}(${P}(1.0) / ${P}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${_}(x[offset + i]);
        x[offset + i] = ${v.type.value}(exp(f32input - max_value) / sum);
      }
    }
      ${i?`
        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
          x[offset + total_seq_id] = ${v.type.value}(${P}(0));
        }`:""};
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${c};${f};${l}`,inputDependencies:y},getShaderSource:I,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:n,z:r*t},programUniforms:u})}},sm=(e,r,t,s,n,o,i,a,l)=>{let c=i+o.kvSequenceLength,p=[o.batchSize,o.numHeads,o.sequenceLength,c],d=e>1&&s,u=o.kvNumHeads?o.kvNumHeads:o.numHeads,f=d?[o.batchSize,u,c,o.headSize]:void 0,_=o.nReps?o.nReps:1,y=o.scale===0?1/Math.sqrt(o.headSize):o.scale,I=or(o.headSize),w=o.headSize/I,v=12,k={x:Math.ceil(c/v),y:Math.ceil(o.sequenceLength/v),z:o.batchSize*o.numHeads},T=[{type:12,data:o.sequenceLength},{type:12,data:w},{type:12,data:c},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:1,data:y},{type:12,data:i},{type:12,data:o.kvSequenceLength},{type:12,data:_}],b=d&&s&&we.size(s.dims)>0,P=["type","type"];b&&P.push("type"),n&&P.push("type"),a&&P.push("type"),l&&P.push("type");let x=[{dims:p,dataType:r.dataType,gpuDataType:0}];d&&x.push({dims:f,dataType:r.dataType,gpuDataType:0});let S=O=>{let F=Ie("q",r.dataType,r.dims,I),H=Ie("key",t.dataType,t.dims,I),W=[F,H];if(b){let le=Ie("past_key",s.dataType,s.dims,I);W.push(le)}n&&W.push(Ie("attention_bias",n.dataType,n.dims));let B=a?Ie("seq_lens",a.dataType,a.dims):void 0;B&&W.push(B);let Y=l?Ie("total_sequence_length_input",l.dataType,l.dims):void 0;Y&&W.push(Y);let X=ot("output",r.dataType,p),J=[X];d&&J.push(ot("present_key",r.dataType,f,I));let re=Rr(1,I),ne=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${v}u;

  var<workgroup> tileQ: array<${F.type.storage}, ${v*v}>;
  var<workgroup> tileK: array<${F.type.storage}, ${v*v}>;
  ${O.registerUniforms(ne).declareVariables(...W,...J)}
  ${O.mainStart([v,v,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${_===1?"headIdx":"headIdx / uniforms.n_reps"};
    let kv_num_heads = ${_===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let sequence_length = uniforms.M;
    var total_sequence_length = uniforms.N;
    ${ua(B,Y,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${b&&d?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${d?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
    var value = ${re}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
      ${b&&d?`
              if (n + local_id.y < past_sequence_length) {
                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
              }`:`
          if (n + local_id.y < uniforms.kv_sequence_length) {
            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
          }`}
      ${d?`if (n + local_id.y < present_sequence_length) {
        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
      }`:""}
      }
      workgroupBarrier();

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

      workgroupBarrier();
    }

    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(I){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: ${I}`)}})()};
        output[outputIdx] = ${X.type.value} (sum * uniforms.alpha) + ${n?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${I};${n!==void 0};${s!==void 0};${e}`,inputDependencies:P},getRunData:()=>({outputs:x,dispatchGroup:k,programUniforms:T}),getShaderSource:S}},nm=(e,r,t,s,n,o,i=void 0,a=void 0)=>{let l=o+n.kvSequenceLength,c=n.nReps?n.nReps:1,p=n.vHiddenSize*c,d=e>1&&s,u=n.kvNumHeads?n.kvNumHeads:n.numHeads,f=d?[n.batchSize,u,l,n.headSize]:void 0,_=[n.batchSize,n.sequenceLength,p],y=12,I={x:Math.ceil(n.vHeadSize/y),y:Math.ceil(n.sequenceLength/y),z:n.batchSize*n.numHeads},w=[{type:12,data:n.sequenceLength},{type:12,data:l},{type:12,data:n.vHeadSize},{type:12,data:n.numHeads},{type:12,data:n.headSize},{type:12,data:p},{type:12,data:o},{type:12,data:n.kvSequenceLength},{type:12,data:c}],v=d&&s&&we.size(s.dims)>0,k=["type","type"];v&&k.push("type"),i&&k.push("type"),a&&k.push("type");let T=[{dims:_,dataType:r.dataType,gpuDataType:0}];d&&T.push({dims:f,dataType:r.dataType,gpuDataType:0});let b=P=>{let x=Ie("probs",r.dataType,r.dims),S=Ie("v",t.dataType,t.dims),O=[x,S];v&&O.push(Ie("past_value",s.dataType,s.dims));let F=i?Ie("seq_lens",i.dataType,i.dims):void 0;i&&O.push(F);let H=a?Ie("total_sequence_length_input",a.dataType,a.dims):void 0;a&&O.push(H);let W=[ot("output",r.dataType,_)];d&&W.push(ot("present_value",r.dataType,f));let B=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${y}u;
  var<workgroup> tileQ: array<${x.type.value}, ${y*y}>;
  var<workgroup> tileV: array<${x.type.value}, ${y*y}>;
  ${P.registerUniforms(B).declareVariables(...O,...W)}
  ${P.mainStart([y,y,1])}
   let headIdx = workgroup_id.z % uniforms.num_heads;
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let kvHeadIdx = ${c===1?"headIdx":"headIdx / uniforms.n_reps"};
   let kv_num_heads = ${c===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
   let m = global_id.y;
   let n = global_id.x;
   let sequence_length = uniforms.M;
   var total_sequence_length = uniforms.K;
   ${ua(F,H,!0)}
   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
   ${v&&d?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${d?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${x.type.storage}(0);
   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
        ${v&&d?`
        if (w + local_id.y < past_sequence_length) {
          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
        }
      `:`
            if (w + local_id.y < uniforms.kv_sequence_length) {
              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
            }`}
        ${d?`
            if (w + local_id.y < present_sequence_length) {
          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
        }`:""}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + headIdx * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`};return{name:"AttentionScore",shaderCache:{hint:`${s!==void 0};${e}`,inputDependencies:k},getRunData:()=>({outputs:T,dispatchGroup:I,programUniforms:w}),getShaderSource:b}},fo=(e,r,t,s,n,o,i,a,l,c,p=void 0,d=void 0)=>{let u=Math.min(e.outputCount,1+(i?1:0)+(a?1:0)),f=u>1?c.pastSequenceLength:0,_=f+c.kvSequenceLength,y=l&&we.size(l.dims)>0?l:void 0,I=[r,t];u>1&&i&&we.size(i.dims)>0&&I.push(i),y&&I.push(y),p&&I.push(p),d&&I.push(d);let w=e.compute(sm(u,r,t,i,y,c,f,p,d),{inputs:I,outputs:u>1?[-1,1]:[-1]})[0];e.compute(rm(w,c.batchSize,c.numHeads,f,c.sequenceLength,_,p,d),{inputs:p&&d?[w,p,d]:[w],outputs:[]});let v=[w,s];u>1&&a&&we.size(a.dims)>0&&v.push(a),p&&v.push(p),d&&v.push(d),e.compute(nm(u,w,s,a,c,f,p,d),{inputs:v,outputs:u>1?[0,2]:[0]})},om=(e,r)=>{let t=[r.batchSize,r.numHeads,r.sequenceLength,r.headSize],s=r.sequenceLength,n=r.inputHiddenSize,o=r.headSize,i=12,a={x:Math.ceil(r.headSize/i),y:Math.ceil(r.sequenceLength/i),z:r.batchSize*r.numHeads},l=[e.inputs[0],e.inputs[1],e.inputs[2]],c=[{type:12,data:s},{type:12,data:n},{type:12,data:o},{type:12,data:r.numHeads},{type:12,data:r.headSize},{type:12,data:r.hiddenSize},{type:12,data:r.hiddenSize+r.hiddenSize+r.vHiddenSize}],p=d=>{let u=ot("output_q",l[0].dataType,t),f=ot("output_k",l[0].dataType,t),_=ot("output_v",l[0].dataType,t),y=Ie("input",l[0].dataType,l[0].dims),I=Ie("weight",l[1].dataType,l[1].dims),w=Ie("bias",l[2].dataType,l[2].dims),v=y.type.storage,k=[{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 = ${i}u;
  var<workgroup> tileInput: array<${v}, ${i*i}>;
  var<workgroup> tileWeightQ: array<${v}, ${i*i}>;
  var<workgroup> tileWeightK: array<${v}, ${i*i}>;
  var<workgroup> tileWeightV: array<${v}, ${i*i}>;
  ${d.registerUniforms(k).declareVariables(y,I,w,u,f,_)}
  ${d.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 = ${v}(0);
    var valueK = ${v}(0);
    var valueV = ${v}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:a,programUniforms:c}),getShaderSource:p},{inputs:l,outputs:[-1,-1,-1]})},am=(e,r)=>{let t=tm(e.inputs,r),[s,n,o]=om(e,t);return fo(e,s,n,o,e.inputs[4],void 0,void 0,void 0,e.inputs[5],t)}}),im,lm,cm,um,wx=Ne(()=>{ps(),gt(),Tt(),dr(),St(),im=(e,r)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let t=(s,n,o)=>{let i=n.length;if(i!==s.length)throw new Error(`${o}: num dimensions != ${i}`);n.forEach((a,l)=>{if(a!==s[l])throw new Error(`${o}: dim[${l}] do not match`)})};if(e[0].dims.length>1){let s=r.format==="NHWC"?r.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,r.spatial?2:void 0);t(e[1].dims,s,"Invalid input scale"),t(e[2].dims,s,"Invalid input B"),t(e[3].dims,s,"Invalid input mean"),t(e[4].dims,s,"Invalid input var")}else t(e[1].dims,[1],"Invalid input scale"),t(e[2].dims,[1],"Invalid input B"),t(e[3].dims,[1],"Invalid input mean"),t(e[4].dims,[1],"Invalid input var")},lm=(e,r)=>{let{epsilon:t,spatial:s,format:n}=r,o=e[0].dims,i=s?or(o[o.length-1]):1,a=n==="NHWC"&&o.length>1?i:1,l=we.size(o)/i,c=s,p=c?o.length:o,d=Ie("x",e[0].dataType,e[0].dims,i),u=Ie("scale",e[1].dataType,e[1].dims,a),f=Ie("bias",e[2].dataType,e[2].dims,a),_=Ie("inputMean",e[3].dataType,e[3].dims,a),y=Ie("inputVar",e[4].dataType,e[4].dims,a),I=ot("y",e[0].dataType,p,i),w=()=>{let k="";if(s)k=`let cOffset = ${o.length===1?"0u":n==="NHWC"?`outputIndices[${o.length-1}] / ${i}`:"outputIndices[1]"};`;else if(n==="NCHW")k=`
            ${I.indicesSet("outputIndices","0","0")}
            let cOffset = ${I.indicesToOffset("outputIndices")};`;else{k=`var cIndices = ${u.type.indices}(0);
                       cIndices[0] = outputIndices[${o.length-1}];`;for(let T=1;T<u.rank;T++)k+=`cIndices[${T}] = outputIndices[${T}];`;k+=`let cOffset = ${u.indicesToOffset("cIndices")};`}return k},v=k=>`
  const epsilon = ${t};
  ${k.registerUniform("outputSize","u32").declareVariables(d,u,f,_,y,I)}
  ${k.mainStart()}
  ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${I.offsetToIndices(`global_idx * ${i}`)};
    ${w()}
    let scale = ${u.getByOffset("cOffset")};
    let bias = ${f.getByOffset("cOffset")};
    let inputMean = ${_.getByOffset("cOffset")};
    let inputVar = ${y.getByOffset("cOffset")};
    let x = ${d.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${I.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${r.epsilon}_${r.format}_${s}_${i}`,inputDependencies:c?["rank","type","type","type","type"]:void 0},getShaderSource:v,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:c?[{type:12,data:l},...ct(o)]:[{type:12,data:l}]})}},cm=e=>Rt(e),um=(e,r)=>{let{inputs:t,outputCount:s}=e,n=cm({...r,outputCount:s});if(Xt.webgpu.validateInputContent&&im(t,n),r.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(lm(t,n))}}),dm,pm,mm,bx=Ne(()=>{Tt(),St(),dm=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},pm=e=>{let r=e[0].dims,t=e[0].dims[2],s=we.size(r)/4,n=e[0].dataType,o=Ie("input",n,r,4),i=Ie("bias",n,[t],4),a=Ie("residual",n,r,4),l=ot("output",n,r,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:c=>`
  const channels = ${t}u / 4;
  ${c.declareVariables(o,i,a,l)}

  ${c.mainStart()}
    ${c.guardAgainstOutOfBoundsWorkgroupSizes(s)}
    let value = ${o.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${a.getByOffset("global_idx")};
    ${l.setByOffset("global_idx","value")}
  }`}},mm=e=>{dm(e.inputs),e.compute(pm(e.inputs))}}),hm,zt,_m,fm,gm,Mm,wm,bm,ym,vm,xm,Tm,Pm,Em,Cm,Sm,go,km,da,$m,Im,Am,Fm,Om,Dm,Lm,zm,Bm,Rm,jm,Nm,Vm,Um,Wm,Gm,ml,Km,hl,_l,Hm,qm,Qm,Xm,Jm,Ym,fl=Ne(()=>{gt(),Tt(),dr(),St(),hm=(e,r,t,s,n,o,i)=>{let a=Math.ceil(r/4),l="";typeof n=="string"?l=`${n}(a)`:l=n("a");let c=Ie("inputData",t,[a],4),p=ot("outputData",s,[a],4),d=[{name:"vec_size",type:"u32"}];return i&&d.push(...i),`
      ${e.registerUniforms(d).declareVariables(c,p)}

  ${o??""}

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

    let a = ${c.getByOffset("global_idx")};
    ${p.setByOffset("global_idx",l)}
  }`},zt=(e,r,t,s,n,o=e.dataType,i,a)=>{let l=[{type:12,data:Math.ceil(we.size(e.dims)/4)}];return i&&l.push(...i),{name:r,shaderCache:{hint:n,inputDependencies:["type"]},getShaderSource:c=>hm(c,we.size(e.dims),e.dataType,o,t,s,a),getRunData:c=>({outputs:[{dims:e.dims,dataType:o}],dispatchGroup:{x:Math.ceil(we.size(c[0].dims)/64/4)},programUniforms:l})}},_m=e=>{e.compute(zt(e.inputs[0],"Abs","abs"))},fm=e=>{e.compute(zt(e.inputs[0],"Acos","acos"))},gm=e=>{e.compute(zt(e.inputs[0],"Acosh","acosh"))},Mm=e=>{e.compute(zt(e.inputs[0],"Asin","asin"))},wm=e=>{e.compute(zt(e.inputs[0],"Asinh","asinh"))},bm=e=>{e.compute(zt(e.inputs[0],"Atan","atan"))},ym=e=>{e.compute(zt(e.inputs[0],"Atanh","atanh"))},vm=e=>Rt(e),xm=(e,r)=>{let t;switch(r.to){case 10:t="vec4<f16>";break;case 1:t="vec4<f32>";break;case 12:t="vec4<u32>";break;case 6:t="vec4<i32>";break;case 9:t="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${r.to}`)}e.compute(zt(e.inputs[0],"Cast",t,void 0,r.cacheKey,r.to))},Tm=e=>{let r,t,s=e.length>=2&&e[1].data!==0,n=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:r=s?e[1].getFloat32Array()[0]:-34028234663852886e22,t=n?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:r=s?e[1].getUint16Array()[0]:64511,t=n?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return Rt({min:r,max:t})},Pm=(e,r)=>{let t=r||Tm(e.inputs),s=Rr(e.inputs[0].dataType);e.compute(zt(e.inputs[0],"Clip",n=>`clamp(${n}, vec4<${s}>(uniforms.min), vec4<${s}>(uniforms.max))`,void 0,t.cacheKey,void 0,[{type:e.inputs[0].dataType,data:t.min},{type:e.inputs[0].dataType,data:t.max}],[{name:"min",type:s},{name:"max",type:s}]),{inputs:[0]})},Em=e=>{e.compute(zt(e.inputs[0],"Ceil","ceil"))},Cm=e=>{e.compute(zt(e.inputs[0],"Cos","cos"))},Sm=e=>{e.compute(zt(e.inputs[0],"Cosh","cosh"))},go=e=>Rt(e),km=(e,r)=>{let t=Rr(e.inputs[0].dataType);e.compute(zt(e.inputs[0],"Elu",s=>`elu_vf32(${s})`,`
  const elu_alpha_ = ${t}(${r.alpha});

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

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

fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {
  let absv = abs(v);
  let x = 1.0 / (1.0 + r0 * absv);
  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
}`,$m=e=>{let r=Rr(e.inputs[0].dataType);e.compute(zt(e.inputs[0],"Erf",t=>`erf_vf32(${t})`,da(r)))},Im=e=>{e.compute(zt(e.inputs[0],"Exp","exp"))},Am=e=>{e.compute(zt(e.inputs[0],"Floor","floor"))},Fm=e=>{let r=Rr(e.inputs[0].dataType);e.compute(zt(e.inputs[0],"Gelu",t=>`0.5 * ${t} * (1.0 + erf_vf32(${t} * 0.7071067811865475))`,da(r)))},Om=(e,r)=>{let t=Rr(e.inputs[0].dataType);e.compute(zt(e.inputs[0],"LeakyRelu",s=>`select(leaky_relu_alpha_ * ${s}, ${s}, ${s} >= vec4<${t}>(0.0))`,`const leaky_relu_alpha_ = ${t}(${r.alpha});`,r.cacheKey))},Dm=e=>{e.compute(zt(e.inputs[0],"Not",r=>`!${r}`))},Lm=e=>{e.compute(zt(e.inputs[0],"Neg",r=>`-${r}`))},zm=e=>{e.compute(zt(e.inputs[0],"Reciprocal",r=>`1.0/${r}`))},Bm=e=>{let r=Rr(e.inputs[0].dataType);e.compute(zt(e.inputs[0],"Relu",t=>`select(vec4<${r}>(0.0), ${t}, ${t} > vec4<${r}>(0.0))`))},Rm=e=>{e.compute(zt(e.inputs[0],"Sigmoid",r=>`(1.0 / (1.0 + exp(-${r})))`))},jm=e=>Rt(e),Nm=(e,r)=>{let t=Rr(e.inputs[0].dataType);e.compute(zt(e.inputs[0],"HardSigmoid",s=>`max(vec4<${t}>(0.0), min(vec4<${t}>(1.0), ${r.alpha} * ${s} + vec4<${t}>(${r.beta})))`,void 0,r.cacheKey))},Vm=e=>{e.compute(zt(e.inputs[0],"Sin","sin"))},Um=e=>{e.compute(zt(e.inputs[0],"Sinh","sinh"))},Wm=e=>{e.compute(zt(e.inputs[0],"Sqrt","sqrt"))},Gm=e=>{e.compute(zt(e.inputs[0],"Tan","tan"))},ml=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Km=e=>{e.compute(zt(e.inputs[0],"Tanh",ml))},hl=(e="f32")=>`
const fast_gelu_a: ${e} = 0.5;
const fast_gelu_b: ${e} = 0.7978845608028654;
const fast_gelu_c: ${e} = 0.035677408136300125;

fn tanh_v(v: vec4<${e}>) -> vec4<${e}> {
  return ${ml("v")};
}
`,_l=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Hm=e=>{let r=Rr(e.inputs[0].dataType);e.compute(zt(e.inputs[0],"FastGelu",_l,hl(r),void 0,e.inputs[0].dataType))},qm=(e,r)=>{let t=Rr(e.inputs[0].dataType);return e.compute(zt(e.inputs[0],"ThresholdedRelu",s=>`select(vec4<${t}>(0.0), ${s}, ${s} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${t}>(${r.alpha});`,r.cacheKey)),0},Qm=e=>{e.compute(zt(e.inputs[0],"Log","log"))},Xm=(e,r)=>`
const alpha = vec4<${e}>(${r});
const one = ${e}(1.0);
const zero = ${e}(0.0);

fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> {
  let v = x *alpha;
  var x1 : vec4<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,Jm=e=>`quick_gelu_impl(${e})`,Ym=(e,r)=>{let t=Rr(e.inputs[0].dataType);e.compute(zt(e.inputs[0],"QuickGelu",Jm,Xm(t,r.alpha),r.cacheKey,e.inputs[0].dataType))}}),Zm,eh,th,yx=Ne(()=>{Tt(),St(),fl(),Zm=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},eh=e=>{let r=e[0].dims.slice();r[2]=r[2]/2;let t=Ie("input",e[0].dataType,e[0].dims,4),s=Ie("bias",e[0].dataType,[e[0].dims[2]],4),n=ot("output",e[0].dataType,r,4),o=we.size(r)/4,i=Sr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:a=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

  ${a.declareVariables(t,s,n)}

  ${da(i)}

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

    ${n.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},th=e=>{Zm(e.inputs),e.compute(eh(e.inputs))}}),rh,sh,Ms,nh,oh,ah,ih,lh,ch,uh,dh,ph,mh,vx=Ne(()=>{gt(),Tt(),St(),rh=(e,r,t,s,n,o,i,a,l,c,p,d)=>{let u,f;typeof a=="string"?u=f=(v,k)=>`${a}((${v}),(${k}))`:typeof a=="function"?u=f=a:(u=a.scalar,f=a.vector);let _=ot("outputData",p,s.length,4),y=Ie("aData",l,r.length,4),I=Ie("bData",c,t.length,4),w;if(n)if(o){let v=we.size(r)===1,k=we.size(t)===1,T=r.length>0&&r[r.length-1]%4===0,b=t.length>0&&t[t.length-1]%4===0;v||k?w=_.setByOffset("global_idx",f(v?`${y.type.value}(${y.getByOffset("0")}.x)`:y.getByOffset("global_idx"),k?`${I.type.value}(${I.getByOffset("0")}.x)`:I.getByOffset("global_idx"))):w=`
            let outputIndices = ${_.offsetToIndices("global_idx * 4u")};
            let offsetA = ${y.broadcastedIndicesToOffset("outputIndices",_)};
            let offsetB = ${I.broadcastedIndicesToOffset("outputIndices",_)};
            ${_.setByOffset("global_idx",f(i||T?y.getByOffset("offsetA / 4u"):`${y.type.value}(${y.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||b?I.getByOffset("offsetB / 4u"):`${I.type.value}(${I.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else w=_.setByOffset("global_idx",f(y.getByOffset("global_idx"),I.getByOffset("global_idx")));else{if(!o)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let v=(k,T,b="")=>{let P=`aData[indexA${T}][componentA${T}]`,x=`bData[indexB${T}][componentB${T}]`;return`
            let outputIndices${T} = ${_.offsetToIndices(`global_idx * 4u + ${T}u`)};
            let offsetA${T} = ${y.broadcastedIndicesToOffset(`outputIndices${T}`,_)};
            let offsetB${T} = ${I.broadcastedIndicesToOffset(`outputIndices${T}`,_)};
            let indexA${T} = offsetA${T} / 4u;
            let indexB${T} = offsetB${T} / 4u;
            let componentA${T} = offsetA${T} % 4u;
            let componentB${T} = offsetB${T} % 4u;
            ${k}[${T}] = ${b}(${u(P,x)});
          `};p===9?w=`
            var data = vec4<u32>(0);
            ${v("data",0,"u32")}
            ${v("data",1,"u32")}
            ${v("data",2,"u32")}
            ${v("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:w=`
            ${v("outputData[global_idx]",0)}
            ${v("outputData[global_idx]",1)}
            ${v("outputData[global_idx]",2)}
            ${v("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(y,I,_)}

        ${d??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${w}
      }`},sh=(e,r,t,s,n,o,i=t.dataType)=>{let a=t.dims.map(y=>Number(y)??1),l=s.dims.map(y=>Number(y)??1),c=!we.areEqual(a,l),p=a,d=we.size(a),u=!1,f=!1,_=[c];if(c){let y=Ln.calcShape(a,l,!1);if(!y)throw new Error("Can't perform binary op on the given tensors");p=y.slice(),d=we.size(p);let I=we.size(a)===1,w=we.size(l)===1,v=a.length>0&&a[a.length-1]%4===0,k=l.length>0&&l[l.length-1]%4===0;_.push(I),_.push(w),_.push(v),_.push(k);let T=1;for(let b=1;b<p.length;b++){let P=a[a.length-b],x=l[l.length-b];if(P===x)T*=P;else break}T%4===0?(f=!0,u=!0):(I||w||v||k)&&(u=!0)}else u=!0;return _.push(u),{name:e,shaderCache:{hint:r+_.map(y=>y.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:y=>rh(y,a,l,p,u,c,f,n,t.dataType,s.dataType,i,o),getRunData:()=>({outputs:[{dims:p,dataType:i}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:Math.ceil(we.size(p)/4)},...ct(a,l,p)]})}},Ms=(e,r,t,s,n,o)=>{e.compute(sh(r,n??"",e.inputs[0],e.inputs[1],t,s,o))},nh=e=>{Ms(e,"Add",(r,t)=>`${r}+${t}`)},oh=e=>{Ms(e,"Div",(r,t)=>`${r}/${t}`)},ah=e=>{Ms(e,"Equal",{scalar:(r,t)=>`u32(${r}==${t})`,vector:(r,t)=>`vec4<u32>(${r}==${t})`},void 0,void 0,9)},ih=e=>{Ms(e,"Mul",(r,t)=>`${r}*${t}`)},lh=e=>{let r=Ie("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Ms(e,"Pow",{scalar:(t,s)=>`pow_custom(${t},${s})`,vector:(t,s)=>`pow_vector_custom(${t},${s})`},`
    fn pow_custom(a : ${r}, b : ${r}) -> ${r} {
      if (b == ${r}(0.0)) {
        return ${r}(1.0);
      } else if (a < ${r}(0.0) && f32(b) != floor(f32(b))) {
        return ${r}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${r}(1.0), round(f32(abs(b) % ${r}(2.0))) != 1.0) * ${r}(${r==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${r}>, b : vec4<${r}>) -> vec4<${r}> {
      // TODO: implement vectorized pow
      return vec4<${r}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},ch=e=>{Ms(e,"Sub",(r,t)=>`${r}-${t}`)},uh=e=>{Ms(e,"Greater",{scalar:(r,t)=>`u32(${r}>${t})`,vector:(r,t)=>`vec4<u32>(${r}>${t})`},void 0,void 0,9)},dh=e=>{Ms(e,"Less",{scalar:(r,t)=>`u32(${r}<${t})`,vector:(r,t)=>`vec4<u32>(${r}<${t})`},void 0,void 0,9)},ph=e=>{Ms(e,"GreaterOrEqual",{scalar:(r,t)=>`u32(${r}>=${t})`,vector:(r,t)=>`vec4<u32>(${r}>=${t})`},void 0,void 0,9)},mh=e=>{Ms(e,"LessOrEqual",{scalar:(r,t)=>`u32(${r}<=${t})`,vector:(r,t)=>`vec4<u32>(${r}<=${t})`},void 0,void 0,9)}}),hh,_h,fh,gh,Mh,wh,xx=Ne(()=>{gt(),Tt(),dr(),St(),hh=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");let t=0,s=e[t],n=s.dataType,o=s.dims.length;e.forEach((i,a)=>{if(a!==t){if(i.dataType!==n)throw new Error("input tensors should be one type");if(i.dims.length!==o)throw new Error("input tensors should have the same shape");i.dims.forEach((l,c)=>{if(c!==r&&l!==s.dims[c])throw new Error("non concat dimensions must match")})}})},_h=(e,r)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${e}u>(${r});
    for (var i: u32 = 0u; i < ${e}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${e}u;
  }`,fh=(e,r)=>{let t=e.length,s=[];for(let n=0;n<t;++n){let o=r.setByOffset("global_idx",e[n].getByIndices("indices"));t===1?s.push(o):n===0?s.push(`if (inputIndex == ${n}u) { ${o} }`):n===t-1?s.push(`else { ${o} }`):s.push(`else if (inputIndex == ${n}) { ${o} }`)}return s.join(`
`)},gh=(e,r,t,s)=>{let n=we.size(t),o=new Array(e.length),i=new Array(e.length),a=0,l=[],c=[],p=[{type:12,data:n}];for(let y=0;y<e.length;++y)a+=e[y].dims[r],o[y]=a,c.push(e[y].dims.length),i[y]=Ie(`input${y}`,s,c[y]),l.push("rank"),p.push({type:12,data:o[y]});for(let y=0;y<e.length;++y)p.push(...ct(e[y].dims));p.push(...ct(t));let d=ot("output",s,t.length),u=d.indicesGet("indices",r),f=Array.from(Array(o.length).keys()).map(y=>`uniforms.sizeInConcatAxis${y}`).join(","),_=y=>`

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

  ${_h(o.length,f)}

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

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

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

    ${fh(i,d)}
  }`;return{name:"Concat",shaderCache:{hint:`${r}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:t,dataType:s}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:p}),getShaderSource:_}},Mh=(e,r)=>{let t=e.inputs,s=t[0].dims,n=we.normalizeAxis(r.axis,s.length);hh(t,n);let o=s.slice();o[n]=t.reduce((a,l)=>a+(l.dims.length>n?l.dims[n]:0),0);let i=t.filter(a=>we.size(a.dims)>0);e.compute(gh(i,n,o,t[0].dataType),{inputs:i})},wh=e=>Rt({axis:e.axis})}),dn,pn,mn,gl,hn=Ne(()=>{gt(),Tt(),dn=(e,r,t="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${r}(0.0));`;case"Sigmoid":return`value = (${r}(1.0) / (${r}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${r}(${t}(uniforms.clip_min)), ${r}(${t}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${r}(0.0), min(${r}(1.0), ${t}(uniforms.alpha) * value + ${t}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${t}(uniforms.alpha) * value, value, value >= ${r}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
        `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},pn=(e,r)=>{e.activation==="Clip"?r.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?r.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&r.push({type:1,data:e.alpha})},mn=(e,r)=>{e.activation==="Clip"?r.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?r.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&r.push({name:"alpha",type:"f32"})},gl=e=>{let r=e?.activation||"";if(r==="HardSigmoid"){let[t,s]=e?.activation_params||[.2,.5];return{activation:r,alpha:t,beta:s}}else if(r==="Clip"){let[t,s]=e?.activation_params||[Vd,Ud];return{activation:r,clipMax:s,clipMin:t}}else if(r==="LeakyRelu"){let[t]=e?.activation_params||[.01];return{activation:r,alpha:t}}return{activation:r}}}),Fr,bh,Ml=Ne(()=>{Fr=(e,r)=>{switch(e){case 1:return r;case 2:return`vec2<${r}>`;case 3:return`vec3<${r}>`;case 4:return`vec4<${r}>`;default:throw new Error(`${e}-component is not supported.`)}},bh=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),yh,Tx=Ne(()=>{yh=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),Mo,wl,bl=Ne(()=>{gt(),Tt(),St(),hn(),Mo=(e,r,t,s,n)=>{let o=s-t;return`
      ${Array.from({length:t}).map((i,a)=>`
      if (${lt(r.shape,a,r.rank)} != 1) {
        ${r.indicesSet(e,a,lt(n,a+o,s))}
      } else {
        ${r.indicesSet(e,a,0)}
      }`).join("")}
`},wl=(e,r,t,s,n=!1,o)=>{let i=e[0].dims,a=e[1].dims,l=i[i.length-2],c=a[a.length-1],p=i[i.length-1],d=or(c),u=or(p),f=or(l),_=we.size(t)/d/f,y=e.length>2,I=s?s.slice(0,-2):t.slice(0,-2),w=[we.size(I),l,c],v=[{type:12,data:_},{type:12,data:l},{type:12,data:c},{type:12,data:p}];pn(r,v),v.push(...ct(I,i,a)),y&&v.push(...ct(e[2].dims)),v.push(...ct(w));let k=T=>{let b=al("batch_dims",e[0].dataType,I.length),P=Ie("a",e[0].dataType,i.length,u),x=Ie("b",e[1].dataType,a.length,d),S=ot("output",e[0].dataType,w.length,d),O=Sr(S.type.tensor),F=dn(r,S.type.value,O),H=[P,x],W="";if(y){let X=n?d:1;H.push(Ie("bias",e[2].dataType,e[2].dims.length,X)),W=`${n?`value += bias[col / ${X}];`:`value += ${S.type.value}(bias[row + i]);`}`}let B=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];mn(r,B);let Y=()=>{let X=`var a_data: ${P.type.value};`;for(let J=0;J<u;J++)X+=`
              let b_data${J} = b[(b_offset + (k + ${J}) * uniforms.N + col) / ${d}];`;for(let J=0;J<f;J++){X+=`a_data = a[(a_offset + (row + ${J}) * uniforms.K + k) / ${u}];`;for(let re=0;re<u;re++)X+=`
            values[${J}] = fma(${x.type.value}(a_data${u===1?"":`[${re}]`}), b_data${re}, values[${J}]);
`}return X};return`
  ${T.registerUniforms(B).registerInternalVariables(b).declareVariables(...H,S)}
  ${T.mainStart()}
    ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${d})) * ${d};
    var index1 = global_idx / (uniforms.N / ${d});
    let stride1 = uniforms.M / ${f};
    let row = (index1 % stride1) * ${f};
    let batch = index1 / stride1;

    ${t.length===2?"":`let batch_indices = ${b.offsetToIndices("batch")};`}

    var a_indices: ${P.type.indices};
    ${Mo("a_indices",P,P.rank-2,b.rank,"batch_indices")}
    ${P.indicesSet("a_indices",P.rank-2,0)}
    ${P.indicesSet("a_indices",P.rank-1,0)}
    let a_offset = ${P.indicesToOffset("a_indices")};

    var b_indices: ${x.type.indices};
    ${Mo("b_indices",x,x.rank-2,b.rank,"batch_indices")}
    ${x.indicesSet("b_indices",x.rank-2,0)}
    ${x.indicesSet("b_indices",x.rank-1,0)}
    let b_offset = ${x.indicesToOffset("b_indices")};
    var values: array<${S.type.value}, ${f}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${u}) {
      ${Y()}
    }
    for (var i = 0u; i < ${f}u; i++) {
      var value = values[i];
      ${W}
      ${F}
      let cur_indices = ${S.type.indices}(batch, row + i, col);
      let offset = ${S.indicesToOffset("cur_indices")};
      ${S.setByOffset(`offset / ${d}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${r.activation};${d};${u};${f};${n}`,inputDependencies:y?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:o?o(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:v}),getShaderSource:k}}}),vh,xh,yl,vl,Th,xl,Ph,pa,Tl=Ne(()=>{gt(),Tt(),St(),hn(),bl(),Ml(),vh=(e,r)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${r?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${r?", batchIndices":""});
        `,xh=(e,r)=>e?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${r===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];
          ${r===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];
          ${r===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,yl=(e,r,t="f32",s,n=!1,o=32,i=!1,a=32)=>{let l=r[1]*e[1],c=r[0]*e[0],p=n?l:o,d=n?o:l,u=p/r[0],f=o/r[1];if(!((n&&u===4&&e[1]===4||!n&&(u===3||u===4))&&p%r[0]===0&&o%r[1]===0&&e[0]===4))throw new Error(`If transposeA ${n} is true, innerElementSize ${u} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${u} must be 3 or 4.
  tileAWidth ${p} must be divisible by workgroupSize[0]${r[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${u}<${t}>, ${p/u}>, ${d}>;
var<workgroup> mm_Bsub: array<array<vec4<${t}>, ${c/e[0]}>, ${o}>;

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

@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[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)"};
  ${s?`let batchIndices = ${s.offsetToIndices("u32(batch)")};`:""}
  let globalRowStart = i32(workgroupId.y) * ${l};

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

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

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

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

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

          ${xh(n,u)}
      }

      workgroupBarrier();
  }

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

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

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

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

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

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

  // Compute acc values for a single thread.
  var BCached : array<${t}, 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) {
      ${Th(n)}
      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<${t}, ${d}>, ${u}>;
  var<workgroup> mm_Bsub : array<array<${t}, ${p}>, ${o}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${o};

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

    var acc : array<array<${t}, colPerThread>, rowPerThread>;
    ${I}
  }
`},Ph=(e,r,t,s,n=!1)=>{let[o,i,a,l]=s,c=Sr(s[0].type.tensor);return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${o.type.indices}) -> ${Fr(e,c)} {
      var value = ${Fr(e,c)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        var aIndices: ${i.type.indices};
        ${Mo("aIndices",i,i.rank-2,o.rank,"batchIndices")}
        ${i.indicesSet("aIndices",i.rank-2,"u32(row)")}
        ${i.indicesSet("aIndices",i.rank-1,"u32(colIn)")}
        value = ${i.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${o.type.indices}) -> ${Fr(e,c)} {
      var value = ${Fr(e,c)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
      {
        var bIndices: ${a.type.indices};
        ${Mo("bIndices",a,a.rank-2,o.rank,"batchIndices")}
        ${a.indicesSet("bIndices",a.rank-2,"u32(row)")}
        ${a.indicesSet("bIndices",a.rank-1,"u32(colIn)")}
        value = ${a.getByIndices("bIndices")};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Fr(e,c)}) {
      let col = colIn * ${e};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${r?`value = value + ${n?"bias[colIn]":`${Fr(e,c)}(bias[row])`};`:""}
        ${t}
        ${l.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},pa=(e,r,t,s,n=!1,o)=>{let i=e[0].dims,a=e[1].dims,l=i.slice(0,-2),c=a.slice(0,-2),p=s?s.slice(0,-2):t.slice(0,-2),d=we.size(p),u=i[i.length-2],f=i[i.length-1],_=a[a.length-1],y=f%4===0&&_%4===0,I=u<=8?[4,1,1]:[4,4,1],w=[8,8,1],v=[Math.ceil(_/w[0]/I[0]),Math.ceil(u/w[1]/I[1]),Math.ceil(d/w[2]/I[2])],k=y?4:1,T=[...l,u,f/k],b=T.length,P=[...c,f,_/k],x=P.length,S=[d,u,_/k],O=[{type:6,data:u},{type:6,data:_},{type:6,data:f}];pn(r,O),O.push(...ct(p,T,P));let F=["rank","rank"],H=e.length>2;H&&(O.push(...ct(e[2].dims)),F.push("rank")),O.push(...ct(S));let W=B=>{let Y=p.length,X=al("batchDims",e[0].dataType,Y,1),J=Sr(e[0].dataType),re=Ie("a",e[0].dataType,b,k),ne=Ie("b",e[1].dataType,x,k),le=ot("result",e[0].dataType,S.length,k),pe=[re,ne];if(H){let te=n?k:1;pe.push(Ie("bias",e[2].dataType,e[2].dims.length,te))}let oe=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];mn(r,oe);let K=Sr(le.type.tensor),j=dn(r,le.type.value,K),D=Ph(k,H,j,[X,re,ne,le],n);return`
  ${B.registerUniforms(oe).registerInternalVariables(X).declareVariables(...pe,le)}
  ${D}
  ${y?yl(I,w,J,X):xl(I,w,J,X)}
                   `};return{name:"MatMul",shaderCache:{hint:`${I};${r.activation};${y};${n}`,inputDependencies:F},getRunData:()=>({outputs:[{dims:o?o(t):t,dataType:e[0].dataType}],dispatchGroup:{x:v[0],y:v[1],z:v[2]},programUniforms:O}),getShaderSource:W}}}),Eh,Ch,Px=Ne(()=>{gt(),Ls(),St(),hn(),Ml(),Tx(),Tl(),Eh=(e,r,t,s,n=!1,o,i=4,a=4,l=4,c="f32")=>{let p=O=>{switch(O){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${c}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${O} is not supported.`)}},d=O=>{switch(O){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 ${O} is not supported.`)}},u=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,f=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,_=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",y=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",I=e?"row":"col",w=e?"col":"row",v=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${I} / outWidth;
    let outCol = ${I} % outWidth;

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

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

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${b}) {
      let col = colIn * ${l};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${f}
      ${bh(n)}
      ${S}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Ch=(e,r,t,s,n,o,i,a,l)=>{let c=r.format==="NHWC",p=c?e[0].dims[3]:e[0].dims[1],d=t[0],u=c?t[2]:t[3],f=c?t[1]:t[2],_=c?t[3]:t[1],y=c&&(p%4===0||p%3===0)&&_%4===0,I=c?_:u*f,w=c?u*f:_,v=[8,8,1],k=s<=8?[4,1,1]:[4,4,1],T=[Math.ceil(I/v[0]/k[0]),Math.ceil(w/v[1]/k[1]),Math.ceil(d/v[2]/k[2])];Ot("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${T}`);let b=y?c&&p%4!==0?3:4:1,P=v[1]*k[1],x=v[0]*k[0],S=Math.max(v[0]*b,v[1]),O=s%P===0,F=n%x===0,H=o%S===0,W=y?[b,4,4]:[1,1,1],B=[{type:6,data:s},{type:6,data:n},{type:6,data:o},{type:6,data:[r.pads[0],r.pads[1]]},{type:6,data:r.strides},{type:6,data:r.dilations}];pn(r,B),B.push(...ct(e[0].dims,e[1].dims));let Y=["rank","rank"];i&&(B.push(...ct(e[2].dims)),Y.push("rank")),B.push(...ct(t));let X=J=>{let re=[{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}];mn(r,re);let ne=y?4:1,le=Sr(e[0].dataType),pe=`
      fn setOutputAtIndex(flatIndex : i32, value : ${y?`vec4<${le}>`:le}) {
        result[flatIndex] = ${y?`vec4<${le}>`:le}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${y?`vec4<${le}>`:le}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${y?"/ 4":""}, value);
      }`,oe=Ie("x",e[0].dataType,e[0].dims.length,b===3?1:b),K=Ie("w",e[1].dataType,e[1].dims.length,ne),j=[oe,K],D=ot("result",e[0].dataType,t.length,ne);if(i){let te=Ie("bias",e[2].dataType,e[2].dims.length,ne);j.push(te),pe+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${y?`vec4<${le}>`:le} {
          return bias[coords.${c?"w":"y"}${y?"/ 4":""}];
        }`}return`
        ${yh("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 };
        ${J.registerUniforms(re).declareVariables(...j,D)}
        ${pe}
        ${Eh(c,O,F,H,i,r,W[0],W[1],W[2],le)}
        ${y?yl(k,v,le,void 0,!c,S):xl(k,v,le,void 0,!c,S,!1,void 0,a)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${r.cacheKey};${b};${y};${O};${F};${H};${P};${x};${S}`,inputDependencies:Y},getRunData:()=>({outputs:[{dims:l?l(t):t,dataType:e[0].dataType}],dispatchGroup:{x:T[0],y:T[1],z:T[2]},programUniforms:B}),getShaderSource:X}}}),Sh,Pl,wo,kh,El,$h,Ih,Ah,Ex=Ne(()=>{gt(),Ls(),Tt(),St(),hn(),Ml(),Sh=e=>{let r=1;for(let t=0;t<e.length;t++)r*=e[t];return r},Pl=e=>typeof e=="number"?[e,e,e]:e,wo=(e,r)=>r<=1?e:e+(e-1)*(r-1),kh=(e,r,t,s=1)=>{let n=wo(r,s);return Math.floor((e[0]*(t-1)-t+n)/2)},El=(e,r,t,s,n)=>{n==null&&(n=kh(e,r[0],s[0]));let o=[0,0,0,t];for(let i=0;i<3;i++)e[i]+2*n>=r[i]&&(o[i]=Math.trunc((e[i]-r[i]+2*n)/s[i]+1));return o},$h=(e,r,t,s,n,o,i,a,l,c)=>{let p,d,u,f;if(e==="VALID"&&(e=0),typeof e=="number"){p={top:e,bottom:e,left:e,right:e,front:e,back:e};let _=El([r,t,s,1],[a,l,c],1,[n,o,i],e);d=_[0],u=_[1],f=_[2]}else if(Array.isArray(e)){if(!e.every((y,I,w)=>y===w[0]))throw Error(`Unsupported padding parameter: ${e}`);p={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let _=El([r,t,s,1],[a,l,c],1,[n,o,i],e[0]);d=_[0],u=_[1],f=_[2]}else if(e==="SAME_UPPER"){d=Math.ceil(r/n),u=Math.ceil(t/o),f=Math.ceil(s/i);let _=(d-1)*n+a-r,y=(u-1)*o+l-t,I=(f-1)*i+c-s,w=Math.floor(_/2),v=_-w,k=Math.floor(y/2),T=y-k,b=Math.floor(I/2),P=I-b;p={top:k,bottom:T,left:b,right:P,front:w,back:v}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:d,outHeight:u,outWidth:f}},Ih=(e,r,t,s,n,o=!1,i="channelsLast")=>{let a,l,c,p,d;if(i==="channelsLast")[a,l,c,p,d]=e;else if(i==="channelsFirst")[a,d,l,c,p]=e;else throw new Error(`Unknown dataFormat ${i}`);let[u,,f,_,y]=r,[I,w,v]=Pl(t),[k,T,b]=Pl(s),P=wo(f,k),x=wo(_,T),S=wo(y,b),{padInfo:O,outDepth:F,outHeight:H,outWidth:W}=$h(n,l,c,p,I,w,v,P,x,S),B=o?u*d:u,Y=[0,0,0,0,0];return i==="channelsFirst"?Y=[a,B,F,H,W]:i==="channelsLast"&&(Y=[a,F,H,W,B]),{batchSize:a,dataFormat:i,inDepth:l,inHeight:c,inWidth:p,inChannels:d,outDepth:F,outHeight:H,outWidth:W,outChannels:B,padInfo:O,strideDepth:I,strideHeight:w,strideWidth:v,filterDepth:f,filterHeight:_,filterWidth:y,effectiveFilterDepth:P,effectiveFilterHeight:x,effectiveFilterWidth:S,dilationDepth:k,dilationHeight:T,dilationWidth:b,inShape:e,outShape:Y,filterShape:r}},Ah=(e,r,t,s,n,o)=>{let i=o==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let a=[64,1,1],l={x:t.map((I,w)=>w)},c=[Math.ceil(Sh(l.x.map(I=>t[I]))/a[0]),1,1];Ot("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${c}`);let p=1,d=we.size(t),u=[{type:12,data:d},{type:12,data:s},{type:12,data:n},{type:12,data:r.strides},{type:12,data:r.dilations}];pn(r,u),u.push(...ct(e[0].dims,e[1].dims));let f=["rank","rank"],_=e.length===3;_&&(u.push(...ct(e[2].dims)),f.push("rank")),u.push(...ct(t));let y=I=>{let w=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:s.length},{name:"pads",type:"u32",length:n.length},{name:"strides",type:"u32",length:r.strides.length},{name:"dilations",type:"u32",length:r.dilations.length}];mn(r,w);let v=1,k=Sr(e[0].dataType),T=Ie("x",e[0].dataType,e[0].dims.length,p),b=Ie("W",e[1].dataType,e[1].dims.length,v),P=[T,b],x=ot("result",e[0].dataType,t.length,v),S="";if(_){let H=Ie("bias",e[2].dataType,e[2].dims.length,v);P.push(H),S+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${k} {
          return bias[${i?lt("coords",4,5):lt("coords",1,5)}];
        }`}let O=Fr(p,k),F=dn(r,O,k);return`
            ${S}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${T.getByIndices("aIndices")};
            }
            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${b.getByIndices("aIndices")};
            }
          ${I.registerUniforms(w).declareVariables(...P,x)}
          ${I.mainStart()}
          ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${x.offsetToIndices("global_idx")};
              let batch = ${lt("coords",0,T.rank)};
              let d2 = ${i?lt("coords",T.rank-1,T.rank):lt("coords",1,T.rank)};
              let xFRCCorner = vec3<u32>(${i?lt("coords",1,T.rank):lt("coords",2,T.rank)},
              ${i?lt("coords",2,T.rank):lt("coords",3,T.rank)},
              ${i?lt("coords",3,T.rank):lt("coords",4,T.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${i?lt("uniforms.x_shape",1,T.rank):lt("uniforms.x_shape",2,T.rank)};
              let xShapeZ = ${i?lt("uniforms.x_shape",2,T.rank):lt("uniforms.x_shape",3,T.rank)};
              let xShapeW = ${i?lt("uniforms.x_shape",3,T.rank):lt("uniforms.x_shape",4,T.rank)};
              let xShapeU = ${i?lt("uniforms.x_shape",4,T.rank):lt("uniforms.x_shape",1,T.rank)};
              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
              let inputDepthVec4Remainder = xShapeU % 4;

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

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

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

                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
                      ${i?`let xValues = vec4<f32>(
                               getX(batch, xF, xR, xC, d1),
                               getX(batch, xF, xR, xC, d1 + 1),
                               getX(batch, xF, xR, xC, d1 + 2),
                               getX(batch, xF, xR, xC, d1 + 3));
                            `:`let xValues = vec4<f32>(
                               getX(batch, d1, xF, xR, xC),
                               getX(batch, d1 + 1, xF, xR, xC),
                               getX(batch, d1 + 2, xF, xR, xC),
                               getX(batch, d1 + 3, xF, xR, xC));
                            `}
                            let wValues = vec4<f32>(
                              getW(d2, d1, wF, wR, wC),
                              getW(d2, d1 + 1, wF, wR, wC),
                              getW(d2, d1 + 2, wF, wR, wC),
                              getW(d2, d1 + 3, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                    if (inputDepthVec4Remainder == 1) {
                        ${i?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
                    } else if (inputDepthVec4Remainder == 2) {
                      ${i?`let xValues = vec2<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
                      `:`let xValues = vec2<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
                    `}
                    let wValues = vec2<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
                      value += dot(xValues, wValues);
                    } else if (inputDepthVec4Remainder == 3) {
                      ${i?`let xValues = vec3<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
                      `:`let xValues = vec3<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
                    `}
                    let wValues = vec3<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                  }
                }
              }
              ${_?"value = value + getBiasByOutputCoords(coords)":""};
              ${F}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${r.cacheKey};${i};${p};${_}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:c[0],y:c[1],z:c[2]},programUniforms:u}),getShaderSource:y}}}),Fh,Oh,Cx=Ne(()=>{gt(),Tt(),St(),hn(),Fh=(e,r,t,s)=>{let n=e.length>2,o=n?"value += b[output_channel];":"",i=e[0].dims,a=e[1].dims,l=r.format==="NHWC",c=l?t[3]:t[1],p=c/r.group,d=l&&p>=4?or(c):1,u=we.size(t)/d,f=[{type:12,data:u},{type:12,data:r.dilations},{type:12,data:[r.strides[0],r.strides[1]]},{type:12,data:[r.pads[0],r.pads[1]]},{type:12,data:p}];pn(r,f),f.push(...ct(i,[a[0],a[1],a[2],a[3]/d]));let _=n?["rank","rank","rank"]:["rank","rank"];f.push(...ct([t[0],t[1],t[2],t[3]/d]));let y=I=>{let w=ot("output",e[0].dataType,t.length,d),v=Sr(w.type.tensor),k=dn(r,w.type.value,v),T=Ie("x",e[0].dataType,i.length),b=Ie("w",e[1].dataType,a.length,d),P=[T,b];n&&P.push(Ie("b",e[2].dataType,e[2].dims,d));let x=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:r.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];mn(r,x);let S=l?`
      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

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

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

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

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

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

            let xVal = ${T.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${b.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${I.registerUniforms(x).declareVariables(...P,w)}

  ${I.mainStart()}
    ${I.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 * ${d} / uniforms.output_channels_per_group;
    var in_channel_offset = group_id * uniforms.w_shape[${l?2:1}];

    var value: ${w.type.value} = ${w.type.value}(0);
    ${S}
    ${o}
    ${k}
    ${w.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:`${r.cacheKey}_${d}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:f}),getShaderSource:y}},Oh=(e,r,t,s)=>{let n=e.length>2,o=or(t[3]),i=or(t[2]),a=we.size(t)/o/i,l=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/o],c=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/o],p=[t[0],t[1],t[2],t[3]/o],d=[{type:12,data:a},{type:6,data:[r.strides[0],r.strides[1]]},{type:6,data:[r.pads[0],r.pads[1]]}];pn(r,d),d.push(...ct(l,c,p));let u=(i-1)*r.strides[1]+c[1],f=_=>{let y=ot("output",e[0].dataType,p.length,o),I=Sr(y.type.tensor),w=dn(r,y.type.value,I),v=Ie("x",e[0].dataType,l.length,o),k=Ie("w",e[1].dataType,c.length,o),T=[v,k];n&&T.push(Ie("b",e[2].dataType,e[2].dims,o));let b=n?"value += b[output_channel];":"",P=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return mn(r,P),`
  ${_.registerUniforms(P).declareVariables(...T,y)}
  ${_.mainStart()}
    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let width0 = uniforms.output_shape[3];
    let output_channel = global_idx % width0;
    var index1 = global_idx / width0;
    let width1 = uniforms.output_shape[2] / ${i}u;
    let col = (index1 % width1) * ${i}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

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

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

    for (var i = 0u; i < ${i}u; i++) {
      var value = values[i];
      ${b}
      ${w}
      ${y.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${r.cacheKey};${o};${i};${u};${c[0]};${c[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:f}}}),Dh,ma,Lh,ha,Cl,Sl,zh,Bh,kl,Sx=Ne(()=>{Tt(),Px(),Ex(),Tl(),Cx(),hn(),bl(),Ks(),Dh=(e,r,t,s,n,o)=>{let i=e[0],a=e.slice(o?1:2,o?3:4),l=a.length,c=r[0],p=r.slice(2).map((u,f)=>u+(u-1)*(t[f]-1)),d=a.map((u,f)=>u+s[f]+s[f+l]).map((u,f)=>Math.floor((u-p[f]+n[f])/n[f]));return d.splice(0,0,i),d.splice(o?3:1,0,c),d},ma=[2,3,1,0],Lh=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],s=e[1].dims[1]*r.group;if(t!==s)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 n=e[0].dims.length-2;if(r.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(r.strides.length!==n)throw new Error(`strides should be ${n}D`);if(r.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ha=(e,r)=>{let t=e.kernelShape.slice();t.length<r[1].dims.length-2&&t.push(...Array(r[1].dims.length-2-t.length).fill(0));for(let o=2;o<r[1].dims.length;++o)t[o-2]===0&&(t[o-2]=r[1].dims[o]);let s=e.pads.slice();na.adjustPadsBasedOnAutoPad(r[0].dims,e.strides,e.dilations,t,s,e.format==="NHWC",e.autoPad);let n=Object.assign({},e);return Object.assign(n,{kernelShape:t,pads:s}),n},Cl=e=>{let r=gl(e),t=e.format,s=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],n=e.dilations,o=e.group,i=e.kernel_shape,a=e.pads,l=e.strides,c=e.w_is_const();return{autoPad:s,format:t,dilations:n,group:o,kernelShape:i,pads:a,strides:l,wIsConst:c,...r,cacheKey:`${e.format};${r.activation};`}},Sl=(e,r,t,s)=>{let n=t.format==="NHWC",o=Dh(r[0].dims,r[1].dims,t.dilations,t.pads,t.strides,n);if(t.group!==1){let P=[r[0]];if(n){let x=e.kernelCustomData.wT??e.compute(Xr(r[1],ma),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=x),P.push(x)}else P.push(r[1]);r.length===3&&P.push(r[2]),!e.adapterInfo.isArchitecture("ampere")&&n&&r[1].dims[0]===t.group&&r[1].dims[1]===1&&t.dilations[0]===1&&t.dilations[1]===1?e.compute(Oh(P,t,o,s),{inputs:P}):e.compute(Fh(P,t,o,s),{inputs:P});return}let i=r.length===3,a=r[0].dims[n?1:2],l=r[0].dims[n?2:3],c=r[0].dims[n?3:1],p=r[1].dims[2],d=r[1].dims[3],u=o[n?1:2],f=o[n?2:3],_=o[n?3:1],y=n&&p===a&&d===l&&t.pads[0]===0&&t.pads[1]===0;if(y||p===1&&d===1&&t.dilations[0]===1&&t.dilations[1]===1&&t.strides[0]===1&&t.strides[1]===1&&t.pads[0]===0&&t.pads[1]===0){let P=o[0],x,S,O,F=[];if(n){let B=e.kernelCustomData.wT??e.compute(Xr(r[1],ma),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=B),y){let Y=a*l*c;x=r[0].reshape([1,P,Y]),S=B.reshape([1,Y,_]),O=[1,P,_]}else x=r[0].reshape([P,a*l,c]),S=B.reshape([1,c,_]),O=[P,u*f,_];F.push(x),F.push(S)}else x=r[0].reshape([P,c,a*l]),S=r[1].reshape([1,_,c]),O=[P,_,u*f],F.push(S),F.push(x);i&&F.push(r[2]);let H=O[2],W=F[0].dims[F[0].dims.length-1];H<8&&W<8?e.compute(wl(F,t,o,O,n,s),{inputs:F}):e.compute(pa(F,t,o,O,n,s),{inputs:F});return}let I=!0,w=e.kernelCustomData.wT??e.compute(Xr(r[1],ma),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=w);let v=[r[0],w];i&&v.push(r[2]);let k=n?u*f:_,T=n?_:u*f,b=p*d*c;e.compute(Ch(v,t,o,k,T,b,i,I,s),{inputs:v})},zh=(e,r)=>{let t=r.format==="NHWC",s=[e.inputs[0].reshape(t?[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&&s.push(e.inputs[2]);let n=[0,r.pads[0],0,r.pads[1]],o=[1].concat(r.strides),i=[1].concat(r.dilations),a=[1].concat(r.kernelShape),l=ha({...r,pads:n,strides:o,dilations:i,kernelShape:a},s);Sl(e,s,l,c=>t?[c[0],c[2],c[3]]:[c[0],c[1],c[3]])},Bh=(e,r,t)=>{let s=t.format==="NHWC"?"channelsLast":"channelsFirst",n=ha(t,r),o=t.autoPad==="NOTSET"?t.pads:t.autoPad,i=Ih(r[0].dims,r[1].dims,t.strides,t.dilations,o,!1,s);e.compute(Ah(r,n,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],s))},kl=(e,r)=>{if(Lh(e.inputs,r),e.inputs[0].dims.length===3)zh(e,r);else if(e.inputs[0].dims.length===5)Bh(e,e.inputs,r);else{let t=ha(r,e.inputs);Sl(e,e.inputs,t)}}}),Rh,kx=Ne(()=>{gt(),Ls(),Tt(),St(),Rh=(e,r,t)=>{let s=e.length>2,n=r.outputShape,o=r.format==="NHWC",i=r.group,a=e[1].dims,l=a[2]/i,c=a[3],p=o?or(l):1,d=o&&c===1&&l>=4,u=d?Math.floor(l/4)*4:Math.floor(l/p)*p,f=l-u,_=o?or(c):1,y=o?c===1?p:_:1,I=we.size(n)/_,w=[Math.ceil(I/64),1,1];Ot("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${w}`);let v=["rank","rank"],k=[r.strides[0],r.strides[1]],T=[r.kernelShape[o?1:2],r.kernelShape[o?2:3]],b=[r.dilations[0],r.dilations[1]],P=[T[0]+(r.dilations[0]<=1?0:(r.kernelShape[o?1:2]-1)*(r.dilations[0]-1)),T[1]+(r.dilations[1]<=1?0:(r.kernelShape[o?2:3]-1)*(r.dilations[1]-1))],x=[P[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),P[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],S=[{type:12,data:I},{type:12,data:k},{type:12,data:T},{type:12,data:b},{type:12,data:P},{type:6,data:x},{type:12,data:u},{type:12,data:l},{type:12,data:c},...ct(e[0].dims,e[1].dims)];s&&(S.push(...ct(e[2].dims)),v.push("rank")),S.push(...ct(n));let O=F=>{let H=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:k.length},{name:"filter_dims",type:"u32",length:T.length},{name:"dilations",type:"u32",length:T.length},{name:"effective_filter_dims",type:"u32",length:P.length},{name:"pads",type:"i32",length:x.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],W=Sr(e[0].dataType),B=o?1:2,Y=o?2:3,X=o?3:1,J=Ie("W",e[1].dataType,e[1].dims.length,y),re=Ie("Dy",e[0].dataType,e[0].dims.length,p),ne=[re,J];s&&ne.push(Ie("bias",e[2].dataType,[n[X]].length,_));let le=ot("result",e[0].dataType,n.length,_),pe=()=>{let j="";if(d)p===4?j+=`
        let xValue = ${re.getByOffset("x_offset")};
        let wValue = ${J.getByOffset("w_offset")};
        dotProd = dotProd + dot(xValue, wValue);
        x_offset += 1u;
        w_offset += 1u;`:p===2?j+=`
          dotProd = dotProd + dot(vec4<${W}>(${re.getByOffset("x_offset")}, ${re.getByOffset("x_offset + 1u")}), vec4<${W}>(${J.getByOffset("w_offset")}, ${J.getByOffset("w_offset + 1u")}));
          x_offset += 2u;
          w_offset += 2u;`:p===1&&(j+=`
          dotProd = dotProd + dot(vec4<${W}>(${re.getByOffset("x_offset")}, ${re.getByOffset("x_offset + 1u")}, ${re.getByOffset("x_offset + 2u")}, ${re.getByOffset("x_offset + 3u")}), vec4<${W}>(${J.getByOffset("w_offset")}, ${J.getByOffset("w_offset + 1u")}, ${J.getByOffset("w_offset + 2u")}, ${J.getByOffset("w_offset + 3u")}));
          x_offset += 4u;
          w_offset += 4u;`);else if(j+=`
                  let xValue = ${o?re.getByOffset(`${re.indicesToOffset(`${re.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}`):re.get("batch","inputChannel","idyR","idyC")};
        `,p===1)j+=`
          let w_offset = ${J.indicesToOffset(`${J.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
          let wValue = ${J.getByOffset(`w_offset / ${y}`)};
          dotProd = dotProd + xValue * wValue;`;else for(let D=0;D<p;D++)j+=`
            let wValue${D} = ${J.getByOffset(`${J.indicesToOffset(`${J.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${D}, wOutChannel)`)} / ${y}`)};
            dotProd = dotProd + xValue[${D}] * wValue${D};`;return j},oe=()=>{if(f===0)return"";if(!d)throw new Error(`packInputAs4 ${d} is not true.`);let j="";if(p===1){j+="dotProd = dotProd";for(let D=0;D<f;D++)j+=`
            + ${re.getByOffset(`x_offset + ${D}`)} * ${J.getByOffset(`w_offset + ${D}`)}`;j+=";"}else if(p===2){if(f!==2)throw new Error(`Invalid inputChannelsRemainder ${f}.`);j+=`
          let xValue = ${re.getByOffset("x_offset")};
          let wValue = ${J.getByOffset("w_offset")};
          dotProd = dotProd + dot(xValue, wValue);`}return j},K=`
            let outputIndices = ${le.offsetToIndices(`global_idx * ${_}`)};
            let batch = ${le.indicesGet("outputIndices",0)};
            let d1 = ${le.indicesGet("outputIndices",X)};
            let r = ${le.indicesGet("outputIndices",B)};
            let c = ${le.indicesGet("outputIndices",Y)};
            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 = ${le.type.value}(0.0);
            var wR: u32 = 0;
            if (uniforms.dilations.x == 1) {
              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
            }
            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
              if (wR % uniforms.dilations.x != 0) {
                continue;
              }
              let dyR = (${W}(dyRCorner) + ${W}(wR)) / ${W}(uniforms.strides[0]);
              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
              if (dyR < 0.0 || dyR >= ${W}(uniforms.Dy_shape[${B}]) || fract(dyR) > 0.0 ||
                  wRPerm < 0) {
                continue;
              }
              let idyR: u32 = u32(dyR);
              var wC: u32 = 0;
              if (uniforms.dilations.y == 1) {
                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
              }
              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
                if (wC % uniforms.dilations.y != 0) {
                  continue;
                }
                let dyC = (${W}(dyCCorner) + ${W}(wC)) / ${W}(uniforms.strides.y);
                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
                if (dyC < 0.0 || dyC >= ${W}(uniforms.Dy_shape[${Y}]) ||
                    fract(dyC) > 0.0 || wCPerm < 0) {
                  continue;
                }
                let idyC: u32 = u32(dyC);
                var inputChannel = groupId * uniforms.input_channels_per_group;
                ${d?`
                var x_offset = ${re.indicesToOffset(`${re.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p};
                var w_offset = ${J.indicesToOffset(`${J.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${y};
                  `:""}
                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${d?4:p}) {
                  ${pe()}
                  inputChannel = inputChannel + ${d?4:p};
                }
                ${oe()}
                wC = wC + uniforms.strides.y - 1;
              }
              wR = wR + uniforms.strides[0] - 1;
            }
            let value = dotProd${s?` + bias[d1 / ${_}]`:""};
            ${le.setByOffset("global_idx","value")};
          `;return`
    ${F.registerUniforms(H).declareVariables(...ne,le)}
      ${F.mainStart()}
      ${F.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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      ${I("")};
      let value = ${f.getByIndices("dataIndices")};
      ${y.setByOffset("global_idx","value")};
      `;return`
      ${u.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(f,_,y)}
      ${u.mainStart()}
        ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${w}
      }`};return{name:"Gather",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:p}),getShaderSource:d}},f_=e=>Rt({axis:e.axis}),g_=(e,r)=>{let t=e.inputs;h_(t),e.compute(__(e.inputs,r))}}),M_,w_,b_,zx=Ne(()=>{gt(),Tt(),St(),M_=(e,r,t,s,n,o,i,a,l)=>{let c=[{type:12,data:o},{type:12,data:s},{type:12,data:n},{type:12,data:t},{type:12,data:i},{type:12,data:a},{type:12,data:l}],p=[o];c.push(...ct(r.dims,p));let d=u=>{let f=Ie("indices_data",r.dataType,r.dims.length),_=ot("input_slice_offsets_data",12,1,1),y=[f,_],I=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:n.length},{name:"sizes_from_slice_dims_data",type:"u32",length:t.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return`
  ${u.registerUniforms(I).declareVariables(...y)}
  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let batch_idx = global_idx / uniforms.num_slices_per_batch;
    let base_offset = batch_idx * uniforms.input_batch_stride;

    let slice_indices_base_offset = global_idx * uniforms.num_slice_dims;
    var relative_slice_offset = 0;
    for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) {
      var index = i32(indices_data[dim_idx + slice_indices_base_offset].x);
      let input_dim_idx = uniforms.batch_dims + dim_idx;
      if (index < 0) {
        ${n.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"}
      }
      ${t.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"}
    }

    input_slice_offsets_data[global_idx] =  base_offset + u32(relative_slice_offset);
  }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${n.length}_${t.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:c}),getShaderSource:d},{inputs:[r],outputs:[-1]})[0]},w_=(e,r)=>{let t=e.inputs,s=t[0].dims,n=t[0].dataType,o=t[1].dims,i=o[o.length-1],a=we.sizeToDimension(o,o.length-1),l=we.sizeFromDimension(s,r.batchDims+i),c=we.sizeToDimension(s,r.batchDims),p=we.sizeFromDimension(s,r.batchDims),d=a/c,u=new Array(i),f=l;for(let T=0;T<i;++T)u[i-1-T]=f,f*=s[r.batchDims+i-1-T];let _=M_(e,t[1],u,r.batchDims,s,a,d,p,i),y=r.batchDims+i;if(y>s.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let I=o.slice(0,-1).concat(s.slice(y)),w=we.size(I),v=[{type:12,data:w},{type:12,data:l},...ct(t[0].dims,_.dims,I)],k=T=>{let b=Ie("data",t[0].dataType,t[0].dims.length),P=Ie("slice_offsets",12,_.dims.length),x=ot("output",t[0].dataType,I.length);return`
          ${T.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(b,P,x)}
            ${T.mainStart()}
            ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
        }`};e.compute({name:"GatherND",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:I,dataType:n}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:v}),getShaderSource:k},{inputs:[t[0],_]})},b_=e=>({batchDims:e.batch_dims,cacheKey:""})}),y_,v_,x_,T_,Bx=Ne(()=>{gt(),Tt(),dr(),St(),y_=(e,r)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let t=we.normalizeAxis(r.quantizeAxis,e[0].dims.length),s=r.blockSize,n=e[0],o=e[2],i=e.length===4?e[3]:void 0;if(o.dims.length!==n.dims.length||!n.dims.map((a,l)=>l===t?Math.ceil(a/s)===o.dims[l]:a===o.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(i){if(i.dataType!==n.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(i.dims.length!==o.dims.length||!i.dims.map((a,l)=>a===o.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},v_=(e,r)=>{let t=e[0].dims,s=e[1].dims,n=t.length,o=we.normalizeAxis(r.gatherAxis,n),i=we.normalizeAxis(r.quantizeAxis,n),a=t.slice(0);a.splice(o,1,...s);let l=we.size(a),c=e[2].dataType,p=e[0].dataType===22,d=[{type:12,data:l},{type:12,data:i},{type:12,data:o},{type:12,data:r.blockSize},...ct(...e.map((f,_)=>f.dims),a)],u=f=>{let _=Ie("data",e[0].dataType,e[0].dims.length),y=Ie("inputIndices",e[1].dataType,e[1].dims.length),I=Ie("scales",e[2].dataType,e[2].dims.length),w=e.length>3?Ie("zeroPoint",e[3].dataType,e[3].dims.length):void 0,v=ot("output",c,a.length),k=[_,y,I];w&&k.push(w);let T=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${f.registerUniforms(T).declareVariables(...k,v)}
        ${f.mainStart()}
        let output_indices = ${v.offsetToIndices("global_idx")};
        var indices_indices = ${y.type.indices}(0);
        ${s.length>1?`
          for (var i: u32 = 0; i < ${s.length}; i++) {
            let index = ${v.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${y.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${v.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${_.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${v.indicesGet("output_indices","i")};
          ${_.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${y.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${t[o]};
        }
        ${_.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${a.length}; i++) {
          let index = ${v.indicesGet("output_indices",`i + ${s.length} - 1`)};
          ${_.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${_.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${_.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
        let quantized_data = quantized_data_vec[data_index / 2];
        var scale_indices = data_indices;
        let quantize_axis_index = ${I.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${I.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${I.getByIndices("scale_indices")};
        ${w?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${w.indicesToOffset("zero_point_indices")};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${w.getByOffset("zero_point_offset / 8")};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${Rr(c)}(quantized_data - zero_point) * scale;
        ${v.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${r.cacheKey};${e.filter((f,_)=>_!==1).map(f=>f.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(f,_)=>"rank")},getRunData:()=>({outputs:[{dims:a,dataType:c}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:u}},x_=(e,r)=>{let t=e.inputs;y_(t,r),e.compute(v_(e.inputs,r))},T_=e=>Rt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),P_,E_,C_,S_,Rx=Ne(()=>{gt(),Tt(),dr(),St(),P_=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},E_=(e,r)=>{let t=e[0].dims,s=e[0].dataType,n=t.length,o=e[1].dims,i=e[1].dataType,a=we.normalizeAxis(r.axis,n),l=t[a],c=o.slice(0),p=we.size(c),d=Ie("input",s,n),u=Ie("indicesInput",i,o.length),f=ot("output",s,c.length),_=[{type:12,data:p},{type:6,data:l},{type:12,data:a}];return _.push(...ct(t,o,c)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:_}),getShaderSource:y=>`
      ${y.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(d,u,f)}
      ${y.mainStart()}
      ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${f.setByOffset("global_idx","value")};
  }`}},C_=e=>Rt({axis:e.axis}),S_=(e,r)=>{let t=e.inputs;P_(t),e.compute(E_(e.inputs,r))}}),k_,$_,I_,A_,jx=Ne(()=>{gt(),Tt(),St(),k_=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},$_=(e,r)=>{let t=e[0].dims.slice(),s=e[1].dims.slice(),[n,o,i]=Nd.getShapeOfGemmResult(t,r.transA,s,r.transB,e.length===3?e[2].dims:void 0),a=[n,o];if(!a)throw new Error("Can't use gemm on the given tensors");let l=16,c=Math.ceil(o/l),p=Math.ceil(n/l),d=!0,u=we.size(a),f=[{type:12,data:d?c:u},{type:12,data:n},{type:12,data:o},{type:12,data:i},{type:1,data:r.alpha},{type:1,data:r.beta}],_=["type","type"];e.length===3&&(f.push(...ct(e[2].dims)),_.push("rank")),f.push(...ct(a));let y=w=>{let v="";r.transA&&r.transB?v="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":r.transA&&!r.transB?v="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!r.transA&&r.transB?v="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!r.transA&&!r.transB&&(v="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let k=r.alpha===1?"":"value *= uniforms.alpha;",T=Ie("a",e[0].dataType,e[0].dims),b=Ie("b",e[1].dataType,e[1].dims),P=T.type.value,x=null,S=[T,b];e.length===3&&(x=Ie("c",e[2].dataType,e[2].dims.length),S.push(x));let O=ot("output",e[0].dataType,a.length);S.push(O);let F=[{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`
  ${w.registerUniforms(F).declareVariables(...S)}

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

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

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

    ${k}
    ${x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",O)}; value += ${P}(uniforms.beta) * ${x.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`},I=w=>{let v=Ie("a",e[0].dataType,e[0].dims),k=Ie("b",e[1].dataType,e[1].dims),T=null,b=[v,k];e.length===3&&(T=Ie("c",e[2].dataType,e[2].dims.length),b.push(T));let P=ot("output",e[0].dataType,a.length);b.push(P);let x=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],S="",O="";r.transA&&r.transB?(O=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${v.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${k.type.value}(0);
      }
      `,S="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):r.transA&&!r.transB?(O=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${v.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${k.type.value}(0);
      }
      `,S="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!r.transA&&r.transB?(O=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${v.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${k.type.value}(0);
      }
      `,S="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!r.transA&&!r.transB&&(O=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${v.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${k.type.value}(0);
      }
      `,S="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let F=r.alpha===1?"":"value *= uniforms.alpha;";return`
  ${w.registerUniforms(x).declareVariables(...b)}
  var<workgroup> tile_a: array<array<${v.type.storage}, ${l}>, ${l}>;
  var<workgroup> tile_b: array<array<${k.type.storage}, ${l}>, ${l}>;
  ${w.mainStart([l,l,1])}
    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${l};
    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${l};
    let num_tiles = (uniforms.K - 1) / ${l} + 1;
    var k_start = 0u;
    var value = ${P.type.value}(0);
    for (var t: u32 = 0u; t < num_tiles; t++) {
      ${O}
      k_start = k_start + ${l};
      workgroupBarrier();

      for (var k: u32 = 0u; k < ${l}; k++) {
        ${S}
      }
      workgroupBarrier();
    }

    ${F}
    let m = tile_row_start + local_id.y;
    let n = tile_col_start + local_id.x;
    ${T!=null?`let cOffset = ${T.broadcastedIndicesToOffset("vec2(m, n)",P)}; value += ${P.type.value}(uniforms.beta) * ${T.getByOffset("cOffset")};`:""}
    if (m < uniforms.M && n < uniforms.N) {
      output[m * uniforms.N + n] = value;
    }
  }`};return d?{name:"GemmShared",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:c*p},programUniforms:f}),getShaderSource:I}:{name:"Gemm",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:f}),getShaderSource:y}},I_=e=>{let r=e.transA,t=e.transB,s=e.alpha,n=e.beta;return{transA:r,transB:t,alpha:s,beta:n,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},A_=(e,r)=>{k_(e.inputs),e.compute($_(e.inputs,r))}}),xs,zs,_n,fn,F_,O_,D_,L_,z_,B_,R_,j_,N_,V_,Nx=Ne(()=>{gt(),Tt(),dr(),St(),[xs,zs,_n,fn]=[0,1,2,3],F_=e=>{if(e[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(e[0].dims.length!==e[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},O_=`
  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
    let cubic_alpha = -0.75f;
    let x_abs = abs(x);
    var coeffs: vec4<f32>;
    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
    return coeffs;
  }
`,D_=e=>`
  fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} {
    var v: vec4<f32>;
    var coeffs = gs_get_cubic_coeffs(x);
    for (var i = 0; i < 4; i++) {
      v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
    }
    coeffs = gs_get_cubic_coeffs(y);
    let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
    return pixel;
  }
`,L_=e=>`
  fn gs_denormalize(n: f32, length: i32) -> f32 {
    ${e.alignCorners===0?`
    // alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
    return ((n + 1.0) * f32(length) - 1.0) / 2.0;
    `:`
    // alignCorners: true => [-1, 1] to [0, length - 1]
    return (n + 1.0) / 2.0 * (f32(length - 1));
    `}
  }
`,z_=e=>`
  ${e.paddingMode==="reflection"?`
      fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
        var dx = 0.0;
        var fx = f32(x);
        let range = x_max - x_min;
        if (fx < x_min) {
          dx = x_min - fx;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_min + r;
          } else {
            fx = x_max - r;
          }
        } else if (fx > x_max) {
          dx = fx - x_max;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_max - r;
          } else {
            fx = x_min + r;
          }
        }
        return u32(fx);
      }`:""}
`,B_=(e,r,t)=>`
  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${r} {
     var pixel = ${r}(0);
     var indices = vec4<u32>(0);
     indices[${xs}] = batch;
     indices[${zs}] = channel;`+(()=>{switch(t.paddingMode){case"zeros":return`
          if (r >= 0 && r < H && c >=0 && c < W) {
            indices[${_n}] = u32(r);
            indices[${fn}] = u32(c);
          } else {
            return ${r}(0);
          }
        `;case"border":return`
          indices[${_n}] = u32(clamp(r, 0, H - 1));
          indices[${fn}] = u32(clamp(c, 0, W - 1));
        `;case"reflection":return`
          indices[${_n}] = gs_reflect(r, border[1], border[3]);
          indices[${fn}] = gs_reflect(c, border[0], border[2]);
        `;default:throw new Error(`padding mode ${t.paddingMode} is not supported`)}})()+`
    return ${e.getByIndices("indices")};
  }
`,R_=(e,r,t)=>(()=>{switch(t.mode){case"nearest":return`
          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${xs}], indices[${zs}], border);
        `;case"bilinear":return`
          let x1 = i32(floor(x));
          let y1 = i32(floor(y));
          let x2 = x1 + 1;
          let y2 = y1 + 1;

          let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${xs}], indices[${zs}], border);
          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${xs}], indices[${zs}], border);
          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${xs}], indices[${zs}], border);
          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${xs}], indices[${zs}], border);

          let dx2 = ${r}(f32(x2) - x);
          let dx1 = ${r}(x - f32(x1));
          let dy2 = ${r}(f32(y2) - y);
          let dy1 = ${r}(y - f32(y1));
          let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22);
        `;case"bicubic":return`
          let x0 = i32(floor(x)) - 1;
          let y0 = i32(floor(y)) - 1;
          var p: mat4x4<${r}>;
          for (var h = 0; h < 4; h++) {
            for (var w = 0; w < 4; w++) {
              p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${xs}], indices[${zs}], border);
            }
          }

          let dx = x - f32(x0 + 1);
          let dy = y - f32(y0 + 1);
          let result = gs_bicubic_interpolate(p, dx, dy);
        `;default:throw new Error(`mode ${t.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,j_=(e,r)=>{let t=Ie("x",e[0].dataType,e[0].dims.length),s=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],n=Ie("grid",e[1].dataType,s.length,2),o=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];r.format==="NHWC"&&(o=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[xs,zs,_n,fn]=[0,3,1,2]);let i=ot("output",e[0].dataType,o.length),a=t.type.value,l=we.size(o),c=[{type:12,data:l},...ct(e[0].dims,s,o)],p=d=>`
  ${d.registerUniform("output_size","u32").declareVariables(t,n,i)}
  ${O_}
  ${D_(a)}
  ${L_(r)}
  ${z_(r)}
  ${B_(t,a,r)}

  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let H_in = i32(uniforms.x_shape[${_n}]);
      let W_in = i32(uniforms.x_shape[${fn}]);

      ${r.alignCorners===0?`
      let x_min = -0.5;
      let x_max = f32(W_in) - 0.5;
      let y_min = -0.5;
      let y_max = f32(H_in) - 0.5;
      `:`
      let x_min = 0.0;
      let x_max = f32(W_in) - 1.0;
      let y_min = 0.0;
      let y_max = f32(H_in) - 1.0;
      `};
      let border = vec4<f32>(x_min, y_min, x_max, y_max);

      let indices = ${i.offsetToIndices("global_idx")};
      var grid_indices = vec3<u32>(indices[${xs}], indices[${_n}], indices[${fn}]);
      let nxy = ${n.getByIndices("grid_indices")};
      var x = gs_denormalize(f32(nxy[0]), W_in);
      var y = gs_denormalize(f32(nxy[1]), H_in);

      ${R_(i,a,r)}
  }`;return{name:"GridSample",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:["type","type"]},getRunData:d=>{let u=we.size(o);return{outputs:[{dims:o,dataType:d[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:c}},getShaderSource:p}},N_=(e,r)=>{F_(e.inputs),e.compute(j_(e.inputs,r))},V_=e=>Rt({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),Vr,U_,W_,Dl,G_,yo,K_,H_=Ne(()=>{gt(),Tt(),dr(),tl(),pl(),St(),Ks(),Vr=(e,r)=>e.length>r&&e[r].dims.length>0?e[r]:void 0,U_=(e,r)=>{let t=e[0],s=Vr(e,1),n=Vr(e,2),o=Vr(e,3),i=Vr(e,4),a=Vr(e,5),l=Vr(e,6),c=Vr(e,7);if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let p=t.dims[0],d=t.dims[1],u=t.dims.length===3?t.dims[2]:r.numHeads*t.dims[4],f=d,_=0,y=0,I=Math.floor(u/r.numHeads);if(l&&c&&we.size(l.dims)&&we.size(c.dims)){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==p||l.dims[1]!==r.numHeads||l.dims[3]!==I)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(c.dims[0]!==p||c.dims[1]!==r.numHeads||c.dims[3]!==I)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==c.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(c.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');_=l.dims[2],y=l.dims[2]}else if(l&&we.size(l.dims)||c&&we.size(c.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let w;if(s&&we.size(s.dims)>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(s.dims[2]!==t.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');w=2,f=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==I)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(n)throw new Error('Expect "value" be none when "key" has packed kv format.');w=5,f=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==I)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');w=0,f=s.dims[2]}}else{if(t.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(t.dims[2]!==r.numHeads||t.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');w=3}if(o&&we.size(o.dims)>0){if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(s&&s.dims.length===5&&s.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let v=_+f,k=0;if(i&&we.size(i.dims)>0){k=8;let x=i.dims;throw x.length===1?x[0]===p?k=1:x[0]===3*p+2&&(k=3):x.length===2&&x[0]===p&&x[1]===v&&(k=5),k===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let T=!1,b=u;if(n&&we.size(n.dims)>0){if(n.dims.length!==3&&n.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.dims[0]!==n.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(n.dims.length===3){if(f!==n.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');b=n.dims[2]}else{if(f!==n.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');b=n.dims[1]*n.dims[3],T=!0}}let P=!1;if(i&&we.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(a&&we.size(a.dims)>0){if(a.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(a.dims[0]!==p||a.dims[1]!==r.numHeads||a.dims[2]!==d||a.dims[3]!==v)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:d,pastSequenceLength:_,kvSequenceLength:f,totalSequenceLength:v,maxSequenceLength:y,inputHiddenSize:0,hiddenSize:u,vHiddenSize:b,headSize:I,vHeadSize:Math.floor(b/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:k,scale:r.scale,broadcastResPosBias:P,passPastInKv:T,qkvFormat:w}},W_=e=>Rt({...e}),Dl=Rt({perm:[0,2,1,3]}),G_=(e,r,t,s,n,o,i)=>{let a=[s,n,o],l=we.size(a),c=[{type:12,data:l},{type:12,data:i},{type:12,data:o}],p=d=>{let u=ot("qkv_with_bias",r.dataType,a),f=Ie("qkv",r.dataType,a),_=Ie("bias",t.dataType,a),y=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${d.registerUniforms(y).declareVariables(f,_,u)}
  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:c}),getShaderSource:p},{inputs:[r,t],outputs:[-1]})[0]},yo=(e,r,t,s,n,o,i,a)=>{let l=o;if(i&&we.size(i.dims)>0){if(s===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=G_(e,o,i,r,s,t*n,a),l=l.reshape([r,s,t,n]),t===1||s===1?l:e.compute(Xr(l,Dl.perm),{inputs:[l],outputs:[-1]})[0]}else return o.dims.length===3&&(l=o.reshape([r,s,t,n])),t===1||s===1?l:e.compute(Xr(l,Dl.perm),{inputs:[l],outputs:[-1]})[0]},K_=(e,r)=>{let t=U_(e.inputs,r),s=e.inputs[0],n=Vr(e.inputs,1),o=Vr(e.inputs,2),i=Vr(e.inputs,3),a=Vr(e.inputs,4),l=Vr(e.inputs,5),c=Vr(e.inputs,6),p=Vr(e.inputs,7);if(s.dims.length===5)throw new Error("Packed QKV is not implemented");if(n?.dims.length===5)throw new Error("Packed KV is not implemented");let d=n&&o&&n.dims.length===4&&o.dims.length===4,u=yo(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,s,i,0);if(d)return fo(e,u,n,o,a,void 0,c,p,l,t);if(!n||!o)throw new Error("key and value must be provided");let f=yo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.headSize,n,i,t.hiddenSize),_=yo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.vHeadSize,o,i,2*t.hiddenSize);fo(e,u,f,_,a,void 0,c,p,l,t)}}),q_,Q_,X_,J_,Ll,Y_,Z_,ef=Ne(()=>{gt(),Tt(),dr(),St(),q_=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Q_=(e,r)=>{let t=[],s=r.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(n=>t.push(Number(n))),s=t.length),Rt({numOutputs:s,axis:r.axis,splitSizes:t})},X_=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${lt("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,J_=e=>{let r=e.length,t=[];for(let s=0;s<r;++s){let n=e[s].setByIndices("indices","input[global_idx]");r===1?t.push(n):s===0?t.push(`if (output_number == ${s}u) { ${n} }`):s===r-1?t.push(`else { ${n} }`):t.push(`else if (output_number == ${s}) { ${n} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${t.join(`
`)}
      }`},Ll=(e,r)=>{let t=e[0].dims,s=we.size(t),n=e[0].dataType,o=we.normalizeAxis(r.axis,t.length),i=new Array(r.numOutputs),a=Ie("input",n,t.length),l=new Array(r.numOutputs),c=[],p=[],d=0,u=[{type:12,data:s}];for(let _=0;_<r.numOutputs;_++){d+=r.splitSizes[_],l[_]=d;let y=t.slice();y[o]=r.splitSizes[_],p.push(y),i[_]=ot(`output${_}`,n,y.length),c.push({dims:p[_],dataType:e[0].dataType})}u.push({type:12,data:l},...ct(t,...p));let f=_=>`
  ${_.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(a,...i)}
  ${X_(l.length)}
  ${J_(i)}

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

    var indices = ${a.offsetToIndices("global_idx")};
    var index = ${a.indicesGet("indices",o)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${lt("uniforms.size_in_split_axis","output_number - 1u",l.length)};
      ${a.indicesSet("indices",o,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getShaderSource:f,getRunData:()=>({outputs:c,dispatchGroup:{x:Math.ceil(s/64)},programUniforms:u})}},Y_=(e,r)=>{q_(e.inputs);let t=e.inputs.length===1?r:Q_(e.inputs,r);e.compute(Ll(e.inputs,t),{inputs:[0]})},Z_=e=>{let r=e.axis,t=e.splitSizes,s=e.numOutputs<0?t.length:e.numOutputs;if(s!==t.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Rt({axis:r,numOutputs:s,splitSizes:t})}}),tf,fa,rf,sf=Ne(()=>{gt(),Tt(),dr(),St(),tf=(e,r)=>{let[t,s,n,o]=e,{numHeads:i,rotaryEmbeddingDim:a}=r;if(t.dims.length!==3&&t.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${t.dims.length}`);if(!we.areEqual(s.dims,[])&&!we.areEqual(s.dims,[1])&&s.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${s.dims.length}`);if(n.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${n.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(!we.areEqual(n.dims,o.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(a>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let l=t.dims[0],c=t.dims[t.dims.length-2],p=n.dims[0],d=we.sizeFromDimension(t.dims,1)/c,u=a===0?n.dims[1]*2:d/i;if(a>u)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(s.dims.length===2){if(l!==s.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${s.dims[0]}`);if(c!==s.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${s.dims[1]}`)}if(u/2!==n.dims[1]&&a/2!==n.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${n.dims[1]}`);if(c>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},fa=(e,r)=>{let{interleaved:t,numHeads:s,rotaryEmbeddingDim:n,scale:o}=r,i=e[0].dims[0],a=we.sizeFromDimension(e[0].dims,1),l=e[0].dims[e[0].dims.length-2],c=a/l,p=e[2].dims[1],d=n===0?p*2:c/s,u=new Array(i,l,c/d,d-p),f=we.computeStrides(u),_=[{type:1,data:o},{type:12,data:u},{type:12,data:f},...e[0].dims.length===3?new Array({type:12,data:[a,c,d,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[a,d,l*d,1]}):[],...ct(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],y=I=>{let w=Ie("input",e[0].dataType,e[0].dims.length),v=Ie("position_ids",e[1].dataType,e[1].dims.length),k=Ie("cos_cache",e[2].dataType,e[2].dims.length),T=Ie("sin_cache",e[3].dataType,e[3].dims.length),b=ot("output",e[0].dataType,e[0].dims.length);return I.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:u.length},{name:"global_strides",type:"u32",length:f.length},{name:"input_output_strides",type:"u32",length:f.length}]),`
        ${I.declareVariables(w,v,k,T,b)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${v.broadcastedIndicesToOffset("bsnh.xy",ot("",v.type.tensor,2))};
            let position_id =
                u32(${v.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${t});
            let j = i + select(half_rotary_emb_dim, 1, ${t});
            let re = ${w.getByOffset("i")} * ${k.get("position_id","bsnh[3]")} -
                ${w.getByOffset("j")} * ${T.get("position_id","bsnh[3]")};
            ${b.setByOffset("i","re")}
            let im = ${w.getByOffset("i")} * ${T.get("position_id","bsnh[3]")} +
                ${w.getByOffset("j")} * ${k.get("position_id","bsnh[3]")};
            ${b.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${b.setByOffset("k",w.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:Rt({interleaved:t}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:y,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(we.size(u)/zn)},programUniforms:_})}},rf=(e,r)=>{tf(e.inputs,r),e.compute(fa(e.inputs,r))}}),nf,of,zl,af,lf,Vx=Ne(()=>{dr(),gt(),pl(),H_(),ef(),Ks(),sf(),St(),nf=(e,r)=>{if(r.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let t=e[0],s=e[1],n=e[2],o=e[3],i=e[4];if(r.doRotary!==0&&e.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(r.localWindowSize!==-1)throw new Error("Local attention is not supported");if(r.softcap!==0)throw new Error("Softcap is not supported");if(r.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(r.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let a=!1,l=t.dims[0],c=t.dims[1],p=t.dims.length===3?a?t.dims[2]/3:t.dims[2]:r.numHeads*t.dims[4],d=c,u=0,f=!s||s.dims.length===0,_=Math.floor(f?p/(r.numHeads+2*r.kvNumHeads):p/r.numHeads);f&&(p=_*r.numHeads);let y=o&&o.dims.length!==0,I=i&&i.dims.length!==0;if(y&&o.dims.length===4&&o.dims[0]===l&&o.dims[1]!==r.kvNumHeads&&o.dims[2]===r.kvNumHeads&&o.dims[3]===_)throw new Error("BSNH pastKey/pastValue is not supported");if(y&&I){if(o.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(i.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');u=o.dims[2]}else if(y||I)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let w=1;if(s&&s.dims.length>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(t.dims[2]%s.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');d=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==_)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(n)throw new Error('Expect "value" be none when "key" has packed kv format.');d=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==_)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');d=s.dims[2]}}else{if(t.dims.length!==3&&t.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(t.dims.length===5&&(t.dims[2]!==r.numHeads||t.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');w=3}let v=0,k=!1,T=r.kvNumHeads?_*r.kvNumHeads:p;if(n&&n.dims.length>0){if(n.dims.length!==3&&n.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.dims[0]!==n.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(n.dims.length===3){if(d!==n.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');T=n.dims[2]}else{if(d!==n.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');T=n.dims[1]*n.dims[3],k=!0}}let b=e.length>4?e[5]:void 0;if(b&&b.dims.length!==1&&b.dims[0]!==l)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:l,sequenceLength:c,pastSequenceLength:u,kvSequenceLength:d,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:p,vHiddenSize:T,headSize:_,vHeadSize:Math.floor(T/r.kvNumHeads),numHeads:r.numHeads,kvNumHeads:r.kvNumHeads,nReps:r.numHeads/r.kvNumHeads,pastPresentShareBuffer:!1,maskType:v,scale:r.scale,broadcastResPosBias:!1,passPastInKv:k,qkvFormat:w}},of=Rt({perm:[0,2,1,3]}),zl=(e,r,t)=>{let s=r,n=t.kvNumHeads;return r.dims.length===3&&t.kvSequenceLength!==0&&(s=r.reshape([t.batchSize,t.kvSequenceLength,n,t.headSize]),s=e.compute(Xr(s,of.perm),{inputs:[s],outputs:[-1]})[0]),s},af=(e,r,t,s)=>{let n=7,o=["type","type"],i=[e*r],a=e*r,l=[{type:12,data:a},{type:12,data:r},{type:12,data:e}],c=p=>{let d=Ie("seq_lens",t.dataType,t.dims),u=Ie("total_seq_lens",s.dataType,s.dims),f=ot("pos_ids",n,i),_=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return`
  ${p.registerUniforms(_).declareVariables(d,u,f)}
  ${p.mainStart()}
    ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let total_sequence_length = u32(${u.getByOffset("0")});
    let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length;
    let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length;
    let batch_idx = global_idx / uniforms.sequence_length;
    let sequence_idx = i32(global_idx % uniforms.sequence_length);
    var pos_id: i32 = 0;
    let seqlen = ${d.getByOffset("batch_idx")};
    let total_seqlen = seqlen + 1;
    if (is_first_prompt) {
      if (sequence_idx < total_seqlen) {
        pos_id = sequence_idx;
      } else {
        pos_id = 1;
      }
      ${f.setByOffset("global_idx","pos_id")}
    } else if (is_subsequent_prompt) {
      let past_seqlen = total_seqlen - i32(uniforms.sequence_length);
      if (past_seqlen + sequence_idx < total_seqlen) {
        pos_id = past_seqlen + sequence_idx;
      } else {
        pos_id = 1;
      }
      ${f.setByOffset("global_idx","pos_id")}
    } else if (global_idx < uniforms.batch_size) {
      ${f.setByOffset("global_idx","seqlen")}
    };
  }
  `};return{name:"GeneratePositionIds",shaderCache:{hint:`${e};${r}`,inputDependencies:o},getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:l}),getShaderSource:c}},lf=(e,r)=>{let t=nf(e.inputs,r);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let s=e.inputs[0],n=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,o=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,i=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,a=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,l=e.inputs.length>4?e.inputs[5]:void 0,c=e.inputs.length>5?e.inputs[6]:void 0,p=t.kvNumHeads?t.kvNumHeads:t.numHeads,d=Rt({axis:2,numOutputs:3,splitSizes:[t.numHeads*t.headSize,p*t.headSize,p*t.headSize]}),[u,f,_]=!n&&!o?e.compute(Ll([s],d),{inputs:[s],outputs:[-1,-1,-1]}):[s,n,o],y,I;if(r.doRotary){let T=e.compute(af(t.batchSize,t.sequenceLength,l,c),{inputs:[l,c],outputs:[-1]})[0],b=e.inputs[7],P=e.inputs[8],x=Rt({interleaved:r.rotaryInterleaved!==0,numHeads:t.numHeads,rotaryEmbeddingDim:0,scale:r.scale}),S=[u,T,b,P],O=[-1];y=e.compute(fa(S,x),{inputs:S,outputs:O})[0],S.splice(0,1,f);let F=Rt({interleaved:r.rotaryInterleaved!==0,numHeads:t.kvNumHeads,rotaryEmbeddingDim:0,scale:r.scale});I=e.compute(fa(S,F),{inputs:S,outputs:O})[0]}let w=yo(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,r.doRotary?y:u,void 0,0),v=zl(e,r.doRotary?I:f,t),k=zl(e,_,t);fo(e,w,v,k,void 0,void 0,i,a,void 0,t,l,c)}}),Bl,cf,uf,df,Ux=Ne(()=>{gt(),Tt(),Ks(),St(),Bl=(e,r,t,s,n,o,i,a)=>{let l=or(o),c=l===1?"f32":`vec${l}f`,p=l===1?"vec2f":`mat2x${l}f`,d=n*i,u=64;d===1&&(u=256);let f=[n,i,o/l],_=[n,i,2],y=["rank","type","type"],I=[];I.push(...ct(f,_));let w=v=>{let k=Ie("x",r.dataType,3,l),T=Ie("scale",t.dataType,t.dims),b=Ie("bias",s.dataType,s.dims),P=ot("output",1,3,2),x=[k,T,b,P];return`
  var<workgroup> workgroup_shared : array<${p}, ${u}>;
  const workgroup_size = ${u}u;
  ${v.declareVariables(...x)}
  ${v.mainStart(u)}
    let batch = workgroup_index / uniforms.x_shape[1];
    let channel = workgroup_index % uniforms.x_shape[1];
    let hight = uniforms.x_shape[2];
    // initialize workgroup memory
    var sum = ${c}(0);
    var squared_sum = ${c}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${c}(${k.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${p}(sum, squared_sum);
    workgroupBarrier();

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

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${a}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${a};${u}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:_,dataType:1}],dispatchGroup:{x:d},programUniforms:I}),getShaderSource:w},{inputs:[r,t,s],outputs:[-1]})[0]},cf=(e,r,t)=>{let s=r[0].dims,n=s,o=2,i=s[0],a=s[1],l=we.sizeFromDimension(s,o),c=or(l),p=we.size(n)/c,d=Bl(e,r[0],r[1],r[2],i,l,a,t.epsilon),u=[i,a,l/c],f=[i,a],_=["type","none"],y=I=>{let w=Ie("x",r[0].dataType,u.length,c),v=Ie("scale_shift",1,f.length,2),k=ot("output",r[0].dataType,u.length,c),T=[w,v,k];return`
  ${I.registerUniform("output_size","u32").declareVariables(...T)}
  ${I.mainStart()}
  ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let outputIndices = ${k.offsetToIndices("global_idx")};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${v.getByIndices("vec2<u32>(batch, channel)")};
      let value = ${w.getByOffset("global_idx")} * ${k.type.value}(scale_shift.x) + ${k.type.value}(scale_shift.y);
      ${k.setByOffset("global_idx","value")};
  }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${c}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:n,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...ct(u,f,u)]}),getShaderSource:y},{inputs:[r[0],d]})},uf=(e,r,t)=>{let s=r[0].dims,n=s,o=s[0],i=s[s.length-1],a=we.sizeFromDimension(s,1)/i,l=or(i),c=we.size(n)/l,p=[{type:12,data:a},{type:12,data:Math.floor(i/l)}],d=["type","type"],u=!1,f=[0,s.length-1];for(let w=0;w<s.length-2;w++)u=u||s[w+1]!==1,f.push(w+1);u=u&&s[s.length-1]!==1;let _=u?e.compute(Xr(e.inputs[0],f),{inputs:[e.inputs[0]],outputs:[-1]})[0]:e.inputs[0].reshape(Array.from({length:s.length},(w,v)=>s[f[v]])),y=Bl(e,_,r[1],r[2],o,a,i,t.epsilon),I=w=>{let v=Sr(r[0].dataType),k=l===1?"vec2f":`mat${l}x2f`,T=x=>{let S=x===0?"x":"y",O=l===1?"f32":`vec${l}f`;switch(l){case 1:return`${v}(${O}(scale.${S}))`;case 2:return`vec2<${v}>(${O}(scale[0].${S}, scale[1].${S}))`;case 4:return`vec4<${v}>(${O}(scale[0].${S}, scale[1].${S}, scale[2].${S}, scale[3].${S}))`;default:throw new Error(`Not supported compoents ${l}`)}},b=Ie("input",r[0].dataType,r[0].dims,l),P=ot("output",r[0].dataType,n,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${b.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${k}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${P.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

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

    let scale_offset = current_image_number * uniforms.C + current_channel_number;
    let scale = scale_input[scale_offset];
    output[global_idx] = fma(input[global_idx], ${T(0)}, ${T(1)});
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:n,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:p}),getShaderSource:I},{inputs:[r[0],y]})},df=(e,r)=>{r.format==="NHWC"?uf(e,e.inputs,r):cf(e,e.inputs,r)}}),pf,mf,hf,Wx=Ne(()=>{gt(),Tt(),St(),pf=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},mf=(e,r,t)=>{let s=r.simplified,n=e[0].dims,o=e[1],i=!s&&e[2],a=n,l=we.normalizeAxis(r.axis,n.length),c=we.sizeToDimension(n,l),p=we.sizeFromDimension(n,l),d=we.size(o.dims),u=i?we.size(i.dims):0;if(d!==p||i&&u!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${d} and bias size of ${u}`);let f=[];for(let b=0;b<n.length;++b)b<l?f.push(n[b]):f.push(1);let _=or(p),y=["type","type"],I=[{type:12,data:c},{type:1,data:p},{type:12,data:Math.floor(p/_)},{type:1,data:r.epsilon}];i&&y.push("type");let w=t>1,v=t>2,k=b=>{let P=Sr(e[0].dataType),x=[Ie("x",e[0].dataType,e[0].dims,_),Ie("scale",o.dataType,o.dims,_)];i&&x.push(Ie("bias",i.dataType,i.dims,_)),x.push(ot("output",e[0].dataType,a,_)),w&&x.push(ot("mean_data_output",1,f)),v&&x.push(ot("inv_std_output",1,f));let S=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${b.registerUniforms(S).declareVariables(...x)}
  ${b.mainStart()}
    ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${ol("f32",_)};
    var mean_square_vector = ${ol("f32",_)};

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

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

    ${w?"mean_data_output[global_idx] = mean":""};
    ${v?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},T=[{dims:a,dataType:e[0].dataType}];return w&&T.push({dims:f,dataType:1}),v&&T.push({dims:f,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${_};${t};${s}`,inputDependencies:y},getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(c/64)},programUniforms:I}),getShaderSource:k}},hf=(e,r)=>{pf(e.inputs),e.compute(mf(e.inputs,r,e.outputCount))}}),_f,ff,Gx=Ne(()=>{Tt(),bl(),Tl(),_f=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.")},ff=e=>{_f(e.inputs);let r=Ln.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!r)throw new Error("Can't use matmul on the given tensors");let t=r[r.length-1],s=e.inputs[0].dims[e.inputs[0].dims.length-1];if(t<8&&s<8)e.compute(wl(e.inputs,{activation:""},r));else{let n=r[r.length-2],o=we.size(e.inputs[0].dims.slice(0,-2)),i=we.size(e.inputs[1].dims.slice(0,-2));if(o!==1&&n===1&&i===1){let a=e.inputs[0].reshape([1,o,s]),l=e.inputs[1].reshape([1,s,t]),c=[1,o,t],p=[a,l];e.compute(pa(p,{activation:""},r,c),{inputs:p})}else e.compute(pa(e.inputs,{activation:""},r))}}}),gf,Mf,wf,bf,yf,Kx=Ne(()=>{gt(),Tt(),dr(),St(),gf=(e,r)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let t=e[0],s=t.dims.length;if(t.dims[s-1]!==r.k)throw new Error("The last dim of input shape does not match the k value");let n=Math.floor((r.k+r.blockSize-1)/r.blockSize),o=r.blockSize/8*r.bits,i=e[1];if(!we.areEqual(i.dims,[r.n,n,o]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let a=e[2].dims;if(we.size(a)!==r.n*n)throw new Error("scales input size error.");if(e.length===4){let l=e[3].dims,c=r.bits>4?r.n*n:r.n*Math.floor((n+1)/2);if(we.size(l)!==c)throw new Error("zeroPoints input size error.")}},Mf=(e,r)=>{let t=e[0].dims,s=t.length,n=t[s-2],o=r.k,i=r.n,a=t.slice(0,s-2),l=we.size(a),c=e[1].dims[2]/4,p=e[0].dataType,d=or(r.k),u=or(c),f=or(i),_=a.concat([n,i]),y=n>1&&i/f%2===0?2:1,I=we.size(_)/f/y,w=64,v=[],k=[l,n,o/d],T=we.convertShape(e[1].dims).slice();T.splice(-1,1,c/u),v.push(...ct(k)),v.push(...ct(T)),v.push(...ct(e[2].dims)),e.length===4&&v.push(...ct(we.convertShape(e[3].dims)));let b=[l,n,i/f];v.push(...ct(b));let P=x=>{let S=k.length,O=Ie("a",e[0].dataType,S,d),F=Ie("b",12,T.length,u),H=Ie("scales",e[2].dataType,e[2].dims.length),W=[O,F,H],B=e.length===4?Ie("zero_points",12,e[3].dims.length):void 0;B&&W.push(B);let Y=b.length,X=ot("output",e[0].dataType,Y,f),J=Sr(e[0].dataType),re=(()=>{switch(d){case 1:return`array<${J}, 8>`;case 2:return`mat4x2<${J}>`;case 4:return`mat2x4<${J}>`;default:throw new Error(`${d}-component is not supported.`)}})(),ne=()=>{let oe=`
          // reuse a data
            var input_offset = ${O.indicesToOffset(`${O.type.indices}(batch, row, word_offset)`)};
            var a_data: ${re};
            for (var j: u32 = 0; j < ${8/d}; j++) {
              a_data[j] = ${O.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let K=0;K<f*y;K++)oe+=`
            b_value = ${u===1?`b${K}_data`:`b${K}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${re}(${Array.from({length:4},(j,D)=>`${J}(b_value_lower[${D}]), ${J}(b_value_upper[${D}])`).join(", ")});
            b_dequantized_values = ${d===1?`${re}(${Array.from({length:8},(j,D)=>`(b_quantized_values[${D}] - ${B?`zero_point${K}`:"zero_point"}) * scale${K}`).join(", ")});`:`(b_quantized_values - ${re}(${Array(8).fill(`${B?`zero_point${K}`:"zero_point"}`).join(",")})) * scale${K};`};
            workgroup_shared[local_id.x * ${y} + ${Math.floor(K/f)}]${f>1?`[${K%f}]`:""} += ${Array.from({length:8/d},(j,D)=>`${d===1?`a_data[${D}] * b_dequantized_values[${D}]`:`dot(a_data[${D}], b_dequantized_values[${D}])`}`).join(" + ")};
          `;return oe},le=()=>{let oe=`
            var col_index = col * ${f};
            ${B?`
            let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
            var zero_point_byte_count: u32;
            var zero_point_word_index: u32;
            var zero_point_byte_offset: u32;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32;
            var zero_point_word: u32;`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${J}(8);`}
            `;for(let K=0;K<f*y;K++)oe+=`
            let scale${K} = ${H.getByOffset("col_index * nBlocksPerCol + block")};
            ${B?`
            zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u);
            zero_point_word_index = zero_point_byte_count >> 0x2u;
            zero_point_byte_offset = zero_point_byte_count & 0x3u;
            zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            zero_point_word = ${B.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${K} = ${J}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return oe},pe=()=>{let oe=`col_index = col * ${f};`;for(let K=0;K<f*y;K++)oe+=`
            let b${K}_data = ${F.getByIndices(`${F.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return oe+=`
            var b_value: u32;
            let b_mask: u32 = 0x0F0F0F0Fu;
            var b_value_lower: vec4<u32>;
            var b_value_upper: vec4<u32>;
            var b_quantized_values: ${re};
            var b_dequantized_values: ${re};`,oe};return`
        var<workgroup> workgroup_shared: array<${X.type.value}, ${y*w}>;
        ${x.declareVariables(...W,X)}
        ${x.mainStart([w,1,1])}
          let output_indices = ${X.offsetToIndices(`(global_idx / ${w}) * ${y}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let nBlocksPerCol = uniforms.b_shape[1];

          for (var block = local_id.x; block < nBlocksPerCol; block += ${w}) {
            //process one block
            var word_offset: u32 = block * ${r.blockSize/d};
            ${le()}
            for (var word: u32 = 0; word < ${c}; word += ${u}) {
              ${pe()}
              for (var i: u32 = 0; i < ${u}; i++) {
                ${ne()}
                word_offset += ${8/d};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${y}) {
            var output_value: ${X.type.value} = ${X.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < ${w}u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${y};
            }
            ${X.setByIndices(`${X.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${r.blockSize};${r.bits};${d};${u};${f};${y};${w}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:_,dataType:p}],dispatchGroup:{x:I},programUniforms:v}),getShaderSource:P}},wf=(e,r)=>{let t=e[0].dims,s=t.length,n=t[s-2],o=r.k,i=r.n,a=t.slice(0,s-2),l=we.size(a),c=e[1].dims[2]/4,p=e[0].dataType,d=or(r.k),u=or(c),f=a.concat([n,i]),_=128,y=i%8===0?8:i%4===0?4:1,I=_/y,w=I*u*8,v=w/d,k=w/r.blockSize,T=we.size(f)/y,b=[],P=[l,n,o/d],x=we.convertShape(e[1].dims).slice();x.splice(-1,1,c/u),b.push(...ct(P)),b.push(...ct(x)),b.push(...ct(e[2].dims)),e.length===4&&b.push(...ct(we.convertShape(e[3].dims)));let S=[l,n,i];b.push(...ct(S));let O=F=>{let H=P.length,W=Ie("a",e[0].dataType,H,d),B=Ie("b",12,x.length,u),Y=Ie("scales",e[2].dataType,e[2].dims.length),X=[W,B,Y],J=e.length===4?Ie("zero_points",12,e[3].dims.length):void 0;J&&X.push(J);let re=S.length,ne=ot("output",e[0].dataType,re),le=Sr(e[0].dataType),pe=()=>{switch(d){case 1:return`
          let a_data0 = vec4<${le}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
          let a_data1 = vec4<${le}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
          let a_data0 = vec4<${le}>(sub_a[word_offset], sub_a[word_offset + 1]);
          let a_data1 = vec4<${le}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${d}-component is not supported.`)}};return`
        var<workgroup> sub_a: array<${W.type.value}, ${v}>;
        var<workgroup> inter_results: array<array<${ne.type.value}, ${I}>, ${y}>;
        ${F.declareVariables(...X,ne)}
        ${F.mainStart([I,y,1])}
          let output_indices = ${ne.offsetToIndices(`workgroup_index * ${y}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let n_blocks_per_col = uniforms.b_shape[1];
          let num_tiles =  (n_blocks_per_col - 1) / ${k} + 1;

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

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

          if (local_idx < ${y}) {
            var output_value: ${ne.type.value} = ${ne.type.value}(0);
            for (var b = 0u; b < ${I}; b++) {
              output_value += inter_results[local_idx][b];
            }
            if (col + local_idx < uniforms.output_shape[2])
            {
              ${ne.setByIndices(`${ne.type.indices}(batch, row, col + local_idx)`,"output_value")}
            }
          }
        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${r.blockSize};${d};${u};${I};${y}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:f,dataType:p}],dispatchGroup:{x:T},programUniforms:b}),getShaderSource:O}},bf=(e,r)=>{gf(e.inputs,r),r.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(wf(e.inputs,r)):e.compute(Mf(e.inputs,r))},yf=e=>Rt(e)}),vf,xf,Tf,Pf,Ef,Cf,Sf,kf,$f,Hx=Ne(()=>{gt(),Tt(),St(),vf=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 r=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(r=e[3].dims[0]*2===e[1].dims[0]),!r)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},xf=(e,r,t)=>{let s="";for(let n=r-1;n>=0;--n)s+=`
            k = i32(${e.indicesGet("indices",n)}) - ${lt("uniforms.pads",n,t)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${lt("uniforms.x_shape",n,r)})) {
              break;
            }
            offset += k * i32(${lt("uniforms.x_strides",n,r)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${s}
            value = x[offset];
          }
      `},Tf=(e,r,t)=>{let s="";for(let n=r-1;n>=0;--n)s+=`
                k = i32(${e.indicesGet("indices",n)}) - ${lt("uniforms.pads",n,t)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${lt("uniforms.x_shape",n,r)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${lt("uniforms.x_shape",n,r)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${lt("uniforms.x_strides",n,r)});
            `;return`
              var offset = 0;
              var k = 0;
              ${s}
              value = x[offset];
          `},Pf=(e,r,t)=>{let s="";for(let n=r-1;n>=0;--n)s+=`
                k = i32(${e.indicesGet("indices",n)}) - ${lt("uniforms.pads",n,t)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${lt("uniforms.x_shape",n,r)})) {
                  k = i32(${lt("uniforms.x_shape",n,r)}) - 1;
                }
                offset += k * i32(${lt("uniforms.x_strides",n,r)});
            `;return`
              var offset = 0;
              var k = 0;
              ${s}
              value = x[offset];
          `},Ef=(e,r,t)=>{let s="";for(let n=r-1;n>=0;--n)s+=`
                k = i32(${e.indicesGet("indices",n)}) - ${lt("uniforms.pads",n,t)};
                if (k < 0)  {
                  k += i32(${lt("uniforms.x_shape",n,r)}]);
                }
                if (k >= i32(${lt("uniforms.x_shape",n,r)})) {
                  k -= i32(${lt("uniforms.x_shape",n,r)});
                }
                offset += k * i32(${lt("uniforms.x_strides",n,r)});
            `;return`
              var offset = 0;
              var k = 0;
              ${s}
              value = x[offset];
          `},Cf=(e,r,t)=>{switch(t.mode){case 0:return xf(e,r,t.pads.length);case 1:return Tf(e,r,t.pads.length);case 2:return Pf(e,r,t.pads.length);case 3:return Ef(e,r,t.pads.length);default:throw new Error("Invalid mode")}},Sf=(e,r)=>{let t=we.padShape(e[0].dims.slice(),r.pads),s=e[0].dims,n=we.size(t),o=[{type:12,data:n},{type:6,data:r.pads}],i=e.length>=3&&e[2].data;r.mode===0&&o.push({type:i?e[2].dataType:1,data:r.value}),o.push(...ct(e[0].dims,t));let a=["rank"],l=c=>{let p=ot("output",e[0].dataType,t.length),d=Ie("x",e[0].dataType,s.length),u=d.type.value,f=Cf(p,s.length,r),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:r.pads.length}];return r.mode===0&&_.push({name:"constant_value",type:i?u:"f32"}),`
            ${c.registerUniforms(_).declareVariables(d,p)}
            ${c.mainStart()}
            ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${u}(0);
            ${f}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${r.mode}${i}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(we.size(t)/64)},programUniforms:o}),getShaderSource:l}},kf=(e,r)=>{if(e.length>1){let t=e[1].getBigInt64Array(),s=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,n=e[0].dims.length,o=new Int32Array(2*n).fill(0);if(e.length>=4){let a=e[3].getBigInt64Array();for(let l=0;l<a.length;l++)o[Number(a[l])]=Number(t[l]),o[Number(a[l])+n]=Number(t[l+a.length])}else t.forEach((a,l)=>o[Number(l)]=Number(a));let i=[];return o.forEach(a=>i.push(a)),{mode:r.mode,value:s,pads:i}}else return r},$f=(e,r)=>{vf(e.inputs);let t=kf(e.inputs,r);e.compute(Sf(e.inputs,t),{inputs:[0]})}}),vo,Rl,jl,Nl,Vl,If,Af,Ul,Wl,Ff,Of,Gl,Df,Lf,Kl,zf,Bf,Rf,jf,qx=Ne(()=>{ps(),gt(),Tt(),St(),vo=e=>{if(Xt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Rl=(e,r,t)=>{let s=r.format==="NHWC",n=e.dims.slice();s&&n.splice(1,0,n.pop());let o=Object.hasOwnProperty.call(r,"dilations"),i=r.kernelShape.slice(),a=r.strides.slice(),l=o?r.dilations.slice():[],c=r.pads.slice();na.adjustPoolAttributes(t,n,i,a,l,c);let p=na.computePoolOutputShape(t,n,a,l,i,c,r.autoPad),d=Object.assign({},r);o?Object.assign(d,{kernelShape:i,strides:a,pads:c,dilations:l,cacheKey:r.cacheKey}):Object.assign(d,{kernelShape:i,strides:a,pads:c,cacheKey:r.cacheKey});let u=p.slice();return u.push(u.splice(1,1)[0]),[d,s?u:p]},jl=(e,r)=>{let t=r.format==="NHWC",s=we.size(e),n=we.size(r.kernelShape),o=[{type:12,data:s},{type:12,data:n}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(r.kernelShape.length<=2){let a=r.kernelShape[r.kernelShape.length-1],l=r.strides[r.strides.length-1],c=r.pads[r.pads.length/2-1],p=r.pads[r.pads.length-1],d=!!(c+p);o.push({type:12,data:a},{type:12,data:l},{type:12,data:c},{type:12,data:p}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let u=!1;if(r.kernelShape.length===2){let f=r.kernelShape[r.kernelShape.length-2],_=r.strides[r.strides.length-2],y=r.pads[r.pads.length/2-2],I=r.pads[r.pads.length-2];u=!!(y+I),o.push({type:12,data:f},{type:12,data:_},{type:12,data:y},{type:12,data:I}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[o,i,!0,d,u]}else{if(t)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let a=we.computeStrides(r.kernelShape);o.push({type:12,data:a},{type:12,data:r.pads},{type:12,data:r.strides}),i.push({name:"kernelStrides",type:"u32",length:a.length},{name:"pads",type:"u32",length:r.pads.length},{name:"strides",type:"u32",length:r.strides.length});let l=r.pads.reduce((c,p)=>c+p);return[o,i,!!l,!1,!1]}},Nl=(e,r,t,s,n,o,i,a,l,c,p,d)=>{let u=n.format==="NHWC",f=r.type.value,_=ot("output",r.type.tensor,s);if(n.kernelShape.length<=2){let y="",I="",w="",v=t-(u?2:1);if(p?y=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${v}] = indices[${v}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${v}] < 0 || xIndices[${v}]
                      >= uniforms.x_shape[${v}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${r.indicesToOffset("xIndices")}];
                  ${o}
                }`:y=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${v}] = indices[${v}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${r.indicesToOffset("xIndices")}];
                  ${o}
                }`,n.kernelShape.length===2){let k=t-(u?3:2);d?I=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${k}] = indices[${k}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${k}] < 0 || xIndices[${k}] >= uniforms.x_shape[${k}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:I=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${k}] = indices[${k}] * uniforms.sh - uniforms.phStart + j;
                `,w=`
              }
            `}return`
            ${e.registerUniforms(l).declareVariables(r,_)}

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

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

              var value = ${f}(${a});
              var pad = 0;
              ${I}
              ${y}
              ${w}
              ${i}

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

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

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

              var value = ${f}(${a});
              var pad = 0;
              var isPad = false;

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

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

              output[global_idx] = value;
            }`}},Vl=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,If=e=>`${Vl(e)};${e.countIncludePad}`,Af=e=>`${Vl(e)};${e.storageOrder};${e.dilations}`,Ul=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}),Wl=(e,r,t,s)=>{let[n,o]=Rl(r,s,t),i=Ie("x",r.dataType,r.dims.length),a=i.type.value,l="value += x_val;",c="";n.countIncludePad?c+=`value /= ${a}(uniforms.kernelSize);`:c+=`value /= ${a}(i32(uniforms.kernelSize) - pad);`;let[p,d,u,f,_]=jl(o,n);p.push(...ct(r.dims,o));let y=["rank"];return{name:e,shaderCache:{hint:`${s.cacheKey};${u};${f};${_}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:o,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(we.size(o)/64)},programUniforms:p}),getShaderSource:I=>Nl(I,i,r.dims.length,o.length,n,l,c,0,d,u,f,_)}},Ff=e=>{let r=e.count_include_pad!==0,t=Ul(e);if(t.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let s={countIncludePad:r,...t,cacheKey:""};return{...s,cacheKey:If(s)}},Of=(e,r)=>{vo(e.inputs),e.compute(Wl("AveragePool",e.inputs[0],!1,r))},Gl={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Df=e=>{let r=e.format;return{format:r,...Gl,cacheKey:r}},Lf=(e,r)=>{vo(e.inputs),e.compute(Wl("GlobalAveragePool",e.inputs[0],!0,r))},Kl=(e,r,t,s)=>{let[n,o]=Rl(r,s,t),i=`
      value = max(x_val, value);
    `,a="",l=Ie("x",r.dataType,r.dims.length),c=["rank"],[p,d,u,f,_]=jl(o,n);return p.push(...ct(r.dims,o)),{name:e,shaderCache:{hint:`${s.cacheKey};${u};${f};${_}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:o,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(we.size(o)/64)},programUniforms:p}),getShaderSource:y=>Nl(y,l,r.dims.length,o.length,n,i,a,r.dataType===10?-65504:-1e5,d,u,f,_)}},zf=(e,r)=>{vo(e.inputs),e.compute(Kl("MaxPool",e.inputs[0],!1,r))},Bf=e=>{let r=e.storage_order,t=e.dilations,s=Ul(e);if(r!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let n={storageOrder:r,dilations:t,...s,cacheKey:""};return{...n,cacheKey:Af(n)}},Rf=e=>{let r=e.format;return{format:r,...Gl,cacheKey:r}},jf=(e,r)=>{vo(e.inputs),e.compute(Kl("GlobalMaxPool",e.inputs[0],!0,r))}}),Nf,Vf,Uf,Wf,Qx=Ne(()=>{gt(),Tt(),dr(),St(),Nf=(e,r)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((t,s)=>t===e[2].dims[s]).reduce((t,s)=>t&&s,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(r.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((n,o)=>o===r.axis||n===e[0].dims[o]).reduce((n,o)=>n&&o,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let t=e[0].dims[r.axis],s=e[1].dims[r.axis];if(r.blockSize<Math.ceil(t/s)||r.blockSize>Math.ceil(t/(s-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},Vf=(e,r)=>{let t=we.normalizeAxis(r.axis,e[0].dims.length),s=e[0].dataType,n=s===3,o=e[0].dims,i=e[1].dataType,a=we.size(o),l=s===3||s===2,c=l?[Math.ceil(we.size(e[0].dims)/4)]:e[0].dims,p=e[1].dims,d=e.length>2?e[2]:void 0,u=d?l?[Math.ceil(we.size(d.dims)/4)]:d.dims:void 0,f=p.length===0||p.length===1&&p[0]===1,_=f===!1&&p.length===1,y=or(a),I=f&&(!l||y===4),w=I?y:1,v=I&&!l?y:1,k=Ie("input",l?12:s,c.length,v),T=Ie("scale",i,p.length),b=d?Ie("zero_point",l?12:s,u.length):void 0,P=ot("output",i,o.length,w),x=[k,T];b&&x.push(b);let S=[c,p];d&&S.push(u);let O=[{type:12,data:a/w},{type:12,data:t},{type:12,data:r.blockSize},...ct(...S,o)],F=H=>{let W=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${H.registerUniforms(W).declareVariables(...x,P)}
      ${H.mainStart()}
          ${H.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${P.offsetToIndices("global_idx")};

          // Set input x
          ${l?`
            let input = ${k.getByOffset("global_idx / 4")};
            let x_vec = ${n?"unpack4xI8(input)":"unpack4xU8(input)"};
            let x_value = ${w===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${k.getByOffset("global_idx")};`};

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

          // Set zero-point input
          ${b?f?l?`
                let zero_point_input = ${b.getByOffset("0")};
                let zero_point_vec =  ${n?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${b.getByOffset("0")}`:_?l?`
                let zero_point_index = ${P.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${b.getByOffset("zero_point_index / 4")};
                let zero_point_vec =  ${n?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${P.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${b.getByOffset("zero_point_index")};`:l?`
                let zero_point_offset = ${T.indicesToOffset("scale_indices")};
                let zero_point_input = ${b.getByOffset("zero_point_offset / 4")};
                let zero_point_vec = ${n?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${b.getByIndices("scale_indices")};`:`let zero_point_value = ${l?n?"i32":"u32":k.type.value}(0);`};
      // Compute and write output
      ${P.setByOffset("global_idx",`${P.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:r.cacheKey,inputDependencies:b?["rank","rank","rank"]:["rank","rank"]},getShaderSource:F,getRunData:()=>({outputs:[{dims:o,dataType:i}],dispatchGroup:{x:Math.ceil(a/w/64),y:1,z:1},programUniforms:O})}},Uf=(e,r)=>{Nf(e.inputs,r),e.compute(Vf(e.inputs,r))},Wf=e=>Rt({axis:e.axis,blockSize:e.blockSize})}),Gf,Kf,Hf,Xx=Ne(()=>{ps(),gt(),St(),Gf=(e,r,t)=>{let s=e===r,n=e<r&&t<0,o=e>r&&t>0;if(s||n||o)throw new Error("Range these inputs' contents are invalid.")},Kf=(e,r,t,s)=>{let n=Math.abs(Math.ceil((r-e)/t)),o=[n],i=n,a=[{type:12,data:i},{type:s,data:e},{type:s,data:t},...ct(o)],l=c=>{let p=ot("output",s,o.length),d=p.type.value,u=[{name:"outputSize",type:"u32"},{name:"start",type:d},{name:"delta",type:d}];return`
        ${c.registerUniforms(u).declareVariables(p)}
        ${c.mainStart()}
        ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${d}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${s}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:o,dataType:s}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:a})}},Hf=e=>{let r=0,t=0,s=0;e.inputs[0].dataType===6?(r=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],s=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(r=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],s=e.inputs[2].getFloat32Array()[0]),Xt.webgpu.validateInputContent&&Gf(r,t,s),e.compute(Kf(r,t,s,e.inputs[0].dataType),{inputs:[]})}}),qf,Hl,ql,Qf,Xf,Jf,Jx=Ne(()=>{gt(),Tt(),dr(),St(),qf=(e,r,t,s)=>{if(e!=="none"&&s!=="i32"&&s!=="u32"&&s!=="f32")throw new Error(`Input ${s} is not supported with reduction ${e}.`);let n=`{
                var oldValue = 0;
                loop {
                  let newValueF32 =`,o=`;
                  let newValue = bitcast<i32>(newValueF32);
                  let res = atomicCompareExchangeWeak(&${r}, oldValue, newValue);
                  if res.exchanged {
                    break;
                  }
                  oldValue = res.old_value;
                }
              }`;switch(e){case"none":return`${r}=${t};`;case"add":return s==="i32"||s==="u32"?`atomicAdd(&${r}, bitcast<${s}>(${t}));`:`
              ${n}bitcast<${s}>(oldValue) + (${t})${o}`;case"max":return s==="i32"||s==="u32"?`atomicMax(&${r}, bitcast<${s}>(${t}));`:`
                ${n}max(bitcast<f32>(oldValue), (${t}))${o}`;case"min":return s==="i32"||s==="u32"?`atomicMin(&${r}, bitcast<${s}>(${t}));`:`${n}min(bitcast<${s}>(oldValue), (${t}))${o}`;case"mul":return`${n}(bitcast<${s}>(oldValue) * (${t}))${o}`;default:throw new Error(`Reduction ${e} is not supported.`)}},Hl=(e,r)=>`${e===1?`
    let element_count_dim = uniforms.output_strides;
    let dim_value = uniforms.output_shape;`:`
    let element_count_dim = uniforms.output_strides[${r?"i - indices_start":"i"}];
    let dim_value = uniforms.output_shape[${r?"i - indices_start":"i"} + uniforms.last_index_dimension];`}
    
    if (index >= 0) {
      if (index >= i32(dim_value)) {
        index = i32(dim_value - 1);
      }
    } else {
      if (index < -i32(dim_value)) {
        index = 0;
      } else {
        index += i32(dim_value);
      }
    }
    data_offset += u32((u32(index) * element_count_dim));`,ql=(e,r,t)=>`for (var i = 0u; i < uniforms.num_updates_elements; i++) {
        let value = updates[uniforms.num_updates_elements * ${t?"global_idx":"idx"} + i];
        ${qf(e.reduction,"output[data_offset + i]","value",r)}
      }`,Qf=(e,r)=>{let t=e[0].dims,s=e[1].dims,n=t,o=1,i=Math.ceil(we.size(s)/o),a=s[s.length-1],l=we.sizeFromDimension(t,a),c=we.sizeFromDimension(s,0)/a,p=[{type:12,data:i},{type:12,data:a},{type:12,data:l},...ct(e[1].dims,e[2].dims,n)],d=u=>{let f=Ie("indices",e[1].dataType,e[1].dims.length),_=Ie("updates",e[2].dataType,e[2].dims.length,o),y=r.reduction!=="none"&&r.reduction!==""?rp("output",e[0].dataType,n.length):ot("output",e[0].dataType,n.length,o);return`
      ${u.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(f,_,y)}
      ${u.mainStart()}
        ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
  var hasDuplicates = false;
  if (${r.reduction==="none"}) {
    for (var i = 0; i < ${c}; i = i + 1) {
      for (var j = i + 1; j < ${c}; j = j + 1) {
        var index_i = i32(indices[i].x);
        var index_j = i32(indices[j].x);
        if (index_i == index_j) {
          hasDuplicates = true;
          break;
        }
      }
      if (hasDuplicates) {
        break;
      }
    }
  }

  if (${r.reduction==="none"} && hasDuplicates) {
    if (global_idx != 0u) {
      return;
    }
    // Process each index-update pair individually when duplicates exist
    for (var idx = 0u; idx < ${c}u; idx++) {
      var data_offset = 0u;
      for (var i = 0u; i < uniforms.last_index_dimension; i++) {
        var index = i32(indices[idx * uniforms.last_index_dimension + i].x);
        ${Hl(t.length,!1)}
      }
      ${ql(r,y.type.value,!1)}
    }
    return;
  }

  var data_offset = 0u;
  var indices_start = uniforms.last_index_dimension * global_idx;
  var indices_end = indices_start + uniforms.last_index_dimension;
  for (var i = indices_start; i < indices_end; i++) {
    var index = i32(indices[i].x);
    ${Hl(t.length,!0)}
  }
  ${ql(r,y.type.value,!0)}
  }`};return{name:"ScatterND",shaderCache:{hint:`${r.cacheKey}_${r.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:p}),getShaderSource:d}},Xf=e=>Rt({reduction:e.reduction}),Jf=(e,r)=>{e.compute(Qf(e.inputs,r),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),Yf,Zf,eg,Ql,tg,rg,sg,ng,og,ag,ig,lg,Xl,cg,ug,dg,pg,mg,hg,_g,Yx=Ne(()=>{gt(),Tt(),dr(),St(),Yf=(e,r)=>{if(e.every(t=>t>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(r.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(r.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")}},Zf=(e,r,t)=>{r.every(n=>n>=0&&n<t||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let s=new Array(t).fill(1);return r.forEach((n,o)=>s[n]=e[o]),s},eg=(e,r,t,s,n,o)=>{let[i,a,l]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],c=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(p=>o.push(p));else if(r.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(a>0&&e.length>a&&e[a].dims.length===1&&e[a].dims[0]>0){if(e[a].getFloat32Array().forEach(p=>s.push(p)),s.length!==0&&s.length!==c&&t>=18&&s.length!==r.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");Yf(s,r),r.axes.length>0&&Zf(s,r.axes,c).forEach((p,d)=>s[d]=p)}if(l>0&&e.length>l&&e[l].dims.length===1&&e[l].dims[0]>0&&(e[l].getBigInt64Array().forEach(p=>n.push(Number(p))),n.length!==0&&n.length!==c&&t>=18&&n.length!==r.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(r.axes.length>0){if(s.length!==0&&s.length!==r.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(n.length!==0&&n.length!==r.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof s<"u"&&typeof n<"u"&&s.length>0&&n.length>c)throw new Error("Resize requires only of scales or sizes to be specified")},Ql=(e,r,t,s)=>`
  // 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 big = (${e}) * (${r});
  let whole = ${s}(big / (${t}));
  let fract = ${s}(big % (${t})) / ${s}(${t});
  return whole + fract;
`,tg=(e,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(e){case"asymmetric":return`
          if (xScale < 1.0 || floor(xScale) != xScale) {
            return ${r}(xResized) / ${r}(xScale);
          } else {
            ${Ql("xResized","lengthOriginal","lengthResized",r)}
          }
        `;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${r}(xResized) + 0.5) / ${r}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    ${Ql("xResized","lengthOriginal - 1","lengthResized - 1",r)}
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${r}(roiStart) * ${r}(lengthOriginal - 1) +
                        (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) /
                        ${r}(lengthResized - 1);
                  } else {
                    return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${r}xScale * ${r}(lengthResized);
                  const adjustment = ${r}(lengthResized) / outputWidth;
                  const center = ${r}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;case"half_pixel":return`return ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",rg=(e,r,t)=>`fn getNearestPixelFromOriginal(xOriginal: ${t}, isDownSample: bool) -> ${t} {`+(()=>{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);                   }";default:if(r<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",sg=(e,r,t)=>{let s=new Array(t).fill(0).concat(new Array(t).fill(1)),n=e.length===0?s:e.slice();return r.length>0?(r.forEach((o,i)=>{s[o]=n[i],s[i+t]=n[r.length+i]}),s):n},ng=(e,r,t,s)=>{let n=[];if(t.length>0)if(s.length>0){if(e.forEach(o=>n.push(o)),Math.max(...s)>e.length)throw new Error("axes is out of bound");s.forEach((o,i)=>n[o]=t[i])}else t.forEach(o=>n.push(o));else{if(r.length===0)throw new Error("Resize requires either scales or sizes.");n=e.map((o,i)=>Math.round(o*r[i]))}return n},og=(e,r,t)=>{let s=(()=>{switch(t.keepAspectRatioPolicy){case"not_larger":return t.axes.length>0?Math.min(...t.axes.map(o=>r[o]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case"not_smaller":return t.axes.length>0?Math.max(...t.axes.map(o=>r[o]),Number.MIN_VALUE):Math.max(...r,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${t.keepAspectRatioPolicy} is not supported`)}})();r.fill(1,0,r.length);let n=e.slice();return t.axes.length>0?(t.axes.forEach(o=>r[o]=s),t.axes.forEach(o=>n[o]=Math.round(e[o]*r[o]))):(r.fill(s,0,r.length),n.forEach((o,i)=>n[i]=Math.round(o*r[i]))),n},ag=(e,r,t,s,n)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${t.length}> {
      var original_indices: array<${e.type.value}, ${t.length}>;
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var scale = ${lt("uniforms.scales","i",s)};
        var roi_low = ${lt("uniforms.roi","i",n)};
        var roi_hi = ${lt("uniforms.roi",`i + ${r.length}`,n)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${lt("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${lt("uniforms.output_shape","i",t.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,ig=(e,r,t,s,n,o,i)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {
      var input_indices: ${e.type.indices};
      for (var i:u32 = 0; i < ${s.length}; i++) {
        var output_index = ${r.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${lt("uniforms.scales","i",n)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${lt("uniforms.roi","i",o)};
          var roi_hi = ${lt("uniforms.roi",`i + ${t.length}`,o)};
          var input_shape_i = ${lt("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${lt("uniforms.output_shape","i",s.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${i} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${r.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${e.indicesSet("input_indices","i","input_index")}
      }
      return input_indices;
    }`,lg=(e,r)=>`
    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var input_index = ${e.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${lt("uniforms.input_shape","i",r.length)}) {
          return false;
        }
      }
      return true;
    }`,Xl=(e,r,t,s)=>e.rank>s?`
    ${e.indicesSet("input_indices",r,"channel")};
    ${e.indicesSet("input_indices",t,"batch")};
`:"",cg=(e,r,t,s,n)=>{let[o,i,a,l]=t.length===2?[-1,0,1,-1]:[0,2,3,1],c=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${c} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",i,`max(0, min(row, ${t[i]} - 1))`)};
      ${e.indicesSet("input_indices",a,`max(0, min(col, ${t[a]} - 1))`)};
      ${Xl(e,l,o,2)}
      return ${e.getByIndices("input_indices")};
    }

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

        if (${a} && (originalIdx < 0 || originalIdx > (${t[y]} - 1))) {
          return ${l};
        }
        var data: array<${f}, 4> = array<${f}, 4>(0.0, 0.0, 0.0, 0.0);
        for (var i: i32 = -1; i < 3; i++) {
          var ${I}: ${f} = originalIdx + ${f}(i);
          if (${I} < 0 || ${I} >= ${t[y]}) {
            ${c?`coefs[i + 1] = 0.0;
                        continue;`:a?`return ${l};`:`${I} = max(0, min(${I}, ${t[y]} - 1));`};
          }
        var input_indices_copy: ${e.type.indices} = input_indices;
          ${e.indicesSet("input_indices_copy",y,`u32(${I})`)};
          data[i + 1] = ${y===d?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
        }
        return cubicInterpolation1D(data, coefs);
      }`};return`
    ${_(d)};
    ${_(u)};
  fn getCubicInterpolationCoefs(s: ${f}) -> array<${f}, 4> {
    var absS = abs(s);
    var coeffs: array<${f}, 4> = array<${f}, 4>(0.0, 0.0, 0.0, 0.0);
    var oneMinusAbsS: ${f} = 1.0 - absS;
    var twoMinusAbsS: ${f} = 2.0 - absS;
    var onePlusAbsS: ${f} = 1.0 + absS;
    coeffs[0] = ((${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<${f}, 4>, coefs: array<${f}, 4>) -> ${f} {
    var coefsSum: ${f} = 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: ${r.type.indices}) -> ${f} {
    var input_indices: ${e.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},dg=(e,r,t,s,n)=>{let[o,i,a,l,c]=t.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],p=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${p} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",i,`max(0, min(depth, ${t[i]} - 1))`)};
      ${e.indicesSet("input_indices",a,`max(0, min(height, ${t[a]} - 1))`)};
      ${e.indicesSet("input_indices",l,`max(0, min(width, ${t[l]} - 1))`)};
      ${Xl(e,c,o,3)}
      return ${e.getByIndices("input_indices")};
    }

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

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

      var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${p} = abs(depth - ${p}(depth1));
      var dx2: ${p} = abs(${p}(depth2) - depth);
      var dy1: ${p} = abs(height - ${p}(height1));
      var dy2: ${p} = abs(${p}(height2) - height);
      var dz1: ${p} = abs(width - ${p}(width1));
      var dz2: ${p} = abs(${p}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},pg=(e,r,t,s,n,o)=>{let i=e.dims,a=sg(o,r.axes,i.length),l=ng(i,s,n,r.axes),c=s.slice();s.length===0&&(c=i.map((v,k)=>v===0?1:l[k]/v),r.keepAspectRatioPolicy!=="stretch"&&(l=og(i,c,r)));let p=ot("output",e.dataType,l.length),d=Ie("input",e.dataType,i.length),u=we.size(l),f=i.length===l.length&&i.every((v,k)=>v===l[k]),_=r.coordinateTransformMode==="tf_crop_and_resize",y=r.extrapolationValue,I=d.type.value,w=v=>`
      ${f?"":`
      ${tg(r.coordinateTransformMode,I)};
      ${(()=>{switch(r.mode){case"nearest":return`
              ${lg(d,i)};
              ${rg(r.nearestMode,t,I)};
              ${ig(d,p,i,l,c.length,a.length,_)};
              `;case"linear":return`
              ${ag(p,i,l,c.length,a.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${cg(d,p,i,_,y)}`;if(i.length===3||i.length===5)return`${dg(d,p,i,_,y)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${ug(d,p,i,l,c,a,r.cubicCoeffA,_,r.extrapolationValue,r.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${v.registerUniform("output_size","u32").registerUniform("scales","f32",c.length).registerUniform("roi","f32",a.length).declareVariables(d,p)}
      ${v.mainStart()}
        ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${f?"output[global_idx] = input[global_idx];":`
        let output_indices = ${p.offsetToIndices("global_idx")};
        var input_indices: ${d.type.indices};
        ${(()=>{switch(r.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${d.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${r.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: ${r.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${r.cacheKey}|${t}|${c.length>0?r.mode==="cubic"?c:c.length:""}|${n.length>0?n:""}|${a.length>0?a:""}|${f}|${r.mode==="nearest"?i.length:i}`,inputDependencies:["rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:l,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:[{type:12,data:u},{type:1,data:c},{type:1,data:a},...ct(i,l)]})}},mg=e=>{let r=e.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]},hg=(e,r)=>{let t=[],s=[],n=[],o=mg(e);if(r.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");eg(e.inputs,r,o,t,s,n),e.compute(pg(e.inputs[0],r,o,t,s,n),{inputs:[0]})},_g=e=>{let r=e.antialias,t=e.axes,s=e.coordinateTransformMode,n=e.cubicCoeffA,o=e.excludeOutside!==0,i=e.extrapolationValue,a=e.keepAspectRatioPolicy,l=e.mode,c=e.nearestMode===""?"simple":e.nearestMode;return Rt({antialias:r,axes:t,coordinateTransformMode:s,cubicCoeffA:n,excludeOutside:o,extrapolationValue:i,keepAspectRatioPolicy:a,mode:l,nearestMode:c})}}),fg,gg,Mg,Zx=Ne(()=>{gt(),Tt(),St(),fg=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let r=e[0],t=e[1],s=e[2];if(r.dataType!==t.dataType||r.dataType!==s.dataType)throw new Error("All inputs must have the same data type");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Input must be 2D or 3D");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Skip must be 2D or 3D");let n=r.dims[r.dims.length-1],o=r.dims[r.dims.length-2];if(t.dims[t.dims.length-1]!==n)throw new Error("Skip must have the same hidden size as input");if(t.dims[t.dims.length-2]!==o)throw new Error("Skip must have the same sequence length as input");if(s.dims.length!==1)throw new Error("Gamma must be 1D");if(s.dims[s.dims.length-1]!==n)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let i=e[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==n)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let i=e[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==n)throw new Error("Bias must have the same hidden size as input")}},gg=(e,r,t,s)=>{let n=r.simplified,o=e[0].dims,i=we.size(o),a=o,l=i,c=o.slice(-1)[0],p=s?o.slice(0,-1).concat(1):[],d=!n&&e.length>3,u=e.length>4,f=s&&t>1,_=s&&t>2,y=t>3,I=64,w=or(c),v=[{type:12,data:l},{type:12,data:w},{type:12,data:c},{type:1,data:r.epsilon}],k=b=>{let P=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],x=[Ie("x",e[0].dataType,e[0].dims,w),Ie("skip",e[1].dataType,e[1].dims,w),Ie("gamma",e[2].dataType,e[2].dims,w)];d&&x.push(Ie("beta",e[3].dataType,e[3].dims,w)),u&&x.push(Ie("bias",e[4].dataType,e[4].dims,w)),x.push(ot("output",e[0].dataType,a,w)),f&&x.push(ot("mean_output",1,p)),_&&x.push(ot("inv_std_output",1,p)),y&&x.push(ot("input_skip_bias_sum",e[0].dataType,a,w));let S=Sr(e[0].dataType),O=Sr(1,w);return`

      ${b.registerUniforms(P).declareVariables(...x)}
      var<workgroup> sum_shared : array<${O}, ${I}>;
      var<workgroup> sum_squared_shared : array<${O}, ${I}>;

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

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

        var reduce_size : u32 = ${I};
        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {
          reduce_size = curr_size + (reduce_size & 1);
          if (ix < curr_size) {
            sum_shared[ix] += sum_shared[ix + reduce_size];
            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
          }
          workgroupBarrier();
        }

        let sum = sum_shared[0];
        let square_sum = sum_squared_shared[0];
        let mean = ${Gs("sum",w)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Gs("square_sum",w)} / f32(uniforms.hidden_size) ${n?"":"- mean * mean"} + uniforms.epsilon);
        ${f?"mean_output[global_idx] = mean;":""}
        ${_?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${n?"":`- ${S}(mean)`}) *
            ${S}(inv_std_dev) * gamma[offset1d + i]
            ${d?"+ beta[offset1d + i]":""};
        }
      }`},T=[{dims:a,dataType:e[0].dataType}];return t>1&&T.push({dims:p,dataType:1}),t>2&&T.push({dims:p,dataType:1}),t>3&&T.push({dims:o,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${w};${f};${_};${y}`,inputDependencies:e.map((b,P)=>"type")},getShaderSource:k,getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(l/c)},programUniforms:v})}},Mg=(e,r)=>{fg(e.inputs);let t=[0];e.outputCount>1&&t.push(-3),e.outputCount>2&&t.push(-3),e.outputCount>3&&t.push(3),e.compute(gg(e.inputs,r,e.outputCount,!1),{outputs:t})}}),wg,xo,bg,Jl,yg,vg,xg,Tg,eT=Ne(()=>{gt(),Tt(),dr(),St(),wg=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");if(r.axes.length!==0){if(r.axes.length!==r.starts.length||r.axes.length!==r.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(r.starts.length!==r.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((t,s)=>{if(e[s+1].dataType!==6&&e[s+1].dataType!==7)throw new Error(`Input ${s} must be an array of int32 or int64`)})},xo=(e,r)=>{let t=[];if(e.length>r)if(e[r].dataType===7)e[r].getBigInt64Array().forEach(s=>t.push(Number(s)));else if(e[r].dataType===6)e[r].getInt32Array().forEach(s=>t.push(Number(s)));else throw new Error(`Input ${r} must be an array of int32 or int64`);return t},bg=(e,r)=>{if(e.length>1){let t=xo(e,1),s=xo(e,2),n=xo(e,3);return n.length===0&&(n=[...Array(e[0].dims.length).keys()]),Rt({starts:t,ends:s,axes:n})}else return r},Jl=(e,r,t,s,n)=>{let o=e;return e<0&&(o+=t[s[r]]),n[r]<0?Math.max(0,Math.min(o,t[s[r]]-1)):Math.max(0,Math.min(o,t[s[r]]))},yg=(e,r,t)=>`fn calculateInputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${t.length}; i >= 0; i--) {
            let input_shape_i = ${lt("uniforms.input_shape","i",t.length)};
            let steps_i = ${lt("uniforms.steps","i",t.length)};
            let signs_i = ${lt("uniforms.signs","i",t.length)};
            let starts_i = ${lt("uniforms.starts","i",t.length)};
            var output_index = ${r.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${e.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,vg=(e,r)=>{let t=e[0].dims,s=we.size(t),n=r.axes.length>0?we.normalizeAxes(r.axes,t.length):[...Array(t.length).keys()],o=xo(e,4);o.forEach(w=>w!==0||(()=>{throw new Error("step cannot be 0")})),o.length===0&&(o=Array(n.length).fill(1));let i=r.starts.map((w,v)=>Jl(w,v,t,n,o)),a=r.ends.map((w,v)=>Jl(w,v,t,n,o));if(n.length!==i.length||n.length!==a.length)throw new Error("start, ends and axes should have the same number of elements");if(n.length!==t.length)for(let w=0;w<t.length;++w)n.includes(w)||(i.splice(w,0,0),a.splice(w,0,t[w]),o.splice(w,0,1));let l=o.map(w=>Math.sign(w));o.forEach((w,v,k)=>{if(w<0){let T=(a[v]-i[v])/w,b=i[v],P=b+T*o[v];i[v]=P,a[v]=b,k[v]=-w}});let c=t.slice(0);n.forEach((w,v)=>{c[w]=Math.ceil((a[w]-i[w])/o[w])});let p={dims:c,dataType:e[0].dataType},d=ot("output",e[0].dataType,c.length),u=Ie("input",e[0].dataType,e[0].dims.length),f=we.size(c),_=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:o.length}],y=[{type:12,data:f},{type:12,data:i},{type:6,data:l},{type:12,data:o},...ct(e[0].dims,c)],I=w=>`
      ${w.registerUniforms(_).declareVariables(u,d)}
        ${yg(u,d,t)}
        ${w.mainStart()}
          ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${d.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${d.setByOffset("global_idx",u.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${i.length}_${o.length}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:y})}},xg=(e,r)=>{wg(e.inputs,r);let t=bg(e.inputs,r);e.compute(vg(e.inputs,t),{inputs:[0]})},Tg=e=>{let r=e.starts,t=e.ends,s=e.axes;return Rt({starts:r,ends:t,axes:s})}}),Pg,Eg,Cg,Sg,tT=Ne(()=>{gt(),Tt(),dr(),Ks(),St(),Pg=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Eg=(e,r)=>{let t=e.inputs[0],s=t.dims,n=we.size(s),o=s.length,i=we.normalizeAxis(r.axis,o),a=i<s.length-1,l,c=[];a?(c=Array.from({length:o},(x,S)=>S),c[i]=o-1,c[o-1]=i,l=e.compute(Xr(t,c),{inputs:[t],outputs:[-1]})[0]):l=t;let p=l.dims,d=p[o-1],u=n/d,f=or(d),_=d/f,y=64;u===1&&(y=256);let I=(x,S)=>S===4?`max(max(${x}.x, ${x}.y), max(${x}.z, ${x}.w))`:S===2?`max(${x}.x, ${x}.y)`:S===3?`max(max(${x}.x, ${x}.y), ${x}.z)`:x,w=Ie("x",l.dataType,l.dims,f),v=ot("result",l.dataType,l.dims,f),k=w.type.value,T=Sr(l.dataType)==="f32"?`var threadMax = ${k}(-3.402823e+38f);`:`var threadMax = ${k}(-65504.0h);`,b=x=>`
      var<workgroup> rowMaxShared : ${k};
      var<workgroup> rowSumShared : ${k};
      var<workgroup> threadShared : array<${k}, ${y}>;

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

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

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

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

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

        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
          if (lindex < currSize) {
            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
          rowSumShared = ${k}(${Gs("threadShared[0]",f)});
        }
        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);
        }
      }`,P=e.compute({name:"Softmax",shaderCache:{hint:`${f};${y}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:p,dataType:l.dataType}],dispatchGroup:{x:u},programUniforms:[{type:6,data:_}]}),getShaderSource:b},{inputs:[l],outputs:[a?-1:0]})[0];a&&e.compute(Xr(P,c),{inputs:[P]})},Cg=(e,r)=>{Pg(e.inputs),Eg(e,r)},Sg=e=>Rt({axis:e.axis})}),Yl,kg,$g,Ig,Ag,rT=Ne(()=>{gt(),Tt(),St(),Yl=e=>Array.from(e.getBigInt64Array(),Number),kg=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==10&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Yl(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},$g=(e,r)=>{let t=[];for(let s=0;s<e.length;++s)t.push(e[s]*r[s]);return t},Ig=(e,r)=>{let t=e[0].dims,s=r??Yl(e[1]),n=$g(t,s),o=we.size(n),i=e[0].dataType,a=Ie("input",i,t.length),l=ot("output",i,n.length),c=p=>`
      const inputShape = ${a.indices(...t)};
      ${p.registerUniform("output_size","u32").declareVariables(a,l)}
      ${p.mainStart()}
      ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${l.offsetToIndices("global_idx")};
      var input_indices: ${a.type.indices};
      for (var i = 0; i < ${t.length}; i++) {
        let input_dim_i = ${a.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${l.indicesGet("output_indices","i")}  % input_dim_i;

        ${a.indicesSet("input_indices","i","input_dim_value")}
      }
      ${l.setByOffset("global_idx",a.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${s}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:[{type:12,data:o},...ct(e[0].dims,n)]}),getShaderSource:c}},Ag=e=>{kg(e.inputs),e.compute(Ig(e.inputs),{inputs:[0]})}}),Fg,Og,Dg,sT=Ne(()=>{gt(),Tt(),St(),Fg=(e,r,t,s,n)=>{let o=ot("output_data",n,t.length,4),i=Ie("a_data",r[1].dataType,r[1].dims.length,4),a=Ie("b_data",r[2].dataType,r[2].dims.length,4),l=Ie("c_data",r[0].dataType,r[0].dims.length,4),c,p=(d,u,f)=>`select(${u}, ${d}, ${f})`;if(!s)c=o.setByOffset("global_idx",p(i.getByOffset("global_idx"),a.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let d=(u,f,_="")=>{let y=`a_data[index_a${f}][component_a${f}]`,I=`b_data[index_b${f}][component_b${f}]`,w=`bool(c_data[index_c${f}] & (0xffu << (component_c${f} * 8)))`;return`
            let output_indices${f} = ${o.offsetToIndices(`global_idx * 4u + ${f}u`)};
            let offset_a${f} = ${i.broadcastedIndicesToOffset(`output_indices${f}`,o)};
            let offset_b${f} = ${a.broadcastedIndicesToOffset(`output_indices${f}`,o)};
            let offset_c${f} = ${l.broadcastedIndicesToOffset(`output_indices${f}`,o)};
            let index_a${f} = offset_a${f} / 4u;
            let index_b${f} = offset_b${f} / 4u;
            let index_c${f} = offset_c${f} / 4u;
            let component_a${f} = offset_a${f} % 4u;
            let component_b${f} = offset_b${f} % 4u;
            let component_c${f} = offset_c${f} % 4u;
            ${u}[${f}] = ${_}(${p(y,I,w)});
          `};n===9?c=`
            var data = vec4<u32>(0);
            ${d("data",0,"u32")}
            ${d("data",1,"u32")}
            ${d("data",2,"u32")}
            ${d("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:c=`
            ${d("output_data[global_idx]",0)}
            ${d("output_data[global_idx]",1)}
            ${d("output_data[global_idx]",2)}
            ${d("output_data[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(l,i,a,o)}
        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${c}
      }`},Og=e=>{let r=e[1].dims,t=e[2].dims,s=e[0].dims,n=e[1].dataType,o=!(we.areEqual(r,t)&&we.areEqual(t,s)),i=r,a=we.size(r);if(o){let c=Ln.calcShape(Ln.calcShape(r,t,!1),s,!1);if(!c)throw new Error("Can't perform where op on the given tensors");i=c,a=we.size(i)}let l=Math.ceil(a/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:c=>Fg(c,e,i,o,n),getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(a/64/4)},programUniforms:[{type:12,data:l},...ct(s,r,t,i)]})}},Dg=e=>{e.compute(Og(e.inputs))}}),Lg,nT=Ne(()=>{Mx(),pl(),wx(),bx(),yx(),vx(),xx(),Sx(),$x(),Ix(),Ax(),Fx(),Ox(),Dx(),Lx(),zx(),Bx(),Rx(),jx(),Nx(),Vx(),Ux(),Wx(),Gx(),Kx(),H_(),Hx(),qx(),Qx(),Xx(),Jx(),cl(),Yx(),sf(),Zx(),eT(),tT(),ef(),rT(),Ks(),fl(),sT(),Lg=new Map([["Abs",[_m]],["Acos",[fm]],["Acosh",[gm]],["Add",[nh]],["ArgMax",[em,dl]],["ArgMin",[Zp,dl]],["Asin",[Mm]],["Asinh",[wm]],["Atan",[bm]],["Atanh",[ym]],["Attention",[am]],["AveragePool",[Of,Ff]],["BatchNormalization",[um]],["BiasAdd",[mm]],["BiasSplitGelu",[th]],["Cast",[xm,vm]],["Ceil",[Em]],["Clip",[Pm]],["Concat",[Mh,wh]],["Conv",[kl,Cl]],["ConvTranspose",[Kh,Uh]],["Cos",[Cm]],["Cosh",[Sm]],["CumSum",[qh,Qh]],["DepthToSpace",[Zh,e_]],["DequantizeLinear",[Uf,Wf]],["Div",[oh]],["Einsum",[a_,i_]],["Elu",[km,go]],["Equal",[ah]],["Erf",[$m]],["Exp",[Im]],["Expand",[d_]],["FastGelu",[m_]],["Floor",[Am]],["FusedConv",[kl,Cl]],["Gather",[g_,f_]],["GatherElements",[S_,C_]],["GatherBlockQuantized",[x_,T_]],["GatherND",[w_,b_]],["Gelu",[Fm]],["Gemm",[A_,I_]],["GlobalAveragePool",[Lf,Df]],["GlobalMaxPool",[jf,Rf]],["Greater",[uh]],["GreaterOrEqual",[ph]],["GridSample",[N_,V_]],["GroupQueryAttention",[lf]],["HardSigmoid",[Nm,jm]],["InstanceNormalization",[df]],["LayerNormalization",[hf]],["LeakyRelu",[Om,go]],["Less",[dh]],["LessOrEqual",[mh]],["Log",[Qm]],["MatMul",[ff]],["MatMulNBits",[bf,yf]],["MaxPool",[zf,Bf]],["Mul",[ih]],["MultiHeadAttention",[K_,W_]],["Neg",[Lm]],["Not",[Dm]],["Pad",[$f]],["Pow",[lh]],["QuickGelu",[Ym,go]],["Range",[Hf]],["Reciprocal",[zm]],["ReduceMin",[qp]],["ReduceMean",[Up]],["ReduceMax",[Hp]],["ReduceSum",[Xp]],["ReduceProd",[Qp]],["ReduceL1",[Wp]],["ReduceL2",[Gp]],["ReduceLogSum",[Yp]],["ReduceLogSumExp",[Kp]],["ReduceSumSquare",[Jp]],["Relu",[Bm]],["Resize",[hg,_g]],["RotaryEmbedding",[rf]],["ScatterND",[Jf,Xf]],["Sigmoid",[Rm]],["Sin",[Vm]],["Sinh",[Um]],["Slice",[xg,Tg]],["SkipLayerNormalization",[Mg]],["Split",[Y_,Z_]],["Sqrt",[Wm]],["Softmax",[Cg,Sg]],["Sub",[ch]],["Tan",[Gm]],["Tanh",[Km]],["ThresholdedRelu",[qm,go]],["Tile",[Ag]],["Transpose",[up,dp]],["Where",[Dg]]])}),zg,oT=Ne(()=>{ps(),Ls(),St(),zg=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,r){this.repo.set(e,r)}run(e,r,t,s,n){ds(e.programInfo.name);let o=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let a=[];for(let c of r)a.push({binding:a.length,resource:{buffer:c.buffer}});for(let c of t)a.push({binding:a.length,resource:{buffer:c.buffer}});n&&a.push({binding:a.length,resource:n});let l=o.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:a,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let c={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:l,dispatchGroup:s};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(c)}i.setPipeline(e.computePipeline),i.setBindGroup(0,l),i.dispatchWorkgroups(...s),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(),rs(e.programInfo.name)}dispose(){}build(e,r){ds(e.name);let t=this.backend.device,s=[];[{feature:"shader-f16",extension:"f16"},{feature:"subgroups",extension:"subgroups"}].forEach(c=>{t.features.has(c.feature)&&s.push(`enable ${c.extension};`)});let n=np(r,this.backend.device.limits),o=e.getShaderSource(n),i=`${s.join(`
`)}
${n.additionalImplementations}
${o}`,a=t.createShaderModule({code:i,label:e.name});Ot("verbose",()=>`[WebGPU] ${e.name} shader code: ${i}`);let l=t.createComputePipeline({compute:{module:a,entryPoint:"main"},layout:"auto",label:e.name});return rs(e.name),{programInfo:e,computePipeline:l,uniformVariablesInfo:n.variablesInfo}}normalizeDispatchGroupSize(e){let r=typeof e=="number"?e:e.x,t=typeof e=="number"?1:e.y||1,s=typeof e=="number"?1:e.z||1,n=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(r<=n&&t<=n&&s<=n)return[r,t,s];let o=r*t*s,i=Math.ceil(Math.sqrt(o));if(i>n){if(i=Math.ceil(Math.cbrt(o)),i>n)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),Bg={};On(Bg,{WebGpuBackend:()=>Vg});var Rg,jg,Ng,Vg,aT=Ne(()=>{ps(),gt(),Ls(),Wd(),fx(),nT(),oT(),Rg=(e,r)=>{if(r.length!==e.length)throw new Error(`inputDependencies length ${r.length} is not equal to inputTensors length ${e.length}.`);let t=[];for(let s=0;s<e.length;++s){let n=e[s].dataType;switch(r[s]){case"none":{t.push("");break}case"type":{t.push(`${n}`);break}case"rank":{let o=e[s].dims.length;t.push(`${n};${o}`);break}case"dims":{let o=e[s].dims.join(",");t.push(`${n};${o}`);break}default:throw new Error(`unsupported input dependency: ${r[s]}`)}}return t.join("|")},jg=(e,r,t)=>{let s=e.name;return e.shaderCache?.hint&&(s+="["+e.shaderCache.hint+"]"),s+=":"+t+`:${Rg(r,e.shaderCache?.inputDependencies??new Array(r.length).fill("dims"))}`,s},Ng=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},Vg=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 e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,r){this.env=e;let t=[],s={requiredLimits:{maxComputeWorkgroupStorageSize:r.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.limits.maxStorageBufferBindingSize,maxBufferSize:r.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:r.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:r.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:r.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:r.limits.maxComputeWorkgroupSizeZ},requiredFeatures:t},n=o=>r.features.has(o)&&t.push(o)&&!0;n("chromium-experimental-timestamp-query-inside-passes")||n("timestamp-query"),n("shader-f16"),n("subgroups"),this.device=await r.requestDevice(s),this.adapterInfo=new Ng(r.info||await r.requestAdapterInfo()),this.gpuDataManager=ep(this),this.programManager=new zg(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Hi(e.logLevel,!!e.debug),this.device.onuncapturederror=o=>{o.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${o.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:r,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),r={};this.queryType==="at-passes"&&(r.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(r)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;ds(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{let r=new BigUint64Array(e.getMappedRange()),t=this.pendingQueries.get(e);for(let s=0;s<r.length/2;s++){let n=t[s],o=n.kernelId,i=this.kernels.get(o),a=i.kernelType,l=i.kernelName,c=n.programName,p=n.inputTensorViews,d=n.outputTensorViews,u=r[s*2],f=r[s*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=u);let _=Number(u-this.queryTimeBase),y=Number(f-this.queryTimeBase);if(!Number.isSafeInteger(_)||!Number.isSafeInteger(y))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:p.map(I=>({dims:I.dims,dataType:Ds(I.dataType)})),outputsMetadata:d.map(I=>({dims:I.dims,dataType:Ds(I.dataType)})),kernelId:o,kernelType:a,kernelName:l,programName:c,startTime:_,endTime:y});else{let I="";p.forEach((v,k)=>{I+=`input[${k}]: [${v.dims}] | ${Ds(v.dataType)}, `});let w="";d.forEach((v,k)=>{w+=`output[${k}]: [${v.dims}] | ${Ds(v.dataType)}, `}),console.log(`[profiling] kernel "${o}|${a}|${l}|${c}" ${I}${w}execution time: ${y-_} ns`)}po("GPU",`${c}::${u}::${f}`)}e.unmap(),this.pendingQueries.delete(e)}),rs()}run(e,r,t,s,n,o){ds(e.name);let i=[];for(let v=0;v<r.length;++v){let k=r[v].data;if(k===0)continue;let T=this.gpuDataManager.get(k);if(!T)throw new Error(`no GPU data for input: ${k}`);i.push(T)}let{outputs:a,dispatchGroup:l,programUniforms:c}=e.getRunData(r),p=t.length===0?a.map((v,k)=>k):t;if(p.length!==a.length)throw new Error(`Output size ${p.length} must be equal to ${a.length}.`);let d=[],u=[];for(let v=0;v<a.length;++v){if(!Number.isInteger(p[v])||p[v]<-3||p[v]>=o)throw new Error(`Invalid output index: ${p[v]}`);if(p[v]===-3)continue;let k=p[v]===-1,T=p[v]===-2,b=k||T?n(a[v].dataType,a[v].dims):s(p[v],a[v].dataType,a[v].dims);if(d.push(b),b.data===0)continue;let P=this.gpuDataManager.get(b.data);if(!P)throw new Error(`no GPU data for output: ${b.data}`);if(k&&this.temporaryData.push(P),T){let x=this.kernelPersistentData.get(this.currentKernelId);x||(x=[],this.kernelPersistentData.set(this.currentKernelId,x)),x.push(P)}u.push(P)}if(i.length!==r.length||u.length!==d.length){if(u.length===0)return rs(e.name),d;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let f;if(c){let v=0,k=[];c.forEach(x=>{let S=typeof x.data=="number"?[x.data]:x.data;if(S.length===0)return;let O=x.type===10?2:4,F,H;x.type===10?(H=S.length>4?16:S.length>2?8:S.length*O,F=S.length>4?16:O*S.length):(H=S.length<=2?S.length*O:16,F=16),v=Math.ceil(v/H)*H,k.push(v);let W=x.type===10?8:4;v+=S.length>4?Math.ceil(S.length/W)*F:S.length*O});let T=16;v=Math.ceil(v/T)*T;let b=new ArrayBuffer(v);c.forEach((x,S)=>{let O=k[S],F=typeof x.data=="number"?[x.data]:x.data;if(x.type===6)new Int32Array(b,O,F.length).set(F);else if(x.type===12)new Uint32Array(b,O,F.length).set(F);else if(x.type===10)new Uint16Array(b,O,F.length).set(F);else if(x.type===1)new Float32Array(b,O,F.length).set(F);else throw new Error(`Unsupported uniform type: ${Ds(x.type)}`)});let P=this.gpuDataManager.create(v,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(P.buffer,0,b,0,v),this.gpuDataManager.release(P.id),f={offset:0,size:v,buffer:P.buffer}}let _=this.programManager.normalizeDispatchGroupSize(l),y=_[1]===1&&_[2]===1,I=jg(e,r,y),w=this.programManager.getArtifact(I);if(w||(w=this.programManager.build(e,_),this.programManager.setArtifact(I,w),Ot("info",()=>`[artifact] key: ${I}, programName: ${e.name}`)),c&&w.uniformVariablesInfo){if(c.length!==w.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${w.uniformVariablesInfo.length}, got ${c.length} in program "${w.programInfo.name}".`);for(let v=0;v<c.length;v++){let k=c[v],T=k.type,b=typeof k.data=="number"?1:k.data.length,[P,x]=w.uniformVariablesInfo[v];if(T!==P||b!==x)throw new Error(`Uniform variable ${v} mismatch: expect type ${P} with size ${x}, got type ${T} with size ${b} in program "${w.programInfo.name}".`)}}if(Ot("info",()=>`[ProgramManager] run "${e.name}" (key=${I}) with ${_[0]}x${_[1]}x${_[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let v={kernelId:this.currentKernelId,programName:w.programInfo.name,inputTensorViews:r,outputTensorViews:d};this.pendingKernels.push(v),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(v)}return this.programManager.run(w,i,u,_,f),rs(e.name),d}upload(e,r){this.gpuDataManager.upload(e,r)}memcpy(e,r){this.gpuDataManager.memcpy(e,r)}async download(e,r){await this.gpuDataManager.download(e,r)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,r,t,s){let n=Lg.get(e);if(!n)throw new Error(`kernel not implemented: ${e}`);let o={kernelType:e,kernelName:s,kernelEntry:n[0],attributes:[n[1],t]};this.kernels.set(r,o)}releaseKernel(e){let r=this.kernelPersistentData.get(e);if(r){for(let t of r)this.gpuDataManager.release(t.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,r,t){let s=this.kernels.get(e);if(!s)throw new Error(`kernel not created: ${e}`);let n=s.kernelType,o=s.kernelName,i=s.kernelEntry,a=s.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${n}] ${o}" is not allowed to be called recursively`);this.currentKernelId=e,a[0]&&(a[1]=a[0](a[1]),a[0]=void 0),Ot("info",()=>`[WebGPU] Start to run kernel "[${n}] ${o}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),i(r,a[1]),0}catch(c){return t.push(Promise.resolve(`[WebGPU] Kernel "[${n}] ${o}" failed. ${c}`)),1}finally{l&&t.push(this.device.popErrorScope().then(c=>c?`GPU validation error for kernel "[${n}] ${o}": ${c.message}`:null));for(let c of this.temporaryData)this.gpuDataManager.release(c.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,r,t,s){let n=this.sessionExternalDataMapping.get(e);n||(n=new Map,this.sessionExternalDataMapping.set(e,n));let o=n.get(r),i=this.gpuDataManager.registerExternalBuffer(t,s,o);return n.set(r,[i,t]),i}unregisterBuffers(e){let r=this.sessionExternalDataMapping.get(e);r&&(r.forEach(t=>this.gpuDataManager.unregisterExternalBuffer(t[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let r=this.gpuDataManager.get(e);if(!r)throw new Error(`no GPU data for buffer: ${e}`);return r.buffer}createDownloader(e,r,t){return async()=>{let s=await nl(this,e,r);return qi(s.buffer,t)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){this.queryType="none",(this.env.webgpu.profiling?.mode==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){Ot("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(){Ot("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){Ot("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),r=this.capturedPendingKernels.get(this.currentSessionId),t=e.length;this.pendingKernels=[];for(let s=0;s<t;s++){let n=this.getComputePassEncoder(),o=e[s];this.writeTimestamp(this.pendingDispatchNumber*2),n.setPipeline(o.computePipeline),n.setBindGroup(0,o.bindGroup),n.dispatchWorkgroups(...o.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(r[s]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),Ug={};On(Ug,{init:()=>Gg});var ga,Wg,Gg,iT=Ne(()=>{gt(),Ls(),Tt(),_x(),ga=class kv{constructor(r,t,s,n){this.module=r,this.dataType=t,this.data=s,this.dims=n}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let r=we.size(this.dims);return r===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,r)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let r=we.size(this.dims);return r===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,r)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let r=we.size(this.dims);return r===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,r)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let r=we.size(this.dims);return r===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,r)}reshape(r){if(we.size(r)!==we.size(this.dims))throw new Error("Invalid new shape");return new kv(this.module,this.dataType,this.data,r)}},Wg=class{constructor(e,r,t){this.module=e,this.backend=r,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=r.adapterInfo;let s=e.PTR_SIZE,n=t/e.PTR_SIZE,o=s===4?"i32":"i64";this.opKernelContext=Number(e.getValue(s*n++,o));let i=Number(e.getValue(s*n++,o));this.outputCount=Number(e.getValue(s*n++,o)),this.customDataOffset=Number(e.getValue(s*n++,"*")),this.customDataSize=Number(e.getValue(s*n++,o));let a=[];for(let l=0;l<i;l++){let c=Number(e.getValue(s*n++,o)),p=Number(e.getValue(s*n++,"*")),d=Number(e.getValue(s*n++,o)),u=[];for(let f=0;f<d;f++)u.push(Number(e.getValue(s*n++,o)));a.push(new ga(e,c,p,u))}this.inputs=a}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(e,r){let t=r?.inputs?.map(i=>typeof i=="number"?this.inputs[i]:i)??this.inputs,s=r?.outputs??[],n=(i,a,l)=>new ga(this.module,a,this.output(i,l),l),o=(i,a)=>{let l=un(i,a);if(!l)throw new Error(`Unsupported data type: ${i}`);let c=l>0?this.backend.gpuDataManager.create(l).id:0;return new ga(this.module,i,c,a)};return this.backend.run(e,t,s,n,o,this.outputCount)}output(e,r){let t=this.module.stackSave();try{let s=this.module.PTR_SIZE,n=s===4?"i32":"i64",o=this.module.stackAlloc((1+r.length)*s);this.module.setValue(o,r.length,n);for(let i=0;i<r.length;i++)this.module.setValue(o+s*(i+1),r[i],n);return this.module._JsepOutput(this.opKernelContext,e,o)}catch(s){throw new Error(`Failed to generate kernel's output[${e}] with dims [${r}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${s}`)}finally{this.module.stackRestore(t)}}},Gg=async(e,r,t,s)=>{let n=r.jsepInit;if(!n)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(e==="webgpu"){let o=(aT(),lo(Bg)).WebGpuBackend,i=new o;await i.initialize(t,s),n("webgpu",[i,a=>i.alloc(Number(a)),a=>i.free(a),(a,l,c,p=!1)=>{if(p)Ot("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${Number(a)}, dst=${Number(l)}, size=${Number(c)}`),i.memcpy(Number(a),Number(l));else{Ot("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${Number(a)}, gpuDataId=${Number(l)}, size=${Number(c)}`);let d=r.HEAPU8.subarray(Number(a>>>0),Number(a>>>0)+Number(c));i.upload(Number(l),d)}},async(a,l,c)=>{Ot("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${a}, dataOffset=${l}, size=${c}`),await i.download(Number(a),()=>r.HEAPU8.subarray(Number(l)>>>0,Number(l+c)>>>0))},(a,l,c)=>i.createKernel(a,Number(l),c,r.UTF8ToString(r._JsepGetNodeName(Number(l)))),a=>i.releaseKernel(a),(a,l,c,p)=>{Ot("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${c}, kernel=${a}, contextDataOffset=${l}`);let d=new Wg(r,i,Number(l));return i.computeKernel(Number(a),d,p)},()=>i.captureBegin(),()=>i.captureEnd(),()=>i.replay()])}else{let o=new Xd(t);n("webnn",[o,()=>o.reserveTensorId(),i=>o.releaseTensorId(i),async(i,a,l,c,p)=>o.ensureTensor(i,a,l,c,p),(i,a)=>{o.uploadTensor(i,a)},async(i,a)=>o.downloadTensor(i,a)])}}}),Kg,Zl,ec,Hs,Hg,tc,Ma,rc,sc,nc,oc,ac,ic,qg=Ne(()=>{px(),mx(),gt(),cn(),Ni(),Od(),Kg=(e,r)=>{Qt()._OrtInit(e,r)!==0&&Gt("Can't initialize onnxruntime.")},Zl=async e=>{Kg(e.wasm.numThreads,sa(e.logLevel))},ec=async(e,r)=>{Qt().asyncInit?.();{let t=(iT(),lo(Ug)).init;if(r==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let s=e.webgpu.adapter;if(s){if(typeof s.limits!="object"||typeof s.features!="object"||typeof s.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let n=e.webgpu.powerPreference;if(n!==void 0&&n!=="low-power"&&n!=="high-performance")throw new Error(`Invalid powerPreference setting: "${n}"`);let o=e.webgpu.forceFallbackAdapter;if(o!==void 0&&typeof o!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${o}"`);if(s=await navigator.gpu.requestAdapter({powerPreference:n,forceFallbackAdapter:o}),!s)throw new Error('Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.')}await t("webgpu",Qt(),e,s)}if(r==="webnn"){if(typeof navigator>"u"||!navigator.ml)throw new Error("WebNN is not supported in current environment");await t("webnn",Qt(),e)}}},Hs=new Map,Hg=e=>{let r=Qt(),t=r.stackSave();try{let s=r.PTR_SIZE,n=r.stackAlloc(2*s);r._OrtGetInputOutputCount(e,n,n+s)!==0&&Gt("Can't get session input/output count.");let o=s===4?"i32":"i64";return[Number(r.getValue(n,o)),Number(r.getValue(n+s,o))]}finally{r.stackRestore(t)}},tc=(e,r)=>{let t=Qt(),s=t.stackSave(),n=0;try{let o=t.PTR_SIZE,i=t.stackAlloc(2*o);t._OrtGetInputOutputMetadata(e,r,i,i+o)!==0&&Gt("Can't get session input/output metadata.");let a=Number(t.getValue(i,"*"));n=Number(t.getValue(i+o,"*"));let l=t.HEAP32[n/4];if(l===0)return[a,0];let c=t.HEAPU32[n/4+1],p=[];for(let d=0;d<c;d++){let u=Number(t.getValue(n+8+d*o,"*"));p.push(u!==0?t.UTF8ToString(u):Number(t.getValue(n+8+(d+c)*o,"*")))}return[a,l,p]}finally{t.stackRestore(s),n!==0&&t._OrtFree(n)}},Ma=e=>{let r=Qt(),t=r._malloc(e.byteLength);if(t===0)throw new Error(`Can't create a session. failed to allocate a buffer of size ${e.byteLength}.`);return r.HEAPU8.set(e,t),[t,e.byteLength]},rc=async(e,r)=>{let t,s,n=Qt();Array.isArray(e)?[t,s]=e:e.buffer===n.HEAPU8.buffer?[t,s]=[e.byteOffset,e.byteLength]:[t,s]=Ma(e);let o=0,i=0,a=0,l=[],c=[],p=[];try{if([i,l]=await Fd(r),r?.externalData&&n.mountExternalData){let T=[];for(let b of r.externalData){let P=typeof b=="string"?b:b.path;T.push(Ki(typeof b=="string"?b:b.data).then(x=>{n.mountExternalData(P,x)}))}await Promise.all(T)}for(let T of r?.executionProviders??[])if((typeof T=="string"?T:T.name)==="webnn"){if(n.shouldTransferToMLTensor=!1,typeof T!="string"){let b=T,P=b?.context,x=b?.gpuDevice,S=b?.deviceType,O=b?.powerPreference;P?n.currentContext=P:x?n.currentContext=await n.webnnCreateMLContext(x):n.currentContext=await n.webnnCreateMLContext({deviceType:S,powerPreference:O})}else n.currentContext=await n.webnnCreateMLContext();break}o=await n._OrtCreateSession(t,s,i),n.webgpuOnCreateSession?.(o),o===0&&Gt("Can't create a session."),n.jsepOnCreateSession?.(),n.currentContext&&(n.webnnRegisterMLContext(o,n.currentContext),n.currentContext=void 0,n.shouldTransferToMLTensor=!0);let[d,u]=Hg(o),f=!!r?.enableGraphCapture,_=[],y=[],I=[],w=[],v=[];for(let T=0;T<d;T++){let[b,P,x]=tc(o,T);b===0&&Gt("Can't get an input name."),c.push(b);let S=n.UTF8ToString(b);_.push(S),I.push(P===0?{name:S,isTensor:!1}:{name:S,isTensor:!0,type:Ds(P),shape:x})}for(let T=0;T<u;T++){let[b,P,x]=tc(o,T+d);b===0&&Gt("Can't get an output name."),p.push(b);let S=n.UTF8ToString(b);y.push(S),w.push(P===0?{name:S,isTensor:!1}:{name:S,isTensor:!0,type:Ds(P),shape:x});{if(f&&r?.preferredOutputLocation===void 0){v.push("gpu-buffer");continue}let O=typeof r?.preferredOutputLocation=="string"?r.preferredOutputLocation:r?.preferredOutputLocation?.[S]??"cpu";if(O!=="cpu"&&O!=="cpu-pinned"&&O!=="gpu-buffer"&&O!=="ml-tensor")throw new Error(`Not supported preferred output location: ${O}.`);if(f&&O!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${O}. 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fe(We(C));if(C instanceof Re)switch(G.value){case"list":return C;case"first":return C.value[0];case"last":return C.value[C.value.length-1];case"length":return new ge(C.value.length);case"reverse":return new Re(C.value.slice().reverse());case"sort":return new Re(C.value.slice().sort((Z,ce)=>kt(Z,ce,!1)));case"join":return new fe(C.value.map(Z=>Z.value).join(""));case"string":return new fe(We(C,null,0,!1));case"unique":{const Z=new Set,ce=[];for(const ye of C.value)Z.has(ye.value)||(Z.add(ye.value),ce.push(ye));return new Re(ce)}default:throw new Error(`Unknown ArrayValue filter: ${G.value}`)}else if(C instanceof fe)switch(G.value){case"length":case"upper":case"lower":case"title":case"capitalize":{const Z=C.builtins.get(G.value);if(Z instanceof Ze)return Z.value([],R);if(Z instanceof ge)return Z;throw new Error(`Unknown StringValue filter: ${G.value}`)}case"trim":return new fe(C.value.trim());case"indent":return new fe(C.value.split(`
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Oe}evaluateUnaryExpression(C,q){const R=this.evaluate(C.argument,q);if(C.operator.value==="not")return new Pe(!R.value);throw new SyntaxError(`Unknown operator: ${C.operator.value}`)}evaluateTernaryExpression(C,q){return this.evaluate(C.condition,q).__bool__().value?this.evaluate(C.trueExpr,q):this.evaluate(C.falseExpr,q)}evalProgram(C,q){return this.evaluateBlock(C.body,q)}evaluateBlock(C,q){let R="";for(const G of C){const Z=this.evaluate(G,q);Z.type!=="NullValue"&&Z.type!=="UndefinedValue"&&(R+=Z.toString())}return new fe(R)}evaluateIdentifier(C,q){return q.lookupVariable(C.value)}evaluateCallExpression(C,q){const[R,G]=this.evaluateArguments(C.args,q);G.size>0&&R.push(new tt(G));const Z=this.evaluate(C.callee,q);if(Z.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${Z.type}`);return Z.value(R,q)}evaluateSliceExpression(C,q,R){if(!(C instanceof Re||C instanceof fe))throw new Error("Slice object must be an array or string");const 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ge)Z=R.value.at(G.value),R instanceof fe&&(Z=new fe(R.value.at(G.value)));else if(G instanceof fe)Z=R.builtins.get(G.value);else throw new Error(`Cannot access property with non-string/non-number: got ${G.type}`);else{if(!(G instanceof fe))throw new Error(`Cannot access property with non-string: got ${G.type}`);Z=R.builtins.get(G.value)}return Z instanceof Ce?Z:new Oe}evaluateSet(C,q){const R=C.value?this.evaluate(C.value,q):this.evaluateBlock(C.body,q);if(C.assignee.type==="Identifier"){const G=C.assignee.value;q.setVariable(G,R)}else if(C.assignee.type==="TupleLiteral"){const G=C.assignee;if(!(R instanceof Re))throw new Error(`Cannot unpack non-iterable type in set: ${R.type}`);const Z=R.value;if(Z.length!==G.value.length)throw new Error(`Too ${G.value.length>Z.length?"few":"many"} items to unpack in set`);for(let ce=0;ce<G.value.length;++ce){const ye=G.value[ce];if(ye.type!=="Identifier")throw new Error(`Cannot unpack to non-identifier in set: 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Pt;if(C.loopvar.type==="Identifier")Pt=It=>It.setVariable(C.loopvar.value,qe);else if(C.loopvar.type==="TupleLiteral"){const It=C.loopvar;if(qe.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${qe.type}`);const Mr=qe;if(It.value.length!==Mr.value.length)throw new Error(`Too ${It.value.length>Mr.value.length?"few":"many"} items to unpack`);Pt=pr=>{for(let ir=0;ir<It.value.length;++ir){if(It.value[ir].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${It.value[ir].type}`);pr.setVariable(It.value[ir].value,Mr.value[ir])}}}else throw new Error(`Invalid loop variable(s): ${C.loopvar.type}`);G&&(Pt(Mt),!this.evaluate(G,Mt).__bool__().value)||(ce.push(qe),ye.push(Pt))}let et="",ut=!0;for(let He=0;He<ce.length;++He){const Mt=new Map([["index",new ge(He+1)],["index0",new ge(He)],["revindex",new ge(ce.length-He)],["revindex0",new ge(ce.length-He-1)],["first",new Pe(He===0)],["last",new Pe(He===ce.length-1)],["length",new 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je}evaluateCallStatement(C,q){const R=new Ze((et,ut)=>{const He=new at(ut);if(C.callerArgs)for(let Mt=0;Mt<C.callerArgs.length;++Mt){const qe=C.callerArgs[Mt];if(qe.type!=="Identifier")throw new Error(`Caller parameter must be an identifier, got ${qe.type}`);He.setVariable(qe.value,et[Mt]??new Oe)}return this.evaluateBlock(C.body,He)}),[G,Z]=this.evaluateArguments(C.call.args,q);G.push(new tt(Z));const ce=this.evaluate(C.call.callee,q);if(ce.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${ce.type}`);const ye=new at(q);return ye.setVariable("caller",R),ce.value(G,ye)}evaluateFilterStatement(C,q){const R=this.evaluateBlock(C.body,q);return this.applyFilter(R,C.filter,q)}evaluate(C,q){if(!C)return new Oe;switch(C.type){case"Program":return this.evalProgram(C,q);case"Set":return this.evaluateSet(C,q);case"If":return this.evaluateIf(C,q);case"For":return this.evaluateFor(C,q);case"Macro":return this.evaluateMacro(C,q);case"CallStatement":return this.evaluateCallStatement(C,q);case"Break":throw new ve;case"Continue":throw new xe;case"IntegerLiteral":return new ge(C.value);case"FloatLiteral":return new De(C.value);case"StringLiteral":return new fe(C.value);case"ArrayLiteral":return new Re(C.value.map(R=>this.evaluate(R,q)));case"TupleLiteral":return new rt(C.value.map(R=>this.evaluate(R,q)));case"ObjectLiteral":{const R=new Map;for(const[G,Z]of C.value){const ce=this.evaluate(G,q);if(!(ce instanceof fe))throw new Error(`Object keys must be strings: got ${ce.type}`);R.set(ce.value,this.evaluate(Z,q))}return new Fe(R)}case"Identifier":return this.evaluateIdentifier(C,q);case"CallExpression":return this.evaluateCallExpression(C,q);case"MemberExpression":return this.evaluateMemberExpression(C,q);case"UnaryExpression":return this.evaluateUnaryExpression(C,q);case"BinaryExpression":return this.evaluateBinaryExpression(C,q);case"FilterExpression":return this.evaluateFilterExpression(C,q);case"FilterStatement":return 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No instance will be fused."),O=new Set);const H=b.class_queries_logits??b.logits,B=(b.masks_queries_logits??b.pred_masks).sigmoid();let[Y,X,J]=H.dims;if(J-=1,F!==null&&F.length!==Y)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let re=[];for(let ne=0;ne<Y;++ne){let le=F!==null?F[ne]:null,pe=H[ne],oe=B[ne],[K,j,D]=_(pe,oe,P,J);if(D.length===0){let[Ae,ke]=le??oe.dims.slice(-2),Ve=new n.Tensor("int32",new Int32Array(Ae*ke).fill(-1),[Ae,ke]);re.push({segmentation:Ve,segments_info:[]});continue}let[te,he]=I(K,j,D,x,S,O,le);re.push({segmentation:te,segments_info:he})}return re}function k(b,P=.5,x=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}class T extends s.Callable{constructor(P){super(),this.image_mean=P.image_mean??P.mean,this.image_std=P.image_std??P.std,this.resample=P.resample??2,this.do_rescale=P.do_rescale??!0,this.rescale_factor=P.rescale_factor??1/255,this.do_normalize=P.do_normalize,this.do_thumbnail=P.do_thumbnail,this.size=P.size??P.image_size,this.do_resize=P.do_resize??this.size!==void 0,this.size_divisibility=P.size_divisibility??P.size_divisor,this.do_center_crop=P.do_center_crop,this.crop_size=P.crop_size,this.do_convert_rgb=P.do_convert_rgb??!0,this.do_crop_margin=P.do_crop_margin,this.pad_size=P.pad_size,this.do_pad=P.do_pad,this.min_pixels=P.min_pixels,this.max_pixels=P.max_pixels,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=P.do_flip_channel_order??!1,this.config=P}async thumbnail(P,x,S=2){const O=P.height,F=P.width,H=x.height,W=x.width;let B=Math.min(O,H),Y=Math.min(F,W);return B===O&&Y===F?P:(O>F?Y=Math.floor(F*B/O):F>O&&(B=Math.floor(O*Y/F)),await P.resize(Y,B,{resample:S}))}async crop_margin(P,x=200){const S=P.clone().grayscale(),O=(0,o.min)(S.data)[0],H=(0,o.max)(S.data)[0]-O;if(H===0)return P;const W=x/255;let B=S.width,Y=S.height,X=0,J=0;const re=S.data;for(let ne=0;ne<S.height;++ne){const le=ne*S.width;for(let pe=0;pe<S.width;++pe)(re[le+pe]-O)/H<W&&(B=Math.min(B,pe),Y=Math.min(Y,ne),X=Math.max(X,pe),J=Math.max(J,ne))}return P=await P.crop([B,Y,X,J]),P}pad_image(P,x,S,{mode:O="constant",center:F=!1,constant_values:H=0}={}){const[W,B,Y]=x;let X,J;if(typeof S=="number"?(X=S,J=S):S==="square"?X=J=Math.max(W,B):(X=S.width,J=S.height),X!==B||J!==W){const re=new Float32Array(X*J*Y);if(Array.isArray(H))for(let pe=0;pe<re.length;++pe)re[pe]=H[pe%Y];else H!==0&&re.fill(H);const[ne,le]=F?[Math.floor((X-B)/2),Math.floor((J-W)/2)]:[0,0];for(let pe=0;pe<W;++pe){const oe=(pe+le)*X,K=pe*B;for(let j=0;j<B;++j){const D=(oe+j+ne)*Y,te=(K+j)*Y;for(let he=0;he<Y;++he)re[D+he]=P[te+he]}}if(O==="symmetric"){if(F)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const pe=W-1,oe=B-1;for(let K=0;K<J;++K){const j=K*X,D=(0,i.calculateReflectOffset)(K,pe)*B;for(let te=0;te<X;++te){if(K<W&&te<B)continue;const he=(j+te)*Y,Ae=(D+(0,i.calculateReflectOffset)(te,oe))*Y;for(let ke=0;ke<Y;++ke)re[he+ke]=P[Ae+ke]}}}P=re,x=[J,X,Y]}return[P,x]}rescale(P){for(let x=0;x<P.length;++x)P[x]=this.rescale_factor*P[x]}get_resize_output_image_size(P,x){const[S,O]=P.size;let F,H;if(this.do_thumbnail){const{height:W,width:B}=x;F=Math.min(W,B)}else Number.isInteger(x)?(F=x,H=this.config.max_size??F):x!==void 0&&(F=x.shortest_edge,H=x.longest_edge);if(F!==void 0||H!==void 0){const W=F===void 0?1:Math.max(F/S,F/O),B=S*W,Y=O*W,X=H===void 0?1:Math.min(H/B,H/Y);let J=Math.floor(Number((B*X).toFixed(2))),re=Math.floor(Number((Y*X).toFixed(2)));return this.size_divisibility!==void 0&&([J,re]=p([J,re],this.size_divisibility)),[J,re]}else if(x!==void 0&&x.width!==void 0&&x.height!==void 0){let W=x.width,B=x.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let Y=B/O,X=W/S;Math.abs(1-X)<Math.abs(1-Y)?Y=X:X=Y,B=c(Y*O,this.config.ensure_multiple_of),W=c(X*S,this.config.ensure_multiple_of)}return[W,B]}else{if(this.size_divisibility!==void 0)return p([S,O],this.size_divisibility);if(this.min_pixels!==void 0&&this.max_pixels!==void 0){const W=this.config.patch_size*this.config.merge_size;return w(O,S,W,this.min_pixels,this.max_pixels)}else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(x)}`)}}async resize(P){const[x,S]=this.get_resize_output_image_size(P,this.size);return await P.resize(x,S,{resample:this.resample})}async preprocess(P,{do_normalize:x=null,do_pad:S=null,do_convert_rgb:O=null,do_convert_grayscale:F=null,do_flip_channel_order:H=null}={}){this.do_crop_margin&&(P=await this.crop_margin(P));const[W,B]=P.size;if(O??this.do_convert_rgb?P=P.rgb():F&&(P=P.grayscale()),this.do_resize&&(P=await this.resize(P)),this.do_thumbnail&&(P=await this.thumbnail(P,this.size,this.resample)),this.do_center_crop){let ne,le;Number.isInteger(this.crop_size)?(ne=this.crop_size,le=this.crop_size):(ne=this.crop_size.width,le=this.crop_size.height),P=await P.center_crop(ne,le)}const Y=[P.height,P.width];let X=Float32Array.from(P.data),J=[P.height,P.width,P.channels];if(this.do_rescale&&this.rescale(X),x??this.do_normalize){let ne=this.image_mean;Array.isArray(this.image_mean)||(ne=new Array(P.channels).fill(ne));let le=this.image_std;if(Array.isArray(this.image_std)||(le=new Array(P.channels).fill(le)),ne.length!==P.channels||le.length!==P.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${ne.length}) and \`image_std\` (${le.length}) must match the number of channels in the image (${P.channels}).`);for(let pe=0;pe<X.length;pe+=P.channels)for(let oe=0;oe<P.channels;++oe)X[pe+oe]=(X[pe+oe]-ne[oe])/le[oe]}if(S??this.do_pad){if(this.pad_size)[X,J]=this.pad_image(X,[P.height,P.width,P.channels],this.pad_size);else if(this.size_divisibility){const[ne,le]=p([J[1],J[0]],this.size_divisibility);[X,J]=this.pad_image(X,J,{width:ne,height:le})}}if(H??this.do_flip_channel_order){if(J[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let ne=0;ne<X.length;ne+=3){const le=X[ne];X[ne]=X[ne+2],X[ne+2]=le}}const re=new n.Tensor("float32",X,J).permute(2,0,1);return{original_size:[B,W],reshaped_input_size:Y,pixel_values:re}}async _call(P,...x){Array.isArray(P)||(P=[P]);const S=await Promise.all(P.map(F=>this.preprocess(F)));return{pixel_values:(0,n.stack)(S.map(F=>F.pixel_values),0),original_sizes:S.map(F=>F.original_size),reshaped_input_sizes:S.map(F=>F.reshaped_input_size)}}static async from_pretrained(P,x={}){const S=await(0,a.getModelJSON)(P,l.IMAGE_PROCESSOR_NAME,!0,x);return new this(S)}}}),"./src/base/processing_utils.js":((e,r,t)=>{t.r(r),t.d(r,{Processor:()=>i});var s=t("./src/utils/constants.js"),n=t("./src/utils/generic.js"),o=t("./src/utils/hub.js");class i extends n.Callable{static classes=["image_processor_class","tokenizer_class","feature_extractor_class"];static uses_processor_config=!1;static uses_chat_template_file=!1;constructor(l,c,p){super(),this.config=l,this.components=c,this.chat_template=p}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(l,c={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(l,{tokenize:!1,chat_template:this.chat_template??void 0,...c})}batch_decode(...l){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...l)}decode(...l){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...l)}async _call(l,...c){for(const p of[this.image_processor,this.feature_extractor,this.tokenizer])if(p)return p(l,...c);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(l,c={}){const[p,d,u]=await Promise.all([this.uses_processor_config?(0,o.getModelJSON)(l,s.PROCESSOR_NAME,!0,c):{},Promise.all(this.classes.filter(f=>f in this).map(async f=>{const _=await this[f].from_pretrained(l,c);return[f.replace(/_class$/,""),_]})).then(Object.fromEntries),this.uses_chat_template_file?(0,o.getModelText)(l,s.CHAT_TEMPLATE_NAME,!0,c):null]);return new this(p,d,u)}}}),"./src/configs.js":((e,r,t)=>{t.r(r),t.d(r,{AutoConfig:()=>p,PretrainedConfig:()=>c,getCacheShapes:()=>a});var s=t("./src/utils/core.js"),n=t("./src/utils/hub.js");async function o(d,u){return 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y=i(d.decoder),I="num_decoder_layers"in y,w=(0,s.pick)(d,["model_type","is_encoder_decoder"]);return I?(w.num_decoder_layers=y.num_decoder_layers,w.num_decoder_heads=y.num_decoder_heads,w.decoder_hidden_size=y.decoder_hidden_size,w.num_encoder_layers=y.num_encoder_layers,w.num_encoder_heads=y.num_encoder_heads,w.encoder_hidden_size=y.encoder_hidden_size):(w.num_layers=y.num_layers,w.num_heads=y.num_heads,w.hidden_size=y.hidden_size),w}const _={...f,...(0,s.pick)(d,["model_type","multi_query","is_encoder_decoder"])};for(const y in u)_[y]=d[u[y]];return _}function a(d,u){if(d.model_type==="lfm2"){const f=u?.prefix??"past_key_values",_=f==="present"?"present":"past",y={},{layer_types:I,num_attention_heads:w,num_key_value_heads:v,hidden_size:k,conv_L_cache:T}=d,b=k/w,P=u?.batch_size??1;for(let x=0;x<I.length;++x)if(I[x]==="full_attention")for(const S of["key","value"])y[`${f}.${x}.${S}`]=[P,v,0,b];else if(I[x]==="conv")y[`${_}_conv.${x}`]=[P,k,T];else throw new Error(`Unsupported layer type: ${I[x]}`);return y}return l(d,u)}function l(d,{prefix:u="past_key_values",batch_size:f=1}={}){const _={},y=d.normalized_config;if(y.is_encoder_decoder&&"num_encoder_heads"in y&&"num_decoder_heads"in y){const I=y.encoder_dim_kv??y.encoder_hidden_size/y.num_encoder_heads,w=y.decoder_dim_kv??y.decoder_hidden_size/y.num_decoder_heads,v=[f,y.num_encoder_heads,0,I],k=[f,y.num_decoder_heads,0,w];for(let T=0;T<y.num_decoder_layers;++T)_[`${u}.${T}.encoder.key`]=v,_[`${u}.${T}.encoder.value`]=v,_[`${u}.${T}.decoder.key`]=k,_[`${u}.${T}.decoder.value`]=k}else{const I=y.num_heads,w=y.num_layers,v=y.dim_kv??y.hidden_size/(y.num_attention_heads??I);if(y.model_type==="falcon"){const k=[f*I,0,v];for(let T=0;T<w;++T)_[`${u}.${T}.key`]=k,_[`${u}.${T}.value`]=k}else if(y.multi_query){const k=[f*I,0,2*v];for(let T=0;T<w;++T)_[`${u}.${T}.key_value`]=k}else if(y.model_type==="bloom"){const k=[f*I,v,0],T=[f*I,0,v];for(let b=0;b<w;++b)_[`${u}.${b}.key`]=k,_[`${u}.${b}.value`]=T}else if(y.model_type==="openelm")for(let k=0;k<w;++k){const T=[f,I[k],0,v];_[`${u}.${k}.key`]=T,_[`${u}.${k}.value`]=T}else{const k=[f,I,0,v];for(let T=0;T<w;++T)_[`${u}.${T}.key`]=k,_[`${u}.${T}.value`]=k}}return _}class c{model_type=null;is_encoder_decoder=!1;max_position_embeddings;"transformers.js_config";constructor(u){Object.assign(this,u),this.normalized_config=i(this)}static async from_pretrained(u,{progress_callback:f=null,config:_=null,cache_dir:y=null,local_files_only:I=!1,revision:w="main"}={}){_&&!(_ instanceof c)&&(_=new c(_));const v=_??await o(u,{progress_callback:f,config:_,cache_dir:y,local_files_only:I,revision:w});return new this(v)}}class p{static async from_pretrained(...u){return c.from_pretrained(...u)}}}),"./src/env.js":((e,r,t)=>{t.r(r),t.d(r,{apis:()=>w,env:()=>x});var s=t("?db59"),n=t("?383f"),o=t("?fa4b");const i="3.8.1",a=typeof window<"u"&&typeof window.document<"u",l=typeof self<"u"&&["DedicatedWorkerGlobalScope","ServiceWorkerGlobalScope","SharedWorkerGlobalScope"].includes(self.constructor?.name),c=typeof self<"u"&&"caches"in self,p=typeof navigator<"u"&&"gpu"in navigator,d=typeof navigator<"u"&&"ml"in navigator,u=typeof process<"u",f=u&&process?.release?.name==="node",_=!S(s),y=!S(n),I=typeof globalThis.Deno<"u",w=Object.freeze({IS_BROWSER_ENV:a,IS_WEBWORKER_ENV:l,IS_WEB_CACHE_AVAILABLE:c,IS_WEBGPU_AVAILABLE:p,IS_WEBNN_AVAILABLE:d,IS_PROCESS_AVAILABLE:u,IS_NODE_ENV:f,IS_FS_AVAILABLE:_,IS_PATH_AVAILABLE:y}),v=_&&y;let k="./";if(v){const O=Object({url:self.location.href}).url;O?k=n.dirname(n.dirname(o.fileURLToPath(O))):typeof __dirname<"u"&&(k=n.dirname(__dirname))}const T=v?n.join(k,"/.cache/"):null,b="/models/",P=v?n.join(k,b):b,x={version:i,backends:{onnx:{}},allowRemoteModels:!0,remoteHost:"https://huggingface.co/",remotePathTemplate:"{model}/resolve/{revision}/",allowLocalModels:!(a||l),localModelPath:P,useFS:_,useBrowserCache:c&&!I,useFSCache:_,cacheDir:T,useCustomCache:!1,customCache:null};function S(O){return Object.keys(O).length===0}}),"./src/generation/configuration_utils.js":((e,r,t)=>{t.r(r),t.d(r,{GenerationConfig:()=>n});var s=t("./src/utils/core.js");class n{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;streamer=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(i){Object.assign(this,(0,s.pick)(i,Object.getOwnPropertyNames(this)))}}}),"./src/generation/logits_process.js":((e,r,t)=>{t.r(r),t.d(r,{ClassifierFreeGuidanceLogitsProcessor:()=>w,ForcedBOSTokenLogitsProcessor:()=>l,ForcedEOSTokenLogitsProcessor:()=>c,LogitsProcessor:()=>o,LogitsProcessorList:()=>a,LogitsWarper:()=>i,MinLengthLogitsProcessor:()=>_,MinNewTokensLengthLogitsProcessor:()=>y,NoBadWordsLogitsProcessor:()=>I,NoRepeatNGramLogitsProcessor:()=>u,RepetitionPenaltyLogitsProcessor:()=>f,SuppressTokensAtBeginLogitsProcessor:()=>p,TemperatureLogitsWarper:()=>v,TopKLogitsWarper:()=>T,TopPLogitsWarper:()=>k,WhisperTimeStampLogitsProcessor:()=>d});var s=t("./src/utils/generic.js");t("./src/utils/tensor.js");var n=t("./src/utils/maths.js");class o extends s.Callable{_call(P,x){throw Error("`_call` should be implemented in a subclass")}}class i extends s.Callable{_call(P,x){throw Error("`_call` should be implemented in a subclass")}}class a extends s.Callable{constructor(){super(),this.processors=[]}push(P){this.processors.push(P)}extend(P){this.processors.push(...P)}_call(P,x){let S=x;for(const O of this.processors)S=O(P,S);return S}[Symbol.iterator](){return this.processors.values()}}class l extends o{constructor(P){super(),this.bos_token_id=P}_call(P,x){for(let S=0;S<P.length;++S)if(P[S].length===1){const O=x[S].data;O.fill(-1/0),O[this.bos_token_id]=0}return x}}class c extends o{constructor(P,x){super(),this.max_length=P,this.eos_token_id=Array.isArray(x)?x:[x]}_call(P,x){for(let S=0;S<P.length;++S)if(P[S].length===this.max_length-1){const O=x[S].data;O.fill(-1/0);for(const F of this.eos_token_id)O[F]=0}return x}}class p extends o{constructor(P,x){super(),this.begin_suppress_tokens=P,this.begin_index=x}_call(P,x){for(let S=0;S<P.length;++S)if(P[S].length===this.begin_index){const O=x[S].data;for(const F of this.begin_suppress_tokens)O[F]=-1/0}return x}}class d extends o{constructor(P,x){super(),this.eos_token_id=Array.isArray(P.eos_token_id)?P.eos_token_id[0]:P.eos_token_id,this.no_timestamps_token_id=P.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=x.length,x.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=P.max_initial_timestamp_index}_call(P,x){for(let S=0;S<P.length;++S){const O=x[S].data;if(O[this.no_timestamps_token_id]=-1/0,P[S].length===this.begin_index-1){O.fill(-1/0),O[this.timestamp_begin]=0;continue}const F=P[S].slice(this.begin_index),H=F.length>=1&&F[F.length-1]>=this.timestamp_begin,W=F.length<2||F[F.length-2]>=this.timestamp_begin;if(H&&(W?O.subarray(this.timestamp_begin).fill(-1/0):O.subarray(0,this.eos_token_id).fill(-1/0)),P[S].length===this.begin_index&&this.max_initial_timestamp_index!==null){const J=this.timestamp_begin+this.max_initial_timestamp_index;O.subarray(J+1).fill(-1/0)}const B=(0,n.log_softmax)(O),Y=Math.log(B.subarray(this.timestamp_begin).map(Math.exp).reduce((J,re)=>J+re)),X=(0,n.max)(B.subarray(0,this.timestamp_begin))[0];Y>X&&O.subarray(0,this.timestamp_begin).fill(-1/0)}return x}}class u extends o{constructor(P){super(),this.no_repeat_ngram_size=P}getNgrams(P){const x=P.length,S=[];for(let F=0;F<x+1-this.no_repeat_ngram_size;++F){const H=[];for(let W=0;W<this.no_repeat_ngram_size;++W)H.push(P[F+W]);S.push(H.map(Number))}const O=new Map;for(const F of S){const H=F.slice(0,F.length-1),W=JSON.stringify(H),B=O.get(W)??[];B.push(F[F.length-1]),O.set(W,B)}return O}getGeneratedNgrams(P,x){const S=x.slice(x.length+1-this.no_repeat_ngram_size,x.length);return P.get(JSON.stringify(S.map(Number)))??[]}calcBannedNgramTokens(P){const x=[];if(P.length+1<this.no_repeat_ngram_size)return x;{const S=this.getNgrams(P);return this.getGeneratedNgrams(S,P)}}_call(P,x){for(let S=0;S<P.length;++S){const O=x[S].data,F=this.calcBannedNgramTokens(P[S]);for(const H of F)O[H]=-1/0}return x}}class f extends o{constructor(P){super(),this.penalty=P}_call(P,x){for(let S=0;S<P.length;++S){const O=x[S].data;for(const F of new Set(P[S])){const H=Number(F);O[H]<0?O[H]*=this.penalty:O[H]/=this.penalty}}return x}}class _ extends o{constructor(P,x){super(),this.min_length=P,this.eos_token_id=Array.isArray(x)?x:[x]}_call(P,x){for(let S=0;S<P.length;++S)if(P[S].length<this.min_length){const O=x[S].data;for(const F of this.eos_token_id)O[F]=-1/0}return x}}class y extends o{constructor(P,x,S){super(),this.prompt_length_to_skip=P,this.min_new_tokens=x,this.eos_token_id=Array.isArray(S)?S:[S]}_call(P,x){for(let S=0;S<P.length;++S)if(P[S].length-this.prompt_length_to_skip<this.min_new_tokens){const F=x[S].data;for(const H of this.eos_token_id)F[H]=-1/0}return x}}class I extends o{constructor(P,x){super(),this.bad_words_ids=P,this.eos_token_id=Array.isArray(x)?x:[x]}_call(P,x){for(let S=0;S<P.length;++S){const O=x[S].data,F=P[S];for(const H of this.bad_words_ids){if(F.length<H.length-1)continue;let W=!0;for(let B=1;B<=H.length-1;++B)if(H.at(-B-1)!=F.at(-B)){W=!1;break}W&&(O[H.at(-1)]=-1/0)}}return x}}class w extends o{constructor(P){if(super(),P<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${P}.`);this.guidance_scale=P}_call(P,x){if(x.dims[0]!==2*P.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 ${x.dims[0]} for the logits and ${P.length} for the input ids.`);const S=P.length,O=x.slice([0,S],null),F=x.slice([S,x.dims[0]],null);for(let H=0;H<F.data.length;++H)F.data[H]+=(O.data[H]-F.data[H])*this.guidance_scale;return F}}class v extends i{constructor(P){super(),this.temperature=P}_call(P,x){const S=x.data;for(let O=0;O<S.length;++O)S[O]/=this.temperature;return x}}class k extends i{constructor(P,{filter_value:x=-1/0,min_tokens_to_keep:S=1}={}){if(super(),P<0||P>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${P}`);if(!Number.isInteger(S)||S<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${S}`);this.top_p=P,this.filter_value=x,this.min_tokens_to_keep=S}}class T extends i{constructor(P,{filter_value:x=-1/0,min_tokens_to_keep:S=1}={}){if(super(),!Number.isInteger(P)||P<0)throw new Error(`\`top_k\` must be a positive integer, but is ${P}`);this.top_k=Math.max(P,S),this.filter_value=x}}}),"./src/generation/logits_sampler.js":((e,r,t)=>{t.r(r),t.d(r,{LogitsSampler:()=>i});var s=t("./src/utils/generic.js"),n=t("./src/utils/tensor.js"),o=t("./src/utils/maths.js");t("./src/generation/configuration_utils.js");class i extends s.Callable{constructor(d){super(),this.generation_config=d}async _call(d){return this.sample(d)}async sample(d){throw Error("sample should be implemented in subclasses.")}getLogits(d,u){let f=d.dims.at(-1),_=d.data;if(u===-1)_=_.slice(-f);else{let y=u*f;_=_.slice(y,y+f)}return _}randomSelect(d){let u=0;for(let _=0;_<d.length;++_)u+=d[_];let f=Math.random()*u;for(let _=0;_<d.length;++_)if(f-=d[_],f<=0)return _;return 0}static getSampler(d){if(d.do_sample)return new l(d);if(d.num_beams>1)return new c(d);if(d.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${d.num_return_sequences}.`);return new a(d)}}class a extends i{async sample(d){const u=(0,o.max)(d.data)[1];return[[BigInt(u),0]]}}class l extends i{async sample(d){let u=d.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const[f,_]=await(0,n.topk)(d,u),y=(0,o.softmax)(f.data);return Array.from({length:this.generation_config.num_beams},()=>{const I=this.randomSelect(y);return[_.data[I],Math.log(y[I])]})}}class c extends i{async sample(d){let u=d.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const[f,_]=await(0,n.topk)(d,u),y=(0,o.softmax)(f.data);return Array.from({length:this.generation_config.num_beams},(I,w)=>[_.data[w],Math.log(y[w])])}}}),"./src/generation/stopping_criteria.js":((e,r,t)=>{t.r(r),t.d(r,{EosTokenCriteria:()=>a,InterruptableStoppingCriteria:()=>l,MaxLengthCriteria:()=>i,StoppingCriteria:()=>n,StoppingCriteriaList:()=>o});var s=t("./src/utils/generic.js");class n extends s.Callable{_call(p,d){throw Error("StoppingCriteria needs to be subclassed")}}class o extends s.Callable{constructor(){super(),this.criteria=[]}push(p){this.criteria.push(p)}extend(p){p instanceof o?p=p.criteria:p instanceof n&&(p=[p]),this.criteria.push(...p)}_call(p,d){const u=new Array(p.length).fill(!1);for(const f of this.criteria){const _=f(p,d);for(let y=0;y<u.length;++y)u[y]||=_[y]}return u}[Symbol.iterator](){return this.criteria.values()}}class i extends n{constructor(p,d=null){super(),this.max_length=p,this.max_position_embeddings=d}_call(p){return p.map(d=>d.length>=this.max_length)}}class a extends n{constructor(p){super(),Array.isArray(p)||(p=[p]),this.eos_token_id=p}_call(p,d){return p.map(u=>{const f=u.at(-1);return this.eos_token_id.some(_=>f==_)})}}class l extends n{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(p,d){return new Array(p.length).fill(this.interrupted)}}}),"./src/generation/streamers.js":((e,r,t)=>{t.r(r),t.d(r,{BaseStreamer:()=>i,TextStreamer:()=>l,WhisperTextStreamer:()=>c});var s=t("./src/utils/core.js"),n=t("./src/tokenizers.js"),o=t("./src/env.js");class i{put(d){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const a=o.apis.IS_PROCESS_AVAILABLE?p=>process.stdout.write(p):p=>console.log(p);class l extends i{constructor(d,{skip_prompt:u=!1,callback_function:f=null,token_callback_function:_=null,skip_special_tokens:y=!0,decode_kwargs:I={},...w}={}){super(),this.tokenizer=d,this.skip_prompt=u,this.callback_function=f??a,this.token_callback_function=_,this.decode_kwargs={skip_special_tokens:y,...I,...w},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(d){if(d.length>1)throw Error("TextStreamer only supports batch size of 1");const u=this.next_tokens_are_prompt;if(u&&(this.next_tokens_are_prompt=!1,this.skip_prompt))return;const f=d[0];this.token_callback_function?.(f),this.token_cache=(0,s.mergeArrays)(this.token_cache,f);const _=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let y;u||_.endsWith(`
`)?(y=_.slice(this.print_len),this.token_cache=[],this.print_len=0):_.length>0&&(0,n.is_chinese_char)(_.charCodeAt(_.length-1))?(y=_.slice(this.print_len),this.print_len+=y.length):(y=_.slice(this.print_len,_.lastIndexOf(" ")+1),this.print_len+=y.length),this.on_finalized_text(y,!1)}end(){let d;this.token_cache.length>0?(d=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):d="",this.next_tokens_are_prompt=!0,this.on_finalized_text(d,!0)}on_finalized_text(d,u){d.length>0&&this.callback_function?.(d),u&&this.callback_function===a&&o.apis.IS_PROCESS_AVAILABLE&&this.callback_function?.(`
`)}}class c extends l{constructor(d,{skip_prompt:u=!1,callback_function:f=null,token_callback_function:_=null,on_chunk_start:y=null,on_chunk_end:I=null,on_finalize:w=null,time_precision:v=.02,skip_special_tokens:k=!0,decode_kwargs:T={}}={}){super(d,{skip_prompt:u,skip_special_tokens:k,callback_function:f,token_callback_function:_,decode_kwargs:T}),this.timestamp_begin=d.timestamp_begin,this.on_chunk_start=y,this.on_chunk_end=I,this.on_finalize=w,this.time_precision=v,this.waiting_for_timestamp=!1}put(d){if(d.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const u=d[0];if(u.length===1){const f=Number(u[0])-this.timestamp_begin;if(f>=0){const _=f*this.time_precision;this.waiting_for_timestamp?this.on_chunk_end?.(_):this.on_chunk_start?.(_),this.waiting_for_timestamp=!this.waiting_for_timestamp,this.token_callback_function?.(u);return}}return 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Se=(0,a.pick)(_e,ae.inputNames);return await W(ae,Se)}function pe({modality_token_id:g,inputs_embeds:M,modality_features:N,input_ids:ae,attention_mask:me}){const _e=ae.tolist().map(st=>st.reduce((wt,bt,Ct)=>(bt==g&&wt.push(Ct),wt),[])),Se=_e.reduce((st,wt)=>st+wt.length,0),Le=N.dims[0];if(Se!==Le)throw new Error(`Number of tokens and features do not match: tokens: ${Se}, features ${Le}`);let Ge=0;for(let st=0;st<_e.length;++st){const wt=_e[st],bt=M[st];for(let Ct=0;Ct<wt.length;++Ct)bt[wt[Ct]].data.set(N[Ge++].data)}return{inputs_embeds:M,attention_mask:me}}function oe({image_token_id:g,inputs_embeds:M,image_features:N,input_ids:ae,attention_mask:me}){return pe({modality_token_id:g,inputs_embeds:M,modality_features:N,input_ids:ae,attention_mask:me})}function K({audio_token_id:g,inputs_embeds:M,audio_features:N,input_ids:ae,attention_mask:me}){return pe({modality_token_id:g,inputs_embeds:M,modality_features:N,input_ids:ae,attention_mask:me})}async function j(g,{encode_function:M,merge_function:N,modality_input_name:ae,modality_output_name:me,input_ids:_e=null,attention_mask:Se=null,position_ids:Le=null,inputs_embeds:Ge=null,past_key_values:st=null,generation_config:wt=null,logits_processor:bt=null,...Ct}){const mt=Ct[ae];if(!Ge){if(Ge=await g.encode_text({input_ids:_e,...Ct}),mt&&_e.dims[1]!==1){const _t=await M({[ae]:mt,...Ct});({inputs_embeds:Ge,attention_mask:Se}=N({[me]:_t,inputs_embeds:Ge,input_ids:_e,attention_mask:Se}))}else if(st&&mt&&_e.dims[1]===1){const _t=_e.dims[1],pt=Object.values(st)[0].dims.at(-2);Se=(0,u.cat)([(0,u.ones)([_e.dims[0],pt]),Se.slice(null,[Se.dims[1]-_t,Se.dims[1]])],1)}}if(!Le&&g.config.model_type==="qwen2_vl"){const{image_grid_thw:_t,video_grid_thw:pt}=Ct;[Le]=g.get_rope_index(_e,_t,pt,Se)}return await le(g,{inputs_embeds:Ge,past_key_values:st,attention_mask:Se,position_ids:Le,generation_config:wt,logits_processor:bt},!0)}async function D(g,M){return await j(g,{...M,modality_input_name:"audio_values",modality_output_name:"audio_features",encode_function:g.encode_audio.bind(g),merge_function:g._merge_input_ids_with_audio_features.bind(g)})}async function te(g,M){return await j(g,{...M,modality_input_name:"pixel_values",modality_output_name:"image_features",encode_function:g.encode_image.bind(g),merge_function:g._merge_input_ids_with_image_features.bind(g)})}function he(g,M=0){const[N,ae]=g.dims,me=g.data,_e=new BigInt64Array(me.length);for(let Se=0;Se<N;++Se){const Le=Se*ae;let Ge=BigInt(M);for(let st=0;st<ae;++st){const wt=Le+st;me[wt]===0n?_e[wt]=BigInt(1):(_e[wt]=Ge,Ge+=me[wt])}}return{data:_e,dims:g.dims}}function Ae(g,M=null,N=0){const{input_ids:ae,inputs_embeds:me,attention_mask:_e}=g,{data:Se,dims:Le}=he(_e,N);let Ge=new u.Tensor("int64",Se,Le);if(M){const st=-(ae??me).dims.at(1);Ge=Ge.slice(null,[st,null])}return Ge}function ke(g,M,N,ae){const me=N.past_key_values?Object.values(N.past_key_values)[0].dims.at(-2):0;if(!N.attention_mask){let _e;for(const Se of["input_ids","inputs_embeds","position_ids"])if(N[Se]){_e=N[Se].dims;break}if(!_e)throw new Error("attention_mask is not provided, and unable to infer its shape from model inputs.");N.attention_mask=(0,u.ones)([_e[0],me+_e[1]])}if(N.past_key_values){const{input_ids:_e,attention_mask:Se}=N;Se&&Se.dims[1]>_e.dims[1]||me<_e.dims[1]&&(N.input_ids=_e.slice(null,[me,null]))}return N}function Ve(g,M,N,ae){return N.past_key_values&&(M=M.map(me=>[me.at(-1)])),{...N,decoder_input_ids:Y(M)}}function Te(g,...M){return g.config.is_encoder_decoder?Ve(g,...M):ke(g,...M)}function Q(g,M,N,ae){const me=!!N.past_key_values;return ae.guidance_scale!==null&&ae.guidance_scale>1&&(me?N.input_ids=(0,u.cat)([N.input_ids,N.input_ids],0):(N.input_ids=(0,u.cat)([N.input_ids,(0,u.full_like)(N.input_ids,BigInt(ae.pad_token_id))],0),N.attention_mask=(0,u.cat)([N.attention_mask,(0,u.full_like)(N.attention_mask,0n)],0))),(me||!N.pixel_values)&&(N.pixel_values=(0,u.full)([0,0,3,384,384],1)),me&&(N.images_seq_mask=new u.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),N.images_emb_mask=new u.Tensor("bool",new Array(0).fill(!1),[1,1,0])),N}class z extends i.Callable{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(M,N,ae){super(),this.config=M,this.sessions=N,this.configs=ae;const me=x.get(this.constructor),_e=b.get(me);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,_e){case T.DecoderOnly:this.can_generate=!0,this._forward=le,this._prepare_inputs_for_generation=ke;break;case T.Seq2Seq:case T.Vision2Seq:case T.Musicgen:this.can_generate=!0,this._forward=J,this._prepare_inputs_for_generation=Ve;break;case T.EncoderDecoder:this._forward=J;break;case T.ImageTextToText:this.can_generate=!0,this._forward=te,this._prepare_inputs_for_generation=Te;break;case T.AudioTextToText:this.can_generate=!0,this._forward=D,this._prepare_inputs_for_generation=Te;break;case T.Phi3V:case T.ImageAudioTextToText:this.can_generate=!0,this._prepare_inputs_for_generation=Te;break;case T.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=Q;break;case T.AutoEncoder:this._forward=ne;break;default:this._forward=re;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const M=[];for(const N of Object.values(this.sessions))N?.handler?.dispose&&M.push(N.handler.dispose());return await Promise.all(M)}static async from_pretrained(M,{progress_callback:N=null,config:ae=null,cache_dir:me=null,local_files_only:_e=!1,revision:Se="main",model_file_name:Le=null,subfolder:Ge="onnx",device:st=null,dtype:wt=null,use_external_data_format:bt=null,session_options:Ct={}}={}){let mt={progress_callback:N,config:ae,cache_dir:me,local_files_only:_e,revision:Se,model_file_name:Le,subfolder:Ge,device:st,dtype:wt,use_external_data_format:bt,session_options:Ct};const Lt=x.get(this),_t=b.get(Lt);ae=mt.config=await s.AutoConfig.from_pretrained(M,mt);let pt;if(_t===T.DecoderOnly)pt=await Promise.all([O(M,{model:mt.model_file_name??"model"},mt),F(M,{generation_config:"generation_config.json"},mt)]);else if(_t===T.Seq2Seq||_t===T.Vision2Seq)pt=await Promise.all([O(M,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},mt),F(M,{generation_config:"generation_config.json"},mt)]);else if(_t===T.MaskGeneration)pt=await Promise.all([O(M,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},mt)]);else if(_t===T.EncoderDecoder)pt=await Promise.all([O(M,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},mt)]);else if(_t===T.ImageTextToText){const Ft={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};ae.is_encoder_decoder&&(Ft.model="encoder_model"),pt=await Promise.all([O(M,Ft,mt),F(M,{generation_config:"generation_config.json"},mt)])}else if(_t===T.AudioTextToText){const Ft={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",decoder_model_merged:"decoder_model_merged"};pt=await Promise.all([O(M,Ft,mt),F(M,{generation_config:"generation_config.json"},mt)])}else if(_t===T.ImageAudioTextToText){const Ft={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};pt=await Promise.all([O(M,Ft,mt),F(M,{generation_config:"generation_config.json"},mt)])}else if(_t===T.Musicgen)pt=await Promise.all([O(M,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},mt),F(M,{generation_config:"generation_config.json"},mt)]);else if(_t===T.MultiModality)pt=await Promise.all([O(M,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},mt),F(M,{generation_config:"generation_config.json"},mt)]);else if(_t===T.Phi3V)pt=await Promise.all([O(M,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},mt),F(M,{generation_config:"generation_config.json"},mt)]);else if(_t===T.AutoEncoder)pt=await Promise.all([O(M,{encoder_model:"encoder_model",decoder_model:"decoder_model"},mt)]);else if(_t===T.Supertonic)pt=await Promise.all([O(M,{text_encoder:"text_encoder",latent_denoiser:"latent_denoiser",voice_decoder:"voice_decoder"},mt)]);else{if(_t!==T.EncoderOnly){const Ft=Lt??ae?.model_type;Ft!=="custom"&&console.warn(`Model type for '${Ft}' not found, assuming encoder-only architecture. Please report this at ${c.GITHUB_ISSUE_URL}.`)}pt=await Promise.all([O(M,{model:mt.model_file_name??"model"},mt)])}return new this(ae,...pt)}async _call(M){return await this.forward(M)}async forward(M){return await this._forward(this,M)}get generation_config(){return this.configs?.generation_config??null}_get_logits_processor(M,N,ae=null){const me=new p.LogitsProcessorList;if(M.repetition_penalty!==null&&M.repetition_penalty!==1&&me.push(new p.RepetitionPenaltyLogitsProcessor(M.repetition_penalty)),M.no_repeat_ngram_size!==null&&M.no_repeat_ngram_size>0&&me.push(new p.NoRepeatNGramLogitsProcessor(M.no_repeat_ngram_size)),M.bad_words_ids!==null&&me.push(new p.NoBadWordsLogitsProcessor(M.bad_words_ids,M.eos_token_id)),M.min_length!==null&&M.eos_token_id!==null&&M.min_length>0&&me.push(new p.MinLengthLogitsProcessor(M.min_length,M.eos_token_id)),M.min_new_tokens!==null&&M.eos_token_id!==null&&M.min_new_tokens>0&&me.push(new p.MinNewTokensLengthLogitsProcessor(N,M.min_new_tokens,M.eos_token_id)),M.forced_bos_token_id!==null&&me.push(new p.ForcedBOSTokenLogitsProcessor(M.forced_bos_token_id)),M.forced_eos_token_id!==null&&me.push(new p.ForcedEOSTokenLogitsProcessor(M.max_length,M.forced_eos_token_id)),M.begin_suppress_tokens!==null){const _e=N>1||M.forced_bos_token_id===null?N:N+1;me.push(new p.SuppressTokensAtBeginLogitsProcessor(M.begin_suppress_tokens,_e))}return M.guidance_scale!==null&&M.guidance_scale>1&&me.push(new p.ClassifierFreeGuidanceLogitsProcessor(M.guidance_scale)),M.temperature===0&&M.do_sample&&(console.warn("`do_sample` changed to false because `temperature: 0` implies greedy sampling (always selecting the most likely token), which is incompatible with `do_sample: true`."),M.do_sample=!1),M.do_sample&&M.temperature!==null&&M.temperature!==1&&me.push(new p.TemperatureLogitsWarper(M.temperature)),ae!==null&&me.extend(ae),me}_prepare_generation_config(M,N,ae=d.GenerationConfig){const me={...this.config};for(const Se of["decoder","generator","text_config"])Se in me&&Object.assign(me,me[Se]);const _e=new ae(me);return Object.assign(_e,this.generation_config??{}),M&&Object.assign(_e,M),N&&Object.assign(_e,(0,a.pick)(N,Object.getOwnPropertyNames(_e))),_e}_get_stopping_criteria(M,N=null){const ae=new y.StoppingCriteriaList;return M.max_length!==null&&ae.push(new y.MaxLengthCriteria(M.max_length,this.config.max_position_embeddings??null)),M.eos_token_id!==null&&ae.push(new y.EosTokenCriteria(M.eos_token_id)),N&&ae.extend(N),ae}_validate_model_class(){if(!this.can_generate){const M=[Lu,zu,Du,Ou],N=x.get(this.constructor),ae=new Set,me=this.config.model_type;for(const Se of M){const Le=Se.get(me);Le&&ae.add(Le[0])}let _e=`The current model class (${N}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw ae.size>0&&(_e+=` Please use the following class instead: ${[...ae].join(", ")}`),Error(_e)}}prepare_inputs_for_generation(...M){return this._prepare_inputs_for_generation(this,...M)}_update_model_kwargs_for_generation({generated_input_ids:M,outputs:N,model_inputs:ae,is_encoder_decoder:me}){return ae.past_key_values=this.getPastKeyValues(N,ae.past_key_values),ae.input_ids=new u.Tensor("int64",M.flat(),[M.length,1]),me||(ae.attention_mask=(0,u.cat)([ae.attention_mask,(0,u.ones)([ae.attention_mask.dims[0],1])],1)),ae.position_ids=null,ae}_prepare_model_inputs({inputs:M,bos_token_id:N,model_kwargs:ae}){const me=(0,a.pick)(ae,this.forward_params),_e=this.main_input_name;if(_e in me){if(M)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else me[_e]=M;return{inputs_tensor:me[_e],model_inputs:me,model_input_name:_e}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:M,model_inputs:N,model_input_name:ae,generation_config:me}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!N.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:Se,pixel_values:Le,attention_mask:Ge,...st}=N,wt=await this._prepare_inputs_embeds(N);N={...st,...(0,a.pick)(wt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:_e}=await re(this,N);if(me.guidance_scale!==null&&me.guidance_scale>1)_e=(0,u.cat)([_e,(0,u.full_like)(_e,0)],0),"attention_mask"in N&&(N.attention_mask=(0,u.cat)([N.attention_mask,(0,u.zeros_like)(N.attention_mask)],0));else if(N.decoder_input_ids){const Se=Y(N.decoder_input_ids).dims[0];if(Se!==_e.dims[0]){if(_e.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${_e.dims[0]}) than the decoder inputs (${Se}).`);_e=(0,u.cat)(Array.from({length:Se},()=>_e),0)}}return N.encoder_outputs=_e,N}_prepare_decoder_input_ids_for_generation({batch_size:M,model_input_name:N,model_kwargs:ae,decoder_start_token_id:me,bos_token_id:_e,generation_config:Se}){let{decoder_input_ids:Le,...Ge}=ae;if(!(Le instanceof u.Tensor)){if(Le)Array.isArray(Le[0])||(Le=Array.from({length:M},()=>Le));else if(me??=_e,this.config.model_type==="musicgen")Le=Array.from({length:M*this.config.decoder.num_codebooks},()=>[me]);else if(Array.isArray(me)){if(me.length!==M)throw new Error(`\`decoder_start_token_id\` expcted to have length ${M} but got ${me.length}`);Le=me}else Le=Array.from({length:M},()=>[me]);Le=Y(Le)}return ae.decoder_attention_mask=(0,u.ones_like)(Le),{input_ids:Le,model_inputs:Ge}}async generate({inputs:M=null,generation_config:N=null,logits_processor:ae=null,stopping_criteria:me=null,streamer:_e=null,...Se}){this._validate_model_class(),N=this._prepare_generation_config(N,Se);let{inputs_tensor:Le,model_inputs:Ge,model_input_name:st}=this._prepare_model_inputs({inputs:M,model_kwargs:Se});const wt=this.config.is_encoder_decoder;wt&&("encoder_outputs"in Ge||(Ge=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Le,model_inputs:Ge,model_input_name:st,generation_config:N})));let bt;wt?{input_ids:bt,model_inputs:Ge}=this._prepare_decoder_input_ids_for_generation({batch_size:Ge[st].dims.at(0),model_input_name:st,model_kwargs:Ge,decoder_start_token_id:N.decoder_start_token_id,bos_token_id:N.bos_token_id,generation_config:N}):bt=Ge[st];let Ct=bt.dims.at(-1);N.max_new_tokens!==null&&(N.max_length=Ct+N.max_new_tokens);const mt=this._get_logits_processor(N,Ct,ae),Lt=this._get_stopping_criteria(N,me),_t=Ge[st].dims.at(0),pt=I.LogitsSampler.getSampler(N),Ft=new Array(_t).fill(0),Wt=bt.tolist();_e&&_e.put(Wt);let er,nr={};for(;;){if(Ge=this.prepare_inputs_for_generation(Wt,Ge,N),er=await this.forward(Ge),N.output_attentions&&N.return_dict_in_generate){const Wr=this.getAttentions(er);for(const ys in Wr)ys in nr||(nr[ys]=[]),nr[ys].push(Wr[ys])}const At=er.logits.slice(null,-1,null),ur=mt(Wt,At),Ar=[];for(let Wr=0;Wr<ur.dims.at(0);++Wr){const ys=ur[Wr],oo=await pt(ys);for(const[ao,mi]of oo){const Qo=BigInt(ao);Ft[Wr]+=mi,Wt[Wr].push(Qo),Ar.push([Qo]);break}}if(_e&&_e.put(Ar),Lt(Wt).every(Wr=>Wr))break;Ge=this._update_model_kwargs_for_generation({generated_input_ids:Ar,outputs:er,model_inputs:Ge,is_encoder_decoder:wt})}_e&&_e.end();const mr=this.getPastKeyValues(er,Ge.past_key_values,!0),fr=new u.Tensor("int64",Wt.flat(),[Wt.length,Wt[0].length]);if(N.return_dict_in_generate)return{sequences:fr,past_key_values:mr,...nr};for(const At of Object.values(er))At.location==="gpu-buffer"&&At.dispose();return fr}getPastKeyValues(M,N,ae=!1){const me=Object.create(null);for(const _e in M)if(_e.startsWith("present")){const Se=_e.replace("present_conv","past_conv").replace("present","past_key_values"),Le=_e.includes("encoder");if(Le&&N?me[Se]=N[Se]:me[Se]=M[_e],N&&(!Le||ae)){const Ge=N[Se];Ge.location==="gpu-buffer"&&Ge.dispose()}}return me}getAttentions(M){const N={};for(const ae of["cross_attentions","encoder_attentions","decoder_attentions"])for(const me in M)me.startsWith(ae)&&(ae in N||(N[ae]=[]),N[ae].push(M[me]));return N}addPastKeyValues(M,N){if(N)Object.assign(M,N);else{const ae=this.sessions.decoder_model_merged??this.sessions.model,me=(M[this.main_input_name]??M.attention_mask)?.dims?.[0]??1,_e=ae?.config?.kv_cache_dtype??"float32",Se=_e==="float16"?u.DataTypeMap.float16:u.DataTypeMap.float32,Le=(0,s.getCacheShapes)(this.config,{batch_size:me});for(const Ge in Le){const st=Le[Ge].reduce((wt,bt)=>wt*bt,1);M[Ge]=new u.Tensor(_e,new Se(st),Le[Ge])}}}async encode_image({pixel_values:M}){return(await W(this.sessions.vision_encoder,{pixel_values:M})).image_features}async encode_text({input_ids:M}){return(await W(this.sessions.embed_tokens,{input_ids:M})).inputs_embeds}async encode_audio({audio_values:M}){return(await W(this.sessions.audio_encoder,{audio_values:M})).audio_features}}class de{}class be extends de{constructor({last_hidden_state:M,hidden_states:N=null,attentions:ae=null}){super(),this.last_hidden_state=M,this.hidden_states=N,this.attentions=ae}}class ve extends z{}class xe extends ve{}class Ce extends ve{async _call(M){return new Ir(await super._call(M))}}class ge extends ve{async _call(M){return new xt(await super._call(M))}}class De extends ve{async _call(M){return new Er(await super._call(M))}}class fe extends ve{async _call(M){return new Br(await super._call(M))}}class Pe extends z{}class We extends Pe{}class Fe extends Pe{async _call(M){return new Ir(await super._call(M))}}class tt extends Pe{async _call(M){return new xt(await super._call(M))}}class Re extends Pe{async _call(M){return new Er(await super._call(M))}}class rt extends Pe{async _call(M){return new Br(await super._call(M))}}class Ze extends z{}class je extends Ze{}class Oe extends Ze{async _call(M){return new Ir(await super._call(M))}}class at extends Ze{async _call(M){return new xt(await super._call(M))}}class ht extends Ze{async _call(M){return new Er(await super._call(M))}}class Nt extends z{}class kt extends Nt{}class gr extends Nt{}class Or extends z{}class Bt extends Or{}class jr extends z{}class Qs extends jr{}class Xs extends jr{async _call(M){return new Ir(await super._call(M))}}class Js extends jr{async _call(M){return new xt(await super._call(M))}}class ar extends jr{async _call(M){return new Er(await super._call(M))}}class kr extends jr{async _call(M){return new Br(await super._call(M))}}class Jr extends z{}class Bs extends Jr{}class ft extends Jr{async _call(M){return new Ir(await super._call(M))}}class qt extends Jr{async _call(M){return new xt(await super._call(M))}}class Ts extends Jr{async _call(M){return new Er(await super._call(M))}}class Ps extends Jr{async _call(M){return new Br(await super._call(M))}}class Gr extends z{}class yt extends Gr{}class Es extends Gr{async _call(M){return new Ir(await super._call(M))}}class C extends Gr{async _call(M){return new xt(await super._call(M))}}class q extends Gr{async _call(M){return new Er(await super._call(M))}}class R extends Gr{async _call(M){return new Br(await super._call(M))}}class G extends z{}class Z extends G{}class ce extends G{async _call(M){return new Ir(await super._call(M))}}class ye extends G{async _call(M){return new xt(await super._call(M))}}class et extends G{async _call(M){return new Er(await super._call(M))}}class ut extends G{async _call(M){return new Br(await super._call(M))}}class He extends z{}class Mt extends He{}class qe extends He{async _call(M){return new Ir(await super._call(M))}}class Pt extends He{async _call(M){return new xt(await super._call(M))}}class It extends He{async _call(M){return new Er(await super._call(M))}}class Mr extends He{async _call(M){return new Br(await super._call(M))}}class pr extends z{}class ir extends pr{}class Tr extends pr{async _call(M){return new Ir(await super._call(M))}}class Cs extends pr{async _call(M){return new xt(await super._call(M))}}class Dr extends pr{async _call(M){return new Er(await super._call(M))}}class Ss extends pr{async _call(M){return new Br(await super._call(M))}}class Lr extends z{}class zr extends Lr{}class ns extends Lr{async _call(M){return new xt(await super._call(M))}}class wr extends Lr{async _call(M){return new Er(await super._call(M))}}class lr extends Lr{async _call(M){return new Br(await super._call(M))}}class Kr extends Lr{async _call(M){return new Ir(await super._call(M))}}class os extends z{}class Rs extends os{}class ks extends os{async _call(M){return new Ir(await super._call(M))}}class $s extends os{async _call(M){return new xt(await super._call(M))}}class Is extends os{async _call(M){return new Er(await super._call(M))}}class as extends z{}class Nr extends as{}class ze extends as{async _call(M){return new Ir(await super._call(M))}}class Ue extends as{async _call(M){return new xt(await super._call(M))}}class nt extends as{async _call(M){return new Br(await super._call(M))}}class Kt extends z{}class js extends Kt{}class As extends Kt{async _call(M){return new Ir(await super._call(M))}}class Ns extends Kt{async _call(M){return new xt(await super._call(M))}}class Nn extends Kt{async _call(M){return new Er(await super._call(M))}}class ue extends Kt{async _call(M){return new Br(await super._call(M))}}class $ extends z{}class U extends ${}class ee extends ${async _call(M){return new Ir(await super._call(M))}}class se extends ${async _call(M){return new xt(await super._call(M))}}class Me extends ${async _call(M){return new Br(await super._call(M))}}class $e extends z{}class Xe extends $e{}class Je extends $e{async _call(M){return new xt(await super._call(M))}}class Ye extends $e{async _call(M){return new Br(await super._call(M))}}class Ke extends $e{async _call(M){return new Ir(await super._call(M))}}class $t extends z{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]}class Et extends $t{}class rr extends $t{}class br extends z{}class Jt extends br{}class sr extends br{}class Ht extends z{}class Hr extends Ht{}class is extends Ht{}class Pr extends z{}class ws extends Pr{}class Yt extends Pr{}class hr extends Pr{async _call(M){return new xt(await super._call(M))}}class $r extends z{}class Yr extends $r{}class bs extends $r{}class yr extends $r{async _call(M){return new xt(await super._call(M))}}class ls extends $r{}class vr extends z{}class Zt extends vr{}class _r extends vr{}class cr extends z{}class Ur extends cr{}class Ys extends cr{}class Fs extends z{}class Eo extends Fs{}class Ea extends Fs{async _call(M){return new Ir(await super._call(M))}}class Ca extends Fs{async _call(M){return new xt(await super._call(M))}}class Sa extends Fs{async _call(M){return new Er(await super._call(M))}}class ka extends Fs{async _call(M){return new Br(await super._call(M))}}class Zs extends z{}class $a extends Zs{}class Ia extends Zs{async _call(M){return new Ir(await super._call(M))}}class Aa extends Zs{async _call(M){return new xt(await super._call(M))}}class Fa extends Zs{async _call(M){return new Er(await super._call(M))}}class Oa extends Zs{async _call(M){return new Br(await super._call(M))}}class en extends z{}class Da extends en{}class Co extends en{async _call(M){return new Ir(await super._call(M))}}class So extends en{async _call(M){return new xt(await super._call(M))}}class ko extends en{async _call(M){return new Er(await super._call(M))}}class La extends en{async _call(M){return new Br(await super._call(M))}}class $o extends z{}class Io extends $o{}class Ao extends $o{}class Vs extends z{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]}class za extends Vs{}class Vn extends Vs{_prepare_generation_config(M,N){return super._prepare_generation_config(M,N,v.WhisperGenerationConfig)}_retrieve_init_tokens(M){const N=[M.decoder_start_token_id];let ae=M.language;const me=M.task;if(M.is_multilingual){ae||(console.warn("No language specified - defaulting to English (en)."),ae="en");const Se=`<|${(0,k.whisper_language_to_code)(ae)}|>`;N.push(M.lang_to_id[Se]),N.push(M.task_to_id[me??"transcribe"])}else if(ae||me)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!M.return_timestamps&&M.no_timestamps_token_id&&N.at(-1)!==M.no_timestamps_token_id?N.push(M.no_timestamps_token_id):M.return_timestamps&&N.at(-1)===M.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),N.pop()),N.filter(_e=>_e!=null)}async generate({inputs:M=null,generation_config:N=null,logits_processor:ae=null,stopping_criteria:me=null,..._e}){N=this._prepare_generation_config(N,_e);const Se=_e.decoder_input_ids??this._retrieve_init_tokens(N);if(N.return_timestamps&&(ae??=new p.LogitsProcessorList,ae.push(new p.WhisperTimeStampLogitsProcessor(N,Se))),N.begin_suppress_tokens&&(ae??=new p.LogitsProcessorList,ae.push(new p.SuppressTokensAtBeginLogitsProcessor(N.begin_suppress_tokens,Se.length))),N.return_token_timestamps){if(!N.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");N.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),N.output_attentions=!0,N.return_dict_in_generate=!0}const Le=await super.generate({inputs:M,generation_config:N,logits_processor:ae,decoder_input_ids:Se,..._e});return N.return_token_timestamps&&(Le.token_timestamps=this._extract_token_timestamps(Le,N.alignment_heads,N.num_frames)),Le}_extract_token_timestamps(M,N,ae=null,me=.02){if(!M.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`.");ae==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let _e=this.config.median_filter_width;_e===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),_e=7);const Se=M.cross_attentions,Le=Array.from({length:this.config.decoder_layers},(_t,pt)=>(0,u.cat)(Se.map(Ft=>Ft[pt]),2)),Ge=(0,u.stack)(N.map(([_t,pt])=>{if(_t>=Le.length)throw new Error(`Layer index ${_t} is out of bounds for cross attentions (length ${Le.length}).`);return ae?Le[_t].slice(null,pt,null,[0,ae]):Le[_t].slice(null,pt)})).transpose(1,0,2,3),[st,wt]=(0,u.std_mean)(Ge,-2,0,!0),bt=Ge.clone();for(let _t=0;_t<bt.dims[0];++_t){const pt=bt[_t];for(let Ft=0;Ft<pt.dims[0];++Ft){const Wt=pt[Ft],er=st[_t][Ft][0].data,nr=wt[_t][Ft][0].data;for(let mr=0;mr<Wt.dims[0];++mr){let fr=Wt[mr].data;for(let At=0;At<fr.length;++At)fr[At]=(fr[At]-nr[At])/er[At];fr.set((0,_.medianFilter)(fr,_e))}}}const Ct=[(0,u.mean)(bt,1)],mt=M.sequences.dims,Lt=new u.Tensor("float32",new Float32Array(mt[0]*mt[1]),mt);for(let _t=0;_t<mt[0];++_t){const pt=Ct[_t].neg().squeeze_(0),[Ft,Wt]=(0,_.dynamic_time_warping)(pt.tolist()),er=Array.from({length:Ft.length-1},(fr,At)=>Ft[At+1]-Ft[At]),nr=(0,a.mergeArrays)([1],er).map(fr=>!!fr),mr=[];for(let fr=0;fr<nr.length;++fr)nr[fr]&&mr.push(Wt[fr]*me);Lt[_t].data.set(mr,1)}return Lt}}class Ba extends Vn{}class Fo extends z{requires_attention_mask=!1;main_input_name="input_values";forward_params=["input_values","decoder_input_ids","past_key_values"]}class fc extends Fo{}class Un extends Fo{}class Wn extends z{main_input_name="pixel_values";forward_params=["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"]}class Oo extends z{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]}class Gn extends Oo{_merge_input_ids_with_image_features(M){const N=M.image_features.dims.at(-1),ae=M.image_features.view(-1,N);return oe({image_token_id:this.config.image_token_index,...M,image_features:ae})}}class Ra extends Gn{}class ja extends Gn{}class Na extends z{forward_params=["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"];main_input_name="inputs_embeds"}class Va extends Na{_merge_input_ids_with_image_features({inputs_embeds:M,image_features:N,input_ids:ae,attention_mask:me}){return{inputs_embeds:(0,u.cat)([N,M],1),attention_mask:(0,u.cat)([(0,u.ones)(N.dims.slice(0,2)),me],1)}}async _prepare_inputs_embeds({input_ids:M,pixel_values:N,inputs_embeds:ae,attention_mask:me}){if(!M&&!N)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let _e,Se;return M&&(_e=await this.encode_text({input_ids:M})),N&&(Se=await this.encode_image({pixel_values:N})),_e&&Se?{inputs_embeds:ae,attention_mask:me}=this._merge_input_ids_with_image_features({inputs_embeds:_e,image_features:Se,input_ids:M,attention_mask:me}):ae=_e||Se,{inputs_embeds:ae,attention_mask:me}}async forward({input_ids:M,pixel_values:N,attention_mask:ae,decoder_input_ids:me,decoder_attention_mask:_e,encoder_outputs:Se,past_key_values:Le,inputs_embeds:Ge,decoder_inputs_embeds:st}){if(Ge||({inputs_embeds:Ge,attention_mask:ae}=await this._prepare_inputs_embeds({input_ids:M,pixel_values:N,inputs_embeds:Ge,attention_mask:ae})),!Se){let{last_hidden_state:Ct}=await re(this,{inputs_embeds:Ge,attention_mask:ae});Se=Ct}if(!st){if(!me)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");st=await this.encode_text({input_ids:me})}return await le(this,{inputs_embeds:st,attention_mask:_e,encoder_attention_mask:ae,encoder_hidden_states:Se,past_key_values:Le},!0)}}class Ua extends z{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]}class Wa extends Ua{_merge_input_ids_with_image_features(M){const N=M.image_features.dims.at(-1),ae=M.image_features.view(-1,N);return oe({image_token_id:this.config.image_token_index,...M,image_features:ae})}}class Do extends Oo{_merge_input_ids_with_image_features(M){const N=M.image_features.dims.at(-1),ae=M.image_features.view(-1,N);return oe({image_token_id:this.config.image_token_index,...M,image_features:ae})}}class bn extends Do{}class tn extends z{forward_params=["input_ids","attention_mask","inputs_embeds","per_layer_inputs","position_ids","pixel_values","input_features","input_features_mask","past_key_values"]}class Kn extends tn{async forward({input_ids:M=null,attention_mask:N=null,pixel_values:ae=null,input_features:me=null,input_features_mask:_e=null,position_ids:Se=null,inputs_embeds:Le=null,per_layer_inputs:Ge=null,past_key_values:st=null,generation_config:wt=null,logits_processor:bt=null,...Ct}){if((!Le||!Ge)&&({inputs_embeds:Le,per_layer_inputs:Ge}=await W(this.sessions.embed_tokens,{input_ids:M}),M.dims[1]!==1)){if(ae){const{image_features:Lt}=await W(this.sessions.vision_encoder,{pixel_values:ae});({inputs_embeds:Le,attention_mask:N}=this._merge_input_ids_with_image_features({image_features:Lt,inputs_embeds:Le,input_ids:M,attention_mask:N}))}if(me){const{audio_features:Lt}=await W(this.sessions.audio_encoder,{input_features:me,input_features_mask:_e});({inputs_embeds:Le,attention_mask:N}=this._merge_input_ids_with_audio_features({audio_features:Lt,inputs_embeds:Le,input_ids:M,attention_mask:N}))}}return await le(this,{inputs_embeds:Le,per_layer_inputs:Ge,past_key_values:st,attention_mask:N,position_ids:Se,generation_config:wt,logits_processor:bt},!0)}_merge_input_ids_with_image_features(M){const N=M.image_features.dims.at(-1),ae=M.image_features.view(-1,N);return oe({image_token_id:this.config.image_token_id,...M,image_features:ae})}_merge_input_ids_with_audio_features(M){const N=M.audio_features.dims.at(-1),ae=M.audio_features.view(-1,N);return K({audio_token_id:this.config.audio_token_id,...M,audio_features:ae})}}class Ga extends z{forward_params=["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"]}class Hn extends Ga{async encode_image({pixel_values:M,pixel_attention_mask:N}){return(await W(this.sessions.vision_encoder,{pixel_values:M,pixel_attention_mask:N})).image_features}_merge_input_ids_with_image_features(M){const N=M.image_features.dims.at(-1),ae=M.image_features.view(-1,N);return oe({image_token_id:this.config.image_token_id,...M,image_features:ae})}}class yn extends Hn{}class qn extends z{forward_params=["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"]}class Qn extends qn{async forward({input_ids:M=null,attention_mask:N=null,pixel_values:ae=null,image_sizes:me=null,position_ids:_e=null,inputs_embeds:Se=null,past_key_values:Le=null,generation_config:Ge=null,logits_processor:st=null,...wt}){if(!Se){let Ct;if(ae&&M.dims[1]!==1){if(!me)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:Ct}=await W(this.sessions.vision_encoder,{pixel_values:ae,image_sizes:me}))}else{const mt=this.config.normalized_config.hidden_size;Ct=new u.Tensor("float32",[],[0,mt])}({inputs_embeds:Se}=await W(this.sessions.prepare_inputs_embeds,{input_ids:M,image_features:Ct}))}return await le(this,{inputs_embeds:Se,past_key_values:Le,attention_mask:N,position_ids:_e,generation_config:Ge,logits_processor:st},!1)}}class Os extends z{}class Lo extends Os{}class Ka extends Os{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"text_model"})}}class zo extends Os{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"text_model"})}}class Ha extends Os{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"vision_model"})}}class qa extends Os{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"vision_model"})}}class Xn extends z{}class Bo extends Xn{}class vn extends Xn{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"text_model"})}}class dt extends Os{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"vision_model"})}}class Qa extends z{}class Ro extends Qa{}class xn extends z{}class Tn extends xn{async forward(M){const N=!M.input_ids,ae=!M.pixel_values;if(N&&ae)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(N&&(M.input_ids=(0,u.ones)([M.pixel_values.dims[0],1])),ae){const{image_size:st}=this.config.vision_config;M.pixel_values=(0,u.full)([0,3,st,st],0)}const{text_embeddings:me,image_embeddings:_e,l2norm_text_embeddings:Se,l2norm_image_embeddings:Le}=await super.forward(M),Ge={};return N||(Ge.text_embeddings=me,Ge.l2norm_text_embeddings=Se),ae||(Ge.image_embeddings=_e,Ge.l2norm_image_embeddings=Le),Ge}}class Zr extends xn{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"text_model"})}}class Pn extends xn{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"vision_model"})}}class En extends z{}class jo extends En{}class No extends En{}class Jn extends z{}class Yn extends Jn{}class Vo extends Jn{}class Zn extends z{}class Uo extends Zn{}class Cn extends Zn{}class Sn extends z{}class eo extends Sn{}class Wo extends Sn{}class to extends z{}class Go extends to{}class Ko extends to{}class ro extends z{}class h extends ro{}class E extends ro{}class A extends z{}class L extends A{}class V extends A{}class ie extends z{}class Ee extends ie{}class Be extends ie{}class Qe extends z{}class it extends Qe{}class vt extends Qe{}class Dt extends z{}class Vt extends Dt{}class xr extends z{}class Us extends xr{}class Xa extends xr{}class gc extends z{}class wM extends gc{}class bM extends gc{}class Mc extends z{}class yM extends Mc{}class vM extends Mc{}class wc extends z{}class xM extends wc{}class TM extends wc{}class bc extends z{}class PM extends bc{}class EM extends bc{}class yc extends z{}class CM extends yc{}class SM extends yc{}class vc extends z{}class kM extends vc{}class $M extends vc{}class xc extends z{}class IM extends xc{}class AM extends xc{}class Tc extends z{}class FM extends Tc{}class OM extends Tc{}class Pc extends z{}class DM extends Pc{}class LM extends Pc{}class Ec extends z{}class zM extends Ec{}class BM extends Ec{}class Cc extends z{}class RM extends Cc{}class jM extends Cc{}class Sc extends z{}class NM extends Sc{}class VM extends Sc{}class kc extends z{}class UM extends kc{}class WM extends kc{}class $c extends z{}class GM extends $c{}class KM extends $c{}class Ic extends z{}class HM extends Ic{}class qM extends Ic{}class Ac extends z{}class QM extends Ac{}class XM extends Ac{}class Fc extends z{}class JM extends Fc{}class YM extends Fc{}class Oc extends z{}class ZM extends Oc{}class ew extends Oc{}class Dc extends z{}class tw extends Dc{}class rw extends Dc{}class sw extends z{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]}class nw extends sw{get_rope_index(M,N,ae,me){const{vision_config:_e,image_token_id:Se,video_token_id:Le,vision_start_token_id:Ge}=this.config,st=_e.spatial_merge_size??2,wt=[];if(N||ae){let bt=M.tolist();me||(me=(0,u.ones_like)(M));const Ct=me.tolist(),mt=Array.from({length:3},Wt=>Array.from({length:M.dims[0]},er=>Array.from({length:M.dims[1]},nr=>1))),Lt=N?N.tolist():[],_t=ae?ae.tolist():[];let pt=0,Ft=0;for(let Wt=0;Wt<bt.length;++Wt){const er=bt[Wt].filter((tr,Cr)=>Ct[Wt][Cr]==1),mr=er.reduce((tr,Cr,nn)=>(Cr==Ge&&tr.push(nn),tr),[]).map(tr=>er[tr+1]),fr=mr.filter(tr=>tr==Se).length,At=mr.filter(tr=>tr==Le).length;let ur=[],Ar=0,kn=fr,Wr=At;for(let tr=0;tr<mr.length;++tr){const Cr=er.findIndex((In,vs)=>vs>Ar&&In==Se),nn=er.findIndex((In,vs)=>vs>Ar&&In==Le),$n=kn>0&&Cr!==-1?Cr:er.length+1,io=Wr>0&&nn!==-1?nn:er.length+1;let hi,Bu,Ru,ju;$n<io?([Bu,Ru,ju]=Lt[pt],++pt,--kn,hi=$n):([Bu,Ru,ju]=_t[Ft],++Ft,--Wr,hi=io);const[vP,Nu,_i]=[Number(Bu),Math.floor(Number(Ru)/st),Math.floor(Number(ju)/st)],Vu=hi-Ar,Ev=ur.length>0?(0,_.max)(ur.at(-1))[0]+1:0;ur.push(Array.from({length:3*Vu},(In,vs)=>Ev+vs%Vu));const Uu=Vu+Ev,fi=vP*Nu*_i,xP=Array.from({length:fi},(In,vs)=>Uu+Math.floor(vs/(Nu*_i))),TP=Array.from({length:fi},(In,vs)=>Uu+Math.floor(vs/_i)%Nu),PP=Array.from({length:fi},(In,vs)=>Uu+vs%_i);ur.push([xP,TP,PP].flat()),Ar=hi+fi}if(Ar<er.length){const tr=ur.length>0?(0,_.max)(ur.at(-1))[0]+1:0,Cr=er.length-Ar;ur.push(Array.from({length:3*Cr},(nn,$n)=>tr+$n%Cr))}const ys=ur.reduce((tr,Cr)=>tr+Cr.length,0),oo=new Array(ys);let ao=0;for(let tr=0;tr<3;++tr)for(let Cr=0;Cr<ur.length;++Cr){const nn=ur[Cr],$n=nn.length/3;for(let io=tr*$n;io<(tr+1)*$n;++io)oo[ao++]=nn[io]}let mi=0;const Qo=Ct[Wt];for(let tr=0;tr<Qo.length;++tr)if(Qo[tr]==1){for(let Cr=0;Cr<3;++Cr)mt[Cr][Wt][tr]=oo[Cr*ys/3+mi];++mi}const yP=(0,_.max)(oo)[0];wt.push(yP+1-bt[Wt].length)}return[new u.Tensor("int64",mt.flat(1/0),[3,M.dims[0],M.dims[1]]),new u.Tensor("int64",wt,[wt.length,1])]}else if(me){const{data:bt,dims:Ct}=he(me),mt=BigInt64Array.from({length:3*bt.length},(_t,pt)=>bt[pt%bt.length]),Lt=Array.from({length:Ct[0]},(_t,pt)=>(0,_.max)(bt.subarray(Ct[1]*pt,Ct[1]*(pt+1)))[0]+1n+BigInt(Ct[1]));return[new u.Tensor("int64",mt,[3,...Ct]),new u.Tensor("int64",Lt,[Lt.length,1])]}else{const[bt,Ct]=M.dims,mt=BigInt64Array.from({length:3*bt*Ct},(Lt,_t)=>BigInt(Math.floor(_t%Ct/bt)));return[new u.Tensor("int64",mt,[3,...M.dims]),(0,u.zeros)([bt,1])]}}async encode_image({pixel_values:M,image_grid_thw:N}){return(await W(this.sessions.vision_encoder,{pixel_values:M,grid_thw:N})).image_features}_merge_input_ids_with_image_features(M){return oe({image_token_id:this.config.image_token_id,...M})}prepare_inputs_for_generation(M,N,ae){if(N.attention_mask&&!N.position_ids)if(!N.past_key_values)[N.position_ids,N.rope_deltas]=this.get_rope_index(N.input_ids,N.image_grid_thw,N.video_grid_thw,N.attention_mask);else{N.pixel_values=null;const me=BigInt(Object.values(N.past_key_values)[0].dims.at(-2)),_e=N.rope_deltas.map(Se=>me+Se);N.position_ids=(0,u.stack)([_e,_e,_e],0)}return N}}class Lc extends z{}class ow extends Lc{}class aw extends Lc{}class zc extends z{}class iw extends zc{}class lw extends zc{}class Bc extends z{}class cw extends Bc{}class uw extends Bc{}class Rc extends z{}class dw extends Rc{}class pw extends Rc{}class jc extends z{}class mw extends jc{}class hw extends jc{}class Nc extends z{}class _w extends Nc{}class fw extends Nc{async _call(M){return new xt(await super._call(M))}}class Vc extends z{}class gw extends Vc{}class Mw extends Vc{async _call(M){return new xt(await super._call(M))}}class ww extends z{}class bw extends ww{}class Uc extends z{}class yw extends Uc{}class vw extends Uc{async _call(M){return new xt(await super._call(M))}}class xw extends z{}class Tw extends xw{}class Wc extends z{}class Pw extends Wc{}class Ew extends Wc{async _call(M){return new xt(await super._call(M))}}class Cw extends z{}class Sw extends Cw{}class Gc extends z{}class kw extends Gc{}class $w extends Gc{async _call(M){return new xt(await super._call(M))}}class Iw extends z{}class Aw extends Iw{async _call(M){return new Tv(await super._call(M))}}class Kc extends z{}class Fw extends Kc{}class Ow extends Kc{async _call(M){return new xt(await super._call(M))}}class Hc extends z{}class Dw extends Hc{}class Lw extends Hc{async _call(M){return new xt(await super._call(M))}}class qc extends z{}class zw extends qc{}class Bw extends qc{}class Qc extends z{}class Rw extends Qc{}class jw extends Qc{}class Xc extends z{}class Nw extends Xc{}class Vw extends Xc{async _call(M){return new xt(await super._call(M))}}class Ja extends z{}class Uw extends Ja{}class Ww extends Ja{async _call(M){return new Yc(await super._call(M))}}class Jc extends Ja{async _call(M){return new Gw(await super._call(M))}}class Yc extends de{constructor({logits:M,pred_boxes:N}){super(),this.logits=M,this.pred_boxes=N}}class Gw extends de{constructor({logits:M,pred_boxes:N,pred_masks:ae}){super(),this.logits=M,this.pred_boxes=N,this.pred_masks=ae}}class Zc extends z{}class Kw extends Zc{}class Hw extends Zc{async _call(M){return new Ho(await super._call(M))}}class Ho extends de{constructor({logits:M,pred_boxes:N}){super(),this.logits=M,this.pred_boxes=N}}class eu extends z{}class qw extends eu{}class Qw extends eu{async _call(M){return new Xw(await super._call(M))}}class Xw extends Ho{}class tu extends z{}class Jw extends tu{}class Yw extends tu{async _call(M){return new Zw(await super._call(M))}}class Zw extends Ho{}class ru extends z{}class eb extends ru{}class tb extends ru{async _call(M){return new Ho(await super._call(M))}}class su extends z{}class rb extends su{}class sb extends su{async _call(M){return new nb(await super._call(M))}}class nb extends Yc{}class nu extends z{}class ob extends nu{}class ab extends nu{async _call(M){return new xt(await super._call(M))}}class ou extends z{}class ib extends ou{}class lb extends ou{async _call(M){return new xt(await super._call(M))}}class au extends z{}class cb extends au{}class ub extends au{async _call(M){return new xt(await super._call(M))}}class Ya extends z{}class db extends Ya{}class pb extends Ya{async _call(M){return new xt(await super._call(M))}}class mb extends Ya{}class iu extends z{}class hb extends iu{}class _b extends iu{}class lu extends z{}class fb extends lu{}class gb extends lu{}class Mb extends z{}class wb extends Mb{}class Za extends z{}class bb extends Za{}class yb extends Za{}class vb extends Za{}class xb extends z{}class Tb extends xb{}class Pb extends z{}class Eb extends Pb{}class Cb extends z{}class Sb extends Cb{}class cu extends z{}class kb extends cu{}class $b extends cu{}class uu extends z{}class Ib extends uu{}class Ab extends uu{}class Fb extends z{}class Ob extends Fb{}class du extends z{}class Db extends du{}class Lb extends du{async _call(M){return new xt(await super._call(M))}}class pu extends z{}class zb extends pu{}class Bb extends pu{async _call(M){return new xt(await super._call(M))}}class mu extends z{}class Rb extends mu{}class jb extends mu{async _call(M){return new xt(await super._call(M))}}class hu extends z{}class Nb extends hu{}class Vb extends hu{async _call(M){return new xt(await super._call(M))}}class Ub extends z{}class Wb extends Ub{}class Gb extends z{}class Kb extends Gb{}class Hb extends z{}class qb extends Hb{}class _u extends z{}class Qb extends _u{}class Xb extends _u{async _call(M){return new Jb(await super._call(M))}}class Jb extends de{constructor({logits:M,pred_boxes:N}){super(),this.logits=M,this.pred_boxes=N}}class Yb extends z{}class Zb extends Yb{async get_image_embeddings({pixel_values:M}){return await re(this,{pixel_values:M})}async forward(M){!M.image_embeddings||!M.image_positional_embeddings?M={...M,...await this.get_image_embeddings(M)}:M={...M},M.input_labels??=(0,u.ones)(M.input_points.dims.slice(0,-1));const N={image_embeddings:M.image_embeddings,image_positional_embeddings:M.image_positional_embeddings};return M.input_points&&(N.input_points=M.input_points),M.input_labels&&(N.input_labels=M.input_labels),M.input_boxes&&(N.input_boxes=M.input_boxes),await W(this.sessions.prompt_encoder_mask_decoder,N)}async _call(M){return new ey(await super._call(M))}}class ey extends de{constructor({iou_scores:M,pred_masks:N}){super(),this.iou_scores=M,this.pred_masks=N}}class ty extends de{constructor({iou_scores:M,pred_masks:N,object_score_logits:ae}){super(),this.iou_scores=M,this.pred_masks=N,this.object_score_logits=ae}}class ry extends z{}class ei extends ry{async get_image_embeddings({pixel_values:M}){return await re(this,{pixel_values:M})}async forward(M){const{num_feature_levels:N}=this.config.vision_config;if(Array.from({length:N},(Se,Le)=>`image_embeddings.${Le}`).some(Se=>!M[Se])?M={...M,...await this.get_image_embeddings(M)}:M={...M},M.input_points){if(M.input_boxes&&M.input_boxes.dims[1]!==1)throw new Error("When both `input_points` and `input_boxes` are provided, the number of boxes per image must be 1.");const Se=M.input_points.dims;M.input_labels??=(0,u.ones)(Se.slice(0,-1)),M.input_boxes??=(0,u.full)([Se[0],0,4],0)}else if(M.input_boxes){const Se=M.input_boxes.dims;M.input_labels=(0,u.full)([Se[0],Se[1],0],-1n),M.input_points=(0,u.full)([Se[0],1,0,2],0)}else throw new Error("At least one of `input_points` or `input_boxes` must be provided.");const me=this.sessions.prompt_encoder_mask_decoder,_e=(0,a.pick)(M,me.inputNames);return await W(me,_e)}async _call(M){return new ty(await super._call(M))}}class sy extends ei{}class ny extends ei{}class fu extends z{}class oy extends fu{}class ay extends fu{}class gu extends z{}class iy extends gu{}class ly extends gu{}class rn extends z{}class cy extends rn{}class uy extends rn{async _call(M){return new sn(await super._call(M))}}class dy extends rn{async _call(M){return new xt(await super._call(M))}}class py extends rn{async _call(M){return new Er(await super._call(M))}}class my extends z{}class hy extends my{async _call(M){return new sn(await super._call(M))}}class Mu extends z{}class _y extends Mu{}class fy extends Mu{async _call(M){return new Er(await super._call(M))}}class gy extends z{}class My extends gy{}class ti extends z{}class wy extends ti{}class by extends ti{async _call(M){return new sn(await super._call(M))}}class yy extends ti{async _call(M){return new xt(await super._call(M))}}class qo extends z{}class vy extends qo{}class xy extends qo{async _call(M){return new sn(await super._call(M))}}class Ty extends qo{async _call(M){return new xt(await super._call(M))}}class Py extends qo{async _call(M){return new Er(await super._call(M))}}class ri extends z{}class Ey extends ri{}class Cy extends ri{async _call(M){return new sn(await super._call(M))}}class Sy extends ri{async _call(M){return new xt(await super._call(M))}}class kT extends z{}class ky extends rn{}class $y extends rn{async _call(M){return new sn(await super._call(M))}}class Iy extends rn{async _call(M){return new xt(await super._call(M))}}class so extends z{}class Ay extends so{}class Fy extends so{async _call(M){return new sn(await super._call(M))}}class Oy extends so{async _call(M){return new xt(await super._call(M))}}class Dy extends so{async _call(M){return new xv(await super._call(M))}}class Ly extends so{async _call(M){return new Er(await super._call(M))}}class zy extends z{}class By extends zy{}class si extends z{}class $T extends si{}class Ry extends si{}class jy extends si{async generate_speech(M,N,{threshold:ae=.5,minlenratio:me=0,maxlenratio:_e=20,vocoder:Se=null}={}){const Le={input_ids:M},{encoder_outputs:Ge,encoder_attention_mask:st}=await re(this,Le),wt=Ge.dims[1]/this.config.reduction_factor,bt=Math.floor(wt*_e),Ct=Math.floor(wt*me),mt=this.config.num_mel_bins;let Lt=[],_t=null,pt=null,Ft=0;for(;;){++Ft;const nr=X(!!pt);let mr;pt?mr=pt.output_sequence_out:mr=new u.Tensor("float32",new Float32Array(mt),[1,1,mt]);let fr={use_cache_branch:nr,output_sequence:mr,encoder_attention_mask:st,speaker_embeddings:N,encoder_hidden_states:Ge};this.addPastKeyValues(fr,_t),pt=await W(this.sessions.decoder_model_merged,fr),_t=this.getPastKeyValues(pt,_t);const{prob:At,spectrum:ur}=pt;if(Lt.push(ur),Ft>=Ct&&(Array.from(At.data).filter(Ar=>Ar>=ae).length>0||Ft>=bt))break}const Wt=(0,u.cat)(Lt),{waveform:er}=await W(Se.sessions.model,{spectrogram:Wt});return{spectrogram:Wt,waveform:er}}}class Ny extends z{main_input_name="spectrogram"}class Vy extends z{}class wu extends Vy{async generate_speech({input_ids:M,attention_mask:N,style:ae,num_inference_steps:me=5,speed:_e=1.05}){const{sampling_rate:Se,chunk_compress_factor:Le,base_chunk_size:Ge,latent_dim:st}=this.config,{last_hidden_state:wt,durations:bt}=await W(this.sessions.text_encoder,{input_ids:M,attention_mask:N,style:ae});bt.div_(_e);const Ct=bt.max().item()*Se,mt=Ge*Le,Lt=Math.floor((Ct+mt-1)/mt),_t=M.dims[0],pt=(0,u.ones)([_t,Lt]),Ft=(0,u.full)([_t],me);let Wt=(0,u.randn)([_t,st*Le,Lt]);for(let nr=0;nr<me;++nr){const mr=(0,u.full)([_t],nr);({denoised_latents:Wt}=await W(this.sessions.latent_denoiser,{style:ae,noisy_latents:Wt,latent_mask:pt,encoder_outputs:wt,attention_mask:N,timestep:mr,num_inference_steps:Ft}))}const{waveform:er}=await W(this.sessions.voice_decoder,{latents:Wt});return{waveform:er,durations:bt}}}class Uy extends z{}class Wy extends Uy{}class bu extends z{}class Gy extends bu{}class Ky extends bu{}class yu extends z{}class Hy extends yu{}class qy extends yu{}class vu extends z{}class Qy extends vu{}class Xy extends vu{}class xu extends z{}class Jy extends xu{}class Yy extends xu{}class Tu extends z{}class Zy extends Tu{}class e0 extends Tu{}class Pu extends z{}class t0 extends Pu{}class r0 extends Pu{}class ni extends z{}class s0 extends ni{}class n0 extends ni{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"text_model"})}}class o0 extends ni{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"audio_model"})}}class a0 extends z{}class Eu extends a0{async _call(M){return new Pv(await super._call(M))}}class oi extends z{}class IT extends oi{}class i0 extends oi{}class l0 extends oi{}class Cu extends z{}class c0 extends Cu{}class u0 extends Cu{}class Su extends z{}class d0 extends Su{}class p0 extends Su{async _call(M){return new xt(await super._call(M))}}class ku extends z{}class AT extends ku{}class FT extends ku{}class $u extends z{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];_apply_and_filter_by_delay_pattern_mask(M){const[N,ae]=M.dims,me=this.config.decoder.num_codebooks,_e=ae-me;let Se=0;for(let st=0;st<M.size;++st){if(M.data[st]===this.config.decoder.pad_token_id)continue;const wt=st%ae,bt=Math.floor(st/ae)%me,Ct=wt-bt;Ct>0&&Ct<=_e&&(M.data[Se++]=M.data[st])}const Le=Math.floor(N/me),Ge=Se/(Le*me);return new u.Tensor(M.type,M.data.slice(0,Se),[Le,me,Ge])}prepare_inputs_for_generation(M,N,ae){let me=structuredClone(M);for(let Se=0;Se<me.length;++Se)for(let Le=0;Le<me[Se].length;++Le)Se%this.config.decoder.num_codebooks>=Le&&(me[Se][Le]=BigInt(this.config.decoder.pad_token_id));return ae.guidance_scale!==null&&ae.guidance_scale>1&&(me=me.concat(me)),super.prepare_inputs_for_generation(me,N,ae)}async generate(M){const N=await super.generate(M),ae=this._apply_and_filter_by_delay_pattern_mask(N).unsqueeze_(0),{audio_values:me}=await W(this.sessions.encodec_decode,{audio_codes:ae});return me}}class ai extends z{}class m0 extends ai{}class h0 extends ai{async _call(M){return new xt(await super._call(M))}}class _0 extends ai{}class ii extends z{}class f0 extends ii{}class g0 extends ii{async _call(M){return new xt(await super._call(M))}}class M0 extends ii{}class li extends z{}class w0 extends li{}class b0 extends li{async _call(M){return new xt(await super._call(M))}}class y0 extends li{}class ci extends z{}class v0 extends ci{}class x0 extends ci{async _call(M){return new xt(await super._call(M))}}class T0 extends ci{}class P0 extends z{}class E0 extends P0{}class C0 extends z{}class S0 extends C0{forward_params=["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"];constructor(...M){super(...M),this._generation_mode="text"}async forward(M){const N=this._generation_mode??"text";let ae;if(N==="text"||!M.past_key_values){const Ge=this.sessions.prepare_inputs_embeds,st=(0,a.pick)(M,Ge.inputNames);ae=await W(Ge,st)}else{const Ge=this.sessions.gen_img_embeds,st=(0,a.pick)({image_ids:M.input_ids},Ge.inputNames);ae=await W(Ge,st)}const me={...M,...ae},_e=await le(this,me),Se=this.sessions[N==="text"?"lm_head":"gen_head"];if(!Se)throw new Error(`Unable to find "${Se}" generation head`);const Le=await W(Se,(0,a.pick)(_e,Se.inputNames));return{...ae,..._e,...Le}}async generate(M){return this._generation_mode="text",super.generate(M)}async generate_images(M){this._generation_mode="image";const N=(M.inputs??M[this.main_input_name]).dims[1],me=(await super.generate(M)).slice(null,[N,null]),_e=this.sessions.image_decode,{decoded_image:Se}=await W(_e,{generated_tokens:me}),Le=Se.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),Ge=[];for(const st of Le){const wt=f.RawImage.fromTensor(st);Ge.push(wt)}return Ge}}class k0 extends de{constructor({char_logits:M,bpe_logits:N,wp_logits:ae}){super(),this.char_logits=M,this.bpe_logits=N,this.wp_logits=ae}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class $0 extends z{}class I0 extends $0{async _call(M){return new k0(await super._call(M))}}class Iu extends z{}class A0 extends Iu{}class F0 extends Iu{}class Au extends z{}class O0 extends Au{}class D0 extends Au{}class L0 extends z{forward_params=["input_ids","attention_mask","position_ids","audio_values","past_key_values"]}class Fu extends L0{_merge_input_ids_with_audio_features(M){const N=M.audio_features.dims.at(-1),ae=M.audio_features.view(-1,N);return K({audio_token_id:this.config.ignore_index??this.config.audio_token_id,...M,audio_features:ae})}}class z0 extends Fu{}class ui extends z{main_input_name="input_values";forward_params=["input_values"]}class B0 extends de{constructor({audio_codes:M}){super(),this.audio_codes=M}}class R0 extends de{constructor({audio_values:M}){super(),this.audio_values=M}}class j0 extends ui{async encode(M){return new B0(await W(this.sessions.encoder_model,M))}async decode(M){return new R0(await W(this.sessions.decoder_model,M))}}class N0 extends ui{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"encoder_model"})}}class V0 extends ui{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"decoder_model"})}}class di extends z{main_input_name="input_values";forward_params=["input_values"]}class U0 extends de{constructor({audio_codes:M}){super(),this.audio_codes=M}}class W0 extends de{constructor({audio_values:M}){super(),this.audio_values=M}}class G0 extends di{async encode(M){return new U0(await W(this.sessions.encoder_model,M))}async decode(M){return new W0(await W(this.sessions.decoder_model,M))}}class K0 extends di{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"encoder_model"})}}class H0 extends di{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"decoder_model"})}}class pi extends z{main_input_name="input_values";forward_params=["input_values"]}class q0 extends pi{async encode(M){return await W(this.sessions.encoder_model,M)}async decode(M){return await W(this.sessions.decoder_model,M)}}class Q0 extends pi{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"encoder_model"})}}class X0 extends pi{static async from_pretrained(M,N={}){return super.from_pretrained(M,{...N,model_file_name:N.model_file_name??"decoder_model"})}}class Ut{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(M,{progress_callback:N=null,config:ae=null,cache_dir:me=null,local_files_only:_e=!1,revision:Se="main",model_file_name:Le=null,subfolder:Ge="onnx",device:st=null,dtype:wt=null,use_external_data_format:bt=null,session_options:Ct={}}={}){const mt={progress_callback:N,config:ae,cache_dir:me,local_files_only:_e,revision:Se,model_file_name:Le,subfolder:Ge,device:st,dtype:wt,use_external_data_format:bt,session_options:Ct};if(mt.config=await s.AutoConfig.from_pretrained(M,mt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);const Lt=mt.config.model_type;for(const _t of this.MODEL_CLASS_MAPPINGS){let pt=_t.get(Lt);if(!pt){for(const Ft of _t.values())if(Ft[0]===Lt){pt=Ft;break}if(!pt)continue}return await pt[1].from_pretrained(M,mt)}if(this.BASE_IF_FAIL)return vv.has(Lt)||console.warn(`Unknown model class "${Lt}", attempting to construct from base class.`),await z.from_pretrained(M,mt);throw Error(`Unsupported model type: ${Lt}`)}}const OT=new Map([["bert",["BertModel",xe]],["neobert",["NeoBertModel",We]],["modernbert",["ModernBertModel",je]],["nomic_bert",["NomicBertModel",Bt]],["roformer",["RoFormerModel",Qs]],["electra",["ElectraModel",yt]],["esm",["EsmModel",Rs]],["convbert",["ConvBertModel",Bs]],["camembert",["CamembertModel",Z]],["deberta",["DebertaModel",Mt]],["deberta-v2",["DebertaV2Model",ir]],["mpnet",["MPNetModel",js]],["albert",["AlbertModel",Xe]],["distilbert",["DistilBertModel",zr]],["roberta",["RobertaModel",Eo]],["xlm",["XLMModel",$a]],["xlm-roberta",["XLMRobertaModel",Da]],["clap",["ClapModel",s0]],["clip",["CLIPModel",Lo]],["clipseg",["CLIPSegModel",jo]],["chinese_clip",["ChineseCLIPModel",Ro]],["siglip",["SiglipModel",Bo]],["jina_clip",["JinaCLIPModel",Tn]],["mobilebert",["MobileBertModel",Nr]],["squeezebert",["SqueezeBertModel",U]],["wav2vec2",["Wav2Vec2Model",cy]],["wav2vec2-bert",["Wav2Vec2BertModel",Ey]],["unispeech",["UniSpeechModel",wy]],["unispeech-sat",["UniSpeechSatModel",vy]],["hubert",["HubertModel",ky]],["wavlm",["WavLMModel",Ay]],["audio-spectrogram-transformer",["ASTModel",Io]],["vits",["VitsModel",Eu]],["pyannote",["PyAnnoteModel",_y]],["wespeaker-resnet",["WeSpeakerResNetModel",My]],["detr",["DetrModel",Uw]],["rt_detr",["RTDetrModel",Kw]],["rt_detr_v2",["RTDetrV2Model",qw]],["rf_detr",["RFDetrModel",Jw]],["d_fine",["DFineModel",eb]],["table-transformer",["TableTransformerModel",rb]],["vit",["ViTModel",_w]],["ijepa",["IJepaModel",gw]],["pvt",["PvtModel",yw]],["vit_msn",["ViTMSNModel",Pw]],["vit_mae",["ViTMAEModel",Tw]],["groupvit",["GroupViTModel",Sw]],["fastvit",["FastViTModel",kw]],["mobilevit",["MobileViTModel",Fw]],["mobilevitv2",["MobileViTV2Model",Dw]],["owlvit",["OwlViTModel",zw]],["owlv2",["Owlv2Model",Rw]],["beit",["BeitModel",Nw]],["deit",["DeiTModel",ob]],["hiera",["HieraModel",ib]],["convnext",["ConvNextModel",Db]],["convnextv2",["ConvNextV2Model",zb]],["dinov2",["Dinov2Model",Rb]],["dinov2_with_registers",["Dinov2WithRegistersModel",Nb]],["dinov3_vit",["DINOv3ViTModel",Wb]],["dinov3_convnext",["DINOv3ConvNextModel",Kb]],["resnet",["ResNetModel",cb]],["swin",["SwinModel",db]],["swin2sr",["Swin2SRModel",hb]],["donut-swin",["DonutSwinModel",Ob]],["yolos",["YolosModel",Qb]],["dpt",["DPTModel",fb]],["glpn",["GLPNModel",Ib]],["hifigan",["SpeechT5HifiGan",Ny]],["efficientnet",["EfficientNetModel",d0]],["decision_transformer",["DecisionTransformerModel",E0]],["patchtst",["PatchTSTForPrediction",A0]],["patchtsmixer",["PatchTSMixerForPrediction",O0]],["mobilenet_v1",["MobileNetV1Model",m0]],["mobilenet_v2",["MobileNetV2Model",f0]],["mobilenet_v3",["MobileNetV3Model",w0]],["mobilenet_v4",["MobileNetV4Model",v0]],["maskformer",["MaskFormerModel",kb]],["mgp-str",["MgpstrForSceneTextRecognition",I0]],["style_text_to_speech_2",["StyleTextToSpeech2Model",By]]]),DT=new Map([["t5",["T5Model",Et]],["longt5",["LongT5Model",Jt]],["mt5",["MT5Model",Hr]],["bart",["BartModel",ws]],["mbart",["MBartModel",Yr]],["marian",["MarianModel",oy]],["whisper",["WhisperModel",za]],["m2m_100",["M2M100Model",iy]],["blenderbot",["BlenderbotModel",Zt]],["blenderbot-small",["BlenderbotSmallModel",Ur]]]),LT=new Map([["mimi",["MimiModel",j0]],["dac",["DacModel",G0]],["snac",["SnacModel",q0]]]),zT=new Map([["bloom",["BloomModel",cw]],["jais",["JAISModel",Uo]],["gpt2",["GPT2Model",Yn]],["gptj",["GPTJModel",h]],["gpt_bigcode",["GPTBigCodeModel",L]],["gpt_neo",["GPTNeoModel",eo]],["gpt_neox",["GPTNeoXModel",Go]],["codegen",["CodeGenModel",Ee]],["llama",["LlamaModel",it]],["nanochat",["NanoChatModel",Us]],["arcee",["ArceeModel",wM]],["lfm2",["Lfm2Model",yM]],["smollm3",["SmolLM3Model",xM]],["exaone",["ExaoneModel",kM]],["olmo",["OlmoModel",FM]],["olmo2",["Olmo2Model",DM]],["mobilellm",["MobileLLMModel",IM]],["granite",["GraniteModel",zM]],["granitemoehybrid",["GraniteMoeHybridModel",RM]],["cohere",["CohereModel",NM]],["gemma",["GemmaModel",UM]],["gemma2",["Gemma2Model",GM]],["vaultgemma",["VaultGemmaModel",HM]],["gemma3_text",["Gemma3Model",QM]],["helium",["HeliumModel",PM]],["glm",["GlmModel",CM]],["openelm",["OpenELMModel",JM]],["qwen2",["Qwen2Model",ZM]],["qwen3",["Qwen3Model",tw]],["phi",["PhiModel",ow]],["phi3",["Phi3Model",iw]],["mpt",["MptModel",dw]],["opt",["OPTModel",mw]],["mistral",["MistralModel",Gy]],["ministral",["MinistralModel",Hy]],["ministral3",["Ministral3Model",Qy]],["ernie4_5",["Ernie4_5Model",Jy]],["starcoder2",["Starcoder2Model",Zy]],["falcon",["FalconModel",t0]],["stablelm",["StableLmModel",c0]],["modernbert-decoder",["ModernBertDecoderModel",kt]]]),Ou=new Map([["speecht5",["SpeechT5ForSpeechToText",Ry]],["whisper",["WhisperForConditionalGeneration",Vn]],["lite-whisper",["LiteWhisperForConditionalGeneration",Ba]],["moonshine",["MoonshineForConditionalGeneration",Un]]]),J0=new Map([["speecht5",["SpeechT5ForTextToSpeech",jy]]]),Y0=new Map([["vits",["VitsModel",Eu]],["musicgen",["MusicgenForConditionalGeneration",$u]],["supertonic",["SupertonicForConditionalGeneration",wu]]]),Z0=new Map([["bert",["BertForSequenceClassification",ge]],["neobert",["NeoBertForSequenceClassification",tt]],["modernbert",["ModernBertForSequenceClassification",at]],["roformer",["RoFormerForSequenceClassification",Js]],["electra",["ElectraForSequenceClassification",C]],["esm",["EsmForSequenceClassification",$s]],["convbert",["ConvBertForSequenceClassification",qt]],["camembert",["CamembertForSequenceClassification",ye]],["deberta",["DebertaForSequenceClassification",Pt]],["deberta-v2",["DebertaV2ForSequenceClassification",Cs]],["mpnet",["MPNetForSequenceClassification",Ns]],["albert",["AlbertForSequenceClassification",Je]],["distilbert",["DistilBertForSequenceClassification",ns]],["roberta",["RobertaForSequenceClassification",Ca]],["xlm",["XLMForSequenceClassification",Aa]],["xlm-roberta",["XLMRobertaForSequenceClassification",So]],["bart",["BartForSequenceClassification",hr]],["mbart",["MBartForSequenceClassification",yr]],["mobilebert",["MobileBertForSequenceClassification",Ue]],["squeezebert",["SqueezeBertForSequenceClassification",se]]]),ev=new Map([["bert",["BertForTokenClassification",De]],["neobert",["NeoBertForTokenClassification",Re]],["modernbert",["ModernBertForTokenClassification",ht]],["roformer",["RoFormerForTokenClassification",ar]],["electra",["ElectraForTokenClassification",q]],["esm",["EsmForTokenClassification",Is]],["convbert",["ConvBertForTokenClassification",Ts]],["camembert",["CamembertForTokenClassification",et]],["deberta",["DebertaForTokenClassification",It]],["deberta-v2",["DebertaV2ForTokenClassification",Dr]],["mpnet",["MPNetForTokenClassification",Nn]],["distilbert",["DistilBertForTokenClassification",wr]],["roberta",["RobertaForTokenClassification",Sa]],["xlm",["XLMForTokenClassification",Fa]],["xlm-roberta",["XLMRobertaForTokenClassification",ko]]]),Du=new Map([["t5",["T5ForConditionalGeneration",rr]],["longt5",["LongT5ForConditionalGeneration",sr]],["mt5",["MT5ForConditionalGeneration",is]],["bart",["BartForConditionalGeneration",Yt]],["mbart",["MBartForConditionalGeneration",bs]],["marian",["MarianMTModel",ay]],["m2m_100",["M2M100ForConditionalGeneration",ly]],["blenderbot",["BlenderbotForConditionalGeneration",_r]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Ys]]]),Lu=new Map([["bloom",["BloomForCausalLM",uw]],["gpt2",["GPT2LMHeadModel",Vo]],["jais",["JAISLMHeadModel",Cn]],["gptj",["GPTJForCausalLM",E]],["gpt_bigcode",["GPTBigCodeForCausalLM",V]],["gpt_neo",["GPTNeoForCausalLM",Wo]],["gpt_neox",["GPTNeoXForCausalLM",Ko]],["codegen",["CodeGenForCausalLM",Be]],["llama",["LlamaForCausalLM",vt]],["nanochat",["NanoChatForCausalLM",Xa]],["llama4_text",["Llama4ForCausalLM",Vt]],["arcee",["ArceeForCausalLM",bM]],["lfm2",["Lfm2ForCausalLM",vM]],["smollm3",["SmolLM3ForCausalLM",TM]],["exaone",["ExaoneForCausalLM",$M]],["olmo",["OlmoForCausalLM",OM]],["olmo2",["Olmo2ForCausalLM",LM]],["mobilellm",["MobileLLMForCausalLM",AM]],["granite",["GraniteForCausalLM",BM]],["granitemoehybrid",["GraniteMoeHybridForCausalLM",jM]],["cohere",["CohereForCausalLM",VM]],["gemma",["GemmaForCausalLM",WM]],["gemma2",["Gemma2ForCausalLM",KM]],["vaultgemma",["VaultGemmaForCausalLM",qM]],["gemma3_text",["Gemma3ForCausalLM",XM]],["helium",["HeliumForCausalLM",EM]],["glm",["GlmForCausalLM",SM]],["openelm",["OpenELMForCausalLM",YM]],["qwen2",["Qwen2ForCausalLM",ew]],["qwen3",["Qwen3ForCausalLM",rw]],["phi",["PhiForCausalLM",aw]],["phi3",["Phi3ForCausalLM",lw]],["mpt",["MptForCausalLM",pw]],["opt",["OPTForCausalLM",hw]],["mbart",["MBartForCausalLM",ls]],["mistral",["MistralForCausalLM",Ky]],["ministral",["MinistralForCausalLM",qy]],["ministral3",["Ministral3ForCausalLM",Xy]],["ernie4_5",["Ernie4_5ForCausalLM",Yy]],["starcoder2",["Starcoder2ForCausalLM",e0]],["falcon",["FalconForCausalLM",r0]],["trocr",["TrOCRForCausalLM",Wy]],["stablelm",["StableLmForCausalLM",u0]],["modernbert-decoder",["ModernBertDecoderForCausalLM",gr]],["phi3_v",["Phi3VForCausalLM",Qn]]]),BT=new Map([["multi_modality",["MultiModalityCausalLM",S0]]]),tv=new Map([["bert",["BertForMaskedLM",Ce]],["neobert",["NeoBertForMaskedLM",Fe]],["modernbert",["ModernBertForMaskedLM",Oe]],["roformer",["RoFormerForMaskedLM",Xs]],["electra",["ElectraForMaskedLM",Es]],["esm",["EsmForMaskedLM",ks]],["convbert",["ConvBertForMaskedLM",ft]],["camembert",["CamembertForMaskedLM",ce]],["deberta",["DebertaForMaskedLM",qe]],["deberta-v2",["DebertaV2ForMaskedLM",Tr]],["mpnet",["MPNetForMaskedLM",As]],["albert",["AlbertForMaskedLM",Ke]],["distilbert",["DistilBertForMaskedLM",Kr]],["roberta",["RobertaForMaskedLM",Ea]],["xlm",["XLMWithLMHeadModel",Ia]],["xlm-roberta",["XLMRobertaForMaskedLM",Co]],["mobilebert",["MobileBertForMaskedLM",ze]],["squeezebert",["SqueezeBertForMaskedLM",ee]]]),rv=new Map([["bert",["BertForQuestionAnswering",fe]],["neobert",["NeoBertForQuestionAnswering",rt]],["roformer",["RoFormerForQuestionAnswering",kr]],["electra",["ElectraForQuestionAnswering",R]],["convbert",["ConvBertForQuestionAnswering",Ps]],["camembert",["CamembertForQuestionAnswering",ut]],["deberta",["DebertaForQuestionAnswering",Mr]],["deberta-v2",["DebertaV2ForQuestionAnswering",Ss]],["mpnet",["MPNetForQuestionAnswering",ue]],["albert",["AlbertForQuestionAnswering",Ye]],["distilbert",["DistilBertForQuestionAnswering",lr]],["roberta",["RobertaForQuestionAnswering",ka]],["xlm",["XLMForQuestionAnswering",Oa]],["xlm-roberta",["XLMRobertaForQuestionAnswering",La]],["mobilebert",["MobileBertForQuestionAnswering",nt]],["squeezebert",["SqueezeBertForQuestionAnswering",Me]]]),zu=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Wn]],["idefics3",["Idefics3ForConditionalGeneration",Hn]],["smolvlm",["SmolVLMForConditionalGeneration",yn]]]),sv=new Map([["llava",["LlavaForConditionalGeneration",Gn]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",Ra]],["moondream1",["Moondream1ForConditionalGeneration",ja]],["florence2",["Florence2ForConditionalGeneration",Va]],["qwen2-vl",["Qwen2VLForConditionalGeneration",nw]],["idefics3",["Idefics3ForConditionalGeneration",Hn]],["smolvlm",["SmolVLMForConditionalGeneration",yn]],["paligemma",["PaliGemmaForConditionalGeneration",Wa]],["llava_qwen2",["LlavaQwen2ForCausalLM",Do]],["gemma3n",["Gemma3nForConditionalGeneration",Kn]],["mistral3",["Mistral3ForConditionalGeneration",bn]]]),nv=new Map([["ultravox",["UltravoxModel",Fu]],["voxtral",["VoxtralForConditionalGeneration",z0]]]),RT=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Wn]]]),ov=new Map([["vit",["ViTForImageClassification",fw]],["ijepa",["IJepaForImageClassification",Mw]],["pvt",["PvtForImageClassification",vw]],["vit_msn",["ViTMSNForImageClassification",Ew]],["fastvit",["FastViTForImageClassification",$w]],["mobilevit",["MobileViTForImageClassification",Ow]],["mobilevitv2",["MobileViTV2ForImageClassification",Lw]],["beit",["BeitForImageClassification",Vw]],["deit",["DeiTForImageClassification",ab]],["hiera",["HieraForImageClassification",lb]],["convnext",["ConvNextForImageClassification",Lb]],["convnextv2",["ConvNextV2ForImageClassification",Bb]],["dinov2",["Dinov2ForImageClassification",jb]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",Vb]],["resnet",["ResNetForImageClassification",ub]],["swin",["SwinForImageClassification",pb]],["segformer",["SegformerForImageClassification",i0]],["efficientnet",["EfficientNetForImageClassification",p0]],["mobilenet_v1",["MobileNetV1ForImageClassification",h0]],["mobilenet_v2",["MobileNetV2ForImageClassification",g0]],["mobilenet_v3",["MobileNetV3ForImageClassification",b0]],["mobilenet_v4",["MobileNetV4ForImageClassification",x0]]]),av=new Map([["detr",["DetrForObjectDetection",Ww]],["rt_detr",["RTDetrForObjectDetection",Hw]],["rt_detr_v2",["RTDetrV2ForObjectDetection",Qw]],["rf_detr",["RFDetrForObjectDetection",Yw]],["d_fine",["DFineForObjectDetection",tb]],["table-transformer",["TableTransformerForObjectDetection",sb]],["yolos",["YolosForObjectDetection",Xb]]]),iv=new Map([["owlvit",["OwlViTForObjectDetection",Bw]],["owlv2",["Owlv2ForObjectDetection",jw]],["grounding-dino",["GroundingDinoForObjectDetection",qb]]]),no=new Map([["detr",["DetrForSegmentation",Jc]],["clipseg",["CLIPSegForImageSegmentation",No]]]),lv=new Map([["segformer",["SegformerForSemanticSegmentation",l0]],["sapiens",["SapiensForSemanticSegmentation",bb]],["swin",["SwinForSemanticSegmentation",mb]],["mobilenet_v1",["MobileNetV1ForSemanticSegmentation",_0]],["mobilenet_v2",["MobileNetV2ForSemanticSegmentation",M0]],["mobilenet_v3",["MobileNetV3ForSemanticSegmentation",y0]],["mobilenet_v4",["MobileNetV4ForSemanticSegmentation",T0]]]),cv=new Map([["detr",["DetrForSegmentation",Jc]],["maskformer",["MaskFormerForInstanceSegmentation",$b]]]),uv=new Map([["sam",["SamModel",Zb]],["sam2",["Sam2Model",ei]],["edgetam",["EdgeTamModel",sy]],["sam3_tracker",["Sam3TrackerModel",ny]]]),dv=new Map([["wav2vec2",["Wav2Vec2ForCTC",uy]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Cy]],["unispeech",["UniSpeechForCTC",by]],["unispeech-sat",["UniSpeechSatForCTC",xy]],["wavlm",["WavLMForCTC",Fy]],["hubert",["HubertForCTC",$y]],["parakeet_ctc",["ParakeetForCTC",hy]]]),pv=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",dy]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Sy]],["unispeech",["UniSpeechForSequenceClassification",yy]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Ty]],["wavlm",["WavLMForSequenceClassification",Oy]],["hubert",["HubertForSequenceClassification",Iy]],["audio-spectrogram-transformer",["ASTForAudioClassification",Ao]]]),mv=new Map([["wavlm",["WavLMForXVector",Dy]]]),hv=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Py]],["wavlm",["WavLMForAudioFrameClassification",Ly]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",py]],["pyannote",["PyAnnoteForAudioFrameClassification",fy]]]),_v=new Map([["vitmatte",["VitMatteForImageMatting",Aw]]]),jT=new Map([["patchtst",["PatchTSTForPrediction",F0]],["patchtsmixer",["PatchTSMixerForPrediction",D0]]]),fv=new Map([["swin2sr",["Swin2SRForImageSuperResolution",_b]]]),gv=new Map([["dpt",["DPTForDepthEstimation",gb]],["depth_anything",["DepthAnythingForDepthEstimation",wb]],["glpn",["GLPNForDepthEstimation",Ab]],["sapiens",["SapiensForDepthEstimation",yb]],["depth_pro",["DepthProForDepthEstimation",Tb]],["metric3d",["Metric3DForDepthEstimation",Eb]],["metric3dv2",["Metric3Dv2ForDepthEstimation",Sb]]]),Mv=new Map([["sapiens",["SapiensForNormalEstimation",vb]]]),wv=new Map([["vitpose",["VitPoseForPoseEstimation",bw]]]),bv=new Map([["clip",["CLIPVisionModelWithProjection",qa]],["siglip",["SiglipVisionModel",dt]],["jina_clip",["JinaCLIPVisionModel",Pn]]]),yv=[[OT,T.EncoderOnly],[DT,T.EncoderDecoder],[zT,T.DecoderOnly],[LT,T.AutoEncoder],[Z0,T.EncoderOnly],[ev,T.EncoderOnly],[Du,T.Seq2Seq],[Ou,T.Seq2Seq],[Lu,T.DecoderOnly],[BT,T.MultiModality],[tv,T.EncoderOnly],[rv,T.EncoderOnly],[zu,T.Vision2Seq],[sv,T.ImageTextToText],[nv,T.AudioTextToText],[ov,T.EncoderOnly],[no,T.EncoderOnly],[cv,T.EncoderOnly],[lv,T.EncoderOnly],[_v,T.EncoderOnly],[jT,T.EncoderOnly],[fv,T.EncoderOnly],[gv,T.EncoderOnly],[Mv,T.EncoderOnly],[wv,T.EncoderOnly],[av,T.EncoderOnly],[iv,T.EncoderOnly],[uv,T.MaskGeneration],[dv,T.EncoderOnly],[pv,T.EncoderOnly],[J0,T.Seq2Seq],[Y0,T.EncoderOnly],[mv,T.EncoderOnly],[hv,T.EncoderOnly],[bv,T.EncoderOnly]];for(const[g,M]of yv)for(const[N,ae]of g.values())b.set(N,M),x.set(ae,N),P.set(N,ae);const NT=[["MusicgenForConditionalGeneration",$u,T.Musicgen],["Phi3VForCausalLM",Qn,T.Phi3V],["CLIPTextModelWithProjection",zo,T.EncoderOnly],["SiglipTextModel",vn,T.EncoderOnly],["JinaCLIPTextModel",Zr,T.EncoderOnly],["ClapTextModelWithProjection",n0,T.EncoderOnly],["ClapAudioModelWithProjection",o0,T.EncoderOnly],["DacEncoderModel",K0,T.EncoderOnly],["DacDecoderModel",H0,T.EncoderOnly],["MimiEncoderModel",N0,T.EncoderOnly],["MimiDecoderModel",V0,T.EncoderOnly],["SnacEncoderModel",Q0,T.EncoderOnly],["SnacDecoderModel",X0,T.EncoderOnly],["Gemma3nForConditionalGeneration",Kn,T.ImageAudioTextToText],["SupertonicForConditionalGeneration",wu,T.Supertonic]];for(const[g,M,N]of NT)b.set(g,N),x.set(M,g),P.set(g,M);const vv=new Map([["modnet",no],["birefnet",no],["isnet",no],["ben",no]]);for(const[g,M]of vv.entries())M.set(g,["PreTrainedModel",z]),b.set(g,T.EncoderOnly),x.set(z,g),P.set(g,z);class VT extends Ut{static MODEL_CLASS_MAPPINGS=yv.map(M=>M[0]);static BASE_IF_FAIL=!0}class UT extends Ut{static MODEL_CLASS_MAPPINGS=[Z0]}class WT extends Ut{static MODEL_CLASS_MAPPINGS=[ev]}class GT extends Ut{static MODEL_CLASS_MAPPINGS=[Du]}class KT extends Ut{static MODEL_CLASS_MAPPINGS=[Ou]}class HT extends Ut{static MODEL_CLASS_MAPPINGS=[J0]}class qT extends Ut{static MODEL_CLASS_MAPPINGS=[Y0]}class QT extends Ut{static MODEL_CLASS_MAPPINGS=[Lu]}class XT extends Ut{static MODEL_CLASS_MAPPINGS=[tv]}class JT extends Ut{static MODEL_CLASS_MAPPINGS=[rv]}class YT extends Ut{static MODEL_CLASS_MAPPINGS=[zu]}class ZT extends Ut{static MODEL_CLASS_MAPPINGS=[ov]}class eP extends Ut{static MODEL_CLASS_MAPPINGS=[no]}class tP extends Ut{static MODEL_CLASS_MAPPINGS=[lv]}class rP extends Ut{static MODEL_CLASS_MAPPINGS=[cv]}class sP extends Ut{static MODEL_CLASS_MAPPINGS=[av]}class nP extends Ut{static MODEL_CLASS_MAPPINGS=[iv]}class oP extends Ut{static MODEL_CLASS_MAPPINGS=[uv]}class aP extends Ut{static MODEL_CLASS_MAPPINGS=[dv]}class iP extends Ut{static MODEL_CLASS_MAPPINGS=[pv]}class lP extends Ut{static MODEL_CLASS_MAPPINGS=[mv]}class cP extends Ut{static MODEL_CLASS_MAPPINGS=[hv]}class uP extends Ut{static MODEL_CLASS_MAPPINGS=[RT]}class dP extends Ut{static MODEL_CLASS_MAPPINGS=[_v]}class pP extends Ut{static MODEL_CLASS_MAPPINGS=[fv]}class mP extends Ut{static MODEL_CLASS_MAPPINGS=[gv]}class hP extends Ut{static MODEL_CLASS_MAPPINGS=[Mv]}class _P extends Ut{static MODEL_CLASS_MAPPINGS=[wv]}class fP extends Ut{static MODEL_CLASS_MAPPINGS=[bv]}class gP extends Ut{static MODEL_CLASS_MAPPINGS=[sv]}class MP extends Ut{static MODEL_CLASS_MAPPINGS=[nv]}class wP extends de{constructor({logits:M,past_key_values:N,encoder_outputs:ae,decoder_attentions:me=null,cross_attentions:_e=null}){super(),this.logits=M,this.past_key_values=N,this.encoder_outputs=ae,this.decoder_attentions=me,this.cross_attentions=_e}}class xt extends de{constructor({logits:M,...N}){super(),this.logits=M;const ae=Object.values(N);ae.length>0&&(this.attentions=ae)}}class xv extends de{constructor({logits:M,embeddings:N}){super(),this.logits=M,this.embeddings=N}}class Er extends de{constructor({logits:M}){super(),this.logits=M}}class Ir extends de{constructor({logits:M}){super(),this.logits=M}}class Br extends de{constructor({start_logits:M,end_logits:N}){super(),this.start_logits=M,this.end_logits=N}}class sn extends de{constructor({logits:M}){super(),this.logits=M}}class bP extends de{constructor({logits:M,past_key_values:N}){super(),this.logits=M,this.past_key_values=N}}class Tv extends de{constructor({alphas:M}){super(),this.alphas=M}}class Pv extends de{constructor({waveform:M,spectrogram:N}){super(),this.waveform=M,this.spectrogram=N}}}),"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":((e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>o});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var n=t("./src/utils/audio.js");class o extends s.FeatureExtractor{constructor(a){super(a);const l=this.config.sampling_rate,c=(0,n.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(l/2),l,null,"kaldi",!0);this.mel_filters=c,this.window=(0,n.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(a,l){return(0,n.spectrogram)(a,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:l,transpose:!0})}async _call(a){(0,s.validate_audio_inputs)(a,"ASTFeatureExtractor");const l=await this._extract_fbank_features(a,this.config.max_length);if(this.config.do_normalize){const c=this.std*2,p=l.data;for(let d=0;d<p.length;++d)p[d]=(p[d]-this.mean)/c}return{input_values:l.unsqueeze_(0)}}}}),"./src/models/auto/feature_extraction_auto.js":((e,r,t)=>{t.r(r),t.d(r,{AutoFeatureExtractor:()=>i});var s=t("./src/utils/constants.js"),n=t("./src/utils/hub.js");t("./src/base/feature_extraction_utils.js");var o=t("./src/models/feature_extractors.js");class i{static async from_pretrained(l,c={}){const p=await(0,n.getModelJSON)(l,s.FEATURE_EXTRACTOR_NAME,!0,c),d=p.feature_extractor_type,u=o[d];if(!u)throw new Error(`Unknown feature_extractor_type: '${d}'. Please report this at ${s.GITHUB_ISSUE_URL}.`);return new u(p)}}}),"./src/models/auto/image_processing_auto.js":((e,r,t)=>{t.r(r),t.d(r,{AutoImageProcessor:()=>a});var s=t("./src/utils/constants.js"),n=t("./src/utils/hub.js"),o=t("./src/base/image_processors_utils.js"),i=t("./src/models/image_processors.js");class a{static async from_pretrained(c,p={}){const d=await(0,n.getModelJSON)(c,s.IMAGE_PROCESSOR_NAME,!0,p),u=d.image_processor_type??d.feature_extractor_type;let f=i[u?.replace(/Fast$/,"")];return f||(u!==void 0&&console.warn(`Image processor type '${u}' not found, assuming base ImageProcessor. Please report this at ${s.GITHUB_ISSUE_URL}.`),f=o.ImageProcessor),new f(d)}}}),"./src/models/auto/processing_auto.js":((e,r,t)=>{t.r(r),t.d(r,{AutoProcessor:()=>c});var s=t("./src/utils/constants.js"),n=t("./src/utils/hub.js"),o=t("./src/base/processing_utils.js"),i=t("./src/models/processors.js"),a=t("./src/models/image_processors.js"),l=t("./src/models/feature_extractors.js");class c{static async from_pretrained(d,u={}){const f=await(0,n.getModelJSON)(d,s.IMAGE_PROCESSOR_NAME,!0,u),{image_processor_type:_,feature_extractor_type:y,processor_class:I}=f;if(I&&i[I])return i[I].from_pretrained(d,u);if(!_&&!y)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const w={};if(_){const k=a[_.replace(/Fast$/,"")];if(!k)throw new Error(`Unknown image_processor_type: '${_}'.`);w.image_processor=new k(f)}if(y){const k=a[y];if(k)w.image_processor=new k(f);else{const T=l[y];if(!T)throw new Error(`Unknown feature_extractor_type: '${y}'.`);w.feature_extractor=new T(f)}}const v={};return new o.Processor(v,w,null)}}}),"./src/models/beit/image_processing_beit.js":((e,r,t)=>{t.r(r),t.d(r,{BeitFeatureExtractor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}}),"./src/models/bit/image_processing_bit.js":((e,r,t)=>{t.r(r),t.d(r,{BitImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}}),"./src/models/chinese_clip/image_processing_chinese_clip.js":((e,r,t)=>{t.r(r),t.d(r,{ChineseCLIPFeatureExtractor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}}),"./src/models/clap/feature_extraction_clap.js":((e,r,t)=>{t.r(r),t.d(r,{ClapFeatureExtractor:()=>o});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var n=t("./src/utils/audio.js");class o extends s.FeatureExtractor{constructor(a){super(a),this.mel_filters=(0,n.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,n.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,n.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(a,l,c,p){let d;const u=a.length-l;if(u>0)if(c==="rand_trunc"){const f=Math.floor(Math.random()*(u+1));a=a.subarray(f,f+l),d=await this._extract_fbank_features(a,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${c}" not implemented`);else{if(u<0){let f=new Float64Array(l);if(f.set(a),p==="repeat")for(let _=a.length;_<l;_+=a.length)f.set(a.subarray(0,Math.min(a.length,l-_)),_);else if(p==="repeatpad")for(let _=a.length;_<-u;_+=a.length)f.set(a,_);a=f}if(c==="fusion")throw new Error(`Truncation strategy "${c}" not implemented`);d=await this._extract_fbank_features(a,this.mel_filters_slaney,this.config.nb_max_samples)}return d.unsqueeze_(0)}async _extract_fbank_features(a,l,c=null){return(0,n.spectrogram)(a,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:l,log_mel:"dB",max_num_frames:c,do_pad:!1,transpose:!0})}async _call(a,{max_length:l=null}={}){return(0,s.validate_audio_inputs)(a,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(a,l??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}}),"./src/models/clip/image_processing_clip.js":((e,r,t)=>{t.r(r),t.d(r,{CLIPFeatureExtractor:()=>o,CLIPImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}class o extends n{}}),"./src/models/convnext/image_processing_convnext.js":((e,r,t)=>{t.r(r),t.d(r,{ConvNextFeatureExtractor:()=>o,ConvNextImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{constructor(a){super(a),this.crop_pct=this.config.crop_pct??224/256}async resize(a){const l=this.size?.shortest_edge;if(l===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(l<384){const c=Math.floor(l/this.crop_pct),[p,d]=this.get_resize_output_image_size(a,{shortest_edge:c});a=await a.resize(p,d,{resample:this.resample}),a=await a.center_crop(l,l)}else a=await a.resize(l,l,{resample:this.resample});return a}}class o extends n{}}),"./src/models/dac/feature_extraction_dac.js":((e,r,t)=>{t.r(r),t.d(r,{DacFeatureExtractor:()=>n});var s=t("./src/models/encodec/feature_extraction_encodec.js");class n extends s.EncodecFeatureExtractor{}}),"./src/models/deit/image_processing_deit.js":((e,r,t)=>{t.r(r),t.d(r,{DeiTFeatureExtractor:()=>o,DeiTImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}class o extends n{}}),"./src/models/detr/image_processing_detr.js":((e,r,t)=>{t.r(r),t.d(r,{DetrFeatureExtractor:()=>i,DetrImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js"),n=t("./src/utils/tensor.js");class o extends s.ImageProcessor{async _call(l){const c=await super._call(l),p=[c.pixel_values.dims[0],64,64],d=(0,n.full)(p,1n);return{...c,pixel_mask:d}}post_process_object_detection(...l){return(0,s.post_process_object_detection)(...l)}post_process_panoptic_segmentation(...l){return(0,s.post_process_panoptic_segmentation)(...l)}post_process_instance_segmentation(...l){return(0,s.post_process_instance_segmentation)(...l)}}class i extends o{}}),"./src/models/dinov3_vit/image_processing_dinov3_vit.js":((e,r,t)=>{t.r(r),t.d(r,{DINOv3ViTImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}}),"./src/models/donut/image_processing_donut.js":((e,r,t)=>{t.r(r),t.d(r,{DonutFeatureExtractor:()=>o,DonutImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{pad_image(a,l,c,p={}){const[d,u,f]=l;let _=this.image_mean;Array.isArray(this.image_mean)||(_=new Array(f).fill(_));let y=this.image_std;Array.isArray(y)||(y=new Array(f).fill(_));const I=_.map((w,v)=>-w/y[v]);return super.pad_image(a,l,c,{center:!0,constant_values:I,...p})}}class o extends n{}}),"./src/models/dpt/image_processing_dpt.js":((e,r,t)=>{t.r(r),t.d(r,{DPTFeatureExtractor:()=>o,DPTImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}class o extends n{}}),"./src/models/efficientnet/image_processing_efficientnet.js":((e,r,t)=>{t.r(r),t.d(r,{EfficientNetImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{constructor(i){super(i),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(a=>a*a))}}}),"./src/models/encodec/feature_extraction_encodec.js":((e,r,t)=>{t.r(r),t.d(r,{EncodecFeatureExtractor:()=>o});var s=t("./src/base/feature_extraction_utils.js"),n=t("./src/utils/tensor.js");class o extends s.FeatureExtractor{async _call(a){(0,s.validate_audio_inputs)(a,"EncodecFeatureExtractor"),a instanceof Float64Array&&(a=new Float32Array(a));const l=this.config.feature_size;if(a.length%l!==0)throw new Error(`The length of the audio data must be a multiple of the number of channels (${l}).`);const c=[1,l,a.length/l];return{input_values:new n.Tensor("float32",a,c)}}}}),"./src/models/feature_extractors.js":((e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>s.ASTFeatureExtractor,ClapFeatureExtractor:()=>o.ClapFeatureExtractor,DacFeatureExtractor:()=>i.DacFeatureExtractor,EncodecFeatureExtractor:()=>n.EncodecFeatureExtractor,Gemma3nAudioFeatureExtractor:()=>a.Gemma3nAudioFeatureExtractor,ImageFeatureExtractor:()=>w.ImageProcessor,MoonshineFeatureExtractor:()=>l.MoonshineFeatureExtractor,ParakeetFeatureExtractor:()=>c.ParakeetFeatureExtractor,PyAnnoteFeatureExtractor:()=>p.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>d.SeamlessM4TFeatureExtractor,SnacFeatureExtractor:()=>u.SnacFeatureExtractor,SpeechT5FeatureExtractor:()=>f.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>_.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>y.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>I.WhisperFeatureExtractor});var s=t("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),n=t("./src/models/encodec/feature_extraction_encodec.js"),o=t("./src/models/clap/feature_extraction_clap.js"),i=t("./src/models/dac/feature_extraction_dac.js"),a=t("./src/models/gemma3n/feature_extraction_gemma3n.js"),l=t("./src/models/moonshine/feature_extraction_moonshine.js"),c=t("./src/models/parakeet/feature_extraction_parakeet.js"),p=t("./src/models/pyannote/feature_extraction_pyannote.js"),d=t("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),u=t("./src/models/snac/feature_extraction_snac.js"),f=t("./src/models/speecht5/feature_extraction_speecht5.js"),_=t("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),y=t("./src/models/wespeaker/feature_extraction_wespeaker.js"),I=t("./src/models/whisper/feature_extraction_whisper.js"),w=t("./src/base/image_processors_utils.js")}),"./src/models/florence2/processing_florence2.js":((e,r,t)=>{t.r(r),t.d(r,{Florence2Processor:()=>i});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js");class i extends s.Processor{static tokenizer_class=o.AutoTokenizer;static image_processor_class=n.AutoImageProcessor;constructor(l,c,p){super(l,c,p);const{tasks_answer_post_processing_type:d,task_prompts_without_inputs:u,task_prompts_with_input:f}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(d??{})),this.task_prompts_without_inputs=new Map(Object.entries(u??{})),this.task_prompts_with_input=new Map(Object.entries(f??{})),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(l){typeof l=="string"&&(l=[l]);const c=[];for(const p of l)if(this.task_prompts_without_inputs.has(p))c.push(this.task_prompts_without_inputs.get(p));else{for(const[d,u]of this.task_prompts_with_input)if(p.includes(d)){c.push(u.replaceAll("{input}",p).replaceAll(d,""));break}c.length!==l.length&&c.push(p)}return c}post_process_generation(l,c,p){const d=this.tasks_answer_post_processing_type.get(c)??"pure_text";l=l.replaceAll("<s>","").replaceAll("</s>","");let u;switch(d){case"pure_text":u=l;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const f=d==="ocr"?"quad_boxes":"bboxes",_=l.matchAll(this.regexes[f]),y=[],I=[];for(const[w,v,...k]of _)y.push(v?v.trim():y.at(-1)??""),I.push(k.map((T,b)=>(Number(T)+.5)/this.size_per_bin*p[b%2]));u={labels:y,[f]:I};break;default:throw new Error(`Task "${c}" (of type "${d}") not yet implemented.`)}return{[c]:u}}async _call(l,c=null,p={}){if(!l&&!c)throw new Error("Either text or images must be provided");const d=await this.image_processor(l,p),u=c?this.tokenizer(this.construct_prompts(c),p):{};return{...d,...u}}}}),"./src/models/gemma3n/feature_extraction_gemma3n.js":((e,r,t)=>{t.r(r),t.d(r,{Gemma3nAudioFeatureExtractor:()=>i});var s=t("./src/base/feature_extraction_utils.js"),n=t("./src/utils/tensor.js"),o=t("./src/utils/audio.js");class i extends s.FeatureExtractor{constructor(l){super(l);const{fft_length:c,feature_size:p,min_frequency:d,max_frequency:u,sampling_rate:f,frame_length:_}=this.config,y=(0,o.mel_filter_bank)(Math.floor(1+c/2),p,d,u,f,null,"htk",!1);this.mel_filters=y,this.window=(0,o.window_function)(_,"hann")}async _extract_fbank_features(l,c){return(0,o.spectrogram)(l,this.window,this.config.frame_length,this.config.hop_length,{fft_length:this.config.fft_length,center:!1,onesided:!0,preemphasis:this.config.preemphasis,preemphasis_htk_flavor:this.config.preemphasis_htk_flavor,mel_filters:this.mel_filters,log_mel:"log",mel_floor:this.config.mel_floor,remove_dc_offset:!1,transpose:!0})}async _call(l,{max_length:c=48e4,truncation:p=!0,padding:d=!0,pad_to_multiple_of:u=128}={}){if((0,s.validate_audio_inputs)(l,"Gemma3nAudioFeatureExtractor"),p&&l.length>c&&(l=l.slice(0,c)),d&&l.length%u!==0){const y=u-l.length%u,I=new Float64Array(l.length+y);I.set(l),this.config.padding_value!==0&&I.fill(this.config.padding_value,l.length),l=I}const f=await this._extract_fbank_features(l,this.config.max_length),_=(0,n.full)([1,f.dims[0]],!0);return{input_features:f.unsqueeze_(0),input_features_mask:_}}}}),"./src/models/gemma3n/processing_gemma3n.js":((e,r,t)=>{t.r(r),t.d(r,{Gemma3nProcessor:()=>a});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/models/auto/feature_extraction_auto.js"),i=t("./src/tokenizers.js");t("./src/utils/image.js"),t("./src/utils/audio.js");class a extends s.Processor{static image_processor_class=n.AutoImageProcessor;static feature_extractor_class=o.AutoFeatureExtractor;static tokenizer_class=i.AutoTokenizer;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(c,p,d){super(c,p,d),this.audio_seq_length=this.config.audio_seq_length,this.image_seq_length=this.config.image_seq_length;const{audio_token_id:u,boa_token:f,audio_token:_,eoa_token:y,image_token_id:I,boi_token:w,image_token:v,eoi_token:k}=this.tokenizer.config;this.audio_token_id=u,this.boa_token=f,this.audio_token=_;const T=_.repeat(this.audio_seq_length);this.full_audio_sequence=`

${f}${T}${y}

`,this.image_token_id=I,this.boi_token=w,this.image_token=v;const b=v.repeat(this.image_seq_length);this.full_image_sequence=`

${w}${b}${k}

`}async _call(c,p=null,d=null,u={}){typeof c=="string"&&(c=[c]);let f;d&&(f=await this.feature_extractor(d,u),c=c.map(I=>I.replaceAll(this.audio_token,this.full_audio_sequence)));let _;return p&&(_=await this.image_processor(p,u),c=c.map(I=>I.replaceAll(this.image_token,this.full_image_sequence))),{...this.tokenizer(c,u),..._,...f}}}}),"./src/models/glpn/image_processing_glpn.js":((e,r,t)=>{t.r(r),t.d(r,{GLPNFeatureExtractor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}}),"./src/models/grounding_dino/image_processing_grounding_dino.js":((e,r,t)=>{t.r(r),t.d(r,{GroundingDinoImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js"),n=t("./src/utils/tensor.js");class o extends s.ImageProcessor{async _call(a){const l=await super._call(a),c=l.pixel_values.dims,p=(0,n.ones)([c[0],c[2],c[3]]);return{...l,pixel_mask:p}}}}),"./src/models/grounding_dino/processing_grounding_dino.js":((e,r,t)=>{t.r(r),t.d(r,{GroundingDinoProcessor:()=>l});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js"),i=t("./src/base/image_processors_utils.js");function a(c,p){const u=c.dims.at(-1)-1,f=c.tolist();f.fill(!1,0,1),f.fill(!1,u);const _=p.tolist();return f.map((y,I)=>y?I:null).filter(y=>y!==null).map(y=>_[y])}class l extends s.Processor{static tokenizer_class=o.AutoTokenizer;static image_processor_class=n.AutoImageProcessor;async _call(p,d,u={}){const f=p?await this.image_processor(p,u):{};return{...d?this.tokenizer(d,u):{},...f}}post_process_grounded_object_detection(p,d,{box_threshold:u=.25,text_threshold:f=.25,target_sizes:_=null}={}){const{logits:y,pred_boxes:I}=p,w=y.dims[0];if(_!==null&&_.length!==w)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const v=y.dims.at(1),k=y.sigmoid(),T=k.max(-1).tolist(),b=I.tolist().map(x=>x.map(S=>(0,i.center_to_corners_format)(S))),P=[];for(let x=0;x<w;++x){const S=_!==null?_[x]:null;S!==null&&(b[x]=b[x].map(B=>B.map((Y,X)=>Y*S[(X+1)%2])));const O=T[x],F=[],H=[],W=[];for(let B=0;B<v;++B){const Y=O[B];if(Y<=u)continue;const X=b[x][B],J=k[x][B];F.push(Y),W.push(X);const re=a(J.gt(f),d[x]);H.push(re)}P.push({scores:F,boxes:W,labels:this.batch_decode(H)})}return P}}}),"./src/models/idefics3/image_processing_idefics3.js":((e,r,t)=>{t.r(r),t.d(r,{Idefics3ImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js"),n=t("./src/utils/tensor.js");class o extends s.ImageProcessor{constructor(a){super(a),this.do_image_splitting=a.do_image_splitting??!0,this.max_image_size=a.max_image_size}get_resize_for_vision_encoder(a,l){let[c,p]=a.dims.slice(-2);const d=p/c;return p>=c?(p=Math.ceil(p/l)*l,c=Math.floor(p/d),c=Math.ceil(c/l)*l):(c=Math.ceil(c/l)*l,p=Math.floor(c*d),p=Math.ceil(p/l)*l),{height:c,width:p}}async _call(a,{do_image_splitting:l=null,return_row_col_info:c=!1}={}){let p;if(!Array.isArray(a))p=[[a]];else{if(a.length===0||!a[0])throw new Error("No images provided.");Array.isArray(a[0])?p=a:p=[a]}let d=[],u=[],f=[];const _=[],y=[];for(const x of p){let S=await Promise.all(x.map(H=>this.preprocess(H)));_.push(...S.map(H=>H.original_size)),y.push(...S.map(H=>H.reshaped_input_size)),S.forEach(H=>H.pixel_values.unsqueeze_(0));const{longest_edge:O}=this.max_image_size;let F;if(l??this.do_image_splitting){let H=new Array(S.length),W=new Array(S.length);F=await Promise.all(S.map(async(B,Y)=>{const X=this.get_resize_for_vision_encoder(B.pixel_values,O),J=await(0,n.interpolate_4d)(B.pixel_values,{size:[X.height,X.width]}),{frames:re,num_splits_h:ne,num_splits_w:le}=await this.split_image(J,this.max_image_size);return H[Y]=ne,W[Y]=le,(0,n.cat)(re,0)})),u.push(H),f.push(W)}else{const H=[O,O];F=await Promise.all(S.map(W=>(0,n.interpolate_4d)(W.pixel_values,{size:H}))),u.push(new Array(S.length).fill(0)),f.push(new Array(S.length).fill(0))}d.push((0,n.cat)(F,0))}const I=d.length,[w,v,k,T]=d[0].dims;let b,P;if(I===1)b=d[0].unsqueeze_(0),P=(0,n.full)([I,w,k,T],!0);else{const x=Math.max(...d.map(F=>F.dims.at(0)));P=(0,n.full)([I,x,k,T],!0);const S=P.data,O=x*k*T;for(let F=0;F<I;++F){const H=d[F].dims[0];if(H<x){d[F]=(0,n.cat)([d[F],(0,n.full)([x-H,v,k,T],0)],0);const W=F*O+H*k*T,B=(F+1)*O;S.fill(!1,W,B)}}b=(0,n.stack)(d,0)}return{pixel_values:b,pixel_attention_mask:P,original_sizes:_,reshaped_input_sizes:y,...c?{rows:u,cols:f}:{}}}async split_image(a,{longest_edge:l}){const c=l,p=l,d=[],[u,f]=a.dims.slice(-2);let _=0,y=0;if(u>c||f>p){_=Math.ceil(u/c),y=Math.ceil(f/p);const I=Math.ceil(u/_),w=Math.ceil(f/y);for(let T=0;T<_;++T)for(let b=0;b<y;++b){let P,x,S,O;T===_-1?(x=u-I,O=u):(x=T*I,O=(T+1)*I),b===y-1?(P=f-w,S=f):(P=b*w,S=(b+1)*w);const F=[x,P],H=[O,S],W=await(0,n.slice)(a,F,H,[2,3]);d.push(W)}const v=c,k=p;(u!==v||f!==k)&&(a=await(0,n.interpolate_4d)(a,{size:[v,k]}))}return d.push(a),{frames:d,num_splits_h:_,num_splits_w:y}}}}),"./src/models/idefics3/processing_idefics3.js":((e,r,t)=>{t.r(r),t.d(r,{Idefics3Processor:()=>p});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js");t("./src/utils/image.js");var i=t("./src/utils/core.js");function a(d,u,f,_,y,I){let w="";for(let v=0;v<u;++v){for(let k=0;k<f;++k)w+=_+`<row_${v+1}_col_${k+1}>`+y.repeat(d);w+=`
`}return w+=`
${_}${I}`+y.repeat(d)+`${_}`,w}function l(d,u,f,_){return`${u}${_}`+f.repeat(d)+`${u}`}function c(d,u,f,_,y,I){return d===0&&u===0?l(f,_,y,I):a(f,d,u,_,y,I)}class p extends s.Processor{static image_processor_class=n.AutoImageProcessor;static tokenizer_class=o.AutoTokenizer;static uses_processor_config=!0;fake_image_token="<fake_token_around_image>";image_token="<image>";global_img_token="<global-img>";async _call(u,f=null,_={}){_.return_row_col_info??=!0;let y;f&&(y=await this.image_processor(f,_)),Array.isArray(u)||(u=[u]);const I=y.rows??[new Array(u.length).fill(0)],w=y.cols??[new Array(u.length).fill(0)],v=this.config.image_seq_len,k=[],T=[];for(let P=0;P<u.length;++P){const x=u[P],S=I[P],O=w[P];k.push((0,i.count)(x,this.image_token));const F=S.map((B,Y)=>c(B,O[Y],v,this.fake_image_token,this.image_token,this.global_img_token)),H=x.split(this.image_token);if(H.length===0)throw new Error("The image token should be present in the text.");let W=H[0];for(let B=0;B<F.length;++B)W+=F[B]+H[B+1];T.push(W)}return{...this.tokenizer(T),...y}}}}),"./src/models/image_processors.js":((e,r,t)=>{t.r(r),t.d(r,{BeitFeatureExtractor:()=>s.BeitFeatureExtractor,BitImageProcessor:()=>n.BitImageProcessor,CLIPFeatureExtractor:()=>i.CLIPFeatureExtractor,CLIPImageProcessor:()=>i.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>o.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>a.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>a.ConvNextImageProcessor,DINOv3ViTImageProcessor:()=>p.DINOv3ViTImageProcessor,DPTFeatureExtractor:()=>u.DPTFeatureExtractor,DPTImageProcessor:()=>u.DPTImageProcessor,DeiTFeatureExtractor:()=>l.DeiTFeatureExtractor,DeiTImageProcessor:()=>l.DeiTImageProcessor,DetrFeatureExtractor:()=>c.DetrFeatureExtractor,DetrImageProcessor:()=>c.DetrImageProcessor,DonutFeatureExtractor:()=>d.DonutFeatureExtractor,DonutImageProcessor:()=>d.DonutImageProcessor,EfficientNetImageProcessor:()=>f.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>_.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>y.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>I.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>v.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>k.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>T.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>b.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>b.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>P.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>P.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>x.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>x.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>S.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>S.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>O.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>O.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>F.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>F.MobileViTImageProcessor,NougatImageProcessor:()=>H.NougatImageProcessor,OwlViTFeatureExtractor:()=>B.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>B.OwlViTImageProcessor,Owlv2ImageProcessor:()=>W.Owlv2ImageProcessor,Phi3VImageProcessor:()=>Y.Phi3VImageProcessor,PixtralImageProcessor:()=>X.PixtralImageProcessor,PvtImageProcessor:()=>J.PvtImageProcessor,Qwen2VLImageProcessor:()=>re.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>ne.RTDetrImageProcessor,Sam2ImageProcessor:()=>pe.Sam2ImageProcessor,Sam3ImageProcessor:()=>oe.Sam3ImageProcessor,SamImageProcessor:()=>le.SamImageProcessor,SegformerFeatureExtractor:()=>K.SegformerFeatureExtractor,SegformerImageProcessor:()=>K.SegformerImageProcessor,SiglipImageProcessor:()=>j.SiglipImageProcessor,SmolVLMImageProcessor:()=>D.SmolVLMImageProcessor,Swin2SRImageProcessor:()=>te.Swin2SRImageProcessor,VLMImageProcessor:()=>w.VLMImageProcessor,ViTFeatureExtractor:()=>he.ViTFeatureExtractor,ViTImageProcessor:()=>he.ViTImageProcessor,VitMatteImageProcessor:()=>Ae.VitMatteImageProcessor,VitPoseImageProcessor:()=>ke.VitPoseImageProcessor,YolosFeatureExtractor:()=>Ve.YolosFeatureExtractor,YolosImageProcessor:()=>Ve.YolosImageProcessor});var s=t("./src/models/beit/image_processing_beit.js"),n=t("./src/models/bit/image_processing_bit.js"),o=t("./src/models/chinese_clip/image_processing_chinese_clip.js"),i=t("./src/models/clip/image_processing_clip.js"),a=t("./src/models/convnext/image_processing_convnext.js"),l=t("./src/models/deit/image_processing_deit.js"),c=t("./src/models/detr/image_processing_detr.js"),p=t("./src/models/dinov3_vit/image_processing_dinov3_vit.js"),d=t("./src/models/donut/image_processing_donut.js"),u=t("./src/models/dpt/image_processing_dpt.js"),f=t("./src/models/efficientnet/image_processing_efficientnet.js"),_=t("./src/models/glpn/image_processing_glpn.js"),y=t("./src/models/grounding_dino/image_processing_grounding_dino.js"),I=t("./src/models/idefics3/image_processing_idefics3.js"),w=t("./src/models/janus/image_processing_janus.js"),v=t("./src/models/jina_clip/image_processing_jina_clip.js"),k=t("./src/models/llava_onevision/image_processing_llava_onevision.js"),T=t("./src/models/mask2former/image_processing_mask2former.js"),b=t("./src/models/maskformer/image_processing_maskformer.js"),P=t("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),x=t("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),S=t("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),O=t("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),F=t("./src/models/mobilevit/image_processing_mobilevit.js"),H=t("./src/models/nougat/image_processing_nougat.js"),W=t("./src/models/owlv2/image_processing_owlv2.js"),B=t("./src/models/owlvit/image_processing_owlvit.js"),Y=t("./src/models/phi3_v/image_processing_phi3_v.js"),X=t("./src/models/pixtral/image_processing_pixtral.js"),J=t("./src/models/pvt/image_processing_pvt.js"),re=t("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),ne=t("./src/models/rt_detr/image_processing_rt_detr.js"),le=t("./src/models/sam/image_processing_sam.js"),pe=t("./src/models/sam2/image_processing_sam2.js"),oe=t("./src/models/sam3/image_processing_sam3.js"),K=t("./src/models/segformer/image_processing_segformer.js"),j=t("./src/models/siglip/image_processing_siglip.js"),D=t("./src/models/smolvlm/image_processing_smolvlm.js"),te=t("./src/models/swin2sr/image_processing_swin2sr.js"),he=t("./src/models/vit/image_processing_vit.js"),Ae=t("./src/models/vitmatte/image_processing_vitmatte.js"),ke=t("./src/models/vitpose/image_processing_vitpose.js"),Ve=t("./src/models/yolos/image_processing_yolos.js")}),"./src/models/janus/image_processing_janus.js":((e,r,t)=>{t.r(r),t.d(r,{VLMImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{constructor(i){super({do_pad:!0,pad_size:{width:i.image_size,height:i.image_size},...i}),this.constant_values=this.config.background_color.map(a=>a*this.rescale_factor)}pad_image(i,a,l,c){return super.pad_image(i,a,l,{constant_values:this.constant_values,center:!0,...c})}}}),"./src/models/janus/processing_janus.js":((e,r,t)=>{t.r(r),t.d(r,{VLChatProcessor:()=>c});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js"),i=t("./src/utils/core.js"),a=t("./src/utils/tensor.js"),l=t("./src/utils/image.js");class c extends s.Processor{static image_processor_class=n.AutoImageProcessor;static tokenizer_class=o.AutoTokenizer;static uses_processor_config=!0;constructor(d,u,f){super(d,u,f),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(d,{images:u=null,chat_template:f="default"}={}){u?Array.isArray(u)||(u=[u]):u=await Promise.all(d.filter(F=>F.images).flatMap(F=>F.images).map(F=>l.RawImage.read(F)));const _=this.tokenizer,y=_.apply_chat_template(d,{tokenize:!1,add_generation_prompt:!0,chat_template:f}),I=F=>_.encode(F,{add_special_tokens:!1}),w=y.split(this.image_tag),v=w.length-1;if(u.length!==v)throw new Error(`Number of images provided (${u.length}) does not match number of "${this.image_tag}" image tags (${v})`);const[k,T,b]=_.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let P=I(w[0]),x=new Array(P.length).fill(!1);for(let F=1;F<w.length;++F){const H=new Array(this.num_image_tokens).fill(k),W=I(w[F]);P=(0,i.mergeArrays)(P,[T],H,[b],W);const B=new Array(this.num_image_tokens).fill(!0);x=(0,i.mergeArrays)(x,[!1],B,[!1],new Array(W.length).fill(!1))}const S=[1,P.length],O={input_ids:new a.Tensor("int64",P,S),attention_mask:new a.Tensor("int64",new Array(P.length).fill(1),S),images_seq_mask:new a.Tensor("bool",x,S),images_emb_mask:new a.Tensor("bool",new Array(v*this.num_image_tokens).fill(!0),[1,v,this.num_image_tokens])};if(u&&u.length>0){const F=await this.image_processor(u);return F.pixel_values.unsqueeze_(0),{...O,...F}}return O}}}),"./src/models/jina_clip/image_processing_jina_clip.js":((e,r,t)=>{t.r(r),t.d(r,{JinaCLIPImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{constructor(i){const{resize_mode:a,fill_color:l,interpolation:c,size:p,...d}=i,u=a==="squash"?{width:p,height:p}:a==="shortest"?{shortest_edge:p}:{longest_edge:p},f=c==="bicubic"?3:2;super({...d,size:u,resample:f,do_center_crop:!0,crop_size:p,do_normalize:!0})}}}),"./src/models/jina_clip/processing_jina_clip.js":((e,r,t)=>{t.r(r),t.d(r,{JinaCLIPProcessor:()=>i});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js");class i extends s.Processor{static tokenizer_class=o.AutoTokenizer;static image_processor_class=n.AutoImageProcessor;async _call(l=null,c=null,p={}){if(!l&&!c)throw new Error("Either text or images must be provided");const d=l?this.tokenizer(l,p):{},u=c?await this.image_processor(c,p):{};return{...d,...u}}}}),"./src/models/llava/processing_llava.js":((e,r,t)=>{t.r(r),t.d(r,{LlavaProcessor:()=>i});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js");class i extends s.Processor{static tokenizer_class=o.AutoTokenizer;static image_processor_class=n.AutoImageProcessor;static uses_processor_config=!0;async _call(l,c=null,p={}){const d=await this.image_processor(l,p);if(c){const[f,_]=d.pixel_values.dims.slice(-2),{image_token:y,patch_size:I,num_additional_image_tokens:w}=this.config,v=Math.floor(f/I)*Math.floor(_/I)+w;c=structuredClone(c),Array.isArray(c)||(c=[c]);for(let k=0;k<c.length;++k)c[k]=c[k].replace(y,y.repeat(v))}const u=c?this.tokenizer(c,p):{};return{...d,...u}}}}),"./src/models/llava_onevision/image_processing_llava_onevision.js":((e,r,t)=>{t.r(r),t.d(r,{LlavaOnevisionImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}}),"./src/models/mask2former/image_processing_mask2former.js":((e,r,t)=>{t.r(r),t.d(r,{Mask2FormerImageProcessor:()=>n});var s=t("./src/models/maskformer/image_processing_maskformer.js");class n extends s.MaskFormerImageProcessor{}}),"./src/models/maskformer/image_processing_maskformer.js":((e,r,t)=>{t.r(r),t.d(r,{MaskFormerFeatureExtractor:()=>o,MaskFormerImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{post_process_panoptic_segmentation(...a){return(0,s.post_process_panoptic_segmentation)(...a)}post_process_instance_segmentation(...a){return(0,s.post_process_instance_segmentation)(...a)}}class o extends n{}}),"./src/models/mgp_str/processing_mgp_str.js":((e,r,t)=>{t.r(r),t.d(r,{MgpstrProcessor:()=>l});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js"),i=t("./src/utils/maths.js");const a={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class l extends s.Processor{static tokenizer_class=o.AutoTokenizer;static image_processor_class=n.AutoImageProcessor;get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(p,d){if(!a.hasOwnProperty(d))throw new Error(`Format ${d} is not supported.`);const[u,f]=a[d],_=this[u].bind(this),[y,I]=p.dims,w=[],v=[],k=p.tolist();for(let b=0;b<y;++b){const P=k[b],x=[],S=[];for(let F=1;F<I;++F){const[H,W]=(0,i.max)((0,i.softmax)(P[F]));if(S.push(H),W==f)break;x.push(W)}const O=S.length>0?S.reduce((F,H)=>F*H,1):0;v.push(x),w.push(O)}return[_(v),w]}char_decode(p){return this.char_tokenizer.batch_decode(p).map(d=>d.replaceAll(" ",""))}bpe_decode(p){return this.bpe_tokenizer.batch_decode(p)}wp_decode(p){return this.wp_tokenizer.batch_decode(p).map(d=>d.replaceAll(" ",""))}batch_decode([p,d,u]){const[f,_]=this._decode_helper(p,"char"),[y,I]=this._decode_helper(d,"bpe"),[w,v]=this._decode_helper(u,"wp"),k=[],T=[];for(let b=0;b<f.length;++b){const[P,x]=(0,i.max)([_[b],I[b],v[b]]);k.push([f[b],y[b],w[b]][x]),T.push(P)}return{generated_text:k,scores:T,char_preds:f,bpe_preds:y,wp_preds:w}}static async from_pretrained(...p){const d=await super.from_pretrained(...p),u=await o.AutoTokenizer.from_pretrained("Xenova/gpt2"),f=await o.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased");return d.components={image_processor:d.image_processor,char_tokenizer:d.tokenizer,bpe_tokenizer:u,wp_tokenizer:f},d}async _call(p,d=null){const u=await this.image_processor(p);return d&&(u.labels=this.tokenizer(d).input_ids),u}}}),"./src/models/mobilenet_v1/image_processing_mobilenet_v1.js":((e,r,t)=>{t.r(r),t.d(r,{MobileNetV1FeatureExtractor:()=>o,MobileNetV1ImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}class o extends n{}}),"./src/models/mobilenet_v2/image_processing_mobilenet_v2.js":((e,r,t)=>{t.r(r),t.d(r,{MobileNetV2FeatureExtractor:()=>o,MobileNetV2ImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}class o extends n{}}),"./src/models/mobilenet_v3/image_processing_mobilenet_v3.js":((e,r,t)=>{t.r(r),t.d(r,{MobileNetV3FeatureExtractor:()=>o,MobileNetV3ImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}class o extends n{}}),"./src/models/mobilenet_v4/image_processing_mobilenet_v4.js":((e,r,t)=>{t.r(r),t.d(r,{MobileNetV4FeatureExtractor:()=>o,MobileNetV4ImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}class o extends n{}}),"./src/models/mobilevit/image_processing_mobilevit.js":((e,r,t)=>{t.r(r),t.d(r,{MobileViTFeatureExtractor:()=>o,MobileViTImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{}class o extends n{}}),"./src/models/moonshine/feature_extraction_moonshine.js":((e,r,t)=>{t.r(r),t.d(r,{MoonshineFeatureExtractor:()=>o});var s=t("./src/base/feature_extraction_utils.js"),n=t("./src/utils/tensor.js");class o extends s.FeatureExtractor{async _call(a){(0,s.validate_audio_inputs)(a,"MoonshineFeatureExtractor"),a instanceof Float64Array&&(a=new Float32Array(a));const l=[1,a.length];return{input_values:new n.Tensor("float32",a,l)}}}}),"./src/models/moonshine/processing_moonshine.js":((e,r,t)=>{t.r(r),t.d(r,{MoonshineProcessor:()=>i});var s=t("./src/models/auto/feature_extraction_auto.js"),n=t("./src/tokenizers.js"),o=t("./src/base/processing_utils.js");class i extends o.Processor{static tokenizer_class=n.AutoTokenizer;static feature_extractor_class=s.AutoFeatureExtractor;async _call(l){return await this.feature_extractor(l)}}}),"./src/models/nougat/image_processing_nougat.js":((e,r,t)=>{t.r(r),t.d(r,{NougatImageProcessor:()=>n});var s=t("./src/models/donut/image_processing_donut.js");class n extends s.DonutImageProcessor{}}),"./src/models/owlv2/image_processing_owlv2.js":((e,r,t)=>{t.r(r),t.d(r,{Owlv2ImageProcessor:()=>n});var s=t("./src/models/owlvit/image_processing_owlvit.js");class n extends s.OwlViTImageProcessor{}}),"./src/models/owlvit/image_processing_owlvit.js":((e,r,t)=>{t.r(r),t.d(r,{OwlViTFeatureExtractor:()=>o,OwlViTImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{post_process_object_detection(...a){return(0,s.post_process_object_detection)(...a)}}class o extends n{}}),"./src/models/owlvit/processing_owlvit.js":((e,r,t)=>{t.r(r),t.d(r,{OwlViTProcessor:()=>i});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js");class i extends s.Processor{static tokenizer_class=o.AutoTokenizer;static image_processor_class=n.AutoImageProcessor}}),"./src/models/paligemma/processing_paligemma.js":((e,r,t)=>{t.r(r),t.d(r,{PaliGemmaProcessor:()=>l});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js");const i="<image>";function a(c,p,d,u,f){return`${u.repeat(d*f)}${p}${c}
`}class l extends s.Processor{static tokenizer_class=o.AutoTokenizer;static image_processor_class=n.AutoImageProcessor;static uses_processor_config=!1;async _call(p,d=null,u={}){d||(console.warn("You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."),d=""),Array.isArray(p)||(p=[p]),Array.isArray(d)||(d=[d]);const f=this.tokenizer.bos_token,_=this.image_processor.config.image_seq_length;let y;d.some(v=>v.includes(i))?y=d.map(v=>{const k=v.replaceAll(i,i.repeat(_)),T=k.lastIndexOf(i),b=T===-1?0:T+i.length;return k.slice(0,b)+f+k.slice(b)+`
`}):(console.warn("You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special image tokens in the text, as many tokens as there are images per each text. It is recommended to add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images each text has and add special tokens."),y=d.map(v=>a(v,f,_,i,p.length)));const I=this.tokenizer(y,u);return{...await this.image_processor(p,u),...I}}}}),"./src/models/parakeet/feature_extraction_parakeet.js":((e,r,t)=>{t.r(r),t.d(r,{ParakeetFeatureExtractor:()=>a});var s=t("./src/base/feature_extraction_utils.js"),n=t("./src/utils/tensor.js"),o=t("./src/utils/audio.js");const i=1e-5;class a extends s.FeatureExtractor{constructor(c){super(c),this.config.mel_filters??=(0,o.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,this.config.sampling_rate/2,this.config.sampling_rate,"slaney","slaney");const p=(0,o.window_function)(this.config.win_length,"hann",{periodic:!1});this.window=new Float64Array(this.config.n_fft);const d=Math.floor((this.config.n_fft-this.config.win_length)/2);this.window.set(p,d)}async _extract_fbank_features(c){const p=this.config.preemphasis;c=new Float64Array(c);for(let u=c.length-1;u>=1;--u)c[u]-=p*c[u-1];return await(0,o.spectrogram)(c,this.window,this.window.length,this.config.hop_length,{fft_length:this.config.n_fft,power:2,mel_filters:this.config.mel_filters,log_mel:"log",mel_floor:-1/0,pad_mode:"constant",center:!0,transpose:!0,mel_offset:2**-24})}async _call(c){(0,s.validate_audio_inputs)(c,"ParakeetFeatureExtractor");const p=await this._extract_fbank_features(c),d=Math.floor((c.length+Math.floor(this.config.n_fft/2)*2-this.config.n_fft)/this.config.hop_length),u=p.data;u.fill(0,d*p.dims[1]);const[f,_]=p.dims,y=new Float64Array(_),I=new Float64Array(_);for(let k=0;k<d;++k){const T=k*_;for(let b=0;b<_;++b){const P=u[T+b];y[b]+=P,I[b]+=P*P}}const w=d>1?d-1:1;for(let k=0;k<_;++k){const T=y[k]/d,b=(I[k]-d*T*T)/w,x=1/(Math.sqrt(b)+i);for(let S=0;S<d;++S){const O=S*_+k;u[O]=(u[O]-T)*x}}const v=new BigInt64Array(f);return v.fill(1n,0,d),{input_features:p.unsqueeze_(0),attention_mask:new n.Tensor("int64",v,[1,f])}}}}),"./src/models/phi3_v/image_processing_phi3_v.js":((e,r,t)=>{t.r(r),t.d(r,{Phi3VImageProcessor:()=>p});var s=t("./src/base/image_processors_utils.js"),n=t("./src/utils/tensor.js");const o=336,i=[2,3],{ceil:a,floor:l,sqrt:c}=Math;class p extends s.ImageProcessor{constructor(u){super({...u,do_normalize:!0,do_pad:!0,pad_size:"custom",do_convert_rgb:!0,do_resize:!0}),this._num_crops=u.num_crops}calc_num_image_tokens_from_image_size(u,f){const{num_img_tokens:_}=this.config;return l((l(f/o)*l(u/o)+1)*_+1+(l(f/o)+1)*c(_))}get_resize_output_image_size(u,f){const _=this._num_crops,[y,I]=u.size;let w=y/I,v=1;for(;v*Math.ceil(v/w)<=_;)v+=1;v-=1;const k=Math.floor(v*336),T=Math.floor(k/w);return[k,T]}pad_image(u,f,_,y={}){const[I,w]=f,v=o*a(I/o),k=o*a(w/o),T=[1,1,1].map((b,P)=>(b-this.image_mean[P])/this.image_std[P]);return super.pad_image(u,f,{width:k,height:v},{center:!0,constant_values:T,...y})}async _call(u,{num_crops:f=null}={}){if(this._num_crops=f??=this.config.num_crops,f<4||c(f)%1!==0)throw new Error("num_crops must be a square number >= 4");Array.isArray(u)||(u=[u]);const _=u.length,y=await Promise.all(u.map(x=>this.preprocess(x))),I=y.map(x=>x.original_size),w=y.map(x=>x.reshaped_input_size),v=[];for(const{pixel_values:x}of y){x.unsqueeze_(0);const[S,O]=x.dims.slice(-2),F=await(0,n.interpolate_4d)(x,{size:[o,o],mode:"bicubic"});if(f>0){const H=[],W=c(f),B=l(O/W),Y=l(S/W);for(let J=0;J<W;++J)for(let re=0;re<W;++re){let ne,le,pe,oe;J===W-1?(le=S-Y,oe=S):(le=J*Y,oe=(J+1)*Y),re===W-1?(ne=O-B,pe=O):(ne=re*B,pe=(re+1)*B);const K=[le,ne],j=[oe,pe],D=await(0,n.slice)(x,K,j,i);H.push(D)}const X=await(0,n.interpolate_4d)((0,n.cat)(H,0),{size:[o,o],mode:"bicubic"});v.push((0,n.cat)([F,X],0))}else v.push(F)}const k=(0,n.stack)(v,0),T=w.map(x=>x.map(S=>o*a(S/o))),b=new n.Tensor("int64",T.flat(),[_,2]),P=T.map(([x,S])=>this.calc_num_image_tokens_from_image_size(S,x));return{pixel_values:k,original_sizes:I,reshaped_input_sizes:w,image_sizes:b,num_img_tokens:P}}}}),"./src/models/phi3_v/processing_phi3_v.js":((e,r,t)=>{t.r(r),t.d(r,{Phi3VProcessor:()=>l});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js");t("./src/utils/image.js");const i="<|image|>",a=/<\|image_\d+\|>/g;class l extends s.Processor{static image_processor_class=n.AutoImageProcessor;static tokenizer_class=o.AutoTokenizer;async _call(p,d=null,{padding:u=!0,truncation:f=!0,num_crops:_=null}={}){Array.isArray(p)||(p=[p]);let y,I;if(d){I=await this.image_processor(d,{num_crops:_});const{num_img_tokens:w}=I,v=p.map((T,b)=>T.split(a).join(i.repeat(w[b])));y=this.tokenizer(v,{padding:u,truncation:f});const k=this.tokenizer.model.convert_tokens_to_ids([i])[0];y.input_ids.map_(T=>T==k?-T:T)}else y=this.tokenizer(p);return{...y,...I}}}}),"./src/models/pixtral/image_processing_pixtral.js":((e,r,t)=>{t.r(r),t.d(r,{PixtralImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{get_resize_output_image_size(i,a){const{longest_edge:l}=a;if(l===void 0)throw new Error("size must contain 'longest_edge'");const[c,p]=i.size,d=Math.max(c,p)/l;let u=c,f=p;d>1&&(u=Math.floor(c/d),f=Math.floor(p/d));const{patch_size:_,spatial_merge_size:y}=this.config;if(!y)throw new Error("config must contain 'spatial_merge_size'");const I=_*y,w=Math.floor((u-1)/I)+1,v=Math.floor((f-1)/I)+1;return[w*I,v*I]}}}),"./src/models/pixtral/processing_pixtral.js":((e,r,t)=>{t.r(r),t.d(r,{PixtralProcessor:()=>i});var s=t("./src/base/processing_utils.js"),n=t("./src/models/auto/image_processing_auto.js"),o=t("./src/tokenizers.js");class i extends s.Processor{static tokenizer_class=o.AutoTokenizer;static image_processor_class=n.AutoImageProcessor;static 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(${_.length}) does not match the number of audio tokens in the text (${k}).`);const T=this.feature_extractor.config.n_samples,b=_.map(F=>p(F,T)),P=b.map(F=>F.length),x=b.flat(),S=(await Promise.all(x.map(F=>this.feature_extractor(F,y)))).map(F=>F.input_features);I.audio_values=S.length>1?(0,i.cat)(S,0):S[0];let O=v[0];for(let F=0;F<P.length;++F){O+=l;for(let H=0;H<P[F];++H)O+=a.repeat(c);O+=v[F+1]}f=O}return{...this.tokenizer(f,{add_special_tokens:!1,...y}),...I}}}}),"./src/models/wav2vec2/feature_extraction_wav2vec2.js":((e,r,t)=>{t.r(r),t.d(r,{Wav2Vec2FeatureExtractor:()=>o});var s=t("./src/base/feature_extraction_utils.js"),n=t("./src/utils/tensor.js");class o extends s.FeatureExtractor{_zero_mean_unit_var_norm(a){const c=a.reduce((d,u)=>d+u,0)/a.length,p=a.reduce((d,u)=>d+(u-c)**2,0)/a.length;return a.map(d=>(d-c)/Math.sqrt(p+1e-7))}async _call(a){(0,s.validate_audio_inputs)(a,"Wav2Vec2FeatureExtractor"),a instanceof Float64Array&&(a=new Float32Array(a));let l=a;this.config.do_normalize&&(l=this._zero_mean_unit_var_norm(l));const c=[1,l.length];return{input_values:new n.Tensor("float32",l,c),attention_mask:new n.Tensor("int64",new BigInt64Array(l.length).fill(1n),c)}}}}),"./src/models/wav2vec2/processing_wav2vec2.js":((e,r,t)=>{t.r(r),t.d(r,{Wav2Vec2Processor:()=>i});var s=t("./src/tokenizers.js"),n=t("./src/models/auto/feature_extraction_auto.js"),o=t("./src/base/processing_utils.js");class i extends o.Processor{static tokenizer_class=s.AutoTokenizer;static feature_extractor_class=n.AutoFeatureExtractor;async _call(l){return await this.feature_extractor(l)}}}),"./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js":((e,r,t)=>{t.r(r),t.d(r,{Wav2Vec2ProcessorWithLM:()=>i});var s=t("./src/tokenizers.js"),n=t("./src/models/auto/feature_extraction_auto.js"),o=t("./src/base/processing_utils.js");class i extends o.Processor{static tokenizer_class=s.AutoTokenizer;static feature_extractor_class=n.AutoFeatureExtractor;async _call(l){return await this.feature_extractor(l)}}}),"./src/models/wespeaker/feature_extraction_wespeaker.js":((e,r,t)=>{t.r(r),t.d(r,{WeSpeakerFeatureExtractor:()=>o});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var n=t("./src/utils/audio.js");class o extends s.FeatureExtractor{constructor(a){super(a);const l=this.config.sampling_rate,c=(0,n.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(l/2),l,null,"kaldi",!0);this.mel_filters=c,this.window=(0,n.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(a){return a=a.map(l=>l*32768),(0,n.spectrogram)(a,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(a){(0,s.validate_audio_inputs)(a,"WeSpeakerFeatureExtractor");const l=(await this._extract_fbank_features(a)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const c=l.mean(1).data,p=l.data,[d,u,f]=l.dims;for(let _=0;_<d;++_){const y=_*u*f,I=_*f;for(let w=0;w<u;++w){const v=y+w*f;for(let k=0;k<f;++k)p[v+k]-=c[I+k]}}}return{input_features:l}}}}),"./src/models/whisper/common_whisper.js":((e,r,t)=>{t.r(r),t.d(r,{WHISPER_LANGUAGE_MAPPING:()=>n,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>o,whisper_language_to_code:()=>i});const s=[["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"]],n=new Map(s),o=new Map([...s.map(([a,l])=>[l,a]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function i(a){a=a.toLowerCase();let l=o.get(a);if(l===void 0){const c=a.match(/^<\|([a-z]{2})\|>$/);if(c&&(a=c[1]),n.has(a))l=a;else{const d=a.length===2?n.keys():n.values();throw new Error(`Language "${a}" is not supported. Must be one of: ${JSON.stringify(Array.from(d))}`)}}return l}}),"./src/models/whisper/feature_extraction_whisper.js":((e,r,t)=>{t.r(r),t.d(r,{WhisperFeatureExtractor:()=>i});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var n=t("./src/utils/audio.js"),o=t("./src/utils/maths.js");class i extends s.FeatureExtractor{constructor(l){super(l),this.config.mel_filters??=(0,n.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=(0,n.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(l){const c=await(0,n.spectrogram)(l,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:Math.min(Math.floor(l.length/this.config.hop_length),this.config.nb_max_frames)}),p=c.data,d=(0,o.max)(p)[0];for(let u=0;u<p.length;++u)p[u]=(Math.max(p[u],d-8)+4)/4;return c}async _call(l,{max_length:c=null}={}){(0,s.validate_audio_inputs)(l,"WhisperFeatureExtractor");let p;const d=c??this.config.n_samples;return l.length>d?(l.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`."),p=l.slice(0,d)):(p=new Float32Array(d),p.set(l)),{input_features:(await this._extract_fbank_features(p)).unsqueeze_(0)}}}}),"./src/models/whisper/generation_whisper.js":((e,r,t)=>{t.r(r),t.d(r,{WhisperGenerationConfig:()=>n});var s=t("./src/generation/configuration_utils.js");class n extends s.GenerationConfig{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1}}),"./src/models/whisper/processing_whisper.js":((e,r,t)=>{t.r(r),t.d(r,{WhisperProcessor:()=>i});var s=t("./src/models/auto/feature_extraction_auto.js"),n=t("./src/tokenizers.js"),o=t("./src/base/processing_utils.js");class i extends o.Processor{static tokenizer_class=n.AutoTokenizer;static feature_extractor_class=s.AutoFeatureExtractor;async _call(l){return await this.feature_extractor(l)}}}),"./src/models/yolos/image_processing_yolos.js":((e,r,t)=>{t.r(r),t.d(r,{YolosFeatureExtractor:()=>o,YolosImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js");class n extends s.ImageProcessor{post_process_object_detection(...a){return(0,s.post_process_object_detection)(...a)}}class o extends n{}}),"./src/ops/registry.js":((e,r,t)=>{t.r(r),t.d(r,{TensorOpRegistry:()=>i});var s=t("./src/backends/onnx.js"),n=t("./src/utils/tensor.js");const o=async(a,l,c)=>{const p=await(0,s.createInferenceSession)(new Uint8Array(a),l);return(async d=>{const u=(0,s.isONNXProxy)(),f=Object.fromEntries(Object.entries(d).map(([y,I])=>[y,(u?I.clone():I).ort_tensor])),_=await(0,s.runInferenceSession)(p,f);return Array.isArray(c)?c.map(y=>new n.Tensor(_[y])):new n.Tensor(_[c])})};class i{static session_options={};static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=o([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=o([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=o([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=o([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=o([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=o([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=o([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}static get slice(){return this._slice||(this._slice=o([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}}),"./src/pipelines.js":((e,r,t)=>{t.r(r),t.d(r,{AudioClassificationPipeline:()=>W,AutomaticSpeechRecognitionPipeline:()=>Y,BackgroundRemovalPipeline:()=>ne,DepthEstimationPipeline:()=>te,DocumentQuestionAnsweringPipeline:()=>K,FeatureExtractionPipeline:()=>F,FillMaskPipeline:()=>k,ImageClassificationPipeline:()=>J,ImageFeatureExtractionPipeline:()=>H,ImageSegmentationPipeline:()=>re,ImageToImagePipeline:()=>D,ImageToTextPipeline:()=>X,ObjectDetectionPipeline:()=>pe,Pipeline:()=>y,QuestionAnsweringPipeline:()=>v,SummarizationPipeline:()=>b,Text2TextGenerationPipeline:()=>T,TextClassificationPipeline:()=>I,TextGenerationPipeline:()=>S,TextToAudioPipeline:()=>j,TokenClassificationPipeline:()=>w,TranslationPipeline:()=>P,ZeroShotAudioClassificationPipeline:()=>B,ZeroShotClassificationPipeline:()=>O,ZeroShotImageClassificationPipeline:()=>le,ZeroShotObjectDetectionPipeline:()=>oe,pipeline:()=>ke});var s=t("./src/tokenizers.js"),n=t("./src/models.js"),o=t("./src/models/auto/processing_auto.js");t("./src/base/processing_utils.js");var i=t("./src/utils/generic.js"),a=t("./src/utils/core.js"),l=t("./src/utils/maths.js"),c=t("./src/utils/audio.js"),p=t("./src/utils/tensor.js"),d=t("./src/utils/image.js");async function u(Te){return Array.isArray(Te)||(Te=[Te]),await Promise.all(Te.map(Q=>d.RawImage.read(Q)))}async function f(Te,Q){return Array.isArray(Te)||(Te=[Te]),await Promise.all(Te.map(z=>typeof z=="string"||z instanceof URL?(0,c.read_audio)(z,Q):z instanceof Float64Array?new Float32Array(z):z))}function _(Te,Q){Q&&(Te=Te.map(xe=>xe|0));const[z,de,be,ve]=Te;return{xmin:z,ymin:de,xmax:be,ymax:ve}}class y extends i.Callable{constructor({task:Q,model:z,tokenizer:de=null,processor:be=null}){super(),this.task=Q,this.model=z,this.tokenizer=de,this.processor=be}async dispose(){await this.model.dispose()}}class I extends y{constructor(Q){super(Q)}async _call(Q,{top_k:z=1}={}){const de=this.tokenizer(Q,{padding:!0,truncation:!0}),be=await this.model(de),ve=this.model.config.problem_type==="multi_label_classification"?ge=>ge.sigmoid():ge=>new p.Tensor("float32",(0,l.softmax)(ge.data),ge.dims),xe=this.model.config.id2label,Ce=[];for(const ge of be.logits){const De=ve(ge),fe=await(0,p.topk)(De,z),Pe=fe[0].tolist(),Fe=fe[1].tolist().map((tt,Re)=>({label:xe?xe[tt]:`LABEL_${tt}`,score:Pe[Re]}));z===1?Ce.push(...Fe):Ce.push(Fe)}return Array.isArray(Q)||z===1?Ce:Ce[0]}}class w extends y{constructor(Q){super(Q)}async _call(Q,{ignore_labels:z=["O"]}={}){const de=Array.isArray(Q),be=this.tokenizer(de?Q:[Q],{padding:!0,truncation:!0}),xe=(await this.model(be)).logits,Ce=this.model.config.id2label,ge=[];for(let De=0;De<xe.dims[0];++De){const fe=be.input_ids[De],Pe=xe[De],We=[];for(let Fe=0;Fe<Pe.dims[0];++Fe){const tt=Pe[Fe],Re=(0,l.max)(tt.data)[1],rt=Ce?Ce[Re]:`LABEL_${Re}`;if(z.includes(rt))continue;const Ze=this.tokenizer.decode([fe[Fe].item()],{skip_special_tokens:!0});if(Ze==="")continue;const je=(0,l.softmax)(tt.data);We.push({entity:rt,score:je[Re],index:Fe,word:Ze})}ge.push(We)}return de?ge:ge[0]}}class v extends y{constructor(Q){super(Q)}async _call(Q,z,{top_k:de=1}={}){const be=this.tokenizer(Q,{text_pair:z,padding:!0,truncation:!0}),{start_logits:ve,end_logits:xe}=await this.model(be),Ce=be.input_ids.tolist(),ge=be.attention_mask.tolist(),De=this.tokenizer.all_special_ids,fe=[];for(let Pe=0;Pe<ve.dims[0];++Pe){const We=Ce[Pe],Fe=We.findIndex(Oe=>Oe==this.tokenizer.sep_token_id);ge[Pe].map((Oe,at)=>Oe==1&&(at===0||at>Fe&&De.findIndex(ht=>ht==We[at])===-1));const tt=ve[Pe].tolist(),Re=xe[Pe].tolist();for(let Oe=1;Oe<tt.length;++Oe)(ge[Pe]==0||Oe<=Fe||De.findIndex(at=>at==We[Oe])!==-1)&&(tt[Oe]=-1/0,Re[Oe]=-1/0);const rt=(0,l.softmax)(tt).map((Oe,at)=>[Oe,at]),Ze=(0,l.softmax)(Re).map((Oe,at)=>[Oe,at]);rt[0][0]=0,Ze[0][0]=0;const je=(0,a.product)(rt,Ze).filter(Oe=>Oe[0][1]<=Oe[1][1]).map(Oe=>[Oe[0][1],Oe[1][1],Oe[0][0]*Oe[1][0]]).sort((Oe,at)=>at[2]-Oe[2]);for(let Oe=0;Oe<Math.min(je.length,de);++Oe){const[at,ht,Nt]=je[Oe],kt=We.slice(at,ht+1),gr=this.tokenizer.decode(kt,{skip_special_tokens:!0});fe.push({answer:gr,score:Nt})}}return de===1?fe[0]:fe}}class k extends y{constructor(Q){super(Q)}async _call(Q,{top_k:z=5}={}){const de=this.tokenizer(Q,{padding:!0,truncation:!0}),{logits:be}=await this.model(de),ve=[],xe=de.input_ids.tolist();for(let Ce=0;Ce<xe.length;++Ce){const ge=xe[Ce],De=ge.findIndex(tt=>tt==this.tokenizer.mask_token_id);if(De===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const fe=be[Ce][De],Pe=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(fe.data),fe.dims),z),We=Pe[0].tolist(),Fe=Pe[1].tolist();ve.push(Fe.map((tt,Re)=>{const rt=ge.slice();return rt[De]=tt,{score:We[Re],token:Number(tt),token_str:this.tokenizer.decode([tt]),sequence:this.tokenizer.decode(rt,{skip_special_tokens:!0})}}))}return Array.isArray(Q)?ve:ve[0]}}class T extends y{_key="generated_text";constructor(Q){super(Q)}async _call(Q,z={}){Array.isArray(Q)||(Q=[Q]),this.model.config.prefix&&(Q=Q.map(ge=>this.model.config.prefix+ge));const de=this.model.config.task_specific_params;de&&de[this.task]&&de[this.task].prefix&&(Q=Q.map(ge=>de[this.task].prefix+ge));const be=this.tokenizer,ve={padding:!0,truncation:!0};let xe;this instanceof P&&"_build_translation_inputs"in be?xe=be._build_translation_inputs(Q,ve,z):xe=be(Q,ve);const Ce=await this.model.generate({...xe,...z});return be.batch_decode(Ce,{skip_special_tokens:!0}).map(ge=>({[this._key]:ge}))}}class b extends T{_key="summary_text";constructor(Q){super(Q)}}class P extends T{_key="translation_text";constructor(Q){super(Q)}}function x(Te){return Array.isArray(Te)&&Te.every(Q=>"role"in Q&&"content"in Q)}class S extends y{constructor(Q){super(Q)}async _call(Q,z={}){let de=!1,be=!1,ve=z.add_special_tokens??(this.tokenizer.add_bos_token||this.tokenizer.add_eos_token)??!1,xe;if(typeof Q=="string")xe=Q=[Q];else if(Array.isArray(Q)&&Q.every(Fe=>typeof Fe=="string"))de=!0,xe=Q;else{if(x(Q))Q=[Q];else if(Array.isArray(Q)&&Q.every(x))de=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");be=!0,xe=Q.map(Fe=>this.tokenizer.apply_chat_template(Fe,{tokenize:!1,add_generation_prompt:!0})),ve=!1}const Ce=be?!1:z.return_full_text??!0;this.tokenizer.padding_side="left";const ge=this.tokenizer(xe,{add_special_tokens:ve,padding:!0,truncation:!0}),De=await this.model.generate({...ge,...z}),fe=this.tokenizer.batch_decode(De,{skip_special_tokens:!0});let Pe;!Ce&&ge.input_ids.dims.at(-1)>0&&(Pe=this.tokenizer.batch_decode(ge.input_ids,{skip_special_tokens:!0}).map(Fe=>Fe.length));const We=Array.from({length:Q.length},Fe=>[]);for(let Fe=0;Fe<fe.length;++Fe){const tt=Math.floor(Fe/De.dims[0]*Q.length);Pe&&(fe[Fe]=fe[Fe].slice(Pe[tt])),We[tt].push({generated_text:be?[...Q[tt],{role:"assistant",content:fe[Fe]}]:fe[Fe]})}return!de&&We.length===1?We[0]:We}}class O extends y{constructor(Q){super(Q),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([z,de])=>[z.toLowerCase(),de])),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(Q,z,{hypothesis_template:de="This example is {}.",multi_label:be=!1}={}){const ve=Array.isArray(Q);ve||(Q=[Q]),Array.isArray(z)||(z=[z]);const xe=z.map(De=>de.replace("{}",De)),Ce=be||z.length===1,ge=[];for(const De of Q){const fe=[];for(const Fe of xe){const tt=this.tokenizer(De,{text_pair:Fe,padding:!0,truncation:!0}),Re=await this.model(tt);Ce?fe.push([Re.logits.data[this.contradiction_id],Re.logits.data[this.entailment_id]]):fe.push(Re.logits.data[this.entailment_id])}const We=(Ce?fe.map(Fe=>(0,l.softmax)(Fe)[1]):(0,l.softmax)(fe)).map((Fe,tt)=>[Fe,tt]).sort((Fe,tt)=>tt[0]-Fe[0]);ge.push({sequence:De,labels:We.map(Fe=>z[Fe[1]]),scores:We.map(Fe=>Fe[0])})}return ve?ge:ge[0]}}class F extends y{constructor(Q){super(Q)}async _call(Q,{pooling:z="none",normalize:de=!1,quantize:be=!1,precision:ve="binary"}={}){const xe=this.tokenizer(Q,{padding:!0,truncation:!0}),Ce=await this.model(xe);let ge=Ce.last_hidden_state??Ce.logits??Ce.token_embeddings;switch(z){case"none":break;case"mean":ge=(0,p.mean_pooling)(ge,xe.attention_mask);break;case"first_token":case"cls":ge=ge.slice(null,0);break;case"last_token":case"eos":ge=ge.slice(null,-1);break;default:throw Error(`Pooling method '${z}' not supported.`)}return de&&(ge=ge.normalize(2,-1)),be&&(ge=(0,p.quantize_embeddings)(ge,ve)),ge}}class H extends y{constructor(Q){super(Q)}async _call(Q,{pool:z=null}={}){const de=await u(Q),{pixel_values:be}=await this.processor(de),ve=await this.model({pixel_values:be});let xe;if(z){if(!("pooler_output"in ve))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");xe=ve.pooler_output}else xe=ve.last_hidden_state??ve.logits??ve.image_embeds;return xe}}class W extends y{constructor(Q){super(Q)}async _call(Q,{top_k:z=5}={}){const de=this.processor.feature_extractor.config.sampling_rate,be=await f(Q,de),ve=this.model.config.id2label,xe=[];for(const Ce of be){const ge=await this.processor(Ce),fe=(await this.model(ge)).logits[0],Pe=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(fe.data),fe.dims),z),We=Pe[0].tolist(),tt=Pe[1].tolist().map((Re,rt)=>({label:ve?ve[Re]:`LABEL_${Re}`,score:We[rt]}));xe.push(tt)}return Array.isArray(Q)?xe:xe[0]}}class B extends y{constructor(Q){super(Q)}async _call(Q,z,{hypothesis_template:de="This is a sound of {}."}={}){const be=!Array.isArray(Q);be&&(Q=[Q]);const ve=z.map(fe=>de.replace("{}",fe)),xe=this.tokenizer(ve,{padding:!0,truncation:!0}),Ce=this.processor.feature_extractor.config.sampling_rate,ge=await f(Q,Ce),De=[];for(const fe of ge){const Pe=await this.processor(fe),We=await this.model({...xe,...Pe}),Fe=(0,l.softmax)(We.logits_per_audio.data);De.push([...Fe].map((tt,Re)=>({score:tt,label:z[Re]})))}return be?De[0]:De}}class Y extends y{constructor(Q){super(Q)}async _call(Q,z={}){switch(this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(Q,z);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":case"parakeet_ctc":return this._call_wav2vec2(Q,z);case"moonshine":return this._call_moonshine(Q,z);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(Q,z){z.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),z.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const de=!Array.isArray(Q);de&&(Q=[Q]);const be=this.processor.feature_extractor.config.sampling_rate,ve=await f(Q,be),xe=[];for(const Ce of ve){const ge=await this.processor(Ce),fe=(await this.model(ge)).logits[0],Pe=[];for(const Fe of fe)Pe.push((0,l.max)(Fe.data)[1]);const We=this.tokenizer.decode(Pe,{skip_special_tokens:!0}).trim();xe.push({text:We})}return de?xe[0]:xe}async _call_whisper(Q,z){const de=z.return_timestamps??!1,be=z.chunk_length_s??0,ve=z.force_full_sequences??!1;let xe=z.stride_length_s??null;const Ce={...z};de==="word"&&(Ce.return_token_timestamps=!0,Ce.return_timestamps=!1);const ge=!Array.isArray(Q);ge&&(Q=[Q]);const De=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,fe=this.processor.feature_extractor.config.hop_length,Pe=this.processor.feature_extractor.config.sampling_rate,We=await f(Q,Pe),Fe=[];for(const tt of We){let Re=[];if(be>0){if(xe===null)xe=be/6;else if(be<=xe)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const je=Pe*be,Oe=Pe*xe,at=je-2*Oe;let ht=0;for(;;){const Nt=ht+je,kt=tt.subarray(ht,Nt),gr=await this.processor(kt),Or=ht===0,Bt=Nt>=tt.length;if(Re.push({stride:[kt.length,Or?0:Oe,Bt?0:Oe],input_features:gr.input_features,is_last:Bt}),Bt)break;ht+=at}}else Re=[{stride:[tt.length,0,0],input_features:(await this.processor(tt)).input_features,is_last:!0}];for(const je of Re){Ce.num_frames=Math.floor(je.stride[0]/fe);const Oe=await this.model.generate({inputs:je.input_features,...Ce});de==="word"?(je.tokens=Oe.sequences.tolist()[0],je.token_timestamps=Oe.token_timestamps.tolist()[0].map(at=>(0,l.round)(at,2))):je.tokens=Oe[0].tolist(),je.stride=je.stride.map(at=>at/Pe)}const[rt,Ze]=this.tokenizer._decode_asr(Re,{time_precision:De,return_timestamps:de,force_full_sequences:ve});Fe.push({text:rt,...Ze})}return ge?Fe[0]:Fe}async _call_moonshine(Q,z){const de=!Array.isArray(Q);de&&(Q=[Q]);const be=this.processor.feature_extractor.config.sampling_rate,ve=await f(Q,be),xe=[];for(const Ce of ve){const ge=await this.processor(Ce),De=Math.floor(Ce.length/be)*6,fe=await this.model.generate({max_new_tokens:De,...z,...ge}),Pe=this.processor.batch_decode(fe,{skip_special_tokens:!0})[0];xe.push({text:Pe})}return de?xe[0]:xe}}class X extends y{constructor(Q){super(Q)}async _call(Q,z={}){const de=Array.isArray(Q),be=await u(Q),{pixel_values:ve}=await this.processor(be),xe=[];for(const Ce of ve){Ce.dims=[1,...Ce.dims];const ge=await this.model.generate({inputs:Ce,...z}),De=this.tokenizer.batch_decode(ge,{skip_special_tokens:!0}).map(fe=>({generated_text:fe.trim()}));xe.push(De)}return de?xe:xe[0]}}class J extends y{constructor(Q){super(Q)}async _call(Q,{top_k:z=5}={}){const de=await u(Q),{pixel_values:be}=await this.processor(de),ve=await this.model({pixel_values:be}),xe=this.model.config.id2label,Ce=[];for(const ge of ve.logits){const De=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(ge.data),ge.dims),z),fe=De[0].tolist(),We=De[1].tolist().map((Fe,tt)=>({label:xe?xe[Fe]:`LABEL_${Fe}`,score:fe[tt]}));Ce.push(We)}return Array.isArray(Q)?Ce:Ce[0]}}class re extends y{constructor(Q){super(Q),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(Q,{threshold:z=.5,mask_threshold:de=.5,overlap_mask_area_threshold:be=.8,label_ids_to_fuse:ve=null,target_sizes:xe=null,subtask:Ce=null}={}){if(Array.isArray(Q)&&Q.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const De=await u(Q),fe=De.map(je=>[je.height,je.width]),Pe=await this.processor(De),{inputNames:We,outputNames:Fe}=this.model.sessions.model;if(!We.includes("pixel_values")){if(We.length!==1)throw Error(`Expected a single input name, but got ${We.length} inputs: ${We}.`);const je=We[0];if(je in Pe)throw Error(`Input name ${je} already exists in the inputs.`);Pe[je]=Pe.pixel_values}const tt=await this.model(Pe);let Re=null;if(Ce!==null)Re=this.subtasks_mapping[Ce];else if(this.processor.image_processor){for(const[je,Oe]of Object.entries(this.subtasks_mapping))if(Oe in this.processor.image_processor){Re=this.processor.image_processor[Oe].bind(this.processor.image_processor),Ce=je;break}}const rt=this.model.config.id2label,Ze=[];if(Ce)if(Ce==="panoptic"||Ce==="instance"){const je=Re(tt,z,de,be,ve,xe??fe)[0],Oe=je.segmentation;for(const at of je.segments_info){const ht=new Uint8ClampedArray(Oe.data.length);for(let kt=0;kt<Oe.data.length;++kt)Oe.data[kt]===at.id&&(ht[kt]=255);const Nt=new d.RawImage(ht,Oe.dims[1],Oe.dims[0],1);Ze.push({score:at.score,label:rt[at.label_id],mask:Nt})}}else if(Ce==="semantic"){const{segmentation:je,labels:Oe}=Re(tt,xe??fe)[0];for(const at of Oe){const ht=new Uint8ClampedArray(je.data.length);for(let kt=0;kt<je.data.length;++kt)je.data[kt]===at&&(ht[kt]=255);const Nt=new d.RawImage(ht,je.dims[1],je.dims[0],1);Ze.push({score:null,label:rt[at],mask:Nt})}}else throw Error(`Subtask ${Ce} not supported.`);else{const Oe=tt[Fe[0]];for(let at=0;at<fe.length;++at){const ht=fe[at],Nt=Oe[at];Nt.data.some(gr=>gr<-1e-5||gr>1+1e-5)&&Nt.sigmoid_();const kt=await d.RawImage.fromTensor(Nt.mul_(255).to("uint8")).resize(ht[1],ht[0]);Ze.push({label:null,score:null,mask:kt})}}return Ze}}class ne extends re{constructor(Q){super(Q)}async _call(Q,z={}){if(Array.isArray(Q)&&Q.length!==1)throw Error("Background removal pipeline currently only supports a batch size of 1.");const be=await u(Q),ve=await super._call(Q,z);return be.map((Ce,ge)=>{const De=Ce.clone();return De.putAlpha(ve[ge].mask),De})}}class le extends y{constructor(Q){super(Q)}async _call(Q,z,{hypothesis_template:de="This is a photo of {}"}={}){const be=Array.isArray(Q),ve=await u(Q),xe=z.map(We=>de.replace("{}",We)),Ce=this.tokenizer(xe,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:ge}=await this.processor(ve),De=await this.model({...Ce,pixel_values:ge}),fe=this.model.config.model_type==="siglip"?We=>We.sigmoid().data:We=>(0,l.softmax)(We.data),Pe=[];for(const We of De.logits_per_image){const tt=[...fe(We)].map((Re,rt)=>({score:Re,label:z[rt]}));tt.sort((Re,rt)=>rt.score-Re.score),Pe.push(tt)}return be?Pe:Pe[0]}}class pe extends y{constructor(Q){super(Q)}async _call(Q,{threshold:z=.9,percentage:de=!1}={}){const be=Array.isArray(Q);if(be&&Q.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const ve=await u(Q),xe=de?null:ve.map(Fe=>[Fe.height,Fe.width]),{pixel_values:Ce,pixel_mask:ge}=await this.processor(ve),De=await this.model({pixel_values:Ce,pixel_mask:ge}),fe=this.processor.image_processor.post_process_object_detection(De,z,xe),Pe=this.model.config.id2label,We=fe.map(Fe=>Fe.boxes.map((tt,Re)=>({score:Fe.scores[Re],label:Pe[Fe.classes[Re]],box:_(tt,!de)})));return be?We:We[0]}}class oe extends y{constructor(Q){super(Q)}async _call(Q,z,{threshold:de=.1,top_k:be=null,percentage:ve=!1}={}){const xe=Array.isArray(Q),Ce=await u(Q),ge=this.tokenizer(z,{padding:!0,truncation:!0}),De=await this.processor(Ce),fe=[];for(let Pe=0;Pe<Ce.length;++Pe){const We=Ce[Pe],Fe=ve?null:[[We.height,We.width]],tt=De.pixel_values[Pe].unsqueeze_(0),Re=await this.model({...ge,pixel_values:tt});let rt;if("post_process_grounded_object_detection"in this.processor){const Ze=this.processor.post_process_grounded_object_detection(Re,ge.input_ids,{box_threshold:de,text_threshold:de,target_sizes:Fe})[0];rt=Ze.boxes.map((je,Oe)=>({score:Ze.scores[Oe],label:Ze.labels[Oe],box:_(je,!ve)}))}else{const Ze=this.processor.image_processor.post_process_object_detection(Re,de,Fe,!0)[0];rt=Ze.boxes.map((je,Oe)=>({score:Ze.scores[Oe],label:z[Ze.classes[Oe]],box:_(je,!ve)}))}rt.sort((Ze,je)=>je.score-Ze.score),be!==null&&(rt=rt.slice(0,be)),fe.push(rt)}return xe?fe:fe[0]}}class K extends y{constructor(Q){super(Q)}async _call(Q,z,de={}){const be=(await u(Q))[0],{pixel_values:ve}=await this.processor(be),xe=`<s_docvqa><s_question>${z}</s_question><s_answer>`,Ce=this.tokenizer(xe,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,ge=await this.model.generate({inputs:ve,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Ce,...de}),fe=this.tokenizer.batch_decode(ge)[0].match(/<s_answer>(.*?)<\/s_answer>/);let Pe=null;return fe&&fe.length>=2&&(Pe=fe[1].trim()),[{answer:Pe}]}}class j extends y{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(Q){super(Q),this.vocoder=Q.vocoder??null}async _prepare_speaker_embeddings(Q){if((typeof Q=="string"||Q instanceof URL)&&(Q=new Float32Array(await(await fetch(Q)).arrayBuffer())),Q instanceof Float32Array)Q=new p.Tensor("float32",Q,[Q.length]);else if(!(Q instanceof p.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");return Q}async _call(Q,{speaker_embeddings:z=null,num_inference_steps:de,speed:be}={}){return this.processor?this._call_text_to_spectrogram(Q,{speaker_embeddings:z}):this.model.config.model_type==="supertonic"?this._call_supertonic(Q,{speaker_embeddings:z,num_inference_steps:de,speed:be}):this._call_text_to_waveform(Q)}async _call_supertonic(Q,{speaker_embeddings:z,num_inference_steps:de,speed:be}){if(!z)throw new Error("Speaker embeddings must be provided for Supertonic models.");z=await this._prepare_speaker_embeddings(z);const{sampling_rate:ve,style_dim:xe}=this.model.config;z=z.view(1,-1,xe);const Ce=this.tokenizer(Q,{padding:!0,truncation:!0}),{waveform:ge}=await this.model.generate_speech({...Ce,style:z,num_inference_steps:de,speed:be});return new c.RawAudio(ge.data,ve)}async _call_text_to_waveform(Q){const z=this.tokenizer(Q,{padding:!0,truncation:!0}),{waveform:de}=await this.model(z),be=this.model.config.sampling_rate;return new c.RawAudio(de.data,be)}async _call_text_to_spectrogram(Q,{speaker_embeddings:z}){this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await n.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"}));const{input_ids:de}=this.tokenizer(Q,{padding:!0,truncation:!0});z=await this._prepare_speaker_embeddings(z),z=z.view(1,-1);const{waveform:be}=await this.model.generate_speech(de,z,{vocoder:this.vocoder}),ve=this.processor.feature_extractor.config.sampling_rate;return new c.RawAudio(be.data,ve)}}class D extends y{constructor(Q){super(Q)}async _call(Q){const z=await u(Q),de=await this.processor(z),be=await this.model(de),ve=[];for(const xe of be.reconstruction){const Ce=xe.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");ve.push(d.RawImage.fromTensor(Ce))}return ve.length>1?ve:ve[0]}}class te extends y{constructor(Q){super(Q)}async _call(Q){const z=await u(Q),de=await this.processor(z),{predicted_depth:be}=await this.model(de),ve=[];for(let xe=0;xe<z.length;++xe){const Ce=be[xe],[ge,De]=Ce.dims.slice(-2),[fe,Pe]=z[xe].size,We=(await(0,p.interpolate_4d)(Ce.view(1,1,ge,De),{size:[Pe,fe],mode:"bilinear"})).view(Pe,fe),Fe=We.min().item(),tt=We.max().item(),Re=We.sub(Fe).div_(tt-Fe).mul_(255).to("uint8").unsqueeze(0),rt=d.RawImage.fromTensor(Re);ve.push({predicted_depth:We,depth:rt})}return ve.length>1?ve:ve[0]}}const he=Object.freeze({"text-classification":{tokenizer:s.AutoTokenizer,pipeline:I,model:n.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:s.AutoTokenizer,pipeline:w,model:n.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:s.AutoTokenizer,pipeline:v,model:n.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:s.AutoTokenizer,pipeline:k,model:n.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:s.AutoTokenizer,pipeline:b,model:n.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:s.AutoTokenizer,pipeline:P,model:n.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:s.AutoTokenizer,pipeline:T,model:n.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:s.AutoTokenizer,pipeline:S,model:n.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:s.AutoTokenizer,pipeline:O,model:n.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:W,model:n.AutoModelForAudioClassification,processor:o.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:s.AutoTokenizer,pipeline:B,model:n.AutoModel,processor:o.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:s.AutoTokenizer,pipeline:Y,model:[n.AutoModelForSpeechSeq2Seq,n.AutoModelForCTC],processor:o.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:s.AutoTokenizer,pipeline:j,model:[n.AutoModelForTextToWaveform,n.AutoModelForTextToSpectrogram],processor:[o.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:s.AutoTokenizer,pipeline:X,model:n.AutoModelForVision2Seq,processor:o.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:J,model:n.AutoModelForImageClassification,processor:o.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:re,model:[n.AutoModelForImageSegmentation,n.AutoModelForSemanticSegmentation,n.AutoModelForUniversalSegmentation],processor:o.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"background-removal":{pipeline:ne,model:[n.AutoModelForImageSegmentation,n.AutoModelForSemanticSegmentation,n.AutoModelForUniversalSegmentation],processor:o.AutoProcessor,default:{model:"Xenova/modnet"},type:"image"},"zero-shot-image-classification":{tokenizer:s.AutoTokenizer,pipeline:le,model:n.AutoModel,processor:o.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:pe,model:n.AutoModelForObjectDetection,processor:o.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:s.AutoTokenizer,pipeline:oe,model:n.AutoModelForZeroShotObjectDetection,processor:o.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:s.AutoTokenizer,pipeline:K,model:n.AutoModelForDocumentQuestionAnswering,processor:o.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:D,model:n.AutoModelForImageToImage,processor:o.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:te,model:n.AutoModelForDepthEstimation,processor:o.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:s.AutoTokenizer,pipeline:F,model:n.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:o.AutoProcessor,pipeline:H,model:[n.AutoModelForImageFeatureExtraction,n.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Ae=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function ke(Te,Q=null,{progress_callback:z=null,config:de=null,cache_dir:be=null,local_files_only:ve=!1,revision:xe="main",device:Ce=null,dtype:ge=null,subfolder:De="onnx",use_external_data_format:fe=null,model_file_name:Pe=null,session_options:We={}}={}){Te=Ae[Te]??Te;const Fe=he[Te.split("_",1)[0]];if(!Fe)throw Error(`Unsupported pipeline: ${Te}. 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P="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",x=new RegExp(`^[${P}]+$`,"gu"),S=".,!?…。，、।۔،",O=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],["(?i:[sdmt]|ll|ve|re)","(?:[sS]|[dD]|[mM]|[tT]|[lL][lL]|[vV][eE]|[rR][eE])"],["[^\\r\\n\\p{L}\\p{N}]?+","[^\\r\\n\\p{L}\\p{N}]?"],["[^\\s\\p{L}\\p{N}]++","[^\\s\\p{L}\\p{N}]+"],[` ?[^(\\s|[${S}])]+`,` ?[^\\s${S}]+`]]);class F{constructor($){this.content=$.content,this.id=$.id,this.single_word=$.single_word??!1,this.lstrip=$.lstrip??!1,this.rstrip=$.rstrip??!1,this.special=$.special??!1,this.normalized=$.normalized??null}}class H extends s.Callable{constructor($){super(),this.config=$,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig($,...U){switch($.type){case"WordPiece":return new W($);case"Unigram":return new B($,...U);case"BPE":return new J($);default:if($.vocab)return Array.isArray($.vocab)?new B($,...U):Object.hasOwn($,"continuing_subword_prefix")&&Object.hasOwn($,"unk_token")?Object.hasOwn($,"merges")?new J($):new W($):new re($,...U);throw new Error(`Unknown TokenizerModel type: ${$.type}`)}}_call($){return $=this.encode($),this.fuse_unk&&($=T($,this.tokens_to_ids,this.unk_token_id)),$}encode($){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids($){return $.map(U=>this.tokens_to_ids.get(U)??this.unk_token_id)}convert_ids_to_tokens($){return $.map(U=>this.vocab[U]??this.unk_token)}}class W extends H{constructor($){super($),this.tokens_to_ids=_($.vocab),this.unk_token_id=this.tokens_to_ids.get($.unk_token),this.unk_token=$.unk_token,this.max_input_chars_per_word=$.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[U,ee]of this.tokens_to_ids)this.vocab[ee]=U}encode($){const U=[];for(const ee of $){const 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TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.max_length_to_cache=256,this.cache_capacity=1e4,this.cache=new l.LRUCache(this.cache_capacity)}clear_cache(){this.cache.clear()}bpe($){if($.length===0)return[];const U=this.cache.get($);if(U!==void 0)return U;const ee=Array.from($);this.end_of_word_suffix&&(ee[ee.length-1]+=this.end_of_word_suffix);let se=[];if(ee.length>1){const Me=new l.PriorityQueue((Je,Ye)=>Je.score<Ye.score);let $e={token:ee[0],bias:0,prev:null,next:null},Xe=$e;for(let Je=1;Je<ee.length;++Je){const Ye={bias:Je/ee.length,token:ee[Je],prev:Xe,next:null};Xe.next=Ye,this._add_node(Me,Xe),Xe=Ye}for(;!Me.isEmpty();){const Je=Me.pop();if(Je.deleted||!Je.next||Je.next.deleted)continue;if(Je.deleted=!0,Je.next.deleted=!0,Je.prev){const Ke={...Je.prev};Je.prev.deleted=!0,Je.prev=Ke,Ke.prev?Ke.prev.next=Ke:$e=Ke}const 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U}}class re extends H{constructor($,U){super($),this.tokens_to_ids=_(U.target_lang?$.vocab[U.target_lang]:$.vocab),this.bos_token=U.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=U.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=U.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=U.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[ee,se]of this.tokens_to_ids)this.vocab[se]=ee}encode($){return $}}class ne extends s.Callable{constructor($){super(),this.config=$}static fromConfig($){if($===null)return null;switch($.type){case"BertNormalizer":return new Te($);case"Precompiled":return new Bt($);case"Sequence":return new Ve($);case"Replace":return new le($);case"NFC":return new oe($);case"NFD":return new K($);case"NFKC":return new j($);case"NFKD":return new D($);case"Strip":return new te($);case"StripAccents":return new he($);case"Lowercase":return new Ae($);case"Prepend":return new ke($);default:throw new Error(`Unknown Normalizer type: ${$.type}`)}}normalize($){throw Error("normalize should be implemented in subclass.")}_call($){return this.normalize($)}}class le extends ne{normalize($){const U=f(this.config.pattern);return U===null?$:$.replaceAll(U,this.config.content)}}class pe extends ne{form=void 0;normalize($){return $=$.normalize(this.form),$}}class oe extends pe{form="NFC"}class K extends pe{form="NFD"}class j extends pe{form="NFKC"}class D extends pe{form="NFKD"}class te extends ne{normalize($){return this.config.strip_left&&this.config.strip_right?$=$.trim():(this.config.strip_left&&($=$.trimStart()),this.config.strip_right&&($=$.trimEnd())),$}}class he extends ne{normalize($){return $=w($),$}}class Ae extends ne{normalize($){return $=$.toLowerCase(),$}}class ke extends ne{normalize($){return $=this.config.prepend+$,$}}class Ve extends ne{constructor($){super($),this.normalizers=$.normalizers.map(U=>ne.fromConfig(U))}normalize($){return this.normalizers.reduce((U,ee)=>ee.normalize(U),$)}}class Te extends ne{_tokenize_chinese_chars($){const U=[];for(let ee=0;ee<$.length;++ee){const se=$[ee],Me=se.charCodeAt(0);k(Me)?(U.push(" "),U.push(se),U.push(" ")):U.push(se)}return U.join("")}stripAccents($){return $.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control($){switch($){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test($)}}_clean_text($){const U=[];for(const ee of $){const se=ee.charCodeAt(0);se===0||se===65533||this._is_control(ee)||(/^\s$/.test(ee)?U.push(" "):U.push(ee))}return U.join("")}normalize($){return this.config.clean_text&&($=this._clean_text($)),this.config.handle_chinese_chars&&($=this._tokenize_chinese_chars($)),this.config.lowercase?($=$.toLowerCase(),this.config.strip_accents!==!1&&($=this.stripAccents($))):this.config.strip_accents&&($=this.stripAccents($)),$}}class Q extends s.Callable{static fromConfig($){if($===null)return null;switch($.type){case"BertPreTokenizer":return new z($);case"Sequence":return new jr($);case"Whitespace":return new Qs($);case"WhitespaceSplit":return new Xs($);case"Metaspace":return new gr($);case"ByteLevel":return new de($);case"Split":return new be($);case"Punctuation":return new ve($);case"Digits":return new xe($);case"Replace":return new Js($);case"FixedLength":return new ar($);default:throw new Error(`Unknown PreTokenizer type: ${$.type}`)}}pre_tokenize_text($,U){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize($,U){return(Array.isArray($)?$.map(ee=>this.pre_tokenize_text(ee,U)):this.pre_tokenize_text($,U)).flat()}_call($,U){return this.pre_tokenize($,U)}}class z extends Q{constructor($){super(),this.pattern=new RegExp(`[^\\s${P}]+|[${P}]`,"gu")}pre_tokenize_text($,U){return $.trim().match(this.pattern)||[]}}class de extends Q{constructor($){super(),this.config=$,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=Y,this.text_encoder=new TextEncoder}pre_tokenize_text($,U){return this.add_prefix_space&&!$.startsWith(" ")&&($=" "+$),(this.use_regex?$.match(this.pattern)||[]:[$]).map(se=>Array.from(this.text_encoder.encode(se),Me=>this.byte_encoder[Me]).join(""))}}class be extends Q{constructor($){super(),this.config=$,this.pattern=f(this.config.pattern,this.config.invert)}pre_tokenize_text($,U){return this.pattern===null?[]:this.config.invert?$.match(this.pattern)||[]:this.config.behavior?.toLowerCase()==="removed"?$.split(this.pattern).filter(ee=>ee):u($,this.pattern)}}class ve extends Q{constructor($){super(),this.config=$,this.pattern=new RegExp(`[^${P}]+|[${P}]+`,"gu")}pre_tokenize_text($,U){return $.match(this.pattern)||[]}}class xe extends Q{constructor($){super(),this.config=$;const U=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(U,"gu")}pre_tokenize_text($,U){return $.match(this.pattern)||[]}}class Ce extends s.Callable{constructor($){super(),this.config=$}static fromConfig($){if($===null)return null;switch($.type){case"TemplateProcessing":return new fe($);case"ByteLevel":return new Pe($);case"RobertaProcessing":return new De($);case"BertProcessing":return new ge($);case"Sequence":return new We($);default:throw new Error(`Unknown PostProcessor type: ${$.type}`)}}post_process($,...U){throw Error("post_process should be implemented in subclass.")}_call($,...U){return this.post_process($,...U)}}class ge extends Ce{constructor($){super($),this.cls=$.cls[0],this.sep=$.sep[0]}post_process($,U=null,{add_special_tokens:ee=!0}={}){ee&&($=(0,n.mergeArrays)([this.cls],$,[this.sep]));let se=new Array($.length).fill(0);if(U!==null){const Me=ee&&this instanceof De?[this.sep]:[],$e=ee?[this.sep]:[];$=(0,n.mergeArrays)($,Me,U,$e),se=(0,n.mergeArrays)(se,new Array(U.length+Me.length+$e.length).fill(1))}return{tokens:$,token_type_ids:se}}}class De extends ge{}class fe extends Ce{constructor($){super($),this.single=$.single,this.pair=$.pair}post_process($,U=null,{add_special_tokens:ee=!0}={}){const se=U===null?this.single:this.pair;let Me=[],$e=[];for(const Xe of se)"SpecialToken"in Xe?ee&&(Me.push(Xe.SpecialToken.id),$e.push(Xe.SpecialToken.type_id)):"Sequence"in Xe&&(Xe.Sequence.id==="A"?(Me=(0,n.mergeArrays)(Me,$),$e=(0,n.mergeArrays)($e,new Array($.length).fill(Xe.Sequence.type_id))):Xe.Sequence.id==="B"&&(Me=(0,n.mergeArrays)(Me,U),$e=(0,n.mergeArrays)($e,new Array(U.length).fill(Xe.Sequence.type_id))));return{tokens:Me,token_type_ids:$e}}}class Pe extends Ce{post_process($,U=null){return U&&($=(0,n.mergeArrays)($,U)),{tokens:$}}}class We extends Ce{constructor($){super($),this.processors=$.processors.map(U=>Ce.fromConfig(U))}post_process($,U=null,ee={}){let se;for(const Me of this.processors)if(Me instanceof Pe)$=Me.post_process($).tokens,U&&(U=Me.post_process(U).tokens);else{const $e=Me.post_process($,U,ee);$=$e.tokens,se=$e.token_type_ids}return{tokens:$,token_type_ids:se}}}class Fe extends s.Callable{constructor($){super(),this.config=$,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=$.trim_offsets}static fromConfig($){if($===null)return null;switch($.type){case"WordPiece":return new je($);case"Metaspace":return new Or($);case"ByteLevel":return new Oe($);case"Replace":return new tt($);case"ByteFallback":return new Re($);case"Fuse":return new rt($);case"Strip":return new Ze($);case"Sequence":return new ht($);case"CTC":return new at($);case"BPEDecoder":return new Nt($);default:throw new Error(`Unknown Decoder type: ${$.type}`)}}_call($){return this.decode($)}decode($){return this.decode_chain($).join("")}decode_chain($){throw Error("`decode_chain` should be implemented in subclass.")}}class tt extends Fe{decode_chain($){const U=f(this.config.pattern);return U===null?$:$.map(ee=>ee.replaceAll(U,this.config.content))}}class Re extends Fe{constructor($){super($),this.text_decoder=new TextDecoder}decode_chain($){const U=[];let ee=[];for(const se of $){let Me=null;if(se.length===6&&se.startsWith("<0x")&&se.endsWith(">")){const $e=parseInt(se.slice(3,5),16);isNaN($e)||(Me=$e)}if(Me!==null)ee.push(Me);else{if(ee.length>0){const $e=this.text_decoder.decode(Uint8Array.from(ee));U.push($e),ee=[]}U.push(se)}}if(ee.length>0){const se=this.text_decoder.decode(Uint8Array.from(ee));U.push(se),ee=[]}return U}}class rt extends Fe{decode_chain($){return[$.join("")]}}class Ze extends Fe{constructor($){super($),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain($){return $.map(U=>{let ee=0;for(let Me=0;Me<this.start&&U[Me]===this.content;++Me){ee=Me+1;continue}let se=U.length;for(let Me=0;Me<this.stop;++Me){const $e=U.length-Me-1;if(U[$e]===this.content){se=$e;continue}else break}return U.slice(ee,se)})}}class je extends Fe{constructor($){super($),this.cleanup=$.cleanup}decode_chain($){return $.map((U,ee)=>(ee!==0&&(U.startsWith(this.config.prefix)?U=U.replace(this.config.prefix,""):U=" "+U),this.cleanup&&(U=I(U)),U))}}class Oe extends Fe{constructor($){super($),this.byte_decoder=X,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string($){const U=$.join(""),ee=new Uint8Array([...U].map(Me=>this.byte_decoder[Me]));return this.text_decoder.decode(ee)}decode_chain($){const U=[];let ee=[];for(const se of $)this.added_tokens.find(Me=>Me.content===se)!==void 0?(ee.length>0&&(U.push(this.convert_tokens_to_string(ee)),ee=[]),U.push(se)):ee.push(se);return ee.length>0&&U.push(this.convert_tokens_to_string(ee)),U}}class at extends Fe{constructor($){super($),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string($){if($.length===0)return"";const U=[$[0]];for(let Me=1;Me<$.length;++Me)$[Me]!==U.at(-1)&&U.push($[Me]);let se=U.filter(Me=>Me!==this.pad_token).join("");return this.cleanup&&(se=I(se).replaceAll(this.word_delimiter_token," ").trim()),se}decode_chain($){return[this.convert_tokens_to_string($)]}}class ht extends Fe{constructor($){super($),this.decoders=$.decoders.map(U=>Fe.fromConfig(U))}decode_chain($){return this.decoders.reduce((U,ee)=>ee.decode_chain(U),$)}}class Nt extends Fe{constructor($){super($),this.suffix=this.config.suffix}decode_chain($){return $.map((U,ee)=>U.replaceAll(this.suffix,ee===$.length-1?"":" "))}}class kt extends Fe{decode_chain($){let U="";for(let ee=1;ee<$.length;ee+=2)U+=$[ee];return[U]}}class gr extends Q{constructor($){super(),this.replacement=$.replacement,this.strRep=$.str_rep||this.replacement,this.prepend_scheme=$.prepend_scheme??"always"}pre_tokenize_text($,{section_index:U=void 0}={}){let ee=$.replaceAll(" ",this.strRep);return!ee.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&U===0)&&(ee=this.strRep+ee),[ee]}}class Or extends Fe{constructor($){super($),this.replacement=$.replacement}decode_chain($){const U=[];for(let ee=0;ee<$.length;++ee){let se=$[ee].replaceAll(this.replacement," ");ee==0&&se.startsWith(" ")&&(se=se.substring(1)),U.push(se)}return U}}class Bt extends ne{constructor($){super($),this.charsmap=$.precompiled_charsmap}normalize($){return $=$.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),$=$.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),$.includes("～")?$=$.split("～").map(ee=>ee.normalize("NFKC")).join("～"):$=$.normalize("NFKC"),$}}class jr extends Q{constructor($){super(),this.tokenizers=$.pretokenizers.map(U=>Q.fromConfig(U))}pre_tokenize_text($,U){return this.tokenizers.reduce((ee,se)=>se.pre_tokenize(ee,U),[$])}}class Qs extends Q{constructor($){super()}pre_tokenize_text($,U){return $.match(/\w+|[^\w\s]+/g)||[]}}class Xs extends Q{constructor($){super()}pre_tokenize_text($,U){return b($)}}class Js extends Q{constructor($){super(),this.config=$,this.pattern=f(this.config.pattern),this.content=this.config.content}pre_tokenize_text($,U){return this.pattern===null?[$]:[$.replaceAll(this.pattern,this.config.content)]}}class ar extends Q{constructor($){super(),this._length=$.length}pre_tokenize_text($,U){const ee=[];for(let se=0;se<$.length;se+=this._length)ee.push($.slice(se,se+this._length));return ee}}const kr=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Jr(ue,$,U,ee){for(const se of Object.keys(ue)){const Me=$-ue[se].length,$e=U(se),Xe=new Array(Me).fill($e);ue[se]=ee==="right"?(0,n.mergeArrays)(ue[se],Xe):(0,n.mergeArrays)(Xe,ue[se])}}function Bs(ue,$){for(const U of Object.keys(ue))ue[U].length=$}class ft extends s.Callable{return_token_type_ids=!1;padding_side="right";constructor($,U){super(),this.config=U,this.normalizer=ne.fromConfig($.normalizer),this.pre_tokenizer=Q.fromConfig($.pre_tokenizer),this.model=H.fromConfig($.model,U),this.post_processor=Ce.fromConfig($.post_processor),this.decoder=Fe.fromConfig($.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ee of $.added_tokens){const se=new F(ee);this.added_tokens.push(se),this.model.tokens_to_ids.set(se.content,se.id),this.model.vocab[se.id]=se.content,se.special&&(this.special_tokens.push(se.content),this.all_special_ids.push(se.id))}if(this.additional_special_tokens=U.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_splitter=new l.DictionarySplitter(this.added_tokens.map(ee=>ee.content)),this.added_tokens_map=new Map(this.added_tokens.map(ee=>[ee.content,ee])),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.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=U.model_max_length,this.remove_space=U.remove_space,this.clean_up_tokenization_spaces=U.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=U.do_lowercase_and_remove_accent??!1,U.padding_side&&(this.padding_side=U.padding_side),this.add_bos_token=U.add_bos_token,this.add_eos_token=U.add_eos_token,this.legacy=!1,this.chat_template=U.chat_template??null,Array.isArray(this.chat_template)){const ee=Object.create(null);for(const{name:se,template:Me}of this.chat_template){if(typeof se!="string"||typeof Me!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ee[se]=Me}this.chat_template=ee}this._compiled_template_cache=new Map}getToken(...$){for(const U of $){const ee=this.config[U];if(ee)if(typeof ee=="object"){if(ee.__type==="AddedToken")return ee.content;throw Error(`Unknown token: ${ee}`)}else return ee}return null}static async from_pretrained($,{progress_callback:U=null,config:ee=null,cache_dir:se=null,local_files_only:Me=!1,revision:$e="main",legacy:Xe=null}={}){const Je=await d($,{progress_callback:U,config:ee,cache_dir:se,local_files_only:Me,revision:$e,legacy:Xe});return new this(...Je)}_call($,{text_pair:U=null,add_special_tokens:ee=!0,padding:se=!1,truncation:Me=null,max_length:$e=null,return_tensor:Xe=!0,return_token_type_ids:Je=null}={}){const Ye=Array.isArray($);let Ke;if(Ye){if($.length===0)throw Error("text array must be non-empty");if(U!==null){if(Array.isArray(U)){if($.length!==U.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");Ke=$.map((Et,rr)=>this._encode_plus(Et,{text_pair:U[rr],add_special_tokens:ee,return_token_type_ids:Je}))}else Ke=$.map(Et=>this._encode_plus(Et,{add_special_tokens:ee,return_token_type_ids:Je}))}else{if($==null)throw Error("text may not be null or undefined");if(Array.isArray(U))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");Ke=[this._encode_plus($,{text_pair:U,add_special_tokens:ee,return_token_type_ids:Je})]}if($e===null?$e=this.model_max_length:Me===null&&(se===!0?(console.warn("`max_length` is ignored when `padding: true` and there is no truncation strategy. To pad to max length, use `padding: 'max_length'`."),$e=this.model_max_length):se===!1&&(console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation: true` to explicitly truncate examples to max length."),Me=!0)),se===!0&&($e=Math.min((0,i.max)(Ke.map(Et=>Et.input_ids.length))[0],$e??1/0)),$e=Math.min($e,this.model_max_length??1/0),se||Me)for(let Et=0;Et<Ke.length;++Et)Ke[Et].input_ids.length!==$e&&(Ke[Et].input_ids.length>$e?Me&&Bs(Ke[Et],$e):se&&Jr(Ke[Et],$e,rr=>rr==="input_ids"?this.pad_token_id:0,this.padding_side));const $t={};if(Xe){if(!(se&&Me)&&Ke.some(rr=>{for(const br of Object.keys(rr))if(rr[br].length!==Ke[0][br]?.length)return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const Et=[Ke.length,Ke[0].input_ids.length];for(const rr of Object.keys(Ke[0]))$t[rr]=new a.Tensor("int64",BigInt64Array.from(Ke.flatMap(br=>br[rr]).map(BigInt)),Et)}else{for(const Et of Object.keys(Ke[0]))$t[Et]=Ke.map(rr=>rr[Et]);if(!Ye)for(const Et of Object.keys($t))$t[Et]=$t[Et][0]}return $t}_encode_text($){if($===null)return null;const U=this.added_tokens_splitter.split($);for(let se=0;se<U.length;++se){const Me=this.added_tokens_map.get(U[se]);Me&&(Me.lstrip&&se>0&&(U[se-1]=U[se-1].trimEnd()),Me.rstrip&&se<U.length-1&&(U[se+1]=U[se+1].trimStart()))}return U.flatMap((se,Me)=>{if(se.length===0)return[];if(this.added_tokens_map.has(se))return[se];if(this.remove_space===!0&&(se=se.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(se=v(se)),this.normalizer!==null&&(se=this.normalizer(se)),se.length===0)return[];const $e=this.pre_tokenizer!==null?this.pre_tokenizer(se,{section_index:Me}):[se];return this.model($e)})}_encode_plus($,{text_pair:U=null,add_special_tokens:ee=!0,return_token_type_ids:se=null}={}){const{tokens:Me,token_type_ids:$e}=this._tokenize_helper($,{pair:U,add_special_tokens:ee}),Xe=this.model.convert_tokens_to_ids(Me),Je={input_ids:Xe,attention_mask:new Array(Xe.length).fill(1)};return(se??this.return_token_type_ids)&&$e&&(Je.token_type_ids=$e),Je}_tokenize_helper($,{pair:U=null,add_special_tokens:ee=!1}={}){const se=this._encode_text($),Me=this._encode_text(U);return this.post_processor?this.post_processor(se,Me,{add_special_tokens:ee}):{tokens:(0,n.mergeArrays)(se??[],Me??[])}}tokenize($,{pair:U=null,add_special_tokens:ee=!1}={}){return this._tokenize_helper($,{pair:U,add_special_tokens:ee}).tokens}encode($,{text_pair:U=null,add_special_tokens:ee=!0,return_token_type_ids:se=null}={}){return this._encode_plus($,{text_pair:U,add_special_tokens:ee,return_token_type_ids:se}).input_ids}batch_decode($,U={}){return $ instanceof a.Tensor&&($=$.tolist()),$.map(ee=>this.decode(ee,U))}decode($,U={}){if($ instanceof a.Tensor&&($=y($)),!Array.isArray($)||$.length===0||!(0,n.isIntegralNumber)($[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single($,U)}decode_single($,{skip_special_tokens:U=!1,clean_up_tokenization_spaces:ee=null}){let se=this.model.convert_ids_to_tokens($);U&&(se=se.filter($e=>!this.special_tokens.includes($e)));let Me=this.decoder?this.decoder(se):se.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Me=Me.replaceAll(this.decoder.end_of_word_suffix," "),U&&(Me=Me.trim())),(ee??this.clean_up_tokenization_spaces)&&(Me=I(Me)),Me}get_chat_template({chat_template:$=null,tools:U=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const ee=this.chat_template;if($!==null&&Object.hasOwn(ee,$))$=ee[$];else if($===null)if(U!==null&&"tool_use"in ee)$=ee.tool_use;else if("default"in ee)$=ee.default;else throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(ee).sort()}.`)}else if($===null)if(this.chat_template)$=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return $}apply_chat_template($,{tools:U=null,documents:ee=null,chat_template:se=null,add_generation_prompt:Me=!1,tokenize:$e=!0,padding:Xe=!1,truncation:Je=!1,max_length:Ye=null,return_tensor:Ke=!0,return_dict:$t=!1,tokenizer_kwargs:Et={},...rr}={}){if(se=this.get_chat_template({chat_template:se,tools:U}),typeof se!="string")throw Error(`chat_template must be a string, but got ${typeof se}`);let br=this._compiled_template_cache.get(se);br===void 0&&(br=new c.Template(se),this._compiled_template_cache.set(se,br));const Jt=Object.create(null);for(const Ht of kr){const Hr=this.getToken(Ht);Hr&&(Jt[Ht]=Hr)}const sr=br.render({messages:$,add_generation_prompt:Me,tools:U,documents:ee,...Jt,...rr});if($e){const Ht=this._call(sr,{add_special_tokens:!1,padding:Xe,truncation:Je,max_length:Ye,return_tensor:Ke,...Et});return $t?Ht:Ht.input_ids}return sr}}class qt extends ft{return_token_type_ids=!0}class Ts extends ft{return_token_type_ids=!0}class Ps extends ft{return_token_type_ids=!0}class Gr extends ft{return_token_type_ids=!0}class yt extends ft{return_token_type_ids=!0}class Es extends ft{return_token_type_ids=!0}class C extends ft{return_token_type_ids=!0}class q extends ft{return_token_type_ids=!0}class R extends ft{return_token_type_ids=!0}class G extends ft{}class Z extends ft{}class ce extends ft{return_token_type_ids=!0;constructor($,U){super($,U),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class ye extends ft{return_token_type_ids=!0}class et extends ft{}class ut extends ft{}class He extends ft{}class Mt extends ft{constructor($,U){super($,U),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(ee=>this.languageRegex.test(ee)),this.lang_to_token=ee=>ee}_build_translation_inputs($,U,ee){return lr(this,$,U,ee)}}class qe extends Mt{}class Pt extends ft{}class It extends ft{}const Mr="▁";class pr extends ft{padding_side="left";constructor($,U){super($,U),this.legacy=U.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new gr({replacement:Mr,prepend_scheme:"first"}))}_encode_text($){if($===null)return null;if(this.legacy||$.length===0)return super._encode_text($);let U=super._encode_text(Mr+$.replaceAll(Mr," "));return U.length>1&&U[0]===Mr&&this.special_tokens.includes(U[1])&&(U=U.slice(1)),U}}class ir extends ft{}class Tr extends ft{}class Cs extends ft{}class Dr extends ft{}class Ss extends ft{}class Lr extends ft{}class zr extends ft{}class ns extends ft{}class wr extends ft{}function lr(ue,$,U,ee){if(!("language_codes"in ue)||!Array.isArray(ue.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in ue)||!(ue.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in ue)||typeof ue.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const se=ee.src_lang,Me=ee.tgt_lang;if(!ue.language_codes.includes(Me))throw new Error(`Target language code "${Me}" is not valid. Must be one of: {${ue.language_codes.join(", ")}}`);if(se!==void 0){if(!ue.language_codes.includes(se))throw new Error(`Source language code "${se}" is not valid. Must be one of: {${ue.language_codes.join(", ")}}`);for(const $e of ue.post_processor.config.single)if("SpecialToken"in $e&&ue.languageRegex.test($e.SpecialToken.id)){$e.SpecialToken.id=ue.lang_to_token(se);break}}return ee.forced_bos_token_id=ue.model.convert_tokens_to_ids([ue.lang_to_token(Me)])[0],ue._call($,U)}class Kr extends ft{constructor($,U){super($,U),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(ee=>this.languageRegex.test(ee)),this.lang_to_token=ee=>ee}_build_translation_inputs($,U,ee){return lr(this,$,U,ee)}}class os extends ft{constructor($,U){super($,U),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(ee=>this.languageRegex.test(ee)).map(ee=>ee.slice(2,-2)),this.lang_to_token=ee=>`__${ee}__`}_build_translation_inputs($,U,ee){return lr(this,$,U,ee)}}class Rs extends ft{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr($,{return_timestamps:U=!1,return_language:ee=!1,time_precision:se=null,force_full_sequences:Me=!0}={}){if(se===null)throw Error("Must specify time_precision");let $e=null;const Xe=U==="word";function Je(){return{language:$e,timestamp:[null,null],text:""}}const Ye=[];let Ke=Je(),$t=0;const Et=this.timestamp_begin,br=Et+1500;let Jt=[],sr=[],Ht=!1,Hr=null;const is=new Set(this.all_special_ids);for(const Yt of $){const hr=Yt.tokens,$r=Xe?Yt.token_timestamps:null;let Yr=null,bs=Et;if("stride"in Yt){const[vr,Zt,_r]=Yt.stride;if($t-=Zt,Hr=vr-_r,Zt&&(bs=Zt/se+Et),_r)for(let cr=hr.length-1;cr>=0;--cr){const Ur=Number(hr[cr]);if(Ur>=Et){if(Yr!==null&&(Ur-Et)*se<Hr)break;Yr=Ur}}}let yr=[],ls=[];for(let vr=0;vr<hr.length;++vr){const Zt=Number(hr[vr]);if(is.has(Zt)){const _r=this.decode([Zt]),cr=p.WHISPER_LANGUAGE_MAPPING.get(_r.slice(2,-2));if(cr!==void 0){if($e!==null&&cr!==$e&&!U){Jt.push(yr);const Ur=this.findLongestCommonSequence(Jt)[0],Ys=this.decode(Ur);Ke.text=Ys,Ye.push(Ke),Jt=[],yr=[],Ke=Je()}$e=Ke.language=cr}}else if(Zt>=Et&&Zt<=br){const _r=(Zt-Et)*se+$t,cr=(0,i.round)(_r,2);if(Yr!==null&&Zt>=Yr)Ht=!0;else if(Ht||Jt.length>0&&Zt<bs)Ht=!1;else if(Ke.timestamp[0]===null)Ke.timestamp[0]=cr;else if(cr!==Ke.timestamp[0]){Ke.timestamp[1]=cr,Jt.push(yr),Xe&&sr.push(ls);const[Ur,Ys]=this.findLongestCommonSequence(Jt,sr),Fs=this.decode(Ur);Ke.text=Fs,Xe&&(Ke.words=this.collateWordTimestamps(Ur,Ys,$e)),Ye.push(Ke),Jt=[],yr=[],sr=[],ls=[],Ke=Je()}}else if(yr.push(Zt),Xe){let _r=(0,i.round)($r[vr]+$t,2),cr;if(vr+1<$r.length){cr=(0,i.round)($r[vr+1]+$t,2);const Ur=this.decode([Zt]);x.test(Ur)&&(cr=(0,i.round)(Math.min(_r+se,cr),2))}else cr=null;ls.push([_r,cr])}}if("stride"in Yt){const[vr,Zt,_r]=Yt.stride;$t+=vr-_r}yr.length>0?(Jt.push(yr),Xe&&sr.push(ls)):Jt.every(vr=>vr.length===0)&&(Ke=Je(),Jt=[],yr=[],sr=[],ls=[])}if(Jt.length>0){if(Me&&U)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[Yt,hr]=this.findLongestCommonSequence(Jt,sr),$r=this.decode(Yt);Ke.text=$r,Xe&&(Ke.words=this.collateWordTimestamps(Yt,hr,$e)),Ye.push(Ke)}let Pr=Object.create(null);const ws=Ye.map(Yt=>Yt.text).join("");if(U||ee){for(let Yt=0;Yt<Ye.length;++Yt){const hr=Ye[Yt];U||delete hr.timestamp,ee||delete hr.language}if(Xe){const Yt=[];for(const hr of Ye)for(const $r of hr.words)Yt.push($r);Pr={chunks:Yt}}else Pr={chunks:Ye}}return[ws,Pr]}findLongestCommonSequence($,U=null){let ee=$[0],se=ee.length,Me=[];const $e=Array.isArray(U)&&U.length>0;let Xe=$e?[]:null,Je=$e?U[0]:null;for(let Ye=1;Ye<$.length;++Ye){const Ke=$[Ye];let $t=0,Et=[se,se,0,0];const rr=Ke.length;for(let Pr=1;Pr<se+rr;++Pr){const ws=Math.max(0,se-Pr),Yt=Math.min(se,se+rr-Pr),hr=ee.slice(ws,Yt),$r=Math.max(0,Pr-se),Yr=Math.min(rr,Pr),bs=Ke.slice($r,Yr);if(hr.length!==bs.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let yr;$e?yr=hr.filter((Zt,_r)=>Zt===bs[_r]&&Je[ws+_r]<=U[Ye][$r+_r]).length:yr=hr.filter((Zt,_r)=>Zt===bs[_r]).length;const ls=Pr/1e4,vr=yr/Pr+ls;yr>1&&vr>$t&&($t=vr,Et=[ws,Yt,$r,Yr])}const[br,Jt,sr,Ht]=Et,Hr=Math.floor((Jt+br)/2),is=Math.floor((Ht+sr)/2);Me.push(...ee.slice(0,Hr)),ee=Ke.slice(is),se=ee.length,$e&&(Xe.push(...Je.slice(0,Hr)),Je=U[Ye].slice(is))}return Me.push(...ee),$e?(Xe.push(...Je),[Me,Xe]):[Me,[]]}collateWordTimestamps($,U,ee){const[se,Me,$e]=this.combineTokensIntoWords($,ee),Xe=[];for(let Je=0;Je<se.length;++Je){const Ye=$e[Je];Xe.push({text:se[Je],timestamp:[U[Ye.at(0)][0],U[Ye.at(-1)][1]]})}return Xe}combineTokensIntoWords($,U,ee=`"'“¡¿([{-`,se=`"'.。,，!！?？:：”)]}、`){U=U??"english";let Me,$e,Xe;return["chinese","japanese","thai","lao","myanmar"].includes(U)?[Me,$e,Xe]=this.splitTokensOnUnicode($):[Me,$e,Xe]=this.splitTokensOnSpaces($),this.mergePunctuations(Me,$e,Xe,ee,se)}decode($,U){let ee;return U?.decode_with_timestamps?($ instanceof a.Tensor&&($=y($)),ee=this.decodeWithTimestamps($,U)):ee=super.decode($,U),ee}decodeWithTimestamps($,U){const ee=U?.time_precision??.02,se=Array.from(this.all_special_ids).at(-1)+1;let Me=[[]];for(let $e of $)if($e=Number($e),$e>=se){const Xe=(($e-se)*ee).toFixed(2);Me.push(`<|${Xe}|>`),Me.push([])}else Me[Me.length-1].push($e);return Me=Me.map($e=>typeof $e=="string"?$e:super.decode($e,U)),Me.join("")}splitTokensOnUnicode($){const U=this.decode($,{decode_with_timestamps:!0}),ee="�",se=[],Me=[],$e=[];let Xe=[],Je=[],Ye=0;for(let Ke=0;Ke<$.length;++Ke){const $t=$[Ke];Xe.push($t),Je.push(Ke);const Et=this.decode(Xe,{decode_with_timestamps:!0});(!Et.includes(ee)||U[Ye+Et.indexOf(ee)]===ee)&&(se.push(Et),Me.push(Xe),$e.push(Je),Xe=[],Je=[],Ye+=Et.length)}return[se,Me,$e]}splitTokensOnSpaces($){const[U,ee,se]=this.splitTokensOnUnicode($),Me=[],$e=[],Xe=[],Je=new RegExp(`^[${P}]$`,"gu");for(let Ye=0;Ye<U.length;++Ye){const Ke=U[Ye],$t=ee[Ye],Et=se[Ye],rr=$t[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),br=Ke.startsWith(" "),Jt=Ke.trim(),sr=Je.test(Jt);if(rr||br||sr||Me.length===0)Me.push(Ke),$e.push($t),Xe.push(Et);else{const Ht=Me.length-1;Me[Ht]+=Ke,$e[Ht].push(...$t),Xe[Ht].push(...Et)}}return[Me,$e,Xe]}mergePunctuations($,U,ee,se,Me){const $e=structuredClone($),Xe=structuredClone(U),Je=structuredClone(ee);let Ye=$e.length-2,Ke=$e.length-1;for(;Ye>=0;)$e[Ye].startsWith(" ")&&se.includes($e[Ye].trim())?($e[Ke]=$e[Ye]+$e[Ke],Xe[Ke]=(0,n.mergeArrays)(Xe[Ye],Xe[Ke]),Je[Ke]=(0,n.mergeArrays)(Je[Ye],Je[Ke]),$e[Ye]="",Xe[Ye]=[],Je[Ye]=[]):Ke=Ye,--Ye;for(Ye=0,Ke=1;Ke<$e.length;)!$e[Ye].endsWith(" ")&&Me.includes($e[Ke])?($e[Ye]+=$e[Ke],Xe[Ye]=(0,n.mergeArrays)(Xe[Ye],Xe[Ke]),Je[Ye]=(0,n.mergeArrays)(Je[Ye],Je[Ke]),$e[Ke]="",Xe[Ke]=[],Je[Ke]=[]):Ye=Ke,++Ke;return[$e.filter($t=>$t),Xe.filter($t=>$t.length>0),Je.filter($t=>$t.length>0)]}}class ks extends ft{}class $s extends ft{}class Is extends ft{}class as extends ft{constructor($,U){super($,U),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(ee=>this.languageRegex.test(ee)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text($){if($===null)return null;const[U,...ee]=$.trim().split(this.languageRegex);if(ee.length===0)return super._encode_text(U);if(ee.length===2){const[se,Me]=ee;return this.supported_language_codes.includes(se)||console.warn(`Unsupported language code "${se}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,n.mergeArrays)([se],super._encode_text(Me))}}}class Nr extends ft{}class ze extends ft{}class Ue extends ft{}class nt extends ft{}class Kt extends ft{}class js extends ft{constructor($,U){super($,U),this.decoder=new kt({})}}class As extends ft{}class Ns extends ft{}class Nn{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:et,DistilBertTokenizer:G,CamembertTokenizer:Z,DebertaTokenizer:yt,DebertaV2Tokenizer:Es,BertTokenizer:qt,HerbertTokenizer:C,ConvBertTokenizer:q,RoFormerTokenizer:R,XLMTokenizer:ce,ElectraTokenizer:ye,MobileBertTokenizer:Ps,SqueezeBertTokenizer:Gr,AlbertTokenizer:Ts,GPT2Tokenizer:ut,BartTokenizer:He,MBartTokenizer:Mt,MBart50Tokenizer:qe,RobertaTokenizer:Pt,WhisperTokenizer:Rs,CodeGenTokenizer:ks,CLIPTokenizer:$s,SiglipTokenizer:Is,MarianTokenizer:as,BloomTokenizer:It,NllbTokenizer:Kr,M2M100Tokenizer:os,LlamaTokenizer:pr,CodeLlamaTokenizer:ir,XLMRobertaTokenizer:Tr,MPNetTokenizer:Cs,FalconTokenizer:Dr,GPTNeoXTokenizer:Ss,EsmTokenizer:Lr,Wav2Vec2CTCTokenizer:Nr,BlenderbotTokenizer:ze,BlenderbotSmallTokenizer:Ue,SpeechT5Tokenizer:nt,NougatTokenizer:Kt,VitsTokenizer:js,Qwen2Tokenizer:zr,GemmaTokenizer:ns,Grok1Tokenizer:wr,CohereTokenizer:As,MgpstrTokenizer:Ns,PreTrainedTokenizer:ft};static async from_pretrained($,{progress_callback:U=null,config:ee=null,cache_dir:se=null,local_files_only:Me=!1,revision:$e="main",legacy:Xe=null}={}){const[Je,Ye]=await d($,{progress_callback:U,config:ee,cache_dir:se,local_files_only:Me,revision:$e,legacy:Xe}),Ke=Ye.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let $t=this.TOKENIZER_CLASS_MAPPING[Ke];return $t||(console.warn(`Unknown tokenizer class "${Ke}", attempting to construct from base class.`),$t=ft),new $t(Je,Ye)}}}),"./src/utils/audio.js":((e,r,t)=>{t.r(r),t.d(r,{RawAudio:()=>W,hamming:()=>u,hanning:()=>d,mel_filter_bank:()=>k,read_audio:()=>c,spectrogram:()=>S,window_function:()=>O});var s=t("./src/utils/hub.js"),n=t("./src/utils/maths.js"),o=t("./src/utils/core.js"),i=t("./src/env.js"),a=t("./src/utils/tensor.js"),l=t("?7992");async function c(B,Y){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. 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