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te=0,se=1,X=2,R=3,I=0,B=D,k=D*2,ue=-1;ee==="RGBA"?(I=0,B=D,k=D*2,ue=D*3):ee==="RGB"?(I=0,B=D,k=D*2):ee==="RBG"&&(I=0,k=D,B=D*2),j=Ce.createImageData(S,V);for(let ve=0;ve<V*S;te+=H,se+=H,X+=H,R+=H,ve++)j.data[te]=(ye.data[I++]-ce[0])*me[0],j.data[se]=(ye.data[B++]-ce[1])*me[1],j.data[X]=(ye.data[k++]-ce[2])*me[2],j.data[R]=ue===-1?255:(ye.data[ue++]-ce[3])*me[3]}else throw new Error("Can not access image data");return j}},"./node_modules/onnxruntime-common/dist/esm/tensor-conversion.js":($t,Ee,N)=>{N.r(Ee)},"./node_modules/onnxruntime-common/dist/esm/tensor-factory-impl.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{bufferToTensor:()=>fe,tensorFromGpuBuffer:()=>Ce,tensorFromImage:()=>ye,tensorFromPinnedBuffer:()=>j,tensorFromTexture:()=>Te});var O=N("./node_modules/onnxruntime-common/dist/esm/tensor-impl.js");const fe=(S,V)=>{if(S===void 0)throw new Error("Image buffer must be defined");if(V.height===void 0||V.width===void 0)throw new Error("Image height and width must be 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O.Tensor("float32",se,[1,3,A,ee])},ye=async(S,V)=>{const A=typeof HTMLImageElement<"u"&&S instanceof HTMLImageElement,ee=typeof ImageData<"u"&&S instanceof ImageData,ne=typeof ImageBitmap<"u"&&S instanceof ImageBitmap,me=typeof S=="string";let ce,D=V??{};const H=()=>{if(typeof document<"u")return document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},te=se=>se instanceof HTMLCanvasElement||se instanceof OffscreenCanvas?se.getContext("2d"):null;if(A){const se=H();se.width=S.width,se.height=S.height;const X=te(se);if(X!=null){let R=S.height,I=S.width;if(V!==void 0&&V.resizedHeight!==void 0&&V.resizedWidth!==void 0&&(R=V.resizedHeight,I=V.resizedWidth),V!==void 0){if(D=V,V.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");D.tensorFormat="RGBA",D.height=R,D.width=I}else D.tensorFormat="RGBA",D.height=R,D.width=I;X.drawImage(S,0,0),ce=X.getImageData(0,0,I,R).data}else throw new Error("Can not access image data")}else if(ee){let se,X;if(V!==void 0&&V.resizedWidth!==void 0&&V.resizedHeight!==void 0?(se=V.resizedHeight,X=V.resizedWidth):(se=S.height,X=S.width),V!==void 0&&(D=V),D.format="RGBA",D.height=se,D.width=X,V!==void 0){const R=H();R.width=X,R.height=se;const I=te(R);if(I!=null)I.putImageData(S,0,0),ce=I.getImageData(0,0,X,se).data;else throw new Error("Can not access image data")}else ce=S.data}else if(ne){if(V===void 0)throw new Error("Please provide image config with format for Imagebitmap");const se=H();se.width=S.width,se.height=S.height;const X=te(se);if(X!=null){const R=S.height,I=S.width;return X.drawImage(S,0,0,I,R),ce=X.getImageData(0,0,I,R).data,D.height=R,D.width=I,fe(ce,D)}else throw new Error("Can not access image data")}else{if(me)return new Promise((se,X)=>{const R=H(),I=te(R);if(!S||!I)return X();const B=new Image;B.crossOrigin="Anonymous",B.src=S,B.onload=()=>{R.width=B.width,R.height=B.height,I.drawImage(B,0,0,R.width,R.height);const k=I.getImageData(0,0,R.width,R.height);D.height=R.height,D.width=R.width,se(fe(k.data,D))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(ce!==void 0)return fe(ce,D);throw new Error("Input data provided is not supported - aborted tensor creation")},Te=(S,V)=>{const{width:A,height:ee,download:ne,dispose:me}=V,ce=[1,ee,A,4];return new O.Tensor({location:"texture",type:"float32",texture:S,dims:ce,download:ne,dispose:me})},Ce=(S,V)=>{const{dataType:A,dims:ee,download:ne,dispose:me}=V;return new O.Tensor({location:"gpu-buffer",type:A??"float32",gpuBuffer:S,dims:ee,download:ne,dispose:me})},j=(S,V,A)=>new O.Tensor({location:"cpu-pinned",type:S,data:V,dims:A??[V.length]})},"./node_modules/onnxruntime-common/dist/esm/tensor-factory.js":($t,Ee,N)=>{N.r(Ee)},"./node_modules/onnxruntime-common/dist/esm/tensor-impl-type-mapping.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP:()=>fe,NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP:()=>O,checkTypedArray:()=>Te});const O=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),fe=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let ye=!1;const Te=()=>{if(!ye){ye=!0;const Ce=typeof BigInt64Array<"u"&&BigInt64Array.from,j=typeof BigUint64Array<"u"&&BigUint64Array.from,S=typeof Float16Array<"u"&&Float16Array.from;Ce&&(O.set("int64",BigInt64Array),fe.set(BigInt64Array,"int64")),j&&(O.set("uint64",BigUint64Array),fe.set(BigUint64Array,"uint64")),S?(O.set("float16",Float16Array),fe.set(Float16Array,"float16")):O.set("float16",Uint16Array)}}},"./node_modules/onnxruntime-common/dist/esm/tensor-impl.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{Tensor:()=>Ce});var O=N("./node_modules/onnxruntime-common/dist/esm/tensor-conversion-impl.js"),fe=N("./node_modules/onnxruntime-common/dist/esm/tensor-factory-impl.js"),ye=N("./node_modules/onnxruntime-common/dist/esm/tensor-impl-type-mapping.js"),Te=N("./node_modules/onnxruntime-common/dist/esm/tensor-utils-impl.js");class Ce{constructor(S,V,A){(0,ye.checkTypedArray)();let ee,ne;if(typeof S=="object"&&"location"in S)switch(this.dataLocation=S.location,ee=S.type,ne=S.dims,S.location){case"cpu-pinned":{const ce=ye.NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(ee);if(!ce)throw new TypeError(`unsupported type "${ee}" to create tensor from 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${S}.`);if(Array.isArray(V)){if(S==="float16"&&H===Uint16Array)throw new TypeError("Creating a float16 tensor from number array is not supported. 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Make sure you're using the correct configuration & WebAssembly files.";class Te{constructor(j,S,V){this.handler=j,this.hasOptimizerModel=S,this.hasEvalModel=V}get trainingInputNames(){return this.handler.inputNames}get trainingOutputNames(){return this.handler.outputNames}get evalInputNames(){if(this.hasEvalModel)return this.handler.evalInputNames;throw new Error("This training session has no evalModel loaded.")}get evalOutputNames(){if(this.hasEvalModel)return this.handler.evalOutputNames;throw new Error("This training session has no evalModel loaded.")}static async create(j,S){const V=j.evalModel||"",A=j.optimizerModel||"",ee=S||{},[ne,me]=await(0,O.resolveBackendAndExecutionProviders)(ee);if(ne.createTrainingSessionHandler){const ce=await ne.createTrainingSessionHandler(j.checkpointState,j.trainModel,V,A,me);return new Te(ce,!!j.optimizerModel,!!j.evalModel)}else throw new Error(ye)}typeNarrowingForRunStep(j,S,V,A,ee){const ne={};let me={};if(typeof V!="object"||V===null||V instanceof fe.Tensor||Array.isArray(V))throw new TypeError("'feeds' must be an object that use input names as keys and OnnxValue as corresponding values.");let ce=!0;if(typeof A=="object"){if(A===null)throw new TypeError("Unexpected argument[1]: cannot be null.");if(A instanceof fe.Tensor)throw new TypeError("'fetches' cannot be a Tensor");if(Array.isArray(A)){if(A.length===0)throw new TypeError("'fetches' cannot be an empty array.");ce=!1;for(const D of A){if(typeof D!="string")throw new TypeError("'fetches' must be a string array or an object.");if(S.indexOf(D)===-1)throw new RangeError(`'fetches' contains invalid output name: ${D}.`);ne[D]=null}if(typeof ee=="object"&&ee!==null)me=ee;else if(typeof ee<"u")throw new TypeError("'options' must be an object.")}else{let D=!1;const H=Object.getOwnPropertyNames(A);for(const te of S)if(H.indexOf(te)!==-1){const se=A[te];(se===null||se instanceof fe.Tensor)&&(D=!0,ce=!1,ne[te]=se)}if(D){if(typeof ee=="object"&&ee!==null)me=ee;else if(typeof ee<"u")throw new TypeError("'options' must be an object.")}else me=A}}else if(typeof A<"u")throw new TypeError("Unexpected argument[1]: must be 'fetches' or 'options'.");for(const D of j)if(typeof V[D]>"u")throw new Error(`input '${D}' is missing in 'feeds'.`);if(ce)for(const D of S)ne[D]=null;return[ne,me]}convertHandlerReturnTypeToMapOfTensors(j){const S={};for(const V in j)if(Object.hasOwnProperty.call(j,V)){const A=j[V];A instanceof fe.Tensor?S[V]=A:S[V]=new fe.Tensor(A.type,A.data,A.dims)}return S}async lazyResetGrad(){await this.handler.lazyResetGrad()}async runTrainStep(j,S,V){const[A,ee]=this.typeNarrowingForRunStep(this.trainingInputNames,this.trainingOutputNames,j,S,V),ne=await this.handler.runTrainStep(j,A,ee);return this.convertHandlerReturnTypeToMapOfTensors(ne)}async runOptimizerStep(j){if(this.hasOptimizerModel)await this.handler.runOptimizerStep(j||{});else throw new Error("This TrainingSession has no OptimizerModel loaded.")}async runEvalStep(j,S,V){if(this.hasEvalModel){const[A,ee]=this.typeNarrowingForRunStep(this.evalInputNames,this.evalOutputNames,j,S,V),ne=await this.handler.runEvalStep(j,A,ee);return this.convertHandlerReturnTypeToMapOfTensors(ne)}else throw new Error("This TrainingSession has no EvalModel loaded.")}async getParametersSize(j=!0){return this.handler.getParametersSize(j)}async loadParametersBuffer(j,S=!0){const V=await this.getParametersSize(S);if(j.length!==4*V)throw new Error("Size of the buffer passed into loadParametersBuffer must match the number of parameters in the model. 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 * ONNX Runtime Web v1.20.0-dev.20240925-a47254eaef
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
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  fn main(${a}) {
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`)+this.internalVariables.map(e=>e.impl()).join(`
`)}get variablesInfo(){if(this.uniforms.length===0)return;let e=t=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(t)];return this.uniforms.map(t=>[e(t.type),t.length??1])}},to=(e,t)=>new Mi(e,t),_s=(e,t)=>{let r=e.length,n=[];for(let s=0;s<r;s++){let a=r-1-s,i=e[a]||1;(t[t.length-1-s]||1)>1&&i===1&&n.unshift(a)}return n}}),ro,vi,ss,no,md,In,_d,so,Qn=j(()=>{Xt(),Qt(),pr(),ar(),ro=e=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.")},vi=(e,t)=>t&&t.length!==e?[...new Array(e).keys()].reverse():t,ss=(e,t)=>Oe.sortBasedOnPerm(e,vi(e.length,t)),no=(e,t,r,n)=>{let s=`fn perm(i: ${n.type.indices}) -> ${r.type.indices} {
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  ${W.registerUniform("output_size","u32").declareVariables(M,E)}
  var<workgroup> tile : array<array<${E.type.value}, ${$+1}>, ${$}>;
  ${W.mainStart([$,$,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] = ${M.getByIndices(`${M.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]) {
      ${E.setByIndices(`${E.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
    }
  }`:L=W=>`
  ${W.registerUniform("output_size","u32").declareVariables(M,E)}

  ${no(s,n,M,E)}

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

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

    ${E.setByOffset("global_idx",M.getByIndices("aIndices"))}
  }`,{name:w?"TransposeShared":"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:()=>{let W=Oe.size(a);return{outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:w?{x:Math.ceil(l[1]/$),y:Math.ceil(l[0]/$)}:{x:Math.ceil(W/64)},programUniforms:[{type:12,data:W},...It(g,l)]}},getShaderSource:L}},_d=(e,t)=>{ro(e.inputs),e.compute(In(e.inputs[0],t.perm))},so=e=>qt({perm:e.perm})}),io,ao,oo,lo,uo,xi,co,po,Ti,ho,Fn,Ei,fo,mo,Ci,_o,go,$i,wo,yo,Si,gd=j(()=>{Xt(),Qt(),ar(),Li(),Qn(),io={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"},ao={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"},oo={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},lo={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},uo=(e,t)=>{let r=[];for(let n=t-e;n<t;++n)r.push(n);return r},xi=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++)t.indexOf(a)===-1&&r.push(e[a]);let s=t.map(a=>e[a]);return[r,s]},co=(e,t)=>{let r=e.length+t.length,n=[],s=0;for(let a=0;a<r;a++)t.indexOf(a)===-1?n.push(e[s++]):n.push(1);return n},po=(e,t)=>{for(let r=0;r<e.length;++r)if(e[e.length-r-1]!==t-1-r)return!1;return!0},Ti=(e,t)=>{let r=[];if(!po(e,t)){for(let n=0;n<t;++n)e.indexOf(n)===-1&&r.push(n);e.forEach(n=>r.push(n))}return r},ho=(e,t,r,n,s,a,i)=>{let u=r[0].dims,d=Oe.size(a),p=Oe.size(i),w=et("_A",r[0].dataType,u),g=Ut("output",s,a),l=32,M=`
          var<workgroup> aBestValues : array<f32, ${l}>;
       `;return{name:e,shaderCache:t,getShaderSource:E=>`
        ${E.registerUniform("reduceSize","u32").declareVariables(w,g)}
        ${M}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${E.mainStart(l)}

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

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

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

         if (local_idx == 0u) {
          ${g.setByOffset("outputIndex",`${n==="mean"?`${g.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${g.type.storage}(${lo[n]})`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:a,dataType:s}],dispatchGroup:{x:d},programUniforms:[{type:12,data:p}]})}},Fn=(e,t,r,n)=>{let s=e.inputs.length===1?r:Pi(e.inputs,r),a=s.axes;a.length===0&&!s.noopWithEmptyAxes&&(a=e.inputs[0].dims.map((M,E)=>E));let i=Oe.normalizeAxes(a,e.inputs[0].dims.length),u=i,d=e.inputs[0],p=Ti(u,e.inputs[0].dims.length);p.length>0&&(d=e.compute(In(e.inputs[0],p),{inputs:[0],outputs:[-1]})[0],u=uo(u.length,d.dims.length));let[w,g]=xi(d.dims,u),l=w;s.keepDims&&(l=co(w,i)),e.compute(ho(t,{hint:s.cacheKey,inputDependencies:["type"]},[d],n,e.inputs[0].dataType,l,g),{inputs:[d]})},Ei=(e,t)=>{Fn(e,"ReduceMeanShared",t,"mean")},fo=(e,t)=>{Fn(e,"ReduceL1Shared",t,"l1")},mo=(e,t)=>{Fn(e,"ReduceL2Shared",t,"l2")},Ci=(e,t)=>{Fn(e,"ReduceLogSumExpShared",t,"logSumExp")},_o=(e,t)=>{Fn(e,"ReduceMaxShared",t,"max")},go=(e,t)=>{Fn(e,"ReduceMinShared",t,"min")},$i=(e,t)=>{Fn(e,"ReduceProdShared",t,"prod")},wo=(e,t)=>{Fn(e,"ReduceSumShared",t,"sum")},yo=(e,t)=>{Fn(e,"ReduceSumSquareShared",t,"sumSquare")},Si=(e,t)=>{Fn(e,"ReduceLogSumShared",t,"logSum")}}),On,ki,ei,Pi,kn,bo,Mo,Ai,vo,xo,Ii,To,Eo,Fi,Co,zn,Oi,$o,So,zi,ko,Po,Di,Ao,Io,Bi,Li=j(()=>{Xt(),Qt(),pr(),ar(),gd(),On=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},ki=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],ei=(e,t,r,n,s,a,i=!1,u=!1)=>{let d=[],p=r[0].dims,w=p.length,g=Oe.normalizeAxes(s,w),l=!u&&g.length===0;p.forEach(($,L)=>{l||g.indexOf(L)>=0?i&&d.push(1):d.push($)});let M=d.length,E=Oe.size(d);return{name:e,shaderCache:t,getShaderSource:$=>{let L=[],W=et("_A",r[0].dataType,w),z=Ut("output",a,M),ie=n(W,z,g),J=ie[2];for(let oe=0,Ge=0;oe<w;oe++)l||g.indexOf(oe)>=0?(i&&Ge++,J=`for(var j${oe}: u32 = 0; j${oe} < ${p[oe]}; j${oe}++) {
                  ${ie[2].includes("last_index")?`let last_index = j${oe};`:""}
                  ${W.indicesSet("input_indices",oe,`j${oe}`)}
                  ${J}
                }`):(L.push(`${W.indicesSet("input_indices",oe,z.indicesGet("output_indices",Ge))};`),Ge++);return`

        ${$.registerUniform("output_size","u32").declareVariables(W,z)}

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

          ${L.join(`
`)}
          ${ie[0]}       // init ops for reduce max/min
          ${ie[1]}
          ${J}
          ${ie[3]}
          ${ie.length===4?z.setByOffset("global_idx","value"):ie.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:d,dataType:a}],dispatchGroup:{x:Math.ceil(E/64)},programUniforms:[{type:12,data:E},...It(p,d)]})}},Pi=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),qt({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},kn=(e,t,r,n)=>{let s=e.inputs,a=s.length===1?r:Pi(s,r);e.compute(ei(t,{hint:a.cacheKey,inputDependencies:["rank"]},[s[0]],a.noopWithEmptyAxes&&a.axes.length===0?ki:n,a.axes,s[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},bo=(e,t)=>{On(e.inputs),kn(e,"ReduceLogSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},Mo=(e,t)=>{On(e.inputs),kn(e,"ReduceL1",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${r.getByIndices("input_indices")});`,""])},Ai=(e,t)=>{On(e.inputs),kn(e,"ReduceL2",t,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},vo=(e,t)=>{On(e.inputs),kn(e,"ReduceLogSumExp",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${r.getByIndices("input_indices")});`,"value = log(value);"])},xo=(e,t)=>{On(e.inputs),kn(e,"ReduceMax",t,(r,n,s)=>{let a=[];for(let i=0;i<r.rank;i++)(s.indexOf(i)>=0||s.length===0)&&a.push(r.indicesSet("input_indices",i,0));return[`${a.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = max(value, ${r.getByIndices("input_indices")});`,""]})},Ii=(e,t)=>{On(e.inputs),kn(e,"ReduceMean",t,(r,n,s)=>{let a=1;for(let i=0;i<r.rank;i++)(s.indexOf(i)>=0||s.length===0)&&(a*=e.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${a});`]})},To=(e,t)=>{On(e.inputs),kn(e,"ReduceMin",t,(r,n,s)=>{let a=[];for(let i=0;i<r.rank;i++)(s.indexOf(i)>=0||s.length===0)&&a.push(`input_indices[${i}] = 0;`);return[`${a.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},Eo=(e,t)=>{On(e.inputs),kn(e,"ReduceProd",t,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},Fi=(e,t)=>{On(e.inputs),kn(e,"ReduceSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},Co=(e,t)=>{On(e.inputs),kn(e,"ReduceSumSquare",t,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},zn=(e,t,r)=>{if(t.length===0)return r;let n=1,s=1;for(let a=0;a<t.length;a++)t.indexOf(a)===-1?n*=e[a]:s*=e[a];return s<32&&n>1024},Oi=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ii(e,t):Ei(e,t)},$o=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Mo(e,t):fo(e,t)},So=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ai(e,t):mo(e,t)},zi=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?vo(e,t):Ci(e,t)},ko=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?xo(e,t):_o(e,t)},Po=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?To(e,t):go(e,t)},Di=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Eo(e,t):$i(e,t)},Ao=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Fi(e,t):wo(e,t)},Io=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Co(e,t):yo(e,t)},Bi=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?bo(e,t):Si(e,t)}}),Ri,Ni,Fo,ji,Oo=j(()=>{Xt(),pr(),Li(),Ri=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Ni=(e,t)=>{Ri(e.inputs);let r=(n,s,a)=>{let i=[];for(let u=0;u<n.rank;u++)(a.indexOf(u)>=0||a.length===0)&&i.push(`input_indices[${u}] = 0;`);return[`${i.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",s.setByOffset("global_idx","best_index")]};e.compute(ei("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Fo=(e,t)=>{Ri(e.inputs);let r=(n,s,a)=>{let i=[];for(let u=0;u<n.rank;u++)(a.indexOf(u)>=0||a.length===0)&&i.push(`input_indices[${u}] = 0;`);return[`${i.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {
         value = ${n.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",s.setByOffset("global_idx","best_index")]};e.compute(ei("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},ji=e=>qt(e)}),zo,Do,Vi,Bo,As,Ui,Lo,Wi=j(()=>{Xt(),Qt(),P(),ar(),zo=(e,t)=>{let r=e[0],n=e[1],s=e[2],a=e[3],i=e[4],u=e[5];if(i&&u)throw new Error("Attention cannot have both past and attention_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let d=r.dims[0],p=r.dims[1],w=r.dims[2];if(s.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==w)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(s.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let g=s.dims[0]/3,l=g,M=l;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let ie of t.qkvHiddenSizes)if(ie%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");g=t.qkvHiddenSizes[0],l=t.qkvHiddenSizes[1],M=t.qkvHiddenSizes[2]}let E=p;if(g!==l)throw new Error("qkv_hidden_sizes first element should be same as the second");if(s.dims[0]!==g+l+M)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let $=0;if(i){if(l!==M)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==d)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==l/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||($=i.dims[3])}let L=E+$,W=-1,z=0;if(a)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");if(u){if(u.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(u.dims[0]!==d||u.dims[1]!==t.numHeads||u.dims[2]!==p||u.dims[3]!==L)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:d,sequenceLength:p,pastSequenceLength:$,kvSequenceLength:E,totalSequenceLength:L,maxSequenceLength:W,inputHiddenSize:w,hiddenSize:g,vHiddenSize:M,headSize:Math.floor(g/t.numHeads),vHeadSize:Math.floor(M/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:z,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Do=(e,t,r)=>{let n=br(r),s=64,a=r/n;a<s&&(s=32);let i=Math.ceil(r/n/s),u=[{type:1,data:1/r},{type:12,data:a},{type:12,data:i}],d=Ar(e.dataType,n),p=Cr(1,n),w=["type"],g=l=>{let M=Ut("x",e.dataType,e.dims,n),E=Cr(e.dataType),$=[{name:"d_inv",type:"f32"},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${s}>;
  var<workgroup> thread_sum: array<f32, ${s}>;
  ${l.registerUniforms($).declareVariables(M)}
  ${l.mainStart([s,1,1])}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${s}) * uniforms.d_comp + local_offset;

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

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

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

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

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        x[offset + i] = ${M.type.value}(${E}(uniforms.d_inv));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) {
        var f32input = ${p}(x[offset + i]);
        x[offset + i] = ${M.type.value}(exp(f32input - max_value) / sum);
      }
    }
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${d};${n}`,inputDependencies:w},getShaderSource:g,getRunData:()=>({outputs:[],dispatchGroup:{x:t},programUniforms:u})}},Vi=(e,t,r,n,s,a,i,u)=>{let d=u+a.kvSequenceLength,p=[a.batchSize,a.numHeads,a.sequenceLength,d],w=a.kvNumHeads===void 0&&e>1&&n,g=w?[a.batchSize,a.numHeads,d,a.headSize]:void 0,l=i.scale===0?1/Math.sqrt(a.headSize):i.scale,M=br(a.headSize),E=a.headSize/M,$=12,L={x:Math.ceil(d/$),y:Math.ceil(a.sequenceLength/$),z:a.batchSize*a.numHeads},W=[{type:12,data:a.sequenceLength},{type:12,data:E},{type:12,data:d},{type:12,data:a.numHeads},{type:1,data:l},{type:12,data:u},{type:12,data:a.kvSequenceLength}],z=w&&n&&Oe.size(n.dims)>0,ie=["type","type"];z&&ie.push("type"),s&&ie.push("type");let J=[{dims:p,dataType:t.dataType,gpuDataType:0}];w&&J.push({dims:g,dataType:t.dataType,gpuDataType:0});let oe=Ge=>{let De=et("q",t.dataType,t.dims,M),pt=et("key",r.dataType,r.dims,M),Dt=[De,pt];if(z){let jr=et("past_key",n.dataType,n.dims,M);Dt.push(jr)}s&&Dt.push(et("attention_bias",s.dataType,s.dims));let Vt=Ut("output",t.dataType,p),lr=[Vt];w&&lr.push(Ut("present_key",t.dataType,g,M));let fr=Cr(1,M),tr=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"}];return`
  const TILE_SIZE = ${$}u;

  var<workgroup> tileQ: array<${De.type.storage}, ${$*$}>;
  var<workgroup> tileK: array<${De.type.storage}, ${$*$}>;
  ${Ge.registerUniforms(tr).declareVariables(...Dt,...lr)}
  ${Ge.mainStart([$,$,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;
    ${z&&w?`
    let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx;
    let pastKeyOffset = uniforms.past_sequence_length * uniforms.K * headIdx;`:`
    let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;`}
    ${w?"let presentKeyOffset = headIdx * uniforms.N * uniforms.K;":""}
    var value = ${fr}(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;
      ${z&&w?`
              if (n + local_id.y < uniforms.past_sequence_length) {
                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
              } else {
                tileK[idx] =
                         key[kOffset + (n + local_id.y - uniforms.past_sequence_length) * uniforms.K + w + local_id.x];
              }`:"tileK[idx] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];"}
      ${w?"present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];":""}
      }
      workgroupBarrier();

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

      workgroupBarrier();
    }

    let headOffset = headIdx * uniforms.M * uniforms.N;
    if (global_id.y < uniforms.M && global_id.x < uniforms.N) {
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(M){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: ${M}`)}})()};
        output[outputIdx] = ${Vt.type.value} (sum * uniforms.alpha) + ${s?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${M};${s!==void 0};${n!==void 0};${e}`,inputDependencies:ie},getRunData:()=>({outputs:J,dispatchGroup:L,programUniforms:W}),getShaderSource:oe}},Bo=(e,t,r,n,s,a)=>{let i=a+s.kvSequenceLength,u=s.nReps?s.nReps:1,d=s.vHiddenSize*u,p=s.kvNumHeads==null&&e>1&&n,w=p?[s.batchSize,s.numHeads,i,s.headSize]:void 0,g=[s.batchSize,s.sequenceLength,d],l=12,M={x:Math.ceil(s.vHeadSize/l),y:Math.ceil(s.sequenceLength/l),z:s.batchSize*s.numHeads},E=[{type:12,data:s.sequenceLength},{type:12,data:i},{type:12,data:s.vHeadSize},{type:12,data:s.numHeads},{type:12,data:d},{type:12,data:a},{type:12,data:s.kvSequenceLength}],$=p&&n&&Oe.size(n.dims)>0,L=["type","type"];$&&L.push("type");let W=[{dims:g,dataType:t.dataType,gpuDataType:0}];p&&W.push({dims:w,dataType:t.dataType,gpuDataType:0});let z=ie=>{let J=et("probs",t.dataType,t.dims),oe=et("v",r.dataType,r.dims),Ge=[J,oe];$&&Ge.push(et("past_value",n.dataType,n.dims));let De=[Ut("output",t.dataType,g)];p&&De.push(Ut("present_value",t.dataType,w));let pt=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"}];return`
  const TILE_SIZE = ${l}u;
  var<workgroup> tileQ: array<${J.type.value}, ${l*l}>;
  var<workgroup> tileK: array<${J.type.value}, ${l*l}>;
  ${ie.registerUniforms(pt).declareVariables(...Ge,...De)}
  ${ie.mainStart([l,l,1])}
   let headIdx = workgroup_id.z;
   let m = global_id.y;
   let n = global_id.x;

   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;
   ${$&&p?`
    let pastValueOffset = headIdx * uniforms.N * uniforms.past_sequence_length + n;
    let vOffset = headIdx * uniforms.N * uniforms.kv_sequence_length + n;
      `:`
   let offsetB = headIdx * uniforms.N * uniforms.K + n;
            `}
    ${p?"let presentValueOffset = headIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${J.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;
        ${$&&p?`
        if (w + local_id.y < uniforms.past_sequence_length) {
          tileK[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
        } else {
          tileK[idx] = v[vOffset + (w + local_id.y - uniforms.past_sequence_length) * uniforms.N];
        }
      `:`
        tileK[idx] = v[offsetB + (w + local_id.y) * uniforms.N];
      `}
        ${p?"present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileK[idx];":""}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + currentBatchHeadNumber * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:L},getRunData:()=>({outputs:W,dispatchGroup:M,programUniforms:E}),getShaderSource:z}},As=(e,t,r,n,s,a,i,u,d,p,w)=>{let g=Math.min(e.outputCount,1+(i?1:0)+(u?1:0)),l=p.kvNumHeads!==void 0||g>1?p.pastSequenceLength:0,M=l+p.kvSequenceLength,E=d&&Oe.size(d.dims)>0?d:void 0,$=[t,r];p.kvNumHeads===void 0&&g>1&&i&&Oe.size(i.dims)>0&&$.push(i),E&&$.push(E);let L=e.compute(Vi(g,t,r,i,E,p,w,l),{inputs:$,outputs:p.kvNumHeads===void 0&&g>1?[-1,1]:[-1]})[0];e.compute(Do(L,p.batchSize*p.numHeads*p.sequenceLength,M),{inputs:[L],outputs:[]});let W=[L,n];p.kvNumHeads===void 0&&g>1&&u&&Oe.size(u.dims)>0&&W.push(u),e.compute(Bo(g,L,n,u,p,l),{inputs:W,outputs:p.kvNumHeads===void 0&&g>1?[0,2]:[0]})},Ui=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,s=t.inputHiddenSize,a=t.headSize,i=12,u={x:Math.ceil(t.headSize/i),y:Math.ceil(t.sequenceLength/i),z:t.batchSize*t.numHeads},d=[e.inputs[0],e.inputs[1],e.inputs[2]],p=[{type:12,data:n},{type:12,data:s},{type:12,data:a},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],w=g=>{let l=Ut("output_q",d[0].dataType,r),M=Ut("output_k",d[0].dataType,r),E=Ut("output_v",d[0].dataType,r),$=et("input",d[0].dataType,d[0].dims),L=et("weight",d[1].dataType,d[1].dims),W=et("bias",d[2].dataType,d[2].dims),z=$.type.storage,ie=[{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 = ${i}u;
  var<workgroup> tileInput: array<${z}, ${i*i}>;
  var<workgroup> tileWeightQ: array<${z}, ${i*i}>;
  var<workgroup> tileWeightK: array<${z}, ${i*i}>;
  var<workgroup> tileWeightV: array<${z}, ${i*i}>;
  ${g.registerUniforms(ie).declareVariables($,L,W,l,M,E)}
  ${g.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 = ${z}(0);
    var valueK = ${z}(0);
    var valueV = ${z}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

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

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:p}),getShaderSource:w},{inputs:d,outputs:[-1,-1,-1]})},Lo=(e,t)=>{let r=zo(e.inputs,t),[n,s,a]=Ui(e,r);return As(e,n,s,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}}),Gi,Ro,No,qi,wd=j(()=>{bt(),Xt(),Qt(),pr(),ar(),Gi=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,s,a)=>{let i=s.length;if(i!==n.length)throw new Error(`${a}: num dimensions != ${i}`);s.forEach((u,d)=>{if(u!==n[d])throw new Error(`${a}: dim[${d}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,n,"Invalid input scale"),r(e[2].dims,n,"Invalid input B"),r(e[3].dims,n,"Invalid input mean"),r(e[4].dims,n,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input var")},Ro=(e,t)=>{let{epsilon:r,spatial:n,format:s}=t,a=e[0].dims,i=n?br(a[a.length-1]):1,u=s==="NHWC"&&a.length>1?i:1,d=Oe.size(a)/i,p=n,w=p?a.length:a,g=et("x",e[0].dataType,e[0].dims,i),l=et("scale",e[1].dataType,e[1].dims,u),M=et("bias",e[2].dataType,e[2].dims,u),E=et("inputMean",e[3].dataType,e[3].dims,u),$=et("inputVar",e[4].dataType,e[4].dims,u),L=Ut("y",e[0].dataType,w,i),W=()=>{let ie="";if(n)ie=`let cOffset = ${a.length===1?"0u":s==="NHWC"?`outputIndices[${a.length-1}] / ${i}`:"outputIndices[1]"};`;else if(s==="NCHW")ie=`
            ${L.indicesSet("outputIndices","0","0")}
            let cOffset = ${L.indicesToOffset("outputIndices")};`;else{ie=`var cIndices = ${l.type.indices}(0);
                       cIndices[0] = outputIndices[${a.length-1}];`;for(let J=1;J<l.rank;J++)ie+=`cIndices[${J}] = outputIndices[${J}];`;ie+=`let cOffset = ${l.indicesToOffset("cIndices")};`}return ie},z=ie=>`
  const epsilon = ${r};
  ${ie.registerUniform("outputSize","u32").declareVariables(g,l,M,E,$,L)}
  ${ie.mainStart()}
  ${ie.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${L.offsetToIndices(`global_idx * ${i}`)};
    ${W()}
    let scale = ${l.getByOffset("cOffset")};
    let bias = ${M.getByOffset("cOffset")};
    let inputMean = ${E.getByOffset("cOffset")};
    let inputVar = ${$.getByOffset("cOffset")};
    let x = ${g.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${L.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${i}`,inputDependencies:p?["rank","type","type","type","type"]:void 0},getShaderSource:z,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p?[{type:12,data:d},...It(a)]:[{type:12,data:d}]})}},No=e=>qt(e),qi=(e,t)=>{let{inputs:r,outputCount:n}=e,s=No({...t,outputCount:n});if(k.webgpu.validateInputContent&&Gi(r,s),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Ro(r,s))}}),jo,Vo,Uo,Wo=j(()=>{Qt(),ar(),jo=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")},Vo=e=>{let t=e[0].dims,r=e[0].dims[2],n=Oe.size(t)/4,s=e[0].dataType,a=et("input",s,t,4),i=et("bias",s,[r],4),u=et("residual",s,t,4),d=Ut("output",s,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:p=>`
  const channels = ${r}u / 4;
  ${p.declareVariables(a,i,u,d)}

  ${p.mainStart()}
    ${p.guardAgainstOutOfBoundsWorkgroupSizes(n)}
    let value = ${a.getByOffset("global_idx")}
      + ${i.getByOffset("global_idx % channels")} + ${u.getByOffset("global_idx")};
    ${d.setByOffset("global_idx","value")}
  }`}},Uo=e=>{jo(e.inputs),e.compute(Vo(e.inputs))}}),Go,$r,qo,Ho,Hi,Ko,Xo,Qo,Yo,Ki,Jo,Zo,Xi,el,tl,Qi,Is,rl,Fs,nl,sl,Yi,il,al,ti,ol,ll,Ji,Zi,ul,ea,dl,cl,pl,ta,ra,hl,na,ri,fl,ml,sa,_l,gl,ia,aa=j(()=>{Xt(),Qt(),pr(),ar(),Go=(e,t,r,n,s,a,i)=>{let u=Math.ceil(t/4),d="";typeof s=="string"?d=`${s}(a)`:d=s("a");let p=et("inputData",r,[u],4),w=Ut("outputData",n,[u],4),g=[{name:"vec_size",type:"u32"}];return i&&g.push(...i),`
      ${e.registerUniforms(g).declareVariables(p,w)}

  ${a??""}

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

    let a = ${p.getByOffset("global_idx")};
    ${w.setByOffset("global_idx",d)}
  }`},$r=(e,t,r,n,s,a=e.dataType,i,u)=>{let d=[{type:12,data:Math.ceil(Oe.size(e.dims)/4)}];return i&&d.push(...i),{name:t,shaderCache:{hint:s,inputDependencies:["type"]},getShaderSource:p=>Go(p,Oe.size(e.dims),e.dataType,a,r,n,u),getRunData:p=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil(Oe.size(p[0].dims)/64/4)},programUniforms:d})}},qo=e=>{e.compute($r(e.inputs[0],"Abs","abs"))},Ho=e=>{e.compute($r(e.inputs[0],"Acos","acos"))},Hi=e=>{e.compute($r(e.inputs[0],"Acosh","acosh"))},Ko=e=>{e.compute($r(e.inputs[0],"Asin","asin"))},Xo=e=>{e.compute($r(e.inputs[0],"Asinh","asinh"))},Qo=e=>{e.compute($r(e.inputs[0],"Atan","atan"))},Yo=e=>{e.compute($r(e.inputs[0],"Atanh","atanh"))},Ki=e=>qt(e),Jo=(e,t)=>{let r;switch(t.to){case 10:r="vec4<f16>";break;case 1:r="vec4<f32>";break;case 12:r="vec4<u32>";break;case 6:r="vec4<i32>";break;case 9:r="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute($r(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},Zo=e=>{let t,r,n=e.length>=2&&e[1].data!==0,s=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=n?e[1].getFloat32Array()[0]:-34028234663852886e22,r=s?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=n?e[1].getUint16Array()[0]:64511,r=s?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return qt({min:t,max:r})},Xi=(e,t)=>{let r=t||Zo(e.inputs),n=Cr(e.inputs[0].dataType);e.compute($r(e.inputs[0],"Clip",s=>`clamp(${s}, vec4<${n}>(uniforms.min), vec4<${n}>(uniforms.max))`,void 0,r.cacheKey,void 0,[{type:e.inputs[0].dataType,data:r.min},{type:e.inputs[0].dataType,data:r.max}],[{name:"min",type:n},{name:"max",type:n}]),{inputs:[0]})},el=e=>{e.compute($r(e.inputs[0],"Ceil","ceil"))},tl=e=>{e.compute($r(e.inputs[0],"Cos","cos"))},Qi=e=>{e.compute($r(e.inputs[0],"Cosh","cosh"))},Is=e=>qt(e),rl=(e,t)=>{let r=Cr(e.inputs[0].dataType);e.compute($r(e.inputs[0],"Elu",n=>`elu_vf32(${n})`,`
  const elu_alpha_ = ${r}(${t.alpha});

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

  fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,t.cacheKey))},Fs=(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));
}`,nl=e=>{let t=Cr(e.inputs[0].dataType);e.compute($r(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,Fs(t)))},sl=e=>{e.compute($r(e.inputs[0],"Exp","exp"))},Yi=e=>{e.compute($r(e.inputs[0],"Floor","floor"))},il=e=>{let t=Cr(e.inputs[0].dataType);e.compute($r(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,Fs(t)))},al=(e,t)=>{let r=Cr(e.inputs[0].dataType);e.compute($r(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},ti=e=>{e.compute($r(e.inputs[0],"Not",t=>`!${t}`))},ol=e=>{e.compute($r(e.inputs[0],"Neg",t=>`-${t}`))},ll=e=>{e.compute($r(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Ji=e=>{let t=Cr(e.inputs[0].dataType);e.compute($r(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Zi=e=>{e.compute($r(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},ul=e=>qt(e),ea=(e,t)=>{let r=Cr(e.inputs[0].dataType);e.compute($r(e.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${n} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},dl=e=>{e.compute($r(e.inputs[0],"Sin","sin"))},cl=e=>{e.compute($r(e.inputs[0],"Sinh","sinh"))},pl=e=>{e.compute($r(e.inputs[0],"Sqrt","sqrt"))},ta=e=>{e.compute($r(e.inputs[0],"Tan","tan"))},ra=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,hl=e=>{e.compute($r(e.inputs[0],"Tanh",ra))},na=(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 ${ra("v")};
}
`,ri=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,fl=e=>{let t=Cr(e.inputs[0].dataType);e.compute($r(e.inputs[0],"FastGelu",ri,na(t),void 0,e.inputs[0].dataType))},ml=(e,t)=>{let r=Cr(e.inputs[0].dataType);return e.compute($r(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},sa=e=>{e.compute($r(e.inputs[0],"Log","log"))},_l=(e,t)=>`
const alpha = vec4<${e}>(${t});
const one = ${e}(1.0);
const zero = ${e}(0.0);

fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> {
  let v = x *alpha;
  var x1 : vec4<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,gl=e=>`quick_gelu_impl(${e})`,ia=(e,t)=>{let r=Cr(e.inputs[0].dataType);e.compute($r(e.inputs[0],"QuickGelu",gl,_l(r,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),wl,oa,yl,yd=j(()=>{Qt(),ar(),aa(),wl=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")},oa=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=et("input",e[0].dataType,e[0].dims,4),n=et("bias",e[0].dataType,[e[0].dims[2]],4),s=Ut("output",e[0].dataType,t,4),a=Oe.size(t)/4,i=Ar(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:u=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

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

  ${Fs(i)}

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

    ${s.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},yl=e=>{wl(e.inputs),e.compute(oa(e.inputs))}}),la,bl,Dn,ua,Ml,vl,xl,Tl,ni,El,Cl,$l,da,bd=j(()=>{Xt(),Qt(),ar(),la=(e,t,r,n,s,a,i,u,d,p,w,g)=>{let l,M;typeof u=="string"?l=M=(z,ie)=>`${u}((${z}),(${ie}))`:typeof u=="function"?l=M=u:(l=u.scalar,M=u.vector);let E=Ut("outputData",w,n.length,4),$=et("aData",d,t.length,4),L=et("bData",p,r.length,4),W;if(s)if(a){let z=Oe.size(t)===1,ie=Oe.size(r)===1,J=t.length>0&&t[t.length-1]%4===0,oe=r.length>0&&r[r.length-1]%4===0;z||ie?W=E.setByOffset("global_idx",M(z?`${$.type.value}(${$.getByOffset("0")}.x)`:$.getByOffset("global_idx"),ie?`${L.type.value}(${L.getByOffset("0")}.x)`:L.getByOffset("global_idx"))):W=`
            let outputIndices = ${E.offsetToIndices("global_idx * 4u")};
            let offsetA = ${$.broadcastedIndicesToOffset("outputIndices",E)};
            let offsetB = ${L.broadcastedIndicesToOffset("outputIndices",E)};
            ${E.setByOffset("global_idx",M(i||J?$.getByOffset("offsetA / 4u"):`${$.type.value}(${$.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||oe?L.getByOffset("offsetB / 4u"):`${L.type.value}(${L.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else W=E.setByOffset("global_idx",M($.getByOffset("global_idx"),L.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let z=(ie,J,oe="")=>{let Ge=`aData[indexA${J}][componentA${J}]`,De=`bData[indexB${J}][componentB${J}]`;return`
            let outputIndices${J} = ${E.offsetToIndices(`global_idx * 4u + ${J}u`)};
            let offsetA${J} = ${$.broadcastedIndicesToOffset(`outputIndices${J}`,E)};
            let offsetB${J} = ${L.broadcastedIndicesToOffset(`outputIndices${J}`,E)};
            let indexA${J} = offsetA${J} / 4u;
            let indexB${J} = offsetB${J} / 4u;
            let componentA${J} = offsetA${J} % 4u;
            let componentB${J} = offsetB${J} % 4u;
            ${ie}[${J}] = ${oe}(${l(Ge,De)});
          `};w===9?W=`
            var data = vec4<u32>(0);
            ${z("data",0,"u32")}
            ${z("data",1,"u32")}
            ${z("data",2,"u32")}
            ${z("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:W=`
            ${z("outputData[global_idx]",0)}
            ${z("outputData[global_idx]",1)}
            ${z("outputData[global_idx]",2)}
            ${z("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables($,L,E)}

        ${g??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${W}
      }`},bl=(e,t,r,n,s,a,i=r.dataType)=>{let u=!Oe.areEqual(r.dims,n.dims),d=r.dims,p=Oe.size(r.dims),w=!1,g=!1,l=[u];if(u){let M=ln.calcShape(r.dims,n.dims,!1);if(!M)throw new Error("Can't perform binary op on the given tensors");d=M,p=Oe.size(d);let E=Oe.size(r.dims)===1,$=Oe.size(n.dims)===1,L=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,W=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;l.push(E),l.push($),l.push(L),l.push(W);let z=1;for(let ie=1;ie<d.length;ie++){let J=r.dims[r.dims.length-ie]??1,oe=n.dims[n.dims.length-ie]??1;if(J===oe)z*=J;else break}z%4===0?(g=!0,w=!0):(E||$||L||W)&&(w=!0)}else w=!0;return l.push(w),{name:e,shaderCache:{hint:t+l.map(M=>M.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:M=>la(M,r.dims,n.dims,d,w,u,g,s,r.dataType,n.dataType,i,a),getRunData:()=>({outputs:[{dims:d,dataType:i}],dispatchGroup:{x:Math.ceil(p/64/4)},programUniforms:[{type:12,data:Math.ceil(Oe.size(d)/4)},...It(r.dims,n.dims,d)]})}},Dn=(e,t,r,n,s,a)=>{e.compute(bl(t,s??"",e.inputs[0],e.inputs[1],r,n,a))},ua=e=>{Dn(e,"Add",(t,r)=>`${t}+${r}`)},Ml=e=>{Dn(e,"Div",(t,r)=>`${t}/${r}`)},vl=e=>{Dn(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},xl=e=>{Dn(e,"Mul",(t,r)=>`${t}*${r}`)},Tl=e=>{let t=et("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Dn(e,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},`
    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {
      if (b == ${t}(0.0)) {
        return ${t}(1.0);
      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {
        return ${t}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {
      // TODO: implement vectorized pow
      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},ni=e=>{Dn(e,"Sub",(t,r)=>`${t}-${r}`)},El=e=>{Dn(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},Cl=e=>{Dn(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},$l=e=>{Dn(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},da=e=>{Dn(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}}),Sl,ca,kl,Pl,Al,Il,Fl=j(()=>{Xt(),Qt(),pr(),ar(),Sl=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],s=n.dataType,a=n.dims.length;e.forEach((i,u)=>{if(u!==r){if(i.dataType!==s)throw new Error("input tensors should be one type");if(i.dims.length!==a)throw new Error("input tensors should have the same shape");i.dims.forEach((d,p)=>{if(p!==t&&d!==n.dims[p])throw new Error("non concat dimensions must match")})}})},ca=(e,t)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${e}u>(${t});
    for (var i: u32 = 0u; i < ${e}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${e}u;
  }`,kl=(e,t)=>{let r=e.length,n=[];for(let s=0;s<r;++s){let a=t.setByOffset("global_idx",e[s].getByIndices("indices"));r===1?n.push(a):s===0?n.push(`if (inputIndex == ${s}u) { ${a} }`):s===r-1?n.push(`else { ${a} }`):n.push(`else if (inputIndex == ${s}) { ${a} }`)}return n.join(`
`)},Pl=(e,t,r,n)=>{let s=Oe.size(r),a=new Array(e.length),i=new Array(e.length),u=0,d=[],p=[],w=[{type:12,data:s}];for(let $=0;$<e.length;++$)u+=e[$].dims[t],a[$]=u,p.push(e[$].dims.length),i[$]=et(`input${$}`,n,p[$]),d.push("rank"),w.push({type:12,data:a[$]});for(let $=0;$<e.length;++$)w.push(...It(e[$].dims));w.push(...It(r));let g=Ut("output",n,r.length),l=g.indicesGet("indices",t),M=Array.from(Array(a.length).keys()).map($=>`uniforms.sizeInConcatAxis${$}`).join(","),E=$=>`

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

  ${ca(a.length,M)}

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

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

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

    ${kl(i,g)}
  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:w}),getShaderSource:E}},Al=(e,t)=>{let r=e.inputs,n=r[0].dims,s=Oe.normalizeAxis(t.axis,n.length);Sl(r,s);let a=n.slice();a[s]=r.reduce((u,d)=>u+(d.dims.length>s?d.dims[s]:0),0);let i=r.filter(u=>Oe.size(u.dims)>0);e.compute(Pl(i,s,a,r[0].dataType),{inputs:i})},Il=e=>qt({axis:e.axis})}),Yn,Jn,Un,pa,Zn=j(()=>{Xt(),Qt(),Yn=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
        `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},Jn=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Un=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},pa=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[r,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=(e==null?void 0:e.activation_params)||[sn,xn];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:r}}return{activation:t}}}),un,ha,si=j(()=>{un=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},ha=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),ii,Ol=j(()=>{ii=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),zl,Dl,ai,fa,Bl,Os,Ll,ma,zs=j(()=>{Xt(),Qt(),ar(),Zn(),si(),zl=(e,t)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${t?", batchIndices":""});
        `,Dl=(e,t)=>e?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
        for (var i = 0; i < rowPerThread; i = i + 1) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
        }`:`
        for (var i = 0; i < rowPerThread; i = i + 1) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,ai=(e,t,r="f32",n,s=!1,a=32,i=!1,u=32)=>{let d=t[1]*e[1],p=t[0]*e[0],w=s?d:a,g=s?a:d,l=w/t[0],M=a/t[1];if(!((s&&l===4&&e[1]===4||!s&&(l===3||l===4))&&w%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${s} is true, innerElementSize ${l} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${l} must be 3 or 4.
  tileAWidth ${w} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${l}<${r}>, ${w/l}>, ${g}>;
var<workgroup> mm_Bsub: array<array<vec4<${r}>, ${p/e[0]}>, ${a}>;

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

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

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

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

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

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

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

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

          ${Dl(s,l)}
      }

      workgroupBarrier();
  }

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

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

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

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

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

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

  // Compute acc values for a single thread.
  var BCached : array<${r}, colPerThread>;
  for (var k = 0; k < tileInner; k = k + 1) {
    for (var inner = 0; inner < colPerThread; inner = inner + 1) {
      BCached[inner] = mm_Bsub[k][tileCol + inner];
    }

    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      ${Bl(s)}
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
      }
    }
  }

  workgroupBarrier();
}

for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
    mm_write(batch, globalRow + innerRow, globalCol + innerCol,
        acc[innerRow][innerCol]);
  }
}
`;return`
  var<workgroup> mm_Asub : array<array<${r}, ${g}>, ${l}>;
  var<workgroup> mm_Bsub : array<array<${r}, ${w}>, ${a}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${a};

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

    var acc : array<array<${r}, colPerThread>, rowPerThread>;
    ${L}
  }
`},Ll=(e,t,r,n,s,a=!1)=>{let[i,u,d]=s,[p,w,g,l]=n,M=_s(i,d),E=_s(u,d),$=Ar(n[0].type.tensor),L=()=>{let z=w.rank,ie=p.rank,J=`var aIndices: ${w.type.indices};`;for(let oe=z-2-1,Ge=ie-1;oe>=0;oe--,Ge--)J+=`
aIndices[${oe}] = ${ie>1?`batchIndices[${Ge}]`:"batchIndices"};`;return M.forEach(oe=>{J+=`
aIndices[${oe}] = 0;`}),J+=`
aIndices[${z-2}] = u32(row);
                   aIndices[${z-1}] = u32(colIn);`,J},W=()=>{let z=g.rank,ie=p.rank,J=`var bIndices: ${g.type.indices};`;for(let oe=z-2-1,Ge=ie-1;oe>=0;oe--,Ge--)J+=`
bIndices[${oe}] = ${ie>1?`batchIndices[${Ge}]`:"batchIndices"};`;return E.forEach(oe=>{J+=`
bIndices[${oe}] = 0;`}),J+=`
bIndices[${z-2}] = u32(row);
                   bIndices[${z-1}] = u32(colIn);`,J};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${un(e,$)} {
      var value = ${un(e,$)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        ${L()}
        value = ${w.getByIndices("aIndices")};
      }
      return value;
    }

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

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${un(e,$)}) {
      let col = colIn * ${e};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${t?`value = value + ${a?"bias[colIn]":`${un(e,$)}(bias[row])`};`:""}
        ${r}
        ${l.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},ma=(e,t,r,n,s=!1,a)=>{let i=e[0].dims,u=e[1].dims,d=i.slice(0,-2),p=u.slice(0,-2),w=n?n.slice(0,-2):r.slice(0,-2),g=Oe.size(w),l=i[i.length-2],M=i[i.length-1],E=u[u.length-1],$=M%4===0&&E%4===0,L=l<=8?[4,1,1]:[4,4,1],W=[8,8,1],z=[Math.ceil(E/W[0]/L[0]),Math.ceil(l/W[1]/L[1]),Math.ceil(g/W[2]/L[2])],ie=$?4:1,J=[...d,l,M/ie],oe=J.length,Ge=[...p,M,E/ie],De=Ge.length,pt=[g,l,E/ie],Dt=[{type:6,data:l},{type:6,data:E},{type:6,data:M}];Jn(t,Dt),Dt.push(...It(w,J,Ge));let Vt=["rank","rank"],lr=e.length>2;lr&&(Dt.push(...It(e[2].dims)),Vt.push("rank")),Dt.push(...It(pt));let fr=tr=>{let jr=w.length,Qr=bi("batchDims",e[0].dataType,jr,1),Mr=Ar(e[0].dataType),Ur=et("a",e[0].dataType,oe,ie),Jt=et("b",e[1].dataType,De,ie),dr=Ut("result",e[0].dataType,pt.length,ie),Or=[Ur,Jt];if(lr){let nn=s?ie:1;Or.push(et("bias",e[2].dataType,e[2].dims.length,nn))}let Ve=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Un(t,Ve);let St=Ar(dr.type.tensor),rr=Yn(t,dr.type.value,St),Vr=Ll(ie,lr,rr,[Qr,Ur,Jt,dr],[d,p,w],s);return`
  ${tr.registerUniforms(Ve).registerInternalVariables(Qr).declareVariables(...Or,dr)}
  ${Vr}
  ${$?ai(L,W,Mr,Qr):Os(L,W,Mr,Qr)}
                   `};return{name:"MatMul",shaderCache:{hint:`${L};${t.activation};${$};${s}`,inputDependencies:Vt},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:z[0],y:z[1],z:z[2]},programUniforms:Dt}),getShaderSource:fr}}}),Rl,Nl,is=j(()=>{Xt(),vn(),ar(),Zn(),si(),Ol(),zs(),Rl=(e,t,r,n,s=!1,a,i=4,u=4,d=4,p="f32")=>{let w=Dt=>{switch(Dt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${p}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Dt} is not supported.`)}},g=Dt=>{switch(Dt){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 ${Dt} is not supported.`)}},l=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,M=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,E=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",$=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",L=e?"row":"col",W=e?"col":"row",z=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${L} / outWidth;
    let outCol = ${L} % 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 = ${un(i,p)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${E} && xCol >= 0 && xCol < ${$}) {
      ${l}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${w(i)}
    }
    return resData;`,ie=e?t&&n?`
    let col = colIn * ${i};
    ${z}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${z}
    }
    return ${un(i,p)}(0.0);`:n&&r?`
    let col = colIn * ${i};
    ${z}`:`
    let col = colIn * ${i};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${z}
    }
    return ${un(i,p)}(0.0);`,J=`${g(u)}`,oe=un(d,p),Ge=un(e?i:u,p),De=un(e?u:i,p),pt=Yn(a,oe,p);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Ge} {
      ${e?ie:J}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${De} {
      ${e?J:ie}
    }

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${oe}) {
      let col = colIn * ${d};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${M}
      ${ha(s)}
      ${pt}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Nl=(e,t,r,n,s,a,i,u,d)=>{let p=t.format==="NHWC",w=p?e[0].dims[3]:e[0].dims[1],g=r[0],l=p?r[2]:r[3],M=p?r[1]:r[2],E=p?r[3]:r[1],$=p&&(w%4===0||w%3===0)&&E%4===0,L=p?E:l*M,W=p?l*M:E,z=[8,8,1],ie=n<=8?[4,1,1]:[4,4,1],J=[Math.ceil(L/z[0]/ie[0]),Math.ceil(W/z[1]/ie[1]),Math.ceil(g/z[2]/ie[2])];Wr("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${J}`);let oe=$?p&&w%4!==0?3:4:1,Ge=z[1]*ie[1],De=z[0]*ie[0],pt=Math.max(z[0]*oe,z[1]),Dt=n%Ge===0,Vt=s%De===0,lr=a%pt===0,fr=$?[oe,4,4]:[1,1,1],tr=[{type:6,data:n},{type:6,data:s},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Jn(t,tr),tr.push(...It(e[0].dims,e[1].dims));let jr=["rank","rank"];i&&(tr.push(...It(e[2].dims)),jr.push("rank")),tr.push(...It(r));let Qr=Mr=>{let Ur=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Un(t,Ur);let Jt=$?4:1,dr=Ar(e[0].dataType),Or=`
      fn setOutputAtIndex(flatIndex : i32, value : ${$?`vec4<${dr}>`:dr}) {
        result[flatIndex] = ${$?`vec4<${dr}>`:dr}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${$?`vec4<${dr}>`:dr}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${$?"/ 4":""}, value);
      }`,Ve=et("x",e[0].dataType,e[0].dims.length,oe===3?1:oe),St=et("w",e[1].dataType,e[1].dims.length,Jt),rr=[Ve,St],Vr=Ut("result",e[0].dataType,r.length,Jt);if(i){let nn=et("bias",e[2].dataType,e[2].dims.length,Jt);rr.push(nn),Or+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${$?`vec4<${dr}>`:dr} {
          return bias[coords.${p?"w":"y"}${$?"/ 4":""}];
        }`}return`
        ${ii("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 };
        ${Mr.registerUniforms(Ur).declareVariables(...rr,Vr)}
        ${Or}
        ${Rl(p,Dt,Vt,lr,i,t,fr[0],fr[1],fr[2],dr)}
        ${$?ai(ie,z,dr,void 0,!p,pt):Os(ie,z,dr,void 0,!p,pt,!1,void 0,u)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${oe};${$};${Dt};${Vt};${lr};${Ge};${De};${pt}`,inputDependencies:jr},getRunData:()=>({outputs:[{dims:d?d(r):r,dataType:e[0].dataType}],dispatchGroup:{x:J[0],y:J[1],z:J[2]},programUniforms:tr}),getShaderSource:Qr}}}),jl,_a,Ds,Vl,oi,Ul,Wl,Gl,Md=j(()=>{Xt(),vn(),Qt(),ar(),Zn(),si(),jl=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t},_a=e=>typeof e=="number"?[e,e,e]:e,Ds=(e,t)=>t<=1?e:e+(e-1)*(t-1),Vl=(e,t,r,n=1)=>{let s=Ds(t,n);return Math.floor((e[0]*(r-1)-r+s)/2)},oi=(e,t,r,n,s)=>{s==null&&(s=Vl(e,t[0],n[0]));let a=[0,0,0,r];for(let i=0;i<3;i++)e[i]+2*s>=t[i]&&(a[i]=Math.trunc((e[i]-t[i]+2*s)/n[i]+1));return a},Ul=(e,t,r,n,s,a,i,u,d,p)=>{let w,g,l,M;if(e==="VALID"&&(e=0),typeof e=="number"){w={top:e,bottom:e,left:e,right:e,front:e,back:e};let E=oi([t,r,n,1],[u,d,p],1,[s,a,i],e);g=E[0],l=E[1],M=E[2]}else if(Array.isArray(e)){if(!e.every(($,L,W)=>$===W[0]))throw Error(`Unsupported padding parameter: ${e}`);w={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let E=oi([t,r,n,1],[u,d,p],1,[s,a,i],e[0]);g=E[0],l=E[1],M=E[2]}else if(e==="SAME_UPPER"){g=Math.ceil(t/s),l=Math.ceil(r/a),M=Math.ceil(n/i);let E=(g-1)*s+u-t,$=(l-1)*a+d-r,L=(M-1)*i+p-n,W=Math.floor(E/2),z=E-W,ie=Math.floor($/2),J=$-ie,oe=Math.floor(L/2),Ge=L-oe;w={top:ie,bottom:J,left:oe,right:Ge,front:W,back:z}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:w,outDepth:g,outHeight:l,outWidth:M}},Wl=(e,t,r,n,s,a=!1,i="channelsLast")=>{let u,d,p,w,g;if(i==="channelsLast")[u,d,p,w,g]=e;else if(i==="channelsFirst")[u,g,d,p,w]=e;else throw new Error(`Unknown dataFormat ${i}`);let[l,,M,E,$]=t,[L,W,z]=_a(r),[ie,J,oe]=_a(n),Ge=Ds(M,ie),De=Ds(E,J),pt=Ds($,oe),{padInfo:Dt,outDepth:Vt,outHeight:lr,outWidth:fr}=Ul(s,d,p,w,L,W,z,Ge,De,pt),tr=a?l*g:l,jr=[0,0,0,0,0];return i==="channelsFirst"?jr=[u,tr,Vt,lr,fr]:i==="channelsLast"&&(jr=[u,Vt,lr,fr,tr]),{batchSize:u,dataFormat:i,inDepth:d,inHeight:p,inWidth:w,inChannels:g,outDepth:Vt,outHeight:lr,outWidth:fr,outChannels:tr,padInfo:Dt,strideDepth:L,strideHeight:W,strideWidth:z,filterDepth:M,filterHeight:E,filterWidth:$,effectiveFilterDepth:Ge,effectiveFilterHeight:De,effectiveFilterWidth:pt,dilationDepth:ie,dilationHeight:J,dilationWidth:oe,inShape:e,outShape:jr,filterShape:t}},Gl=(e,t,r,n,s,a)=>{let i=a==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let u=[64,1,1],d={x:r.map((L,W)=>W)},p=[Math.ceil(jl(d.x.map(L=>r[L]))/u[0]),1,1];Wr("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${p}`);let w=1,g=Oe.size(r),l=[{type:12,data:g},{type:12,data:n},{type:12,data:s},{type:12,data:t.strides},{type:12,data:t.dilations}];Jn(t,l),l.push(...It(e[0].dims,e[1].dims));let M=["rank","rank"],E=e.length===3;E&&(l.push(...It(e[2].dims)),M.push("rank")),l.push(...It(r));let $=L=>{let W=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:n.length},{name:"pads",type:"u32",length:s.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];Un(t,W);let z=1,ie=Ar(e[0].dataType),J=et("x",e[0].dataType,e[0].dims.length,w),oe=et("W",e[1].dataType,e[1].dims.length,z),Ge=[J,oe],De=Ut("result",e[0].dataType,r.length,z),pt="";if(E){let lr=et("bias",e[2].dataType,e[2].dims.length,z);Ge.push(lr),pt+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${ie} {
          return bias[${i?jt("coords",4,5):jt("coords",1,5)}];
        }`}let Dt=un(w,ie),Vt=Yn(t,Dt,ie);return`
            ${pt}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${J.getByIndices("aIndices")};
            }
            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${oe.getByIndices("aIndices")};
            }
          ${L.registerUniforms(W).declareVariables(...Ge,De)}
          ${L.mainStart()}
          ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${De.offsetToIndices("global_idx")};
              let batch = ${jt("coords",0,J.rank)};
              let d2 = ${i?jt("coords",J.rank-1,J.rank):jt("coords",1,J.rank)};
              let xFRCCorner = vec3<u32>(${i?jt("coords",1,J.rank):jt("coords",2,J.rank)},
              ${i?jt("coords",2,J.rank):jt("coords",3,J.rank)},
              ${i?jt("coords",3,J.rank):jt("coords",4,J.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${i?jt("uniforms.x_shape",1,J.rank):jt("uniforms.x_shape",2,J.rank)};
              let xShapeZ = ${i?jt("uniforms.x_shape",2,J.rank):jt("uniforms.x_shape",3,J.rank)};
              let xShapeW = ${i?jt("uniforms.x_shape",3,J.rank):jt("uniforms.x_shape",4,J.rank)};
              let xShapeU = ${i?jt("uniforms.x_shape",4,J.rank):jt("uniforms.x_shape",1,J.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);
                    }
                  }
                }
              }
              ${E?"value = value + getBiasByOutputCoords(coords)":""};
              ${Vt}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${i};${w};${E}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:p[0],y:p[1],z:p[2]},programUniforms:l}),getShaderSource:$}}}),li,ql,vd=j(()=>{Xt(),Qt(),ar(),Zn(),li=(e,t,r,n)=>{let s=e.length>2,a=s?"value += b[output_channel];":"",i=e[0].dims,u=e[1].dims,d=t.format==="NHWC",p=d?r[3]:r[1],w=p/t.group,g=d&&w>=4?br(p):1,l=Oe.size(r)/g,M=[{type:12,data:l},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:w}];Jn(t,M),M.push(...It(i,[u[0],u[1],u[2],u[3]/g]));let E=s?["rank","rank","rank"]:["rank","rank"];M.push(...It([r[0],r[1],r[2],r[3]/g]));let $=L=>{let W=Ut("output",e[0].dataType,r.length,g),z=Ar(W.type.tensor),ie=Yn(t,W.type.value,z),J=et("x",e[0].dataType,i.length),oe=et("w",e[1].dataType,u.length,g),Ge=[J,oe];s&&Ge.push(et("b",e[2].dataType,e[2].dims,g));let De=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];Un(t,De);let pt=d?`
      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

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

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

          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
            let input_channel = in_channel_offset + wInChannel;
            let xVal = ${J.get("batch","xHeight","xWidth","input_channel")};
            let wVal = ${oe.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 = ${J.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${oe.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${L.registerUniforms(De).declareVariables(...Ge,W)}

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

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

    var value: ${W.type.value} = ${W.type.value}(0);
    ${pt}
    ${a}
    ${ie}
    ${W.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${g}`,inputDependencies:E},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:M}),getShaderSource:$}},ql=(e,t,r,n)=>{let s=e.length>2,a=br(r[3]),i=br(r[2]),u=Oe.size(r)/a/i,d=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],p=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],w=[r[0],r[1],r[2],r[3]/a],g=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Jn(t,g),g.push(...It(d,p,w));let l=(i-1)*t.strides[1]+p[1],M=E=>{let $=Ut("output",e[0].dataType,w.length,a),L=Ar($.type.tensor),W=Yn(t,$.type.value,L),z=et("x",e[0].dataType,d.length,a),ie=et("w",e[1].dataType,p.length,a),J=[z,ie];s&&J.push(et("b",e[2].dataType,e[2].dims,a));let oe=s?"value += b[output_channel];":"",Ge=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Un(t,Ge),`
  ${E.registerUniforms(Ge).declareVariables(...J,$)}
  ${E.mainStart()}
    ${E.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<${z.type.value}, ${l}>;
    var values: array<${$.type.value}, ${i}>;
    let input_channel = output_channel;
    // Use constant instead of uniform can give better performance for w's height/width.
    for (var w_height: u32 = 0u; w_height < ${p[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${l}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${z.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${z.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${p[1]}; w_width++) {
          let w_val = ${ie.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];
      ${oe}
      ${W}
      ${$.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${a};${i};${l};${p[0]};${p[1]}`,inputDependencies:s?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:g}),getShaderSource:M}}}),ga,Bs,Hl,Kl=j(()=>{Xt(),Qt(),zs(),ar(),Zn(),ga=(e,t,r,n,s=!1,a)=>{let i=e[0].dims,u=e[1].dims,d=i[i.length-2],p=u[u.length-1],w=i[i.length-1],g=br(p),l=br(w),M=br(d),E=Oe.size(r)/g/M,$=e.length>2,L=n?n.slice(0,-2):r.slice(0,-2),W=[Oe.size(L),d,p],z=[{type:12,data:E},{type:12,data:d},{type:12,data:p},{type:12,data:w}];Jn(t,z),z.push(...It(L,i,u)),$&&z.push(...It(e[2].dims)),z.push(...It(W));let ie=J=>{let oe=bi("batch_dims",e[0].dataType,L.length),Ge=et("a",e[0].dataType,i.length,l),De=et("b",e[1].dataType,u.length,g),pt=Ut("output",e[0].dataType,W.length,g),Dt=Ar(pt.type.tensor),Vt=Yn(t,pt.type.value,Dt),lr=[Ge,De],fr="";if($){let Or=s?g:1;lr.push(et("bias",e[2].dataType,e[2].dims.length,Or)),fr=`${s?`value += bias[col / ${Or}];`:`value += ${pt.type.value}(bias[row + i]);`}`}let tr=i.slice(0,-2),jr=u.slice(0,-2),Qr=_s(tr,L),Mr=_s(jr,L),Ur=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Un(t,Ur);let Jt=(Or,Ve)=>{let St=Or.rank,rr=Or.name;if(St===2)return`var ${rr}_indices = ${Or.type.indices}(0u, 0u);`;let Vr=oe.rank,nn=`var ${rr}_indices: ${Or.type.indices};`;for(let fn=St-2-1,Ws=Vr-1;fn>=0;fn--,Ws--)nn+=`
${rr}_indices[${fn}] = ${Vr>1?`batch_indices[${Ws}]`:"batch_indices"};`;return Ve.forEach(fn=>{nn+=`
${rr}_indices[${fn}] = 0;`}),nn+=`${rr}_indices[${St-2}] = 0u;
                     ${rr}_indices[${St-1}] = 0u;`,nn},dr=()=>{let Or=`var a_data: ${Ge.type.value};`;for(let Ve=0;Ve<l;Ve++)Or+=`
              let b_data${Ve} = b[(b_offset + (k + ${Ve}) * uniforms.N + col) / ${g}];`;for(let Ve=0;Ve<M;Ve++){Or+=`a_data = a[(a_offset + (row + ${Ve}) * uniforms.K + k) / ${l}];`;for(let St=0;St<l;St++)Or+=`
            values[${Ve}] = fma(${De.type.value}(a_data${l===1?"":`[${St}]`}), b_data${St}, values[${Ve}]);
`}return Or};return`
  ${J.registerUniforms(Ur).registerInternalVariables(oe).declareVariables(...lr,pt)}
  ${J.mainStart()}
    ${J.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${g})) * ${g};
    var index1 = global_idx / (uniforms.N / ${g});
    let stride1 = uniforms.M / ${M};
    let row = (index1 % stride1) * ${M};
    let batch = index1 / stride1;

    ${r.length===2?"":`let batch_indices = ${oe.offsetToIndices("batch")};`}
    ${Jt(Ge,Qr)}
    let a_offset = ${Ge.indicesToOffset("a_indices")};
    ${Jt(De,Mr)}
    let b_offset = ${De.indicesToOffset("b_indices")};
    var values: array<${pt.type.value}, ${M}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${l}) {
      ${dr()}
    }
    for (var i = 0u; i < ${M}u; i++) {
      var value = values[i];
      ${fr}
      ${Vt}
      let cur_indices = ${pt.type.indices}(batch, row + i, col);
      let offset = ${pt.indicesToOffset("cur_indices")};
      ${pt.setByOffset(`offset / ${g}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${g};${l};${M};${s}`,inputDependencies:$?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(E/64)},programUniforms:z}),getShaderSource:ie}},Bs=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.")},Hl=e=>{Bs(e.inputs);let t=ln.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let r=t[t.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&n<8?e.compute(ga(e.inputs,{activation:""},t)):e.compute(ma(e.inputs,{activation:""},t))}}),Xl,ui,di,ci,wa,ya,xd,Ql,ba,Td=j(()=>{Qt(),is(),Md(),zs(),vd(),Zn(),Kl(),Qn(),Xl=(e,t,r,n,s,a)=>{let i=e[0],u=e.slice(a?1:2,a?3:4),d=u.length,p=t[0],w=t.slice(2).map((l,M)=>l+(l-1)*(r[M]-1)),g=u.map((l,M)=>l+n[M]+n[M+d]).map((l,M)=>Math.floor((l-w[M]+s[M])/s[M]));return g.splice(0,0,i),g.splice(a?3:1,0,p),g},ui=[2,3,1,0],di=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[1]*t.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let s=e[0].dims.length-2;if(t.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(t.strides.length!==s)throw new Error(`strides should be ${s}D`);if(t.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ci=(e,t)=>{let r=e.kernelShape.slice();r.length<t[1].dims.length-2&&r.push(...Array(t[1].dims.length-2-r.length).fill(0));for(let a=2;a<t[1].dims.length;++a)r[a-2]===0&&(r[a-2]=t[1].dims[a]);let n=e.pads.slice();Sn.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,n,e.format==="NHWC",e.autoPad);let s=Object.assign({},e);return Object.assign(s,{kernelShape:r,pads:n}),s},wa=e=>{let t=pa(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],s=e.dilations,a=e.group,i=e.kernel_shape,u=e.pads,d=e.strides,p=e.w_is_const();return{autoPad:n,format:r,dilations:s,group:a,kernelShape:i,pads:u,strides:d,wIsConst:p,...t,cacheKey:`${e.format};${t.activation};`}},ya=(e,t,r,n)=>{let s=r.format==="NHWC",a=Xl(t[0].dims,t[1].dims,r.dilations,r.pads,r.strides,s);if(r.group!==1){let Ge=[t[0]];if(s){let De=e.kernelCustomData.wT??e.compute(In(t[1],ui),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=De),Ge.push(De)}else Ge.push(t[1]);t.length===3&&Ge.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&s&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1?e.compute(ql(Ge,r,a,n),{inputs:Ge}):e.compute(li(Ge,r,a,n),{inputs:Ge});return}let i=t.length===3,u=t[0].dims[s?1:2],d=t[0].dims[s?2:3],p=t[0].dims[s?3:1],w=t[1].dims[2],g=t[1].dims[3],l=a[s?1:2],M=a[s?2:3],E=a[s?3:1],$=s&&w===u&&g===d&&r.pads[0]===0&&r.pads[1]===0;if($||w===1&&g===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let Ge=a[0],De,pt,Dt,Vt=[];if(s){let tr=e.kernelCustomData.wT??e.compute(In(t[1],ui),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=tr),$){let jr=u*d*p;De=t[0].reshape([1,Ge,jr]),pt=tr.reshape([1,jr,E]),Dt=[1,Ge,E]}else De=t[0].reshape([Ge,u*d,p]),pt=tr.reshape([1,p,E]),Dt=[Ge,l*M,E];Vt.push(De),Vt.push(pt)}else De=t[0].reshape([Ge,p,u*d]),pt=t[1].reshape([1,E,p]),Dt=[Ge,E,l*M],Vt.push(pt),Vt.push(De);i&&Vt.push(t[2]);let lr=Dt[2],fr=Vt[0].dims[Vt[0].dims.length-1];lr<8&&fr<8?e.compute(ga(Vt,r,a,Dt,s,n),{inputs:Vt}):e.compute(ma(Vt,r,a,Dt,s,n),{inputs:Vt});return}let L=!0,W=e.kernelCustomData.wT??e.compute(In(t[1],ui),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=W);let z=[t[0],W];i&&z.push(t[2]);let ie=s?l*M:E,J=s?E:l*M,oe=w*g*p;e.compute(Nl(z,r,a,ie,J,oe,i,L,n),{inputs:z})},xd=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let s=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),i=[1].concat(t.dilations),u=[1].concat(t.kernelShape),d=ci({...t,pads:s,strides:a,dilations:i,kernelShape:u},n);ya(e,n,d,p=>r?[p[0],p[2],p[3]]:[p[0],p[1],p[3]])},Ql=(e,t,r)=>{let n=r.format==="NHWC"?"channelsLast":"channelsFirst",s=ci(r,t),a=r.autoPad==="NOTSET"?r.pads:r.autoPad,i=Wl(t[0].dims,t[1].dims,r.strides,r.dilations,a,!1,n);e.compute(Gl(t,s,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],n))},ba=(e,t)=>{if(di(e.inputs,t),e.inputs[0].dims.length===3)xd(e,t);else if(e.inputs[0].dims.length===5)Ql(e,e.inputs,t);else{let r=ci(t,e.inputs);ya(e,e.inputs,r)}}}),gs,Yl,Ed=j(()=>{Xt(),vn(),ar(),Zn(),si(),Ol(),zs(),gs=(e,t=!1,r,n,s=4)=>{let a=L=>{switch(L){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`
            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];
            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];
            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];
            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];
            return ${n}(v0, v1, v2, v3);
            `;default:throw new Error(`innerElementSize ${L} is not supported.`)}},i=e?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,u=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,d=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",p=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",w=e?"row":"col",g=e?"col":"row",l=`
      let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${w} / outWidth;
      let outCol = ${w} % outWidth;

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

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

  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) {
    let col = colIn * ${s};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
      var value = valueInput;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${u}
      ${ha(t)}
      ${$}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${s}] = value;
    }
  }`},Yl=(e,t,r,n,s,a,i,u)=>{let d=t.format==="NHWC",p=d?e[0].dims[3]:e[0].dims[1],w=r[0],g=d?r[2]:r[3],l=d?r[1]:r[2],M=d?r[3]:r[1],E=d&&p%4===0&&p%3&&M%4===0,$=d?M:g*l,L=d?g*l:M,W=[8,8,1],z=n<=8?[4,1,1]:[4,4,1],ie=[Math.ceil($/W[0]/z[0]),Math.ceil(L/W[1]/z[1]),Math.ceil(w/W[2]/z[2])];Wr("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${ie}`);let J=E?4:1,oe=Math.max(W[0]*J,W[1]),Ge=E?4:1,De=[t.kernelShape[d?1:2],t.kernelShape[d?2:3]],pt=[De[0]+(t.dilations[0]<=1?0:(De[0]-1)*(t.dilations[0]-1)),De[1]+(t.dilations[1]<=1?0:(De[1]-1)*(t.dilations[1]-1))],Dt=[pt[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),pt[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Vt=[{type:6,data:n},{type:6,data:s},{type:6,data:a},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:De},{type:6,data:Dt}];Jn(t,Vt),Vt.push(...It(e[0].dims,e[1].dims));let lr=["rank","rank"];i&&(Vt.push(...It(e[2].dims)),lr.push("rank")),Vt.push(...It(r));let fr=tr=>{let jr=et("x",e[0].dataType,e[0].dims.length,Ge),Qr=et("w",e[1].dataType,e[1].dims.length,1),Mr=Ut("result",e[0].dataType,r.length,Ge),Ur=[jr,Qr],Jt="";if(i){let Ve=et("bias",e[2].dataType,e[2].dims.length,Ge);Ur.push(Ve),Jt+=`
          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${Ve.type.value} {
            return bias[coords.${d?"w":"y"}${E?"/ 4":""}];
          }`}let dr=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:De.length},{name:"pads",type:"i32",length:Dt.length}];Un(t,dr);let Or=Ar(e[0].dataType,1);if(Or!=="f16"&&Or!=="f32")throw new Error(`elemType ${Or} is not supported.`);return`
        ${ii("uniforms.result_strides")}
        ${tr.registerUniforms(dr).declareVariables(...Ur,Mr)};
        ${Jt}
        ${gs(d,i,t,jr.type.value,J)}
        ${E?ai(z,W,Or,void 0,!d,oe):Os(z,W,Or,void 0,!d,oe,!1,void 0,u)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${z};${W};${E}`,inputDependencies:lr},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:ie[0],y:ie[1],z:ie[2]},programUniforms:Vt}),getShaderSource:fr}}}),Jl,Ma,Cd=j(()=>{Xt(),vn(),Qt(),ar(),Jl=(e,t,r,n,s,a=!1,i,u,d=!1)=>{let p=d?1:2,w=d?2:3,g=d?3:1,l=a?2:1,M=`
  fn setOutputAtIndex(flatIndex : u32, value : ${a?`vec4<${i}>`:i}) {
    result[flatIndex] = ${a?`vec4<${i}>`:i}(value);
  }`;n&&(M+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${a?`vec4<${i}>`:i} {
      return bias[coords.${d?"w":"y"}${a?"/ 4":""}];
    }`);let E=a?4:1,$=et("W",t[1].dataType,t[1].dims.length,E),L=et("Dy",t[0].dataType,t[0].dims.length,E),W=[L,$];n&&W.push(et("bias",t[2].dataType,[r[g]].length,E));let z=Ut("result",t[0].dataType,r.length,E),ie=`{
        let batch: u32 = ${s?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];
        let r = ${s?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];
        let c = ${s?"global_id.y":"workgroup_id.y"} * ${l};
        let d1: u32 = ${s?"global_id.x":"workgroup_id.x"} * 4;

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

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

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

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

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

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

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

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

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

        for (var i: u32 = 0; i < ${l}; i = i + 1) {
          let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${i}>(0.0)`};
          ${z.set("batch","r","c + i","d1","value")};
        }
      }`,J=`
          let outputIndices = ${z.offsetToIndices("global_idx")};
          let batch = ${z.indicesGet("outputIndices",0)};
          let d1 = ${z.indicesGet("outputIndices",g)};
          let r = ${z.indicesGet("outputIndices",p)};
          let c = ${z.indicesGet("outputIndices",w)};
          let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;
          let dyRCorner = dyCorner.x;
          let dyCCorner = dyCorner.y;
          let groupId = d1 / uniforms.output_channels_per_group;
          let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;
          // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
          // ? = to be determined. : = across all values in that axis.
          var dotProd = ${i}(0.0);
          for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
            if (wR % uniforms.dilations.x != 0) {
              continue;
            }
            let dyR = (${i}(dyRCorner) + ${i}(wR)) / ${i}(uniforms.strides[0]);
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            }
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                continue;
              }
              let dyC = (${i}(dyCCorner) + ${i}(wC)) / ${i}(uniforms.strides.y);
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              }
              let idyC: u32 = u32(dyC);
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d=va({...t,pads:u,strides:i,dilations:a,kernelShape:s},n);e.compute(Ma(n,d,p=>r?[p[0],p[2],p[3]]:[p[0],p[1],p[3]]))},su=(e,t)=>{tu(e.inputs,t),e.inputs[0].dims.length===3?nu(e,t):xa(e,e.inputs,t)}}),au,ou,Ta,Sd=j(()=>{Xt(),Qt(),pr(),ar(),au=(e,t,r,n)=>{let s=Oe.size(t),a=t.length,i=et("input",e,a),u=Ut("output",e,a),d=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),p=Oe.normalizeAxis(d,a),w=g=>{let l=` i32(${i.indicesGet("inputIndices","uniforms.axis")}) `,M=jt("uniforms.input_shape","uniforms.axis",a),E=n.reverse?l+(n.exclusive?" + 1":""):"0",$=n.reverse?M:l+(n.exclusive?"":" + 1");return`
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                  var inputIndices = ${u.offsetToIndices("global_idx")};
                  var sum = ${u.type.value}(0);
                  let first : i32 = ${E};
                  let last : i32 = ${$};
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    let indices = ${$.offsetToIndices("global_idx")};
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    ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
    ${l}`},d=[{type:12,data:i},...It(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:u,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:d})}},bu=e=>{Pd(e.inputs),e.compute(ka(e.inputs),{inputs:[0]})}}),Pa,Mu,Id=j(()=>{Xt(),Qt(),ar(),aa(),Pa=e=>{let t=e[0].dataType,r=Oe.size(e[0].dims),n=Oe.size(e[1].dims),s=n%4===0,a=i=>{let u=et("x",t,[1],4),d=et("bias",t,[1],4),p=Ut("y",t,[1],4),w=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],g=M=>`
      let bias${M}_offset: u32 = (global_idx * 4 + ${M}) % uniforms.bias_size;
      let bias${M} = ${d.getByOffset(`bias${M}_offset / 4`)}[bias${M}_offset % 4];`,l=s?`
      let bias = ${d.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${g(0)}${g(1)}${g(2)}${g(3)}
      let bias = ${u.type.value}(bias0, bias1, bias2, bias3);`;return`${i.registerUniforms(w).declareVariables(u,d,p)}

    ${na(Cr(t))}

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

      let x = ${u.getByOffset("global_idx")};
      ${l}
      let x_in = x + bias;
      ${p.setByOffset("global_idx",ri("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${s}`,inputDependencies:["type","type"]},getShaderSource:a,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/Tn/4)}})}},Mu=e=>{e.inputs.length<2||Oe.size(e.inputs[1].dims)===0?fl(e):e.compute(Pa(e.inputs))}}),Aa,vu,xu,Ia,Fd=j(()=>{Xt(),Qt(),pr(),ar(),Aa=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},vu=(e,t)=>{let r=e[0].dims,n=e[1].dims,s=r.length,a=Oe.normalizeAxis(t.axis,s),i=r.slice(0);i.splice(a,1,...n);let u=r[a],d=e[0].dataType===9?4:1,p=Math.ceil(Oe.size(i)/d),w=[{type:12,data:p},{type:6,data:u},{type:12,data:a},...It(e[0].dims,e[1].dims,i)],g=l=>{let M=et("data",e[0].dataType,e[0].dims.length,d),E=et("inputIndices",e[1].dataType,e[1].dims.length),$=Ut("output",e[0].dataType,i.length,d),L=z=>{let ie=n.length,J=`var indicesIndices${z}  = ${E.type.indices}(0);`;for(let oe=0;oe<ie;oe++)J+=`${ie>1?`indicesIndices${z}[${oe}]`:`indicesIndices${z}`} = ${i.length>1?`outputIndices${z}[uniforms.axis + ${oe}]`:`outputIndices${z}`};`;J+=`
          var idx${z} = ${E.getByIndices(`indicesIndices${z}`)};
          if (idx${z} < 0) {
            idx${z} = idx${z} + uniforms.axisDimLimit;
          }
          var dataIndices${z} : ${M.type.indices};
        `;for(let oe=0,Ge=0;oe<s;oe++)oe===a?(J+=`${s>1?`dataIndices${z}[${oe}]`:`dataIndices${z}`} = u32(idx${z});`,Ge+=ie):(J+=`${s>1?`dataIndices${z}[${oe}]`:`dataIndices${z}`} = ${i.length>1?`outputIndices${z}[${Ge}]`:`outputIndices${z}`};`,Ge++);return J},W;if(e[0].dataType===9){let z=(ie,J,oe="")=>`
          let outputIndices${J} = ${$.offsetToIndices(`outputOffset + ${J}u`)};
          ${L(J)};
          let offset${J} = ${M.indicesToOffset(`dataIndices${J}`)};
          let index${J} = offset${J} / 4u;
          let component${J} = offset${J} % 4u;
          ${ie}[${J}] = ${oe}(${M.getByOffset(`index${J}`)}[component${J}]);
        `;W=`
        let outputOffset = global_idx * ${d};
        var value = vec4<u32>(0);
        ${z("value",0,"u32")}
        ${z("value",1,"u32")}
        ${z("value",2,"u32")}
        ${z("value",3,"u32")}
        ${$.setByOffset("global_idx","value")}
      `}else W=`
      let outputIndices = ${$.offsetToIndices("global_idx")};
      ${L("")};
      let value = ${M.getByIndices("dataIndices")};
      ${$.setByOffset("global_idx","value")};
      `;return`
      ${l.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(M,E,$)}
      ${l.mainStart()}
        ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${W}
      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:w}),getShaderSource:g}},xu=e=>qt({axis:e.axis}),Ia=(e,t)=>{let r=e.inputs;Aa(r),e.compute(vu(e.inputs,t))}}),Tu,Eu,Cu,Dr,wc=j(()=>{Xt(),Qt(),pr(),ar(),Tu=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let r=Oe.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,s=e[0],a=e[2],i=e.length===4?e[3]:void 0;if(a.dims.length!==s.dims.length||!s.dims.map((u,d)=>d===r?Math.ceil(u/n)===a.dims[d]:u===a.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(i){if(i.dataType!==s.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(i.dims.length!==a.dims.length||!i.dims.map((u,d)=>u===a.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},Eu=(e,t)=>{let r=e[0].dims,n=e[1].dims,s=r.length,a=Oe.normalizeAxis(t.gatherAxis,s),i=Oe.normalizeAxis(t.quantizeAxis,s),u=r.slice(0);u.splice(a,1,...n);let d=Oe.size(u),p=e[2].dataType,w=e[0].dataType===22,g=[{type:12,data:d},{type:12,data:i},{type:12,data:a},{type:12,data:t.blockSize},...It(...e.map((M,E)=>M.dims),u)],l=M=>{let E=et("data",e[0].dataType,e[0].dims.length),$=et("inputIndices",e[1].dataType,e[1].dims.length),L=et("scales",e[2].dataType,e[2].dims.length),W=e.length>3?et("zeroPoint",e[3].dataType,e[3].dims.length):void 0,z=Ut("output",p,u.length),ie=[E,$,L];W&&ie.push(W);let J=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${M.registerUniforms(J).declareVariables(...ie,z)}
        ${M.mainStart()}
        let output_indices = ${z.offsetToIndices("global_idx")};
        var indices_indices = ${$.type.indices}(0);
        ${n.length>1?`
          for (var i: u32 = 0; i < ${n.length}; i++) {
            let index = ${z.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${$.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${z.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${E.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${z.indicesGet("output_indices","i")};
          ${E.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${$.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${r[a]};
        }
        ${E.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${u.length}; i++) {
          let index = ${z.indicesGet("output_indices",`i + ${n.length} - 1`)};
          ${E.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${E.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${E.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${w?"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 = ${L.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${L.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${L.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 = ${w?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${Cr(p)}(quantized_data - zero_point) * scale;
        ${z.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((M,E)=>E!==1).map(M=>M.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(M,E)=>"rank")},getRunData:()=>({outputs:[{dims:u,dataType:p}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:g}),getShaderSource:l}},Cu=(e,t)=>{let r=e.inputs;Tu(r,t),e.compute(Eu(e.inputs,t))},Dr=e=>qt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),Od,zd,Fa,$u,Dd=j(()=>{Xt(),Qt(),pr(),ar(),Od=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.`)},zd=(e,t)=>{let r=e[0].dims,n=e[0].dataType,s=r.length,a=e[1].dims,i=e[1].dataType,u=Oe.normalizeAxis(t.axis,s),d=r[u],p=a.slice(0),w=Oe.size(p),g=et("input",n,s),l=et("indicesInput",i,a.length),M=Ut("output",n,p.length),E=[{type:12,data:w},{type:6,data:d},{type:12,data:u}];return E.push(...It(r,a,p)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:E}),getShaderSource:$=>`
      ${$.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(g,l,M)}
      ${$.mainStart()}
      ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

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

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

      ${M.setByOffset("global_idx","value")};
  }`}},Fa=e=>qt({axis:e.axis}),$u=(e,t)=>{let r=e.inputs;Od(r),e.compute(zd(e.inputs,t))}}),Su,ku,Oa,za,Bd=j(()=>{Xt(),Qt(),ar(),Su=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")},ku=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[s,a,i]=Er.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),u=[s,a];if(!u)throw new Error("Can't use gemm on the given tensors");let d=Oe.size(u),p=[{type:12,data:d},{type:12,data:s},{type:12,data:a},{type:12,data:i},{type:1,data:t.alpha},{type:1,data:t.beta}],w=["type","type"];e.length===3&&(p.push(...It(e[2].dims)),w.push("rank")),p.push(...It(u));let g=l=>{let M="";t.transA&&t.transB?M="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?M="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?M="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(M="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let E=t.alpha===1?"":"value *= uniforms.alpha;",$=et("a",e[0].dataType,e[0].dims),L=et("b",e[1].dataType,e[1].dims),W=$.type.value,z=null,ie=[$,L];e.length===3&&(z=et("c",e[2].dataType,e[2].dims.length),ie.push(z));let J=Ut("output",e[0].dataType,u.length);ie.push(J);let oe=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`
  ${l.registerUniforms(oe).declareVariables(...ie)}

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

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

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

    ${E}
    ${z!=null?`let cOffset = ${z.broadcastedIndicesToOffset("vec2(m, n)",J)}; value += ${W}(uniforms.beta) * ${z.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:g}},Oa=e=>{let t=e.transA,r=e.transB,n=e.alpha,s=e.beta;return{transA:t,transB:r,alpha:n,beta:s,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},za=(e,t)=>{Su(e.inputs),e.compute(ku(e.inputs,t))}}),_n,Da,Ld,Pu,Au,Rs,Iu,Fu=j(()=>{Xt(),Qt(),pr(),P(),Wi(),ar(),Qn(),_n=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,Da=(e,t)=>{let r=e[0],n=_n(e,1),s=_n(e,2),a=_n(e,3),i=_n(e,4),u=_n(e,5),d=_n(e,6),p=_n(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let w=r.dims[0],g=r.dims[1],l=r.dims.length===3?r.dims[2]:t.numHeads*r.dims[4],M=g,E=0,$=0,L=Math.floor(l/t.numHeads);if(d&&p&&Oe.size(d.dims)&&Oe.size(p.dims)){if(d.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims[0]!==w||d.dims[1]!==t.numHeads||d.dims[3]!==L)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(p.dims[0]!==w||p.dims[1]!==t.numHeads||p.dims[3]!==L)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[2]!==p.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(p.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');E=d.dims[2],$=d.dims[2]}else if(d&&Oe.size(d.dims)||p&&Oe.size(p.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let W;if(n&&Oe.size(n.dims)>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');W=2,M=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==L)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(s)throw new Error('Expect "value" be none when "key" has packed kv format.');W=5,M=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==L)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');W=0,M=n.dims[2]}}else{if(r.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(r.dims[2]!==t.numHeads||r.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');W=3}if(a&&Oe.size(a.dims)>0){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let z=E+M,ie=0;if(i&&Oe.size(i.dims)>0){ie=8;let De=i.dims;throw De.length===1?De[0]===w?ie=1:De[0]===3*w+2&&(ie=3):De.length===2&&De[0]===w&&De[1]===z&&(ie=5),ie===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let J=!1,oe=l;if(s&&Oe.size(s.dims)>0){if(s.dims.length!==3&&s.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==s.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(s.dims.length===3){if(M!==s.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');oe=s.dims[2]}else{if(M!==s.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');oe=s.dims[1]*s.dims[3],J=!0}}let Ge=!1;if(i&&Oe.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(u&&Oe.size(u.dims)>0){if(u.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(u.dims[0]!==w||u.dims[1]!==t.numHeads||u.dims[2]!==g||u.dims[3]!==z)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:w,sequenceLength:g,pastSequenceLength:E,kvSequenceLength:M,totalSequenceLength:z,maxSequenceLength:$,inputHiddenSize:0,hiddenSize:l,vHiddenSize:oe,headSize:L,vHeadSize:Math.floor(oe/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:ie,scale:t.scale,broadcastResPosBias:Ge,passPastInKv:J,qkvFormat:W}},Ld=e=>qt({...e}),Pu=qt({perm:[0,2,1,3]}),Au=(e,t,r,n,s,a,i)=>{let u=[n,s,a],d=Oe.size(u),p=[{type:12,data:d},{type:12,data:i},{type:12,data:a}],w=g=>{let l=Ut("qkv_with_bias",t.dataType,u),M=et("qkv",t.dataType,u),E=et("bias",r.dataType,u),$=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${g.registerUniforms($).declareVariables(M,E,l)}
  ${g.mainStart()}
    ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:w},{inputs:[t,r],outputs:[-1]})[0]},Rs=(e,t,r,n,s,a,i,u)=>{let d=a;if(i&&Oe.size(i.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return d=Au(e,a,i,t,n,r*s,u),d=d.reshape([t,n,r,s]),e.compute(In(d,Pu.perm),{inputs:[d],outputs:[-1]})[0]}else return a.dims.length===3&&(d=a.reshape([t,n,r,s])),e.compute(In(d,Pu.perm),{inputs:[d],outputs:[-1]})[0]},Iu=(e,t)=>{let r=Da(e.inputs,t),n=e.inputs[0],s=_n(e.inputs,1),a=_n(e.inputs,2),i=_n(e.inputs,3),u=_n(e.inputs,4),d=_n(e.inputs,5),p=_n(e.inputs,6),w=_n(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((s==null?void 0:s.dims.length)===5)throw new Error("Packed KV is not implemented");let g=s&&a&&s.dims.length===4&&a.dims.length===4,l=Rs(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(g)return As(e,l,s,a,u,void 0,p,w,d,r,t);if(!s||!a)throw new Error("key and value must be provided");let M=Rs(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,s,i,r.hiddenSize),E=Rs(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,a,i,2*r.hiddenSize);As(e,l,M,E,u,void 0,p,w,d,r,t)}}),Ba,Ou,zu,La,Du,Bu=j(()=>{Xt(),Qt(),ar(),Ba=e=>Array.from(e.getBigInt64Array(),Number),Ou=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(Ba(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")},zu=(e,t)=>{let r=[];for(let n=0;n<e.length;++n)r.push(e[n]*t[n]);return r},La=(e,t)=>{let r=e[0].dims,n=t??Ba(e[1]),s=zu(r,n),a=Oe.size(s),i=e[0].dataType,u=et("input",i,r.length),d=Ut("output",i,s.length),p=w=>`
      const inputShape = ${u.indices(...r)};
      ${w.registerUniform("output_size","u32").declareVariables(u,d)}
      ${w.mainStart()}
      ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${d.offsetToIndices("global_idx")};
      var input_indices: ${u.type.indices};
      for (var i = 0; i < ${r.length}; i++) {
        let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${d.indicesGet("output_indices","i")}  % input_dim_i;

        ${u.indicesSet("input_indices","i","input_dim_value")}
      }
      ${d.setByOffset("global_idx",u.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...It(e[0].dims,s)]}),getShaderSource:p}},Du=e=>{Ou(e.inputs),e.compute(La(e.inputs),{inputs:[0]})}}),Lu,Ra,Ru,Nu,Na,ju,Rd=j(()=>{Xt(),Qt(),pr(),Wi(),ar(),Fu(),Bu(),Qn(),Lu=(e,t)=>{let r=e[0],n=e[1],s=e[2],a=e[3],i=e[4];if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let u=!1,d=r.dims[0],p=r.dims[1],w=r.dims.length===3?u?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],g=p,l=0,M=0,E=Math.floor(w/t.numHeads),$=a&&a.dims.length!==0,L=i&&i.dims.length!==0,W=!0;if($&&L){if(a.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(i.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');l=a.dims[1],M=a.dims[1]}else if($||L)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let z;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(r.dims[2]%n.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');z=2,g=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==E)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(s)throw new Error('Expect "value" be none when "key" has packed kv format.');z=5,g=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==E)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');z=0,g=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');z=3}let ie=0,J=!1,oe=w;if(s){if(s.dims.length!==3&&s.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==s.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(s.dims.length===3){if(g!==s.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');oe=s.dims[2]}else{if(g!==s.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');oe=s.dims[1]*s.dims[3],J=!0}}let Ge=l+g;return{batchSize:d,sequenceLength:p,pastSequenceLength:l,kvSequenceLength:g,totalSequenceLength:Ge,maxSequenceLength:M,inputHiddenSize:0,hiddenSize:w,vHiddenSize:oe,headSize:E,vHeadSize:Math.floor(oe/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:ie,scale:t.scale,broadcastResPosBias:!1,passPastInKv:J,qkvFormat:z,isPastkvBSNH:W}},Ra=(e,t,r,n)=>{let s=[n.batchSize,n.totalSequenceLength,n.kvNumHeads,n.headSize],a=4,i=Oe.size(s)/a,u=n.totalSequenceLength,d=Ut("present_kv",r,s.length,a),p=et("new_kv",e.dataType,e.dims.length,a),w=t?et("past_kv",t.dataType,t.dims.length,a):void 0,g=Math.ceil(n.headSize/a),l={x:u,y:e.dims[0],z:1},M=t?["rank","rank"]:["rank"],E=[{type:12,data:i},{type:12,data:n.pastSequenceLength},{type:12,data:n.kvSequenceLength},{type:12,data:n.totalSequenceLength}],$=[p];w?(E.push(...It(e.dims),...It(t.dims),...It(s)),$.push(w)):E.push(...It(e.dims),...It(s));let L=[{name:"output_size",type:"u32"},{name:"past_seqlen",type:"u32"},{name:"new_seqlen",type:"u32"},{name:"present_seqlen",type:"u32"}],W=`      let past_batch_stride = uniforms.past_seqlen * num_heads * H;
        var past_head_stride = uniforms.past_seqlen * H;
        if (is_bsnh) {
          past_head_stride = H;
        }
        let in_offset = b * past_batch_stride + s * row_stride + n * past_head_stride + h;
        present_kv[out_offset] = past_kv[in_offset];`,z=`      let new_batch_stride = uniforms.new_seqlen * num_heads * H;
        let new_row_stride = num_heads * H;
        let new_head_stride = H;
        let in_offset = b * new_batch_stride + (s - past_seqlen) * new_row_stride + n * new_head_stride + h;
        present_kv[out_offset] = new_kv[in_offset];`,ie=t?`if (s < past_seqlen) {
        ${W}
        } else if (s < past_seqlen + uniforms.new_seqlen) {
        ${z}
        }`:`if (s < past_seqlen + uniforms.new_seqlen) {
          ${z}
        }`,J=oe=>`

  ${oe.registerUniforms(L).declareVariables(...$,d)}
  ${oe.mainStart([g,n.kvNumHeads,1])}
    ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    var indices = ${d.offsetToIndices("global_idx")};
    let h = local_id.x;
    let n = local_id.y;
    let s = workgroup_id.x;
    let b = workgroup_id.y;
    let num_heads = ${n.kvNumHeads}u;
    let H = ${g}u;

    let present_seqlen = uniforms.present_seqlen;
    let present_batch_stride = present_seqlen * num_heads * H;
    var row_stride = H;
    let is_bsnh = ${n.isPastkvBSNH};

    if (is_bsnh) {
      row_stride = num_heads * H;
    }
    var present_head_stride = present_seqlen * H;
    if (is_bsnh) {
      present_head_stride = H;
    }

    let past_seqlen = uniforms.past_seqlen;

    let out_offset = b * present_batch_stride + s * row_stride + n * present_head_stride + h;
    ${ie}
  }`;return{name:"ConcatPastNew",shaderCache:{hint:`${n.kvNumHeads}${g}${!!t}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:s,dataType:r}],dispatchGroup:l,programUniforms:E}),getShaderSource:J}},Ru=e=>qt({...e}),Nu=qt({perm:[0,2,1,3]}),Na=(e,t,r,n,s)=>{let a=t,i=n.kvNumHeads,u=n.nReps;return t.dims.length===3&&n.kvSequenceLength!==0&&(a=t.reshape([n.batchSize,n.kvSequenceLength,i,n.headSize])),r?a=e.compute(Ra(a,r,a.dataType,n),{inputs:[a,r],outputs:[n.isPastkvBSNH?s:-1]})[0]:a=e.compute(Ra(a,void 0,a.dataType,n),{inputs:[a],outputs:[n.isPastkvBSNH?s:-1]})[0],u!==1&&(a=e.compute(La([a],[1,1,1,u]),{inputs:[a],outputs:[-1]})[0],a=a.reshape([n.batchSize,n.totalSequenceLength,i*u,n.headSize])),e.compute(In(a,Nu.perm),{inputs:[a],outputs:[-1]})[0]},ju=(e,t)=>{var d;let r=Lu(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((d=e.inputs[1])==null?void 0:d.dims.length)===5)throw new Error("Packed KV is not implemented");let n=Rs(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,e.inputs[0],void 0,0),s=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,a=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,i=Na(e,e.inputs[1],s,r,1),u=Na(e,e.inputs[2],a,r,2);As(e,n,i,u,void 0,void 0,void 0,void 0,void 0,r,t)}}),Vu,ja,Uu,Wu,Nd=j(()=>{Xt(),Qt(),Qn(),ar(),Vu=(e,t,r,n,s,a,i,u)=>{let d=br(a),p=d===1?"f32":`vec${d}f`,w=d===1?"vec2f":`mat2x${d}f`,g=s*i,l=[s,i,a/d],M=[s,i,2],E=["rank","type","type"],$=[];$.push(...It(l,M));let L=W=>{let z=et("x",t.dataType,3,d),ie=et("scale",r.dataType,r.dims),J=et("bias",n.dataType,n.dims),oe=Ut("output",1,3,2),Ge=[z,ie,J,oe],De=64;return`
  var<workgroup> workgroup_shared : array<${w}, ${De}>;
  const workgroup_size = ${De}u;
  ${W.declareVariables(...Ge)}
  ${W.mainStart(De)}
    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 = ${p}(0);
    var squared_sum = ${p}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${p}(${z.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${w}(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 = ${En("workgroup_shared[0][0]",d)} / f32(hight * ${d});
      let squared_sum_final = ${En("workgroup_shared[0][1]",d)} / f32(hight * ${d});

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${u}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${d};${u}`,inputDependencies:E},getRunData:()=>({outputs:[{dims:M,dataType:1}],dispatchGroup:{x:g},programUniforms:$}),getShaderSource:L},{inputs:[t,r,n],outputs:[-1]})[0]},ja=(e,t,r)=>{let n=t[0].dims,s=n,a=2,i=n[0],u=n[1],d=Oe.sizeFromDimension(n,a),p=br(d),w=Oe.size(s)/p,g=Vu(e,t[0],t[1],t[2],i,d,u,r.epsilon),l=[i,u,d/p],M=[i,u],E=["type","none"],$=L=>{let W=et("x",t[0].dataType,l.length,p),z=et("scale_shift",1,M.length,2),ie=Ut("output",t[0].dataType,l.length,p),J=[W,z,ie];return`
  ${L.registerUniform("output_size","u32").declareVariables(...J)}
  ${L.mainStart()}
  ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let outputIndices = ${ie.offsetToIndices("global_idx")};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${z.getByIndices("vec2<u32>(batch, channel)")};
      let value = ${W.getByOffset("global_idx")} * ${ie.type.value}(scale_shift.x) + ${ie.type.value}(scale_shift.y);
      ${ie.setByOffset("global_idx","value")};
  }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${p}`,inputDependencies:E},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:[{type:12,data:w},...It(l,M,l)]}),getShaderSource:$},{inputs:[t[0],g]})},Uu=(e,t,r)=>{let n=t[0].dims,s=n,a=n[0],i=n[n.length-1],u=Oe.sizeFromDimension(n,1)/i,d=br(i),p=Oe.size(s)/d,w=[{type:12,data:u},{type:12,data:Math.floor(i/d)}],g=["type","type"],l=[0,n.length-1];for(let L=0;L<n.length-2;L++)l.push(L+1);let M=e.compute(In(e.inputs[0],l),{inputs:[e.inputs[0]],outputs:[-1]})[0],E=Vu(e,M,t[1],t[2],a,u,i,r.epsilon),$=L=>{let W=Ar(t[0].dataType),z=d===1?"vec2f":`mat${d}x2f`,ie=Ge=>{let De=Ge===0?"x":"y",pt=d===1?"f32":`vec${d}f`;switch(d){case 1:return`${W}(${pt}(scale.${De}))`;case 2:return`vec2<${W}>(${pt}(scale[0].${De}, scale[1].${De}))`;case 4:return`vec4<${W}>(${pt}(scale[0].${De}, scale[1].${De}, scale[2].${De}, scale[3].${De}))`;default:throw new Error(`Not supported compoents ${d}`)}},J=et("input",t[0].dataType,t[0].dims,d),oe=Ut("output",t[0].dataType,s,d);return`
  @group(0) @binding(0) var<storage, read> input : array<${J.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${z}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${oe.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${L.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], ${ie(0)}, ${ie(1)});
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${d}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:w}),getShaderSource:$},{inputs:[t[0],E]})},Wu=(e,t)=>{t.format==="NHWC"?Uu(e,e.inputs,t):ja(e,e.inputs,t)}}),Gu,qu,Hu,jd=j(()=>{Xt(),Qt(),ar(),Gu=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},qu=(e,t,r)=>{let n=t.simplified,s=e[0].dims,a=e[1],i=!n&&e[2],u=s,d=Oe.normalizeAxis(t.axis,s.length),p=Oe.sizeToDimension(s,d),w=Oe.sizeFromDimension(s,d),g=Oe.size(a.dims),l=i?Oe.size(i.dims):0;if(g!==w||i&&l!==w)throw new Error(`Size of X.shape()[axis:] == ${w}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${g} and bias size of ${l}`);let M=[];for(let oe=0;oe<s.length;++oe)oe<d?M.push(s[oe]):M.push(1);let E=br(w),$=["type","type"],L=[{type:12,data:p},{type:1,data:w},{type:12,data:Math.floor(w/E)},{type:1,data:t.epsilon}];i&&$.push("type");let W=r>1,z=r>2,ie=oe=>{let Ge=Ar(e[0].dataType),De=[et("x",e[0].dataType,e[0].dims,E),et("scale",a.dataType,a.dims,E)];i&&De.push(et("bias",i.dataType,i.dims,E)),De.push(Ut("output",e[0].dataType,u,E)),W&&De.push(Ut("mean_data_output",1,M)),z&&De.push(Ut("inv_std_output",1,M));let pt=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${oe.registerUniforms(pt).declareVariables(...De)}
  ${oe.mainStart()}
    ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${Gr("f32",E)};
    var mean_square_vector = ${Gr("f32",E)};

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

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

    ${W?"mean_data_output[global_idx] = mean":""};
    ${z?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},J=[{dims:u,dataType:e[0].dataType}];return W&&J.push({dims:M,dataType:1}),z&&J.push({dims:M,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${E};${r};${n}`,inputDependencies:$},getRunData:()=>({outputs:J,dispatchGroup:{x:Math.ceil(p/64)},programUniforms:L}),getShaderSource:ie}},Hu=(e,t)=>{Gu(e.inputs),e.compute(qu(e.inputs,t,e.outputCount))}}),Ku,Xu,Qu,Yu,Vd=j(()=>{Xt(),Qt(),pr(),ar(),Ku=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],n=r.dims.length;if(r.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let s=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,i=e[1];if(!Oe.areEqual(i.dims,[t.n,s,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=e[2].dims;if(Oe.size(u)!==t.n*s)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,p=t.bits>4?t.n*s:t.n*Math.floor((s+1)/2);if(Oe.size(d)!==p)throw new Error("zeroPoints input size error.")}},Xu=(e,t)=>{let r=e[0].dims,n=r.length,s=r[n-2],a=t.k,i=t.n,u=r.slice(0,n-2),d=Oe.size(u),p=e[1].dims[2]/4,w=e[0].dataType,g=br(t.k),l=br(p),M=br(i),E=u.concat([s,i]),$=s>1&&i/M%2===0?2:1,L=Oe.size(E)/M/$,W=64,z=[],ie=[d,s,a/g],J=Oe.convertShape(e[1].dims).slice();J.splice(-1,1,p/l),z.push(...It(ie)),z.push(...It(J)),z.push(...It(e[2].dims)),e.length===4&&z.push(...It(Oe.convertShape(e[3].dims)));let oe=[d,s,i/M];z.push(...It(oe));let Ge=De=>{let pt=ie.length,Dt=et("a",e[0].dataType,pt,g),Vt=et("b",12,J.length,l),lr=et("scales",e[2].dataType,e[2].dims.length),fr=[Dt,Vt,lr],tr=e.length===4?et("zero_points",12,e[3].dims.length):void 0;tr&&fr.push(tr);let jr=oe.length,Qr=Ut("output",e[0].dataType,jr,M),Mr=Ar(e[0].dataType),Ur=(()=>{switch(g){case 1:return`array<${Mr}, 8>`;case 2:return`mat4x2<${Mr}>`;case 4:return`mat2x4<${Mr}>`;default:throw new Error(`${g}-component is not supported.`)}})(),Jt=()=>{let Ve=`
          // reuse a data
            var input_offset = ${Dt.indicesToOffset(`${Dt.type.indices}(batch, row, word_offset)`)};
            var a_data: ${Ur};
            for (var j: u32 = 0; j < ${8/g}; j++) {
              a_data[j] = ${Dt.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let St=0;St<M*$;St++)Ve+=`
            b_value = ${l===1?`b${St}_data`:`b${St}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${Ur}(${Array.from({length:4},(rr,Vr)=>`${Mr}(b_value_lower[${Vr}]), ${Mr}(b_value_upper[${Vr}])`).join(", ")});
            b_dequantized_values = ${g===1?`${Ur}(${Array.from({length:8},(rr,Vr)=>`(b_quantized_values[${Vr}] - ${tr?`zero_point${St}`:"zero_point"}) * scale${St}`).join(", ")});`:`(b_quantized_values - ${Ur}(${Array(8).fill(`${tr?`zero_point${St}`:"zero_point"}`).join(",")})) * scale${St};`};
            workgroup_shared[local_id.x * ${$} + ${Math.floor(St/M)}]${M>1?`[${St%M}]`:""} += ${Array.from({length:8/g},(rr,Vr)=>`${g===1?`a_data[${Vr}] * b_dequantized_values[${Vr}]`:`dot(a_data[${Vr}], b_dequantized_values[${Vr}])`}`).join(" + ")};
          `;return Ve},dr=()=>{let Ve=`
            var col_index = col * ${M};
            ${tr?`
            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 = ${Mr}(8);`}
            `;for(let St=0;St<M*$;St++)Ve+=`
            let scale${St} = ${lr.getByOffset("col_index * nBlocksPerCol + block")};
            ${tr?`
            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 = ${tr.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${St} = ${Mr}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return Ve},Or=()=>{let Ve=`col_index = col * ${M};`;for(let St=0;St<M*$;St++)Ve+=`
            let b${St}_data = ${Vt.getByIndices(`${Vt.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return Ve+=`
            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: ${Ur};
            var b_dequantized_values: ${Ur};`,Ve};return`
        var<workgroup> workgroup_shared: array<${Qr.type.value}, ${$*W}>;
        ${De.declareVariables(...fr,Qr)}
        ${De.mainStart([W,1,1])}
          let output_indices = ${Qr.offsetToIndices(`(global_idx / ${W}) * ${$}`)};
          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 * ${t.blockSize/g};
            ${dr()}
            for (var word: u32 = 0; word < ${p}; word += ${l}) {
              ${Or()}
              for (var i: u32 = 0; i < ${l}; i++) {
                ${Jt()}
                word_offset += ${8/g};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${$}) {
            var output_value: ${Qr.type.value} = ${Qr.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 += ${$};
            }
            ${Qr.setByIndices(`${Qr.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${g};${l};${M};${$};${W}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:E,dataType:w}],dispatchGroup:{x:L},programUniforms:z}),getShaderSource:Ge}},Qu=(e,t)=>{Ku(e.inputs,t),e.compute(Xu(e.inputs,t))},Yu=e=>qt(e)}),Ju,Zu,ed,td,rd,nd,sd,id,ad,Ud=j(()=>{Xt(),Qt(),ar(),Ju=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Zu=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
            k = i32(${e.indicesGet("indices",s)}) - ${jt("uniforms.pads",s,r)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${jt("uniforms.x_shape",s,t)})) {
              break;
            }
            offset += k * i32(${jt("uniforms.x_strides",s,t)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${n}
            value = x[offset];
          }
      `},ed=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
                k = i32(${e.indicesGet("indices",s)}) - ${jt("uniforms.pads",s,r)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${jt("uniforms.x_shape",s,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${jt("uniforms.x_shape",s,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${jt("uniforms.x_strides",s,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},td=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
                k = i32(${e.indicesGet("indices",s)}) - ${jt("uniforms.pads",s,r)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${jt("uniforms.x_shape",s,t)})) {
                  k = i32(${jt("uniforms.x_shape",s,t)}) - 1;
                }
                offset += k * i32(${jt("uniforms.x_strides",s,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},rd=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=`
                k = i32(${e.indicesGet("indices",s)}) - ${jt("uniforms.pads",s,r)};
                if (k < 0)  {
                  k += i32(${jt("uniforms.x_shape",s,t)}]);
                }
                if (k >= i32(${jt("uniforms.x_shape",s,t)})) {
                  k -= i32(${jt("uniforms.x_shape",s,t)});
                }
                offset += k * i32(${jt("uniforms.x_strides",s,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${n}
              value = x[offset];
          `},nd=(e,t,r)=>{switch(r.mode){case 0:return Zu(e,t,r.pads.length);case 1:return ed(e,t,r.pads.length);case 2:return td(e,t,r.pads.length);case 3:return rd(e,t,r.pads.length);default:throw new Error("Invalid mode")}},sd=(e,t)=>{let r=Oe.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,s=Oe.size(r),a=[{type:12,data:s},{type:6,data:t.pads}],i=e.length>=3&&e[2].data;t.mode===0&&a.push({type:i?e[2].dataType:1,data:t.value}),a.push(...It(e[0].dims,r));let u=["rank"],d=p=>{let w=Ut("output",e[0].dataType,r.length),g=et("x",e[0].dataType,n.length),l=g.type.value,M=nd(w,n.length,t),E=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&E.push({name:"constant_value",type:i?l:"f32"}),`
            ${p.registerUniforms(E).declareVariables(g,w)}
            ${p.mainStart()}
            ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

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

            var value = ${l}(0);
            ${M}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${i}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Oe.size(r)/64)},programUniforms:a}),getShaderSource:d}},id=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,s=e[0].dims.length,a=new Int32Array(2*s).fill(0);if(e.length>=4){let u=e[3].getBigInt64Array();for(let d=0;d<u.length;d++)a[Number(u[d])]=Number(r[d]),a[Number(u[d])+s]=Number(r[d+u.length])}else r.forEach((u,d)=>a[Number(d)]=Number(u));let i=[];return a.forEach(u=>i.push(u)),{mode:t.mode,value:n,pads:i}}else return t},ad=(e,t)=>{Ju(e.inputs);let r=id(e.inputs,t);e.compute(sd(e.inputs,r),{inputs:[0]})}}),Ns,Va,Ua,Wa,Ga,Wd,or,qa,en,dn,hn,es,Gd,od,Ha,f,m,T,Y,Fe=j(()=>{bt(),Xt(),Qt(),ar(),Ns=e=>{if(k.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Va=(e,t,r)=>{let n=t.format==="NHWC",s=e.dims.slice();n&&s.splice(1,0,s.pop());let a=Object.hasOwnProperty.call(t,"dilations"),i=t.kernelShape.slice(),u=t.strides.slice(),d=a?t.dilations.slice():[],p=t.pads.slice();Sn.adjustPoolAttributes(r,s,i,u,d,p);let w=Sn.computePoolOutputShape(r,s,u,d,i,p,t.autoPad),g=Object.assign({},t);a?Object.assign(g,{kernelShape:i,strides:u,pads:p,dilations:d,cacheKey:t.cacheKey}):Object.assign(g,{kernelShape:i,strides:u,pads:p,cacheKey:t.cacheKey});let l=w.slice();return l.push(l.splice(1,1)[0]),[g,n?l:w]},Ua=(e,t)=>{let r=t.format==="NHWC",n=Oe.size(e),s=Oe.size(t.kernelShape),a=[{type:12,data:n},{type:12,data:s}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let u=t.kernelShape[t.kernelShape.length-1],d=t.strides[t.strides.length-1],p=t.pads[t.pads.length/2-1],w=t.pads[t.pads.length-1],g=!!(p+w);a.push({type:12,data:u},{type:12,data:d},{type:12,data:p},{type:12,data:w}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let l=!1;if(t.kernelShape.length===2){let M=t.kernelShape[t.kernelShape.length-2],E=t.strides[t.strides.length-2],$=t.pads[t.pads.length/2-2],L=t.pads[t.pads.length-2];l=!!($+L),a.push({type:12,data:M},{type:12,data:E},{type:12,data:$},{type:12,data:L}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,i,!0,g,l]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let u=Oe.computeStrides(t.kernelShape);a.push({type:12,data:u},{type:12,data:t.pads},{type:12,data:t.strides}),i.push({name:"kernelStrides",type:"u32",length:u.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let d=t.pads.reduce((p,w)=>p+w);return[a,i,!!d,!1,!1]}},Wa=(e,t,r,n,s,a,i,u,d,p,w,g)=>{let l=s.format==="NHWC",M=t.type.value,E=Ut("output",t.type.tensor,n);if(s.kernelShape.length<=2){let $="",L="",W="",z=r-(l?2:1);if(w?$=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${z}] = indices[${z}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${z}] < 0 || xIndices[${z}]
                      >= uniforms.x_shape[${z}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`:$=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${z}] = indices[${z}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`,s.kernelShape.length===2){let ie=r-(l?3:2);g?L=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${ie}] = indices[${ie}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${ie}] < 0 || xIndices[${ie}] >= uniforms.x_shape[${ie}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:L=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${ie}] = indices[${ie}] * uniforms.sh - uniforms.phStart + j;
                `,W=`
              }
            `}return`
            ${e.registerUniforms(d).declareVariables(t,E)}

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

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

              var value = ${M}(${u});
              var pad = 0;
              ${L}
              ${$}
              ${W}
              ${i}

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

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

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

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

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

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

              output[global_idx] = value;
            }`}},Ga=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Wd=e=>`${Ga(e)};${e.countIncludePad}`,or=e=>`${Ga(e)};${e.storageOrder};${e.dilations}`,qa=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}),en=(e,t,r,n)=>{let[s,a]=Va(t,n,r),i=et("x",t.dataType,t.dims.length),u=i.type.value,d="value += x_val;",p="";s.countIncludePad?p+=`value /= ${u}(uniforms.kernelSize);`:p+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[w,g,l,M,E]=Ua(a,s);w.push(...It(t.dims,a));let $=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${l};${M};${E}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Oe.size(a)/64)},programUniforms:w}),getShaderSource:L=>Wa(L,i,t.dims.length,a.length,s,d,p,0,g,l,M,E)}},dn=e=>{let t=e.count_include_pad!==0,r=qa(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...r,cacheKey:""};return{...n,cacheKey:Wd(n)}},hn=(e,t)=>{Ns(e.inputs),e.compute(en("AveragePool",e.inputs[0],!1,t))},es={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Gd=e=>{let t=e.format;return{format:t,...es,cacheKey:t}},od=(e,t)=>{Ns(e.inputs),e.compute(en("GlobalAveragePool",e.inputs[0],!0,t))},Ha=(e,t,r,n)=>{let[s,a]=Va(t,n,r),i=`
      value = max(x_val, value);
    `,u="",d=et("x",t.dataType,t.dims.length),p=["rank"],[w,g,l,M,E]=Ua(a,s);return w.push(...It(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${l};${M};${E}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Oe.size(a)/64)},programUniforms:w}),getShaderSource:$=>Wa($,d,t.dims.length,a.length,s,i,u,t.dataType===10?-65504:-1e5,g,l,M,E)}},f=(e,t)=>{Ns(e.inputs),e.compute(Ha("MaxPool",e.inputs[0],!1,t))},m=e=>{let t=e.storage_order,r=e.dilations,n=qa(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let s={storageOrder:t,dilations:r,...n,cacheKey:""};return{...s,cacheKey:or(s)}},T=e=>{let t=e.format;return{format:t,...es,cacheKey:t}},Y=(e,t)=>{Ns(e.inputs),e.compute(Ha("GlobalMaxPool",e.inputs[0],!0,t))}}),ze,ht,Ct,Wt,sr=j(()=>{Xt(),Qt(),pr(),ar(),ze=(e,t)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((r,n)=>r===e[2].dims[n]).reduce((r,n)=>r&&n,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(t.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((s,a)=>a===t.axis||s===e[0].dims[a]).reduce((s,a)=>s&&a,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let r=e[0].dims[t.axis],n=e[1].dims[t.axis];if(t.blockSize<Math.ceil(r/n)||t.blockSize>Math.ceil(r/(n-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},ht=(e,t)=>{let r=Oe.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,s=n===3,a=e[0].dims,i=e[1].dataType,u=Oe.size(a),d=n===3||n===2,p=d?[Math.ceil(Oe.size(e[0].dims)/4)]:e[0].dims,w=e[1].dims,g=e.length>2?e[2]:void 0,l=g?d?[Math.ceil(Oe.size(g.dims)/4)]:g.dims:void 0,M=w.length===0||w.length===1&&w[0]===1,E=M===!1&&w.length===1,$=br(u),L=M&&(!d||$===4),W=L?$:1,z=L&&!d?$:1,ie=et("input",d?12:n,p.length,z),J=et("scale",i,w.length),oe=g?et("zero_point",d?12:n,l.length):void 0,Ge=Ut("output",i,a.length,W),De=[ie,J];oe&&De.push(oe);let pt=[p,w];g&&pt.push(l);let Dt=[{type:12,data:u/W},{type:12,data:r},{type:12,data:t.blockSize},...It(...pt,a)],Vt=lr=>{let fr=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${lr.registerUniforms(fr).declareVariables(...De,Ge)}
      ${lr.mainStart()}
          ${lr.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${Ge.offsetToIndices("global_idx")};

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

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

          // Set zero-point input
          ${oe?M?d?`
                let zero_point_input = ${oe.getByOffset("0")};
                let zero_point_vec =  ${s?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${oe.getByOffset("0")}`:E?d?`
                let zero_point_index = ${Ge.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${oe.getByOffset("zero_point_index / 4")};
                let zero_point_vec =  ${s?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${Ge.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${oe.getByOffset("zero_point_index")};`:d?`
                let zero_point_offset = ${J.indicesToOffset("scale_indices")};
                let zero_point_input = ${oe.getByOffset("zero_point_offset / 4")};
                let zero_point_vec = ${s?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${oe.getByIndices("scale_indices")};`:`let zero_point_value = ${d?s?"i32":"u32":ie.type.value}(0);`};
      // Compute and write output
      ${Ge.setByOffset("global_idx",`${Ge.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:oe?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Vt,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(u/W/64),y:1,z:1},programUniforms:Dt})}},Ct=(e,t)=>{ze(e.inputs,t),e.compute(ht(e.inputs,t))},Wt=e=>qt({axis:e.axis,blockSize:e.blockSize})}),Ir,ur,hr,wr=j(()=>{bt(),Xt(),ar(),Ir=(e,t,r)=>{let n=e===t,s=e<t&&r<0,a=e>t&&r>0;if(n||s||a)throw new Error("Range these inputs' contents are invalid.")},ur=(e,t,r,n)=>{let s=Math.abs(Math.ceil((t-e)/r)),a=[s],i=s,u=[{type:12,data:i},{type:n,data:e},{type:n,data:r},...It(a)],d=p=>{let w=Ut("output",n,a.length),g=w.type.value,l=[{name:"outputSize",type:"u32"},{name:"start",type:g},{name:"delta",type:g}];return`
        ${p.registerUniforms(l).declareVariables(w)}
        ${p.mainStart()}
        ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${g}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:u})}},hr=e=>{let t=0,r=0,n=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),k.webgpu.validateInputContent&&Ir(t,r,n),e.compute(ur(t,r,n,e.inputs[0].dataType),{inputs:[]})}}),Sr,Fr,kr,_r,tn,rn,wn,cn,an,yn,ws,js,Ka,yc,Rn,ys,qd,Hd,Kd,Xd=j(()=>{Xt(),Qt(),pr(),ar(),Sr=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},Fr=(e,t,r)=>{t.every(s=>s>=0&&s<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return t.forEach((s,a)=>n[s]=e[a]),n},kr=(e,t,r,n,s,a)=>{let[i,u,d]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],p=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(w=>a.push(w));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(u>0&&e.length>u&&e[u].dims.length>0){if(e[u].getFloat32Array().forEach(w=>n.push(w)),n.length!==0&&n.length!==p&&r>=18&&n.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");Sr(n,t),t.axes.length>0&&Fr(n,t.axes,p).forEach((w,g)=>n[g]=w)}if(d>0&&e.length>d&&(e[d].getBigInt64Array().forEach(w=>s.push(Number(w))),s.length!==p||r>=18&&s.length===t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(n.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(s.length!==t.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof s<"u"&&n.length>0&&s.length>p)throw new Error("Resize requires only of scales or sizes to be specified")},_r=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating
                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
                    // offset-by-one error later in floor().
                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));
                    let fract =
                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);
                    return whole + fract;
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +
                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /
                        ${t}(lengthResized - 1);
                  } else {
                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);
                  const adjustment = ${t}(lengthResized) / outputWidth;
                  const center = ${t}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",tn=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",rn=(e,t,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),s=e.length===0?n:e.slice();return t.length>0?(t.forEach((a,i)=>{n[a]=s[i],n[i+r]=s[t.length+i]}),n):s},wn=(e,t,r,n)=>{let s=[];if(r.length>0)if(n.length>0){if(e.forEach(a=>s.push(a)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((a,i)=>s[a]=r[i])}else r.forEach(a=>s.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");s=e.map((a,i)=>Math.round(a*t[i]))}return s},cn=(e,t,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(a=>t[a]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(a=>t[a]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let s=e.slice();return r.axes.length>0?(r.axes.forEach(a=>t[a]=n),r.axes.forEach(a=>s[a]=Math.round(e[a]*t[a]))):(t.fill(n,0,t.length),s.forEach((a,i)=>s[i]=Math.round(a*t[i]))),s},an=(e,t,r,n,s)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {
      var original_indices: array<${e.type.value}, ${r.length}>;
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var scale = ${jt("uniforms.scales","i",n)};
        var roi_low = ${jt("uniforms.roi","i",s)};
        var roi_hi = ${jt("uniforms.roi",`i + ${t.length}`,s)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${jt("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${jt("uniforms.output_shape","i",r.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,yn=(e,t,r,n,s,a,i)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
      var input_indices: ${e.type.indices};
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${t.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${jt("uniforms.scales","i",s)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${jt("uniforms.roi","i",a)};
          var roi_hi = ${jt("uniforms.roi",`i + ${r.length}`,a)};
          var input_shape_i = ${jt("uniforms.input_shape","i",r.length)};
          var output_shape_i = ${jt("uniforms.output_shape","i",n.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${i} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${e.indicesSet("input_indices","i"," input_index")}
      }
      return input_indices;
    }`,ws=(e,t)=>`
    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var input_index = ${e.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${jt("uniforms.input_shape","i",t.length)}) {
          return false;
        }
      }
      return true;
    }`,js=(e,t,r,n)=>e.rank>n?`
    ${e.indicesSet("input_indices",t,"channel")};
    ${e.indicesSet("input_indices",r,"batch")};
`:"",Ka=(e,t,r,n,s)=>{let[a,i,u,d]=r.length===2?[-1,0,1,-1]:[0,2,3,1],p=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${p} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",i,`max(0, min(row, ${r[i]} - 1))`)};
      ${e.indicesSet("input_indices",u,`max(0, min(col, ${r[u]} - 1))`)};
      ${js(e,d,a,2)}
      return ${e.getByIndices("input_indices")};
    }

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

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

  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${M} {
    var input_indices: ${e.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},Rn=(e,t,r,n,s)=>{let[a,i,u,d,p]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],w=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${w} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",i,`max(0, min(depth, ${r[i]} - 1))`)};
      ${e.indicesSet("input_indices",u,`max(0, min(height, ${r[u]} - 1))`)};
      ${e.indicesSet("input_indices",d,`max(0, min(width, ${r[d]} - 1))`)};
      ${js(e,p,a,3)}
      return ${e.getByIndices("input_indices")};
    }

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

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

      var x111: ${w} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${w} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${w} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${w} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${w} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${w} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${w} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${w} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${w} = abs(depth - ${w}(depth1));
      var dx2: ${w} = abs(${w}(depth2) - depth);
      var dy1: ${w} = abs(height - ${w}(height1));
      var dy2: ${w} = abs(${w}(height2) - height);
      var dz1: ${w} = abs(width - ${w}(width1));
      var dz2: ${w} = abs(${w}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},ys=(e,t,r,n,s,a)=>{let i=e.dims,u=rn(a,t.axes,i.length),d=wn(i,n,s,t.axes),p=n.slice();n.length===0&&(p=i.map((z,ie)=>z===0?1:d[ie]/z),t.keepAspectRatioPolicy!=="stretch"&&(d=cn(i,p,t)));let w=Ut("output",e.dataType,d.length),g=et("input",e.dataType,i.length),l=Oe.size(d),M=i.length===d.length&&i.every((z,ie)=>z===d[ie]),E=t.coordinateTransformMode==="tf_crop_and_resize",$=t.extrapolationValue,L=g.type.value,W=z=>`
      ${M?"":`
      ${_r(t.coordinateTransformMode,L)};
      ${(()=>{switch(t.mode){case"nearest":return`
              ${ws(g,i)};
              ${tn(t.nearestMode,r,L)};
              ${yn(g,w,i,d,p.length,u.length,E)};
              `;case"linear":return`
              ${an(w,i,d,p.length,u.length)};
              ${(()=>{if(i.length===2||i.length===4)return`${Ka(g,w,i,E,$)}`;if(i.length===3||i.length===5)return`${Rn(g,w,i,E,$)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(i.length===2||i.length===4)return`${yc(g,w,i,d,p,u,t.cubicCoeffA,E,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${z.registerUniform("output_size","u32").registerUniform("scales","f32",p.length).registerUniform("roi","f32",u.length).declareVariables(g,w)}
      ${z.mainStart()}
        ${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${M?"output[global_idx] = input[global_idx];":`
        let output_indices = ${w.offsetToIndices("global_idx")};
        var input_indices: ${g.type.indices};
        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${g.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${t.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${p.length>0?p:""}|${s.length>0?s:""}|${u.length>0?u:""}|${M}|${i}`,inputDependencies:["rank"]},getShaderSource:W,getRunData:()=>({outputs:[{dims:d,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:[{type:12,data:l},{type:1,data:p},{type:1,data:u},...It(i,d)]})}},qd=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Hd=(e,t)=>{let r=[],n=[],s=[],a=qd(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");kr(e.inputs,t,a,r,n,s),e.compute(ys(e.inputs[0],t,a,r,n,s),{inputs:[0]})},Kd=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,s=e.cubicCoeffA,a=e.excludeOutside!==0,i=e.extrapolationValue,u=e.keepAspectRatioPolicy,d=e.mode,p=e.nearestMode===""?"simple":e.nearestMode;return qt({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:s,excludeOutside:a,extrapolationValue:i,keepAspectRatioPolicy:u,mode:d,nearestMode:p})}}),rp,np,sp,Pf=j(()=>{Xt(),Qt(),pr(),ar(),rp=(e,t)=>{let[r,n,s,a]=e,{numHeads:i,rotaryEmbeddingDim:u}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!Oe.areEqual(n.dims,[])&&!Oe.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(s.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${s.dims.length}`);if(a.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!Oe.areEqual(s.dims,a.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(u>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let d=r.dims[0],p=r.dims[r.dims.length-2],w=s.dims[0],g=Oe.sizeFromDimension(r.dims,1)/p,l=u===0?s.dims[1]*2:g/i;if(u>l)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(d!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(p!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(l/2!==s.dims[1]&&u/2!==s.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${s.dims[1]}`);if(p>w)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},np=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:s,scale:a}=t,i=e[0].dims[0],u=Oe.sizeFromDimension(e[0].dims,1),d=e[0].dims[e[0].dims.length-2],p=u/d,w=e[2].dims[1],g=s===0?w*2:p/n,l=new Array(i,d,p/g,g-w),M=Oe.computeStrides(l),E=[{type:1,data:a},{type:12,data:l},{type:12,data:M},...e[0].dims.length===3?new Array({type:12,data:[u,p,g,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[u,g,d*g,1]}):[],...It(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],$=L=>{let W=et("input",e[0].dataType,e[0].dims.length),z=et("position_ids",e[1].dataType,e[1].dims.length),ie=et("cos_cache",e[2].dataType,e[2].dims.length),J=et("sin_cache",e[3].dataType,e[3].dims.length),oe=Ut("output",e[0].dataType,e[0].dims.length);return L.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:l.length},{name:"global_strides",type:"u32",length:M.length},{name:"input_output_strides",type:"u32",length:M.length}]),`
        ${L.declareVariables(W,z,ie,J,oe)}

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

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${z.broadcastedIndicesToOffset("bsnh.xy",Ut("",z.type.tensor,2))};
            let position_id =
                u32(${z.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r});
            let j = i + select(half_rotary_emb_dim, 1, ${r});
            let re = ${W.getByOffset("i")} * ${ie.get("position_id","bsnh[3]")} -
                ${W.getByOffset("j")} * ${J.get("position_id","bsnh[3]")};
            ${oe.setByOffset("i","re")}
            let im = ${W.getByOffset("i")} * ${J.get("position_id","bsnh[3]")} +
                ${W.getByOffset("j")} * ${ie.get("position_id","bsnh[3]")};
            ${oe.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${oe.setByOffset("k",W.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:qt({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:$,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Oe.size(l)/Tn)},programUniforms:E})}},sp=(e,t)=>{rp(e.inputs,t),e.compute(np(e.inputs,t))}}),ip,ap,op,Af=j(()=>{Xt(),Qt(),ar(),ip=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],n=e[2];if(t.dataType!==r.dataType||t.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let s=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==s)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==a)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==s)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let i=e[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==s)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let i=e[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==s)throw new Error("Bias must have the same hidden size as input")}},ap=(e,t,r,n)=>{let s=t.simplified,a=e[0].dims,i=Oe.size(a),u=a,d=i,p=a.slice(-1)[0],w=n?a.slice(0,-1).concat(1):[],g=!s&&e.length>3,l=e.length>4,M=n&&r>1,E=n&&r>2,$=r>3,L=64,W=br(p),z=[{type:12,data:d},{type:12,data:W},{type:12,data:p},{type:1,data:t.epsilon}],ie=oe=>{let Ge=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],De=[et("x",e[0].dataType,e[0].dims,W),et("skip",e[1].dataType,e[1].dims,W),et("gamma",e[2].dataType,e[2].dims,W)];g&&De.push(et("beta",e[3].dataType,e[3].dims,W)),l&&De.push(et("bias",e[4].dataType,e[4].dims,W)),De.push(Ut("output",e[0].dataType,u,W)),M&&De.push(Ut("mean_output",1,w)),E&&De.push(Ut("inv_std_output",1,w)),$&&De.push(Ut("input_skip_bias_sum",e[0].dataType,u,W));let pt=Ar(e[0].dataType),Dt=Ar(1,W);return`

      ${oe.registerUniforms(Ge).declareVariables(...De)}
      var<workgroup> sum_shared : array<${Dt}, ${L}>;
      var<workgroup> sum_squared_shared : array<${Dt}, ${L}>;

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

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

        var reduce_size : u32 = ${L};
        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 = ${En("sum",W)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${En("square_sum",W)} / f32(uniforms.hidden_size) ${s?"":"- mean * mean"} + uniforms.epsilon);
        ${M?"mean_output[global_idx] = mean;":""}
        ${E?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${s?"":`- ${pt}(mean)`}) *
            ${pt}(inv_std_dev) * gamma[offset1d + i]
            ${g?"+ beta[offset1d + i]":""};
        }
      }`},J=[{dims:u,dataType:e[0].dataType}];return r>1&&J.push({dims:w,dataType:1}),r>2&&J.push({dims:w,dataType:1}),r>3&&J.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${W};${M};${E};${$}`,inputDependencies:e.map((oe,Ge)=>"type")},getShaderSource:ie,getRunData:()=>({outputs:J,dispatchGroup:{x:Math.ceil(d/p)},programUniforms:z})}},op=(e,t)=>{ip(e.inputs);let r=[0];e.outputCount>1&&r.push(-3),e.outputCount>2&&r.push(-3),e.outputCount>3&&r.push(3),e.compute(ap(e.inputs,t,e.outputCount,!1),{outputs:r})}}),lp,ld,up,bc,dp,cp,pp,hp,If=j(()=>{Xt(),Qt(),pr(),ar(),lp=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,n)=>{if(e[n+1].dataType!==6&&e[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},ld=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},up=(e,t)=>{if(e.length>1){let r=ld(e,1),n=ld(e,2),s=ld(e,3);return s.length===0&&(s=[...Array(e[0].dims.length).keys()]),qt({starts:r,ends:n,axes:s})}else return t},bc=(e,t,r,n,s)=>{let a=e;return e<0&&(a+=r[n[t]]),s[t]<0?Math.max(0,Math.min(a,r[n[t]]-1)):Math.max(0,Math.min(a,r[n[t]]))},dp=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${r.length}; i >= 0; i--) {
            let input_shape_i = ${jt("uniforms.input_shape","i",r.length)};
            let steps_i = ${jt("uniforms.steps","i",r.length)};
            let signs_i = ${jt("uniforms.signs","i",r.length)};
            let starts_i = ${jt("uniforms.starts","i",r.length)};
            var output_index = ${t.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${e.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,cp=(e,t)=>{let r=e[0].dims,n=Oe.size(r),s=t.axes.length>0?Oe.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],a=ld(e,4);a.forEach(W=>W!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(s.length).fill(1));let i=t.starts.map((W,z)=>bc(W,z,r,s,a)),u=t.ends.map((W,z)=>bc(W,z,r,s,a));if(s.length!==i.length||s.length!==u.length)throw new Error("start, ends and axes should have the same number of elements");if(s.length!==r.length)for(let W=0;W<r.length;++W)s.includes(W)||(i.splice(W,0,0),u.splice(W,0,r[W]),a.splice(W,0,1));let d=a.map(W=>Math.sign(W));a.forEach((W,z,ie)=>{if(W<0){let J=(u[z]-i[z])/W,oe=i[z],Ge=oe+J*a[z];i[z]=Ge,u[z]=oe,ie[z]=-W}});let p=r.slice(0);s.forEach((W,z)=>{p[W]=Math.ceil((u[W]-i[W])/a[W])});let w={dims:p,dataType:e[0].dataType},g=Ut("output",e[0].dataType,p.length),l=et("input",e[0].dataType,e[0].dims.length),M=Oe.size(p),E=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:d.length},{name:"steps",type:"u32",length:a.length}],$=[{type:12,data:M},{type:12,data:i},{type:6,data:d},{type:12,data:a},...It(e[0].dims,p)],L=W=>`
      ${W.registerUniforms(E).declareVariables(l,g)}
        ${dp(l,g,r)}
        ${W.mainStart()}
          ${W.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${g.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${g.setByOffset("global_idx",l.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${d.length}_${i.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:L,getRunData:()=>({outputs:[w],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:$})}},pp=(e,t)=>{lp(e.inputs,t);let r=up(e.inputs,t);e.compute(cp(e.inputs,r),{inputs:[0]})},hp=e=>{let t=e.starts,r=e.ends,n=e.axes;return qt({starts:t,ends:r,axes:n})}}),fp,mp,_p,gp,Ff=j(()=>{Xt(),Qt(),pr(),ar(),fp=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},mp=(e,t)=>{let r=e.dims,n=Oe.size(r),s=64,a=t.axis;if(a<0&&(a=r.length+a),a<r.length-1)throw new Error("softmax only supports last axis for now.");let i=r[a],u=n/i,d=br(i),p=i/d,w=(L,W)=>W===4?`max(max(${L}.x, ${L}.y), max(${L}.z, ${L}.w))`:W===2?`max(${L}.x, ${L}.y)`:W===3?`max(max(${L}.x, ${L}.y), ${L}.z)`:L,g=et("x",e.dataType,e.dims,d),l=Ut("result",e.dataType,e.dims,d),M=g.type.value,E=Ar(e.dataType)==="f32"?`var threadMax = ${M}(-3.402823e+38f);`:`var threadMax = ${M}(-65504.0h);`,$=L=>`
      var<workgroup> rowMaxShared : ${M};
      var<workgroup> rowSumShared : ${M};
      var<workgroup> threadShared : array<${M}, ${s}>;

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

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${M}) {
        let index = row * row_stride + col;
        result[index] = value;
      }
      ${L.registerUniform("packedCols","i32").declareVariables(g,l)}
      ${L.mainStart()}
        let gindex = i32(global_idx);
        let lindex = i32(local_idx);
        const wg = ${s};
        let row = gindex / wg;
        let cols = uniforms.packedCols;
        let row_stride : i32 = uniforms.packedCols;

        // find the rows max
        ${E}
        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 = ${M}(${w("threadShared[0]",d)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${M}(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 = ${M}(${En("threadShared[0]",d)});
        }
        workgroupBarrier();

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          setValue(row, col, row_stride, value);
        }
      }`;return{name:"Softmax",shaderCache:{hint:`${d}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:u},programUniforms:[{type:6,data:p}]}),getShaderSource:$}},_p=(e,t)=>{fp(e.inputs),e.compute(mp(e.inputs[0],t))},gp=e=>qt({axis:e.axis})}),wp,yp,bp,Mp,vp,xp,Tp,Of=j(()=>{Xt(),Qt(),pr(),ar(),wp=e=>{if(!e||e.length<1)throw new Error("too few inputs")},yp=(e,t)=>{let r=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(s=>r.push(Number(s))),n=r.length),qt({numOutputs:n,axis:t.axis,splitSizes:r})},bp=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${jt("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,Mp=e=>{let t=e.length,r=[];for(let n=0;n<t;++n){let s=e[n].setByIndices("indices","input[global_idx]");t===1?r.push(s):n===0?r.push(`if (output_number == ${n}u) { ${s} }`):n===t-1?r.push(`else { ${s} }`):r.push(`else if (output_number == ${n}) { ${s} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${r.join(`
`)}
      }`},vp=(e,t)=>{let r=e[0].dims,n=Oe.size(r),s=e[0].dataType,a=Oe.normalizeAxis(t.axis,r.length),i=new Array(t.numOutputs),u=et("input",s,r.length),d=new Array(t.numOutputs),p=[],w=[],g=0,l=[{type:12,data:n}];for(let E=0;E<t.numOutputs;E++){g+=t.splitSizes[E],d[E]=g;let $=r.slice();$[a]=t.splitSizes[E],w.push($),i[E]=Ut(`output${E}`,s,$.length),p.push({dims:w[E],dataType:e[0].dataType})}l.push({type:12,data:d},...It(r,...w));let M=E=>`
  ${E.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",d.length).declareVariables(u,...i)}
  ${bp(d.length)}
  ${Mp(i)}

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

    var indices = ${u.offsetToIndices("global_idx")};
    var index = ${u.indicesGet("indices",a)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${jt("uniforms.size_in_split_axis","output_number - 1u",d.length)};
      ${u.indicesSet("indices",a,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:M,getRunData:()=>({outputs:p,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:l})}},xp=(e,t)=>{wp(e.inputs);let r=e.inputs.length===1?t:yp(e.inputs,t);e.compute(vp(e.inputs,r),{inputs:[0]})},Tp=e=>{let t=e.axis,r=e.splitSizes,n=e.numOutputs<0?r.length:e.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return qt({axis:t,numOutputs:n,splitSizes:r})}}),Ep,Cp,$p,zf=j(()=>{Xt(),Qt(),ar(),Ep=(e,t,r,n,s)=>{let a=Ut("output_data",s,r.length,4),i=et("a_data",t[1].dataType,t[1].dims.length,4),u=et("b_data",t[2].dataType,t[2].dims.length,4),d=et("c_data",t[0].dataType,t[0].dims.length,4),p,w=(g,l,M)=>`select(${l}, ${g}, ${M})`;if(!n)p=a.setByOffset("global_idx",w(i.getByOffset("global_idx"),u.getByOffset("global_idx"),d.getByOffset("global_idx")));else{let g=(l,M,E="")=>{let $=`a_data[index_a${M}][component_a${M}]`,L=`b_data[index_b${M}][component_b${M}]`,W=`bool(c_data[index_c${M}] & (0xffu << (component_c${M} * 8)))`;return`
            let output_indices${M} = ${a.offsetToIndices(`global_idx * 4u + ${M}u`)};
            let offset_a${M} = ${i.broadcastedIndicesToOffset(`output_indices${M}`,a)};
            let offset_b${M} = ${u.broadcastedIndicesToOffset(`output_indices${M}`,a)};
            let offset_c${M} = ${d.broadcastedIndicesToOffset(`output_indices${M}`,a)};
            let index_a${M} = offset_a${M} / 4u;
            let index_b${M} = offset_b${M} / 4u;
            let index_c${M} = offset_c${M} / 4u;
            let component_a${M} = offset_a${M} % 4u;
            let component_b${M} = offset_b${M} % 4u;
            let component_c${M} = offset_c${M} % 4u;
            ${l}[${M}] = ${E}(${w($,L,W)});
          `};s===9?p=`
            var data = vec4<u32>(0);
            ${g("data",0,"u32")}
            ${g("data",1,"u32")}
            ${g("data",2,"u32")}
            ${g("data",3,"u32")}
            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:p=`
            ${g("output_data[global_idx]",0)}
            ${g("output_data[global_idx]",1)}
            ${g("output_data[global_idx]",2)}
            ${g("output_data[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(d,i,u,a)}
        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${p}
      }`},Cp=e=>{let t=e[1].dims,r=e[2].dims,n=e[0].dims,s=e[1].dataType,a=!(Oe.areEqual(t,r)&&Oe.areEqual(r,n)),i=t,u=Oe.size(t);if(a){let p=ln.calcShape(ln.calcShape(t,r,!1),n,!1);if(!p)throw new Error("Can't perform where op on the given tensors");i=p,u=Oe.size(i)}let d=Math.ceil(u/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:p=>Ep(p,e,i,a,s),getRunData:()=>({outputs:[{dims:i,dataType:s}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:d},...It(n,t,r,i)]})}},$p=e=>{e.compute(Cp(e.inputs))}}),Sp,Df=j(()=>{Oo(),Wi(),wd(),Wo(),yd(),bd(),Fl(),Td(),iu(),Sd(),kd(),wu(),Ad(),Id(),Fd(),wc(),Dd(),Bd(),Rd(),Nd(),jd(),Kl(),Vd(),Fu(),Ud(),Fe(),sr(),wr(),Li(),Xd(),Pf(),Af(),If(),Ff(),Of(),Bu(),Qn(),aa(),zf(),Sp=new Map([["Abs",[qo]],["Acos",[Ho]],["Acosh",[Hi]],["Add",[ua]],["ArgMax",[Fo,ji]],["ArgMin",[Ni,ji]],["Asin",[Ko]],["Asinh",[Xo]],["Atan",[Qo]],["Atanh",[Yo]],["Attention",[Lo]],["AveragePool",[hn,dn]],["BatchNormalization",[qi]],["BiasAdd",[Uo]],["BiasSplitGelu",[yl]],["Cast",[Jo,Ki]],["Ceil",[el]],["Clip",[Xi]],["Concat",[Al,Il]],["Conv",[ba,wa]],["ConvTranspose",[su,eu]],["Cos",[tl]],["Cosh",[Qi]],["CumSum",[ou,Ta]],["DepthToSpace",[du,cu]],["DequantizeLinear",[Ct,Wt]],["Div",[Ml]],["Einsum",[_u,gu]],["Elu",[rl,Is]],["Equal",[vl]],["Erf",[nl]],["Exp",[sl]],["Expand",[bu]],["FastGelu",[Mu]],["Floor",[Yi]],["FusedConv",[ba,wa]],["Gather",[Ia,xu]],["GatherElements",[$u,Fa]],["GatherBlockQuantized",[Cu,Dr]],["Gelu",[il]],["Gemm",[za,Oa]],["GlobalAveragePool",[od,Gd]],["GlobalMaxPool",[Y,T]],["Greater",[El]],["GreaterOrEqual",[$l]],["GroupQueryAttention",[ju,Ru]],["HardSigmoid",[ea,ul]],["InstanceNormalization",[Wu]],["LayerNormalization",[Hu]],["LeakyRelu",[al,Is]],["Less",[Cl]],["LessOrEqual",[da]],["Log",[sa]],["MatMul",[Hl]],["MatMulNBits",[Qu,Yu]],["MaxPool",[f,m]],["Mul",[xl]],["MultiHeadAttention",[Iu,Ld]],["Neg",[ol]],["Not",[ti]],["Pad",[ad]],["Pow",[Tl]],["QuickGelu",[ia,Is]],["Range",[hr]],["Reciprocal",[ll]],["ReduceMin",[Po]],["ReduceMean",[Oi]],["ReduceMax",[ko]],["ReduceSum",[Ao]],["ReduceProd",[Di]],["ReduceL1",[$o]],["ReduceL2",[So]],["ReduceLogSum",[Bi]],["ReduceLogSumExp",[zi]],["ReduceSumSquare",[Io]],["Relu",[Ji]],["Resize",[Hd,Kd]],["RotaryEmbedding",[sp]],["Sigmoid",[Zi]],["Sin",[dl]],["Sinh",[cl]],["Slice",[pp,hp]],["SkipLayerNormalization",[op]],["Split",[xp,Tp]],["Sqrt",[pl]],["Softmax",[_p,gp]],["Sub",[ni]],["Tan",[ta]],["Tanh",[hl]],["ThresholdedRelu",[ml,Is]],["Tile",[Du]],["Transpose",[_d,so]],["Where",[$p]]])}),kp,Bf=j(()=>{bt(),vn(),ar(),kp=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,t){this.repo.set(e,t)}run(e,t,r,n,s){qe(e.programInfo.name);let a=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let u=[];for(let p of t)u.push({binding:u.length,resource:{buffer:p.buffer}});for(let p of r)u.push({binding:u.length,resource:{buffer:p.buffer}});s&&u.push({binding:u.length,resource:s});let d=a.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:u,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let p={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:d,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(p)}i.setPipeline(e.computePipeline),i.setBindGroup(0,d),i.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Ue(e.programInfo.name)}dispose(){}build(e,t){qe(e.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let s=to(t,this.backend.device.limits),a=e.getShaderSource(s),i=`${n.join(`
`)}
${s.additionalImplementations}
${a}`,u=r.createShaderModule({code:i,label:e.name});Wr("verbose",()=>`[WebGPU] ${e.name} shader code: ${i}`);let d=r.createComputePipeline({compute:{module:u,entryPoint:"main"},layout:"auto",label:e.name});return Ue(e.name),{programInfo:e,computePipeline:d,uniformVariablesInfo:s.variablesInfo}}normalizeDispatchGroupSize(e){let t=typeof e=="number"?e:e.x,r=typeof e=="number"?1:e.y||1,n=typeof e=="number"?1:e.z||1,s=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(t<=s&&r<=s&&n<=s)return[t,r,n];let a=t*r*n,i=Math.ceil(Math.sqrt(a));if(i>s){if(i=Math.ceil(Math.cbrt(a)),i>s)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),Pp,Ap,Ip,Fp,Lf=j(()=>{bt(),Xt(),vn(),_(),zr(),Df(),Bf(),Pp=(e,t)=>{if(t.length!==e.length)throw new Error(`inputDependencies length ${t.length} is not equal to inputTensors length ${e.length}.`);let r=[];for(let n=0;n<e.length;++n){let s=e[n].dataType;switch(t[n]){case"none":{r.push("");break}case"type":{r.push(`${s}`);break}case"rank":{let a=e[n].dims.length;r.push(`${s};${a}`);break}case"dims":{let a=e[n].dims.join(",");r.push(`${s};${a}`);break}default:throw new Error(`unsupported input dependency: ${t[n]}`)}}return r.join("|")},Ap=(e,t,r)=>{var s,a;let n=e.name;return(s=e.shaderCache)!=null&&s.hint&&(n+="["+e.shaderCache.hint+"]"),n+=":"+r+`:${Pp(t,((a=e.shaderCache)==null?void 0:a.inputDependencies)??new Array(t.length).fill("dims"))}`,n},Ip=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},Fp=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,t){this.env=e;let r=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:t.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:t.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:t.limits.maxStorageBufferBindingSize,maxBufferSize:t.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:t.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:t.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:t.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:t.limits.maxComputeWorkgroupSizeZ},requiredFeatures:r};t.features.has("chromium-experimental-timestamp-query-inside-passes")?r.push("chromium-experimental-timestamp-query-inside-passes"):t.features.has("timestamp-query")&&r.push("timestamp-query"),t.features.has("shader-f16")&&r.push("shader-f16"),this.device=await t.requestDevice(n),this.adapterInfo=new Ip(t.info||await t.requestAdapterInfo()),this.gpuDataManager=er(this),this.programManager=new kp(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,ms(e.logLevel,!!e.debug),this.device.onuncapturederror=s=>{s.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${s.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:t,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),t={};this.queryType==="at-passes"&&(t.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(t)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;qe(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var n;let t=new BigUint64Array(e.getMappedRange()),r=this.pendingQueries.get(e);for(let s=0;s<t.length/2;s++){let a=r[s],i=a.kernelId,u=this.kernels.get(i),d=u.kernelType,p=u.kernelName,w=a.programName,g=a.inputTensorViews,l=a.outputTensorViews,M=t[s*2],E=t[s*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=M);let $=Number(M-this.queryTimeBase),L=Number(E-this.queryTimeBase);if(!Number.isSafeInteger($)||!Number.isSafeInteger(L))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:g.map(W=>({dims:W.dims,dataType:Ln(W.dataType)})),outputsMetadata:l.map(W=>({dims:W.dims,dataType:Ln(W.dataType)})),kernelId:i,kernelType:d,kernelName:p,programName:w,startTime:$,endTime:L});else{let W="";g.forEach((ie,J)=>{W+=`input[${J}]: [${ie.dims}] | ${Ln(ie.dataType)}, `});let z="";l.forEach((ie,J)=>{z+=`output[${J}]: [${ie.dims}] | ${Ln(ie.dataType)}, `}),console.log(`[profiling] kernel "${i}|${d}|${p}|${w}" ${W}${z}execution time: ${L-$} ns`)}ke("GPU",`${w}::${M}::${E}`)}e.unmap(),this.pendingQueries.delete(e)}),Ue()}run(e,t,r,n,s,a){qe(e.name);let i=[];for(let z=0;z<t.length;++z){let ie=t[z].data;if(ie===0)continue;let J=this.gpuDataManager.get(ie);if(!J)throw new Error(`no GPU data for input: ${ie}`);i.push(J)}let{outputs:u,dispatchGroup:d,programUniforms:p}=e.getRunData(t),w=r.length===0?u.map((z,ie)=>ie):r;if(w.length!==u.length)throw new Error(`Output size ${w.length} must be equal to ${u.length}.`);let g=[],l=[];for(let z=0;z<u.length;++z){if(!Number.isInteger(w[z])||w[z]<-3||w[z]>=a)throw new Error(`Invalid output index: ${w[z]}`);if(w[z]===-3)continue;let ie=w[z]===-1,J=w[z]===-2,oe=ie||J?s(u[z].dataType,u[z].dims):n(w[z],u[z].dataType,u[z].dims);if(g.push(oe),oe.data===0)continue;let Ge=this.gpuDataManager.get(oe.data);if(!Ge)throw new Error(`no GPU data for output: ${oe.data}`);if(ie&&this.temporaryData.push(Ge),J){let De=this.kernelPersistentData.get(this.currentKernelId);De||(De=[],this.kernelPersistentData.set(this.currentKernelId,De)),De.push(Ge)}l.push(Ge)}if(i.length!==t.length||l.length!==g.length){if(l.length===0)return Ue(e.name),g;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let M;if(p){let z=0,ie=[];p.forEach(De=>{let pt=typeof De.data=="number"?[De.data]:De.data;if(pt.length===0)return;let Dt=De.type===10?2:4,Vt,lr;De.type===10?(lr=pt.length>4?16:pt.length>2?8:pt.length*Dt,Vt=pt.length>4?16:Dt*pt.length):(lr=pt.length<=2?pt.length*Dt:16,Vt=16),z=Math.ceil(z/lr)*lr,ie.push(z);let fr=De.type===10?8:4;z+=pt.length>4?Math.ceil(pt.length/fr)*Vt:pt.length*Dt});let J=16;z=Math.ceil(z/J)*J;let oe=new ArrayBuffer(z);p.forEach((De,pt)=>{let Dt=ie[pt],Vt=typeof De.data=="number"?[De.data]:De.data;if(De.type===6)new Int32Array(oe,Dt,Vt.length).set(Vt);else if(De.type===12)new Uint32Array(oe,Dt,Vt.length).set(Vt);else if(De.type===10)new Uint16Array(oe,Dt,Vt.length).set(Vt);else if(De.type===1)new Float32Array(oe,Dt,Vt.length).set(Vt);else throw new Error(`Unsupported uniform type: ${Ln(De.type)}`)});let Ge=this.gpuDataManager.create(z,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Ge.buffer,0,oe,0,z),this.gpuDataManager.release(Ge.id),M={offset:0,size:z,buffer:Ge.buffer}}let E=this.programManager.normalizeDispatchGroupSize(d),$=E[1]===1&&E[2]===1,L=Ap(e,t,$),W=this.programManager.getArtifact(L);if(W||(W=this.programManager.build(e,E),this.programManager.setArtifact(L,W),Wr("info",()=>`[artifact] key: ${L}, programName: ${e.name}`)),p&&W.uniformVariablesInfo){if(p.length!==W.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${W.uniformVariablesInfo.length}, got ${p.length} in program "${W.programInfo.name}".`);for(let z=0;z<p.length;z++){let ie=p[z],J=ie.type,oe=typeof ie.data=="number"?1:ie.data.length,[Ge,De]=W.uniformVariablesInfo[z];if(J!==Ge||oe!==De)throw new Error(`Uniform variable ${z} mismatch: expect type ${Ge} with size ${De}, got type ${J} with size ${oe} in program "${W.programInfo.name}".`)}}if(Wr("info",()=>`[ProgramManager] run "${e.name}" (key=${L}) with ${E[0]}x${E[1]}x${E[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let z={kernelId:this.currentKernelId,programName:W.programInfo.name,inputTensorViews:t,outputTensorViews:g};this.pendingKernels.push(z),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(z)}return this.programManager.run(W,i,l,E,M),Ue(e.name),g}upload(e,t){this.gpuDataManager.upload(e,t)}memcpy(e,t){this.gpuDataManager.memcpy(e,t)}async download(e,t){await this.gpuDataManager.download(e,t)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,t,r,n){let s=Sp.get(e);if(!s)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:s[0],attributes:[s[1],r]};this.kernels.set(t,a)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let r of t)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,r){let n=this.kernels.get(e);if(!n)throw new Error(`kernel not created: ${e}`);let s=n.kernelType,a=n.kernelName,i=n.kernelEntry,u=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${s}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,u[0]&&(u[1]=u[0](u[1]),u[0]=void 0),Wr("info",()=>`[WebGPU] Start to run kernel "[${s}] ${a}"...`);let d=this.env.debug;this.temporaryData=[];try{return d&&this.device.pushErrorScope("validation"),i(t,u[1]),0}catch(p){return r.push(Promise.resolve(`[WebGPU] Kernel "[${s}] ${a}" failed. ${p}`)),1}finally{d&&r.push(this.device.popErrorScope().then(p=>p?`GPU validation error for kernel "[${s}] ${a}": ${p.message}`:null));for(let p of this.temporaryData)this.gpuDataManager.release(p.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,r,n){let s=this.sessionExternalDataMapping.get(e);s||(s=new Map,this.sessionExternalDataMapping.set(e,s));let a=s.get(t),i=this.gpuDataManager.registerExternalBuffer(r,n,a==null?void 0:a[1]);return s.set(t,[i,r]),i}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[1])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let t=this.gpuDataManager.get(e);if(!t)throw new Error(`no GPU data for buffer: ${e}`);return t.buffer}createDownloader(e,t,r){return async()=>{let n=await Mt(this,e,t);return be(n.buffer,r)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){var e;this.queryType="none",(((e=this.env.webgpu.profiling)==null?void 0:e.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){Wr("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(){Wr("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){Wr("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),r=e.length;this.pendingKernels=[];for(let n=0;n<r;n++){let s=this.getComputePassEncoder(),a=e[n];this.writeTimestamp(this.pendingDispatchNumber*2),s.setPipeline(a.computePipeline),s.setBindGroup(0,a.bindGroup),s.dispatchWorkgroups(...a.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(t[n]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),Op={};S(Op,{init:()=>Dp});var Qd,zp,Dp,Rf=j(()=>{Xt(),Lf(),vn(),Qt(),Qd=class bf{constructor(t,r,n,s){this.module=t,this.dataType=r,this.data=n,this.dims=s}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let t=Oe.size(this.dims);return t===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,t)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let t=Oe.size(this.dims);return t===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let t=Oe.size(this.dims);return t===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let t=Oe.size(this.dims);return t===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(Oe.size(t)!==Oe.size(this.dims))throw new Error("Invalid new shape");return new bf(this.module,this.dataType,this.data,t)}},zp=class{constructor(e,t,r){this.module=e,this.backend=t,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=t.adapterInfo;let n=e.HEAPU32,s=r>>>2;this.opKernelContext=n[s++];let a=n[s++];this.outputCount=n[s++],this.customDataOffset=n[s++],this.customDataSize=n[s++];let i=[];for(let u=0;u<a;u++){let d=n[s++],p=n[s++],w=n[s++],g=[];for(let l=0;l<w;l++)g.push(n[s++]);i.push(new Qd(e,d,p,g))}this.inputs=i}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}getMaxComputeWorkgroupSizes(){return[this.backend.device.limits.maxComputeWorkgroupSizeX,this.backend.device.limits.maxComputeWorkgroupSizeY,this.backend.device.limits.maxComputeWorkgroupSizeZ]}getMaxComputeWorkgroupStoragesize(){return this.backend.device.limits.maxComputeWorkgroupStorageSize}compute(e,t){var i;let r=((i=t==null?void 0:t.inputs)==null?void 0:i.map(u=>typeof u=="number"?this.inputs[u]:u))??this.inputs,n=(t==null?void 0:t.outputs)??[],s=(u,d,p)=>new Qd(this.module,d,this.output(u,p),p),a=(u,d)=>{let p=Xn(u,d);if(!p)throw new Error(`Unsupported data type: ${u}`);let w=p>0?this.backend.gpuDataManager.create(p).id:0;return new Qd(this.module,u,w,d)};return this.backend.run(e,r,n,s,a,this.outputCount)}output(e,t){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+t.length)*4),s=n>>2;this.module.HEAPU32[s++]=t.length;for(let a=0;a<t.length;a++)this.module.HEAPU32[s++]=t[a];return this.module._JsepOutput(this.opKernelContext,e,n)}catch(n){throw new Error(`Failed to generate kernel's output[${e}] with dims [${t}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(r)}}},Dp=async(e,t,r,n)=>{let s=t.jsepInit;if(!s)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(e==="webgpu"){let a=new Fp;await a.initialize(r,n),s("webgpu",[a,i=>a.alloc(i),i=>a.free(i),(i,u,d,p=!1)=>{if(p)Wr("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${i}, dst=${u}, size=${d}`),a.memcpy(i,u);else{Wr("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${i}, gpuDataId=${u}, size=${d}`);let w=t.HEAPU8.subarray(i>>>0,(i>>>0)+d);a.upload(u,w)}},async(i,u,d)=>{Wr("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${i}, dataOffset=${u}, size=${d}`),await a.download(i,()=>t.HEAPU8.subarray(u>>>0,(u>>>0)+d))},(i,u,d)=>a.createKernel(i,u,d,t.UTF8ToString(t._JsepGetNodeName(u))),i=>a.releaseKernel(i),(i,u,d,p)=>{Wr("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${d}, kernel=${i}, contextDataOffset=${u}`);let w=new zp(t,a,u);return a.computeKernel(i,w,p)},()=>a.captureBegin(),()=>a.captureEnd(),()=>a.replay()])}else s("webnn")}}),Bp,Mc,vc,Vs,Lp,Yd,xc,Tc,Ec,Cc,$c,Sc,Rp=j(()=>{Ys(),Js(),Xt(),yr(),Hn(),Cs(),Bp=(e,t)=>{mr()._OrtInit(e,t)!==0&&Rr("Can't initialize onnxruntime.")},Mc=async e=>{Bp(e.wasm.numThreads,rs(e.logLevel))},vc=async(e,t)=>{{let r=(Rf(),A(Op)).init;if(t==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let n=e.webgpu.adapter;if(n){if(typeof n.limits!="object"||typeof n.features!="object"||typeof n.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let s=e.webgpu.powerPreference;if(s!==void 0&&s!=="low-power"&&s!=="high-performance")throw new Error(`Invalid powerPreference setting: "${s}"`);let a=e.webgpu.forceFallbackAdapter;if(a!==void 0&&typeof a!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${a}"`);if(n=await navigator.gpu.requestAdapter({powerPreference:s,forceFallbackAdapter:a}),!n)throw new Error('Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.')}await r("webgpu",mr(),e,n)}if(t==="webnn"){if(typeof navigator>"u"||!navigator.ml)throw new Error("WebNN is not supported in current environment");await r("webnn",mr(),e)}}},Vs=new Map,Lp=e=>{let t=mr(),r=t.stackSave();try{let n=t.stackAlloc(8);return t._OrtGetInputOutputCount(e,n,n+4)!==0&&Rr("Can't get session input/output count."),[t.HEAP32[n/4],t.HEAP32[n/4+1]]}finally{t.stackRestore(r)}},Yd=e=>{let t=mr(),r=t._malloc(e.byteLength);if(r===0)throw new Error(`Can't create a session. failed to allocate a buffer of size ${e.byteLength}.`);return t.HEAPU8.set(e,r),[r,e.byteLength]},xc=async(e,t)=>{var g,l;let r,n,s=mr();Array.isArray(e)?[r,n]=e:e.buffer===s.HEAPU8.buffer?[r,n]=[e.byteOffset,e.byteLength]:[r,n]=Yd(e);let a=0,i=0,u=0,d=[],p=[],w=[];try{if([i,d]=Kn(t),(t==null?void 0:t.externalData)&&s.mountExternalData){let J=[];for(let oe of t.externalData){let Ge=typeof oe=="string"?oe:oe.path;J.push(ns(typeof oe=="string"?oe:oe.data).then(De=>{s.mountExternalData(Ge,De)}))}await Promise.all(J)}for(let J of(t==null?void 0:t.executionProviders)??[])if((typeof J=="string"?J:J.name)==="webnn"){if(s.currentContext)throw new Error("WebNN execution provider is already set.");if(typeof J!="string"){let oe=J,Ge=oe==null?void 0:oe.context,De=oe==null?void 0:oe.gpuDevice,pt=oe==null?void 0:oe.deviceType,Dt=oe==null?void 0:oe.numThreads,Vt=oe==null?void 0:oe.powerPreference;Ge?s.currentContext=Ge:De?s.currentContext=await navigator.ml.createContext(De):s.currentContext=await navigator.ml.createContext({deviceType:pt,numThreads:Dt,powerPreference:Vt})}else s.currentContext=await navigator.ml.createContext();break}a=await s._OrtCreateSession(r,n,i),a===0&&Rr("Can't create a session."),s.currentContext&&(s.currentContext=void 0);let[M,E]=Lp(a),$=!!(t!=null&&t.enableGraphCapture),L=[],W=[],z=[];for(let J=0;J<M;J++){let oe=s._OrtGetInputName(a,J);oe===0&&Rr("Can't get an input name."),p.push(oe),L.push(s.UTF8ToString(oe))}for(let J=0;J<E;J++){let oe=s._OrtGetOutputName(a,J);oe===0&&Rr("Can't get an output name."),w.push(oe);let Ge=s.UTF8ToString(oe);W.push(Ge);{if($&&(t==null?void 0:t.preferredOutputLocation)===void 0){z.push("gpu-buffer");continue}let De=typeof(t==null?void 0:t.preferredOutputLocation)=="string"?t.preferredOutputLocation:((g=t==null?void 0:t.preferredOutputLocation)==null?void 0:g[Ge])??"cpu";if(De!=="cpu"&&De!=="cpu-pinned"&&De!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${De}.`);if($&&De!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${De}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);z.push(De)}}let ie=null;return z.some(J=>J==="gpu-buffer")&&(u=s._OrtCreateBinding(a),u===0&&Rr("Can't create IO binding."),ie={handle:u,outputPreferredLocations:z,outputPreferredLocationsEncoded:z.map(J=>hs(J))}),Vs.set(a,[a,p,w,ie,$,!1]),[a,L,W]}catch(M){throw p.forEach(E=>s._OrtFree(E)),w.forEach(E=>s._OrtFree(E)),u!==0&&s._OrtReleaseBinding(u),a!==0&&s._OrtReleaseSession(a),M}finally{s._free(r),i!==0&&s._OrtReleaseSessionOptions(i),d.forEach(M=>s._free(M)),(l=s.unmountExternalData)==null||l.call(s)}},Tc=e=>{var d;let t=mr(),r=Vs.get(e);if(!r)throw new Error(`cannot release session. invalid session id: ${e}`);let[n,s,a,i,u]=r;i&&(u&&t._OrtClearBoundOutputs(i.handle),t._OrtReleaseBinding(i.handle)),(d=t.jsepOnReleaseSession)==null||d.call(t,e),s.forEach(p=>t._OrtFree(p)),a.forEach(p=>t._OrtFree(p)),t._OrtReleaseSession(n),Vs.delete(e)},Ec=(e,t,r,n,s,a=!1)=>{if(!e){t.push(0);return}let i=mr(),u=e[0],d=e[1],p=e[3],w,g;if(u==="string"&&p==="gpu-buffer")throw new Error("String tensor is not supported on GPU.");if(a&&p!=="gpu-buffer")throw new Error(`External buffer must be provided for input/output index ${s} when enableGraphCapture is true.`);if(p==="gpu-buffer"){let E=e[2].gpuBuffer;g=Xn(cs(u),d);let $=i.jsepRegisterBuffer;if(!$)throw new Error('Tensor location "gpu-buffer" is not supported without using WebGPU.');w=$(n,s,E,g)}else{let E=e[2];if(Array.isArray(E)){g=4*E.length,w=i._malloc(g),r.push(w);let $=w/4;for(let L=0;L<E.length;L++){if(typeof E[L]!="string")throw new TypeError(`tensor data at index ${L} is not a string`);i.HEAPU32[$++]=Tr(E[L],r)}}else g=E.byteLength,w=i._malloc(g),r.push(w),i.HEAPU8.set(new Uint8Array(E.buffer,E.byteOffset,g),w)}let l=i.stackSave(),M=i.stackAlloc(4*d.length);try{let E=M/4;d.forEach(L=>i.HEAP32[E++]=L);let $=i._OrtCreateTensor(cs(u),w,g,M,d.length,hs(p));$===0&&Rr(`Can't create tensor for input/output. session=${n}, 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 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */},"./src/backends/onnx.js":($t,Ee,N)=>{var O;N.r(Ee),N.d(Ee,{Tensor:()=>Ce.Tensor,createInferenceSession:()=>ce,deviceToExecutionProviders:()=>ne,isONNXProxy:()=>te,isONNXTensor:()=>D});var fe=N("./src/env.js"),ye=N("?2ce3"),Te=N("./node_modules/onnxruntime-web/dist/ort.webgpu.bundle.min.mjs"),Ce=N("./node_modules/onnxruntime-common/dist/esm/index.js");const j=Object.freeze({auto:null,gpu:null,cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",webnn:{name:"webnn",deviceType:"cpu"},"webnn-npu":{name:"webnn",deviceType:"npu"},"webnn-gpu":{name:"webnn",deviceType:"gpu"},"webnn-cpu":{name:"webnn",deviceType:"cpu"}}),S=[];let V,A;if(fe.apis.IS_NODE_ENV){switch(A=ye??(O||(O=N.t(ye,2))),process.platform){case"win32":S.push("dml");break;case"linux":process.arch==="x64"&&S.push("cuda");break}S.push("cpu"),V=["cpu"]}else A=Te,fe.apis.IS_WEBNN_AVAILABLE&&S.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),fe.apis.IS_WEBGPU_AVAILABLE&&S.push("webgpu"),S.push("wasm"),V=["wasm"];const ee=A.InferenceSession;function ne(se=null){if(!se)return V;switch(se){case"auto":return S;case"gpu":return S.filter(X=>["webgpu","cuda","dml","webnn-gpu"].includes(X))}if(S.includes(se))return[j[se]??se];throw new Error(`Unsupported device: "${se}". 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se=0;se<ne.num_decoder_layers;++se)ee[`${A}.${se}.encoder.key`]=H,ee[`${A}.${se}.encoder.value`]=H,ee[`${A}.${se}.decoder.key`]=te,ee[`${A}.${se}.decoder.value`]=te}else{const ce=ne.num_heads,D=ne.num_layers,H=ne.dim_kv??ne.hidden_size/(ne.num_attention_heads??ce);if(ne.model_type==="falcon"){const te=[me*ce,0,H];for(let se=0;se<D;++se)ee[`${A}.${se}.key`]=te,ee[`${A}.${se}.value`]=te}else if(ne.multi_query){const te=[me*ce,0,2*H];for(let se=0;se<D;++se)ee[`${A}.${se}.key_value`]=te}else if(ne.model_type==="bloom"){const te=[me*ce,H,0],se=[me*ce,0,H];for(let X=0;X<D;++X)ee[`${A}.${X}.key`]=te,ee[`${A}.${X}.value`]=se}else if(ne.model_type==="openelm")for(let te=0;te<D;++te){const se=[me,ce[te],0,H];ee[`${A}.${te}.key`]=se,ee[`${A}.${te}.value`]=se}else{const te=[me,ce,0,H];for(let se=0;se<D;++se)ee[`${A}.${se}.key`]=te,ee[`${A}.${se}.value`]=te}}return ee}class j{constructor(A){xe(this,"model_type",null);xe(this,"is_encoder_decoder",!1);xe(this,"max_position_embeddings");xe(this,"transformers.js_config");Object.assign(this,A),this.normalized_config=Te(this)}static async from_pretrained(A,{progress_callback:ee=null,config:ne=null,cache_dir:me=null,local_files_only:ce=!1,revision:D="main"}={}){ne&&!(ne instanceof j)&&(ne=new j(ne));const H=ne??await ye(A,{progress_callback:ee,config:ne,cache_dir:me,local_files_only:ce,revision:D});return new this(H)}}class S{static async from_pretrained(...A){return j.from_pretrained(...A)}}},"./src/env.js":($t,Ee,N)=>{var k;N.r(Ee),N.d(Ee,{apis:()=>D,env:()=>I});var O=N("?569f"),fe=N("?3f59"),ye=N("?154a");const Te="3.0.0-alpha.17",Ce=typeof self<"u",j=Ce&&self.constructor.name==="DedicatedWorkerGlobalScope",S=Ce&&"caches"in self,V=typeof navigator<"u"&&"gpu"in navigator,A=typeof navigator<"u"&&"ml"in navigator,ee=typeof process<"u",ne=ee&&((k=process==null?void 0:process.release)==null?void 0:k.name)==="node",me=!B(O),ce=!B(fe),D=Object.freeze({IS_BROWSER_ENV:Ce,IS_WEBWORKER_ENV:j,IS_WEB_CACHE_AVAILABLE:S,IS_WEBGPU_AVAILABLE:V,IS_WEBNN_AVAILABLE:A,IS_PROCESS_AVAILABLE:ee,IS_NODE_ENV:ne,IS_FS_AVAILABLE:me,IS_PATH_AVAILABLE:ce}),H=me&&ce,te=H?fe.dirname(fe.dirname(ye.fileURLToPath(self.location.href))):"./",se=H?fe.join(te,"/.cache/"):null,X="/models/",R=H?fe.join(te,X):X,I={version:Te,backends:{onnx:{}},allowRemoteModels:!0,remoteHost:"https://huggingface.co./",remotePathTemplate:"{model}/resolve/{revision}/",allowLocalModels:!Ce,localModelPath:R,useFS:me,useBrowserCache:S,useFSCache:me,cacheDir:se,useCustomCache:!1,customCache:null};function B(ue){return Object.keys(ue).length===0}},"./src/generation/configuration_utils.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{GenerationConfig:()=>fe});var O=N("./src/utils/core.js");class fe{constructor(Te){xe(this,"max_length",20);xe(this,"max_new_tokens",null);xe(this,"min_length",0);xe(this,"min_new_tokens",null);xe(this,"early_stopping",!1);xe(this,"max_time",null);xe(this,"do_sample",!1);xe(this,"num_beams",1);xe(this,"num_beam_groups",1);xe(this,"penalty_alpha",null);xe(this,"use_cache",!0);xe(this,"temperature",1);xe(this,"top_k",50);xe(this,"top_p",1);xe(this,"typical_p",1);xe(this,"epsilon_cutoff",0);xe(this,"eta_cutoff",0);xe(this,"diversity_penalty",0);xe(this,"repetition_penalty",1);xe(this,"encoder_repetition_penalty",1);xe(this,"length_penalty",1);xe(this,"no_repeat_ngram_size",0);xe(this,"bad_words_ids",null);xe(this,"force_words_ids",null);xe(this,"renormalize_logits",!1);xe(this,"constraints",null);xe(this,"forced_bos_token_id",null);xe(this,"forced_eos_token_id",null);xe(this,"remove_invalid_values",!1);xe(this,"exponential_decay_length_penalty",null);xe(this,"suppress_tokens",null);xe(this,"begin_suppress_tokens",null);xe(this,"forced_decoder_ids",null);xe(this,"guidance_scale",null);xe(this,"num_return_sequences",1);xe(this,"output_attentions",!1);xe(this,"output_hidden_states",!1);xe(this,"output_scores",!1);xe(this,"return_dict_in_generate",!1);xe(this,"pad_token_id",null);xe(this,"bos_token_id",null);xe(this,"eos_token_id",null);xe(this,"encoder_no_repeat_ngram_size",0);xe(this,"decoder_start_token_id",null);xe(this,"generation_kwargs",{});Object.assign(this,(0,O.pick)(Te,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{ClassifierFreeGuidanceLogitsProcessor:()=>H,ForcedBOSTokenLogitsProcessor:()=>j,ForcedEOSTokenLogitsProcessor:()=>S,LogitsProcessor:()=>ye,LogitsProcessorList:()=>Ce,LogitsWarper:()=>Te,MinLengthLogitsProcessor:()=>me,MinNewTokensLengthLogitsProcessor:()=>ce,NoBadWordsLogitsProcessor:()=>D,NoRepeatNGramLogitsProcessor:()=>ee,RepetitionPenaltyLogitsProcessor:()=>ne,SuppressTokensAtBeginLogitsProcessor:()=>V,TemperatureLogitsWarper:()=>te,TopKLogitsWarper:()=>X,TopPLogitsWarper:()=>se,WhisperTimeStampLogitsProcessor:()=>A});var O=N("./src/utils/generic.js");N("./src/utils/tensor.js");var fe=N("./src/utils/maths.js");class ye extends O.Callable{_call(I,B){throw Error("`_call` should be implemented in a subclass")}}class Te extends O.Callable{_call(I,B){throw Error("`_call` should be implemented in a subclass")}}class Ce extends O.Callable{constructor(){super(),this.processors=[]}push(I){this.processors.push(I)}extend(I){this.processors.push(...I)}_call(I,B){let k=B;for(const ue of this.processors)k=ue(I,k);return k}[Symbol.iterator](){return this.processors.values()}}class j extends ye{constructor(I){super(),this.bos_token_id=I}_call(I,B){for(let k=0;k<I.length;++k)if(I[k].length===1){const ue=B[k].data;ue.fill(-1/0),ue[this.bos_token_id]=0}return B}}class S extends ye{constructor(I,B){super(),this.max_length=I,this.eos_token_id=Array.isArray(B)?B:[B]}_call(I,B){for(let k=0;k<I.length;++k)if(I[k].length===this.max_length-1){const ue=B[k].data;ue.fill(-1/0);for(const ve of this.eos_token_id)ue[ve]=0}return B}}class V extends ye{constructor(I,B){super(),this.begin_suppress_tokens=I,this.begin_index=B}_call(I,B){for(let k=0;k<I.length;++k)if(I[k].length===this.begin_index){const ue=B[k].data;for(const ve of this.begin_suppress_tokens)ue[ve]=-1/0}return B}}class A extends ye{constructor(I,B){super(),this.eos_token_id=Array.isArray(I.eos_token_id)?I.eos_token_id[0]:I.eos_token_id,this.no_timestamps_token_id=I.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=B.length,B.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=I.max_initial_timestamp_index}_call(I,B){for(let k=0;k<I.length;++k){const ue=B[k].data;if(ue[this.no_timestamps_token_id]=-1/0,I[k].length===this.begin_index-1){ue.fill(-1/0),ue[this.timestamp_begin]=0;continue}const ve=I[k].slice(this.begin_index),$e=ve.length>=1&&ve[ve.length-1]>=this.timestamp_begin,Ie=ve.length<2||ve[ve.length-2]>=this.timestamp_begin;if($e&&(Ie?ue.subarray(this.timestamp_begin).fill(-1/0):ue.subarray(0,this.eos_token_id).fill(-1/0)),I[k].length===this.begin_index&&this.max_initial_timestamp_index!==null){const dt=this.timestamp_begin+this.max_initial_timestamp_index;ue.subarray(dt+1).fill(-1/0)}const Ae=(0,fe.log_softmax)(ue),tt=Math.log(Ae.subarray(this.timestamp_begin).map(Math.exp).reduce((dt,ge)=>dt+ge)),Xe=(0,fe.max)(Ae.subarray(0,this.timestamp_begin))[0];tt>Xe&&ue.subarray(0,this.timestamp_begin).fill(-1/0)}return B}}class ee extends ye{constructor(I){super(),this.no_repeat_ngram_size=I}getNgrams(I){const B=I.length,k=[];for(let ve=0;ve<B+1-this.no_repeat_ngram_size;++ve){const $e=[];for(let Ie=0;Ie<this.no_repeat_ngram_size;++Ie)$e.push(I[ve+Ie]);k.push($e.map(Number))}const ue=new Map;for(const ve of k){const $e=ve.slice(0,ve.length-1),Ie=JSON.stringify($e),Ae=ue.get(Ie)??[];Ae.push(ve[ve.length-1]),ue.set(Ie,Ae)}return ue}getGeneratedNgrams(I,B){const k=B.slice(B.length+1-this.no_repeat_ngram_size,B.length);return I.get(JSON.stringify(k.map(Number)))??[]}calcBannedNgramTokens(I){const B=[];if(I.length+1<this.no_repeat_ngram_size)return B;{const k=this.getNgrams(I);return this.getGeneratedNgrams(k,I)}}_call(I,B){for(let k=0;k<I.length;++k){const ue=B[k].data,ve=this.calcBannedNgramTokens(I[k]);for(const $e of ve)ue[$e]=-1/0}return B}}class ne extends ye{constructor(I){super(),this.penalty=I}_call(I,B){for(let k=0;k<I.length;++k){const ue=B[k].data;for(const ve of I[k]){const $e=Number(ve);ue[$e]<0?ue[$e]*=this.penalty:ue[$e]/=this.penalty}}return B}}class me extends ye{constructor(I,B){super(),this.min_length=I,this.eos_token_id=Array.isArray(B)?B:[B]}_call(I,B){for(let k=0;k<I.length;++k)if(I[k].length<this.min_length){const ue=B[k].data;for(const ve of this.eos_token_id)ue[ve]=-1/0}return B}}class ce extends ye{constructor(I,B,k){super(),this.prompt_length_to_skip=I,this.min_new_tokens=B,this.eos_token_id=Array.isArray(k)?k:[k]}_call(I,B){for(let k=0;k<I.length;++k)if(I[k].length-this.prompt_length_to_skip<this.min_new_tokens){const ve=B[k].data;for(const $e of this.eos_token_id)ve[$e]=-1/0}return B}}class D extends ye{constructor(I,B){super(),this.bad_words_ids=I,this.eos_token_id=Array.isArray(B)?B:[B]}_call(I,B){for(let k=0;k<I.length;++k){const ue=B[k].data,ve=I[k];for(const $e of this.bad_words_ids){let Ie=!0;for(let Ae=1;Ae<=$e.length-1&&$e.length<ve.length;++Ae)if($e.at(-Ae-1)!=ve.at(-Ae)){Ie=!1;break}Ie&&(ue[$e.at(-1)]=-1/0)}}return B}}class H extends ye{constructor(I){if(super(),I<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${I}.`);this.guidance_scale=I}_call(I,B){if(B.dims[0]!==2*I.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${B.dims[0]} for the logits and ${I.length} for the input ids.`);const k=I.length,ue=B.slice([0,k],null),ve=B.slice([k,B.dims[0]],null);for(let $e=0;$e<ve.data.length;++$e)ve.data[$e]+=(ue.data[$e]-ve.data[$e])*this.guidance_scale;return ve}}class te extends Te{constructor(I){super(),this.temperature=I}_call(I,B){const k=B.data;for(let ue=0;ue<k.length;++ue)k[ue]/=this.temperature;return B}}class se extends Te{constructor(I,{filter_value:B=-1/0,min_tokens_to_keep:k=1}={}){if(super(),I<0||I>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${I}`);if(!Number.isInteger(k)||k<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${k}`);this.top_p=I,this.filter_value=B,this.min_tokens_to_keep=k}}class X extends Te{constructor(I,{filter_value:B=-1/0,min_tokens_to_keep:k=1}={}){if(super(),!Number.isInteger(I)||I<0)throw new Error(`\`top_k\` must be a positive integer, but is ${I}`);this.top_k=Math.max(I,k),this.filter_value=B}}},"./src/generation/logits_sampler.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{LogitsSampler:()=>Te});var O=N("./src/utils/generic.js"),fe=N("./src/utils/tensor.js"),ye=N("./src/utils/maths.js");N("./src/generation/configuration_utils.js");class Te extends O.Callable{constructor(A){super(),this.generation_config=A}async _call(A){return this.sample(A)}async sample(A){throw Error("sample should be implemented in subclasses.")}getLogits(A,ee){let ne=A.dims.at(-1),me=A.data;if(ee===-1)me=me.slice(-ne);else{let ce=ee*ne;me=me.slice(ce,ce+ne)}return me}randomSelect(A){let ee=0;for(let me=0;me<A.length;++me)ee+=A[me];let ne=Math.random()*ee;for(let me=0;me<A.length;++me)if(ne-=A[me],ne<=0)return me;return 0}static getSampler(A){if(A.do_sample)return new j(A);if(A.num_beams>1)return new S(A);if(A.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${A.num_return_sequences}.`);return new Ce(A)}}class Ce extends Te{async sample(A){const ee=(0,ye.max)(A.data)[1];return[[BigInt(ee),0]]}}class j extends Te{async sample(A){let ee=A.dims.at(-1);this.generation_config.top_k>0&&(ee=Math.min(this.generation_config.top_k,ee));const[ne,me]=await(0,fe.topk)(A,ee),ce=(0,ye.softmax)(ne.data);return Array.from({length:this.generation_config.num_beams},()=>{const D=this.randomSelect(ce);return[me.data[D],Math.log(ce[D])]})}}class S extends Te{async sample(A){let ee=A.dims.at(-1);this.generation_config.top_k>0&&(ee=Math.min(this.generation_config.top_k,ee));const[ne,me]=await(0,fe.topk)(A,ee),ce=(0,ye.softmax)(ne.data);return Array.from({length:this.generation_config.num_beams},(D,H)=>[me.data[H],Math.log(ce[H])])}}},"./src/generation/stopping_criteria.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{EosTokenCriteria:()=>Ce,InterruptableStoppingCriteria:()=>j,MaxLengthCriteria:()=>Te,StoppingCriteria:()=>fe,StoppingCriteriaList:()=>ye});var O=N("./src/utils/generic.js");class fe extends O.Callable{_call(V,A){throw Error("StoppingCriteria needs to be subclassed")}}class ye extends O.Callable{constructor(){super(),this.criteria=[]}push(V){this.criteria.push(V)}extend(V){V instanceof ye?V=V.criteria:V instanceof fe&&(V=[V]),this.criteria.push(...V)}_call(V,A){const ee=new Array(V.length).fill(!1);for(const ne of this.criteria){const me=ne(V,A);for(let ce=0;ce<ee.length;++ce)ee[ce]||(ee[ce]=me[ce])}return ee}[Symbol.iterator](){return this.criteria.values()}}class Te extends fe{constructor(V,A=null){super(),this.max_length=V,this.max_position_embeddings=A}_call(V){return V.map(A=>A.length>=this.max_length)}}class Ce extends fe{constructor(V){super(),Array.isArray(V)||(V=[V]),this.eos_token_id=V}_call(V,A){return V.map(ee=>{const ne=ee.at(-1);return this.eos_token_id.some(me=>ne==me)})}}class j extends fe{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(V,A){return new Array(V.length).fill(this.interrupted)}}},"./src/generation/streamers.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{BaseStreamer:()=>Te,TextStreamer:()=>j,WhisperTextStreamer:()=>S});var O=N("./src/utils/core.js"),fe=N("./src/tokenizers.js"),ye=N("./src/env.js");class Te{put(A){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const Ce=ye.apis.IS_PROCESS_AVAILABLE?V=>process.stdout.write(V):V=>console.log(V);class j extends Te{constructor(A,{skip_prompt:ee=!1,callback_function:ne=null,token_callback_function:me=null,decode_kwargs:ce={},...D}={}){super(),this.tokenizer=A,this.skip_prompt=ee,this.callback_function=ne??Ce,this.token_callback_function=me,this.decode_kwargs={...ce,...D},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(A){var ce;if(A.length>1)throw Error("TextStreamer only supports batch size of 1");if(this.skip_prompt&&this.next_tokens_are_prompt){this.next_tokens_are_prompt=!1;return}const ee=A[0];(ce=this.token_callback_function)==null||ce.call(this,ee),this.token_cache=(0,O.mergeArrays)(this.token_cache,ee);const ne=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let me;ne.endsWith(`
`)?(me=ne.slice(this.print_len),this.token_cache=[],this.print_len=0):ne.length>0&&(0,fe.is_chinese_char)(ne.charCodeAt(ne.length-1))?(me=ne.slice(this.print_len),this.print_len+=me.length):(me=ne.slice(this.print_len,ne.lastIndexOf(" ")+1),this.print_len+=me.length),this.on_finalized_text(me,!1)}end(){let A;this.token_cache.length>0?(A=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):A="",this.next_tokens_are_prompt=!0,this.on_finalized_text(A,!0)}on_finalized_text(A,ee){var ne,me;A.length>0&&((ne=this.callback_function)==null||ne.call(this,A)),ee&&this.callback_function===Ce&&ye.apis.IS_PROCESS_AVAILABLE&&((me=this.callback_function)==null||me.call(this,`
`))}}class S extends j{constructor(A,{skip_prompt:ee=!1,callback_function:ne=null,token_callback_function:me=null,on_chunk_start:ce=null,on_chunk_end:D=null,on_finalize:H=null,time_precision:te=.02,skip_special_tokens:se=!0,decode_kwargs:X={}}={}){super(A,{skip_prompt:ee,callback_function:ne,token_callback_function:me,decode_kwargs:{skip_special_tokens:se,...X}}),this.timestamp_begin=A.timestamp_begin,this.on_chunk_start=ce,this.on_chunk_end=D,this.on_finalize=H,this.time_precision=te,this.waiting_for_timestamp=!1}put(A){var ne,me;if(A.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const ee=A[0];if(ee.length===1){const ce=Number(ee[0])-this.timestamp_begin;if(ce>=0){const D=ce*this.time_precision;this.waiting_for_timestamp?(ne=this.on_chunk_end)==null||ne.call(this,D):(me=this.on_chunk_start)==null||me.call(this,D),this.waiting_for_timestamp=!this.waiting_for_timestamp,A=[[]]}}return super.put(A)}end(){var 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Xe(f,m,T=!1){const Y=f.sessions[T?"decoder_model_merged":"model"],{past_key_values:Fe,...ze}=m;Y.inputNames.includes("use_cache_branch")&&(ze.use_cache_branch=Ie(!!Fe)),Y.inputNames.includes("position_ids")&&ze.attention_mask&&!ze.position_ids&&(ze.position_ids=ge(ze,Fe)),f.addPastKeyValues(ze,Fe);const ht=(0,Ce.pick)(ze,Y.inputNames);return await ue(Y,ht)}async function dt(f,{input_ids:m=null,attention_mask:T=null,pixel_values:Y=null,position_ids:Fe=null,inputs_embeds:ze=null,past_key_values:ht=null,generation_config:Ct=null,logits_processor:Wt=null,...sr}){if(!ze){if(ze=await f.encode_text({input_ids:m}),Y&&m.dims[1]!==1){const ur=await f.encode_image({pixel_values:Y});({inputs_embeds:ze,attention_mask:T}=f._merge_input_ids_with_image_features({image_features:ur,inputs_embeds:ze,input_ids:m,attention_mask:T}))}else if(ht&&Y&&m.dims[1]===1){const ur=m.dims[1],hr=Object.values(ht)[0].dims.at(-2);T=(0,A.cat)([(0,A.ones)([m.dims[0],hr]),T.slice(null,[T.dims[1]-ur,T.dims[1]])],1)}}return await Xe(f,{inputs_embeds:ze,past_key_values:ht,attention_mask:T,position_ids:Fe,generation_config:Ct,logits_processor:Wt},!0)}function ge(f,m=null){const{input_ids:T,inputs_embeds:Y,attention_mask:Fe}=f,[ze,ht]=Fe.dims,Ct=new BigInt64Array(Fe.data.length);for(let sr=0;sr<ze;++sr){const Ir=sr*ht;let ur=BigInt(0);for(let hr=0;hr<ht;++hr){const wr=Ir+hr;Fe.data[wr]===0n?Ct[wr]=BigInt(1):(Ct[wr]=ur,ur+=Fe.data[wr])}}let Wt=new A.Tensor("int64",Ct,Fe.dims);if(m){const sr=-(T??Y).dims.at(1);Wt=Wt.slice(null,[sr,null])}return Wt}function G(f,m,T,Y){if(T.past_key_values){const Fe=Object.values(T.past_key_values)[0].dims.at(-2),{input_ids:ze,attention_mask:ht}=T;if(!(ht&&ht.dims[1]>ze.dims[1])){if(Fe<ze.dims[1])T.input_ids=ze.slice(null,[Fe,null]);else if(f.config.image_token_index!=null&&ze.data.some(Ct=>Ct==f.config.image_token_index)){const Ct=f.config.num_image_tokens;if(!Ct)throw new Error("`num_image_tokens` is missing in the model configuration.");const Wt=ze.dims[1]-(Fe-Ct);T.input_ids=ze.slice(null,[-Wt,null]),T.attention_mask=(0,A.ones)([1,Fe+Wt])}}}return T}function de(f,m,T,Y){return T.past_key_values&&(m=m.map(Fe=>[Fe.at(-1)])),{...T,decoder_input_ids:$e(m)}}function Se(f,...m){return f.config.is_encoder_decoder?de(f,...m):G(f,...m)}class Z extends Te.Callable{constructor(T,Y){super();xe(this,"main_input_name","input_ids");xe(this,"forward_params",["input_ids","attention_mask"]);this.config=T,this.sessions=Y;const Fe=R.get(this.constructor),ze=se.get(Fe);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,ze){case te.DecoderOnly:this.can_generate=!0,this._forward=Xe,this._prepare_inputs_for_generation=G;break;case te.Seq2Seq:case te.Vision2Seq:case te.Musicgen:this.can_generate=!0,this._forward=Ae,this._prepare_inputs_for_generation=de;break;case te.EncoderDecoder:this._forward=Ae;break;case te.ImageTextToText:this.can_generate=!0,this._forward=dt,this._prepare_inputs_for_generation=Se;break;default:this._forward=tt;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var Y;const T=[];for(const Fe of Object.values(this.sessions))(Y=Fe==null?void 0:Fe.handler)!=null&&Y.dispose&&T.push(Fe.handler.dispose());return await Promise.all(T)}static async from_pretrained(T,{progress_callback:Y=null,config:Fe=null,cache_dir:ze=null,local_files_only:ht=!1,revision:Ct="main",model_file_name:Wt=null,subfolder:sr="onnx",device:Ir=null,dtype:ur=null,use_external_data_format:hr=null,session_options:wr={}}={}){let Sr={progress_callback:Y,config:Fe,cache_dir:ze,local_files_only:ht,revision:Ct,model_file_name:Wt,subfolder:sr,device:Ir,dtype:ur,use_external_data_format:hr,session_options:wr};const Fr=R.get(this),kr=se.get(Fr);Fe=Sr.config=await O.AutoConfig.from_pretrained(T,Sr);let _r;if(kr===te.DecoderOnly)_r=await Promise.all([B(T,{model:Sr.model_file_name??"model"},Sr),(0,j.getModelJSON)(T,"generation_config.json",!1,Sr)]);else if(kr===te.Seq2Seq||kr===te.Vision2Seq)_r=await Promise.all([B(T,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Sr),(0,j.getModelJSON)(T,"generation_config.json",!1,Sr)]);else if(kr===te.MaskGeneration)_r=await Promise.all([B(T,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},Sr)]);else if(kr===te.EncoderDecoder)_r=await Promise.all([B(T,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Sr)]);else if(kr===te.ImageTextToText){const tn={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Fe.is_encoder_decoder&&(tn.model="encoder_model"),_r=await Promise.all([B(T,tn,Sr),(0,j.getModelJSON)(T,"generation_config.json",!1,Sr)])}else kr===te.Musicgen?_r=await Promise.all([B(T,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},Sr),(0,j.getModelJSON)(T,"generation_config.json",!1,Sr)]):(kr!==te.EncoderOnly&&console.warn(`Model type for '${Fr??(Fe==null?void 0:Fe.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),_r=await Promise.all([B(T,{model:Sr.model_file_name??"model"},Sr)]));return new this(Fe,..._r)}async _call(T){return await this.forward(T)}async forward(T){return await this._forward(this,T)}_get_logits_warper(T){const Y=new S.LogitsProcessorList;return T.temperature!==null&&T.temperature!==1&&Y.push(new S.TemperatureLogitsWarper(T.temperature)),T.top_k!==null&&T.top_k!==0&&Y.push(new S.TopKLogitsWarper(T.top_k)),T.top_p!==null&&T.top_p<1&&Y.push(new S.TopPLogitsWarper(T.top_p)),Y}_get_logits_processor(T,Y,Fe=null){const ze=new S.LogitsProcessorList;if(T.repetition_penalty!==null&&T.repetition_penalty!==1&&ze.push(new S.RepetitionPenaltyLogitsProcessor(T.repetition_penalty)),T.no_repeat_ngram_size!==null&&T.no_repeat_ngram_size>0&&ze.push(new S.NoRepeatNGramLogitsProcessor(T.no_repeat_ngram_size)),T.bad_words_ids!==null&&ze.push(new S.NoBadWordsLogitsProcessor(T.bad_words_ids,T.eos_token_id)),T.min_length!==null&&T.eos_token_id!==null&&T.min_length>0&&ze.push(new S.MinLengthLogitsProcessor(T.min_length,T.eos_token_id)),T.min_new_tokens!==null&&T.eos_token_id!==null&&T.min_new_tokens>0&&ze.push(new S.MinNewTokensLengthLogitsProcessor(Y,T.min_new_tokens,T.eos_token_id)),T.forced_bos_token_id!==null&&ze.push(new S.ForcedBOSTokenLogitsProcessor(T.forced_bos_token_id)),T.forced_eos_token_id!==null&&ze.push(new S.ForcedEOSTokenLogitsProcessor(T.max_length,T.forced_eos_token_id)),T.begin_suppress_tokens!==null){const ht=Y>1||T.forced_bos_token_id===null?Y:Y+1;ze.push(new S.SuppressTokensAtBeginLogitsProcessor(T.begin_suppress_tokens,ht))}return T.guidance_scale!==null&&T.guidance_scale>1&&ze.push(new S.ClassifierFreeGuidanceLogitsProcessor(T.guidance_scale)),Fe!==null&&ze.extend(Fe),ze}_prepare_generation_config(T,Y,Fe=V.GenerationConfig){const ze={...this.config};for(const Ct of["decoder","generator","text_config"])Ct in ze&&Object.assign(ze,ze[Ct]);const ht=new Fe(ze);return"generation_config"in this&&Object.assign(ht,this.generation_config),T&&Object.assign(ht,T),Y&&Object.assign(ht,(0,Ce.pick)(Y,Object.getOwnPropertyNames(ht))),ht}_get_stopping_criteria(T,Y=null){const Fe=new ne.StoppingCriteriaList;return T.max_length!==null&&Fe.push(new ne.MaxLengthCriteria(T.max_length,this.config.max_position_embeddings??null)),T.eos_token_id!==null&&Fe.push(new ne.EosTokenCriteria(T.eos_token_id)),Y&&Fe.extend(Y),Fe}_validate_model_class(){if(!this.can_generate){const T=[za,Da,Oa,Fa],Y=R.get(this.constructor),Fe=new Set,ze=this.config.model_type;for(const Ct of T){const Wt=Ct.get(ze);Wt&&Fe.add(Wt[0])}let ht=`The current model class (${Y}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Fe.size>0&&(ht+=` Please use the following class instead: ${[...Fe].join(", ")}`),Error(ht)}}prepare_inputs_for_generation(...T){return this._prepare_inputs_for_generation(this,...T)}_update_model_kwargs_for_generation({generated_input_ids:T,outputs:Y,model_inputs:Fe,is_encoder_decoder:ze}){return Fe.past_key_values=this.getPastKeyValues(Y,Fe.past_key_values),Fe.input_ids=new A.Tensor("int64",T.flat(),[T.length,1]),ze||(Fe.attention_mask=(0,A.cat)([Fe.attention_mask,(0,A.ones)([Fe.attention_mask.dims[0],1])],1)),Fe.position_ids=null,Fe}_prepare_model_inputs({inputs:T,bos_token_id:Y,model_kwargs:Fe}){const ze=(0,Ce.pick)(Fe,this.forward_params),ht=this.main_input_name;if(ht in ze){if(T)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else ze[ht]=T;return{inputs_tensor:ze[ht],model_inputs:ze,model_input_name:ht}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:T,model_inputs:Y,model_input_name:Fe,generation_config:ze}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!Y.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:Ct,pixel_values:Wt,attention_mask:sr,...Ir}=Y,ur=await this._prepare_inputs_embeds(Y);Y={...Ir,...(0,Ce.pick)(ur,["inputs_embeds","attention_mask"])}}let{last_hidden_state:ht}=await tt(this,Y);if(ze.guidance_scale!==null&&ze.guidance_scale>1)ht=(0,A.cat)([ht,(0,A.full_like)(ht,0)],0),"attention_mask"in Y&&(Y.attention_mask=(0,A.cat)([Y.attention_mask,(0,A.zeros_like)(Y.attention_mask)],0));else if(Y.decoder_input_ids){const Ct=$e(Y.decoder_input_ids).dims[0];if(Ct!==ht.dims[0]){if(ht.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${ht.dims[0]}) than the decoder inputs (${Ct}).`);ht=(0,A.cat)(Array.from({length:Ct},()=>ht),0)}}return Y.encoder_outputs=ht,Y}_prepare_decoder_input_ids_for_generation({batch_size:T,model_input_name:Y,model_kwargs:Fe,decoder_start_token_id:ze,bos_token_id:ht,generation_config:Ct}){let{decoder_input_ids:Wt,...sr}=Fe;if(Wt)Array.isArray(Wt[0])||(Wt=Array.from({length:T},()=>Wt));else if(ze??(ze=ht),this.config.model_type==="musicgen")Wt=Array.from({length:T*this.config.decoder.num_codebooks},()=>[ze]);else if(Array.isArray(ze)){if(ze.length!==T)throw new Error(`\`decoder_start_token_id\` expcted to have length ${T} but got ${ze.length}`);Wt=ze}else Wt=Array.from({length:T},()=>[ze]);return Wt=$e(Wt),Fe.decoder_attention_mask=(0,A.ones_like)(Wt),{input_ids:Wt,model_inputs:sr}}async generate({inputs:T=null,generation_config:Y=null,logits_processor:Fe=null,stopping_criteria:ze=null,streamer:ht=null,...Ct}){this._validate_model_class(),Y=this._prepare_generation_config(Y,Ct);let{inputs_tensor:Wt,model_inputs:sr,model_input_name:Ir}=this._prepare_model_inputs({inputs:T,model_kwargs:Ct});const ur=this.config.is_encoder_decoder;ur&&("encoder_outputs"in sr||(sr=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Wt,model_inputs:sr,model_input_name:Ir,generation_config:Y})));let hr;ur?{input_ids:hr,model_inputs:sr}=this._prepare_decoder_input_ids_for_generation({batch_size:sr[Ir].dims.at(0),model_input_name:Ir,model_kwargs:sr,decoder_start_token_id:Y.decoder_start_token_id,bos_token_id:Y.bos_token_id,generation_config:Y}):hr=sr[Ir];let wr=hr.dims.at(-1);Y.max_new_tokens!==null&&(Y.max_length=wr+Y.max_new_tokens);const Sr=this._get_logits_processor(Y,wr,Fe),Fr=this._get_stopping_criteria(Y,ze),kr=sr[Ir].dims.at(0),_r=me.LogitsSampler.getSampler(Y),tn=new Array(kr).fill(0),rn=hr.tolist();ht&&ht.put(rn);let wn,cn={};for(;;){if(sr=this.prepare_inputs_for_generation(rn,sr,Y),wn=await this.forward(sr),Y.output_attentions&&Y.return_dict_in_generate){const Rn=this.getAttentions(wn);for(const ys in Rn)ys in cn||(cn[ys]=[]),cn[ys].push(Rn[ys])}const ws=wn.logits.slice(null,-1,null),js=Sr(rn,ws),Ka=[];for(let Rn=0;Rn<js.dims.at(0);++Rn){const ys=js[Rn],qd=await _r(ys);for(const[Hd,Kd]of qd){const Xd=BigInt(Hd);tn[Rn]+=Kd,rn[Rn].push(Xd),Ka.push([Xd]);break}}if(ht&&ht.put(Ka),Fr(rn).every(Rn=>Rn))break;sr=this._update_model_kwargs_for_generation({generated_input_ids:Ka,outputs:wn,model_inputs:sr,is_encoder_decoder:ur})}ht&&ht.end();const an=this.getPastKeyValues(wn,sr.past_key_values,!0),yn=new A.Tensor("int64",rn.flat(),[rn.length,rn[0].length]);if(Y.return_dict_in_generate)return{sequences:yn,past_key_values:an,...cn};for(const ws of Object.values(wn))ws.location==="gpu-buffer"&&ws.dispose();return yn}getPastKeyValues(T,Y,Fe=!1){const ze=Object.create(null);for(const ht in T)if(ht.startsWith("present")){const Ct=ht.replace("present","past_key_values"),Wt=ht.includes("encoder");if(Wt&&Y?ze[Ct]=Y[Ct]:ze[Ct]=T[ht],Y&&(!Wt||Fe)){const sr=Y[Ct];sr.location==="gpu-buffer"&&sr.dispose()}}return ze}getAttentions(T){const Y={};for(const Fe of["cross_attentions","encoder_attentions","decoder_attentions"])for(const ze in T)ze.startsWith(Fe)&&(Fe in Y||(Y[Fe]=[]),Y[Fe].push(T[ze]));return Y}addPastKeyValues(T,Y){if(Y)Object.assign(T,Y);else{const Fe=this.custom_config.kv_cache_dtype??"float32",ze=Fe==="float16"?new Uint16Array:[],ht=(0,O.getKeyValueShapes)(this.config);for(const Ct in ht)T[Ct]=new A.Tensor(Fe,ze,ht[Ct])}}async encode_image({pixel_values:T}){const Y=(await ue(this.sessions.vision_encoder,{pixel_values:T})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${Y.dims[1]}).`),this.config.num_image_tokens=Y.dims[1]),Y}async encode_text({input_ids:T}){return(await ue(this.sessions.embed_tokens,{input_ids:T})).inputs_embeds}}class He{}class ct extends He{constructor({last_hidden_state:m,hidden_states:T=null,attentions:Y=null}){super(),this.last_hidden_state=m,this.hidden_states=T,this.attentions=Y}}class nt extends Z{}class lt extends nt{}class Ne extends nt{async _call(m){return new dn(await super._call(m))}}class st extends nt{async _call(m){return new or(await super._call(m))}}class Pt extends nt{async _call(m){return new en(await super._call(m))}}class Re extends nt{async _call(m){return new hn(await super._call(m))}}class re extends Z{}class ke extends re{}class je extends Z{}class qe extends je{}class Ue extends je{async _call(m){return new dn(await super._call(m))}}class Ke extends je{async _call(m){return new or(await super._call(m))}}class ut extends je{async _call(m){return new en(await super._call(m))}}class yt extends je{async _call(m){return new hn(await super._call(m))}}class vt extends Z{}class kt extends vt{}class v extends vt{async _call(m){return new dn(await super._call(m))}}class q extends vt{async _call(m){return new or(await super._call(m))}}class C extends vt{async _call(m){return new en(await super._call(m))}}class Q extends vt{async _call(m){return new hn(await super._call(m))}}class he extends Z{}class Qe extends he{}class Ye extends he{async _call(m){return new dn(await super._call(m))}}class Bt extends he{async _call(m){return new or(await super._call(m))}}class ft extends he{async _call(m){return new en(await super._call(m))}}class Tt extends he{async _call(m){return new hn(await super._call(m))}}class bt extends Z{}class Ot extends bt{}class cr extends bt{async _call(m){return new dn(await super._call(m))}}class xr extends bt{async _call(m){return new or(await super._call(m))}}class Yr extends bt{async _call(m){return new en(await super._call(m))}}class Br extends bt{async _call(m){return new hn(await super._call(m))}}class Kr extends Z{}class at extends Kr{}class U extends Kr{async _call(m){return new dn(await super._call(m))}}class _e extends Kr{async _call(m){return new or(await super._call(m))}}class Pe extends Kr{async _call(m){return new en(await super._call(m))}}class rt extends Kr{async _call(m){return new hn(await super._call(m))}}class we extends Z{}class Je extends we{}class gt extends we{async _call(m){return new dn(await super._call(m))}}class mt extends we{async _call(m){return new or(await super._call(m))}}class Et extends we{async _call(m){return new en(await super._call(m))}}class _t extends we{async _call(m){return new hn(await super._call(m))}}class Ft extends Z{}class Nt extends Ft{}class Rt extends Ft{async _call(m){return new or(await super._call(m))}}class Gt extends Ft{async _call(m){return new en(await super._call(m))}}class Me extends Ft{async _call(m){return new hn(await super._call(m))}}class Ze extends Ft{async _call(m){return new dn(await super._call(m))}}class ot extends Z{}class Ht extends ot{}class gr extends ot{async _call(m){return new dn(await super._call(m))}}class Lr extends ot{async _call(m){return new or(await super._call(m))}}class mr extends ot{async _call(m){return new en(await super._call(m))}}class yr extends Z{}class Tr extends yr{}class $n extends yr{async _call(m){return new dn(await super._call(m))}}class Rr extends yr{async _call(m){return new or(await super._call(m))}}class Hn extends yr{async _call(m){return new hn(await super._call(m))}}class jn extends Z{}class Ys extends jn{}class vs extends jn{async _call(m){return new dn(await super._call(m))}}class xs extends jn{async _call(m){return new or(await super._call(m))}}class Ts extends jn{async _call(m){return new en(await super._call(m))}}class Es extends jn{async _call(m){return new hn(await super._call(m))}}class Kn extends Z{}class Js extends Kn{}class cs extends Kn{async _call(m){return new dn(await super._call(m))}}class Ln extends Kn{async _call(m){return new or(await super._call(m))}}class Xn extends Kn{async _call(m){return new hn(await super._call(m))}}class Vn extends Z{}class rs extends Vn{}class ps extends Vn{async _call(m){return new or(await super._call(m))}}class hs extends Vn{async _call(m){return new hn(await super._call(m))}}class Xt extends Vn{async _call(m){return new dn(await super._call(m))}}class ns extends Z{constructor(T,Y,Fe){super(T,Y);xe(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Fe}}class Cs extends ns{}class $s extends ns{}class fs extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Ss extends fs{}class ks extends fs{}class ms extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Ps extends ms{}class Wr extends ms{}class vn extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class be extends vn{}class _ extends vn{}class P extends vn{async _call(m){return new or(await super._call(m))}}class K extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class ae extends K{}class pe extends K{}class Be extends K{async _call(m){return new or(await super._call(m))}}class wt extends K{}class xt extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Mt extends xt{}class zt extends xt{}class er extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class zr extends er{}class ir extends er{}class qt extends Z{}class pr extends qt{}class mn extends qt{async _call(m){return new dn(await super._call(m))}}class ln extends qt{async _call(m){return new or(await super._call(m))}}class Oe extends qt{async _call(m){return new en(await super._call(m))}}class Sn extends qt{async _call(m){return new hn(await super._call(m))}}class Er extends Z{}class sn extends Er{}class xn extends Er{async _call(m){return new dn(await super._call(m))}}class Qt extends Er{async _call(m){return new or(await super._call(m))}}class Tn extends Er{async _call(m){return new en(await super._call(m))}}class pn extends Er{async _call(m){return new hn(await super._call(m))}}class Ar extends Z{}class Cr extends Ar{}class It extends Ar{async _call(m){return new dn(await super._call(m))}}class br extends Ar{async _call(m){return new or(await super._call(m))}}class Gr extends Ar{async _call(m){return new en(await super._call(m))}}class Xr extends Ar{async _call(m){return new hn(await super._call(m))}}class En extends Z{}class jt extends En{}class Zs extends En{}class et extends Z{constructor(T,Y,Fe){super(T,Y);xe(this,"requires_attention_mask",!1);xe(this,"main_input_name","input_features");xe(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Fe}}class Ut extends et{}class bi extends et{_prepare_generation_config(m,T){return super._prepare_generation_config(m,T,D.WhisperGenerationConfig)}_retrieve_init_tokens(m){const T=[m.decoder_start_token_id];let Y=m.language;const Fe=m.task;if(m.is_multilingual){Y||(console.warn("No language specified - defaulting to English (en)."),Y="en");const ht=`<|${(0,H.whisper_language_to_code)(Y)}|>`;T.push(m.lang_to_id[ht]),T.push(m.task_to_id[Fe??"transcribe"])}else if(Y||Fe)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&&T.at(-1)!==m.no_timestamps_token_id?T.push(m.no_timestamps_token_id):m.return_timestamps&&T.at(-1)===m.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),T.pop()),T.filter(ze=>ze!=null)}async generate({inputs:m=null,generation_config:T=null,logits_processor:Y=null,stopping_criteria:Fe=null,...ze}){T=this._prepare_generation_config(T,ze);const ht=ze.decoder_input_ids??this._retrieve_init_tokens(T);if(T.return_timestamps&&(Y??(Y=new S.LogitsProcessorList),Y.push(new S.WhisperTimeStampLogitsProcessor(T,ht))),T.begin_suppress_tokens&&(Y??(Y=new S.LogitsProcessorList),Y.push(new S.SuppressTokensAtBeginLogitsProcessor(T.begin_suppress_tokens,ht.length))),T.return_token_timestamps){if(!T.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.");T.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),T.output_attentions=!0,T.return_dict_in_generate=!0}const Ct=await super.generate({inputs:m,generation_config:T,logits_processor:Y,decoder_input_ids:ht,...ze});return T.return_token_timestamps&&(Ct.token_timestamps=this._extract_token_timestamps(Ct,T.alignment_heads,T.num_frames)),Ct}_extract_token_timestamps(m,T,Y=null,Fe=.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`.");Y==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 ze=this.config.median_filter_width;ze===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),ze=7);const ht=m.cross_attentions,Ct=Array.from({length:this.config.decoder_layers},(Fr,kr)=>(0,A.cat)(ht.map(_r=>_r[kr]),2)),Wt=(0,A.stack)(T.map(([Fr,kr])=>{if(Fr>=Ct.length)throw new Error(`Layer index ${Fr} is out of bounds for cross attentions (length ${Ct.length}).`);return Y?Ct[Fr].slice(null,kr,null,[0,Y]):Ct[Fr].slice(null,kr)})).transpose(1,0,2,3),[sr,Ir]=(0,A.std_mean)(Wt,-2,0,!0),ur=Wt.clone();for(let Fr=0;Fr<ur.dims[0];++Fr){const kr=ur[Fr];for(let _r=0;_r<kr.dims[0];++_r){const tn=kr[_r],rn=sr[Fr][_r][0].data,wn=Ir[Fr][_r][0].data;for(let cn=0;cn<tn.dims[0];++cn){let an=tn[cn].data;for(let yn=0;yn<an.length;++yn)an[yn]=(an[yn]-wn[yn])/rn[yn];an.set((0,ee.medianFilter)(an,ze))}}}const hr=[(0,A.mean)(ur,1)],wr=m.sequences.dims,Sr=new A.Tensor("float32",new Float32Array(wr[0]*wr[1]),wr);for(let Fr=0;Fr<wr[0];++Fr){const kr=hr[Fr].neg().squeeze_(0),[_r,tn]=(0,ee.dynamic_time_warping)(kr.tolist()),rn=Array.from({length:_r.length-1},(an,yn)=>_r[yn+1]-_r[yn]),wn=(0,Ce.mergeArrays)([1],rn).map(an=>!!an),cn=[];for(let an=0;an<wn.length;++an)wn[an]&&cn.push(tn[an]*Fe);Sr[Fr].data.set(cn,1)}return Sr}}class Mi extends Z{constructor(T,Y,Fe){super(T,Y);xe(this,"main_input_name","pixel_values");xe(this,"forward_params",["pixel_values","input_ids","encoder_hidden_states","past_key_values"]);this.generation_config=Fe}}class to extends Z{constructor(T,Y,Fe){super(T,Y);xe(this,"forward_params",["input_ids","pixel_values","attention_mask","position_ids","past_key_values"]);this.generation_config=Fe}}class _s extends to{_merge_input_ids_with_image_features({inputs_embeds:m,image_features:T,input_ids:Y,attention_mask:Fe}){const ze=this.config.image_token_index,Ct=Y.tolist().map(hr=>hr.findIndex(wr=>wr==ze)),Wt=Ct.every(hr=>hr===-1),sr=Ct.every(hr=>hr!==-1);if(!Wt&&!sr)throw new Error("Every input should contain either 0 or 1 image token.");if(Wt)return{inputs_embeds:m,attention_mask:Fe};const Ir=[],ur=[];for(let hr=0;hr<Ct.length;++hr){const wr=Ct[hr],Sr=m[hr],Fr=T[hr],kr=Fe[hr];Ir.push((0,A.cat)([Sr.slice([0,wr]),Fr,Sr.slice([wr+1,Sr.dims[0]])],0)),ur.push((0,A.cat)([kr.slice([0,wr]),(0,A.ones)([Fr.dims[0]]),kr.slice([wr+1,kr.dims[0]])],0))}return{inputs_embeds:(0,A.stack)(Ir,0),attention_mask:(0,A.stack)(ur,0)}}}class ar extends _s{}class ro extends Z{constructor(T,Y,Fe){super(T,Y);xe(this,"forward_params",["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"]);xe(this,"main_input_name","inputs_embeds");this.generation_config=Fe}}class vi extends ro{_merge_input_ids_with_image_features({inputs_embeds:m,image_features:T,input_ids:Y,attention_mask:Fe}){return{inputs_embeds:(0,A.cat)([T,m],1),attention_mask:(0,A.cat)([(0,A.ones)(T.dims.slice(0,2)),Fe],1)}}async _prepare_inputs_embeds({input_ids:m,pixel_values:T,inputs_embeds:Y,attention_mask:Fe}){if(!m&&!T)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let ze,ht;return m&&(ze=await this.encode_text({input_ids:m})),T&&(ht=await this.encode_image({pixel_values:T})),ze&&ht?{inputs_embeds:Y,attention_mask:Fe}=this._merge_input_ids_with_image_features({inputs_embeds:ze,image_features:ht,input_ids:m,attention_mask:Fe}):Y=ze||ht,{inputs_embeds:Y,attention_mask:Fe}}async forward({input_ids:m,pixel_values:T,attention_mask:Y,decoder_input_ids:Fe,decoder_attention_mask:ze,encoder_outputs:ht,past_key_values:Ct,inputs_embeds:Wt,decoder_inputs_embeds:sr}){if(Wt||({inputs_embeds:Wt,attention_mask:Y}=await this._prepare_inputs_embeds({input_ids:m,pixel_values:T,inputs_embeds:Wt,attention_mask:Y})),!ht){let{last_hidden_state:hr}=await tt(this,{inputs_embeds:Wt,attention_mask:Y});ht=hr}if(!sr){if(!Fe)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");sr=await this.encode_text({input_ids:Fe})}return await Xe(this,{inputs_embeds:sr,attention_mask:ze,encoder_attention_mask:Y,encoder_hidden_states:ht,past_key_values:Ct},!0)}}class ss extends Z{}class no extends ss{}class md extends ss{static async from_pretrained(m,T={}){return T.model_file_name??(T.model_file_name="text_model"),super.from_pretrained(m,T)}}class In extends ss{static async from_pretrained(m,T={}){return T.model_file_name??(T.model_file_name="text_model"),super.from_pretrained(m,T)}}class _d extends ss{static async from_pretrained(m,T={}){return T.model_file_name??(T.model_file_name="vision_model"),super.from_pretrained(m,T)}}class so extends ss{static async 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yo extends $i{}class Si extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class gd extends Si{}class On extends Si{}class ki extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class ei extends ki{}class Pi extends ki{}class kn extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class bo extends kn{}class Mo extends kn{}class Ai extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class vo extends Ai{}class xo extends Ai{}class Ii extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class To extends Ii{}class Eo extends Ii{}class Fi extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Co extends Fi{}class zn extends Fi{}class Oi extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class $o extends Oi{}class So extends Oi{}class zi extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class ko extends zi{}class Po extends zi{}class Di extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Ao extends Di{}class Io extends Di{}class Bi extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Li extends Bi{}class Ri extends Bi{}class Ni extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Fo extends Ni{}class ji extends Ni{}class Oo extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class zo extends Oo{}class Do extends Oo{}class Vi extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Bo extends Vi{}class As extends Vi{}class Ui extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Lo extends Ui{}class Wi extends Ui{}class Gi extends Z{}class Ro extends Gi{}class No extends Gi{async _call(m){return new or(await super._call(m))}}class qi extends Z{}class wd extends qi{}class jo extends qi{async _call(m){return new or(await super._call(m))}}class Vo extends Z{}class Uo extends Vo{}class Wo extends Z{}class Go extends Wo{}class $r extends Wo{async _call(m){return new or(await super._call(m))}}class qo extends Z{}class Ho extends qo{}class Hi extends Z{}class Ko extends Hi{}class Xo extends Hi{async _call(m){return new or(await super._call(m))}}class Qo extends Z{}class Yo extends Qo{async _call(m){return new od(await super._call(m))}}class Ki extends Z{}class Jo extends Ki{}class Zo extends Ki{async _call(m){return new or(await super._call(m))}}class Xi extends Z{}class el extends Xi{}class tl extends Xi{async _call(m){return new or(await super._call(m))}}class Qi extends Z{}class Is extends Qi{}class rl extends Qi{}class Fs extends Z{}class nl extends Fs{}class sl extends Fs{}class Yi extends Z{}class il extends Yi{}class al extends Yi{async _call(m){return new or(await super._call(m))}}class ti extends Z{}class ol extends ti{}class ll extends ti{async _call(m){return new Zi(await super._call(m))}}class Ji extends ti{async _call(m){return new ul(await super._call(m))}}class Zi extends He{constructor({logits:m,pred_boxes:T}){super(),this.logits=m,this.pred_boxes=T}}class ul extends He{constructor({logits:m,pred_boxes:T,pred_masks:Y}){super(),this.logits=m,this.pred_boxes=T,this.pred_masks=Y}}class ea extends Z{}class dl extends ea{}class cl extends ea{async _call(m){return new pl(await super._call(m))}}class pl extends He{constructor({logits:m,pred_boxes:T}){super(),this.logits=m,this.pred_boxes=T}}class ta extends Z{}class ra extends ta{}class hl extends ta{async _call(m){return new na(await super._call(m))}}class na extends Zi{}class ri extends Z{}class fl extends ri{}class ml extends ri{async _call(m){return new or(await super._call(m))}}class sa extends Z{}class _l extends sa{}class gl extends sa{async _call(m){return new or(await super._call(m))}}class ia extends Z{}class aa extends ia{}class wl extends ia{async _call(m){return new or(await super._call(m))}}class oa extends Z{}class yl extends oa{}class yd extends oa{async _call(m){return new or(await super._call(m))}}class la extends Z{}class bl extends la{}class Dn extends la{}class ua extends Z{}class Ml extends ua{}class vl extends ua{}class xl extends Z{}class Tl extends xl{}class ni extends Z{}class El extends ni{}class Cl extends ni{}class $l extends ni{}class da extends Z{}class bd extends da{}class Sl extends da{}class ca extends Z{}class kl extends ca{}class Pl extends ca{}class Al extends Z{}class Il extends Al{}class Fl extends Z{}class Yn extends Fl{}class Jn extends Fl{async _call(m){return new or(await super._call(m))}}class Un extends Z{}class pa extends Un{}class Zn extends Un{async _call(m){return new or(await super._call(m))}}class un extends Z{}class ha extends un{}class si extends un{async _call(m){return new or(await super._call(m))}}class ii extends Z{}class Ol extends ii{}class zl extends ii{async _call(m){return new Dl(await super._call(m))}}class Dl extends He{constructor({logits:m,pred_boxes:T}){super(),this.logits=m,this.pred_boxes=T}}class ai extends Z{}class fa extends ai{async get_image_embeddings({pixel_values:m}){return await tt(this,{pixel_values:m})}async forward(m){if((!m.image_embeddings||!m.image_positional_embeddings)&&(m={...m,...await this.get_image_embeddings(m)}),!m.input_labels&&m.input_points){const Y=m.input_points.dims.slice(0,-1),Fe=Y.reduce((ze,ht)=>ze*ht,1);m.input_labels=new A.Tensor("int64",new BigInt64Array(Fe).fill(1n),Y)}const T={image_embeddings:m.image_embeddings,image_positional_embeddings:m.image_positional_embeddings};return m.input_points&&(T.input_points=m.input_points),m.input_labels&&(T.input_labels=m.input_labels),m.input_boxes&&(T.input_boxes=m.input_boxes),await ue(this.sessions.prompt_encoder_mask_decoder,T)}async _call(m){return new Bl(await super._call(m))}}class Bl extends He{constructor({iou_scores:m,pred_masks:T}){super(),this.iou_scores=m,this.pred_masks=T}}class Os extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Ll extends Os{}class ma extends Os{}class zs extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Rl extends zs{}class Nl extends zs{}class is extends Z{}class jl extends is{}class _a extends is{async _call(m){return new es(await super._call(m))}}class Ds extends is{async _call(m){return new or(await super._call(m))}}class Vl extends is{async _call(m){return new en(await super._call(m))}}class oi extends Z{}class Ul extends oi{}class Wl extends oi{async _call(m){return new en(await super._call(m))}}class Gl extends Z{}class Md extends Gl{}class li extends Z{}class ql extends li{}class vd extends li{async _call(m){return new es(await super._call(m))}}class ga extends li{async _call(m){return new or(await super._call(m))}}class Bs extends Z{}class Hl extends Bs{}class Kl extends Bs{async _call(m){return new es(await super._call(m))}}class Xl extends Bs{async _call(m){return new or(await super._call(m))}}class ui extends Bs{async _call(m){return new en(await super._call(m))}}class di extends Z{}class ci extends di{}class wa extends di{async _call(m){return new es(await super._call(m))}}class ya extends di{async _call(m){return new or(await super._call(m))}}class xd extends Z{}class Ql extends is{}class ba extends is{async _call(m){return new es(await super._call(m))}}class Td extends is{async _call(m){return new or(await super._call(m))}}class gs extends Z{}class Yl extends gs{}class Ed extends gs{async _call(m){return new es(await super._call(m))}}class Jl extends gs{async _call(m){return new or(await super._call(m))}}class Ma extends gs{async _call(m){return new qa(await super._call(m))}}class Cd extends gs{async _call(m){return new en(await super._call(m))}}class pi extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class $d extends pi{}class Zl extends pi{}class va extends pi{async generate_speech(m,T,{threshold:Y=.5,minlenratio:Fe=0,maxlenratio:ze=20,vocoder:ht=null}={}){const Ct={input_ids:m},{encoder_outputs:Wt,encoder_attention_mask:sr}=await tt(this,Ct),Ir=Wt.dims[1]/this.config.reduction_factor,ur=Math.floor(Ir*ze),hr=Math.floor(Ir*Fe),wr=this.config.num_mel_bins;let Sr=[],Fr=null,kr=null,_r=0;for(;;){++_r;const wn=Ie(!!kr);let cn;kr?cn=kr.output_sequence_out:cn=new A.Tensor("float32",new Float32Array(wr),[1,1,wr]);let an={use_cache_branch:wn,output_sequence:cn,encoder_attention_mask:sr,speaker_embeddings:T,encoder_hidden_states:Wt};this.addPastKeyValues(an,Fr),kr=await ue(this.sessions.decoder_model_merged,an),Fr=this.getPastKeyValues(kr,Fr);const{prob:yn,spectrum:ws}=kr;if(Sr.push(ws),_r>=hr&&(Array.from(yn.data).filter(js=>js>=Y).length>0||_r>=ur))break}const tn=(0,A.cat)(Sr),{waveform:rn}=await ue(ht.sessions.model,{spectrogram:tn});return{spectrogram:tn,waveform:rn}}}class eu extends Z{constructor(){super(...arguments);xe(this,"main_input_name","spectrogram")}}class tu extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class ru extends tu{}class xa extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class nu extends xa{}class su extends xa{}class iu extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class au extends iu{}class ou extends iu{}class Ta extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class Sd extends Ta{}class lu extends Ta{}class hi extends Z{}class uu extends hi{}class du extends hi{static async from_pretrained(m,T={}){return T.model_file_name??(T.model_file_name="text_model"),super.from_pretrained(m,T)}}class cu extends hi{static async from_pretrained(m,T={}){return T.model_file_name??(T.model_file_name="audio_model"),super.from_pretrained(m,T)}}class kd extends Z{}class Ls extends kd{async _call(m){return new Ha(await super._call(m))}}class as extends Z{}class pu extends as{}class hu extends as{}class fu extends as{}class Ea extends Z{constructor(m,T,Y){super(m,T),this.generation_config=Y}}class mu extends Ea{}class Ca extends Ea{}class $a extends Z{}class _u extends $a{}class gu extends $a{async _call(m){return new or(await super._call(m))}}class wu extends Z{}class Pd extends wu{}class yu extends wu{}class Sa extends Z{constructor(T,Y,Fe){super(T,Y);xe(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Fe}_apply_and_filter_by_delay_pattern_mask(T){const[Y,Fe]=T.dims,ze=this.config.decoder.num_codebooks,ht=Fe-ze;let Ct=0;for(let Ir=0;Ir<T.size;++Ir){if(T.data[Ir]===this.config.decoder.pad_token_id)continue;const ur=Ir%Fe,hr=Math.floor(Ir/Fe)%ze,wr=ur-hr;wr>0&&wr<=ht&&(T.data[Ct++]=T.data[Ir])}const Wt=Math.floor(Y/ze),sr=Ct/(Wt*ze);return new A.Tensor(T.type,T.data.slice(0,Ct),[Wt,ze,sr])}prepare_inputs_for_generation(T,Y,Fe){let ze=structuredClone(T);for(let Ct=0;Ct<ze.length;++Ct)for(let Wt=0;Wt<ze[Ct].length;++Wt)Ct%this.config.decoder.num_codebooks>=Wt&&(ze[Ct][Wt]=BigInt(this.config.decoder.pad_token_id));return Fe.guidance_scale!==null&&Fe.guidance_scale>1&&(ze=ze.concat(ze)),super.prepare_inputs_for_generation(ze,Y,Fe)}async generate(T){const Y=await super.generate(T),Fe=this._apply_and_filter_by_delay_pattern_mask(Y).unsqueeze_(0),{audio_values:ze}=await ue(this.sessions.encodec_decode,{audio_codes:Fe});return ze}}class ka extends Z{}class bu extends ka{}class Ad extends ka{async _call(m){return new or(await super._call(m))}}class Pa extends Z{}class Mu extends Pa{}class Id extends Pa{async _call(m){return new or(await super._call(m))}}class Aa extends Z{}class vu extends Aa{}class xu extends Aa{async _call(m){return new or(await super._call(m))}}class Ia extends Z{}class Fd extends Ia{}class Tu extends Ia{async _call(m){return new or(await super._call(m))}}class Eu extends Z{}class Cu extends Eu{}class Dr{static async from_pretrained(m,{progress_callback:T=null,config:Y=null,cache_dir:Fe=null,local_files_only:ze=!1,revision:ht="main",model_file_name:Ct=null,subfolder:Wt="onnx",device:sr=null,dtype:Ir=null,use_external_data_format:ur=null,session_options:hr={}}={}){const wr={progress_callback:T,config:Y,cache_dir:Fe,local_files_only:ze,revision:ht,model_file_name:Ct,subfolder:Wt,device:sr,dtype:Ir,use_external_data_format:ur,session_options:hr};if(wr.config=await O.AutoConfig.from_pretrained(m,wr),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const Sr of this.MODEL_CLASS_MAPPINGS){const Fr=Sr.get(wr.config.model_type);if(Fr)return await Fr[1].from_pretrained(m,wr)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${wr.config.model_type}", attempting to construct from base class.`),await Z.from_pretrained(m,wr);throw Error(`Unsupported model type: 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Map([["speecht5",["SpeechT5ForSpeechToText",Zl]],["whisper",["WhisperForConditionalGeneration",bi]]]),$u=new Map([["speecht5",["SpeechT5ForTextToSpeech",va]]]),Dd=new Map([["vits",["VitsModel",Ls]],["musicgen",["MusicgenForConditionalGeneration",Sa]]]),Su=new Map([["bert",["BertForSequenceClassification",st]],["roformer",["RoFormerForSequenceClassification",Ke]],["electra",["ElectraForSequenceClassification",Bt]],["esm",["EsmForSequenceClassification",Lr]],["convbert",["ConvBertForSequenceClassification",q]],["camembert",["CamembertForSequenceClassification",xr]],["deberta",["DebertaForSequenceClassification",_e]],["deberta-v2",["DebertaV2ForSequenceClassification",mt]],["mpnet",["MPNetForSequenceClassification",xs]],["albert",["AlbertForSequenceClassification",ps]],["distilbert",["DistilBertForSequenceClassification",Rt]],["roberta",["RobertaForSequenceClassification",ln]],["xlm",["XLMForSequenceClassification",Qt]],["xlm-roberta",["XLMRobertaForSequenceClassification",br]],["bart",["BartForSequenceClassification",P]],["mbart",["MBartForSequenceClassification",Be]],["mobilebert",["MobileBertForSequenceClassification",Rr]],["squeezebert",["SqueezeBertForSequenceClassification",Ln]]]),ku=new Map([["bert",["BertForTokenClassification",Pt]],["roformer",["RoFormerForTokenClassification",ut]],["electra",["ElectraForTokenClassification",ft]],["esm",["EsmForTokenClassification",mr]],["convbert",["ConvBertForTokenClassification",C]],["camembert",["CamembertForTokenClassification",Yr]],["deberta",["DebertaForTokenClassification",Pe]],["deberta-v2",["DebertaV2ForTokenClassification",Et]],["mpnet",["MPNetForTokenClassification",Ts]],["distilbert",["DistilBertForTokenClassification",Gt]],["roberta",["RobertaForTokenClassification",Oe]],["xlm",["XLMForTokenClassification",Tn]],["xlm-roberta",["XLMRobertaForTokenClassification",Gr]]]),Oa=new Map([["t5",["T5ForConditionalGeneration",$s]],["longt5",["LongT5ForConditionalGeneration",ks]],["mt5",["MT5ForConditionalGeneration",Wr]],["bart",["BartForConditionalGeneration",_]],["mbart",["MBartForConditionalGeneration",pe]],["marian",["MarianMTModel",ma]],["m2m_100",["M2M100ForConditionalGeneration",Nl]],["blenderbot",["BlenderbotForConditionalGeneration",zt]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",ir]]]),za=new Map([["bloom",["BloomForCausalLM",Do]],["gpt2",["GPT2LMHeadModel",Fn]],["jais",["JAISLMHeadModel",mo]],["gptj",["GPTJForCausalLM",On]],["gpt_bigcode",["GPTBigCodeForCausalLM",Pi]],["gpt_neo",["GPTNeoForCausalLM",go]],["gpt_neox",["GPTNeoXForCausalLM",yo]],["codegen",["CodeGenForCausalLM",Mo]],["llama",["LlamaForCausalLM",xo]],["cohere",["CohereForCausalLM",Eo]],["gemma",["GemmaForCausalLM",zn]],["gemma2",["Gemma2ForCausalLM",So]],["openelm",["OpenELMForCausalLM",Po]],["qwen2",["Qwen2ForCausalLM",Io]],["phi",["PhiForCausalLM",Ri]],["phi3",["Phi3ForCausalLM",ji]],["mpt",["MptForCausalLM",As]],["opt",["OPTForCausalLM",Wi]],["mbart",["MBartForCausalLM",wt]],["mistral",["MistralForCausalLM",su]],["starcoder2",["Starcoder2ForCausalLM",ou]],["falcon",["FalconForCausalLM",lu]],["trocr",["TrOCRForCausalLM",ru]],["stablelm",["StableLmForCausalLM",Ca]]]),Bd=new Map([["bert",["BertForMaskedLM",Ne]],["roformer",["RoFormerForMaskedLM",Ue]],["electra",["ElectraForMaskedLM",Ye]],["esm",["EsmForMaskedLM",gr]],["convbert",["ConvBertForMaskedLM",v]],["camembert",["CamembertForMaskedLM",cr]],["deberta",["DebertaForMaskedLM",U]],["deberta-v2",["DebertaV2ForMaskedLM",gt]],["mpnet",["MPNetForMaskedLM",vs]],["albert",["AlbertForMaskedLM",Xt]],["distilbert",["DistilBertForMaskedLM",Ze]],["roberta",["RobertaForMaskedLM",mn]],["xlm",["XLMWithLMHeadModel",xn]],["xlm-roberta",["XLMRobertaForMaskedLM",It]],["mobilebert",["MobileBertForMaskedLM",$n]],["squeezebert",["SqueezeBertForMaskedLM",cs]]]),_n=new Map([["bert",["BertForQuestionAnswering",Re]],["roformer",["RoFormerForQuestionAnswering",yt]],["electra",["ElectraForQuestionAnswering",Tt]],["convbert",["ConvBertForQuestionAnswering",Q]],["camembert",["CamembertForQuestionAnswering",Br]],["deberta",["DebertaForQuestionAnswering",rt]],["deberta-v2",["DebertaV2ForQuestionAnswering",_t]],["mpnet",["MPNetForQuestionAnswering",Es]],["albert",["AlbertForQuestionAnswering",hs]],["distilbert",["DistilBertForQuestionAnswering",Me]],["roberta",["RobertaForQuestionAnswering",Sn]],["xlm",["XLMForQuestionAnswering",pn]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Xr]],["mobilebert",["MobileBertForQuestionAnswering",Hn]],["squeezebert",["SqueezeBertForQuestionAnswering",Xn]]]),Da=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Mi]]]),Ld=new Map([["llava",["LlavaForConditionalGeneration",_s]],["moondream1",["Moondream1ForConditionalGeneration",ar]],["florence2",["Florence2ForConditionalGeneration",vi]]]),Pu=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Mi]]]),Au=new Map([["vit",["ViTForImageClassification",No]],["pvt",["PvtForImageClassification",jo]],["vit_msn",["ViTMSNForImageClassification",$r]],["fastvit",["FastViTForImageClassification",Xo]],["mobilevit",["MobileViTForImageClassification",Zo]],["mobilevitv2",["MobileViTV2ForImageClassification",tl]],["beit",["BeitForImageClassification",al]],["deit",["DeiTForImageClassification",ml]],["hiera",["HieraForImageClassification",gl]],["convnext",["ConvNextForImageClassification",Jn]],["convnextv2",["ConvNextV2ForImageClassification",Zn]],["dinov2",["Dinov2ForImageClassification",si]],["resnet",["ResNetForImageClassification",wl]],["swin",["SwinForImageClassification",yd]],["segformer",["SegformerForImageClassification",hu]],["efficientnet",["EfficientNetForImageClassification",gu]],["mobilenet_v1",["MobileNetV1ForImageClassification",Ad]],["mobilenet_v2",["MobileNetV2ForImageClassification",Id]],["mobilenet_v3",["MobileNetV3ForImageClassification",xu]],["mobilenet_v4",["MobileNetV4ForImageClassification",Tu]]]),Rs=new Map([["detr",["DetrForObjectDetection",ll]],["rt_detr",["RTDetrForObjectDetection",cl]],["table-transformer",["TableTransformerForObjectDetection",hl]],["yolos",["YolosForObjectDetection",zl]]]),Iu=new Map([["owlvit",["OwlViTForObjectDetection",rl]],["owlv2",["Owlv2ForObjectDetection",sl]]]),Fu=new Map([["detr",["DetrForSegmentation",Ji]],["clipseg",["CLIPSegForImageSegmentation",po]]]),Ba=new Map([["segformer",["SegformerForSemanticSegmentation",fu]],["sapiens",["SapiensForSemanticSegmentation",El]]]),Ou=new Map([["detr",["DetrForSegmentation",Ji]],["maskformer",["MaskFormerForInstanceSegmentation",Sl]]]),zu=new Map([["sam",["SamModel",fa]]]),La=new Map([["wav2vec2",["Wav2Vec2ForCTC",_a]],["wav2vec2-bert",["Wav2Vec2BertForCTC",wa]],["unispeech",["UniSpeechForCTC",vd]],["unispeech-sat",["UniSpeechSatForCTC",Kl]],["wavlm",["WavLMForCTC",Ed]],["hubert",["HubertForCTC",ba]]]),Du=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Ds]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",ya]],["unispeech",["UniSpeechForSequenceClassification",ga]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Xl]],["wavlm",["WavLMForSequenceClassification",Jl]],["hubert",["HubertForSequenceClassification",Td]],["audio-spectrogram-transformer",["ASTForAudioClassification",Zs]]]),Bu=new Map([["wavlm",["WavLMForXVector",Ma]]]),Lu=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",ui]],["wavlm",["WavLMForAudioFrameClassification",Cd]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Vl]],["pyannote",["PyAnnoteForAudioFrameClassification",Wl]]]),Ra=new Map([["vitmatte",["VitMatteForImageMatting",Yo]]]),Ru=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Dn]]]),Nu=new Map([["dpt",["DPTForDepthEstimation",vl]],["depth_anything",["DepthAnythingForDepthEstimation",Tl]],["glpn",["GLPNForDepthEstimation",Pl]],["sapiens",["SapiensForDepthEstimation",Cl]]]),Na=new Map([["sapiens",["SapiensForNormalEstimation",$l]]]),ju=new Map([["clip",["CLIPVisionModelWithProjection",so]],["siglip",["SiglipVisionModel",oo]]]),Rd=[[wc,te.EncoderOnly],[Od,te.EncoderDecoder],[zd,te.DecoderOnly],[Su,te.EncoderOnly],[ku,te.EncoderOnly],[Oa,te.Seq2Seq],[Fa,te.Seq2Seq],[za,te.DecoderOnly],[Bd,te.EncoderOnly],[_n,te.EncoderOnly],[Da,te.Vision2Seq],[Ld,te.ImageTextToText],[Au,te.EncoderOnly],[Fu,te.EncoderOnly],[Ou,te.EncoderOnly],[Ba,te.EncoderOnly],[Ra,te.EncoderOnly],[Ru,te.EncoderOnly],[Nu,te.EncoderOnly],[Na,te.EncoderOnly],[Rs,te.EncoderOnly],[Iu,te.EncoderOnly],[zu,te.MaskGeneration],[La,te.EncoderOnly],[Du,te.EncoderOnly],[$u,te.Seq2Seq],[Dd,te.EncoderOnly],[Bu,te.EncoderOnly],[Lu,te.EncoderOnly],[ju,te.EncoderOnly]];for(const[f,m]of Rd)for(const[T,Y]of f.values())se.set(T,m),R.set(Y,T),X.set(T,Y);const Vu=[["MusicgenForConditionalGeneration",Sa,te.Musicgen],["CLIPTextModelWithProjection",In,te.EncoderOnly],["SiglipTextModel",ao,te.EncoderOnly],["ClapTextModelWithProjection",du,te.EncoderOnly],["ClapAudioModelWithProjection",cu,te.EncoderOnly]];for(const[f,m,T]of Vu)se.set(f,T),R.set(m,f),X.set(f,m);class ja extends Dr{}xe(ja,"MODEL_CLASS_MAPPINGS",Rd.map(m=>m[0])),xe(ja,"BASE_IF_FAIL",!0);class Uu extends Dr{}xe(Uu,"MODEL_CLASS_MAPPINGS",[Su]);class Wu extends Dr{}xe(Wu,"MODEL_CLASS_MAPPINGS",[ku]);class Nd extends Dr{}xe(Nd,"MODEL_CLASS_MAPPINGS",[Oa]);class Gu extends Dr{}xe(Gu,"MODEL_CLASS_MAPPINGS",[Fa]);class qu extends Dr{}xe(qu,"MODEL_CLASS_MAPPINGS",[$u]);class Hu extends Dr{}xe(Hu,"MODEL_CLASS_MAPPINGS",[Dd]);class jd extends Dr{}xe(jd,"MODEL_CLASS_MAPPINGS",[za]);class Ku extends Dr{}xe(Ku,"MODEL_CLASS_MAPPINGS",[Bd]);class Xu extends Dr{}xe(Xu,"MODEL_CLASS_MAPPINGS",[_n]);class Qu extends Dr{}xe(Qu,"MODEL_CLASS_MAPPINGS",[Da]);class Yu extends Dr{}xe(Yu,"MODEL_CLASS_MAPPINGS",[Au]);class Vd extends Dr{}xe(Vd,"MODEL_CLASS_MAPPINGS",[Fu]);class Ju extends Dr{}xe(Ju,"MODEL_CLASS_MAPPINGS",[Ba]);class Zu extends Dr{}xe(Zu,"MODEL_CLASS_MAPPINGS",[Ou]);class ed extends Dr{}xe(ed,"MODEL_CLASS_MAPPINGS",[Rs]);class td extends Dr{}xe(td,"MODEL_CLASS_MAPPINGS",[Iu]);class rd extends Dr{}xe(rd,"MODEL_CLASS_MAPPINGS",[zu]);class nd extends Dr{}xe(nd,"MODEL_CLASS_MAPPINGS",[La]);class sd extends Dr{}xe(sd,"MODEL_CLASS_MAPPINGS",[Du]);class id extends Dr{}xe(id,"MODEL_CLASS_MAPPINGS",[Bu]);class ad extends Dr{}xe(ad,"MODEL_CLASS_MAPPINGS",[Lu]);class Ud extends Dr{}xe(Ud,"MODEL_CLASS_MAPPINGS",[Pu]);class Ns extends Dr{}xe(Ns,"MODEL_CLASS_MAPPINGS",[Ra]);class Va extends Dr{}xe(Va,"MODEL_CLASS_MAPPINGS",[Ru]);class Ua extends Dr{}xe(Ua,"MODEL_CLASS_MAPPINGS",[Nu]);class Wa extends Dr{}xe(Wa,"MODEL_CLASS_MAPPINGS",[Na]);class Ga extends Dr{}xe(Ga,"MODEL_CLASS_MAPPINGS",[ju]);class Wd extends He{constructor({logits:m,past_key_values:T,encoder_outputs:Y,decoder_attentions:Fe=null,cross_attentions:ze=null}){super(),this.logits=m,this.past_key_values=T,this.encoder_outputs=Y,this.decoder_attentions=Fe,this.cross_attentions=ze}}class or extends He{constructor({logits:m}){super(),this.logits=m}}class qa extends He{constructor({logits:m,embeddings:T}){super(),this.logits=m,this.embeddings=T}}class en extends He{constructor({logits:m}){super(),this.logits=m}}class dn extends He{constructor({logits:m}){super(),this.logits=m}}class hn extends He{constructor({start_logits:m,end_logits:T}){super(),this.start_logits=m,this.end_logits=T}}class es extends He{constructor({logits:m}){super(),this.logits=m}}class Gd extends He{constructor({logits:m,past_key_values:T}){super(),this.logits=m,this.past_key_values=T}}class od extends He{constructor({alphas:m}){super(),this.alphas=m}}class Ha extends He{constructor({waveform:m,spectrogram:T}){super(),this.waveform=m,this.spectrogram=T}}},"./src/models/whisper/common_whisper.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{WHISPER_LANGUAGE_MAPPING:()=>fe,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>ye,whisper_language_to_code:()=>Te});const 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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"]],fe=new Map(O),ye=new Map([...O.map(([Ce,j])=>[j,Ce]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function Te(Ce){Ce=Ce.toLowerCase();let j=ye.get(Ce);if(j===void 0)if(fe.has(Ce))j=Ce;else{const V=Ce.length===2?fe.keys():fe.values();throw new Error(`Language "${Ce}" is not supported. Must be one of: ${JSON.stringify(V)}`)}return j}},"./src/models/whisper/generation_whisper.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{WhisperGenerationConfig:()=>fe});var O=N("./src/generation/configuration_utils.js");class fe extends O.GenerationConfig{constructor(){super(...arguments);xe(this,"return_timestamps",null);xe(this,"return_token_timestamps",null);xe(this,"num_frames",null);xe(this,"alignment_heads",null);xe(this,"task",null);xe(this,"language",null);xe(this,"no_timestamps_token_id",null);xe(this,"prompt_ids",null);xe(this,"is_multilingual",null);xe(this,"lang_to_id",null);xe(this,"task_to_id",null);xe(this,"max_initial_timestamp_index",1)}}},"./src/ops/registry.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{TensorOpRegistry:()=>Te});var O=N("./src/backends/onnx.js"),fe=N("./src/utils/tensor.js");const ye=async(Ce,j,S)=>{const V=await(0,O.createInferenceSession)(new Uint8Array(Ce),j);return async A=>{const ee=Object.fromEntries(Object.entries(A).map(([me,ce])=>[me,ce.ort_tensor])),ne=await V.run(ee);return Array.isArray(S)?S.map(me=>new fe.Tensor(ne[me])):new fe.Tensor(ne[S])}};class Te{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=ye([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=ye([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=ye([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=ye([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=ye([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=ye([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}}xe(Te,"session_options",{})},"./src/pipelines.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{AudioClassificationPipeline:()=>Ie,AutomaticSpeechRecognitionPipeline:()=>tt,DepthEstimationPipeline:()=>nt,DocumentQuestionAnsweringPipeline:()=>Z,FeatureExtractionPipeline:()=>ve,FillMaskPipeline:()=>se,ImageClassificationPipeline:()=>dt,ImageFeatureExtractionPipeline:()=>$e,ImageSegmentationPipeline:()=>ge,ImageToImagePipeline:()=>ct,ImageToTextPipeline:()=>Xe,ObjectDetectionPipeline:()=>de,Pipeline:()=>ce,QuestionAnsweringPipeline:()=>te,SummarizationPipeline:()=>R,Text2TextGenerationPipeline:()=>X,TextClassificationPipeline:()=>D,TextGenerationPipeline:()=>k,TextToAudioPipeline:()=>He,TokenClassificationPipeline:()=>H,TranslationPipeline:()=>I,ZeroShotAudioClassificationPipeline:()=>Ae,ZeroShotClassificationPipeline:()=>ue,ZeroShotImageClassificationPipeline:()=>G,ZeroShotObjectDetectionPipeline:()=>Se,pipeline:()=>st});var O=N("./src/tokenizers.js"),fe=N("./src/models.js"),ye=N("./src/processors.js"),Te=N("./src/utils/generic.js"),Ce=N("./src/utils/core.js"),j=N("./src/utils/maths.js"),S=N("./src/utils/audio.js"),V=N("./src/utils/tensor.js"),A=N("./src/utils/image.js");async function ee(Re){return Array.isArray(Re)||(Re=[Re]),await Promise.all(Re.map(re=>A.RawImage.read(re)))}async function ne(Re,re){return Array.isArray(Re)||(Re=[Re]),await Promise.all(Re.map(ke=>typeof ke=="string"||ke instanceof URL?(0,S.read_audio)(ke,re):ke instanceof Float64Array?new Float32Array(ke):ke))}function me(Re,re){re&&(Re=Re.map(Ke=>Ke|0));const[ke,je,qe,Ue]=Re;return{xmin:ke,ymin:je,xmax:qe,ymax:Ue}}class ce extends Te.Callable{constructor({task:re,model:ke,tokenizer:je=null,processor:qe=null}){super(),this.task=re,this.model=ke,this.tokenizer=je,this.processor=qe}async dispose(){await this.model.dispose()}}class D extends ce{constructor(re){super(re)}async _call(re,{top_k:ke=1}={}){const je=this.tokenizer(re,{padding:!0,truncation:!0}),qe=await this.model(je),Ue=this.model.config.problem_type==="multi_label_classification"?yt=>yt.sigmoid():yt=>new V.Tensor("float32",(0,j.softmax)(yt.data),yt.dims),Ke=this.model.config.id2label,ut=[];for(const yt of qe.logits){const vt=Ue(yt),kt=await(0,V.topk)(vt,ke),v=kt[0].tolist(),C=kt[1].tolist().map((Q,he)=>({label:Ke?Ke[Q]:`LABEL_${Q}`,score:v[he]}));ke===1?ut.push(...C):ut.push(C)}return Array.isArray(re)||ke===1?ut:ut[0]}}class H extends ce{constructor(re){super(re)}async _call(re,{ignore_labels:ke=["O"]}={}){const je=Array.isArray(re),qe=this.tokenizer(je?re:[re],{padding:!0,truncation:!0}),Ke=(await this.model(qe)).logits,ut=this.model.config.id2label,yt=[];for(let vt=0;vt<Ke.dims[0];++vt){const kt=qe.input_ids[vt],v=Ke[vt],q=[];for(let C=0;C<v.dims[0];++C){const Q=v[C],he=(0,j.max)(Q.data)[1],Qe=ut?ut[he]:`LABEL_${he}`;if(ke.includes(Qe))continue;const Ye=this.tokenizer.decode([kt[C].item()],{skip_special_tokens:!0});if(Ye==="")continue;const Bt=(0,j.softmax)(Q.data);q.push({entity:Qe,score:Bt[he],index:C,word:Ye})}yt.push(q)}return je?yt:yt[0]}}class te extends ce{constructor(re){super(re)}async _call(re,ke,{top_k:je=1}={}){const qe=this.tokenizer(re,{text_pair:ke,padding:!0,truncation:!0}),{start_logits:Ue,end_logits:Ke}=await this.model(qe),ut=qe.input_ids.tolist(),yt=qe.attention_mask.tolist(),vt=this.tokenizer.all_special_ids,kt=[];for(let v=0;v<Ue.dims[0];++v){const q=ut[v],C=q.findIndex(ft=>ft==this.tokenizer.sep_token_id);yt[v].map((ft,Tt)=>ft==1&&(Tt===0||Tt>C&&vt.findIndex(bt=>bt==q[Tt])===-1));const Q=Ue[v].tolist(),he=Ke[v].tolist();for(let ft=1;ft<Q.length;++ft)(yt[v]==0||ft<=C||vt.findIndex(Tt=>Tt==q[ft])!==-1)&&(Q[ft]=-1/0,he[ft]=-1/0);const Qe=(0,j.softmax)(Q).map((ft,Tt)=>[ft,Tt]),Ye=(0,j.softmax)(he).map((ft,Tt)=>[ft,Tt]);Qe[0][0]=0,Ye[0][0]=0;const Bt=(0,Ce.product)(Qe,Ye).filter(ft=>ft[0][1]<=ft[1][1]).map(ft=>[ft[0][1],ft[1][1],ft[0][0]*ft[1][0]]).sort((ft,Tt)=>Tt[2]-ft[2]);for(let ft=0;ft<Math.min(Bt.length,je);++ft){const[Tt,bt,Ot]=Bt[ft],cr=q.slice(Tt,bt+1),xr=this.tokenizer.decode(cr,{skip_special_tokens:!0});kt.push({answer:xr,score:Ot})}}return je===1?kt[0]:kt}}class se extends ce{constructor(re){super(re)}async _call(re,{top_k:ke=5}={}){const je=this.tokenizer(re,{padding:!0,truncation:!0}),{logits:qe}=await this.model(je),Ue=[],Ke=je.input_ids.tolist();for(let ut=0;ut<Ke.length;++ut){const yt=Ke[ut],vt=yt.findIndex(Q=>Q==this.tokenizer.mask_token_id);if(vt===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const kt=qe[ut][vt],v=await(0,V.topk)(new V.Tensor("float32",(0,j.softmax)(kt.data),kt.dims),ke),q=v[0].tolist(),C=v[1].tolist();Ue.push(C.map((Q,he)=>{const Qe=yt.slice();return Qe[vt]=Q,{score:q[he],token:Number(Q),token_str:this.tokenizer.model.vocab[Q],sequence:this.tokenizer.decode(Qe,{skip_special_tokens:!0})}}))}return Array.isArray(re)?Ue:Ue[0]}}class X extends ce{constructor(ke){super(ke);xe(this,"_key","generated_text")}async _call(ke,je={}){Array.isArray(ke)||(ke=[ke]),this.model.config.prefix&&(ke=ke.map(vt=>this.model.config.prefix+vt));const qe=this.model.config.task_specific_params;qe&&qe[this.task]&&qe[this.task].prefix&&(ke=ke.map(vt=>qe[this.task].prefix+vt));const Ue=this.tokenizer,Ke={padding:!0,truncation:!0};let ut;this instanceof I&&"_build_translation_inputs"in Ue?ut=Ue._build_translation_inputs(ke,Ke,je):ut=Ue(ke,Ke);const yt=await this.model.generate({...ut,...je});return Ue.batch_decode(yt,{skip_special_tokens:!0}).map(vt=>({[this._key]:vt}))}}class R extends X{constructor(ke){super(ke);xe(this,"_key","summary_text")}}class I extends X{constructor(ke){super(ke);xe(this,"_key","translation_text")}}function B(Re){return Array.isArray(Re)&&Re.every(re=>"role"in re&&"content"in re)}class k extends ce{constructor(re){super(re)}async _call(re,ke={}){let je=!1,qe=!1,Ue;if(typeof re=="string")Ue=re=[re];else if(Array.isArray(re)&&re.every(C=>typeof C=="string"))je=!0,Ue=re;else{if(B(re))re=[re];else if(Array.isArray(re)&&re.every(B))je=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");qe=!0,Ue=re.map(C=>this.tokenizer.apply_chat_template(C,{tokenize:!1,add_generation_prompt:!0}))}const Ke=ke.add_special_tokens??!1,ut=qe?!1:ke.return_full_text??!0;this.tokenizer.padding_side="left";const yt=this.tokenizer(Ue,{add_special_tokens:Ke,padding:!0,truncation:!0}),vt=await this.model.generate({...yt,...ke}),kt=this.tokenizer.batch_decode(vt,{skip_special_tokens:!0});let v;!ut&&yt.input_ids.dims.at(-1)>0&&(v=this.tokenizer.batch_decode(yt.input_ids,{skip_special_tokens:!0}).map(C=>C.length));const q=Array.from({length:re.length},C=>[]);for(let C=0;C<kt.length;++C){const Q=Math.floor(C/vt.dims[0]*re.length);v&&(kt[C]=kt[C].slice(v[Q])),q[Q].push({generated_text:qe?[...re[Q],{role:"assistant",content:kt[C]}]:kt[C]})}return!je&&q.length===1?q[0]:q}}class ue extends ce{constructor(re){super(re),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([ke,je])=>[ke.toLowerCase(),je])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(re,ke,{hypothesis_template:je="This example is {}.",multi_label:qe=!1}={}){const Ue=Array.isArray(re);Ue||(re=[re]),Array.isArray(ke)||(ke=[ke]);const Ke=ke.map(vt=>je.replace("{}",vt)),ut=qe||ke.length===1,yt=[];for(const vt of re){const kt=[];for(const C of Ke){const Q=this.tokenizer(vt,{text_pair:C,padding:!0,truncation:!0}),he=await this.model(Q);ut?kt.push([he.logits.data[this.contradiction_id],he.logits.data[this.entailment_id]]):kt.push(he.logits.data[this.entailment_id])}const q=(ut?kt.map(C=>(0,j.softmax)(C)[1]):(0,j.softmax)(kt)).map((C,Q)=>[C,Q]).sort((C,Q)=>Q[0]-C[0]);yt.push({sequence:vt,labels:q.map(C=>ke[C[1]]),scores:q.map(C=>C[0])})}return Ue?yt:yt[0]}}class ve extends ce{constructor(re){super(re)}async _call(re,{pooling:ke="none",normalize:je=!1,quantize:qe=!1,precision:Ue="binary"}={}){const Ke=this.tokenizer(re,{padding:!0,truncation:!0}),ut=await this.model(Ke);let yt=ut.last_hidden_state??ut.logits??ut.token_embeddings;if(ke!=="none")if(ke==="mean")yt=(0,V.mean_pooling)(yt,Ke.attention_mask);else if(ke==="cls")yt=yt.slice(null,0);else throw Error(`Pooling method '${ke}' not supported.`);return je&&(yt=yt.normalize(2,-1)),qe&&(yt=(0,V.quantize_embeddings)(yt,Ue)),yt}}class $e extends ce{constructor(re){super(re)}async _call(re,{pool:ke=null}={}){const je=await ee(re),{pixel_values:qe}=await this.processor(je),Ue=await this.model({pixel_values:qe});let Ke;if(ke){if(!("pooler_output"in Ue))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Ke=Ue.pooler_output}else Ke=Ue.last_hidden_state??Ue.logits??Ue.image_embeds;return Ke}}class Ie extends ce{constructor(re){super(re)}async _call(re,{top_k:ke=5}={}){const je=this.processor.feature_extractor.config.sampling_rate,qe=await ne(re,je),Ue=this.model.config.id2label,Ke=[];for(const ut of qe){const yt=await this.processor(ut),kt=(await this.model(yt)).logits[0],v=await(0,V.topk)(new V.Tensor("float32",(0,j.softmax)(kt.data),kt.dims),ke),q=v[0].tolist(),Q=v[1].tolist().map((he,Qe)=>({label:Ue?Ue[he]:`LABEL_${he}`,score:q[Qe]}));Ke.push(Q)}return Array.isArray(re)?Ke:Ke[0]}}class Ae extends ce{constructor(re){super(re)}async _call(re,ke,{hypothesis_template:je="This is a sound of {}."}={}){const qe=!Array.isArray(re);qe&&(re=[re]);const Ue=ke.map(kt=>je.replace("{}",kt)),Ke=this.tokenizer(Ue,{padding:!0,truncation:!0}),ut=this.processor.feature_extractor.config.sampling_rate,yt=await ne(re,ut),vt=[];for(const kt of yt){const v=await this.processor(kt),q=await this.model({...Ke,...v}),C=(0,j.softmax)(q.logits_per_audio.data);vt.push([...C].map((Q,he)=>({score:Q,label:ke[he]})))}return qe?vt[0]:vt}}class tt extends ce{constructor(re){super(re)}async _call(re,ke={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(re,ke);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(re,ke);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(re,ke){ke.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),ke.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const je=!Array.isArray(re);je&&(re=[re]);const qe=this.processor.feature_extractor.config.sampling_rate,Ue=await ne(re,qe),Ke=[];for(const ut of Ue){const yt=await this.processor(ut),kt=(await this.model(yt)).logits[0],v=[];for(const C of kt)v.push((0,j.max)(C.data)[1]);const q=this.tokenizer.decode(v);Ke.push({text:q})}return je?Ke[0]:Ke}async _call_whisper(re,ke){const je=ke.return_timestamps??!1,qe=ke.chunk_length_s??0,Ue=ke.force_full_sequences??!1;let Ke=ke.stride_length_s??null;const ut={...ke};je==="word"&&(ut.return_token_timestamps=!0,ut.return_timestamps=!1);const yt=!Array.isArray(re);yt&&(re=[re]);const vt=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,kt=this.processor.feature_extractor.config.hop_length,v=this.processor.feature_extractor.config.sampling_rate,q=await ne(re,v),C=[];for(const Q of q){let he=[];if(qe>0){if(Ke===null)Ke=qe/6;else if(qe<=Ke)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const Bt=v*qe,ft=v*Ke,Tt=Bt-2*ft;let bt=0;for(;;){const Ot=bt+Bt,cr=Q.subarray(bt,Ot),xr=await this.processor(cr),Yr=bt===0,Br=Ot>=Q.length;if(he.push({stride:[cr.length,Yr?0:ft,Br?0:ft],input_features:xr.input_features,is_last:Br}),Br)break;bt+=Tt}}else he=[{stride:[Q.length,0,0],input_features:(await this.processor(Q)).input_features,is_last:!0}];for(const Bt of he){ut.num_frames=Math.floor(Bt.stride[0]/kt);const ft=await this.model.generate({inputs:Bt.input_features,...ut});je==="word"?(Bt.tokens=ft.sequences.tolist()[0],Bt.token_timestamps=ft.token_timestamps.tolist()[0].map(Tt=>(0,j.round)(Tt,2))):Bt.tokens=ft[0].tolist(),Bt.stride=Bt.stride.map(Tt=>Tt/v)}const[Qe,Ye]=this.tokenizer._decode_asr(he,{time_precision:vt,return_timestamps:je,force_full_sequences:Ue});C.push({text:Qe,...Ye})}return yt?C[0]:C}}class Xe extends ce{constructor(re){super(re)}async _call(re,ke={}){const je=Array.isArray(re),qe=await ee(re),{pixel_values:Ue}=await this.processor(qe),Ke=[];for(const ut of Ue){ut.dims=[1,...ut.dims];const yt=await this.model.generate({inputs:ut,...ke}),vt=this.tokenizer.batch_decode(yt,{skip_special_tokens:!0}).map(kt=>({generated_text:kt.trim()}));Ke.push(vt)}return je?Ke:Ke[0]}}class dt extends ce{constructor(re){super(re)}async _call(re,{top_k:ke=5}={}){const je=await ee(re),{pixel_values:qe}=await this.processor(je),Ue=await this.model({pixel_values:qe}),Ke=this.model.config.id2label,ut=[];for(const yt of Ue.logits){const vt=await(0,V.topk)(new V.Tensor("float32",(0,j.softmax)(yt.data),yt.dims),ke),kt=vt[0].tolist(),q=vt[1].tolist().map((C,Q)=>({label:Ke?Ke[C]:`LABEL_${C}`,score:kt[Q]}));ut.push(q)}return Array.isArray(re)?ut:ut[0]}}class ge extends ce{constructor(re){super(re),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(re,{threshold:ke=.5,mask_threshold:je=.5,overlap_mask_area_threshold:qe=.8,label_ids_to_fuse:Ue=null,target_sizes:Ke=null,subtask:ut=null}={}){if(Array.isArray(re)&&re.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const vt=await ee(re),kt=vt.map(Ye=>[Ye.height,Ye.width]),{pixel_values:v,pixel_mask:q}=await this.processor(vt),C=await this.model({pixel_values:v,pixel_mask:q});let Q=null;if(ut!==null)Q=this.subtasks_mapping[ut];else for(let[Ye,Bt]of Object.entries(this.subtasks_mapping))if(Bt in this.processor.feature_extractor){Q=this.processor.feature_extractor[Bt].bind(this.processor.feature_extractor),ut=Ye;break}const he=this.model.config.id2label,Qe=[];if(ut==="panoptic"||ut==="instance"){const Ye=Q(C,ke,je,qe,Ue,Ke??kt)[0],Bt=Ye.segmentation;for(const ft of Ye.segments_info){const Tt=new Uint8ClampedArray(Bt.data.length);for(let Ot=0;Ot<Bt.data.length;++Ot)Bt.data[Ot]===ft.id&&(Tt[Ot]=255);const bt=new A.RawImage(Tt,Bt.dims[1],Bt.dims[0],1);Qe.push({score:ft.score,label:he[ft.label_id],mask:bt})}}else if(ut==="semantic"){const{segmentation:Ye,labels:Bt}=Q(C,Ke??kt)[0];for(const ft of Bt){const Tt=new Uint8ClampedArray(Ye.data.length);for(let Ot=0;Ot<Ye.data.length;++Ot)Ye.data[Ot]===ft&&(Tt[Ot]=255);const bt=new A.RawImage(Tt,Ye.dims[1],Ye.dims[0],1);Qe.push({score:null,label:he[ft],mask:bt})}}else throw Error(`Subtask ${ut} not supported.`);return Qe}}class G extends ce{constructor(re){super(re)}async _call(re,ke,{hypothesis_template:je="This is a photo of {}"}={}){const qe=Array.isArray(re),Ue=await ee(re),Ke=ke.map(q=>je.replace("{}",q)),ut=this.tokenizer(Ke,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:yt}=await this.processor(Ue),vt=await this.model({...ut,pixel_values:yt}),kt=this.model.config.model_type==="siglip"?q=>q.sigmoid().data:q=>(0,j.softmax)(q.data),v=[];for(const q of vt.logits_per_image){const Q=[...kt(q)].map((he,Qe)=>({score:he,label:ke[Qe]}));Q.sort((he,Qe)=>Qe.score-he.score),v.push(Q)}return qe?v:v[0]}}class de extends ce{constructor(re){super(re)}async _call(re,{threshold:ke=.9,percentage:je=!1}={}){const qe=Array.isArray(re);if(qe&&re.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ue=await ee(re),Ke=je?null:Ue.map(C=>[C.height,C.width]),{pixel_values:ut,pixel_mask:yt}=await this.processor(Ue),vt=await this.model({pixel_values:ut,pixel_mask:yt}),kt=this.processor.feature_extractor.post_process_object_detection(vt,ke,Ke),v=this.model.config.id2label,q=kt.map(C=>C.boxes.map((Q,he)=>({score:C.scores[he],label:v[C.classes[he]],box:me(Q,!je)})));return qe?q:q[0]}}class Se extends ce{constructor(re){super(re)}async _call(re,ke,{threshold:je=.1,top_k:qe=null,percentage:Ue=!1}={}){const Ke=Array.isArray(re),ut=await ee(re),yt=this.tokenizer(ke,{padding:!0,truncation:!0}),vt=await this.processor(ut),kt=[];for(let v=0;v<ut.length;++v){const q=ut[v],C=Ue?null:[[q.height,q.width]],Q=vt.pixel_values[v].unsqueeze_(0),he=await this.model({...yt,pixel_values:Q}),Qe=this.processor.feature_extractor.post_process_object_detection(he,je,C,!0)[0];let Ye=Qe.boxes.map((Bt,ft)=>({score:Qe.scores[ft],label:ke[Qe.classes[ft]],box:me(Bt,!Ue)})).sort((Bt,ft)=>ft.score-Bt.score);qe!==null&&(Ye=Ye.slice(0,qe)),kt.push(Ye)}return Ke?kt:kt[0]}}class Z extends ce{constructor(re){super(re)}async _call(re,ke,je={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class He extends ce{constructor(ke){super(ke);xe(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=ke.vocoder??null}async _call(ke,{speaker_embeddings:je=null}={}){return this.processor?this._call_text_to_spectrogram(ke,{speaker_embeddings:je}):this._call_text_to_waveform(ke)}async _call_text_to_waveform(ke){const je=this.tokenizer(ke,{padding:!0,truncation:!0}),{waveform:qe}=await this.model(je),Ue=this.model.config.sampling_rate;return{audio:qe.data,sampling_rate:Ue}}async _call_text_to_spectrogram(ke,{speaker_embeddings:je}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await fe.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof je=="string"||je instanceof URL)&&(je=new Float32Array(await(await fetch(je)).arrayBuffer())),je instanceof Float32Array)je=new V.Tensor("float32",je,[1,je.length]);else if(!(je instanceof V.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:qe}=this.tokenizer(ke,{padding:!0,truncation:!0}),{waveform:Ue}=await this.model.generate_speech(qe,je,{vocoder:this.vocoder}),Ke=this.processor.feature_extractor.config.sampling_rate;return{audio:Ue.data,sampling_rate:Ke}}}class ct extends ce{constructor(re){super(re)}async _call(re){const ke=await ee(re),je=await this.processor(ke),qe=await this.model(je),Ue=[];for(const Ke of qe.reconstruction){const ut=Ke.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ue.push(A.RawImage.fromTensor(ut))}return Ue.length>1?Ue:Ue[0]}}class nt extends ce{constructor(re){super(re)}async _call(re){const ke=await ee(re),je=await this.processor(ke),{predicted_depth:qe}=await this.model(je),Ue=[];for(let Ke=0;Ke<ke.length;++Ke){const ut=(0,V.interpolate)(qe[Ke],ke[Ke].size.reverse(),"bilinear",!1),yt=ut.mul_(255/(0,j.max)(ut.data)[0]).to("uint8");Ue.push({predicted_depth:qe[Ke],depth:A.RawImage.fromTensor(yt)})}return Ue.length>1?Ue:Ue[0]}}const lt=Object.freeze({"text-classification":{tokenizer:O.AutoTokenizer,pipeline:D,model:fe.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:O.AutoTokenizer,pipeline:H,model:fe.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:O.AutoTokenizer,pipeline:te,model:fe.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:O.AutoTokenizer,pipeline:se,model:fe.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:O.AutoTokenizer,pipeline:R,model:fe.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:O.AutoTokenizer,pipeline:I,model:fe.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:O.AutoTokenizer,pipeline:X,model:fe.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:O.AutoTokenizer,pipeline:k,model:fe.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:O.AutoTokenizer,pipeline:ue,model:fe.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:Ie,model:fe.AutoModelForAudioClassification,processor:ye.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:O.AutoTokenizer,pipeline:Ae,model:fe.AutoModel,processor:ye.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:O.AutoTokenizer,pipeline:tt,model:[fe.AutoModelForSpeechSeq2Seq,fe.AutoModelForCTC],processor:ye.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:O.AutoTokenizer,pipeline:He,model:[fe.AutoModelForTextToWaveform,fe.AutoModelForTextToSpectrogram],processor:[ye.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:O.AutoTokenizer,pipeline:Xe,model:fe.AutoModelForVision2Seq,processor:ye.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:dt,model:fe.AutoModelForImageClassification,processor:ye.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ge,model:[fe.AutoModelForImageSegmentation,fe.AutoModelForSemanticSegmentation,fe.AutoModelForUniversalSegmentation],processor:ye.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:O.AutoTokenizer,pipeline:G,model:fe.AutoModel,processor:ye.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:de,model:fe.AutoModelForObjectDetection,processor:ye.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:O.AutoTokenizer,pipeline:Se,model:fe.AutoModelForZeroShotObjectDetection,processor:ye.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:O.AutoTokenizer,pipeline:Z,model:fe.AutoModelForDocumentQuestionAnswering,processor:ye.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:ct,model:fe.AutoModelForImageToImage,processor:ye.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:nt,model:fe.AutoModelForDepthEstimation,processor:ye.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:O.AutoTokenizer,pipeline:ve,model:fe.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:ye.AutoProcessor,pipeline:$e,model:[fe.AutoModelForImageFeatureExtraction,fe.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Ne=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function st(Re,re=null,{progress_callback:ke=null,config:je=null,cache_dir:qe=null,local_files_only:Ue=!1,revision:Ke="main",device:ut=null,dtype:yt=null,model_file_name:vt=null,session_options:kt={}}={}){Re=Ne[Re]??Re;const v=lt[Re.split("_",1)[0]];if(!v)throw Error(`Unsupported pipeline: ${Re}. 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Using default model: "${re}".`));const q={progress_callback:ke,config:je,cache_dir:qe,local_files_only:Ue,revision:Ke,device:ut,dtype:yt,model_file_name:vt,session_options:kt},C=new Map([["tokenizer",v.tokenizer],["model",v.model],["processor",v.processor]]),Q=await Pt(C,re,q);Q.task=Re,(0,Ce.dispatchCallback)(ke,{status:"ready",task:Re,model:re});const he=v.pipeline;return new he(Q)}async function Pt(Re,re,ke){const je=Object.create(null),qe=[];for(const[Ue,Ke]of Re.entries()){if(!Ke)continue;let ut;Array.isArray(Ke)?ut=new Promise(async(yt,vt)=>{var v,q;let kt;for(const C of Ke){if(C===null){yt(null);return}try{yt(await C.from_pretrained(re,ke));return}catch(Q){if((v=Q.message)!=null&&v.includes("Unsupported model type"))kt=Q;else if((q=Q.message)!=null&&q.includes("Could not locate file"))kt=Q;else{vt(Q);return}}}vt(kt)}):ut=Ke.from_pretrained(re,ke),je[Ue]=ut,qe.push(ut)}await Promise.all(qe);for(const[Ue,Ke]of Object.entries(je))je[Ue]=await Ke;return je}},"./src/processors.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{ASTFeatureExtractor:()=>Q,AutoProcessor:()=>Kr,BeitFeatureExtractor:()=>ke,BitImageProcessor:()=>ve,CLIPFeatureExtractor:()=>Ie,CLIPImageProcessor:()=>Ae,ChineseCLIPFeatureExtractor:()=>tt,ClapFeatureExtractor:()=>he,ConvNextFeatureExtractor:()=>dt,ConvNextImageProcessor:()=>ge,DPTFeatureExtractor:()=>k,DPTImageProcessor:()=>ue,DeiTFeatureExtractor:()=>re,DetrFeatureExtractor:()=>Ue,DonutFeatureExtractor:()=>je,EfficientNetImageProcessor:()=>Se,FeatureExtractor:()=>se,Florence2Processor:()=>Br,GLPNFeatureExtractor:()=>$e,ImageFeatureExtractor:()=>X,MaskFormerFeatureExtractor:()=>Ke,MobileNetV1FeatureExtractor:()=>Z,MobileNetV2FeatureExtractor:()=>He,MobileNetV3FeatureExtractor:()=>ct,MobileNetV4FeatureExtractor:()=>nt,MobileViTFeatureExtractor:()=>lt,MobileViTImageProcessor:()=>Ne,NougatImageProcessor:()=>qe,OwlViTFeatureExtractor:()=>st,OwlViTProcessor:()=>Yr,Owlv2ImageProcessor:()=>Pt,Processor:()=>ft,PvtImageProcessor:()=>B,PyAnnoteFeatureExtractor:()=>Qe,PyAnnoteProcessor:()=>cr,RTDetrImageProcessor:()=>Re,SamImageProcessor:()=>yt,SamProcessor:()=>Tt,SapiensFeatureExtractor:()=>R,SeamlessM4TFeatureExtractor:()=>C,SegformerFeatureExtractor:()=>I,SiglipImageProcessor:()=>Xe,SpeechT5FeatureExtractor:()=>Bt,SpeechT5Processor:()=>xr,Swin2SRImageProcessor:()=>vt,ViTFeatureExtractor:()=>G,ViTImageProcessor:()=>de,VitMatteImageProcessor:()=>kt,Wav2Vec2FeatureExtractor:()=>q,Wav2Vec2ProcessorWithLM:()=>Ot,WeSpeakerFeatureExtractor:()=>Ye,WhisperFeatureExtractor:()=>v,WhisperProcessor:()=>bt,YolosFeatureExtractor:()=>ut});var O=N("./src/utils/generic.js"),fe=N("./src/utils/core.js"),ye=N("./src/utils/hub.js"),Te=N("./src/utils/maths.js"),Ce=N("./src/utils/tensor.js");N("./src/utils/image.js");var j=N("./src/utils/audio.js");function S([at,U,_e,Pe]){return[at-_e/2,U-Pe/2,at+_e/2,U+Pe/2]}function V(at,U=.5,_e=null,Pe=!1){const rt=at.logits,we=at.pred_boxes,[Je,gt,mt]=rt.dims;if(_e!==null&&_e.length!==Je)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let Et=[];for(let _t=0;_t<Je;++_t){let Ft=_e!==null?_e[_t]:null,Nt={boxes:[],classes:[],scores:[]},Rt=rt[_t],Gt=we[_t];for(let Me=0;Me<gt;++Me){let Ze=Rt[Me],ot=[],Ht;if(Pe){Ht=Ze.sigmoid().data;for(let gr=0;gr<Ht.length;++gr)Ht[gr]>U&&ot.push(gr)}else{let gr=(0,Te.max)(Ze.data)[1];if(gr===mt-1||(Ht=(0,Te.softmax)(Ze.data),Ht[gr]<U))continue;ot.push(gr)}for(const gr of ot){let Lr=Gt[Me].data;Lr=S(Lr),Ft!==null&&(Lr=Lr.map((mr,yr)=>mr*Ft[(yr+1)%2])),Nt.boxes.push(Lr),Nt.classes.push(gr),Nt.scores.push(Ht[gr])}}Et.push(Nt)}return Et}function A(at,U=null){const _e=at.logits,Pe=_e.dims[0];if(U!==null&&U.length!==Pe)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const rt=[];for(let we=0;we<Pe;++we){const Je=U!==null?U[we]:null;let gt=_e[we];Je!==null&&(gt=(0,Ce.interpolate)(gt,Je,"bilinear",!1));const[mt,Et]=Je??gt.dims.slice(-2),_t=new Ce.Tensor("int32",new Int32Array(mt*Et),[mt,Et]),Ft=gt[0].data,Nt=_t.data;for(let Me=1;Me<gt.dims[0];++Me){const Ze=gt[Me].data;for(let ot=0;ot<Ze.length;++ot)Ze[ot]>Ft[ot]&&(Ft[ot]=Ze[ot],Nt[ot]=Me)}const Rt=new Array(gt.dims[0]);for(let Me=0;Me<Nt.length;++Me){const Ze=Nt[Me];Rt[Ze]=Ze}const Gt=Rt.filter(Me=>Me!==void 0);rt.push({segmentation:_t,labels:Gt})}return rt}function ee(at,U,_e,Pe){const rt=[],we=[],Je=[];for(let gt=0;gt<at.dims[0];++gt){const 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If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function H(at,U,_e=0,Pe=null){const rt=at/U;let we=(0,Te.bankers_round)(rt)*U;return Pe!==null&&we>Pe&&(we=Math.floor(rt)*U),we<_e&&(we=Math.ceil(rt)*U),we}function te([at,U],_e){return[Math.max(Math.floor(at/_e),1)*_e,Math.max(Math.floor(U/_e),1)*_e]}class se extends O.Callable{constructor(U){super(),this.config=U}}class X extends se{constructor(U){super(U),this.image_mean=this.config.image_mean??this.config.mean,this.image_std=this.config.image_std??this.config.std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??.00392156862745098,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.size_divisibility=this.config.size_divisibility??this.config.size_divisor,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=this.config.do_flip_channel_order??!1}async thumbnail(U,_e,Pe=2){const rt=U.height,we=U.width,Je=_e.height,gt=_e.width;let mt=Math.min(rt,Je),Et=Math.min(we,gt);return mt===rt&&Et===we?U:(rt>we?Et=Math.floor(we*mt/rt):we>rt&&(mt=Math.floor(rt*Et/we)),await U.resize(Et,mt,{resample:Pe}))}async crop_margin(U,_e=200){const Pe=U.clone().grayscale(),rt=(0,Te.min)(Pe.data)[0],Je=(0,Te.max)(Pe.data)[0]-rt;if(Je===0)return U;const gt=_e/255;let mt=Pe.width,Et=Pe.height,_t=0,Ft=0;const Nt=Pe.data;for(let Rt=0;Rt<Pe.height;++Rt){const Gt=Rt*Pe.width;for(let Me=0;Me<Pe.width;++Me)(Nt[Gt+Me]-rt)/Je<gt&&(mt=Math.min(mt,Me),Et=Math.min(Et,Rt),_t=Math.max(_t,Me),Ft=Math.max(Ft,Rt))}return U=await U.crop([mt,Et,_t,Ft]),U}pad_image(U,_e,Pe,{mode:rt="constant",center:we=!1,constant_values:Je=0}={}){const[gt,mt,Et]=_e;let _t,Ft;if(typeof Pe=="number"?(_t=Pe,Ft=Pe):(_t=Pe.width,Ft=Pe.height),_t!==mt||Ft!==gt){const Nt=new Float32Array(_t*Ft*Et);if(Array.isArray(Je))for(let Me=0;Me<Nt.length;++Me)Nt[Me]=Je[Me%Et];else Je!==0&&Nt.fill(Je);const[Rt,Gt]=we?[Math.floor((_t-mt)/2),Math.floor((Ft-gt)/2)]:[0,0];for(let Me=0;Me<gt;++Me){const Ze=(Me+Gt)*_t,ot=Me*mt;for(let Ht=0;Ht<mt;++Ht){const gr=(Ze+Ht+Rt)*Et,Lr=(ot+Ht)*Et;for(let mr=0;mr<Et;++mr)Nt[gr+mr]=U[Lr+mr]}}if(rt==="symmetric"){if(we)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const Me=gt-1,Ze=mt-1;for(let ot=0;ot<Ft;++ot){const Ht=ot*_t,gr=(0,fe.calculateReflectOffset)(ot,Me)*mt;for(let Lr=0;Lr<_t;++Lr){if(ot<gt&&Lr<mt)continue;const mr=(Ht+Lr)*Et,yr=(gr+(0,fe.calculateReflectOffset)(Lr,Ze))*Et;for(let Tr=0;Tr<Et;++Tr)Nt[mr+Tr]=U[yr+Tr]}}}U=Nt,_e=[Ft,_t,Et]}return[U,_e]}rescale(U){for(let _e=0;_e<U.length;++_e)U[_e]=this.rescale_factor*U[_e]}get_resize_output_image_size(U,_e){const[Pe,rt]=U.size;let we,Je;if(this.do_thumbnail){const{height:gt,width:mt}=_e;we=Math.min(gt,mt)}else Number.isInteger(_e)?(we=_e,Je=this.config.max_size??we):_e!==void 0&&(we=_e.shortest_edge,Je=_e.longest_edge);if(we!==void 0||Je!==void 0){const gt=we===void 0?1:Math.max(we/Pe,we/rt),mt=Pe*gt,Et=rt*gt,_t=Je===void 0?1:Math.min(Je/mt,Je/Et);let Ft=Math.floor(Number((mt*_t).toFixed(2))),Nt=Math.floor(Number((Et*_t).toFixed(2)));return this.size_divisibility!==void 0&&([Ft,Nt]=te([Ft,Nt],this.size_divisibility)),[Ft,Nt]}else if(_e!==void 0&&_e.width!==void 0&&_e.height!==void 0){let gt=_e.width,mt=_e.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let Et=mt/rt,_t=gt/Pe;Math.abs(1-_t)<Math.abs(1-Et)?Et=_t:_t=Et,mt=H(Et*rt,this.config.ensure_multiple_of),gt=H(_t*Pe,this.config.ensure_multiple_of)}return[gt,mt]}else{if(this.size_divisibility!==void 0)return te([Pe,rt],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(_e)}`)}}async resize(U){const[_e,Pe]=this.get_resize_output_image_size(U,this.size);return await U.resize(_e,Pe,{resample:this.resample})}async preprocess(U,{do_normalize:_e=null,do_pad:Pe=null,do_convert_rgb:rt=null,do_convert_grayscale:we=null,do_flip_channel_order:Je=null}={}){this.do_crop_margin&&(U=await this.crop_margin(U));const[gt,mt]=U.size;if(rt??this.do_convert_rgb?U=U.rgb():we&&(U=U.grayscale()),this.do_resize&&(U=await this.resize(U)),this.do_thumbnail&&(U=await this.thumbnail(U,this.size,this.resample)),this.do_center_crop){let Rt,Gt;Number.isInteger(this.crop_size)?(Rt=this.crop_size,Gt=this.crop_size):(Rt=this.crop_size.width,Gt=this.crop_size.height),U=await U.center_crop(Rt,Gt)}const Et=[U.height,U.width];let _t=Float32Array.from(U.data),Ft=[U.height,U.width,U.channels];if(this.do_rescale&&this.rescale(_t),_e??this.do_normalize){let Rt=this.image_mean;Array.isArray(this.image_mean)||(Rt=new Array(U.channels).fill(Rt));let Gt=this.image_std;if(Array.isArray(this.image_std)||(Gt=new Array(U.channels).fill(Rt)),Rt.length!==U.channels||Gt.length!==U.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${Rt.length}) and \`image_std\` (${Gt.length}) must match the number of channels in the image (${U.channels}).`);for(let Me=0;Me<_t.length;Me+=U.channels)for(let Ze=0;Ze<U.channels;++Ze)_t[Me+Ze]=(_t[Me+Ze]-Rt[Ze])/Gt[Ze]}if(Pe??this.do_pad){if(this.pad_size)[_t,Ft]=this.pad_image(_t,[U.height,U.width,U.channels],this.pad_size);else if(this.size_divisibility){const[Rt,Gt]=te([Ft[1],Ft[0]],this.size_divisibility);[_t,Ft]=this.pad_image(_t,Ft,{width:Rt,height:Gt})}}if(Je??this.do_flip_channel_order){if(Ft[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let Rt=0;Rt<_t.length;Rt+=3){const Gt=_t[Rt];_t[Rt]=_t[Rt+2],_t[Rt+2]=Gt}}const Nt=new Ce.Tensor("float32",_t,Ft).permute(2,0,1);return{original_size:[mt,gt],reshaped_input_size:Et,pixel_values:Nt}}async _call(U,..._e){Array.isArray(U)||(U=[U]);const Pe=await Promise.all(U.map(we=>this.preprocess(we)));return{pixel_values:(0,Ce.stack)(Pe.map(we=>we.pixel_values),0),original_sizes:Pe.map(we=>we.original_size),reshaped_input_sizes:Pe.map(we=>we.reshaped_input_size)}}}class R extends X{post_process_semantic_segmentation(...U){return A(...U)}}class I extends X{post_process_semantic_segmentation(...U){return A(...U)}}class B extends X{}class k extends X{}class ue extends k{}class ve extends X{}class $e extends X{}class Ie extends X{}class Ae extends Ie{}class tt extends X{}class Xe extends X{}class dt extends X{constructor(U){super(U),this.crop_pct=this.config.crop_pct??.875}async resize(U){var Pe;const _e=(Pe=this.size)==null?void 0:Pe.shortest_edge;if(_e===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(_e<384){const rt=Math.floor(_e/this.crop_pct),[we,Je]=this.get_resize_output_image_size(U,{shortest_edge:rt});U=await U.resize(we,Je,{resample:this.resample}),U=await U.center_crop(_e,_e)}else U=await U.resize(_e,_e,{resample:this.resample});return U}}class ge extends dt{}class G extends X{}class de extends X{}class Se extends X{constructor(U){super(U),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(_e=>_e*_e))}}class Z extends X{}class He extends X{}class ct extends X{}class nt extends X{}class lt extends X{}class Ne extends lt{}class st extends X{post_process_object_detection(...U){return V(...U)}}class Pt extends st{}class Re extends X{post_process_object_detection(...U){return V(...U)}}class re extends X{}class ke extends X{}class je extends X{pad_image(U,_e,Pe,rt={}){const[we,Je,gt]=_e;let mt=this.image_mean;Array.isArray(this.image_mean)||(mt=new Array(gt).fill(mt));let Et=this.image_std;Array.isArray(Et)||(Et=new Array(gt).fill(mt));const _t=mt.map((Ft,Nt)=>-Ft/Et[Nt]);return super.pad_image(U,_e,Pe,{center:!0,constant_values:_t,...rt})}}class qe extends je{}class Ue extends X{async _call(U){const _e=await super._call(U),Pe=[_e.pixel_values.dims[0],64,64],rt=(0,Ce.full)(Pe,1n);return{..._e,pixel_mask:rt}}post_process_object_detection(...U){return V(...U)}post_process_panoptic_segmentation(...U){return ce(...U)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class Ke extends X{post_process_panoptic_segmentation(...U){return ce(...U)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class ut extends X{post_process_object_detection(...U){return V(...U)}}class yt extends X{reshape_input_points(U,_e,Pe,rt=!1){U=structuredClone(U);let we=(0,fe.calculateDimensions)(U);if(we.length===3)rt||(we=[1,...we]),U=[U];else if(we.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let Je=0;Je<U.length;++Je){let gt=_e[Je],mt=Pe[Je],Et=[mt[0]/gt[0],mt[1]/gt[1]];for(let _t=0;_t<U[Je].length;++_t)for(let Ft=0;Ft<U[Je][_t].length;++Ft)for(let Nt=0;Nt<U[Je][_t][Ft].length;++Nt)U[Je][_t][Ft][Nt]*=Et[Nt%2]}return new Ce.Tensor("float32",Float32Array.from(U.flat(1/0)),we)}add_input_labels(U,_e){let Pe=(0,fe.calculateDimensions)(U);if(Pe.length===2)Pe=[1,...Pe],U=[U];else if(Pe.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(Pe.some((rt,we)=>rt!==_e.dims[we]))throw Error(`The first ${Pe.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Ce.Tensor("int64",U.flat(1/0).map(BigInt),Pe)}async _call(U,{input_points:_e=null,input_labels:Pe=null,input_boxes:rt=null}={}){const we=await super._call(U);if(_e&&(we.input_points=this.reshape_input_points(_e,we.original_sizes,we.reshaped_input_sizes)),Pe){if(!we.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");we.input_labels=this.add_input_labels(Pe,we.input_points)}return rt&&(we.input_boxes=this.reshape_input_points(rt,we.original_sizes,we.reshaped_input_sizes,!0)),we}async post_process_masks(U,_e,Pe,{mask_threshold:rt=0,binarize:we=!0,pad_size:Je=null}={}){const gt=[];Je=Je??this.pad_size;const mt=[Je.height,Je.width];for(let Et=0;Et<_e.length;++Et){const _t=_e[Et],Ft=Pe[Et];let Nt=await(0,Ce.interpolate_4d)(U[Et],{mode:"bilinear",size:mt});if(Nt=Nt.slice(null,null,[0,Ft[0]],[0,Ft[1]]),Nt=await(0,Ce.interpolate_4d)(Nt,{mode:"bilinear",size:_t}),we){const Rt=Nt.data,Gt=new Uint8Array(Rt.length);for(let Me=0;Me<Rt.length;++Me)Rt[Me]>rt&&(Gt[Me]=1);Nt=new Ce.Tensor("bool",Gt,Nt.dims)}gt.push(Nt)}return gt}generate_crop_boxes(U,_e,{crop_n_layers:Pe=0,overlap_ratio:rt=.3413333333333333,points_per_crop:we=32,crop_n_points_downscale_factor:Je=1}={}){}}class vt extends X{pad_image(U,_e,Pe,rt={}){const[we,Je,gt]=_e;return super.pad_image(U,_e,{width:Je+(Pe-Je%Pe)%Pe,height:we+(Pe-we%Pe)%Pe},{mode:"symmetric",center:!1,constant_values:-1,...rt})}}class kt extends X{async _call(U,_e){Array.isArray(U)||(U=[U]),Array.isArray(_e)||(_e=[_e]);const Pe=await Promise.all(U.map(Je=>this.preprocess(Je))),rt=await Promise.all(_e.map(Je=>this.preprocess(Je,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,Ce.stack)(Pe.map((Je,gt)=>(0,Ce.cat)([Je.pixel_values,rt[gt].pixel_values],0)),0),original_sizes:Pe.map(Je=>Je.original_size),reshaped_input_sizes:Pe.map(Je=>Je.reshaped_input_size)}}}class v extends se{constructor(U){var _e;super(U),(_e=this.config).mel_filters??(_e.mel_filters=(0,j.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,j.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(U){const _e=await(0,j.spectrogram)(U,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),Pe=_e.data,rt=(0,Te.max)(Pe)[0];for(let we=0;we<Pe.length;++we)Pe[we]=(Math.max(Pe[we],rt-8)+4)/4;return _e}async _call(U){D(U,"WhisperFeatureExtractor");let _e;return U.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`."),_e=U.slice(0,this.config.n_samples)):(_e=new Float32Array(this.config.n_samples),_e.set(U)),{input_features:(await this._extract_fbank_features(_e)).unsqueeze_(0)}}}class q extends se{_zero_mean_unit_var_norm(U){const Pe=U.reduce((we,Je)=>we+Je,0)/U.length,rt=U.reduce((we,Je)=>we+(Je-Pe)**2,0)/U.length;return U.map(we=>(we-Pe)/Math.sqrt(rt+1e-7))}async _call(U){D(U,"Wav2Vec2FeatureExtractor"),U instanceof Float64Array&&(U=new Float32Array(U));let _e=U;this.config.do_normalize&&(_e=this._zero_mean_unit_var_norm(_e));const Pe=[1,_e.length];return{input_values:new Ce.Tensor("float32",_e,Pe),attention_mask:new Ce.Tensor("int64",new BigInt64Array(_e.length).fill(1n),Pe)}}}class C extends se{constructor(U){super(U);const _e=this.config.sampling_rate,Pe=(0,j.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(_e/2),_e,null,"kaldi",!0);for(let rt=0;rt<Pe.length;++rt)Pe[rt].push(0);this.mel_filters=Pe,this.window=(0,j.window_function)(400,"povey",{periodic:!1})}async _extract_fbank_features(U,_e){return U=U.map(Pe=>Pe*32768),(0,j.spectrogram)(U,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:_e,transpose:!0})}async _call(U,{padding:_e=!0,pad_to_multiple_of:Pe=2,do_normalize_per_mel_bins:rt=!0,return_attention_mask:we=!0}={}){D(U,"SeamlessM4TFeatureExtractor");let Je=await this._extract_fbank_features(U,this.config.max_length);if(rt){const[Gt,Me]=Je.dims,Ze=Je.data;for(let ot=0;ot<Me;++ot){let Ht=0;for(let yr=0;yr<Gt;++yr)Ht+=Ze[yr*Me+ot];const gr=Ht/Gt;let Lr=0;for(let yr=0;yr<Gt;++yr)Lr+=(Ze[yr*Me+ot]-gr)**2;Lr/=Gt-1;const mr=Math.sqrt(Lr+1e-7);for(let yr=0;yr<Gt;++yr){const Tr=yr*Me+ot;Ze[Tr]=(Ze[Tr]-gr)/mr}}}let gt;if(_e){const[Gt,Me]=Je.dims,Ze=Je.data,ot=Gt%Pe;if(ot>0){const Ht=new Float32Array(Me*(Gt+ot));Ht.set(Ze),Ht.fill(this.config.padding_value,Ze.length);const gr=Gt+ot;Je=new Ce.Tensor(Je.type,Ht,[gr,Me]),we&&(gt=new Ce.Tensor("int64",new BigInt64Array(gr),[1,gr]),gt.data.fill(1n,0,Gt))}}const[mt,Et]=Je.dims,_t=this.config.stride;if(mt%_t!==0)throw new Error(`The number of frames (${mt}) must be a multiple of the stride (${_t}).`);const Nt=Je.view(1,Math.floor(mt/_t),Et*_t),Rt={input_features:Nt};if(we){const Gt=Nt.dims[1],Me=new BigInt64Array(Gt);if(gt){const Ze=gt.data;for(let ot=1,Ht=0;ot<mt;ot+=_t,++Ht)Me[Ht]=Ze[ot]}else Me.fill(1n);Rt.attention_mask=new Ce.Tensor("int64",Me,[1,Gt])}return Rt}}class Q extends se{constructor(U){super(U);const _e=this.config.sampling_rate,Pe=(0,j.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(_e/2),_e,null,"kaldi",!0);for(let rt=0;rt<Pe.length;++rt)Pe[rt].push(0);this.mel_filters=Pe,this.window=(0,j.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(U,_e){return(0,j.spectrogram)(U,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:_e,transpose:!0})}async _call(U){D(U,"ASTFeatureExtractor");const _e=await this._extract_fbank_features(U,this.config.max_length);if(this.config.do_normalize){const Pe=this.std*2,rt=_e.data;for(let we=0;we<rt.length;++we)rt[we]=(rt[we]-this.mean)/Pe}return{input_values:_e.unsqueeze_(0)}}}class he extends se{constructor(U){super(U),this.mel_filters=(0,j.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,j.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,j.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(U,_e,Pe,rt){let we;const Je=U.length-_e;if(Je>0)if(Pe==="rand_trunc"){const gt=Math.floor(Math.random()*(Je+1));U=U.subarray(gt,gt+_e),we=await this._extract_fbank_features(U,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${Pe}" not implemented`);else{if(Je<0){let gt=new Float64Array(_e);if(gt.set(U),rt==="repeat")for(let mt=U.length;mt<_e;mt+=U.length)gt.set(U.subarray(0,Math.min(U.length,_e-mt)),mt);else if(rt==="repeatpad")for(let mt=U.length;mt<-Je;mt+=U.length)gt.set(U,mt);U=gt}if(Pe==="fusion")throw new Error(`Truncation strategy "${Pe}" not implemented`);we=await this._extract_fbank_features(U,this.mel_filters_slaney,this.config.nb_max_samples)}return we.unsqueeze_(0)}async _extract_fbank_features(U,_e,Pe=null){return(0,j.spectrogram)(U,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:_e,log_mel:"dB",max_num_frames:Pe,do_pad:!1,transpose:!0})}async _call(U,{max_length:_e=null}={}){return D(U,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(U,_e??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}class Qe extends se{async _call(U){D(U,"PyAnnoteFeatureExtractor"),U instanceof Float64Array&&(U=new Float32Array(U));const _e=[1,1,U.length];return{input_values:new Ce.Tensor("float32",U,_e)}}samples_to_frames(U){return(U-this.config.offset)/this.config.step}post_process_speaker_diarization(U,_e){const Pe=_e/this.samples_to_frames(_e)/this.config.sampling_rate,rt=[];for(const we of U.tolist()){const Je=[];let gt=-1;for(let mt=0;mt<we.length;++mt){const Et=(0,Te.softmax)(we[mt]),[_t,Ft]=(0,Te.max)(Et),[Nt,Rt]=[mt,mt+1];Ft!==gt?(gt=Ft,Je.push({id:Ft,start:Nt,end:Rt,score:_t})):(Je.at(-1).end=Rt,Je.at(-1).score+=_t)}rt.push(Je.map(({id:mt,start:Et,end:_t,score:Ft})=>({id:mt,start:Et*Pe,end:_t*Pe,confidence:Ft/(_t-Et)})))}return rt}}class Ye extends se{constructor(U){super(U);const _e=this.config.sampling_rate,Pe=(0,j.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(_e/2),_e,null,"kaldi",!0);for(let rt=0;rt<Pe.length;++rt)Pe[rt].push(0);this.mel_filters=Pe,this.window=(0,j.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(U){return U=U.map(_e=>_e*32768),(0,j.spectrogram)(U,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(U){D(U,"WeSpeakerFeatureExtractor");const _e=(await this._extract_fbank_features(U)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const Pe=_e.mean(1).data,rt=_e.data,[we,Je,gt]=_e.dims;for(let mt=0;mt<we;++mt){const Et=mt*Je*gt,_t=mt*gt;for(let Ft=0;Ft<Je;++Ft){const Nt=Et+Ft*gt;for(let Rt=0;Rt<gt;++Rt)rt[Nt+Rt]-=Pe[_t+Rt]}}}return{input_features:_e}}}class Bt extends se{}class ft extends O.Callable{constructor(U){super(),this.feature_extractor=U}async _call(U,..._e){return await this.feature_extractor(U,..._e)}}class Tt extends ft{async _call(...U){return await this.feature_extractor(...U)}post_process_masks(...U){return this.feature_extractor.post_process_masks(...U)}reshape_input_points(...U){return this.feature_extractor.reshape_input_points(...U)}}class bt extends ft{async _call(U){return await this.feature_extractor(U)}}class Ot extends ft{async _call(U){return await this.feature_extractor(U)}}class cr extends ft{async _call(U){return await this.feature_extractor(U)}post_process_speaker_diarization(...U){return this.feature_extractor.post_process_speaker_diarization(...U)}}class xr extends ft{async _call(U){return await this.feature_extractor(U)}}class Yr extends ft{}class Br extends ft{constructor(U){super(U);const{tasks_answer_post_processing_type:_e,task_prompts_without_inputs:Pe,task_prompts_with_input:rt}=U.config;this.tasks_answer_post_processing_type=new Map(Object.entries(_e??{})),this.task_prompts_without_inputs=new Map(Object.entries(Pe??{})),this.task_prompts_with_input=new Map(Object.entries(rt??{})),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(U){typeof U=="string"&&(U=[U]);const _e=[];for(const Pe of U)if(this.task_prompts_without_inputs.has(Pe))_e.push(this.task_prompts_without_inputs.get(Pe));else{for(const[rt,we]of this.task_prompts_with_input)if(Pe.includes(rt)){_e.push(we.replaceAll("{input}",Pe).replaceAll(rt,""));break}_e.length!==U.length&&_e.push(Pe)}return _e}post_process_generation(U,_e,Pe){const rt=this.tasks_answer_post_processing_type.get(_e)??"pure_text";U=U.replaceAll("<s>","").replaceAll("</s>","");let we;switch(rt){case"pure_text":we=U;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const Je=rt==="ocr"?"quad_boxes":"bboxes",gt=U.matchAll(this.regexes[Je]),mt=[],Et=[];for(const[_t,Ft,...Nt]of gt)mt.push(Ft?Ft.trim():mt.at(-1)??""),Et.push(Nt.map((Rt,Gt)=>(Number(Rt)+.5)/this.size_per_bin*Pe[Gt%2]));we={labels:mt,[Je]:Et};break;default:throw new Error(`Task "${_e}" (of type "${rt}") not yet implemented.`)}return{[_e]:we}}}class Kr{static async from_pretrained(U,{progress_callback:_e=null,config:Pe=null,cache_dir:rt=null,local_files_only:we=!1,revision:Je="main"}={}){let gt=Pe??await(0,ye.getModelJSON)(U,"preprocessor_config.json",!0,{progress_callback:_e,config:Pe,cache_dir:rt,local_files_only:we,revision:Je}),mt=gt.feature_extractor_type??gt.image_processor_type,Et=this.FEATURE_EXTRACTOR_CLASS_MAPPING[mt];if(!Et)if(gt.size!==void 0)console.warn(`Feature extractor type "${mt}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),Et=X;else throw new Error(`Unknown Feature Extractor type: ${mt}`);let _t=this.PROCESSOR_CLASS_MAPPING[gt.processor_class]??ft,Ft=new Et(gt);return new _t(Ft)}}xe(Kr,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:X,WhisperFeatureExtractor:v,ViTFeatureExtractor:G,MobileViTFeatureExtractor:lt,MobileViTImageProcessor:Ne,MobileNetV1FeatureExtractor:Z,MobileNetV2FeatureExtractor:He,MobileNetV3FeatureExtractor:ct,MobileNetV4FeatureExtractor:nt,OwlViTFeatureExtractor:st,Owlv2ImageProcessor:Pt,CLIPFeatureExtractor:Ie,CLIPImageProcessor:Ae,Florence2Processor:Br,ChineseCLIPFeatureExtractor:tt,SiglipImageProcessor:Xe,ConvNextFeatureExtractor:dt,ConvNextImageProcessor:ge,SegformerFeatureExtractor:I,SapiensFeatureExtractor:R,BitImageProcessor:ve,DPTImageProcessor:ue,DPTFeatureExtractor:k,PvtImageProcessor:B,GLPNFeatureExtractor:$e,BeitFeatureExtractor:ke,DeiTFeatureExtractor:re,DetrFeatureExtractor:Ue,RTDetrImageProcessor:Re,MaskFormerFeatureExtractor:Ke,YolosFeatureExtractor:ut,DonutFeatureExtractor:je,NougatImageProcessor:qe,EfficientNetImageProcessor:Se,ViTImageProcessor:de,VitMatteImageProcessor:kt,SamImageProcessor:yt,Swin2SRImageProcessor:vt,Wav2Vec2FeatureExtractor:q,SeamlessM4TFeatureExtractor:C,SpeechT5FeatureExtractor:Bt,ASTFeatureExtractor:Q,ClapFeatureExtractor:he,PyAnnoteFeatureExtractor:Qe,WeSpeakerFeatureExtractor:Ye}),xe(Kr,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:bt,Wav2Vec2ProcessorWithLM:Ot,PyAnnoteProcessor:cr,SamProcessor:Tt,SpeechT5Processor:xr,OwlViTProcessor:Yr,Florence2Processor:Br})},"./src/tokenizers.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{AlbertTokenizer:()=>gt,AutoTokenizer:()=>vn,BartTokenizer:()=>mr,BertTokenizer:()=>Je,BlenderbotSmallTokenizer:()=>Ss,BlenderbotTokenizer:()=>fs,BloomTokenizer:()=>Rr,CLIPTokenizer:()=>Xt,CamembertTokenizer:()=>Ze,CodeGenTokenizer:()=>hs,CodeLlamaTokenizer:()=>Ys,CohereTokenizer:()=>Wr,ConvBertTokenizer:()=>Rt,DebertaTokenizer:()=>_t,DebertaV2Tokenizer:()=>Ft,DistilBertTokenizer:()=>Me,ElectraTokenizer:()=>Ht,EsmTokenizer:()=>Kn,FalconTokenizer:()=>Ts,GPT2Tokenizer:()=>Lr,GPTNeoXTokenizer:()=>Es,GemmaTokenizer:()=>cs,Grok1Tokenizer:()=>Ln,HerbertTokenizer:()=>Nt,LlamaTokenizer:()=>jn,M2M100Tokenizer:()=>rs,MBart50Tokenizer:()=>Tr,MBartTokenizer:()=>yr,MPNetTokenizer:()=>xs,MarianTokenizer:()=>Cs,MobileBertTokenizer:()=>mt,NllbTokenizer:()=>Vn,NougatTokenizer:()=>ms,PreTrainedTokenizer:()=>we,Qwen2Tokenizer:()=>Js,RoFormerTokenizer:()=>Gt,RobertaTokenizer:()=>$n,SiglipTokenizer:()=>ns,SpeechT5Tokenizer:()=>ks,SqueezeBertTokenizer:()=>Et,T5Tokenizer:()=>gr,TokenizerModel:()=>$e,VitsTokenizer:()=>Ps,Wav2Vec2CTCTokenizer:()=>$s,WhisperTokenizer:()=>ps,XLMRobertaTokenizer:()=>vs,XLMTokenizer:()=>ot,is_chinese_char:()=>X});var O=N("./src/utils/generic.js"),fe=N("./src/utils/core.js"),ye=N("./src/utils/hub.js"),Te=N("./src/utils/maths.js"),Ce=N("./src/utils/tensor.js"),j=N("./src/utils/data-structures.js"),S=N("./node_modules/@huggingface/jinja/dist/index.js"),V=N("./src/models/whisper/common_whisper.js"),A=N("./src/utils/constants.js");async function ee(be,_){const P=await Promise.all([(0,ye.getModelJSON)(be,"tokenizer.json",!0,_),(0,ye.getModelJSON)(be,"tokenizer_config.json",!0,_)]);return _.legacy!==null&&(P[1].legacy=_.legacy),P}function ne(be,_){const P=[];let K=0;for(const ae of be.matchAll(_)){const pe=ae[0];K<ae.index&&P.push(be.slice(K,ae.index)),pe.length>0&&P.push(pe),K=ae.index+pe.length}return K<be.length&&P.push(be.slice(K)),P}function me(be,_=!0){if(be.Regex!==void 0){let P=be.Regex.replace(/\\([#&~])/g,"$1");for(const[K,ae]of ue)P=P.replaceAll(K,ae);return new RegExp(P,"gu")}else if(be.String!==void 0){const P=(0,fe.escapeRegExp)(be.String);return new RegExp(_?P:`(${P})`,"gu")}else return console.warn("Unknown pattern type:",be),null}function ce(be){return new Map(Object.entries(be))}function D(be){const _=be.dims;switch(_.length){case 1:return be.tolist();case 2:if(_[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return be.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${_.length}.`)}}function H(be){return be.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function te(be){return be.replace(new RegExp("\\p{M}","gu"),"")}function se(be){return te(be.toLowerCase())}function X(be){return be>=19968&&be<=40959||be>=13312&&be<=19903||be>=131072&&be<=173791||be>=173824&&be<=177983||be>=177984&&be<=178207||be>=178208&&be<=183983||be>=63744&&be<=64255||be>=194560&&be<=195103}function R(be,_,P){const K=[];let ae=0;for(;ae<be.length;){if(K.push(be[ae]),(_.get(be[ae])??P)!==P){++ae;continue}for(;++ae<be.length&&(_.get(be[ae])??P)===P;)_.get(K.at(-1))!==P&&(K[K.length-1]+=be[ae])}return K}function I(be){return be.match(/\S+/g)||[]}const B="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",k=new RegExp(`^[${B}]+$`,"gu"),ue=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class ve{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 $e extends O.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(_,...P){switch(_.type){case"WordPiece":return new Ie(_);case"Unigram":return new Ae(_,...P);case"BPE":return new dt(_);default:if(_.vocab)return new ge(_,...P);throw new Error(`Unknown TokenizerModel type: ${_.type}`)}}_call(_){return _=this.encode(_),this.fuse_unk&&(_=R(_,this.tokens_to_ids,this.unk_token_id)),_}encode(_){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(_){return _.map(P=>this.tokens_to_ids.get(P)??this.unk_token_id)}convert_ids_to_tokens(_){return _.map(P=>this.vocab[P]??this.unk_token)}}class Ie extends $e{constructor(_){super(_),this.tokens_to_ids=ce(_.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[P,K]of this.tokens_to_ids)this.vocab[K]=P}encode(_){const P=[];for(const K of _){const ae=[...K];if(ae.length>this.max_input_chars_per_word){P.push(this.unk_token);continue}let pe=!1,Be=0;const wt=[];for(;Be<ae.length;){let xt=ae.length,Mt=null;for(;Be<xt;){let zt=ae.slice(Be,xt).join("");if(Be>0&&(zt=this.config.continuing_subword_prefix+zt),this.tokens_to_ids.has(zt)){Mt=zt;break}--xt}if(Mt===null){pe=!0;break}wt.push(Mt),Be=xt}pe?P.push(this.unk_token):P.push(...wt)}return P}}class Ae extends $e{constructor(_,P){super(_);const K=_.vocab.length;this.vocab=new Array(K),this.scores=new Array(K);for(let ae=0;ae<K;++ae){const pe=_.vocab[ae];this.vocab[ae]=pe[0],this.scores[ae]=pe[1]}this.unk_token_id=_.unk_id,this.unk_token=this.vocab[_.unk_id],this.tokens_to_ids=new Map(this.vocab.map((ae,pe)=>[ae,pe])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=P.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,Te.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new j.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(_){const P=_.chars,K=1;let ae=0;for(;ae<P.length;){let pe=!1;const Be=P.slice(ae).join(""),wt=this.trie.commonPrefixSearch(Be);for(const xt of wt){const Mt=this.tokens_to_ids.get(xt),zt=this.scores[Mt],er=(0,fe.len)(xt);_.insert(ae,er,zt,Mt),!pe&&er===K&&(pe=!0)}pe||_.insert(ae,K,this.unk_score,this.unk_token_id),ae+=K}}tokenize(_){const P=new j.TokenLattice(_,this.bos_token_id,this.eos_token_id);return this.populateNodes(P),P.tokens()}encode(_){const P=[];for(const K of _){const ae=this.tokenize(K);P.push(...ae)}return P}}const tt=(()=>{const be=[...Array.from({length:94},(ae,pe)=>pe+33),...Array.from({length:12},(ae,pe)=>pe+161),...Array.from({length:82},(ae,pe)=>pe+174)],_=be.slice();let P=0;for(let ae=0;ae<256;++ae)be.includes(ae)||(be.push(ae),_.push(256+P),P+=1);const K=_.map(ae=>String.fromCharCode(ae));return Object.fromEntries(be.map((ae,pe)=>[ae,K[pe]]))})(),Xe=(0,fe.reverseDictionary)(tt);class dt extends $e{constructor(_){super(_),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=ce(_.vocab),this.unk_token_id=this.tokens_to_ids.get(_.unk_token),this.unk_token=_.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[P,K]of this.tokens_to_ids)this.vocab[K]=P;this.bpe_ranks=new Map(_.merges.map((P,K)=>[P,K])),this.merges=_.merges.map(P=>P.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=_.end_of_word_suffix,this.continuing_subword_suffix=_.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(_){if(_.length===0)return[];const P=this.cache.get(_);if(P!==void 0)return P;const K=Array.from(_);this.end_of_word_suffix&&(K[K.length-1]+=this.end_of_word_suffix);let ae=[];if(K.length>1){const pe=new j.PriorityQueue((xt,Mt)=>xt.score<Mt.score);let Be={token:K[0],bias:0,prev:null,next:null},wt=Be;for(let xt=1;xt<K.length;++xt){const Mt={bias:xt/K.length,token:K[xt],prev:wt,next:null};wt.next=Mt,this._add_node(pe,wt),wt=Mt}for(;!pe.isEmpty();){const xt=pe.pop();if(xt.deleted||!xt.next||xt.next.deleted)continue;if(xt.deleted=!0,xt.next.deleted=!0,xt.prev){const zt={...xt.prev};xt.prev.deleted=!0,xt.prev=zt,zt.prev?zt.prev.next=zt:Be=zt}const Mt={token:xt.token+xt.next.token,bias:xt.bias,prev:xt.prev,next:xt.next.next};Mt.prev?(Mt.prev.next=Mt,this._add_node(pe,Mt.prev)):Be=Mt,Mt.next&&(Mt.next.prev=Mt,this._add_node(pe,Mt))}for(let xt=Be;xt!==null;xt=xt.next)ae.push(xt.token)}else ae=K;if(this.continuing_subword_suffix)for(let pe=0;pe<ae.length-1;++pe)ae[pe]+=this.continuing_subword_suffix;return this.cache.set(_,ae),ae}_add_node(_,P){const K=this.bpe_ranks.get(P.token+this.BPE_SPLIT_TOKEN+P.next.token);K!==void 0&&(P.score=K+P.bias,_.push(P))}encode(_){const P=[];for(const K of _){if(this.ignore_merges&&this.tokens_to_ids.has(K)){P.push(K);continue}const ae=this.bpe(K);for(const pe of ae)if(this.tokens_to_ids.has(pe))P.push(pe);else if(this.byte_fallback){const Be=Array.from(this.text_encoder.encode(pe)).map(wt=>`<0x${wt.toString(16).toUpperCase().padStart(2,"0")}>`);Be.every(wt=>this.tokens_to_ids.has(wt))?P.push(...Be):P.push(this.unk_token)}else P.push(this.unk_token)}return P}}class ge extends $e{constructor(_,P){super(_),this.tokens_to_ids=ce(P.target_lang?_.vocab[P.target_lang]:_.vocab),this.bos_token=P.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=P.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=P.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=P.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[K,ae]of this.tokens_to_ids)this.vocab[ae]=K}encode(_){return _}}class G extends O.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"BertNormalizer":return new Pt(_);case"Precompiled":return new Yr(_);case"Sequence":return new st(_);case"Replace":return new de(_);case"NFC":return new Se(_);case"NFKC":return new Z(_);case"NFKD":return new He(_);case"Strip":return new ct(_);case"StripAccents":return new nt(_);case"Lowercase":return new lt(_);case"Prepend":return new Ne(_);default:throw new Error(`Unknown Normalizer type: ${_.type}`)}}normalize(_){throw Error("normalize should be implemented in subclass.")}_call(_){return this.normalize(_)}}class de extends G{normalize(_){const P=me(this.config.pattern);return P===null?_:_.replaceAll(P,this.config.content)}}class Se extends G{normalize(_){return _=_.normalize("NFC"),_}}class Z extends G{normalize(_){return _=_.normalize("NFKC"),_}}class He extends G{normalize(_){return _=_.normalize("NFKD"),_}}class ct extends G{normalize(_){return this.config.strip_left&&this.config.strip_right?_=_.trim():(this.config.strip_left&&(_=_.trimStart()),this.config.strip_right&&(_=_.trimEnd())),_}}class nt extends G{normalize(_){return _=te(_),_}}class lt extends G{normalize(_){return _=_.toLowerCase(),_}}class Ne extends G{normalize(_){return _=this.config.prepend+_,_}}class st extends G{constructor(_){super(_),this.normalizers=_.normalizers.map(P=>G.fromConfig(P))}normalize(_){return this.normalizers.reduce((P,K)=>K.normalize(P),_)}}class Pt extends G{_tokenize_chinese_chars(_){const P=[];for(let K=0;K<_.length;++K){const ae=_[K],pe=ae.charCodeAt(0);X(pe)?(P.push(" "),P.push(ae),P.push(" ")):P.push(ae)}return P.join("")}stripAccents(_){return _.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control(_){switch(_){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(_)}}_clean_text(_){const P=[];for(const K of _){const ae=K.charCodeAt(0);ae===0||ae===65533||this._is_control(K)||(/^\s$/.test(K)?P.push(" "):P.push(K))}return P.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 Re extends O.Callable{static fromConfig(_){if(_===null)return null;switch(_.type){case"BertPreTokenizer":return new re(_);case"Sequence":return new Br(_);case"Whitespace":return new Kr(_);case"WhitespaceSplit":return new at(_);case"Metaspace":return new cr(_);case"ByteLevel":return new ke(_);case"Split":return new je(_);case"Punctuation":return new qe(_);case"Digits":return new Ue(_);case"Replace":return new U(_);default:throw new Error(`Unknown PreTokenizer type: ${_.type}`)}}pre_tokenize_text(_,P){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(_,P){return(Array.isArray(_)?_.map(K=>this.pre_tokenize_text(K,P)):this.pre_tokenize_text(_,P)).flat()}_call(_,P){return this.pre_tokenize(_,P)}}class re extends Re{constructor(_){super(),this.pattern=new RegExp(`[^\\s${B}]+|[${B}]`,"gu")}pre_tokenize_text(_,P){return _.trim().match(this.pattern)||[]}}class ke extends Re{constructor(_){super(),this.config=_,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=tt,this.text_encoder=new TextEncoder}pre_tokenize_text(_,P){return this.add_prefix_space&&!_.startsWith(" ")&&(_=" "+_),(this.use_regex?_.match(this.pattern)||[]:[_]).map(ae=>Array.from(this.text_encoder.encode(ae),pe=>this.byte_encoder[pe]).join(""))}}class je extends Re{constructor(_){super(),this.config=_,this.pattern=me(this.config.pattern,this.config.invert)}pre_tokenize_text(_,P){return this.pattern===null?[]:this.config.invert?_.match(this.pattern)||[]:ne(_,this.pattern)}}class qe extends Re{constructor(_){super(),this.config=_,this.pattern=new RegExp(`[^${B}]+|[${B}]+`,"gu")}pre_tokenize_text(_,P){return _.match(this.pattern)||[]}}class Ue extends Re{constructor(_){super(),this.config=_;const P=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(P,"gu")}pre_tokenize_text(_,P){return _.match(this.pattern)||[]}}class Ke extends O.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"TemplateProcessing":return new vt(_);case"ByteLevel":return new kt(_);case"RobertaProcessing":return new yt(_);case"BertProcessing":return new ut(_);case"Sequence":return new v(_);default:throw new Error(`Unknown PostProcessor type: ${_.type}`)}}post_process(_,...P){throw Error("post_process should be implemented in subclass.")}_call(_,...P){return this.post_process(_,...P)}}class ut extends Ke{constructor(_){super(_),this.cls=_.cls[0],this.sep=_.sep[0]}post_process(_,P=null,{add_special_tokens:K=!0}={}){K&&(_=(0,fe.mergeArrays)([this.cls],_,[this.sep]));let ae=new Array(_.length).fill(0);if(P!==null){const pe=K&&this instanceof yt?[this.sep]:[],Be=K?[this.sep]:[];_=(0,fe.mergeArrays)(_,pe,P,Be),ae=(0,fe.mergeArrays)(ae,new Array(P.length+pe.length+Be.length).fill(1))}return{tokens:_,token_type_ids:ae}}}class yt extends ut{}class vt extends Ke{constructor(_){super(_),this.single=_.single,this.pair=_.pair}post_process(_,P=null,{add_special_tokens:K=!0}={}){const ae=P===null?this.single:this.pair;let pe=[],Be=[];for(const wt of ae)"SpecialToken"in wt?K&&(pe.push(wt.SpecialToken.id),Be.push(wt.SpecialToken.type_id)):"Sequence"in wt&&(wt.Sequence.id==="A"?(pe=(0,fe.mergeArrays)(pe,_),Be=(0,fe.mergeArrays)(Be,new Array(_.length).fill(wt.Sequence.type_id))):wt.Sequence.id==="B"&&(pe=(0,fe.mergeArrays)(pe,P),Be=(0,fe.mergeArrays)(Be,new Array(P.length).fill(wt.Sequence.type_id))));return{tokens:pe,token_type_ids:Be}}}class kt extends Ke{post_process(_,P=null){return P&&(_=(0,fe.mergeArrays)(_,P)),{tokens:_}}}class v extends Ke{constructor(_){super(_),this.processors=_.processors.map(P=>Ke.fromConfig(P))}post_process(_,P=null,K={}){let ae;for(const pe of this.processors)if(pe instanceof kt)_=pe.post_process(_).tokens,P&&(P=pe.post_process(P).tokens);else{const Be=pe.post_process(_,P,K);_=Be.tokens,ae=Be.token_type_ids}return{tokens:_,token_type_ids:ae}}}class q extends O.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 Ye(_);case"Metaspace":return new xr(_);case"ByteLevel":return new Bt(_);case"Replace":return new C(_);case"ByteFallback":return new Q(_);case"Fuse":return new he(_);case"Strip":return new Qe(_);case"Sequence":return new Tt(_);case"CTC":return new ft(_);case"BPEDecoder":return new bt(_);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 C extends q{decode_chain(_){const P=me(this.config.pattern);return P===null?_:_.map(K=>K.replaceAll(P,this.config.content))}}class Q extends q{constructor(_){super(_),this.text_decoder=new TextDecoder}decode_chain(_){const P=[];let K=[];for(const ae of _){let pe=null;if(ae.length===6&&ae.startsWith("<0x")&&ae.endsWith(">")){const Be=parseInt(ae.slice(3,5),16);isNaN(Be)||(pe=Be)}if(pe!==null)K.push(pe);else{if(K.length>0){const Be=this.text_decoder.decode(Uint8Array.from(K));P.push(Be),K=[]}P.push(ae)}}if(K.length>0){const ae=this.text_decoder.decode(Uint8Array.from(K));P.push(ae),K=[]}return P}}class he extends q{decode_chain(_){return[_.join("")]}}class Qe extends q{constructor(_){super(_),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(_){return _.map(P=>{let K=0;for(let pe=0;pe<this.start&&P[pe]===this.content;++pe){K=pe+1;continue}let ae=P.length;for(let pe=0;pe<this.stop;++pe){const Be=P.length-pe-1;if(P[Be]===this.content){ae=Be;continue}else break}return P.slice(K,ae)})}}class Ye extends q{constructor(_){super(_),this.cleanup=_.cleanup}decode_chain(_){return _.map((P,K)=>(K!==0&&(P.startsWith(this.config.prefix)?P=P.replace(this.config.prefix,""):P=" "+P),this.cleanup&&(P=H(P)),P))}}class Bt extends q{constructor(_){super(_),this.byte_decoder=Xe,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(_){const P=_.join(""),K=new Uint8Array([...P].map(pe=>this.byte_decoder[pe]));return this.text_decoder.decode(K)}decode_chain(_){const P=[];let K=[];for(const ae of _)this.added_tokens.find(pe=>pe.content===ae)!==void 0?(K.length>0&&(P.push(this.convert_tokens_to_string(K)),K=[]),P.push(ae)):K.push(ae);return K.length>0&&P.push(this.convert_tokens_to_string(K)),P}}class ft extends q{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 P=[_[0]];for(let pe=1;pe<_.length;++pe)_[pe]!==P.at(-1)&&P.push(_[pe]);let ae=P.filter(pe=>pe!==this.pad_token).join("");return this.cleanup&&(ae=H(ae).replaceAll(this.word_delimiter_token," ").trim()),ae}decode_chain(_){return[this.convert_tokens_to_string(_)]}}class Tt extends q{constructor(_){super(_),this.decoders=_.decoders.map(P=>q.fromConfig(P))}decode_chain(_){return this.decoders.reduce((P,K)=>K.decode_chain(P),_)}}class bt extends q{constructor(_){super(_),this.suffix=this.config.suffix}decode_chain(_){return _.map((P,K)=>P.replaceAll(this.suffix,K===_.length-1?"":" "))}}class Ot extends q{decode_chain(_){let P="";for(let K=1;K<_.length;K+=2)P+=_[K];return[P]}}class cr extends Re{constructor(_){super(),this.addPrefixSpace=_.add_prefix_space,this.replacement=_.replacement,this.strRep=_.str_rep||this.replacement,this.prepend_scheme=_.prepend_scheme??"always"}pre_tokenize_text(_,{section_index:P=void 0}={}){let K=_.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!K.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&P===0)&&(K=this.strRep+K),[K]}}class xr extends q{constructor(_){super(_),this.addPrefixSpace=_.add_prefix_space,this.replacement=_.replacement}decode_chain(_){const P=[];for(let K=0;K<_.length;++K){let ae=_[K].replaceAll(this.replacement," ");this.addPrefixSpace&&K==0&&ae.startsWith(" ")&&(ae=ae.substring(1)),P.push(ae)}return P}}class Yr extends G{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(K=>K.normalize("NFKC")).join("～"):_=_.normalize("NFKC"),_}}class Br extends Re{constructor(_){super(),this.tokenizers=_.pretokenizers.map(P=>Re.fromConfig(P))}pre_tokenize_text(_,P){return this.tokenizers.reduce((K,ae)=>ae.pre_tokenize(K,P),[_])}}class Kr extends Re{constructor(_){super()}pre_tokenize_text(_,P){return _.match(/\w+|[^\w\s]+/g)||[]}}class at extends Re{constructor(_){super()}pre_tokenize_text(_,P){return I(_)}}class U extends Re{constructor(_){super(),this.config=_,this.pattern=me(this.config.pattern),this.content=this.config.content}pre_tokenize_text(_,P){return this.pattern===null?[_]:[_.replaceAll(this.pattern,this.config.content)]}}const _e=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Pe(be,_,P,K){for(const ae of Object.keys(be)){const pe=_-be[ae].length,Be=P(ae),wt=new Array(pe).fill(Be);be[ae]=K==="right"?(0,fe.mergeArrays)(be[ae],wt):(0,fe.mergeArrays)(wt,be[ae])}}function rt(be,_){for(const P of Object.keys(be))be[P].length=_}class we extends O.Callable{constructor(P,K){super();xe(this,"return_token_type_ids",!1);xe(this,"padding_side","right");this._tokenizer_config=K,this.normalizer=G.fromConfig(P.normalizer),this.pre_tokenizer=Re.fromConfig(P.pre_tokenizer),this.model=$e.fromConfig(P.model,K),this.post_processor=Ke.fromConfig(P.post_processor),this.decoder=q.fromConfig(P.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ae of P.added_tokens){const pe=new ve(ae);this.added_tokens.push(pe),this.model.tokens_to_ids.set(pe.content,pe.id),this.model.vocab[pe.id]=pe.content,pe.special&&(this.special_tokens.push(pe.content),this.all_special_ids.push(pe.id))}if(this.additional_special_tokens=K.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort((ae,pe)=>pe.content.length-ae.content.length).map(ae=>`${ae.lstrip?"\\s*":""}(${(0,fe.escapeRegExp)(ae.content)})${ae.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=K.model_max_length,this.remove_space=K.remove_space,this.clean_up_tokenization_spaces=K.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=K.do_lowercase_and_remove_accent??!1,K.padding_side&&(this.padding_side=K.padding_side),this.legacy=!1,this.chat_template=K.chat_template??null,Array.isArray(this.chat_template)){const ae=Object.create(null);for(const{name:pe,template:Be}of this.chat_template){if(typeof pe!="string"||typeof Be!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ae[pe]=Be}this.chat_template=ae}this._compiled_template_cache=new Map}getToken(...P){for(const K of P){const ae=this._tokenizer_config[K];if(ae)if(typeof ae=="object"){if(ae.__type==="AddedToken")return ae.content;throw Error(`Unknown token: ${ae}`)}else return ae}return null}static async from_pretrained(P,{progress_callback:K=null,config:ae=null,cache_dir:pe=null,local_files_only:Be=!1,revision:wt="main",legacy:xt=null}={}){const Mt=await ee(P,{progress_callback:K,config:ae,cache_dir:pe,local_files_only:Be,revision:wt,legacy:xt});return new this(...Mt)}_call(P,{text_pair:K=null,add_special_tokens:ae=!0,padding:pe=!1,truncation:Be=null,max_length:wt=null,return_tensor:xt=!0,return_token_type_ids:Mt=null}={}){const zt=Array.isArray(P);let er;if(zt){if(P.length===0)throw Error("text array must be non-empty");if(K!==null){if(Array.isArray(K)){if(P.length!==K.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");er=P.map((ir,qt)=>this._encode_plus(ir,{text_pair:K[qt],add_special_tokens:ae,return_token_type_ids:Mt}))}else er=P.map(ir=>this._encode_plus(ir,{add_special_tokens:ae,return_token_type_ids:Mt}))}else{if(P==null)throw Error("text may not be null or undefined");if(Array.isArray(K))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");er=[this._encode_plus(P,{text_pair:K,add_special_tokens:ae,return_token_type_ids:Mt})]}if(wt===null?pe==="max_length"?wt=this.model_max_length:wt=(0,Te.max)(er.map(ir=>ir.input_ids.length))[0]:Be||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."),wt=Math.min(wt,this.model_max_length??1/0),pe||Be)for(let ir=0;ir<er.length;++ir)er[ir].input_ids.length!==wt&&(er[ir].input_ids.length>wt?Be&&rt(er[ir],wt):pe&&Pe(er[ir],wt,qt=>qt==="input_ids"?this.pad_token_id:0,this.padding_side));const zr={};if(xt){if(!(pe&&Be)&&er.some(qt=>{var pr;for(const mn of Object.keys(qt))if(qt[mn].length!==((pr=er[0][mn])==null?void 0:pr.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 ir=[er.length,er[0].input_ids.length];for(const qt of Object.keys(er[0]))zr[qt]=new Ce.Tensor("int64",BigInt64Array.from(er.flatMap(pr=>pr[qt]).map(BigInt)),ir)}else{for(const ir of Object.keys(er[0]))zr[ir]=er.map(qt=>qt[ir]);if(!zt)for(const ir of Object.keys(zr))zr[ir]=zr[ir][0]}return zr}_encode_text(P){return P===null?null:(this.added_tokens_regex?P.split(this.added_tokens_regex).filter(pe=>pe):[P]).map((pe,Be)=>{if(this.added_tokens.find(xt=>xt.content===pe)!==void 0)return pe;{if(this.remove_space===!0&&(pe=pe.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(pe=se(pe)),this.normalizer!==null&&(pe=this.normalizer(pe)),pe.length===0)return[];const xt=this.pre_tokenizer!==null?this.pre_tokenizer(pe,{section_index:Be}):[pe];return this.model(xt)}}).flat()}_encode_plus(P,{text_pair:K=null,add_special_tokens:ae=!0,return_token_type_ids:pe=null}={}){const{tokens:Be,token_type_ids:wt}=this._tokenize_helper(P,{pair:K,add_special_tokens:ae}),xt=this.model.convert_tokens_to_ids(Be),Mt={input_ids:xt,attention_mask:new Array(xt.length).fill(1)};return(pe??this.return_token_type_ids)&&wt&&(Mt.token_type_ids=wt),Mt}_tokenize_helper(P,{pair:K=null,add_special_tokens:ae=!1}={}){const pe=this._encode_text(P),Be=this._encode_text(K);return this.post_processor?this.post_processor(pe,Be,{add_special_tokens:ae}):{tokens:(0,fe.mergeArrays)(pe??[],Be??[])}}tokenize(P,{pair:K=null,add_special_tokens:ae=!1}={}){return this._tokenize_helper(P,{pair:K,add_special_tokens:ae}).tokens}encode(P,{text_pair:K=null,add_special_tokens:ae=!0,return_token_type_ids:pe=null}={}){return this._encode_plus(P,{text_pair:K,add_special_tokens:ae,return_token_type_ids:pe}).input_ids}batch_decode(P,K={}){return P instanceof Ce.Tensor&&(P=P.tolist()),P.map(ae=>this.decode(ae,K))}decode(P,K={}){if(P instanceof Ce.Tensor&&(P=D(P)),!Array.isArray(P)||P.length===0||!(0,fe.isIntegralNumber)(P[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(P,K)}decode_single(P,{skip_special_tokens:K=!1,clean_up_tokenization_spaces:ae=null}){let pe=this.model.convert_ids_to_tokens(P);K&&(pe=pe.filter(wt=>!this.special_tokens.includes(wt)));let Be=this.decoder?this.decoder(pe):pe.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Be=Be.replaceAll(this.decoder.end_of_word_suffix," "),K&&(Be=Be.trim())),(ae??this.clean_up_tokenization_spaces)&&(Be=H(Be)),Be}get_chat_template({chat_template:P=null,tools:K=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const ae=this.chat_template;if(P!==null&&Object.hasOwn(ae,P))P=ae[P];else if(P===null)if(K!==null&&"tool_use"in ae)P=ae.tool_use;else if("default"in ae)P=ae.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(ae).sort()}.`)}else if(P===null)if(this.chat_template)P=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 P}apply_chat_template(P,{tools:K=null,documents:ae=null,chat_template:pe=null,add_generation_prompt:Be=!1,tokenize:wt=!0,padding:xt=!1,truncation:Mt=!1,max_length:zt=null,return_tensor:er=!0,return_dict:zr=!1,tokenizer_kwargs:ir={},...qt}={}){if(pe=this.get_chat_template({chat_template:pe,tools:K}),typeof pe!="string")throw Error(`chat_template must be a string, but got ${typeof pe}`);let pr=this._compiled_template_cache.get(pe);pr===void 0&&(pr=new S.Template(pe),this._compiled_template_cache.set(pe,pr));const mn=Object.create(null);for(const Oe of _e){const Sn=this.getToken(Oe);Sn&&(mn[Oe]=Sn)}const ln=pr.render({messages:P,add_generation_prompt:Be,tools:K,documents:ae,...mn,...qt});if(wt){const Oe=this._call(ln,{add_special_tokens:!1,padding:xt,truncation:Mt,max_length:zt,return_tensor:er,...ir});return zr?Oe:Oe.input_ids}return ln}}class Je extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class gt extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class mt extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class Et extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class _t extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class Ft extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class Nt extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class Rt extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class Gt extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class Me extends we{}class Ze extends we{}class ot extends we{constructor(P,K){super(P,K);xe(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Ht extends we{constructor(){super(...arguments);xe(this,"return_token_type_ids",!0)}}class gr extends we{}class Lr extends we{}class mr extends we{}class yr extends we{constructor(_,P){super(_,P),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(K=>this.languageRegex.test(K)),this.lang_to_token=K=>K}_build_translation_inputs(_,P,K){return Xn(this,_,P,K)}}class Tr extends yr{}class $n extends we{}class Rr extends we{constructor(_,P){var pe,Be;const K=".,!?…。，、।۔،",ae=(Be=(pe=_.pre_tokenizer)==null?void 0:pe.pretokenizers[0])==null?void 0:Be.pattern;ae&&ae.Regex===` ?[^(\\s|[${K}])]+`&&(ae.Regex=` ?[^\\s${K}]+`),super(_,P)}}const Hn="▁";class jn extends we{constructor(P,K){super(P,K);xe(this,"padding_side","left");this.legacy=K.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new cr({replacement:Hn,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(P){if(P===null)return null;if(this.legacy||P.length===0)return super._encode_text(P);let K=super._encode_text(Hn+P.replaceAll(Hn," "));return K.length>1&&K[0]===Hn&&this.special_tokens.includes(K[1])&&(K=K.slice(1)),K}}class Ys extends we{}class vs extends we{}class xs extends we{}class Ts extends we{}class Es extends we{}class Kn extends we{}class Js extends we{}class cs extends we{}class Ln extends we{}function Xn(be,_,P,K){if(!("language_codes"in be)||!Array.isArray(be.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in be)||!(be.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in be)||typeof be.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const ae=K.src_lang,pe=K.tgt_lang;if(!be.language_codes.includes(pe))throw new Error(`Target language code "${pe}" is not valid. Must be one of: {${be.language_codes.join(", ")}}`);if(ae!==void 0){if(!be.language_codes.includes(ae))throw new Error(`Source language code "${ae}" is not valid. Must be one of: {${be.language_codes.join(", ")}}`);for(const Be of be.post_processor.config.single)if("SpecialToken"in Be&&be.languageRegex.test(Be.SpecialToken.id)){Be.SpecialToken.id=be.lang_to_token(ae);break}}return K.forced_bos_token_id=be.model.convert_tokens_to_ids([be.lang_to_token(pe)])[0],be._call(_,P)}class Vn extends we{constructor(_,P){super(_,P),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(K=>this.languageRegex.test(K)),this.lang_to_token=K=>K}_build_translation_inputs(_,P,K){return Xn(this,_,P,K)}}class rs extends we{constructor(_,P){super(_,P),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(K=>this.languageRegex.test(K)).map(K=>K.slice(2,-2)),this.lang_to_token=K=>`__${K}__`}_build_translation_inputs(_,P,K){return Xn(this,_,P,K)}}class ps extends we{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(_,{return_timestamps:P=!1,return_language:K=!1,time_precision:ae=null,force_full_sequences:pe=!0}={}){if(ae===null)throw Error("Must specify time_precision");let Be=null;const wt=P==="word";function xt(){return{language:Be,timestamp:[null,null],text:""}}const Mt=[];let zt=xt(),er=0;const zr=this.timestamp_begin;let ir=[],qt=[],pr=!1,mn=null;const ln=new Set(this.all_special_ids);for(const Er of _){const sn=Er.tokens,xn=wt?Er.token_timestamps:null;let Qt=null,Tn=zr;if("stride"in Er){const[Cr,It,br]=Er.stride;if(er-=It,mn=Cr-br,It&&(Tn=It/ae+zr),br)for(let Gr=sn.length-1;Gr>=0;--Gr){const Xr=Number(sn[Gr]);if(Xr>=zr){if(Qt!==null&&(Xr-zr)*ae<mn)break;Qt=Xr}}}let pn=[],Ar=[];for(let Cr=0;Cr<sn.length;++Cr){const It=Number(sn[Cr]);if(ln.has(It)){const br=this.decode([It]),Gr=V.WHISPER_LANGUAGE_MAPPING.get(br.slice(2,-2));if(Gr!==void 0){if(Be!==null&&Gr!==Be&&!P){ir.push(pn);const Xr=this.findLongestCommonSequence(ir)[0],En=this.decode(Xr);zt.text=En,Mt.push(zt),ir=[],pn=[],zt=xt()}Be=zt.language=Gr}}else if(It>=zr){const br=(It-zr)*ae+er,Gr=(0,Te.round)(br,2);if(Qt!==null&&It>=Qt)pr=!0;else if(pr||ir.length>0&&It<Tn)pr=!1;else if(zt.timestamp[0]===null)zt.timestamp[0]=Gr;else if(Gr!==zt.timestamp[0]){zt.timestamp[1]=Gr,ir.push(pn),wt&&qt.push(Ar);const[Xr,En]=this.findLongestCommonSequence(ir,qt),jt=this.decode(Xr);zt.text=jt,wt&&(zt.words=this.collateWordTimestamps(Xr,En,Be)),Mt.push(zt),ir=[],pn=[],qt=[],Ar=[],zt=xt()}}else if(pn.push(It),wt){let br=(0,Te.round)(xn[Cr]+er,2),Gr;if(Cr+1<xn.length){Gr=(0,Te.round)(xn[Cr+1]+er,2);const Xr=this.decode([It]);k.test(Xr)&&(Gr=(0,Te.round)(Math.min(br+ae,Gr),2))}else Gr=null;Ar.push([br,Gr])}}if("stride"in Er){const[Cr,It,br]=Er.stride;er+=Cr-br}pn.length>0?(ir.push(pn),wt&&qt.push(Ar)):ir.every(Cr=>Cr.length===0)&&(zt=xt(),ir=[],pn=[],qt=[],Ar=[])}if(ir.length>0){if(pe&&P)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[Er,sn]=this.findLongestCommonSequence(ir,qt),xn=this.decode(Er);zt.text=xn,wt&&(zt.words=this.collateWordTimestamps(Er,sn,Be)),Mt.push(zt)}let Oe=Object.create(null);const Sn=Mt.map(Er=>Er.text).join("");if(P||K){for(let Er=0;Er<Mt.length;++Er){const sn=Mt[Er];P||delete sn.timestamp,K||delete sn.language}if(wt){const Er=[];for(const sn of Mt)for(const xn of sn.words)Er.push(xn);Oe={chunks:Er}}else Oe={chunks:Mt}}return[Sn,Oe]}findLongestCommonSequence(_,P=null){let K=_[0],ae=K.length,pe=[];const Be=Array.isArray(P)&&P.length>0;let wt=Be?[]:null,xt=Be?P[0]:null;for(let Mt=1;Mt<_.length;++Mt){const zt=_[Mt];let er=0,zr=[ae,ae,0,0];const ir=zt.length;for(let Er=1;Er<ae+ir;++Er){const sn=Math.max(0,ae-Er),xn=Math.min(ae,ae+ir-Er),Qt=K.slice(sn,xn),Tn=Math.max(0,Er-ae),pn=Math.min(ir,Er),Ar=zt.slice(Tn,pn);if(Qt.length!==Ar.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. 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Using bilinear instead.`),se="bilinear");case"nearest":case"bilinear":case"bicubic":X=X.affine([D/this.width,0,0,H/this.height],{interpolator:se});break;case"lanczos":X=X.resize({width:D,height:H,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${se} is not supported.`)}return await A(X)}}async pad([D,H,te,se]){if(D=Math.max(D,0),H=Math.max(H,0),te=Math.max(te,0),se=Math.max(se,0),D===0&&H===0&&te===0&&se===0)return this;if(Ce){const X=this.channels,R=this.toCanvas(),I=this.width+D+H,B=this.height+te+se,k=S(I,B).getContext("2d");return k.drawImage(R,0,0,this.width,this.height,D,te,I,B),new me(k.getImageData(0,0,I,B).data,I,B,4).convert(X)}else{const X=this.toSharp().extend({left:D,right:H,top:te,bottom:se});return await A(X)}}async crop([D,H,te,se]){if(D=Math.max(D,0),H=Math.max(H,0),te=Math.min(te,this.width-1),se=Math.min(se,this.height-1),D===0&&H===0&&te===this.width-1&&se===this.height-1)return this;const X=te-D+1,R=se-H+1;if(Ce){const I=this.channels,B=this.toCanvas(),k=S(X,R).getContext("2d");return k.drawImage(B,D,H,X,R,0,0,X,R),new me(k.getImageData(0,0,X,R).data,X,R,4).convert(I)}else{const I=this.toSharp().extract({left:D,top:H,width:X,height:R});return await A(I)}}async center_crop(D,H){if(this.width===D&&this.height===H)return this;const te=(this.width-D)/2,se=(this.height-H)/2;if(Ce){const X=this.channels,R=this.toCanvas(),I=S(D,H).getContext("2d");let B=0,k=0,ue=0,ve=0;return te>=0?B=te:ue=-te,se>=0?k=se:ve=-se,I.drawImage(R,B,k,D,H,ue,ve,D,H),new me(I.getImageData(0,0,D,H).data,D,H,4).convert(X)}else{let X=this.toSharp();if(te>=0&&se>=0)X=X.extract({left:Math.floor(te),top:Math.floor(se),width:D,height:H});else if(te<=0&&se<=0){const R=Math.floor(-se),I=Math.floor(-te);X=X.extend({top:R,left:I,right:D-this.width-I,bottom:H-this.height-R})}else{let R=[0,0],I=0;se<0?(R[0]=Math.floor(-se),R[1]=H-this.height-R[0]):I=Math.floor(se);let B=[0,0],k=0;te<0?(B[0]=Math.floor(-te),B[1]=D-this.width-B[0]):k=Math.floor(te),X=X.extend({top:R[0],bottom:R[1],left:B[0],right:B[1]}).extract({left:k,top:I,width:D,height:H})}return await A(X)}}async toBlob(D="image/png",H=1){if(!Ce)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:D,quality:H})}toTensor(D="CHW"){let H=new ye.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(D!=="HWC")if(D==="CHW")H=H.permute(2,0,1);else throw new Error(`Unsupported channel format: ${D}`);return H}toCanvas(){if(!Ce)throw new Error("toCanvas() is only supported in browser environments.");const D=this.clone().rgba(),H=S(D.width,D.height),te=new V(D.data,D.width,D.height);return H.getContext("2d").putImageData(te,0,0),H}_update(D,H,te,se=null){return this.data=D,this.width=H,this.height=te,se!==null&&(this.channels=se),this}clone(){return new me(this.data.slice(),this.width,this.height,this.channels)}convert(D){if(this.channels===D)return this;switch(D){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(D){if(Ce){if(j)throw new Error("Unable to save an image from a Web Worker.");const H=D.split(".").pop().toLowerCase(),te=ne.get(H)??"image/png",se=await this.toBlob(te),X=URL.createObjectURL(se),R=document.createElement("a");R.href=X,R.download=D,R.click(),R.remove()}else{if(fe.env.useFS)return await this.toSharp().toFile(D);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Ce)throw new Error("toSharp() is only supported in server-side environments.");return Te(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}},"./src/utils/maths.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{FFT:()=>ce,bankers_round:()=>te,cos_sim:()=>j,dot:()=>Ce,dynamic_time_warping:()=>se,interpolate_data:()=>O,log_softmax:()=>Te,magnitude:()=>S,max:()=>A,medianFilter:()=>D,min:()=>V,permute_data:()=>fe,round:()=>H,softmax:()=>ye});function O(X,[R,I,B],[k,ue],ve="bilinear",$e=!1){const Ie=ue/B,Ae=k/I,tt=new X.constructor(k*ue*R),Xe=I*B,dt=k*ue;for(let ge=0;ge<k;++ge)for(let G=0;G<ue;++G){const de=ge*ue+G,Se=(G+.5)/Ie-.5,Z=(ge+.5)/Ae-.5;let He=Math.floor(Se),ct=Math.floor(Z);const nt=Math.min(He+1,B-1),lt=Math.min(ct+1,I-1);He=Math.max(He,0),ct=Math.max(ct,0);const Ne=Se-He,st=Z-ct,Pt=(1-Ne)*(1-st),Re=Ne*(1-st),re=(1-Ne)*st,ke=Ne*st,je=ct*B,qe=lt*B,Ue=je+He,Ke=je+nt,ut=qe+He,yt=qe+nt;for(let vt=0;vt<R;++vt){const kt=vt*Xe;tt[vt*dt+de]=Pt*X[kt+Ue]+Re*X[kt+Ke]+re*X[kt+ut]+ke*X[kt+yt]}}return tt}function fe(X,R,I){const B=new Array(I.length),k=new 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Error("Input and output buffers must be different");this._transform4(R,I,1)}realTransform(R,I){if(R===I)throw new Error("Input and output buffers must be different");this._realTransform4(R,I,1)}inverseTransform(R,I){if(R===I)throw new Error("Input and output buffers must be different");this._transform4(R,I,-1);for(let B=0;B<R.length;++B)R[B]/=this.size}_transform4(R,I,B){const k=this._csize;let ve=1<<this._width,$e=k/ve<<1,Ie,Ae;const tt=this._bitrev;if($e===4)for(Ie=0,Ae=0;Ie<k;Ie+=$e,++Ae){const dt=tt[Ae];this._singleTransform2(I,R,Ie,dt,ve)}else for(Ie=0,Ae=0;Ie<k;Ie+=$e,++Ae){const dt=tt[Ae];this._singleTransform4(I,R,Ie,dt,ve,B)}const Xe=this.table;for(ve>>=2;ve>=2;ve>>=2){$e=k/ve<<1;const dt=$e>>>2;for(Ie=0;Ie<k;Ie+=$e){const ge=Ie+dt-1;for(let G=Ie,de=0;G<ge;G+=2,de+=ve){const Se=G,Z=Se+dt,He=Z+dt,ct=He+dt,nt=R[Se],lt=R[Se+1],Ne=R[Z],st=R[Z+1],Pt=R[He],Re=R[He+1],re=R[ct],ke=R[ct+1],je=Xe[de],qe=B*Xe[de+1],Ue=Ne*je-st*qe,Ke=Ne*qe+st*je,ut=Xe[2*de],yt=B*Xe[2*de+1],vt=Pt*ut-Re*yt,kt=Pt*yt+Re*ut,v=Xe[3*de],q=B*Xe[3*de+1],C=re*v-ke*q,Q=re*q+ke*v,he=nt+vt,Qe=lt+kt,Ye=nt-vt,Bt=lt-kt,ft=Ue+C,Tt=Ke+Q,bt=B*(Ue-C),Ot=B*(Ke-Q);R[Se]=he+ft,R[Se+1]=Qe+Tt,R[Z]=Ye+Ot,R[Z+1]=Bt-bt,R[He]=he-ft,R[He+1]=Qe-Tt,R[ct]=Ye-Ot,R[ct+1]=Bt+bt}}}}_singleTransform2(R,I,B,k,ue){const ve=R[k],$e=R[k+1],Ie=R[k+ue],Ae=R[k+ue+1];I[B]=ve+Ie,I[B+1]=$e+Ae,I[B+2]=ve-Ie,I[B+3]=$e-Ae}_singleTransform4(R,I,B,k,ue,ve){const $e=ue*2,Ie=ue*3,Ae=R[k],tt=R[k+1],Xe=R[k+ue],dt=R[k+ue+1],ge=R[k+$e],G=R[k+$e+1],de=R[k+Ie],Se=R[k+Ie+1],Z=Ae+ge,He=tt+G,ct=Ae-ge,nt=tt-G,lt=Xe+de,Ne=dt+Se,st=ve*(Xe-de),Pt=ve*(dt-Se);I[B]=Z+lt,I[B+1]=He+Ne,I[B+2]=ct+Pt,I[B+3]=nt-st,I[B+4]=Z-lt,I[B+5]=He-Ne,I[B+6]=ct-Pt,I[B+7]=nt+st}_realTransform4(R,I,B){const k=this._csize;let ve=1<<this._width,$e=k/ve<<1,Ie,Ae;const tt=this._bitrev;if($e===4)for(Ie=0,Ae=0;Ie<k;Ie+=$e,++Ae){const ge=tt[Ae];this._singleRealTransform2(I,R,Ie,ge>>>1,ve>>>1)}else for(Ie=0,Ae=0;Ie<k;Ie+=$e,++Ae){const ge=tt[Ae];this._singleRealTransform4(I,R,Ie,ge>>>1,ve>>>1,B)}const Xe=this.table;for(ve>>=2;ve>=2;ve>>=2){$e=k/ve<<1;const ge=$e>>>1,G=ge>>>1,de=G>>>1;for(Ie=0;Ie<k;Ie+=$e)for(let Se=0,Z=0;Se<=de;Se+=2,Z+=ve){const He=Ie+Se,ct=He+G,nt=ct+G,lt=nt+G,Ne=R[He],st=R[He+1],Pt=R[ct],Re=R[ct+1],re=R[nt],ke=R[nt+1],je=R[lt],qe=R[lt+1],Ue=Ne,Ke=st,ut=Xe[Z],yt=B*Xe[Z+1],vt=Pt*ut-Re*yt,kt=Pt*yt+Re*ut,v=Xe[2*Z],q=B*Xe[2*Z+1],C=re*v-ke*q,Q=re*q+ke*v,he=Xe[3*Z],Qe=B*Xe[3*Z+1],Ye=je*he-qe*Qe,Bt=je*Qe+qe*he,ft=Ue+C,Tt=Ke+Q,bt=Ue-C,Ot=Ke-Q,cr=vt+Ye,xr=kt+Bt,Yr=B*(vt-Ye),Br=B*(kt-Bt);if(R[He]=ft+cr,R[He+1]=Tt+xr,R[ct]=bt+Br,R[ct+1]=Ot-Yr,Se===0){R[nt]=ft-cr,R[nt+1]=Tt-xr;continue}if(Se===de)continue;const Kr=Ie+G-Se,at=Ie+ge-Se;R[Kr]=bt-B*Br,R[Kr+1]=-Ot-B*Yr,R[at]=ft-B*cr,R[at+1]=-Tt+B*xr}}const dt=k>>>1;for(let ge=2;ge<dt;ge+=2)R[k-ge]=R[ge],R[k-ge+1]=-R[ge+1]}_singleRealTransform2(R,I,B,k,ue){const ve=R[k],$e=R[k+ue];I[B]=ve+$e,I[B+1]=0,I[B+2]=ve-$e,I[B+3]=0}_singleRealTransform4(R,I,B,k,ue,ve){const $e=ue*2,Ie=ue*3,Ae=R[k],tt=R[k+ue],Xe=R[k+$e],dt=R[k+Ie],ge=Ae+Xe,G=Ae-Xe,de=tt+dt,Se=ve*(tt-dt);I[B]=ge+de,I[B+1]=0,I[B+2]=G,I[B+3]=-Se,I[B+4]=ge-de,I[B+5]=0,I[B+6]=G,I[B+7]=Se}}class me{constructor(R){const I=2*(R-1),B=2*(2*R-1),k=2**Math.ceil(Math.log2(B));this.bufferSize=k,this._a=I;const ue=new Float64Array(B),ve=new Float64Array(k);this._chirpBuffer=new Float64Array(k),this._buffer1=new Float64Array(k),this._buffer2=new Float64Array(k),this._outBuffer1=new Float64Array(k),this._outBuffer2=new Float64Array(k);const $e=-2*Math.PI/R,Ie=Math.cos($e),Ae=Math.sin($e);for(let tt=0;tt<B>>1;++tt){const Xe=(tt+1-R)**2/2,dt=Math.sqrt(Ie**2+Ae**2)**Xe,ge=Xe*Math.atan2(Ae,Ie),G=2*tt;ue[G]=dt*Math.cos(ge),ue[G+1]=dt*Math.sin(ge),ve[G]=ue[G],ve[G+1]=-ue[G+1]}this._slicedChirpBuffer=ue.subarray(I,B),this._f=new ne(k>>1),this._f.transform(this._chirpBuffer,ve)}_transform(R,I,B){const k=this._buffer1,ue=this._buffer2,ve=this._outBuffer1,$e=this._outBuffer2,Ie=this._chirpBuffer,Ae=this._slicedChirpBuffer,tt=this._a;if(B)for(let Xe=0;Xe<Ae.length;Xe+=2){const dt=Xe+1,ge=Xe>>1,G=I[ge];k[Xe]=G*Ae[Xe],k[dt]=G*Ae[dt]}else for(let Xe=0;Xe<Ae.length;Xe+=2){const dt=Xe+1;k[Xe]=I[Xe]*Ae[Xe]-I[dt]*Ae[dt],k[dt]=I[Xe]*Ae[dt]+I[dt]*Ae[Xe]}this._f.transform(ve,k);for(let Xe=0;Xe<Ie.length;Xe+=2){const dt=Xe+1;ue[Xe]=ve[Xe]*Ie[Xe]-ve[dt]*Ie[dt],ue[dt]=ve[Xe]*Ie[dt]+ve[dt]*Ie[Xe]}this._f.inverseTransform($e,ue);for(let Xe=0;Xe<$e.length;Xe+=2){const dt=$e[Xe+tt],ge=$e[Xe+tt+1],G=Ae[Xe],de=Ae[Xe+1];R[Xe]=dt*G-ge*de,R[Xe+1]=dt*de+ge*G}}transform(R,I){this._transform(R,I,!1)}realTransform(R,I){this._transform(R,I,!0)}}class ce{constructor(R){this.fft_length=R,this.isPowerOfTwo=ee(R),this.isPowerOfTwo?(this.fft=new ne(R),this.outputBufferSize=2*R):(this.fft=new me(R),this.outputBufferSize=this.fft.bufferSize)}realTransform(R,I){this.fft.realTransform(R,I)}transform(R,I){this.fft.transform(R,I)}}function D(X,R){if(R%2===0||R<=0)throw new Error("Window size must be a positive odd number");const I=new X.constructor(X.length),B=new X.constructor(R),k=Math.floor(R/2);for(let ue=0;ue<X.length;++ue){let ve=0;for(let $e=-k;$e<=k;++$e){let Ie=ue+$e;Ie<0?Ie=Math.abs(Ie):Ie>=X.length&&(Ie=2*(X.length-1)-Ie),B[ve++]=X[Ie]}B.sort(),I[ue]=B[k]}return I}function H(X,R){const I=Math.pow(10,R);return Math.round(X*I)/I}function te(X){const R=Math.round(X);return Math.abs(X)%1===.5?R%2===0?R:R-1:R}function se(X){const R=X.length,I=X[0].length,B=[R+1,I+1],k=Array.from({length:B[0]},()=>Array(B[1]).fill(1/0));k[0][0]=0;const ue=Array.from({length:B[0]},()=>Array(B[1]).fill(-1));for(let tt=1;tt<B[1];++tt)for(let Xe=1;Xe<B[0];++Xe){const dt=k[Xe-1][tt-1],ge=k[Xe-1][tt],G=k[Xe][tt-1];let de,Se;dt<ge&&dt<G?(de=dt,Se=0):ge<dt&&ge<G?(de=ge,Se=1):(de=G,Se=2),k[Xe][tt]=X[Xe-1][tt-1]+de,ue[Xe][tt]=Se}for(let tt=0;tt<B[1];++tt)ue[0][tt]=2;for(let tt=0;tt<B[0];++tt)ue[tt][0]=1;let ve=R,$e=I,Ie=[],Ae=[];for(;ve>0||$e>0;)switch(Ie.push(ve-1),Ae.push($e-1),ue[ve][$e]){case 0:--ve,--$e;break;case 1:--ve;break;case 2:--$e;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${ve}, ${$e}]. Please file a bug report.`)}return Ie.reverse(),Ae.reverse(),[Ie,Ae]}},"./src/utils/tensor.js":($t,Ee,N)=>{N.r(Ee),N.d(Ee,{Tensor:()=>Ce,cat:()=>X,full:()=>ve,full_like:()=>$e,interpolate:()=>V,interpolate_4d:()=>A,layer_norm:()=>D,matmul:()=>ee,mean:()=>B,mean_pooling:()=>ce,ones:()=>Ie,ones_like:()=>Ae,permute:()=>S,quantize_embeddings:()=>dt,rfft:()=>ne,stack:()=>R,std_mean:()=>I,topk:()=>me,zeros:()=>tt,zeros_like:()=>Xe});var O=N("./src/utils/maths.js"),fe=N("./src/backends/onnx.js"),ye=N("./src/ops/registry.js");const Te=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array});class Ce{constructor(...G){xe(this,"ort_tensor");return(0,fe.isONNXTensor)(G[0])?this.ort_tensor=G[0]:this.ort_tensor=new fe.Tensor(G[0],G[1],G[2]),new Proxy(this,{get:(de,Se)=>{if(typeof Se=="string"){let Z=Number(Se);if(Number.isInteger(Z))return de._getitem(Z)}return de[Se]},set:(de,Se,Z)=>de[Se]=Z})}get dims(){return this.ort_tensor.dims}set dims(G){this.ort_tensor.dims=G}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[G,...de]=this.dims;if(de.length>0){const Se=de.reduce((Z,He)=>Z*He);for(let Z=0;Z<G;++Z)yield this._subarray(Z,Se,de)}else yield*this.data}_getitem(G){const[de,...Se]=this.dims;if(G=se(G,de),Se.length>0){const Z=Se.reduce((He,ct)=>He*ct);return this._subarray(G,Z,Se)}else return new Ce(this.type,[this.data[G]],Se)}indexOf(G){const de=this.data;for(let Se=0;Se<de.length;++Se)if(de[Se]==G)return Se;return-1}_subarray(G,de,Se){const Z=G*de,He=(G+1)*de,ct="subarray"in this.data?this.data.subarray(Z,He):this.data.slice(Z,He);return new Ce(this.type,ct,Se)}item(){const G=this.data;if(G.length!==1)throw new Error(`a Tensor with ${G.length} elements cannot be converted to Scalar`);return G[0]}tolist(){return j(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){const G=this.data;for(let de=0;de<G.length;++de)G[de]=1/(1+Math.exp(-G[de]));return this}mul(G){return this.clone().mul_(G)}mul_(G){const de=this.data;for(let Se=0;Se<de.length;++Se)de[Se]*=G;return this}div(G){return this.clone().div_(G)}div_(G){const de=this.data;for(let Se=0;Se<de.length;++Se)de[Se]/=G;return this}add(G){return this.clone().add_(G)}add_(G){const de=this.data;for(let Se=0;Se<de.length;++Se)de[Se]+=G;return this}sub(G){return this.clone().sub_(G)}sub_(G){const de=this.data;for(let Se=0;Se<de.length;++Se)de[Se]-=G;return this}clone(){return new Ce(this.type,this.data.slice(),this.dims.slice())}slice(...G){const de=[],Se=[];for(let Ne=0;Ne<this.dims.length;++Ne){let st=G[Ne];if(st==null)Se.push([0,this.dims[Ne]]),de.push(this.dims[Ne]);else if(typeof st=="number")st=se(st,this.dims[Ne],Ne),Se.push([st,st+1]);else if(Array.isArray(st)&&st.length===2){let[Pt,Re]=st;if(Pt=Pt===null?0:se(Pt,this.dims[Ne],Ne,!1),Re=Re===null?this.dims[Ne]:se(Re,this.dims[Ne],Ne,!1),Pt>Re)throw new Error(`Invalid slice: ${st}`);const re=[Math.max(Pt,0),Math.min(Re,this.dims[Ne])];Se.push(re),de.push(re[1]-re[0])}else throw new Error(`Invalid slice: ${st}`)}const Z=Se.map(([Ne,st])=>st-Ne),He=Z.reduce((Ne,st)=>Ne*st),ct=this.data,nt=new ct.constructor(He),lt=this.stride();for(let Ne=0;Ne<He;++Ne){let st=0;for(let Pt=Z.length-1,Re=Ne;Pt>=0;--Pt){const re=Z[Pt];st+=(Re%re+Se[Pt][0])*lt[Pt],Re=Math.floor(Re/re)}nt[Ne]=ct[st]}return new Ce(this.type,nt,de)}permute(...G){return S(this,G)}transpose(...G){return this.permute(...G)}sum(G=null,de=!1){return this.norm(1,G,de)}norm(G="fro",de=null,Se=!1){if(G==="fro")G=2;else if(typeof G=="string")throw Error(`Unsupported norm: ${G}`);const Z=this.data;if(de===null){let nt=Z.reduce((lt,Ne)=>lt+Ne**G,0)**(1/G);return new Ce(this.type,[nt],[])}de=se(de,this.dims.length);const He=this.dims.slice();He[de]=1;const ct=new Z.constructor(Z.length/this.dims[de]);for(let nt=0;nt<Z.length;++nt){let lt=0;for(let Ne=this.dims.length-1,st=nt,Pt=1;Ne>=0;--Ne){const Re=this.dims[Ne];if(Ne!==de){const re=st%Re;lt+=re*Pt,Pt*=He[Ne]}st=Math.floor(st/Re)}ct[lt]+=Z[nt]**G}if(G!==1)for(let nt=0;nt<ct.length;++nt)ct[nt]=ct[nt]**(1/G);return Se||He.splice(de,1),new Ce(this.type,ct,He)}normalize_(G=2,de=1){de=se(de,this.dims.length);const Se=this.norm(G,de,!0),Z=this.data,He=Se.data;for(let ct=0;ct<Z.length;++ct){let nt=0;for(let lt=this.dims.length-1,Ne=ct,st=1;lt>=0;--lt){const Pt=this.dims[lt];if(lt!==de){const Re=Ne%Pt;nt+=Re*st,st*=this.dims[lt]}Ne=Math.floor(Ne/Pt)}Z[ct]/=He[nt]}return this}normalize(G=2,de=1){return this.clone().normalize_(G,de)}stride(){return k(this.dims)}squeeze(G=null){return new Ce(this.type,this.data,H(this.dims,G))}squeeze_(G=null){return this.dims=H(this.dims,G),this}unsqueeze(G=null){return new Ce(this.type,this.data,te(this.dims,G))}unsqueeze_(G=null){return this.dims=te(this.dims,G),this}flatten_(G=0,de=-1){de=(de+this.dims.length)%this.dims.length;let Se=this.dims.slice(0,G),Z=this.dims.slice(G,de+1),He=this.dims.slice(de+1);return this.dims=[...Se,Z.reduce((ct,nt)=>ct*nt,1),...He],this}flatten(G=0,de=-1){return this.clone().flatten_(G,de)}view(...G){let de=-1;for(let Z=0;Z<G.length;++Z)if(G[Z]===-1){if(de!==-1)throw new Error("Only one dimension can be inferred");de=Z}const Se=this.data;if(de!==-1){const Z=G.reduce((He,ct,nt)=>nt!==de?He*ct:He,1);G[de]=Se.length/Z}return new Ce(this.type,Se,G)}neg_(){const G=this.data;for(let de=0;de<G.length;++de)G[de]=-G[de];return this}neg(){return this.clone().neg_()}clamp_(G,de){const Se=this.data;for(let Z=0;Z<Se.length;++Z)Se[Z]=Math.min(Math.max(Se[Z],G),de);return this}clamp(G,de){return this.clone().clamp_(G,de)}round_(){const G=this.data;for(let de=0;de<G.length;++de)G[de]=Math.round(G[de]);return this}round(){return this.clone().round_()}mean(G=null,de=!1){return B(this,G,de)}to(G){if(this.type===G)return this;if(!Te.hasOwnProperty(G))throw new Error(`Unsupported type: ${G}`);return new Ce(G,Te[G].from(this.data),this.dims)}}function j(ge,G){const de=ge.length,Se=G.reduce((He,ct)=>He*ct);if(de!==Se)throw Error(`cannot reshape array of size ${de} into shape (${G})`);let Z=ge;for(let He=G.length-1;He>=0;He--)Z=Z.reduce((ct,nt)=>{let lt=ct[ct.length-1];return lt.length<G[He]?lt.push(nt):ct.push([nt]),ct},[[]]);return Z[0]}function S(ge,G){const[de,Se]=(0,O.permute_data)(ge.data,ge.dims,G);return new Ce(ge.type,de,Se)}function V(ge,[G,de],Se="bilinear",Z=!1){const He=ge.dims.at(-3)??1,ct=ge.dims.at(-2),nt=ge.dims.at(-1);let lt=(0,O.interpolate_data)(ge.data,[He,ct,nt],[G,de],Se,Z);return new Ce(ge.type,lt,[He,G,de])}async function A(ge,{size:G=null,mode:de="bilinear"}={}){if(ge.dims.length!==4)throw new Error("`interpolate_4d` currently only supports 4D input.");if(!G)throw new Error("`interpolate_4d` requires a `size` argument.");let Se;if(G.length===2)Se=[...ge.dims.slice(0,2),...G];else if(G.length===3)Se=[ge.dims[0],...G];else if(G.length===4)Se=G;else throw new Error("`size` must be of length 2, 3, or 4.");let Z;if(de==="bilinear")Z=await ye.TensorOpRegistry.bilinear_interpolate_4d;else if(de==="bicubic")Z=await ye.TensorOpRegistry.bicubic_interpolate_4d;else throw new Error(`Unsupported mode: ${de}`);const He=new Ce("int64",new BigInt64Array(Se.map(BigInt)),[Se.length]);return await Z({x:ge,s:He})}async function ee(ge,G){return await(await ye.TensorOpRegistry.matmul)({a:ge,b:G})}async function ne(ge,G){return await(await ye.TensorOpRegistry.rfft)({x:ge,a:G})}async function me(ge,G){const de=await ye.TensorOpRegistry.top_k;return G===null?G=ge.dims.at(-1):G=Math.min(G,ge.dims.at(-1)),await de({x:ge,k:new Ce("int64",[BigInt(G)],[1])})}function ce(ge,G){const de=ge.data,Se=G.data,Z=[ge.dims[0],ge.dims[2]],He=new de.constructor(Z[0]*Z[1]),[ct,nt,lt]=ge.dims;let Ne=0;for(let st=0;st<ct;++st){const Pt=st*lt*nt;for(let Re=0;Re<lt;++Re){let re=0,ke=0;const je=st*nt,qe=Pt+Re;for(let Ke=0;Ke<nt;++Ke){const ut=Number(Se[je+Ke]);ke+=ut,re+=de[qe+Ke*lt]*ut}const Ue=re/ke;He[Ne++]=Ue}}return new Ce(ge.type,He,Z)}function D(ge,G,{eps:de=1e-5}={}){if(ge.dims.length!==2)throw new Error("`layer_norm` currently only supports 2D input.");const[Se,Z]=ge.dims;if(G.length!==1&&G[0]!==Z)throw new Error("`normalized_shape` must be a 1D array with shape `[input.dims[1]]`.");const[He,ct]=I(ge,1,0,!0),nt=He.data,lt=ct.data,Ne=ge.data,st=new Ne.constructor(Ne.length);for(let Pt=0;Pt<Se;++Pt){const Re=Pt*Z;for(let re=0;re<Z;++re){const ke=Re+re;st[ke]=(Ne[ke]-lt[Pt])/(nt[Pt]+de)}}return new Ce(ge.type,st,ge.dims)}function H(ge,G){return ge=ge.slice(),G===null?ge=ge.filter(de=>de!==1):typeof 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xf=c.AutoModelForCausalLM;c.AutoModelForDepthEstimation,c.AutoModelForDocumentQuestionAnswering,c.AutoModelForImageClassification,c.AutoModelForImageFeatureExtraction,c.AutoModelForImageMatting,c.AutoModelForImageSegmentation,c.AutoModelForImageToImage,c.AutoModelForMaskGeneration,c.AutoModelForMaskedLM,c.AutoModelForNormalEstimation,c.AutoModelForObjectDetection,c.AutoModelForQuestionAnswering,c.AutoModelForSemanticSegmentation,c.AutoModelForSeq2SeqLM,c.AutoModelForSequenceClassification,c.AutoModelForSpeechSeq2Seq,c.AutoModelForTextToSpectrogram,c.AutoModelForTextToWaveform,c.AutoModelForTokenClassification,c.AutoModelForUniversalSegmentation,c.AutoModelForVision2Seq,c.AutoModelForXVector,c.AutoModelForZeroShotObjectDetection,c.AutoProcessor;var Tf=c.AutoTokenizer;c.AutomaticSpeechRecognitionPipeline,c.BartForConditionalGeneration,c.BartForSequenceClassification,c.BartModel,c.BartPretrainedModel,c.BartTokenizer,c.BaseModelOutput,c.BaseStreamer,c.BeitFeatureExtractor,c.BeitForImageClassification,c.BeitModel,c.BeitPreTrainedModel,c.BertForMaskedLM,c.BertForQuestionAnswering,c.BertForSequenceClassification,c.BertForTokenClassification,c.BertModel,c.BertPreTrainedModel,c.BertTokenizer,c.BitImageProcessor,c.BlenderbotForConditionalGeneration,c.BlenderbotModel,c.BlenderbotPreTrainedModel,c.BlenderbotSmallForConditionalGeneration,c.BlenderbotSmallModel,c.BlenderbotSmallPreTrainedModel,c.BlenderbotSmallTokenizer,c.BlenderbotTokenizer,c.BloomForCausalLM,c.BloomModel,c.BloomPreTrainedModel,c.BloomTokenizer,c.CLIPFeatureExtractor,c.CLIPImageProcessor,c.CLIPModel,c.CLIPPreTrainedModel,c.CLIPSegForImageSegmentation,c.CLIPSegModel,c.CLIPSegPreTrainedModel,c.CLIPTextModel,c.CLIPTextModelWithProjection,c.CLIPTokenizer,c.CLIPVisionModel,c.CLIPVisionModelWithProjection,c.CamembertForMaskedLM,c.CamembertForQuestionAnswering,c.CamembertForSequenceClassification,c.CamembertForTokenClassification,c.CamembertModel,c.CamembertPreTrainedModel,c.CamembertTokenizer,c.CausalLMOutput,c.CausalLMOutputWithPast,c.ChineseCLIPFeatureExtractor,c.ChineseCLIPModel,c.ChineseCLIPPreTrainedModel,c.ClapAudioModelWithProjection,c.ClapFeatureExtractor,c.ClapModel,c.ClapPreTrainedModel,c.ClapTextModelWithProjection,c.CodeGenForCausalLM,c.CodeGenModel,c.CodeGenPreTrainedModel,c.CodeGenTokenizer,c.CodeLlamaTokenizer,c.CohereForCausalLM,c.CohereModel,c.CoherePreTrainedModel,c.CohereTokenizer,c.ConvBertForMaskedLM,c.ConvBertForQuestionAnswering,c.ConvBertForSequenceClassification,c.ConvBertForTokenClassification,c.ConvBertModel,c.ConvBertPreTrainedModel,c.ConvBertTokenizer,c.ConvNextFeatureExtractor,c.ConvNextForImageClassification,c.ConvNextImageProcessor,c.ConvNextModel,c.ConvNextPreTrainedModel,c.ConvNextV2ForImageClassification,c.ConvNextV2Model,c.ConvNextV2PreTrainedModel,c.DPTFeatureExtractor,c.DPTForDepthEstimation,c.DPTImageProcessor,c.DPTModel,c.DPTPreTrainedModel,c.DebertaForMaskedLM,c.DebertaForQuestionAnswering,c.DebertaForSequenceClassification,c.DebertaForTokenClassification,c.DebertaModel,c.DebertaPreTrainedModel,c.DebertaTokenizer,c.DebertaV2ForMaskedLM,c.DebertaV2ForQuestionAnswering,c.DebertaV2ForSequenceClassification,c.DebertaV2ForTokenClassification,c.DebertaV2Model,c.DebertaV2PreTrainedModel,c.DebertaV2Tokenizer,c.DecisionTransformerModel,c.DecisionTransformerPreTrainedModel,c.DeiTFeatureExtractor,c.DeiTForImageClassification,c.DeiTModel,c.DeiTPreTrainedModel,c.DepthAnythingForDepthEstimation,c.DepthAnythingPreTrainedModel,c.DepthEstimationPipeline,c.DetrFeatureExtractor,c.DetrForObjectDetection,c.DetrForSegmentation,c.DetrModel,c.DetrObjectDetectionOutput,c.DetrPreTrainedModel,c.DetrSegmentationOutput,c.Dinov2ForImageClassification,c.Dinov2Model,c.Dinov2PreTrainedModel,c.DistilBertForMaskedLM,c.DistilBertForQuestionAnswering,c.DistilBertForSequenceClassification,c.DistilBertForTokenClassification,c.DistilBertModel,c.DistilBertPreTrainedModel,c.DistilBertTokenizer,c.DocumentQuestionAnsweringPipeline,c.DonutFeatureExtractor,c.DonutSwinModel,c.DonutSwinPreTrainedModel,c.EfficientNetForImageClassification,c.EfficientNetImageProcessor,c.EfficientNetModel,c.EfficientNetPreTrainedModel,c.ElectraForMaskedLM,c.ElectraForQuestionAnswering,c.ElectraForSequenceClassification,c.ElectraForTokenClassification,c.ElectraModel,c.ElectraPreTrainedModel,c.ElectraTokenizer,c.EosTokenCriteria,c.EsmForMaskedLM,c.EsmForSequenceClassification,c.EsmForTokenClassification,c.EsmModel,c.EsmPreTrainedModel,c.EsmTokenizer,c.FFT,c.FalconForCausalLM,c.FalconModel,c.FalconPreTrainedModel,c.FalconTokenizer,c.FastViTForImageClassification,c.FastViTModel,c.FastViTPreTrainedModel,c.FeatureExtractionPipeline,c.FeatureExtractor,c.FillMaskPipeline,c.Florence2ForConditionalGeneration,c.Florence2PreTrainedModel,c.Florence2Processor,c.GLPNFeatureExtractor,c.GLPNForDepthEstimation,c.GLPNModel,c.GLPNPreTrainedModel,c.GPT2LMHeadModel,c.GPT2Model,c.GPT2PreTrainedModel,c.GPT2Tokenizer,c.GPTBigCodeForCausalLM,c.GPTBigCodeModel,c.GPTBigCodePreTrainedModel,c.GPTJForCausalLM,c.GPTJModel,c.GPTJPreTrainedModel,c.GPTNeoForCausalLM,c.GPTNeoModel,c.GPTNeoPreTrainedModel,c.GPTNeoXForCausalLM,c.GPTNeoXModel,c.GPTNeoXPreTrainedModel,c.GPTNeoXTokenizer,c.Gemma2ForCausalLM,c.Gemma2Model,c.Gemma2PreTrainedModel,c.GemmaForCausalLM,c.GemmaModel,c.GemmaPreTrainedModel,c.GemmaTokenizer,c.Grok1Tokenizer,c.GroupViTModel,c.GroupViTPreTrainedModel,c.HerbertTokenizer,c.HieraForImageClassification,c.HieraModel,c.HieraPreTrainedModel,c.HubertForCTC,c.HubertForSequenceClassification,c.HubertModel,c.HubertPreTrainedModel,c.ImageClassificationPipeline,c.ImageFeatureExtractionPipeline,c.ImageFeatureExtractor,c.ImageMattingOutput,c.ImageSegmentationPipeline,c.ImageToImagePipeline,c.ImageToTextPipeline;var Ef=c.InterruptableStoppingCriteria;c.JAISLMHeadModel,c.JAISModel,c.JAISPreTrainedModel,c.LlamaForCausalLM,c.LlamaModel,c.LlamaPreTrainedModel,c.LlamaTokenizer,c.LlavaForConditionalGeneration,c.LlavaPreTrainedModel,c.LongT5ForConditionalGeneration,c.LongT5Model,c.LongT5PreTrainedModel,c.M2M100ForConditionalGeneration,c.M2M100Model,c.M2M100PreTrainedModel,c.M2M100Tokenizer,c.MBart50Tokenizer,c.MBartForCausalLM,c.MBartForConditionalGeneration,c.MBartForSequenceClassification,c.MBartModel,c.MBartPreTrainedModel,c.MBartTokenizer,c.MPNetForMaskedLM,c.MPNetForQuestionAnswering,c.MPNetForSequenceClassification,c.MPNetForTokenClassification,c.MPNetModel,c.MPNetPreTrainedModel,c.MPNetTokenizer,c.MT5ForConditionalGeneration,c.MT5Model,c.MT5PreTrainedModel,c.MarianMTModel,c.MarianModel,c.MarianPreTrainedModel,c.MarianTokenizer,c.MaskFormerFeatureExtractor,c.MaskFormerForInstanceSegmentation,c.MaskFormerModel,c.MaskFormerPreTrainedModel,c.MaskedLMOutput,c.MaxLengthCriteria,c.MistralForCausalLM,c.MistralModel,c.MistralPreTrainedModel,c.MobileBertForMaskedLM,c.MobileBertForQuestionAnswering,c.MobileBertForSequenceClassification,c.MobileBertModel,c.MobileBertPreTrainedModel,c.MobileBertTokenizer,c.MobileNetV1FeatureExtractor,c.MobileNetV1ForImageClassification,c.MobileNetV1Model,c.MobileNetV1PreTrainedModel,c.MobileNetV2FeatureExtractor,c.MobileNetV2ForImageClassification,c.MobileNetV2Model,c.MobileNetV2PreTrainedModel,c.MobileNetV3FeatureExtractor,c.MobileNetV3ForImageClassification,c.MobileNetV3Model,c.MobileNetV3PreTrainedModel,c.MobileNetV4FeatureExtractor,c.MobileNetV4ForImageClassification,c.MobileNetV4Model,c.MobileNetV4PreTrainedModel,c.MobileViTFeatureExtractor,c.MobileViTForImageClassification,c.MobileViTImageProcessor,c.MobileViTModel,c.MobileViTPreTrainedModel,c.MobileViTV2ForImageClassification,c.MobileViTV2Model,c.MobileViTV2PreTrainedModel,c.ModelOutput,c.Moondream1ForConditionalGeneration,c.MptForCausalLM,c.MptModel,c.MptPreTrainedModel,c.MusicgenForCausalLM,c.MusicgenForConditionalGeneration,c.MusicgenModel,c.MusicgenPreTrainedModel,c.NllbTokenizer,c.NomicBertModel,c.NomicBertPreTrainedModel,c.NougatImageProcessor,c.NougatTokenizer,c.OPTForCausalLM,c.OPTModel,c.OPTPreTrainedModel,c.ObjectDetectionPipeline,c.OpenELMForCausalLM,c.OpenELMModel,c.OpenELMPreTrainedModel,c.OwlViTFeatureExtractor,c.OwlViTForObjectDetection,c.OwlViTModel,c.OwlViTPreTrainedModel,c.OwlViTProcessor,c.Owlv2ForObjectDetection,c.Owlv2ImageProcessor,c.Owlv2Model,c.Owlv2PreTrainedModel,c.Phi3ForCausalLM,c.Phi3Model,c.Phi3PreTrainedModel,c.PhiForCausalLM,c.PhiModel,c.PhiPreTrainedModel,c.Pipeline,c.PreTrainedModel,c.PreTrainedTokenizer,c.PretrainedConfig,c.PretrainedMixin,c.Processor,c.PvtForImageClassification,c.PvtImageProcessor,c.PvtModel,c.PvtPreTrainedModel,c.PyAnnoteFeatureExtractor,c.PyAnnoteForAudioFrameClassification,c.PyAnnoteModel,c.PyAnnotePreTrainedModel,c.PyAnnoteProcessor,c.QuestionAnsweringModelOutput,c.QuestionAnsweringPipeline,c.Qwen2ForCausalLM,c.Qwen2Model,c.Qwen2PreTrainedModel,c.Qwen2Tokenizer,c.RTDetrForObjectDetection,c.RTDetrImageProcessor,c.RTDetrModel,c.RTDetrObjectDetectionOutput,c.RTDetrPreTrainedModel,c.RawImage,c.ResNetForImageClassification,c.ResNetModel,c.ResNetPreTrainedModel,c.RoFormerForMaskedLM,c.RoFormerForQuestionAnswering,c.RoFormerForSequenceClassification,c.RoFormerForTokenClassification,c.RoFormerModel,c.RoFormerPreTrainedModel,c.RoFormerTokenizer,c.RobertaForMaskedLM,c.RobertaForQuestionAnswering,c.RobertaForSequenceClassification,c.RobertaForTokenClassification,c.RobertaModel,c.RobertaPreTrainedModel,c.RobertaTokenizer,c.SamImageProcessor,c.SamImageSegmentationOutput,c.SamModel,c.SamPreTrainedModel,c.SamProcessor,c.SapiensFeatureExtractor,c.SapiensForDepthEstimation,c.SapiensForNormalEstimation,c.SapiensForSemanticSegmentation,c.SapiensPreTrainedModel,c.SeamlessM4TFeatureExtractor,c.SegformerFeatureExtractor,c.SegformerForImageClassification,c.SegformerForSemanticSegmentation,c.SegformerModel,c.SegformerPreTrainedModel,c.Seq2SeqLMOutput,c.SequenceClassifierOutput,c.SiglipImageProcessor,c.SiglipModel,c.SiglipPreTrainedModel,c.SiglipTextModel,c.SiglipTokenizer,c.SiglipVisionModel,c.SpeechT5FeatureExtractor,c.SpeechT5ForSpeechToText,c.SpeechT5ForTextToSpeech,c.SpeechT5HifiGan,c.SpeechT5Model,c.SpeechT5PreTrainedModel,c.SpeechT5Processor,c.SpeechT5Tokenizer,c.SqueezeBertForMaskedLM,c.SqueezeBertForQuestionAnswering,c.SqueezeBertForSequenceClassification,c.SqueezeBertModel,c.SqueezeBertPreTrainedModel,c.SqueezeBertTokenizer,c.StableLmForCausalLM,c.StableLmModel,c.StableLmPreTrainedModel,c.Starcoder2ForCausalLM,c.Starcoder2Model,c.Starcoder2PreTrainedModel,c.StoppingCriteria,c.StoppingCriteriaList,c.SummarizationPipeline,c.Swin2SRForImageSuperResolution,c.Swin2SRImageProcessor,c.Swin2SRModel,c.Swin2SRPreTrainedModel,c.SwinForImageClassification,c.SwinModel,c.SwinPreTrainedModel,c.T5ForConditionalGeneration,c.T5Model,c.T5PreTrainedModel,c.T5Tokenizer,c.TableTransformerForObjectDetection,c.TableTransformerModel,c.TableTransformerObjectDetectionOutput,c.TableTransformerPreTrainedModel,c.Tensor,c.Text2TextGenerationPipeline,c.TextClassificationPipeline,c.TextGenerationPipeline;var Cf=c.TextStreamer;c.TextToAudioPipeline,c.TokenClassificationPipeline,c.TokenClassifierOutput,c.TokenizerModel,c.TrOCRForCausalLM,c.TrOCRPreTrainedModel,c.TranslationPipeline,c.UniSpeechForCTC,c.UniSpeechForSequenceClassification,c.UniSpeechModel,c.UniSpeechPreTrainedModel,c.UniSpeechSatForAudioFrameClassification,c.UniSpeechSatForCTC,c.UniSpeechSatForSequenceClassification,c.UniSpeechSatModel,c.UniSpeechSatPreTrainedModel,c.ViTFeatureExtractor,c.ViTForImageClassification,c.ViTImageProcessor,c.ViTMAEModel,c.ViTMAEPreTrainedModel,c.ViTMSNForImageClassification,c.ViTMSNModel,c.ViTMSNPreTrainedModel,c.ViTModel,c.ViTPreTrainedModel,c.VisionEncoderDecoderModel,c.VitMatteForImageMatting,c.VitMatteImageProcessor,c.VitMattePreTrainedModel,c.VitsModel,c.VitsModelOutput,c.VitsPreTrainedModel,c.VitsTokenizer,c.Wav2Vec2BertForCTC,c.Wav2Vec2BertForSequenceClassification,c.Wav2Vec2BertModel,c.Wav2Vec2BertPreTrainedModel,c.Wav2Vec2CTCTokenizer,c.Wav2Vec2FeatureExtractor,c.Wav2Vec2ForAudioFrameClassification,c.Wav2Vec2ForCTC,c.Wav2Vec2ForSequenceClassification,c.Wav2Vec2Model,c.Wav2Vec2PreTrainedModel,c.Wav2Vec2ProcessorWithLM,c.WavLMForAudioFrameClassification,c.WavLMForCTC,c.WavLMForSequenceClassification,c.WavLMForXVector,c.WavLMModel,c.WavLMPreTrainedModel,c.WeSpeakerFeatureExtractor,c.WeSpeakerResNetModel,c.WeSpeakerResNetPreTrainedModel,c.WhisperFeatureExtractor,c.WhisperForConditionalGeneration,c.WhisperModel,c.WhisperPreTrainedModel,c.WhisperProcessor,c.WhisperTextStreamer,c.WhisperTokenizer,c.XLMForQuestionAnswering,c.XLMForSequenceClassification,c.XLMForTokenClassification,c.XLMModel,c.XLMPreTrainedModel,c.XLMRobertaForMaskedLM,c.XLMRobertaForQuestionAnswering,c.XLMRobertaForSequenceClassification,c.XLMRobertaForTokenClassification,c.XLMRobertaModel,c.XLMRobertaPreTrainedModel,c.XLMRobertaTokenizer,c.XLMTokenizer,c.XLMWithLMHeadModel,c.XVectorOutput,c.YolosFeatureExtractor,c.YolosForObjectDetection,c.YolosModel,c.YolosObjectDetectionOutput,c.YolosPreTrainedModel,c.ZeroShotAudioClassificationPipeline,c.ZeroShotClassificationPipeline,c.ZeroShotImageClassificationPipeline,c.ZeroShotObjectDetectionPipeline,c.bankers_round,c.cat,c.cos_sim,c.dot,c.dynamic_time_warping,c.env,c.full,c.full_like,c.getKeyValueShapes,c.hamming,c.hanning,c.interpolate,c.interpolate_4d,c.interpolate_data,c.is_chinese_char,c.layer_norm,c.log_softmax,c.magnitude,c.matmul,c.max,c.mean,c.mean_pooling,c.medianFilter,c.mel_filter_bank,c.min,c.ones,c.ones_like,c.permute,c.permute_data,c.pipeline,c.quantize_embeddings,c.read_audio,c.rfft,c.round,c.softmax,c.spectrogram,c.stack,c.std_mean,c.topk,c.window_function,c.zeros,c.zeros_like;class gc{static async getInstance(Ee=null){return this.tokenizer??(this.tokenizer=Tf.from_pretrained(this.model_id,{progress_callback:Ee})),this.model??(this.model=xf.from_pretrained(this.model_id,{device:"webgpu",use_external_data_format:!0,progress_callback:Ee})),Promise.all([this.tokenizer,this.model])}}xe(gc,"model_id","onnx-community/Llama-3.2-1B-Instruct-q4f16");const fd=new Ef;async function $f($t){const[Ee,N]=await gc.getInstance(),O=Ee.apply_chat_template($t,{add_generation_prompt:!0,return_dict:!0});let fe,ye=0,Te;const Ce=()=>{fe??(fe=performance.now()),ye++>0&&(Te=ye/(performance.now()-fe)*1e3)},j=ee=>{self.postMessage({status:"update",output:ee,tps:Te,numTokens:ye})},S=new Cf(Ee,{skip_prompt:!0,skip_special_tokens:!0,callback_function:j,token_callback_function:Ce});self.postMessage({status:"start"});const V=await N.generate({...O,max_new_tokens:1024,streamer:S,stopping_criteria:fd}),A=Ee.batch_decode(V,{skip_special_tokens:!0});self.postMessage({status:"complete",output:A})}async function Sf(){try{if(!await navigator.gpu.requestAdapter())throw new Error("WebGPU is not supported (no adapter found)")}catch($t){self.postMessage({status:"error",data:$t.toString()})}}async function kf(){self.postMessage({status:"loading",data:"Loading model..."});const[$t,Ee]=await gc.getInstance(O=>{self.postMessage(O)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const N=$t("a");await Ee.generate({...N,max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async $t=>{const{type:Ee,data:N}=$t.data;switch(Ee){case"check":Sf();break;case"load":kf();break;case"generate":fd.reset(),$f(N);break;case"interrupt":fd.interrupt();break;case"reset":fd.reset();break}})})();